Split (1)

train · 7.77k rows

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https://github.com/huggingface/datasets/issues/6504	6,504	Error Pushing to Hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/55055083?v=4", "events_url": "https://api.github.com/users/Jiayi-Pan/events{/privacy}", "followers_url": "https://api.github.com/users/Jiayi-Pan/followers", "following_url": "https://api.github.com/users/Jiayi-Pan/following{/other_user}", "gists_url": "https://api.github.com/users/Jiayi-Pan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Jiayi-Pan", "id": 55055083, "login": "Jiayi-Pan", "node_id": "MDQ6VXNlcjU1MDU1MDgz", "organizations_url": "https://api.github.com/users/Jiayi-Pan/orgs", "received_events_url": "https://api.github.com/users/Jiayi-Pan/received_events", "repos_url": "https://api.github.com/users/Jiayi-Pan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Jiayi-Pan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Jiayi-Pan/subscriptions", "type": "User", "url": "https://api.github.com/users/Jiayi-Pan", "user_view_type": "public" }	[]	closed	false	[]	2023-12-16T01:05:22Z	2023-12-16T06:20:53Z	2023-12-16T06:20:53Z	NONE	null	null	### Describe the bug Error when trying to push a dataset in a special format to hub ### Steps to reproduce the bug ``` import datasets from datasets import Dataset dataset_dict = { "filename": ["apple", "banana"], "token": [[[1,2],[3,4]],[[1,2],[3,4]]], "label": [0, 1], } dataset = Dataset.from_dict(dataset_dict) dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16")) dataset.push_to_hub("SequenceModel/imagenet_val_256") ``` Error: ``` ... ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple' in "<unicode string>", line 8, column 16: shape: !!python/tuple ^ ``` ### Expected behavior Dataset being pushed to hub ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/55055083?v=4", "events_url": "https://api.github.com/users/Jiayi-Pan/events{/privacy}", "followers_url": "https://api.github.com/users/Jiayi-Pan/followers", "following_url": "https://api.github.com/users/Jiayi-Pan/following{/other_user}", "gists_url": "https://api.github.com/users/Jiayi-Pan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Jiayi-Pan", "id": 55055083, "login": "Jiayi-Pan", "node_id": "MDQ6VXNlcjU1MDU1MDgz", "organizations_url": "https://api.github.com/users/Jiayi-Pan/orgs", "received_events_url": "https://api.github.com/users/Jiayi-Pan/received_events", "repos_url": "https://api.github.com/users/Jiayi-Pan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Jiayi-Pan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Jiayi-Pan/subscriptions", "type": "User", "url": "https://api.github.com/users/Jiayi-Pan", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6504/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6503	6,503	Fix streaming xnli	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6503). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005003 / 0.011353 (-0.006350) \| 0.003020 / 0.011008 (-0.007988) \| 0.061370 / 0.038508 (0.022862) \| 0.050996 / 0.023109 (0.027887) \| 0.243434 / 0.275898 (-0.032464) \| 0.266317 / 0.323480 (-0.057163) \| 0.003888 / 0.007986 (-0.004098) \| 0.002607 / 0.004328 (-0.001721) \| 0.047541 / 0.004250 (0.043290) \| 0.037933 / 0.037052 (0.000881) \| 0.259695 / 0.258489 (0.001206) \| 0.279374 / 0.293841 (-0.014467) \| 0.027258 / 0.128546 (-0.101288) \| 0.010184 / 0.075646 (-0.065462) \| 0.207412 / 0.419271 (-0.211860) \| 0.034978 / 0.043533 (-0.008554) \| 0.247871 / 0.255139 (-0.007267) \| 0.265273 / 0.283200 (-0.017927) \| 0.017886 / 0.141683 (-0.123796) \| 1.090451 / 1.452155 (-0.361704) \| 1.152034 / 1.492716 (-0.340682) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094383 / 0.018006 (0.076377) \| 0.301151 / 0.000490 (0.300661) \| 0.000211 / 0.000200 (0.000011) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018927 / 0.037411 (-0.018484) \| 0.062152 / 0.014526 (0.047626) \| 0.072177 / 0.176557 (-0.104380) \| 0.119792 / 0.737135 (-0.617343) \| 0.073333 / 0.296338 (-0.223005) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282671 / 0.215209 (0.067462) \| 2.721148 / 2.077655 (0.643494) \| 1.472689 / 1.504120 (-0.031431) \| 1.355226 / 1.541195 (-0.185969) \| 1.375935 / 1.468490 (-0.092556) \| 0.562600 / 4.584777 (-4.022177) \| 2.364046 / 3.745712 (-1.381666) \| 2.714984 / 5.269862 (-2.554878) \| 1.738413 / 4.565676 (-2.827263) \| 0.062564 / 0.424275 (-0.361711) \| 0.004964 / 0.007607 (-0.002643) \| 0.341300 / 0.226044 (0.115255) \| 3.345187 / 2.268929 (1.076259) \| 1.857822 / 55.444624 (-53.586803) \| 1.581002 / 6.876477 (-5.295475) \| 1.585919 / 2.142072 (-0.556153) \| 0.640105 / 4.805227 (-4.165122) \| 0.117880 / 6.500664 (-6.382784) \| 0.042032 / 0.075469 (-0.033437) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962701 / 1.841788 (-0.879086) \| 11.309251 / 8.074308 (3.234943) \| 10.462520 / 10.191392 (0.271128) \| 0.127399 / 0.680424 (-0.553025) \| 0.014549 / 0.534201 (-0.519652) \| 0.297017 / 0.579283 (-0.282266) \| 0.266152 / 0.434364 (-0.168212) \| 0.349252 / 0.540337 (-0.191085) \| 0.457015 / 1.386936 (-0.929921) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005341 / 0.011353 (-0.006012) \| 0.003108 / 0.011008 (-0.007900) \| 0.048862 / 0.038508 (0.010353) \| 0.053354 / 0.023109 (0.030245) \| 0.274499 / 0.275898 (-0.001399) \| 0.296698 / 0.323480 (-0.026782) \| 0.003974 / 0.007986 (-0.004012) \| 0.002631 / 0.004328 (-0.001697) \| 0.048013 / 0.004250 (0.043762) \| 0.040416 / 0.037052 (0.003363) \| 0.276581 / 0.258489 (0.018092) \| 0.301296 / 0.293841 (0.007455) \| 0.029049 / 0.128546 (-0.099497) \| 0.010253 / 0.075646 (-0.065393) \| 0.057157 / 0.419271 (-0.362114) \| 0.031830 / 0.043533 (-0.011703) \| 0.274341 / 0.255139 (0.019202) \| 0.292583 / 0.283200 (0.009383) \| 0.018449 / 0.141683 (-0.123234) \| 1.145099 / 1.452155 (-0.307055) \| 1.192958 / 1.492716 (-0.299758) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091596 / 0.018006 (0.073590) \| 0.300917 / 0.000490 (0.300427) \| 0.000225 / 0.000200 (0.000025) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021657 / 0.037411 (-0.015754) \| 0.068464 / 0.014526 (0.053938) \| 0.079869 / 0.176557 (-0.096687) \| 0.117523 / 0.737135 (-0.619613) \| 0.081257 / 0.296338 (-0.215082) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294876 / 0.215209 (0.079667) \| 2.879372 / 2.077655 (0.801718) \| 1.619887 / 1.504120 (0.115767) \| 1.482154 / 1.541195 (-0.059041) \| 1.494656 / 1.468490 (0.026166) \| 0.558914 / 4.584777 (-4.025862) \| 2.420948 / 3.745712 (-1.324765) \| 2.728992 / 5.269862 (-2.540869) \| 1.722135 / 4.565676 (-2.843542) \| 0.062182 / 0.424275 (-0.362093) \| 0.004933 / 0.007607 (-0.002674) \| 0.342759 / 0.226044 (0.116715) \| 3.424083 / 2.268929 (1.155154) \| 1.950673 / 55.444624 (-53.493951) \| 1.683126 / 6.876477 (-5.193351) \| 1.673135 / 2.142072 (-0.468937) \| 0.633711 / 4.805227 (-4.171516) \| 0.114898 / 6.500664 (-6.385766) \| 0.040332 / 0.075469 (-0.035137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975102 / 1.841788 (-0.866685) \| 11.975731 / 8.074308 (3.901423) \| 10.961103 / 10.191392 (0.769711) \| 0.131152 / 0.680424 (-0.549272) \| 0.016268 / 0.534201 (-0.517933) \| 0.285031 / 0.579283 (-0.294252) \| 0.279556 / 0.434364 (-0.154808) \| 0.324183 / 0.540337 (-0.216154) \| 0.571404 / 1.386936 (-0.815532) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f67312956fc15572b6a0ca0dfcc0ceb90fbb794 \"CML watermark\")\n" ]	2023-12-15T14:40:57Z	2023-12-15T14:51:06Z	2023-12-15T14:44:47Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6503.diff", "html_url": "https://github.com/huggingface/datasets/pull/6503", "merged_at": "2023-12-15T14:44:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/6503.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6503" }	This code was failing ```python In [1]: from datasets import load_dataset In [2]: ...: ds = load_dataset("xnli", "all_languages", split="test", streaming=True) ...: ...: sample_data = next(iter(ds))["premise"] # pick up one data ...: input_text = list(sample_data.values()) ``` ``` File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict) 102 return translation_dict 103 elif self.languages and set(translation_dict) - lang_set: --> 104 raise ValueError( 105 f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).' 106 ) 108 # Convert dictionary into tuples, splitting out cases where there are 109 # multiple translations for a single language. 110 translation_tuples = [] ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es). ``` because in streaming mode we expect features encode methods to be no-ops if the example is already encoded. I fixed `TranslationVariableLanguages` to account for that	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6503/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6502	6,502	Pickle support for `torch.Generator` objects	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005472 / 0.011353 (-0.005881) \| 0.003715 / 0.011008 (-0.007293) \| 0.063257 / 0.038508 (0.024749) \| 0.060683 / 0.023109 (0.037574) \| 0.250885 / 0.275898 (-0.025013) \| 0.271685 / 0.323480 (-0.051795) \| 0.003051 / 0.007986 (-0.004934) \| 0.002799 / 0.004328 (-0.001530) \| 0.049113 / 0.004250 (0.044863) \| 0.038965 / 0.037052 (0.001912) \| 0.252688 / 0.258489 (-0.005801) \| 0.282536 / 0.293841 (-0.011305) \| 0.028722 / 0.128546 (-0.099824) \| 0.010586 / 0.075646 (-0.065060) \| 0.205145 / 0.419271 (-0.214127) \| 0.036996 / 0.043533 (-0.006537) \| 0.248874 / 0.255139 (-0.006265) \| 0.266148 / 0.283200 (-0.017051) \| 0.018540 / 0.141683 (-0.123143) \| 1.120216 / 1.452155 (-0.331938) \| 1.191072 / 1.492716 (-0.301644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095721 / 0.018006 (0.077714) \| 0.313401 / 0.000490 (0.312911) \| 0.000234 / 0.000200 (0.000034) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018604 / 0.037411 (-0.018807) \| 0.061571 / 0.014526 (0.047045) \| 0.075343 / 0.176557 (-0.101213) \| 0.121272 / 0.737135 (-0.615864) \| 0.076448 / 0.296338 (-0.219890) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286885 / 0.215209 (0.071676) \| 2.809100 / 2.077655 (0.731445) \| 1.485365 / 1.504120 (-0.018755) \| 1.367672 / 1.541195 (-0.173523) \| 1.423570 / 1.468490 (-0.044920) \| 0.571063 / 4.584777 (-4.013714) \| 2.385248 / 3.745712 (-1.360464) \| 2.855251 / 5.269862 (-2.414610) \| 1.799371 / 4.565676 (-2.766306) \| 0.063491 / 0.424275 (-0.360784) \| 0.004942 / 0.007607 (-0.002665) \| 0.346181 / 0.226044 (0.120137) \| 3.388123 / 2.268929 (1.119195) \| 1.819093 / 55.444624 (-53.625532) \| 1.552998 / 6.876477 (-5.323479) \| 1.627930 / 2.142072 (-0.514143) \| 0.653438 / 4.805227 (-4.151789) \| 0.123831 / 6.500664 (-6.376833) \| 0.043340 / 0.075469 (-0.032129) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.952167 / 1.841788 (-0.889621) \| 12.149515 / 8.074308 (4.075207) \| 10.665085 / 10.191392 (0.473693) \| 0.127768 / 0.680424 (-0.552656) \| 0.014022 / 0.534201 (-0.520179) \| 0.285959 / 0.579283 (-0.293324) \| 0.269727 / 0.434364 (-0.164637) \| 0.336646 / 0.540337 (-0.203692) \| 0.442932 / 1.386936 (-0.944005) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005351 / 0.011353 (-0.006002) \| 0.003561 / 0.011008 (-0.007448) \| 0.048890 / 0.038508 (0.010382) \| 0.054093 / 0.023109 (0.030984) \| 0.274397 / 0.275898 (-0.001501) \| 0.296980 / 0.323480 (-0.026500) \| 0.004126 / 0.007986 (-0.003860) \| 0.002751 / 0.004328 (-0.001578) \| 0.049131 / 0.004250 (0.044880) \| 0.040769 / 0.037052 (0.003716) \| 0.279147 / 0.258489 (0.020658) \| 0.302014 / 0.293841 (0.008173) \| 0.029847 / 0.128546 (-0.098699) \| 0.010710 / 0.075646 (-0.064936) \| 0.057626 / 0.419271 (-0.361645) \| 0.032801 / 0.043533 (-0.010732) \| 0.272698 / 0.255139 (0.017559) \| 0.289238 / 0.283200 (0.006039) \| 0.017876 / 0.141683 (-0.123807) \| 1.152059 / 1.452155 (-0.300096) \| 1.212289 / 1.492716 (-0.280427) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092914 / 0.018006 (0.074908) \| 0.303092 / 0.000490 (0.302603) \| 0.000214 / 0.000200 (0.000014) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022074 / 0.037411 (-0.015337) \| 0.070109 / 0.014526 (0.055583) \| 0.083360 / 0.176557 (-0.093196) \| 0.122445 / 0.737135 (-0.614690) \| 0.083625 / 0.296338 (-0.212714) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282788 / 0.215209 (0.067579) \| 2.789229 / 2.077655 (0.711574) \| 1.571077 / 1.504120 (0.066957) \| 1.452627 / 1.541195 (-0.088567) \| 1.493176 / 1.468490 (0.024686) \| 0.556892 / 4.584777 (-4.027885) \| 2.442771 / 3.745712 (-1.302941) \| 2.826316 / 5.269862 (-2.443545) \| 1.758276 / 4.565676 (-2.807401) \| 0.063039 / 0.424275 (-0.361236) \| 0.004928 / 0.007607 (-0.002679) \| 0.338247 / 0.226044 (0.112202) \| 3.346344 / 2.268929 (1.077416) \| 1.952520 / 55.444624 (-53.492104) \| 1.664520 / 6.876477 (-5.211956) \| 1.701528 / 2.142072 (-0.440544) \| 0.634746 / 4.805227 (-4.170481) \| 0.116879 / 6.500664 (-6.383786) \| 0.040990 / 0.075469 (-0.034479) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969521 / 1.841788 (-0.872267) \| 12.431395 / 8.074308 (4.357087) \| 10.907503 / 10.191392 (0.716111) \| 0.131028 / 0.680424 (-0.549396) \| 0.015239 / 0.534201 (-0.518962) \| 0.290793 / 0.579283 (-0.288490) \| 0.275072 / 0.434364 (-0.159292) \| 0.331036 / 0.540337 (-0.209301) \| 0.567858 / 1.386936 (-0.819078) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#092118fc00f7dd718ab3643739d7b23ff16c9eff \"CML watermark\")\n" ]	2023-12-15T13:55:12Z	2023-12-15T15:04:33Z	2023-12-15T14:58:22Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6502.diff", "html_url": "https://github.com/huggingface/datasets/pull/6502", "merged_at": "2023-12-15T14:58:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6502.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6502" }	Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6502/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6501	6,501	OverflowError: value too large to convert to int32_t	{ "avatar_url": "https://avatars.githubusercontent.com/u/47747764?v=4", "events_url": "https://api.github.com/users/zhangfan-algo/events{/privacy}", "followers_url": "https://api.github.com/users/zhangfan-algo/followers", "following_url": "https://api.github.com/users/zhangfan-algo/following{/other_user}", "gists_url": "https://api.github.com/users/zhangfan-algo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zhangfan-algo", "id": 47747764, "login": "zhangfan-algo", "node_id": "MDQ6VXNlcjQ3NzQ3NzY0", "organizations_url": "https://api.github.com/users/zhangfan-algo/orgs", "received_events_url": "https://api.github.com/users/zhangfan-algo/received_events", "repos_url": "https://api.github.com/users/zhangfan-algo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zhangfan-algo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zhangfan-algo/subscriptions", "type": "User", "url": "https://api.github.com/users/zhangfan-algo", "user_view_type": "public" }	[]	open	false	[ "+1\n" ]	2023-12-15T10:10:21Z	2025-06-27T04:27:14Z	null	NONE	null	null	### Describe the bug ![image](https://github.com/huggingface/datasets/assets/47747764/f58044fb-ddda-48b6-ba68-7bbfef781630) ### Steps to reproduce the bug just loading datasets ### Expected behavior how can I fix it ### Environment info pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl done	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6501/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6500	6,500	Enable setting config as default when push_to_hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6500). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This is ready for review @huggingface/datasets. ", "Also what if the config is being overwritten and it was the default config and the user doesn't pass `set_default` ?\r\nI'd expect the config to keep being the default one but lmk what you think", "How can you unset a config as the default one? In the case you mentioned, I would expect the config not being the default one.", "Maybe by passing `set_default=False` ? (set_default can be None by default)", "I think that way we are unnecessarily complicating the logic of `push_to_hub` and as I told you, I would expect the contrary: the result of calling `push_to_hub` with a determined set of arguments should always be the same, independently of previous calls and the current state of the config on the Hub. Push to hub should be somehow stateless in that sense, and IMO the user expects that the push overwrites previous config if already present on the Hub. I find very confusing making it to partially update the config on the Hub.", "That makes sense, having it stateless is simpler and no need to do something too fancy indeed", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005329 / 0.011353 (-0.006024) \| 0.002998 / 0.011008 (-0.008010) \| 0.063756 / 0.038508 (0.025248) \| 0.051713 / 0.023109 (0.028603) \| 0.248135 / 0.275898 (-0.027763) \| 0.269136 / 0.323480 (-0.054344) \| 0.002970 / 0.007986 (-0.005015) \| 0.002566 / 0.004328 (-0.001763) \| 0.048110 / 0.004250 (0.043859) \| 0.038415 / 0.037052 (0.001363) \| 0.254012 / 0.258489 (-0.004477) \| 0.281915 / 0.293841 (-0.011926) \| 0.027503 / 0.128546 (-0.101043) \| 0.010370 / 0.075646 (-0.065276) \| 0.208965 / 0.419271 (-0.210306) \| 0.035508 / 0.043533 (-0.008024) \| 0.249116 / 0.255139 (-0.006023) \| 0.266350 / 0.283200 (-0.016850) \| 0.018440 / 0.141683 (-0.123243) \| 1.101089 / 1.452155 (-0.351066) \| 1.164870 / 1.492716 (-0.327847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090909 / 0.018006 (0.072903) \| 0.298041 / 0.000490 (0.297551) \| 0.000211 / 0.000200 (0.000012) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018137 / 0.037411 (-0.019275) \| 0.059574 / 0.014526 (0.045048) \| 0.071754 / 0.176557 (-0.104803) \| 0.117980 / 0.737135 (-0.619155) \| 0.072903 / 0.296338 (-0.223435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282844 / 0.215209 (0.067635) \| 2.740916 / 2.077655 (0.663261) \| 1.444546 / 1.504120 (-0.059574) \| 1.321904 / 1.541195 (-0.219291) \| 1.356957 / 1.468490 (-0.111533) \| 0.568389 / 4.584777 (-4.016388) \| 2.354042 / 3.745712 (-1.391671) \| 2.719427 / 5.269862 (-2.550435) \| 1.719616 / 4.565676 (-2.846061) \| 0.062537 / 0.424275 (-0.361738) \| 0.004915 / 0.007607 (-0.002692) \| 0.334716 / 0.226044 (0.108672) \| 3.299499 / 2.268929 (1.030571) \| 1.814629 / 55.444624 (-53.629996) \| 1.515245 / 6.876477 (-5.361232) \| 1.553085 / 2.142072 (-0.588987) \| 0.643859 / 4.805227 (-4.161368) \| 0.116650 / 6.500664 (-6.384014) \| 0.041432 / 0.075469 (-0.034037) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948227 / 1.841788 (-0.893561) \| 11.331103 / 8.074308 (3.256795) \| 10.209658 / 10.191392 (0.018266) \| 0.126721 / 0.680424 (-0.553703) \| 0.013638 / 0.534201 (-0.520563) \| 0.282540 / 0.579283 (-0.296743) \| 0.262635 / 0.434364 (-0.171729) \| 0.335357 / 0.540337 (-0.204981) \| 0.441798 / 1.386936 (-0.945138) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005200 / 0.011353 (-0.006153) \| 0.003012 / 0.011008 (-0.007996) \| 0.047571 / 0.038508 (0.009063) \| 0.055069 / 0.023109 (0.031959) \| 0.271150 / 0.275898 (-0.004748) \| 0.294957 / 0.323480 (-0.028523) \| 0.003922 / 0.007986 (-0.004064) \| 0.002627 / 0.004328 (-0.001702) \| 0.047777 / 0.004250 (0.043527) \| 0.039507 / 0.037052 (0.002454) \| 0.276314 / 0.258489 (0.017825) \| 0.300436 / 0.293841 (0.006595) \| 0.028951 / 0.128546 (-0.099595) \| 0.010583 / 0.075646 (-0.065063) \| 0.056535 / 0.419271 (-0.362737) \| 0.032654 / 0.043533 (-0.010879) \| 0.272945 / 0.255139 (0.017806) \| 0.291909 / 0.283200 (0.008709) \| 0.017545 / 0.141683 (-0.124138) \| 1.195897 / 1.452155 (-0.256258) \| 1.171855 / 1.492716 (-0.320861) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091919 / 0.018006 (0.073913) \| 0.299297 / 0.000490 (0.298807) \| 0.000225 / 0.000200 (0.000025) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022271 / 0.037411 (-0.015140) \| 0.068903 / 0.014526 (0.054377) \| 0.083767 / 0.176557 (-0.092790) \| 0.120239 / 0.737135 (-0.616896) \| 0.083448 / 0.296338 (-0.212891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295353 / 0.215209 (0.080144) \| 2.911452 / 2.077655 (0.833798) \| 1.577941 / 1.504120 (0.073821) \| 1.454514 / 1.541195 (-0.086681) \| 1.459575 / 1.468490 (-0.008915) \| 0.572475 / 4.584777 (-4.012302) \| 2.443634 / 3.745712 (-1.302078) \| 2.801171 / 5.269862 (-2.468691) \| 1.724214 / 4.565676 (-2.841462) \| 0.063539 / 0.424275 (-0.360736) \| 0.004939 / 0.007607 (-0.002668) \| 0.347705 / 0.226044 (0.121660) \| 3.489591 / 2.268929 (1.220663) \| 1.944952 / 55.444624 (-53.499672) \| 1.652810 / 6.876477 (-5.223667) \| 1.656361 / 2.142072 (-0.485712) \| 0.647052 / 4.805227 (-4.158176) \| 0.117286 / 6.500664 (-6.383379) \| 0.040979 / 0.075469 (-0.034490) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971761 / 1.841788 (-0.870027) \| 11.770547 / 8.074308 (3.696239) \| 10.402502 / 10.191392 (0.211110) \| 0.128280 / 0.680424 (-0.552144) \| 0.015160 / 0.534201 (-0.519041) \| 0.286706 / 0.579283 (-0.292578) \| 0.274539 / 0.434364 (-0.159825) \| 0.324591 / 0.540337 (-0.215747) \| 0.573846 / 1.386936 (-0.813090) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3329be80b9abfe83285ef940a590a4e9f68835a3 \"CML watermark\")\n" ]	2023-12-15T09:17:41Z	2023-12-18T11:56:11Z	2023-12-18T11:50:03Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6500.diff", "html_url": "https://github.com/huggingface/datasets/pull/6500", "merged_at": "2023-12-18T11:50:03Z", "patch_url": "https://github.com/huggingface/datasets/pull/6500.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6500" }	Fix #6497.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 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https://github.com/huggingface/datasets/pull/6499	6,499	docs: add reference Git over SSH	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6499). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005701 / 0.011353 (-0.005652) \| 0.003546 / 0.011008 (-0.007463) \| 0.063335 / 0.038508 (0.024827) \| 0.051987 / 0.023109 (0.028878) \| 0.240429 / 0.275898 (-0.035469) \| 0.260659 / 0.323480 (-0.062820) \| 0.003866 / 0.007986 (-0.004120) \| 0.002617 / 0.004328 (-0.001712) \| 0.048653 / 0.004250 (0.044403) \| 0.038176 / 0.037052 (0.001124) \| 0.245496 / 0.258489 (-0.012993) \| 0.277141 / 0.293841 (-0.016700) \| 0.027886 / 0.128546 (-0.100660) \| 0.010738 / 0.075646 (-0.064908) \| 0.211255 / 0.419271 (-0.208016) \| 0.045205 / 0.043533 (0.001672) \| 0.243062 / 0.255139 (-0.012077) \| 0.262877 / 0.283200 (-0.020323) \| 0.023426 / 0.141683 (-0.118257) \| 1.092247 / 1.452155 (-0.359908) \| 1.161074 / 1.492716 (-0.331642) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090488 / 0.018006 (0.072482) \| 0.300993 / 0.000490 (0.300504) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018543 / 0.037411 (-0.018868) \| 0.061418 / 0.014526 (0.046892) \| 0.073242 / 0.176557 (-0.103314) \| 0.120757 / 0.737135 (-0.616378) \| 0.073967 / 0.296338 (-0.222372) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282341 / 0.215209 (0.067132) \| 2.741106 / 2.077655 (0.663451) \| 1.416573 / 1.504120 (-0.087547) \| 1.287904 / 1.541195 (-0.253291) \| 1.309425 / 1.468490 (-0.159065) \| 0.582592 / 4.584777 (-4.002184) \| 2.404866 / 3.745712 (-1.340846) \| 2.895397 / 5.269862 (-2.374464) \| 1.799864 / 4.565676 (-2.765812) \| 0.064386 / 0.424275 (-0.359889) \| 0.004920 / 0.007607 (-0.002687) \| 0.330879 / 0.226044 (0.104835) \| 3.287064 / 2.268929 (1.018135) \| 1.765169 / 55.444624 (-53.679456) \| 1.490442 / 6.876477 (-5.386034) \| 1.530960 / 2.142072 (-0.611113) \| 0.655939 / 4.805227 (-4.149288) \| 0.118529 / 6.500664 (-6.382135) \| 0.042350 / 0.075469 (-0.033119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959027 / 1.841788 (-0.882761) \| 11.911284 / 8.074308 (3.836976) \| 10.576898 / 10.191392 (0.385506) \| 0.141038 / 0.680424 (-0.539386) \| 0.014184 / 0.534201 (-0.520017) \| 0.305335 / 0.579283 (-0.273948) \| 0.267531 / 0.434364 (-0.166832) \| 0.353362 / 0.540337 (-0.186975) \| 0.466258 / 1.386936 (-0.920678) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005194 / 0.011353 (-0.006159) \| 0.003561 / 0.011008 (-0.007448) \| 0.049181 / 0.038508 (0.010673) \| 0.056664 / 0.023109 (0.033555) \| 0.267142 / 0.275898 (-0.008756) \| 0.291871 / 0.323480 (-0.031609) \| 0.003996 / 0.007986 (-0.003990) \| 0.003147 / 0.004328 (-0.001181) \| 0.048527 / 0.004250 (0.044276) \| 0.040239 / 0.037052 (0.003187) \| 0.269728 / 0.258489 (0.011239) \| 0.295531 / 0.293841 (0.001690) \| 0.030316 / 0.128546 (-0.098231) \| 0.010666 / 0.075646 (-0.064981) \| 0.058176 / 0.419271 (-0.361095) \| 0.033218 / 0.043533 (-0.010315) \| 0.265383 / 0.255139 (0.010244) \| 0.285102 / 0.283200 (0.001902) \| 0.018295 / 0.141683 (-0.123388) \| 1.117830 / 1.452155 (-0.334325) \| 1.196919 / 1.492716 (-0.295798) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088547 / 0.018006 (0.070541) \| 0.293220 / 0.000490 (0.292730) \| 0.000211 / 0.000200 (0.000011) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022060 / 0.037411 (-0.015351) \| 0.071973 / 0.014526 (0.057448) \| 0.081721 / 0.176557 (-0.094836) \| 0.119990 / 0.737135 (-0.617145) \| 0.081639 / 0.296338 (-0.214700) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293712 / 0.215209 (0.078503) \| 2.872986 / 2.077655 (0.795331) \| 1.568944 / 1.504120 (0.064824) \| 1.434555 / 1.541195 (-0.106639) \| 1.457747 / 1.468490 (-0.010743) \| 0.559296 / 4.584777 (-4.025481) \| 2.471845 / 3.745712 (-1.273867) \| 2.840916 / 5.269862 (-2.428946) \| 1.754909 / 4.565676 (-2.810768) \| 0.064585 / 0.424275 (-0.359690) \| 0.004992 / 0.007607 (-0.002615) \| 0.349149 / 0.226044 (0.123104) \| 3.385906 / 2.268929 (1.116977) \| 1.940644 / 55.444624 (-53.503980) \| 1.638300 / 6.876477 (-5.238177) \| 1.649939 / 2.142072 (-0.492133) \| 0.645680 / 4.805227 (-4.159547) \| 0.118080 / 6.500664 (-6.382584) \| 0.040643 / 0.075469 (-0.034826) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969965 / 1.841788 (-0.871822) \| 12.099766 / 8.074308 (4.025457) \| 10.550650 / 10.191392 (0.359258) \| 0.131736 / 0.680424 (-0.548688) \| 0.015483 / 0.534201 (-0.518718) \| 0.289231 / 0.579283 (-0.290052) \| 0.287505 / 0.434364 (-0.146858) \| 0.327326 / 0.540337 (-0.213011) \| 0.570364 / 1.386936 (-0.816572) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#533c38cef16111e9e8154eeb76c207f1f4936ddf \"CML watermark\")\n" ]	2023-12-15T08:38:31Z	2023-12-15T11:48:47Z	2023-12-15T11:42:38Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6499.diff", "html_url": "https://github.com/huggingface/datasets/pull/6499", "merged_at": "2023-12-15T11:42:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6499.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6499" }	see https://discuss.huggingface.co/t/update-datasets-getting-started-to-new-git-security/65893	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6499/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6498	6,498	Fallback on dataset script if user wants to load default config	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6498). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> I was just thinking: what if the user does not pass a config name and the dataset has only a config with a name different from \"default\"?\r\n\r\nYou mean if there is a DEFAULT_CONFIG_NAME defined in the script but the dataset only has one configuration ? We can't easily get the number of configs without running the python code so I don't think we can support detect this case\r\n", "Most datasets with a script don't define DEFAULT_CONFIG_NAME if there is only one configuration anyway.\r\n\r\nSo there is no issue e.g. for `squad`", "> I was trying to mean the case where DEFAULT_CONFIG_NAME is None but there is only a single config in BUILDER_CONFIGS, with a name different from \"default\".\r\n\r\nIn this case we can detect if \"DEFAULT_CONFIG_NAME\" is not mentioned and use the Parquet export. If it is mentioned (and maybe it is set to None or to the single config) I consider that it may have multiple configs and fall back on using the script", "... but the user does not pass the config name.", "In this case we load the single configuration (this is how a DatasetBuilder works)", "see \r\n\r\nhttps://github.com/huggingface/datasets/blob/2feaa589de86dd85941301fc8c3fa091731a67c0/src/datasets/builder.py#L532-L532", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005122 / 0.011353 (-0.006231) \| 0.003565 / 0.011008 (-0.007443) \| 0.062706 / 0.038508 (0.024198) \| 0.049314 / 0.023109 (0.026205) \| 0.247325 / 0.275898 (-0.028573) \| 0.269788 / 0.323480 (-0.053692) \| 0.003895 / 0.007986 (-0.004090) \| 0.002788 / 0.004328 (-0.001540) \| 0.048615 / 0.004250 (0.044365) \| 0.037591 / 0.037052 (0.000539) \| 0.253495 / 0.258489 (-0.004994) \| 0.281200 / 0.293841 (-0.012641) \| 0.027712 / 0.128546 (-0.100834) \| 0.010901 / 0.075646 (-0.064745) \| 0.205577 / 0.419271 (-0.213694) \| 0.035989 / 0.043533 (-0.007544) \| 0.252978 / 0.255139 (-0.002161) \| 0.268042 / 0.283200 (-0.015157) \| 0.017857 / 0.141683 (-0.123826) \| 1.096633 / 1.452155 (-0.355521) \| 1.147026 / 1.492716 (-0.345691) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095609 / 0.018006 (0.077603) \| 0.311941 / 0.000490 (0.311451) \| 0.000211 / 0.000200 (0.000011) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019042 / 0.037411 (-0.018369) \| 0.060549 / 0.014526 (0.046023) \| 0.074761 / 0.176557 (-0.101796) \| 0.121729 / 0.737135 (-0.615406) \| 0.075661 / 0.296338 (-0.220677) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284774 / 0.215209 (0.069565) \| 2.764576 / 2.077655 (0.686921) \| 1.489926 / 1.504120 (-0.014194) \| 1.387276 / 1.541195 (-0.153919) \| 1.400931 / 1.468490 (-0.067559) \| 0.555623 / 4.584777 (-4.029154) \| 2.409488 / 3.745712 (-1.336224) \| 2.781053 / 5.269862 (-2.488808) \| 1.750472 / 4.565676 (-2.815204) \| 0.062232 / 0.424275 (-0.362043) \| 0.004974 / 0.007607 (-0.002633) \| 0.336324 / 0.226044 (0.110280) \| 3.286619 / 2.268929 (1.017691) \| 1.825070 / 55.444624 (-53.619554) \| 1.537993 / 6.876477 (-5.338484) \| 1.586520 / 2.142072 (-0.555553) \| 0.640090 / 4.805227 (-4.165138) \| 0.117637 / 6.500664 (-6.383027) \| 0.042318 / 0.075469 (-0.033151) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964051 / 1.841788 (-0.877736) \| 11.706259 / 8.074308 (3.631951) \| 10.752311 / 10.191392 (0.560919) \| 0.128117 / 0.680424 (-0.552307) \| 0.014001 / 0.534201 (-0.520200) \| 0.286255 / 0.579283 (-0.293028) \| 0.263810 / 0.434364 (-0.170554) \| 0.329347 / 0.540337 (-0.210991) \| 0.437349 / 1.386936 (-0.949587) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005303 / 0.011353 (-0.006050) \| 0.003586 / 0.011008 (-0.007422) \| 0.049339 / 0.038508 (0.010831) \| 0.051287 / 0.023109 (0.028178) \| 0.274397 / 0.275898 (-0.001501) \| 0.292977 / 0.323480 (-0.030503) \| 0.004029 / 0.007986 (-0.003957) \| 0.002727 / 0.004328 (-0.001602) \| 0.048779 / 0.004250 (0.044528) \| 0.040075 / 0.037052 (0.003022) \| 0.277676 / 0.258489 (0.019187) \| 0.301963 / 0.293841 (0.008122) \| 0.029340 / 0.128546 (-0.099206) \| 0.010714 / 0.075646 (-0.064932) \| 0.057253 / 0.419271 (-0.362018) \| 0.033426 / 0.043533 (-0.010107) \| 0.276673 / 0.255139 (0.021534) \| 0.291053 / 0.283200 (0.007854) \| 0.017660 / 0.141683 (-0.124023) \| 1.122354 / 1.452155 (-0.329800) \| 1.180381 / 1.492716 (-0.312335) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091903 / 0.018006 (0.073897) \| 0.300720 / 0.000490 (0.300231) \| 0.000288 / 0.000200 (0.000088) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021521 / 0.037411 (-0.015890) \| 0.068233 / 0.014526 (0.053707) \| 0.081245 / 0.176557 (-0.095312) \| 0.119996 / 0.737135 (-0.617139) \| 0.082483 / 0.296338 (-0.213856) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302776 / 0.215209 (0.087567) \| 2.950776 / 2.077655 (0.873122) \| 1.631032 / 1.504120 (0.126912) \| 1.502021 / 1.541195 (-0.039174) \| 1.514213 / 1.468490 (0.045723) \| 0.578246 / 4.584777 (-4.006531) \| 2.443768 / 3.745712 (-1.301944) \| 2.827811 / 5.269862 (-2.442051) \| 1.771529 / 4.565676 (-2.794148) \| 0.064479 / 0.424275 (-0.359797) \| 0.005061 / 0.007607 (-0.002546) \| 0.350966 / 0.226044 (0.124922) \| 3.458616 / 2.268929 (1.189687) \| 1.967917 / 55.444624 (-53.476707) \| 1.704661 / 6.876477 (-5.171815) \| 1.698895 / 2.142072 (-0.443178) \| 0.663259 / 4.805227 (-4.141968) \| 0.122140 / 6.500664 (-6.378525) \| 0.041099 / 0.075469 (-0.034371) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972080 / 1.841788 (-0.869708) \| 12.123286 / 8.074308 (4.048978) \| 10.819854 / 10.191392 (0.628462) \| 0.131486 / 0.680424 (-0.548938) \| 0.015785 / 0.534201 (-0.518416) \| 0.290048 / 0.579283 (-0.289235) \| 0.277822 / 0.434364 (-0.156542) \| 0.325949 / 0.540337 (-0.214388) \| 0.577681 / 1.386936 (-0.809255) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#30f6a2d9af183eba4501f0b8d90e9200bdca6bb1 \"CML watermark\")\n" ]	2023-12-14T16:46:01Z	2023-12-15T13:16:56Z	2023-12-15T13:10:48Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6498.diff", "html_url": "https://github.com/huggingface/datasets/pull/6498", "merged_at": "2023-12-15T13:10:48Z", "patch_url": "https://github.com/huggingface/datasets/pull/6498.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6498" }	Right now this code is failing on `main`: ```python load_dataset("openbookqa") ``` This is because it tries to load the dataset from the Parquet export but the dataset has multiple configurations and the Parquet export doesn't know which one is the default one. I fixed this by simply falling back on using the dataset script (which tells the user to pass `trust_remote_code=True`): ```python load_dataset("openbookqa", trust_remote_code=True) ``` Note that if the user happened to specify a config name I don't fall back on the script since we can use the Parquet export in this case (no need to know which config is the default) ```python load_dataset("openbookqa", "main") ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6498/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6497	6,497	Support setting a default config name in push_to_hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	[]	2023-12-14T15:59:03Z	2023-12-18T11:50:04Z	2023-12-18T11:50:04Z	MEMBER	null	null	In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6497/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6496	6,496	Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.	{ "avatar_url": "https://avatars.githubusercontent.com/u/35808396?v=4", "events_url": "https://api.github.com/users/GeorgesLorre/events{/privacy}", "followers_url": "https://api.github.com/users/GeorgesLorre/followers", "following_url": "https://api.github.com/users/GeorgesLorre/following{/other_user}", "gists_url": "https://api.github.com/users/GeorgesLorre/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/GeorgesLorre", "id": 35808396, "login": "GeorgesLorre", "node_id": "MDQ6VXNlcjM1ODA4Mzk2", "organizations_url": "https://api.github.com/users/GeorgesLorre/orgs", "received_events_url": "https://api.github.com/users/GeorgesLorre/received_events", "repos_url": "https://api.github.com/users/GeorgesLorre/repos", "site_admin": false, "starred_url": "https://api.github.com/users/GeorgesLorre/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/GeorgesLorre/subscriptions", "type": "User", "url": "https://api.github.com/users/GeorgesLorre", "user_view_type": "public" }	[]	open	false	[ "I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`." ]	2023-12-14T11:24:54Z	2023-12-14T12:22:21Z	null	NONE	null	null	Describe the bug Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub. ``` huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc) A commit has happened since. Please refresh and try again. ``` Steps to reproduce the bug This is a minimal reproducer: ``` import dask.dataframe as dd import pandas as pd import random import os import huggingface_hub import datasets huggingface_hub.login(token=os.getenv("HF_TOKEN")) data = {"number": [random.randint(0,10) for _ in range(1000)]} df = pd.DataFrame.from_dict(data) dataframe = dd.from_pandas(df, npartitions=1) dataframe = dataframe.repartition(npartitions=3) schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema repo_id = "GLorr/test-dask" repo_path = f"hf://datasets/{repo_id}" huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True) dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema) ``` Expected behavior Would expect to write to the hub without any problem. Environment info ``` datasets==2.15.0 huggingface-hub==0.19.4 ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6496/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6494	6,494	Image Data loaded Twice	{ "avatar_url": "https://avatars.githubusercontent.com/u/28867010?v=4", "events_url": "https://api.github.com/users/ArcaneLex/events{/privacy}", "followers_url": "https://api.github.com/users/ArcaneLex/followers", "following_url": "https://api.github.com/users/ArcaneLex/following{/other_user}", "gists_url": "https://api.github.com/users/ArcaneLex/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ArcaneLex", "id": 28867010, "login": "ArcaneLex", "node_id": "MDQ6VXNlcjI4ODY3MDEw", "organizations_url": "https://api.github.com/users/ArcaneLex/orgs", "received_events_url": "https://api.github.com/users/ArcaneLex/received_events", "repos_url": "https://api.github.com/users/ArcaneLex/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ArcaneLex/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ArcaneLex/subscriptions", "type": "User", "url": "https://api.github.com/users/ArcaneLex", "user_view_type": "public" }	[]	open	false	[]	2023-12-13T13:11:42Z	2023-12-13T13:11:42Z	null	NONE	null	null	### Describe the bug ![1702472610561](https://github.com/huggingface/datasets/assets/28867010/4b7ef5e7-32c3-4b73-84cb-5de059caa0b6) When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images ### Steps to reproduce the bug from datasets import Dataset, load_dataset dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False) # print(dataset["train"][0]["image"] == dataset["train"][1]["image"]) print(dataset) print(dataset["train"]["image"]) print(len(dataset["train"]["image"])) ### Expected behavior DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 8 }) }) [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>] 8 ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.17 - Huggingface_hub version: 0.19.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6494/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6495	6,495	Newline characters don't behave as expected when calling dataset.info	{ "avatar_url": "https://avatars.githubusercontent.com/u/32300890?v=4", "events_url": "https://api.github.com/users/gerald-wrona/events{/privacy}", "followers_url": "https://api.github.com/users/gerald-wrona/followers", "following_url": "https://api.github.com/users/gerald-wrona/following{/other_user}", "gists_url": "https://api.github.com/users/gerald-wrona/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/gerald-wrona", "id": 32300890, "login": "gerald-wrona", "node_id": "MDQ6VXNlcjMyMzAwODkw", "organizations_url": "https://api.github.com/users/gerald-wrona/orgs", "received_events_url": "https://api.github.com/users/gerald-wrona/received_events", "repos_url": "https://api.github.com/users/gerald-wrona/repos", "site_admin": false, "starred_url": "https://api.github.com/users/gerald-wrona/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/gerald-wrona/subscriptions", "type": "User", "url": "https://api.github.com/users/gerald-wrona", "user_view_type": "public" }	[]	open	false	[]	2023-12-12T23:07:51Z	2023-12-13T13:24:22Z	null	NONE	null	null	### System Info - `transformers` version: 4.32.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.2 - Accelerate version: not installed - Accelerate config: not found - PyTorch version (GPU?): 2.1.1+cpu (False) - Tensorflow version (GPU?): 2.15.0 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: no - Using distributed or parallel set-up in script?: no ### Who can help? @marios ### Information - [X] The official example scripts - [ ] My own modified scripts ### Tasks - [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction [Source](https://huggingface.co/docs/datasets/v2.2.1/en/access) ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673) ### Expected behavior ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo( description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': '60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': 'a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784'}}, download_size=1494541, post_processing_size=None, dataset_size=1492156, size_in_bytes=2986697 )	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6495/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, 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https://github.com/huggingface/datasets/pull/6493	6,493	Lazy data files resolution and offline cache reload	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6493). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Naive question: is there any breaking change when loading?\r\n\r\nNo breaking changes except that the cache folders are different\r\n\r\ne.g. for glue sst2 (has parquet export)\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/fd8e86499fa5c264fcaad392a8f49ddf58bf4037\r\nOn main\r\n~/.cache/huggingface/datasets/glue/sst2/0.0.0/74a75637ac4acd3f\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\r\n```\r\n\r\ne.g. for wikimedia/wikipedia 20231101.ab (has metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/4cb9b0d719291f1a10f96f67d609c5d442980dc9\r\nOn main (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/cfa627e27933df13\r\nOn 2.15.0 (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/e92ee7a91c466564\r\n```\r\n\r\n\r\ne.g. for lhoestq/demo1 (no metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default/0.0.0/87ecf163bedca9d80598b528940a9c4f99e14c11\r\nOn main\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-8a4a0b7a240d3c5e/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-59d4029e0bb36ae0/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\n```", "There was a last bug I just fixed: if you modify a dataset and reload it from the hub it won't download the new version - I think I need to use another hash to name the cache directory\r\nedit: fixed", "I switched to using the git commit sha for the cache directory, which is now `config/version/commit_sha` :) much cleaner than before.\r\n\r\nAnd for local file it's a hash that takes into account the resolved files (and their last modified dates)", "I also ran the `transformers` CI on this branch and it's green", "FYI `huggingface_hub` will have a release on tuesday/wednesday (will speed up load_dataset data files resolution which is now needed for datasets loaded from parquet export) so we can aim on merging this around the same time and do a release on thursday", "Merging this one, and hopefully the cache backward compatibility PR soon too :)\r\n\r\nThen it will be release time", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005444 / 0.011353 (-0.005909) \| 0.003562 / 0.011008 (-0.007446) \| 0.063183 / 0.038508 (0.024675) \| 0.048885 / 0.023109 (0.025776) \| 0.248422 / 0.275898 (-0.027476) \| 0.277844 / 0.323480 (-0.045636) \| 0.003019 / 0.007986 (-0.004966) \| 0.002660 / 0.004328 (-0.001669) \| 0.048928 / 0.004250 (0.044677) \| 0.044850 / 0.037052 (0.007798) \| 0.248505 / 0.258489 (-0.009984) \| 0.282231 / 0.293841 (-0.011610) \| 0.028302 / 0.128546 (-0.100244) \| 0.010829 / 0.075646 (-0.064818) \| 0.206738 / 0.419271 (-0.212533) \| 0.035485 / 0.043533 (-0.008048) \| 0.244575 / 0.255139 (-0.010564) \| 0.281411 / 0.283200 (-0.001789) \| 0.019563 / 0.141683 (-0.122120) \| 1.113769 / 1.452155 (-0.338386) \| 1.176831 / 1.492716 (-0.315885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004718 / 0.018006 (-0.013288) \| 0.304103 / 0.000490 (0.303614) \| 0.000214 / 0.000200 (0.000014) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019642 / 0.037411 (-0.017769) \| 0.060275 / 0.014526 (0.045749) \| 0.073072 / 0.176557 (-0.103484) \| 0.119789 / 0.737135 (-0.617346) \| 0.074535 / 0.296338 (-0.221804) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278799 / 0.215209 (0.063590) \| 2.725320 / 2.077655 (0.647665) \| 1.419048 / 1.504120 (-0.085071) \| 1.335041 / 1.541195 (-0.206154) \| 1.373029 / 1.468490 (-0.095461) \| 0.566774 / 4.584777 (-4.018003) \| 2.383796 / 3.745712 (-1.361916) \| 2.734804 / 5.269862 (-2.535057) \| 1.712277 / 4.565676 (-2.853399) \| 0.062119 / 0.424275 (-0.362156) \| 0.004949 / 0.007607 (-0.002658) \| 0.336126 / 0.226044 (0.110082) \| 3.298602 / 2.268929 (1.029674) \| 1.842815 / 55.444624 (-53.601809) \| 1.544028 / 6.876477 (-5.332449) \| 1.566717 / 2.142072 (-0.575355) \| 0.643006 / 4.805227 (-4.162221) \| 0.118241 / 6.500664 (-6.382423) \| 0.042453 / 0.075469 (-0.033016) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949015 / 1.841788 (-0.892773) \| 11.717958 / 8.074308 (3.643649) \| 10.482448 / 10.191392 (0.291056) \| 0.128564 / 0.680424 (-0.551860) \| 0.014792 / 0.534201 (-0.519408) \| 0.288636 / 0.579283 (-0.290647) \| 0.263345 / 0.434364 (-0.171019) \| 0.325753 / 0.540337 (-0.214584) \| 0.421294 / 1.386936 (-0.965642) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005367 / 0.011353 (-0.005985) \| 0.003802 / 0.011008 (-0.007206) \| 0.049322 / 0.038508 (0.010814) \| 0.055201 / 0.023109 (0.032092) \| 0.287811 / 0.275898 (0.011913) \| 0.305141 / 0.323480 (-0.018339) \| 0.004095 / 0.007986 (-0.003890) \| 0.002733 / 0.004328 (-0.001595) \| 0.049508 / 0.004250 (0.045258) \| 0.039199 / 0.037052 (0.002147) \| 0.282719 / 0.258489 (0.024230) \| 0.311156 / 0.293841 (0.017315) \| 0.029469 / 0.128546 (-0.099077) \| 0.010709 / 0.075646 (-0.064937) \| 0.057646 / 0.419271 (-0.361626) \| 0.032696 / 0.043533 (-0.010837) \| 0.285087 / 0.255139 (0.029948) \| 0.294142 / 0.283200 (0.010942) \| 0.019779 / 0.141683 (-0.121904) \| 1.176844 / 1.452155 (-0.275310) \| 1.190925 / 1.492716 (-0.301792) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092885 / 0.018006 (0.074879) \| 0.301129 / 0.000490 (0.300640) \| 0.000232 / 0.000200 (0.000032) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023202 / 0.037411 (-0.014210) \| 0.076850 / 0.014526 (0.062325) \| 0.090058 / 0.176557 (-0.086499) \| 0.128091 / 0.737135 (-0.609045) \| 0.091098 / 0.296338 (-0.205240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292973 / 0.215209 (0.077764) \| 2.876022 / 2.077655 (0.798367) \| 1.672115 / 1.504120 (0.167995) \| 1.555103 / 1.541195 (0.013909) \| 1.559832 / 1.468490 (0.091342) \| 0.558017 / 4.584777 (-4.026760) \| 2.428448 / 3.745712 (-1.317264) \| 2.812024 / 5.269862 (-2.457837) \| 1.738470 / 4.565676 (-2.827207) \| 0.062669 / 0.424275 (-0.361607) \| 0.005071 / 0.007607 (-0.002536) \| 0.351804 / 0.226044 (0.125759) \| 3.412207 / 2.268929 (1.143279) \| 2.023478 / 55.444624 (-53.421147) \| 1.761281 / 6.876477 (-5.115195) \| 1.770789 / 2.142072 (-0.371283) \| 0.643062 / 4.805227 (-4.162165) \| 0.116616 / 6.500664 (-6.384048) \| 0.041816 / 0.075469 (-0.033653) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988430 / 1.841788 (-0.853357) \| 12.278636 / 8.074308 (4.204328) \| 11.066185 / 10.191392 (0.874793) \| 0.141191 / 0.680424 (-0.539233) \| 0.015547 / 0.534201 (-0.518654) \| 0.288045 / 0.579283 (-0.291238) \| 0.279651 / 0.434364 (-0.154713) \| 0.329869 / 0.540337 (-0.210469) \| 0.420391 / 1.386936 (-0.966545) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3b5dd3633995c95d77f35fb17f89ff44990bc4 \"CML watermark\")\n" ]	2023-12-12T17:15:17Z	2023-12-21T15:19:20Z	2023-12-21T15:13:11Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6493.diff", "html_url": "https://github.com/huggingface/datasets/pull/6493", "merged_at": "2023-12-21T15:13:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/6493.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6493" }	Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459 This PR should be merged instead of the two individually, since they are conflicting ## Offline cache reload it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023). DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` - Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>` - Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one - I instantiate a BuilderConfig even if there is no metadata config with the right config_name - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs) - We can reload this config if offline mode by specifying the right config_name (same as online !) - Consequences of this change: - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash. - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :) Fix https://github.com/huggingface/datasets/issues/3547 ## Lazy data files resolution this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6493/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6492	6,492	Make push_to_hub return CommitInfo	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6492). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This PR is ready to review @huggingface/datasets.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005093 / 0.011353 (-0.006259) \| 0.003695 / 0.011008 (-0.007313) \| 0.064648 / 0.038508 (0.026140) \| 0.054677 / 0.023109 (0.031568) \| 0.242007 / 0.275898 (-0.033891) \| 0.265216 / 0.323480 (-0.058264) \| 0.003847 / 0.007986 (-0.004138) \| 0.003773 / 0.004328 (-0.000556) \| 0.048595 / 0.004250 (0.044345) \| 0.038122 / 0.037052 (0.001070) \| 0.245698 / 0.258489 (-0.012791) \| 0.278095 / 0.293841 (-0.015746) \| 0.027488 / 0.128546 (-0.101058) \| 0.011002 / 0.075646 (-0.064644) \| 0.211443 / 0.419271 (-0.207829) \| 0.035664 / 0.043533 (-0.007869) \| 0.244754 / 0.255139 (-0.010385) \| 0.261078 / 0.283200 (-0.022121) \| 0.017768 / 0.141683 (-0.123915) \| 1.130765 / 1.452155 (-0.321390) \| 1.189825 / 1.492716 (-0.302891) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093027 / 0.018006 (0.075021) \| 0.302193 / 0.000490 (0.301703) \| 0.000207 / 0.000200 (0.000007) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018413 / 0.037411 (-0.018999) \| 0.062715 / 0.014526 (0.048190) \| 0.073287 / 0.176557 (-0.103269) \| 0.120394 / 0.737135 (-0.616741) \| 0.077573 / 0.296338 (-0.218765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284445 / 0.215209 (0.069236) \| 2.780718 / 2.077655 (0.703063) \| 1.460988 / 1.504120 (-0.043132) \| 1.345799 / 1.541195 (-0.195395) \| 1.399892 / 1.468490 (-0.068598) \| 0.576051 / 4.584777 (-4.008726) \| 2.418792 / 3.745712 (-1.326921) \| 2.901330 / 5.269862 (-2.368532) \| 1.765083 / 4.565676 (-2.800593) \| 0.063555 / 0.424275 (-0.360720) \| 0.004991 / 0.007607 (-0.002616) \| 0.339657 / 0.226044 (0.113613) \| 3.372963 / 2.268929 (1.104034) \| 1.853667 / 55.444624 (-53.590958) \| 1.552022 / 6.876477 (-5.324454) \| 1.616452 / 2.142072 (-0.525620) \| 0.652309 / 4.805227 (-4.152919) \| 0.121125 / 6.500664 (-6.379539) \| 0.042420 / 0.075469 (-0.033049) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.954514 / 1.841788 (-0.887274) \| 11.853736 / 8.074308 (3.779428) \| 10.624571 / 10.191392 (0.433179) \| 0.134118 / 0.680424 (-0.546306) \| 0.014200 / 0.534201 (-0.520001) \| 0.290106 / 0.579283 (-0.289177) \| 0.270637 / 0.434364 (-0.163727) \| 0.336155 / 0.540337 (-0.204182) \| 0.443962 / 1.386936 (-0.942974) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005282 / 0.011353 (-0.006071) \| 0.003526 / 0.011008 (-0.007482) \| 0.048994 / 0.038508 (0.010486) \| 0.055345 / 0.023109 (0.032236) \| 0.271587 / 0.275898 (-0.004311) \| 0.294676 / 0.323480 (-0.028804) \| 0.003989 / 0.007986 (-0.003996) \| 0.002594 / 0.004328 (-0.001735) \| 0.048310 / 0.004250 (0.044059) \| 0.039945 / 0.037052 (0.002893) \| 0.277304 / 0.258489 (0.018815) \| 0.312017 / 0.293841 (0.018176) \| 0.028364 / 0.128546 (-0.100182) \| 0.010683 / 0.075646 (-0.064963) \| 0.057990 / 0.419271 (-0.361281) \| 0.032418 / 0.043533 (-0.011115) \| 0.273835 / 0.255139 (0.018697) \| 0.288585 / 0.283200 (0.005385) \| 0.018964 / 0.141683 (-0.122719) \| 1.148863 / 1.452155 (-0.303292) \| 1.195684 / 1.492716 (-0.297032) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091967 / 0.018006 (0.073960) \| 0.303236 / 0.000490 (0.302747) \| 0.000214 / 0.000200 (0.000015) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021960 / 0.037411 (-0.015452) \| 0.068744 / 0.014526 (0.054218) \| 0.081167 / 0.176557 (-0.095390) \| 0.119623 / 0.737135 (-0.617513) \| 0.084965 / 0.296338 (-0.211373) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297740 / 0.215209 (0.082531) \| 2.924856 / 2.077655 (0.847201) \| 1.602080 / 1.504120 (0.097960) \| 1.494083 / 1.541195 (-0.047112) \| 1.544662 / 1.468490 (0.076172) \| 0.581212 / 4.584777 (-4.003565) \| 2.451064 / 3.745712 (-1.294648) \| 2.875213 / 5.269862 (-2.394649) \| 1.780777 / 4.565676 (-2.784900) \| 0.063751 / 0.424275 (-0.360524) \| 0.004967 / 0.007607 (-0.002641) \| 0.350321 / 0.226044 (0.124276) \| 3.449585 / 2.268929 (1.180657) \| 1.977666 / 55.444624 (-53.466958) \| 1.685125 / 6.876477 (-5.191351) \| 1.734466 / 2.142072 (-0.407606) \| 0.657477 / 4.805227 (-4.147750) \| 0.116767 / 6.500664 (-6.383898) \| 0.041400 / 0.075469 (-0.034069) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985751 / 1.841788 (-0.856037) \| 12.300065 / 8.074308 (4.225756) \| 10.608238 / 10.191392 (0.416846) \| 0.139907 / 0.680424 (-0.540517) \| 0.015379 / 0.534201 (-0.518822) \| 0.283528 / 0.579283 (-0.295755) \| 0.278751 / 0.434364 (-0.155613) \| 0.328811 / 0.540337 (-0.211527) \| 0.584041 / 1.386936 (-0.802895) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef0f986518bd252c5314a7e3a419dedcbb166630 \"CML watermark\")\n" ]	2023-12-12T15:18:16Z	2023-12-13T14:29:01Z	2023-12-13T14:22:41Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6492.diff", "html_url": "https://github.com/huggingface/datasets/pull/6492", "merged_at": "2023-12-13T14:22:41Z", "patch_url": "https://github.com/huggingface/datasets/pull/6492.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6492" }	Make `push_to_hub` return `CommitInfo`. This is useful, for example, if we pass `create_pr=True` and we want to know the created PR ID. CC: @severo for the use case in https://huggingface.co/datasets/jmhessel/newyorker_caption_contest/discussions/4	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6492/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6491	6,491	Fix metrics dead link	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6491). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005193 / 0.011353 (-0.006160) \| 0.003246 / 0.011008 (-0.007762) \| 0.063053 / 0.038508 (0.024545) \| 0.049636 / 0.023109 (0.026527) \| 0.240990 / 0.275898 (-0.034908) \| 0.263732 / 0.323480 (-0.059747) \| 0.004062 / 0.007986 (-0.003923) \| 0.002681 / 0.004328 (-0.001648) \| 0.048527 / 0.004250 (0.044277) \| 0.044159 / 0.037052 (0.007107) \| 0.248031 / 0.258489 (-0.010458) \| 0.275705 / 0.293841 (-0.018136) \| 0.028210 / 0.128546 (-0.100336) \| 0.010314 / 0.075646 (-0.065332) \| 0.209887 / 0.419271 (-0.209384) \| 0.035649 / 0.043533 (-0.007884) \| 0.251321 / 0.255139 (-0.003818) \| 0.266672 / 0.283200 (-0.016528) \| 0.017382 / 0.141683 (-0.124301) \| 1.088937 / 1.452155 (-0.363217) \| 1.143692 / 1.492716 (-0.349024) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092558 / 0.018006 (0.074552) \| 0.301648 / 0.000490 (0.301159) \| 0.000208 / 0.000200 (0.000008) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018305 / 0.037411 (-0.019106) \| 0.059836 / 0.014526 (0.045310) \| 0.072926 / 0.176557 (-0.103631) \| 0.119826 / 0.737135 (-0.617309) \| 0.074357 / 0.296338 (-0.221982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279051 / 0.215209 (0.063842) \| 2.711402 / 2.077655 (0.633747) \| 1.431782 / 1.504120 (-0.072338) \| 1.316592 / 1.541195 (-0.224603) \| 1.352062 / 1.468490 (-0.116428) \| 0.562553 / 4.584777 (-4.022224) \| 2.387719 / 3.745712 (-1.357993) \| 2.693330 / 5.269862 (-2.576532) \| 1.682040 / 4.565676 (-2.883636) \| 0.061832 / 0.424275 (-0.362443) \| 0.005066 / 0.007607 (-0.002541) \| 0.332730 / 0.226044 (0.106685) \| 3.315503 / 2.268929 (1.046575) \| 1.787129 / 55.444624 (-53.657496) \| 1.508955 / 6.876477 (-5.367522) \| 1.512620 / 2.142072 (-0.629453) \| 0.637120 / 4.805227 (-4.168107) \| 0.116005 / 6.500664 (-6.384660) \| 0.041973 / 0.075469 (-0.033496) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936996 / 1.841788 (-0.904792) \| 11.485975 / 8.074308 (3.411667) \| 10.604481 / 10.191392 (0.413089) \| 0.130803 / 0.680424 (-0.549621) \| 0.014561 / 0.534201 (-0.519640) \| 0.285905 / 0.579283 (-0.293378) \| 0.271573 / 0.434364 (-0.162791) \| 0.329206 / 0.540337 (-0.211132) \| 0.411977 / 1.386936 (-0.974959) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005333 / 0.011353 (-0.006020) \| 0.003519 / 0.011008 (-0.007489) \| 0.050880 / 0.038508 (0.012372) \| 0.053681 / 0.023109 (0.030571) \| 0.269359 / 0.275898 (-0.006539) \| 0.291498 / 0.323480 (-0.031982) \| 0.004006 / 0.007986 (-0.003979) \| 0.002676 / 0.004328 (-0.001653) \| 0.049652 / 0.004250 (0.045401) \| 0.040588 / 0.037052 (0.003536) \| 0.271701 / 0.258489 (0.013212) \| 0.308384 / 0.293841 (0.014543) \| 0.028713 / 0.128546 (-0.099833) \| 0.010423 / 0.075646 (-0.065223) \| 0.058099 / 0.419271 (-0.361172) \| 0.032372 / 0.043533 (-0.011161) \| 0.269395 / 0.255139 (0.014256) \| 0.292252 / 0.283200 (0.009052) \| 0.020038 / 0.141683 (-0.121645) \| 1.124761 / 1.452155 (-0.327393) \| 1.177609 / 1.492716 (-0.315107) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092187 / 0.018006 (0.074181) \| 0.301936 / 0.000490 (0.301446) \| 0.000230 / 0.000200 (0.000030) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022932 / 0.037411 (-0.014480) \| 0.076552 / 0.014526 (0.062027) \| 0.088729 / 0.176557 (-0.087827) \| 0.127198 / 0.737135 (-0.609937) \| 0.091902 / 0.296338 (-0.204436) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299964 / 0.215209 (0.084755) \| 2.929352 / 2.077655 (0.851697) \| 1.598715 / 1.504120 (0.094595) \| 1.462438 / 1.541195 (-0.078756) \| 1.474308 / 1.468490 (0.005818) \| 0.567120 / 4.584777 (-4.017657) \| 2.481757 / 3.745712 (-1.263955) \| 2.795375 / 5.269862 (-2.474487) \| 1.740346 / 4.565676 (-2.825331) \| 0.064048 / 0.424275 (-0.360227) \| 0.004995 / 0.007607 (-0.002612) \| 0.349084 / 0.226044 (0.123040) \| 3.417679 / 2.268929 (1.148750) \| 1.910615 / 55.444624 (-53.534009) \| 1.694120 / 6.876477 (-5.182356) \| 1.658654 / 2.142072 (-0.483419) \| 0.638158 / 4.805227 (-4.167069) \| 0.115509 / 6.500664 (-6.385156) \| 0.040650 / 0.075469 (-0.034819) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988081 / 1.841788 (-0.853707) \| 12.210089 / 8.074308 (4.135781) \| 11.090203 / 10.191392 (0.898811) \| 0.131861 / 0.680424 (-0.548563) \| 0.015461 / 0.534201 (-0.518740) \| 0.287737 / 0.579283 (-0.291546) \| 0.284170 / 0.434364 (-0.150194) \| 0.324949 / 0.540337 (-0.215388) \| 0.414912 / 1.386936 (-0.972024) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf71653947cecd84050daf0448dc5a73c2c071f3 \"CML watermark\")\n" ]	2023-12-12T12:51:49Z	2023-12-21T15:15:08Z	2023-12-21T15:08:53Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6491.diff", "html_url": "https://github.com/huggingface/datasets/pull/6491", "merged_at": "2023-12-21T15:08:53Z", "patch_url": "https://github.com/huggingface/datasets/pull/6491.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6491" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6491/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6490	6,490	`load_dataset(...,save_infos=True)` not working without loading script	{ "avatar_url": "https://avatars.githubusercontent.com/u/114978051?v=4", "events_url": "https://api.github.com/users/morganveyret/events{/privacy}", "followers_url": "https://api.github.com/users/morganveyret/followers", "following_url": "https://api.github.com/users/morganveyret/following{/other_user}", "gists_url": "https://api.github.com/users/morganveyret/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/morganveyret", "id": 114978051, "login": "morganveyret", "node_id": "U_kgDOBtptAw", "organizations_url": "https://api.github.com/users/morganveyret/orgs", "received_events_url": "https://api.github.com/users/morganveyret/received_events", "repos_url": "https://api.github.com/users/morganveyret/repos", "site_admin": false, "starred_url": "https://api.github.com/users/morganveyret/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/morganveyret/subscriptions", "type": "User", "url": "https://api.github.com/users/morganveyret", "user_view_type": "public" }	[]	open	false	[ "Also, once the README.md exists in the python environment it is used when loading another dataset in the same format (e.g. json) since it always resolves the path to the same directory.\r\nThe consequence here is any other dataset won't load because of infos mismatch.\r\nTo reproduce this aspect:\r\n1. Do a `load_datasets(...,save_infos=True)` with one dataset without a loading script\r\n2. Try to load another dataset without a loading script in the same format (e.g. json) but with a different schema " ]	2023-12-12T08:09:18Z	2023-12-12T08:36:22Z	null	NONE	null	null	### Describe the bug It seems that saving a dataset infos back into the card file is not working for datasets without a loading script. After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory. Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). ### Steps to reproduce the bug 1. Have a local dataset without any loading script 2. Make sure there are no dataset infos in the README.md 3. Load with `save_infos=True` 4. No change in the dataset README.md 5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`) ### Expected behavior The dataset README.md should be updated and no file should be created in the python environment. ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.6.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6490/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6489	6,489	load_dataset imageflder for aws s3 path	{ "avatar_url": "https://avatars.githubusercontent.com/u/9353106?v=4", "events_url": "https://api.github.com/users/segalinc/events{/privacy}", "followers_url": "https://api.github.com/users/segalinc/followers", "following_url": "https://api.github.com/users/segalinc/following{/other_user}", "gists_url": "https://api.github.com/users/segalinc/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/segalinc", "id": 9353106, "login": "segalinc", "node_id": "MDQ6VXNlcjkzNTMxMDY=", "organizations_url": "https://api.github.com/users/segalinc/orgs", "received_events_url": "https://api.github.com/users/segalinc/received_events", "repos_url": "https://api.github.com/users/segalinc/repos", "site_admin": false, "starred_url": "https://api.github.com/users/segalinc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/segalinc/subscriptions", "type": "User", "url": "https://api.github.com/users/segalinc", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	[]	2023-12-12T00:08:43Z	2023-12-12T00:09:27Z	null	NONE	null	null	### Feature request I would like to load a dataset from S3 using the imagefolder option something like `dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) ` ### Motivation no need of data_files ### Your contribution no experience with this	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6489/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6488	6,488	429 Client Error	{ "avatar_url": "https://avatars.githubusercontent.com/u/7882383?v=4", "events_url": "https://api.github.com/users/sasaadi/events{/privacy}", "followers_url": "https://api.github.com/users/sasaadi/followers", "following_url": "https://api.github.com/users/sasaadi/following{/other_user}", "gists_url": "https://api.github.com/users/sasaadi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sasaadi", "id": 7882383, "login": "sasaadi", "node_id": "MDQ6VXNlcjc4ODIzODM=", "organizations_url": "https://api.github.com/users/sasaadi/orgs", "received_events_url": "https://api.github.com/users/sasaadi/received_events", "repos_url": "https://api.github.com/users/sasaadi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sasaadi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sasaadi/subscriptions", "type": "User", "url": "https://api.github.com/users/sasaadi", "user_view_type": "public" }	[]	open	false	[ "Transferring repos as this is a datasets issue ", "I'm getting a similar issue even though I've already downloaded the dataset 😅 \r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/HuggingFaceM4/WebSight\r\n```" ]	2023-12-11T15:06:01Z	2024-06-20T05:55:45Z	null	NONE	null	null	Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it? Thanks Dataset: https://huggingface.co/datasets/cerebras/SlimPajama-627B Error: `requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6488/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6487	6,487	Update builder hash with info	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458" ]	2023-12-11T11:09:16Z	2024-01-11T06:35:07Z	2023-12-11T11:41:34Z	MEMBER	true	{ "diff_url": "https://github.com/huggingface/datasets/pull/6487.diff", "html_url": "https://github.com/huggingface/datasets/pull/6487", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6487.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6487" }	Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change. This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub) Ideally we should take the resolved files into account as well but this will be for another PR	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6487/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6486	6,486	Fix docs phrasing about supported formats when sharing a dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6486). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005042 / 0.011353 (-0.006311) \| 0.003452 / 0.011008 (-0.007557) \| 0.061845 / 0.038508 (0.023337) \| 0.052042 / 0.023109 (0.028933) \| 0.241791 / 0.275898 (-0.034107) \| 0.264639 / 0.323480 (-0.058841) \| 0.003940 / 0.007986 (-0.004045) \| 0.002768 / 0.004328 (-0.001560) \| 0.047851 / 0.004250 (0.043600) \| 0.037599 / 0.037052 (0.000547) \| 0.251462 / 0.258489 (-0.007028) \| 0.274737 / 0.293841 (-0.019104) \| 0.027723 / 0.128546 (-0.100823) \| 0.010510 / 0.075646 (-0.065137) \| 0.205581 / 0.419271 (-0.213691) \| 0.035504 / 0.043533 (-0.008029) \| 0.242380 / 0.255139 (-0.012759) \| 0.259791 / 0.283200 (-0.023409) \| 0.017752 / 0.141683 (-0.123931) \| 1.089289 / 1.452155 (-0.362865) \| 1.161958 / 1.492716 (-0.330759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094288 / 0.018006 (0.076282) \| 0.303253 / 0.000490 (0.302763) \| 0.000216 / 0.000200 (0.000016) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018496 / 0.037411 (-0.018915) \| 0.060411 / 0.014526 (0.045885) \| 0.074294 / 0.176557 (-0.102262) \| 0.122934 / 0.737135 (-0.614201) \| 0.074710 / 0.296338 (-0.221629) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286394 / 0.215209 (0.071185) \| 2.806145 / 2.077655 (0.728490) \| 1.497071 / 1.504120 (-0.007049) \| 1.362254 / 1.541195 (-0.178940) \| 1.389642 / 1.468490 (-0.078848) \| 0.554503 / 4.584777 (-4.030274) \| 2.348029 / 3.745712 (-1.397684) \| 2.780862 / 5.269862 (-2.489000) \| 1.728058 / 4.565676 (-2.837619) \| 0.062617 / 0.424275 (-0.361658) \| 0.004901 / 0.007607 (-0.002707) \| 0.346267 / 0.226044 (0.120223) \| 3.363744 / 2.268929 (1.094815) \| 1.826994 / 55.444624 (-53.617630) \| 1.560656 / 6.876477 (-5.315820) \| 1.561083 / 2.142072 (-0.580990) \| 0.643395 / 4.805227 (-4.161832) \| 0.116206 / 6.500664 (-6.384458) \| 0.042008 / 0.075469 (-0.033461) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953416 / 1.841788 (-0.888371) \| 11.461665 / 8.074308 (3.387357) \| 10.623865 / 10.191392 (0.432473) \| 0.128071 / 0.680424 (-0.552353) \| 0.014277 / 0.534201 (-0.519924) \| 0.288810 / 0.579283 (-0.290474) \| 0.267575 / 0.434364 (-0.166788) \| 0.327422 / 0.540337 (-0.212916) \| 0.435151 / 1.386936 (-0.951785) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005242 / 0.011353 (-0.006111) \| 0.003515 / 0.011008 (-0.007493) \| 0.048483 / 0.038508 (0.009975) \| 0.051684 / 0.023109 (0.028575) \| 0.276564 / 0.275898 (0.000666) \| 0.297582 / 0.323480 (-0.025898) \| 0.004117 / 0.007986 (-0.003869) \| 0.002610 / 0.004328 (-0.001719) \| 0.047811 / 0.004250 (0.043561) \| 0.040622 / 0.037052 (0.003569) \| 0.280265 / 0.258489 (0.021776) \| 0.311719 / 0.293841 (0.017878) \| 0.028811 / 0.128546 (-0.099735) \| 0.010600 / 0.075646 (-0.065047) \| 0.056660 / 0.419271 (-0.362611) \| 0.032638 / 0.043533 (-0.010894) \| 0.276434 / 0.255139 (0.021295) \| 0.299095 / 0.283200 (0.015896) \| 0.018483 / 0.141683 (-0.123200) \| 1.156382 / 1.452155 (-0.295773) \| 1.252205 / 1.492716 (-0.240511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097868 / 0.018006 (0.079862) \| 0.309438 / 0.000490 (0.308948) \| 0.000229 / 0.000200 (0.000029) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021838 / 0.037411 (-0.015573) \| 0.068358 / 0.014526 (0.053832) \| 0.080432 / 0.176557 (-0.096125) \| 0.119788 / 0.737135 (-0.617348) \| 0.081742 / 0.296338 (-0.214597) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301239 / 0.215209 (0.086030) \| 2.962242 / 2.077655 (0.884587) \| 1.693918 / 1.504120 (0.189798) \| 1.573663 / 1.541195 (0.032468) \| 1.583125 / 1.468490 (0.114635) \| 0.557267 / 4.584777 (-4.027510) \| 2.440048 / 3.745712 (-1.305664) \| 2.727572 / 5.269862 (-2.542290) \| 1.713557 / 4.565676 (-2.852120) \| 0.062526 / 0.424275 (-0.361749) \| 0.004982 / 0.007607 (-0.002625) \| 0.353850 / 0.226044 (0.127806) \| 3.530887 / 2.268929 (1.261958) \| 2.047864 / 55.444624 (-53.396761) \| 1.770776 / 6.876477 (-5.105701) \| 1.757621 / 2.142072 (-0.384451) \| 0.633847 / 4.805227 (-4.171381) \| 0.114055 / 6.500664 (-6.386609) \| 0.040078 / 0.075469 (-0.035391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983721 / 1.841788 (-0.858066) \| 11.896537 / 8.074308 (3.822229) \| 10.529883 / 10.191392 (0.338491) \| 0.129593 / 0.680424 (-0.550831) \| 0.016213 / 0.534201 (-0.517988) \| 0.289623 / 0.579283 (-0.289660) \| 0.280073 / 0.434364 (-0.154291) \| 0.327446 / 0.540337 (-0.212892) \| 0.574847 / 1.386936 (-0.812089) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2684a98fe38e0c87bb11e050586004108e32b79d \"CML watermark\")\n" ]	2023-12-11T09:21:22Z	2023-12-13T14:21:29Z	2023-12-13T14:15:21Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6486.diff", "html_url": "https://github.com/huggingface/datasets/pull/6486", "merged_at": "2023-12-13T14:15:21Z", "patch_url": "https://github.com/huggingface/datasets/pull/6486.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6486" }	Fix docs phrasing.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6486/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6485	6,485	FileNotFoundError: [Errno 2] No such file or directory: 'nul'	{ "avatar_url": "https://avatars.githubusercontent.com/u/73683903?v=4", "events_url": "https://api.github.com/users/amanyara/events{/privacy}", "followers_url": "https://api.github.com/users/amanyara/followers", "following_url": "https://api.github.com/users/amanyara/following{/other_user}", "gists_url": "https://api.github.com/users/amanyara/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/amanyara", "id": 73683903, "login": "amanyara", "node_id": "MDQ6VXNlcjczNjgzOTAz", "organizations_url": "https://api.github.com/users/amanyara/orgs", "received_events_url": "https://api.github.com/users/amanyara/received_events", "repos_url": "https://api.github.com/users/amanyara/repos", "site_admin": false, "starred_url": "https://api.github.com/users/amanyara/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/amanyara/subscriptions", "type": "User", "url": "https://api.github.com/users/amanyara", "user_view_type": "public" }	[]	closed	false	[ "Hi! It seems like the problem is your environment. Maybe this issue can help: https://github.com/pytest-dev/pytest/issues/9519. " ]	2023-12-11T08:52:13Z	2023-12-14T08:09:08Z	2023-12-14T08:09:08Z	NONE	null	null	### Describe the bug it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets" i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul' ![image](https://github.com/huggingface/datasets/assets/73683903/3973c120-ebb1-42b7-bede-b9de053e861d) ![image](https://github.com/huggingface/datasets/assets/73683903/0496adff-a7a7-4dcb-929e-ec11ede71f04) ### Steps to reproduce the bug 1.import datasets ### Expected behavior i just run a single line code and stuct in this bug ### Environment info OS: Windows10 Datasets==2.15.0 python=3.10	{ "avatar_url": "https://avatars.githubusercontent.com/u/73683903?v=4", "events_url": "https://api.github.com/users/amanyara/events{/privacy}", "followers_url": "https://api.github.com/users/amanyara/followers", "following_url": "https://api.github.com/users/amanyara/following{/other_user}", "gists_url": "https://api.github.com/users/amanyara/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/amanyara", "id": 73683903, "login": "amanyara", "node_id": "MDQ6VXNlcjczNjgzOTAz", "organizations_url": "https://api.github.com/users/amanyara/orgs", "received_events_url": "https://api.github.com/users/amanyara/received_events", "repos_url": "https://api.github.com/users/amanyara/repos", "site_admin": false, "starred_url": "https://api.github.com/users/amanyara/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/amanyara/subscriptions", "type": "User", "url": "https://api.github.com/users/amanyara", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6485/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6483	6,483	Iterable Dataset: rename column clashes with remove column	{ "avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4", "events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}", "followers_url": "https://api.github.com/users/sanchit-gandhi/followers", "following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}", "gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sanchit-gandhi", "id": 93869735, "login": "sanchit-gandhi", "node_id": "U_kgDOBZhWpw", "organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs", "received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events", "repos_url": "https://api.github.com/users/sanchit-gandhi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions", "type": "User", "url": "https://api.github.com/users/sanchit-gandhi", "user_view_type": "public" }	[ { "color": "fef2c0", "default": false, "description": "", "id": 3287858981, "name": "streaming", "node_id": "MDU6TGFiZWwzMjg3ODU4OTgx", "url": "https://api.github.com/repos/huggingface/datasets/labels/streaming" } ]	closed	false	[ "Column \"text\" doesn't exist anymore so you can't remove it", "You can get the expected result by fixing typos in the snippet :)\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset.features) - COLUMNS_TO_KEEP)\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Fixed code:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\ndataset_features = dataset.features.keys()\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Whoops 😅 Thanks for the swift reply both! Works like a charm!" ]	2023-12-08T16:11:30Z	2023-12-08T16:27:16Z	2023-12-08T16:27:04Z	CONTRIBUTOR	null	null	### Describe the bug Suppose I have a two iterable datasets, one with the features: * `{"audio", "text", "column_a"}` And the other with the features: * `{"audio", "sentence", "column_b"}` I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by: 1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`) 2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`) However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets. ### Steps to reproduce the bug ```python from datasets import load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features dataset_features = dataset.features.keys() print("Original features: ", dataset_features) # rename "text" -> "sentence" dataset = dataset.rename_column("text", "sentence") # remove unwanted columns COLUMNS_TO_KEEP = {"audio", "sentence"} dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) # stream first sample, should return "audio" and "sentence" columns print(next(iter(dataset))) ``` Traceback: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[5], line 17 14 COLUMNS_TO_KEEP = {"audio", "sentence"} 15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) ---> 17 print(next(iter(dataset))) File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self) 650 yield from ArrowExamplesIterable(self._iter_arrow, {}) 651 else: --> 652 yield from self._iter() File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self) 727 if self.remove_columns: 728 for c in self.remove_columns: --> 729 del transformed_example[c] 730 yield key, transformed_example 731 current_idx += 1 KeyError: 'text' ``` => we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset. ### Expected behavior Should be able to rename and remove columns from iterable dataset. ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: macOS-13.5.1-arm64-arm-64bit - Python version: 3.11.6 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4", "events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}", "followers_url": "https://api.github.com/users/sanchit-gandhi/followers", "following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}", "gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sanchit-gandhi", "id": 93869735, "login": "sanchit-gandhi", "node_id": "U_kgDOBZhWpw", "organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs", "received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events", "repos_url": "https://api.github.com/users/sanchit-gandhi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions", "type": "User", "url": "https://api.github.com/users/sanchit-gandhi", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6483/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6484	6,484	[Feature Request] Dataset versioning	{ "avatar_url": "https://avatars.githubusercontent.com/u/47979198?v=4", "events_url": "https://api.github.com/users/kenfus/events{/privacy}", "followers_url": "https://api.github.com/users/kenfus/followers", "following_url": "https://api.github.com/users/kenfus/following{/other_user}", "gists_url": "https://api.github.com/users/kenfus/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kenfus", "id": 47979198, "login": "kenfus", "node_id": "MDQ6VXNlcjQ3OTc5MTk4", "organizations_url": "https://api.github.com/users/kenfus/orgs", "received_events_url": "https://api.github.com/users/kenfus/received_events", "repos_url": "https://api.github.com/users/kenfus/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kenfus/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kenfus/subscriptions", "type": "User", "url": "https://api.github.com/users/kenfus", "user_view_type": "public" }	[]	open	false	[ "Hello @kenfus, this is meant to be possible to do yes. Let me ping @lhoestq or @mariosasko from the `datasets` team (`huggingface_hub` is only the underlying library to download files from the Hub but here it looks more like a `datasets` problem). ", "Hi! https://github.com/huggingface/datasets/pull/6459 will fix this." ]	2023-12-08T16:01:35Z	2023-12-11T19:13:46Z	null	NONE	null	null	Is your feature request related to a problem? Please describe. I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different. Of course, I may have done something wrong or missed a setting somewhere! Describe the solution you'd like The solution would allow me to easily work with revisions: - create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this: `dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')` - then, get the current revision as follows: ``` dataset = load_dataset( 'kenfus/xy', revision='v1.0.2', ) ``` this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision. - if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision. Describe alternatives you've considered I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere. Additional context Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface. This is the data loading in my script: ``` ## CREATE PATHS prepared_dataset_path = os.path.join( DATA_FOLDER, str(DATA_VERSION), "prepared_dataset" ) os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True) ## LOAD DATASET if os.path.exists(prepared_dataset_path): print("Loading prepared dataset from disk...") dataset_prepared = load_from_disk(prepared_dataset_path) else: print("Loading dataset from HuggingFace Datasets...") dataset = load_dataset( PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload" ) print("Preparing dataset...") dataset_prepared = dataset.map( prepare_dataset, remove_columns=["audio", "transcription"], num_proc=os.cpu_count(), load_from_cache_file=False, ) dataset_prepared.save_to_disk(prepared_dataset_path) del dataset if CHECK_DATASET: ## CHECK DATASET dataset_prepared = dataset_prepared.map( check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False ) dataset_filtered = dataset_prepared.filter( lambda example: not example["incorrect_dimension"], load_from_cache_file=False, ) for example in dataset_prepared.filter( lambda example: example["incorrect_dimension"], load_from_cache_file=False ): print(example["path"]) print( f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}" ) print("Number of examples train: ", len(dataset_filtered["train"])) print("Number of examples test: ", len(dataset_filtered["test"])) ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6484/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6482	6,482	Fix max lock length on unix	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - reverting", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005294 / 0.011353 (-0.006059) \| 0.003562 / 0.011008 (-0.007446) \| 0.062030 / 0.038508 (0.023522) \| 0.053335 / 0.023109 (0.030226) \| 0.233303 / 0.275898 (-0.042595) \| 0.252029 / 0.323480 (-0.071451) \| 0.002835 / 0.007986 (-0.005151) \| 0.002732 / 0.004328 (-0.001597) \| 0.047973 / 0.004250 (0.043723) \| 0.038380 / 0.037052 (0.001328) \| 0.235028 / 0.258489 (-0.023461) \| 0.265555 / 0.293841 (-0.028286) \| 0.027136 / 0.128546 (-0.101410) \| 0.010806 / 0.075646 (-0.064840) \| 0.205040 / 0.419271 (-0.214231) \| 0.035063 / 0.043533 (-0.008470) \| 0.236351 / 0.255139 (-0.018788) \| 0.254556 / 0.283200 (-0.028643) \| 0.019528 / 0.141683 (-0.122155) \| 1.099012 / 1.452155 (-0.353142) \| 1.156250 / 1.492716 (-0.336466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093952 / 0.018006 (0.075946) \| 0.304181 / 0.000490 (0.303692) \| 0.000227 / 0.000200 (0.000027) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018568 / 0.037411 (-0.018844) \| 0.060323 / 0.014526 (0.045798) \| 0.073010 / 0.176557 (-0.103546) \| 0.121723 / 0.737135 (-0.615412) \| 0.075668 / 0.296338 (-0.220670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288429 / 0.215209 (0.073220) \| 2.797834 / 2.077655 (0.720180) \| 1.480957 / 1.504120 (-0.023163) \| 1.360872 / 1.541195 (-0.180323) \| 1.406828 / 1.468490 (-0.061663) \| 0.587596 / 4.584777 (-3.997181) \| 2.533997 / 3.745712 (-1.211715) \| 2.906697 / 5.269862 (-2.363164) \| 1.801753 / 4.565676 (-2.763923) \| 0.064360 / 0.424275 (-0.359915) \| 0.005016 / 0.007607 (-0.002591) \| 0.347334 / 0.226044 (0.121290) \| 3.426344 / 2.268929 (1.157416) \| 1.856014 / 55.444624 (-53.588610) \| 1.581774 / 6.876477 (-5.294703) \| 1.640036 / 2.142072 (-0.502037) \| 0.656096 / 4.805227 (-4.149131) \| 0.120212 / 6.500664 (-6.380452) \| 0.044003 / 0.075469 (-0.031466) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943933 / 1.841788 (-0.897855) \| 11.846572 / 8.074308 (3.772263) \| 10.330705 / 10.191392 (0.139313) \| 0.129767 / 0.680424 (-0.550657) \| 0.013508 / 0.534201 (-0.520693) \| 0.289672 / 0.579283 (-0.289611) \| 0.266427 / 0.434364 (-0.167937) \| 0.342766 / 0.540337 (-0.197571) \| 0.452068 / 1.386936 (-0.934868) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005308 / 0.011353 (-0.006045) \| 0.003712 / 0.011008 (-0.007296) \| 0.048848 / 0.038508 (0.010340) \| 0.055156 / 0.023109 (0.032047) \| 0.271942 / 0.275898 (-0.003956) \| 0.293166 / 0.323480 (-0.030314) \| 0.004056 / 0.007986 (-0.003930) \| 0.002722 / 0.004328 (-0.001606) \| 0.048418 / 0.004250 (0.044167) \| 0.039320 / 0.037052 (0.002268) \| 0.277184 / 0.258489 (0.018695) \| 0.312398 / 0.293841 (0.018557) \| 0.029392 / 0.128546 (-0.099155) \| 0.011314 / 0.075646 (-0.064332) \| 0.057883 / 0.419271 (-0.361389) \| 0.032603 / 0.043533 (-0.010930) \| 0.273025 / 0.255139 (0.017886) \| 0.289265 / 0.283200 (0.006065) \| 0.017553 / 0.141683 (-0.124129) \| 1.127725 / 1.452155 (-0.324430) \| 1.202293 / 1.492716 (-0.290423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097179 / 0.018006 (0.079173) \| 0.309712 / 0.000490 (0.309222) \| 0.000269 / 0.000200 (0.000069) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024742 / 0.037411 (-0.012670) \| 0.070097 / 0.014526 (0.055571) \| 0.082273 / 0.176557 (-0.094283) \| 0.121696 / 0.737135 (-0.615439) \| 0.082983 / 0.296338 (-0.213355) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292688 / 0.215209 (0.077479) \| 2.853436 / 2.077655 (0.775781) \| 1.588999 / 1.504120 (0.084879) \| 1.454547 / 1.541195 (-0.086648) \| 1.476342 / 1.468490 (0.007852) \| 0.559464 / 4.584777 (-4.025313) \| 2.564597 / 3.745712 (-1.181115) \| 2.900460 / 5.269862 (-2.369402) \| 1.782156 / 4.565676 (-2.783520) \| 0.061768 / 0.424275 (-0.362507) \| 0.005042 / 0.007607 (-0.002565) \| 0.345168 / 0.226044 (0.119124) \| 3.412273 / 2.268929 (1.143344) \| 1.953154 / 55.444624 (-53.491470) \| 1.667347 / 6.876477 (-5.209130) \| 1.685138 / 2.142072 (-0.456934) \| 0.643270 / 4.805227 (-4.161958) \| 0.115955 / 6.500664 (-6.384709) \| 0.041090 / 0.075469 (-0.034379) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976324 / 1.841788 (-0.865464) \| 12.252294 / 8.074308 (4.177986) \| 10.598062 / 10.191392 (0.406670) \| 0.129779 / 0.680424 (-0.550644) \| 0.015697 / 0.534201 (-0.518504) \| 0.287241 / 0.579283 (-0.292042) \| 0.287331 / 0.434364 (-0.147033) \| 0.331710 / 0.540337 (-0.208628) \| 0.574571 / 1.386936 (-0.812365) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#702344140461b7a111139860c944d3dd0a2689e3 \"CML watermark\")\n" ]	2023-12-08T13:39:30Z	2023-12-12T11:53:32Z	2023-12-12T11:47:27Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6482.diff", "html_url": "https://github.com/huggingface/datasets/pull/6482", "merged_at": "2023-12-12T11:47:27Z", "patch_url": "https://github.com/huggingface/datasets/pull/6482.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6482" }	reported in https://github.com/huggingface/datasets/pull/6482	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6482/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6481	6,481	using torchrun, save_to_disk suddenly shows SIGTERM	{ "avatar_url": "https://avatars.githubusercontent.com/u/85916625?v=4", "events_url": "https://api.github.com/users/Ariya12138/events{/privacy}", "followers_url": "https://api.github.com/users/Ariya12138/followers", "following_url": "https://api.github.com/users/Ariya12138/following{/other_user}", "gists_url": "https://api.github.com/users/Ariya12138/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Ariya12138", "id": 85916625, "login": "Ariya12138", "node_id": "MDQ6VXNlcjg1OTE2NjI1", "organizations_url": "https://api.github.com/users/Ariya12138/orgs", "received_events_url": "https://api.github.com/users/Ariya12138/received_events", "repos_url": "https://api.github.com/users/Ariya12138/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Ariya12138/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ariya12138/subscriptions", "type": "User", "url": "https://api.github.com/users/Ariya12138", "user_view_type": "public" }	[]	open	false	[]	2023-12-08T13:22:03Z	2023-12-08T13:22:03Z	null	NONE	null	null	### Describe the bug When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages: Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard. WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967. ### Steps to reproduce the bug ds_shard = ds_shard.map(map_fn, args, kwargs) ds_shard.save_to_disk(ds_shard_filepaths[rank]) Saving the dataset (14/70 shards): 20%\|██ \| 875350/4376702 [00:19<01:53, 30863.15 examples/s] WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python Traceback (most recent call last): File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module> sys.exit(main()) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(args, **kwargs) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main run(args) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run elastic_launch( File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ========================================================== run.py FAILED ---------------------------------------------------------- Failures: <NO_OTHER_FAILURES> ---------------------------------------------------------- Root Cause (first observed failure): [0]: time : 2023-12-08_20:09:04 rank : 0 (local_rank: 0) exitcode : -7 (pid: 2224967) error_file: <N/A> traceback : Signal 7 (SIGBUS) received by PID 2224967 ### Expected behavior I hope it can save successfully without any issues, but it seems there is a problem. ### Environment info `datasets` version: 2.14.6 - Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 14.0.0 - Pandas version: 2.1.2	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6481/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6480	6,480	Add IterableDataset `__repr__`	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6480). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005392 / 0.011353 (-0.005960) \| 0.003120 / 0.011008 (-0.007888) \| 0.062017 / 0.038508 (0.023509) \| 0.048824 / 0.023109 (0.025715) \| 0.232300 / 0.275898 (-0.043598) \| 0.262045 / 0.323480 (-0.061435) \| 0.002909 / 0.007986 (-0.005077) \| 0.003916 / 0.004328 (-0.000413) \| 0.049469 / 0.004250 (0.045218) \| 0.038965 / 0.037052 (0.001913) \| 0.247841 / 0.258489 (-0.010648) \| 0.268259 / 0.293841 (-0.025582) \| 0.027588 / 0.128546 (-0.100958) \| 0.010334 / 0.075646 (-0.065312) \| 0.205811 / 0.419271 (-0.213460) \| 0.035456 / 0.043533 (-0.008077) \| 0.242774 / 0.255139 (-0.012365) \| 0.260377 / 0.283200 (-0.022823) \| 0.017469 / 0.141683 (-0.124214) \| 1.199665 / 1.452155 (-0.252489) \| 1.259316 / 1.492716 (-0.233400) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092357 / 0.018006 (0.074350) \| 0.303745 / 0.000490 (0.303255) \| 0.000212 / 0.000200 (0.000012) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018820 / 0.037411 (-0.018592) \| 0.061548 / 0.014526 (0.047022) \| 0.072527 / 0.176557 (-0.104030) \| 0.119696 / 0.737135 (-0.617440) \| 0.074153 / 0.296338 (-0.222185) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283952 / 0.215209 (0.068743) \| 2.769844 / 2.077655 (0.692189) \| 1.526100 / 1.504120 (0.021980) \| 1.417584 / 1.541195 (-0.123611) \| 1.440523 / 1.468490 (-0.027967) \| 0.556994 / 4.584777 (-4.027783) \| 2.400392 / 3.745712 (-1.345320) \| 2.727794 / 5.269862 (-2.542068) \| 1.724671 / 4.565676 (-2.841006) \| 0.062111 / 0.424275 (-0.362164) \| 0.004925 / 0.007607 (-0.002682) \| 0.342748 / 0.226044 (0.116704) \| 3.376790 / 2.268929 (1.107862) \| 1.856498 / 55.444624 (-53.588127) \| 1.574143 / 6.876477 (-5.302334) \| 1.591828 / 2.142072 (-0.550245) \| 0.644416 / 4.805227 (-4.160811) \| 0.116862 / 6.500664 (-6.383802) \| 0.041484 / 0.075469 (-0.033985) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975704 / 1.841788 (-0.866084) \| 11.196447 / 8.074308 (3.122139) \| 10.567518 / 10.191392 (0.376126) \| 0.126786 / 0.680424 (-0.553638) \| 0.013768 / 0.534201 (-0.520433) \| 0.284531 / 0.579283 (-0.294752) \| 0.260855 / 0.434364 (-0.173509) \| 0.328888 / 0.540337 (-0.211450) \| 0.439911 / 1.386936 (-0.947025) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005108 / 0.011353 (-0.006245) \| 0.003006 / 0.011008 (-0.008003) \| 0.048673 / 0.038508 (0.010165) \| 0.051066 / 0.023109 (0.027957) \| 0.279578 / 0.275898 (0.003680) \| 0.298356 / 0.323480 (-0.025123) \| 0.003965 / 0.007986 (-0.004020) \| 0.002662 / 0.004328 (-0.001667) \| 0.049037 / 0.004250 (0.044786) \| 0.039385 / 0.037052 (0.002333) \| 0.284545 / 0.258489 (0.026055) \| 0.314240 / 0.293841 (0.020399) \| 0.028493 / 0.128546 (-0.100053) \| 0.010400 / 0.075646 (-0.065247) \| 0.057375 / 0.419271 (-0.361896) \| 0.032382 / 0.043533 (-0.011151) \| 0.283163 / 0.255139 (0.028024) \| 0.298967 / 0.283200 (0.015768) \| 0.017564 / 0.141683 (-0.124119) \| 1.172425 / 1.452155 (-0.279730) \| 1.219975 / 1.492716 (-0.272742) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090664 / 0.018006 (0.072658) \| 0.298419 / 0.000490 (0.297929) \| 0.000211 / 0.000200 (0.000011) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021739 / 0.037411 (-0.015672) \| 0.068274 / 0.014526 (0.053748) \| 0.080820 / 0.176557 (-0.095736) \| 0.119809 / 0.737135 (-0.617326) \| 0.081612 / 0.296338 (-0.214727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303346 / 0.215209 (0.088137) \| 2.971648 / 2.077655 (0.893993) \| 1.634828 / 1.504120 (0.130708) \| 1.510851 / 1.541195 (-0.030344) \| 1.515236 / 1.468490 (0.046745) \| 0.558487 / 4.584777 (-4.026289) \| 2.436263 / 3.745712 (-1.309449) \| 2.718525 / 5.269862 (-2.551336) \| 1.727421 / 4.565676 (-2.838255) \| 0.061396 / 0.424275 (-0.362879) \| 0.004951 / 0.007607 (-0.002656) \| 0.352950 / 0.226044 (0.126906) \| 3.473766 / 2.268929 (1.204838) \| 1.971299 / 55.444624 (-53.473325) \| 1.712173 / 6.876477 (-5.164304) \| 1.711334 / 2.142072 (-0.430738) \| 0.627291 / 4.805227 (-4.177936) \| 0.113779 / 6.500664 (-6.386885) \| 0.046561 / 0.075469 (-0.028908) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989507 / 1.841788 (-0.852280) \| 11.777883 / 8.074308 (3.703575) \| 10.525453 / 10.191392 (0.334061) \| 0.129118 / 0.680424 (-0.551306) \| 0.014989 / 0.534201 (-0.519212) \| 0.282324 / 0.579283 (-0.296959) \| 0.280688 / 0.434364 (-0.153676) \| 0.322579 / 0.540337 (-0.217758) \| 0.554327 / 1.386936 (-0.832609) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79e94fcdf3d4378ddcdf7e130bb1ae23d99c6fce \"CML watermark\")\n" ]	2023-12-07T16:31:50Z	2023-12-08T13:33:06Z	2023-12-08T13:26:54Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6480.diff", "html_url": "https://github.com/huggingface/datasets/pull/6480", "merged_at": "2023-12-08T13:26:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/6480.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6480" }	Example for glue sst2: Dataset ``` DatasetDict({ test: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 1821 }) train: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 67349 }) validation: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 872 }) }) ``` IterableDataset (new) ``` IterableDatasetDict({ test: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) train: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) validation: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) }) ``` IterableDataset (before) ``` {'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>} {'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0} ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6480/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6479	6,479	More robust preupload retry mechanism	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6479). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005669 / 0.011353 (-0.005683) \| 0.003684 / 0.011008 (-0.007324) \| 0.063477 / 0.038508 (0.024969) \| 0.068760 / 0.023109 (0.045651) \| 0.252741 / 0.275898 (-0.023157) \| 0.286499 / 0.323480 (-0.036981) \| 0.003311 / 0.007986 (-0.004674) \| 0.003487 / 0.004328 (-0.000842) \| 0.049636 / 0.004250 (0.045385) \| 0.040983 / 0.037052 (0.003931) \| 0.262230 / 0.258489 (0.003740) \| 0.292131 / 0.293841 (-0.001710) \| 0.028231 / 0.128546 (-0.100315) \| 0.010912 / 0.075646 (-0.064734) \| 0.211248 / 0.419271 (-0.208023) \| 0.036679 / 0.043533 (-0.006854) \| 0.258139 / 0.255139 (0.003000) \| 0.277568 / 0.283200 (-0.005631) \| 0.019576 / 0.141683 (-0.122107) \| 1.102588 / 1.452155 (-0.349567) \| 1.178587 / 1.492716 (-0.314130) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098968 / 0.018006 (0.080962) \| 0.298777 / 0.000490 (0.298287) \| 0.000220 / 0.000200 (0.000020) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020408 / 0.037411 (-0.017003) \| 0.062832 / 0.014526 (0.048306) \| 0.076047 / 0.176557 (-0.100509) \| 0.125209 / 0.737135 (-0.611926) \| 0.079098 / 0.296338 (-0.217240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285603 / 0.215209 (0.070394) \| 2.811530 / 2.077655 (0.733875) \| 1.481012 / 1.504120 (-0.023108) \| 1.362740 / 1.541195 (-0.178455) \| 1.448999 / 1.468490 (-0.019491) \| 0.557740 / 4.584777 (-4.027037) \| 2.391377 / 3.745712 (-1.354335) \| 2.973181 / 5.269862 (-2.296681) \| 1.837147 / 4.565676 (-2.728530) \| 0.064445 / 0.424275 (-0.359831) \| 0.004992 / 0.007607 (-0.002615) \| 0.339207 / 0.226044 (0.113162) \| 3.378508 / 2.268929 (1.109580) \| 1.843969 / 55.444624 (-53.600655) \| 1.597794 / 6.876477 (-5.278682) \| 1.657665 / 2.142072 (-0.484407) \| 0.654267 / 4.805227 (-4.150961) \| 0.120408 / 6.500664 (-6.380256) \| 0.045298 / 0.075469 (-0.030171) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949030 / 1.841788 (-0.892758) \| 12.922161 / 8.074308 (4.847852) \| 11.115660 / 10.191392 (0.924268) \| 0.130556 / 0.680424 (-0.549868) \| 0.016278 / 0.534201 (-0.517923) \| 0.288137 / 0.579283 (-0.291146) \| 0.265978 / 0.434364 (-0.168386) \| 0.331491 / 0.540337 (-0.208847) \| 0.437782 / 1.386936 (-0.949154) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005342 / 0.011353 (-0.006010) \| 0.003636 / 0.011008 (-0.007373) \| 0.049527 / 0.038508 (0.011019) \| 0.054856 / 0.023109 (0.031746) \| 0.271922 / 0.275898 (-0.003976) \| 0.295654 / 0.323480 (-0.027826) \| 0.004023 / 0.007986 (-0.003963) \| 0.002814 / 0.004328 (-0.001515) \| 0.048963 / 0.004250 (0.044712) \| 0.039936 / 0.037052 (0.002884) \| 0.274336 / 0.258489 (0.015847) \| 0.310100 / 0.293841 (0.016259) \| 0.030006 / 0.128546 (-0.098540) \| 0.010750 / 0.075646 (-0.064896) \| 0.057989 / 0.419271 (-0.361283) \| 0.033692 / 0.043533 (-0.009841) \| 0.274084 / 0.255139 (0.018945) \| 0.289428 / 0.283200 (0.006229) \| 0.018739 / 0.141683 (-0.122944) \| 1.126224 / 1.452155 (-0.325931) \| 1.171595 / 1.492716 (-0.321121) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093983 / 0.018006 (0.075977) \| 0.298516 / 0.000490 (0.298026) \| 0.000221 / 0.000200 (0.000022) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022498 / 0.037411 (-0.014914) \| 0.071909 / 0.014526 (0.057383) \| 0.083940 / 0.176557 (-0.092617) \| 0.121059 / 0.737135 (-0.616076) \| 0.084141 / 0.296338 (-0.212198) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301792 / 0.215209 (0.086583) \| 2.971971 / 2.077655 (0.894317) \| 1.618718 / 1.504120 (0.114598) \| 1.495816 / 1.541195 (-0.045379) \| 1.546709 / 1.468490 (0.078219) \| 0.571448 / 4.584777 (-4.013329) \| 2.459182 / 3.745712 (-1.286531) \| 2.937584 / 5.269862 (-2.332278) \| 1.804670 / 4.565676 (-2.761007) \| 0.062264 / 0.424275 (-0.362011) \| 0.004915 / 0.007607 (-0.002692) \| 0.355054 / 0.226044 (0.129009) \| 3.490468 / 2.268929 (1.221539) \| 1.978948 / 55.444624 (-53.465677) \| 1.701020 / 6.876477 (-5.175457) \| 1.744684 / 2.142072 (-0.397388) \| 0.635880 / 4.805227 (-4.169347) \| 0.115933 / 6.500664 (-6.384732) \| 0.042646 / 0.075469 (-0.032823) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.999486 / 1.841788 (-0.842302) \| 13.373854 / 8.074308 (5.299546) \| 10.959784 / 10.191392 (0.768392) \| 0.131032 / 0.680424 (-0.549392) \| 0.015059 / 0.534201 (-0.519142) \| 0.289892 / 0.579283 (-0.289391) \| 0.279383 / 0.434364 (-0.154981) \| 0.337670 / 0.540337 (-0.202668) \| 0.597102 / 1.386936 (-0.789834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd9044cdaabc1f9abce02c1b71bdb48fd3525d4e \"CML watermark\")\n" ]	2023-12-06T17:19:38Z	2023-12-06T19:47:29Z	2023-12-06T19:41:06Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6479.diff", "html_url": "https://github.com/huggingface/datasets/pull/6479", "merged_at": "2023-12-06T19:41:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/6479.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6479" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6479/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6478	6,478	How to load data from lakefs	{ "avatar_url": "https://avatars.githubusercontent.com/u/12895488?v=4", "events_url": "https://api.github.com/users/d710055071/events{/privacy}", "followers_url": "https://api.github.com/users/d710055071/followers", "following_url": "https://api.github.com/users/d710055071/following{/other_user}", "gists_url": "https://api.github.com/users/d710055071/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/d710055071", "id": 12895488, "login": "d710055071", "node_id": "MDQ6VXNlcjEyODk1NDg4", "organizations_url": "https://api.github.com/users/d710055071/orgs", "received_events_url": "https://api.github.com/users/d710055071/received_events", "repos_url": "https://api.github.com/users/d710055071/repos", "site_admin": false, "starred_url": "https://api.github.com/users/d710055071/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/d710055071/subscriptions", "type": "User", "url": "https://api.github.com/users/d710055071", "user_view_type": "public" }	[]	closed	false	[ "You can create a `pandas` DataFrame following [this](https://lakefs.io/data-version-control/dvc-using-python/) tutorial, and then convert this DataFrame to a `Dataset` with `datasets.Dataset.from_pandas`. For larger datasets (to memory map them), you can use `Dataset.from_generator` with a generator function that reads lakeFS files with `s3fs`.", "@mariosasko hello,\r\nThis can achieve and https://huggingface.co/datasets Does the same effect apply to the dataset? For example, downloading while using", "There is a blogspot from lakes on this topic: https://lakefs.io/blog/data-version-control-hugging-face-datasets/" ]	2023-12-06T09:04:11Z	2024-07-03T19:13:57Z	2024-07-03T19:13:56Z	CONTRIBUTOR	null	null	My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references	{ "avatar_url": "https://avatars.githubusercontent.com/u/9143109?v=4", "events_url": "https://api.github.com/users/andimarafioti/events{/privacy}", "followers_url": "https://api.github.com/users/andimarafioti/followers", "following_url": "https://api.github.com/users/andimarafioti/following{/other_user}", "gists_url": "https://api.github.com/users/andimarafioti/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andimarafioti", "id": 9143109, "login": "andimarafioti", "node_id": "MDQ6VXNlcjkxNDMxMDk=", "organizations_url": "https://api.github.com/users/andimarafioti/orgs", "received_events_url": "https://api.github.com/users/andimarafioti/received_events", "repos_url": "https://api.github.com/users/andimarafioti/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andimarafioti/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andimarafioti/subscriptions", "type": "User", "url": "https://api.github.com/users/andimarafioti", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6478/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6477	6,477	Fix PermissionError on Windows CI	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6477). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005383 / 0.011353 (-0.005969) \| 0.003644 / 0.011008 (-0.007364) \| 0.063375 / 0.038508 (0.024866) \| 0.055567 / 0.023109 (0.032457) \| 0.261376 / 0.275898 (-0.014522) \| 0.283731 / 0.323480 (-0.039749) \| 0.004022 / 0.007986 (-0.003964) \| 0.002780 / 0.004328 (-0.001549) \| 0.049407 / 0.004250 (0.045156) \| 0.038208 / 0.037052 (0.001156) \| 0.256275 / 0.258489 (-0.002214) \| 0.293203 / 0.293841 (-0.000638) \| 0.028411 / 0.128546 (-0.100135) \| 0.010753 / 0.075646 (-0.064894) \| 0.210420 / 0.419271 (-0.208851) \| 0.036062 / 0.043533 (-0.007471) \| 0.260455 / 0.255139 (0.005317) \| 0.294991 / 0.283200 (0.011791) \| 0.019020 / 0.141683 (-0.122662) \| 1.118334 / 1.452155 (-0.333821) \| 1.227391 / 1.492716 (-0.265325) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094700 / 0.018006 (0.076694) \| 0.302378 / 0.000490 (0.301888) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018745 / 0.037411 (-0.018667) \| 0.061103 / 0.014526 (0.046578) \| 0.075369 / 0.176557 (-0.101188) \| 0.121573 / 0.737135 (-0.615563) \| 0.076898 / 0.296338 (-0.219440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284143 / 0.215209 (0.068934) \| 2.774298 / 2.077655 (0.696644) \| 1.483557 / 1.504120 (-0.020563) \| 1.365091 / 1.541195 (-0.176104) \| 1.390170 / 1.468490 (-0.078320) \| 0.561179 / 4.584777 (-4.023598) \| 2.401654 / 3.745712 (-1.344058) \| 2.782628 / 5.269862 (-2.487233) \| 1.731497 / 4.565676 (-2.834179) \| 0.061798 / 0.424275 (-0.362477) \| 0.004998 / 0.007607 (-0.002609) \| 0.336920 / 0.226044 (0.110875) \| 3.371891 / 2.268929 (1.102963) \| 1.832173 / 55.444624 (-53.612452) \| 1.573515 / 6.876477 (-5.302962) \| 1.595609 / 2.142072 (-0.546463) \| 0.647652 / 4.805227 (-4.157575) \| 0.118501 / 6.500664 (-6.382164) \| 0.042521 / 0.075469 (-0.032948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939310 / 1.841788 (-0.902478) \| 11.459855 / 8.074308 (3.385547) \| 10.677954 / 10.191392 (0.486562) \| 0.141029 / 0.680424 (-0.539395) \| 0.014321 / 0.534201 (-0.519880) \| 0.306679 / 0.579283 (-0.272604) \| 0.262303 / 0.434364 (-0.172061) \| 0.327422 / 0.540337 (-0.212915) \| 0.436159 / 1.386936 (-0.950777) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005430 / 0.011353 (-0.005923) \| 0.003646 / 0.011008 (-0.007362) \| 0.049272 / 0.038508 (0.010764) \| 0.075367 / 0.023109 (0.052257) \| 0.275959 / 0.275898 (0.000061) \| 0.296317 / 0.323480 (-0.027163) \| 0.004129 / 0.007986 (-0.003857) \| 0.002731 / 0.004328 (-0.001597) \| 0.048475 / 0.004250 (0.044225) \| 0.041571 / 0.037052 (0.004518) \| 0.277993 / 0.258489 (0.019504) \| 0.298709 / 0.293841 (0.004868) \| 0.033117 / 0.128546 (-0.095429) \| 0.010914 / 0.075646 (-0.064732) \| 0.057599 / 0.419271 (-0.361673) \| 0.033354 / 0.043533 (-0.010179) \| 0.275669 / 0.255139 (0.020530) \| 0.288451 / 0.283200 (0.005251) \| 0.019953 / 0.141683 (-0.121729) \| 1.148608 / 1.452155 (-0.303547) \| 1.184818 / 1.492716 (-0.307898) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099566 / 0.018006 (0.081560) \| 0.344935 / 0.000490 (0.344445) \| 0.000221 / 0.000200 (0.000021) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021925 / 0.037411 (-0.015486) \| 0.068623 / 0.014526 (0.054097) \| 0.081533 / 0.176557 (-0.095024) \| 0.120996 / 0.737135 (-0.616139) \| 0.082495 / 0.296338 (-0.213844) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294990 / 0.215209 (0.079781) \| 2.892344 / 2.077655 (0.814690) \| 1.611090 / 1.504120 (0.106970) \| 1.496072 / 1.541195 (-0.045123) \| 1.486069 / 1.468490 (0.017579) \| 0.569769 / 4.584777 (-4.015008) \| 2.477623 / 3.745712 (-1.268089) \| 2.819576 / 5.269862 (-2.450286) \| 1.745717 / 4.565676 (-2.819959) \| 0.063763 / 0.424275 (-0.360512) \| 0.004970 / 0.007607 (-0.002637) \| 0.344879 / 0.226044 (0.118834) \| 3.452795 / 2.268929 (1.183867) \| 1.964468 / 55.444624 (-53.480156) \| 1.674526 / 6.876477 (-5.201951) \| 1.679716 / 2.142072 (-0.462356) \| 0.650005 / 4.805227 (-4.155222) \| 0.117019 / 6.500664 (-6.383646) \| 0.048297 / 0.075469 (-0.027172) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965422 / 1.841788 (-0.876366) \| 11.989414 / 8.074308 (3.915106) \| 10.938462 / 10.191392 (0.747070) \| 0.140089 / 0.680424 (-0.540334) \| 0.015533 / 0.534201 (-0.518668) \| 0.292188 / 0.579283 (-0.287095) \| 0.277903 / 0.434364 (-0.156461) \| 0.326164 / 0.540337 (-0.214173) \| 0.565674 / 1.386936 (-0.821262) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d78f07091bc42c41bea068bf1b6116e2bde46a6f \"CML watermark\")\n" ]	2023-12-06T08:34:53Z	2023-12-06T09:24:11Z	2023-12-06T09:17:52Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6477.diff", "html_url": "https://github.com/huggingface/datasets/pull/6477", "merged_at": "2023-12-06T09:17:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/6477.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6477" }	Fix #6476.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": 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https://github.com/huggingface/datasets/issues/6476	6,476	CI on windows is broken: PermissionError	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	[]	2023-12-06T08:32:53Z	2023-12-06T09:17:53Z	2023-12-06T09:17:53Z	MEMBER	null	null	See: https://github.com/huggingface/datasets/actions/runs/7104781624/job/19340572394 ``` FAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\tmpfcnps56i\\hf-internal-testing___dataset_with_script\\default\\0.0.0\\c240e2be3370bdbd\\dataset_with_script-train.arrow' ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6476/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6475	6,475	laion2B-en failed to load on Windows with PrefetchVirtualMemory failed	{ "avatar_url": "https://avatars.githubusercontent.com/u/2229300?v=4", "events_url": "https://api.github.com/users/doctorpangloss/events{/privacy}", "followers_url": "https://api.github.com/users/doctorpangloss/followers", "following_url": "https://api.github.com/users/doctorpangloss/following{/other_user}", "gists_url": "https://api.github.com/users/doctorpangloss/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/doctorpangloss", "id": 2229300, "login": "doctorpangloss", "node_id": "MDQ6VXNlcjIyMjkzMDA=", "organizations_url": "https://api.github.com/users/doctorpangloss/orgs", "received_events_url": "https://api.github.com/users/doctorpangloss/received_events", "repos_url": "https://api.github.com/users/doctorpangloss/repos", "site_admin": false, "starred_url": "https://api.github.com/users/doctorpangloss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/doctorpangloss/subscriptions", "type": "User", "url": "https://api.github.com/users/doctorpangloss", "user_view_type": "public" }	[]	open	false	[ "~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~", "This is a real issue and not related to paths.", "Based on the StackOverflow answer, this causes the error to go away:\r\n```diff\r\ndiff --git a/table.py b/table.py\r\n--- a/table.py\t\r\n+++ b/table.py\t(date 1701824849806)\r\n@@ -47,7 +47,7 @@\r\n \r\n \r\n def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:\r\n- memory_mapped_stream = pa.memory_map(filename)\r\n+ memory_mapped_stream = pa.memory_map(filename, \"r+\")\r\n return pa.ipc.open_stream(memory_mapped_stream)\r\n```\r\nBut now loading the dataset goes very, very slowly, which is unexpected.", "I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...", "Hi! \r\n\r\nInstead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e.g. to \"4GB\") can fix the issue for you.\r\n\r\n> I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...\r\n\r\nOur `.arrow` files are in the [Arrow streaming format](https://arrow.apache.org/docs/python/ipc.html#using-streams). To load them as a `polars` DataFrame, do the following:\r\n```python\r\ndf = pl.from_arrow(Dataset.from_from(path_to_arrow_file).data.table)\r\n```\r\n\r\nWe plan to switch to the IPC version eventually.\r\n", "Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug." ]	2023-12-06T00:07:34Z	2023-12-06T23:26:23Z	null	NONE	null	null	### Describe the bug I have downloaded laion2B-en, and I'm receiving the following error trying to load it: ``` Resolving data files: 100%\|██████████\| 128/128 [00:00<00:00, 1173.79it/s] Traceback (most recent call last): File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module> count = compute_frequencies() ^^^^^^^^^^^^^^^^^^^^^ File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset datasets = map_nested( ^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested return function(data_struct) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset ds = self._as_dataset( ^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() ^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command. ``` This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux. I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128. ### Steps to reproduce the bug ``` # as a huggingface developer, you may already have laion2B-en somewhere _CACHE_DIR = "." from datasets import load_dataset load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ``` ### Expected behavior This should correctly load as a memory mapped Arrow dataset. ### Environment info - `datasets` version: 2.15.0 - Platform: Windows-10-10.0.20348-SP0 (this is windows 2022) - Python version: 3.11.4 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.10.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/2229300?v=4", "events_url": "https://api.github.com/users/doctorpangloss/events{/privacy}", "followers_url": "https://api.github.com/users/doctorpangloss/followers", "following_url": "https://api.github.com/users/doctorpangloss/following{/other_user}", "gists_url": "https://api.github.com/users/doctorpangloss/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/doctorpangloss", "id": 2229300, "login": "doctorpangloss", "node_id": "MDQ6VXNlcjIyMjkzMDA=", "organizations_url": "https://api.github.com/users/doctorpangloss/orgs", "received_events_url": "https://api.github.com/users/doctorpangloss/received_events", "repos_url": "https://api.github.com/users/doctorpangloss/repos", "site_admin": false, "starred_url": "https://api.github.com/users/doctorpangloss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/doctorpangloss/subscriptions", "type": "User", "url": "https://api.github.com/users/doctorpangloss", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6475/reactions" }	reopened	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6474	6,474	Deprecate Beam API and download from HF GCS bucket	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005280 / 0.011353 (-0.006073) \| 0.003770 / 0.011008 (-0.007238) \| 0.064320 / 0.038508 (0.025812) \| 0.031250 / 0.023109 (0.008141) \| 0.245113 / 0.275898 (-0.030785) \| 0.268646 / 0.323480 (-0.054834) \| 0.003147 / 0.007986 (-0.004839) \| 0.002736 / 0.004328 (-0.001592) \| 0.050802 / 0.004250 (0.046551) \| 0.044539 / 0.037052 (0.007487) \| 0.261702 / 0.258489 (0.003213) \| 0.304696 / 0.293841 (0.010855) \| 0.028601 / 0.128546 (-0.099945) \| 0.010731 / 0.075646 (-0.064915) \| 0.208183 / 0.419271 (-0.211089) \| 0.036597 / 0.043533 (-0.006936) \| 0.245107 / 0.255139 (-0.010032) \| 0.265936 / 0.283200 (-0.017264) \| 0.019430 / 0.141683 (-0.122253) \| 1.184697 / 1.452155 (-0.267458) \| 1.206073 / 1.492716 (-0.286643) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.110242 / 0.018006 (0.092236) \| 0.304185 / 0.000490 (0.303695) \| 0.000220 / 0.000200 (0.000020) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018839 / 0.037411 (-0.018573) \| 0.062840 / 0.014526 (0.048314) \| 0.075849 / 0.176557 (-0.100708) \| 0.122983 / 0.737135 (-0.614153) \| 0.075352 / 0.296338 (-0.220987) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283617 / 0.215209 (0.068408) \| 2.801903 / 2.077655 (0.724248) \| 1.447678 / 1.504120 (-0.056442) \| 1.327561 / 1.541195 (-0.213633) \| 1.348714 / 1.468490 (-0.119776) \| 0.574283 / 4.584777 (-4.010493) \| 2.449396 / 3.745712 (-1.296316) \| 2.908659 / 5.269862 (-2.361203) \| 1.813935 / 4.565676 (-2.751742) \| 0.063141 / 0.424275 (-0.361134) \| 0.005024 / 0.007607 (-0.002583) \| 0.338854 / 0.226044 (0.112810) \| 3.346680 / 2.268929 (1.077752) \| 1.833792 / 55.444624 (-53.610832) \| 1.553680 / 6.876477 (-5.322796) \| 1.590403 / 2.142072 (-0.551670) \| 0.657442 / 4.805227 (-4.147785) \| 0.119961 / 6.500664 (-6.380703) \| 0.042602 / 0.075469 (-0.032867) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977510 / 1.841788 (-0.864278) \| 12.105448 / 8.074308 (4.031140) \| 9.635282 / 10.191392 (-0.556110) \| 0.132453 / 0.680424 (-0.547971) \| 0.014225 / 0.534201 (-0.519976) \| 0.292847 / 0.579283 (-0.286436) \| 0.271498 / 0.434364 (-0.162866) \| 0.329785 / 0.540337 (-0.210552) \| 0.435058 / 1.386936 (-0.951879) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005656 / 0.011353 (-0.005697) \| 0.003903 / 0.011008 (-0.007106) \| 0.050670 / 0.038508 (0.012162) \| 0.032405 / 0.023109 (0.009296) \| 0.271443 / 0.275898 (-0.004455) \| 0.298989 / 0.323480 (-0.024491) \| 0.004397 / 0.007986 (-0.003589) \| 0.003027 / 0.004328 (-0.001301) \| 0.050227 / 0.004250 (0.045977) \| 0.047798 / 0.037052 (0.010745) \| 0.287610 / 0.258489 (0.029120) \| 0.314084 / 0.293841 (0.020243) \| 0.030406 / 0.128546 (-0.098140) \| 0.011159 / 0.075646 (-0.064487) \| 0.059510 / 0.419271 (-0.359762) \| 0.056842 / 0.043533 (0.013309) \| 0.274653 / 0.255139 (0.019514) \| 0.290175 / 0.283200 (0.006976) \| 0.019822 / 0.141683 (-0.121861) \| 1.168894 / 1.452155 (-0.283261) \| 1.205867 / 1.492716 (-0.286850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094907 / 0.018006 (0.076901) \| 0.304920 / 0.000490 (0.304431) \| 0.000218 / 0.000200 (0.000018) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023015 / 0.037411 (-0.014396) \| 0.076868 / 0.014526 (0.062342) \| 0.089099 / 0.176557 (-0.087458) \| 0.128786 / 0.737135 (-0.608350) \| 0.090836 / 0.296338 (-0.205503) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284552 / 0.215209 (0.069343) \| 2.800870 / 2.077655 (0.723215) \| 1.577167 / 1.504120 (0.073047) \| 1.448788 / 1.541195 (-0.092406) \| 1.475493 / 1.468490 (0.007003) \| 0.566639 / 4.584777 (-4.018138) \| 2.503671 / 3.745712 (-1.242041) \| 2.796769 / 5.269862 (-2.473092) \| 1.829344 / 4.565676 (-2.736332) \| 0.064200 / 0.424275 (-0.360075) \| 0.005066 / 0.007607 (-0.002541) \| 0.335390 / 0.226044 (0.109346) \| 3.421265 / 2.268929 (1.152336) \| 1.942245 / 55.444624 (-53.502379) \| 1.641401 / 6.876477 (-5.235076) \| 1.824574 / 2.142072 (-0.317499) \| 0.661379 / 4.805227 (-4.143849) \| 0.117207 / 6.500664 (-6.383457) \| 0.041630 / 0.075469 (-0.033839) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009636 / 1.841788 (-0.832152) \| 12.998379 / 8.074308 (4.924071) \| 10.609013 / 10.191392 (0.417621) \| 0.142794 / 0.680424 (-0.537630) \| 0.015648 / 0.534201 (-0.518553) \| 0.292825 / 0.579283 (-0.286458) \| 0.280790 / 0.434364 (-0.153574) \| 0.329016 / 0.540337 (-0.211322) \| 0.442440 / 1.386936 (-0.944496) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f90b65d6ccaceaa532b851a1010ba3dcc77b8b42 \"CML watermark\")\n" ]	2023-12-05T19:51:33Z	2024-03-12T14:56:25Z	2024-03-12T14:50:12Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6474.diff", "html_url": "https://github.com/huggingface/datasets/pull/6474", "merged_at": "2024-03-12T14:50:12Z", "patch_url": "https://github.com/huggingface/datasets/pull/6474.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6474" }	Deprecate the Beam API and download from the HF GCS bucked. TODO: - [x] Convert the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) to an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19)) - [x] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset ([Hub PR](https://huggingface.co/datasets/natural_questions/discussions/7)) - [x] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset ([Hub PR](https://huggingface.co/datasets/wiki40b/discussions/5)) - [x] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wiki_dpr/discussions/14))	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6474/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6473	6,473	Fix CI quality	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6473). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005270 / 0.011353 (-0.006083) \| 0.003471 / 0.011008 (-0.007537) \| 0.061942 / 0.038508 (0.023434) \| 0.052671 / 0.023109 (0.029562) \| 0.250541 / 0.275898 (-0.025357) \| 0.270677 / 0.323480 (-0.052803) \| 0.002933 / 0.007986 (-0.005053) \| 0.003264 / 0.004328 (-0.001064) \| 0.048055 / 0.004250 (0.043804) \| 0.037459 / 0.037052 (0.000407) \| 0.254926 / 0.258489 (-0.003563) \| 0.292547 / 0.293841 (-0.001294) \| 0.027959 / 0.128546 (-0.100587) \| 0.010762 / 0.075646 (-0.064884) \| 0.204961 / 0.419271 (-0.214310) \| 0.035488 / 0.043533 (-0.008045) \| 0.254102 / 0.255139 (-0.001037) \| 0.273654 / 0.283200 (-0.009546) \| 0.018126 / 0.141683 (-0.123556) \| 1.082330 / 1.452155 (-0.369825) \| 1.147179 / 1.492716 (-0.345538) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093223 / 0.018006 (0.075217) \| 0.301912 / 0.000490 (0.301422) \| 0.000219 / 0.000200 (0.000019) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018407 / 0.037411 (-0.019004) \| 0.060412 / 0.014526 (0.045886) \| 0.074063 / 0.176557 (-0.102494) \| 0.118743 / 0.737135 (-0.618392) \| 0.076484 / 0.296338 (-0.219854) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289929 / 0.215209 (0.074720) \| 2.825096 / 2.077655 (0.747442) \| 1.511444 / 1.504120 (0.007324) \| 1.394812 / 1.541195 (-0.146383) \| 1.419751 / 1.468490 (-0.048739) \| 0.569995 / 4.584777 (-4.014782) \| 2.402586 / 3.745712 (-1.343126) \| 2.826223 / 5.269862 (-2.443639) \| 1.751554 / 4.565676 (-2.814123) \| 0.064266 / 0.424275 (-0.360009) \| 0.005047 / 0.007607 (-0.002561) \| 0.341513 / 0.226044 (0.115469) \| 3.372106 / 2.268929 (1.103177) \| 1.872693 / 55.444624 (-53.571931) \| 1.588200 / 6.876477 (-5.288276) \| 1.630800 / 2.142072 (-0.511272) \| 0.654266 / 4.805227 (-4.150961) \| 0.124292 / 6.500664 (-6.376372) \| 0.042876 / 0.075469 (-0.032593) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948406 / 1.841788 (-0.893382) \| 11.652947 / 8.074308 (3.578639) \| 10.218195 / 10.191392 (0.026803) \| 0.128447 / 0.680424 (-0.551976) \| 0.014092 / 0.534201 (-0.520109) \| 0.287631 / 0.579283 (-0.291652) \| 0.264843 / 0.434364 (-0.169521) \| 0.329997 / 0.540337 (-0.210340) \| 0.439597 / 1.386936 (-0.947339) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005418 / 0.011353 (-0.005935) \| 0.003589 / 0.011008 (-0.007419) \| 0.050074 / 0.038508 (0.011566) \| 0.052566 / 0.023109 (0.029456) \| 0.293447 / 0.275898 (0.017549) \| 0.320518 / 0.323480 (-0.002962) \| 0.004094 / 0.007986 (-0.003892) \| 0.002690 / 0.004328 (-0.001639) \| 0.048200 / 0.004250 (0.043949) \| 0.040692 / 0.037052 (0.003640) \| 0.297086 / 0.258489 (0.038597) \| 0.323827 / 0.293841 (0.029986) \| 0.029511 / 0.128546 (-0.099035) \| 0.011079 / 0.075646 (-0.064568) \| 0.058562 / 0.419271 (-0.360709) \| 0.032897 / 0.043533 (-0.010636) \| 0.297244 / 0.255139 (0.042105) \| 0.316812 / 0.283200 (0.033612) \| 0.018468 / 0.141683 (-0.123215) \| 1.140948 / 1.452155 (-0.311207) \| 1.195453 / 1.492716 (-0.297263) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092677 / 0.018006 (0.074671) \| 0.300775 / 0.000490 (0.300285) \| 0.000225 / 0.000200 (0.000025) \| 0.000054 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021617 / 0.037411 (-0.015794) \| 0.077135 / 0.014526 (0.062610) \| 0.079848 / 0.176557 (-0.096709) \| 0.118475 / 0.737135 (-0.618661) \| 0.081174 / 0.296338 (-0.215164) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294424 / 0.215209 (0.079215) \| 2.863989 / 2.077655 (0.786334) \| 1.590604 / 1.504120 (0.086484) \| 1.474345 / 1.541195 (-0.066849) \| 1.482120 / 1.468490 (0.013630) \| 0.567829 / 4.584777 (-4.016948) \| 2.493782 / 3.745712 (-1.251930) \| 2.823460 / 5.269862 (-2.446402) \| 1.732677 / 4.565676 (-2.833000) \| 0.065518 / 0.424275 (-0.358757) \| 0.004923 / 0.007607 (-0.002684) \| 0.349313 / 0.226044 (0.123268) \| 3.428618 / 2.268929 (1.159689) \| 1.970641 / 55.444624 (-53.473983) \| 1.655884 / 6.876477 (-5.220593) \| 1.657151 / 2.142072 (-0.484921) \| 0.661208 / 4.805227 (-4.144019) \| 0.119129 / 6.500664 (-6.381535) \| 0.040770 / 0.075469 (-0.034699) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964865 / 1.841788 (-0.876923) \| 12.050218 / 8.074308 (3.975910) \| 10.458749 / 10.191392 (0.267357) \| 0.141856 / 0.680424 (-0.538568) \| 0.015091 / 0.534201 (-0.519109) \| 0.288897 / 0.579283 (-0.290387) \| 0.275343 / 0.434364 (-0.159021) \| 0.328363 / 0.540337 (-0.211975) \| 0.579243 / 1.386936 (-0.807693) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7721021e284859ea0952444bae6300a0d00794f \"CML watermark\")\n" ]	2023-12-05T15:36:23Z	2023-12-05T18:14:50Z	2023-12-05T18:08:41Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6473.diff", "html_url": "https://github.com/huggingface/datasets/pull/6473", "merged_at": "2023-12-05T18:08:41Z", "patch_url": "https://github.com/huggingface/datasets/pull/6473.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6473" }	Fix #6472.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6473/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6472	6,472	CI quality is broken	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" }, { "color": "d4c5f9", "default": false, "description": "Maintenance tasks", "id": 4296013012, "name": "maintenance", "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance" } ]	closed	false	[]	2023-12-05T15:35:34Z	2023-12-06T08:17:34Z	2023-12-05T18:08:43Z	MEMBER	null	null	See: https://github.com/huggingface/datasets/actions/runs/7100835633/job/19327734359 ``` Would reformat: src/datasets/features/image.py 1 file would be reformatted, 253 files left unchanged ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6472/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6471	6,471	Remove delete doc CI	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6471). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005573 / 0.011353 (-0.005780) \| 0.003449 / 0.011008 (-0.007559) \| 0.063323 / 0.038508 (0.024815) \| 0.049369 / 0.023109 (0.026260) \| 0.254280 / 0.275898 (-0.021618) \| 0.267721 / 0.323480 (-0.055759) \| 0.002894 / 0.007986 (-0.005092) \| 0.002646 / 0.004328 (-0.001683) \| 0.049284 / 0.004250 (0.045033) \| 0.037947 / 0.037052 (0.000895) \| 0.251654 / 0.258489 (-0.006836) \| 0.279729 / 0.293841 (-0.014112) \| 0.028022 / 0.128546 (-0.100525) \| 0.010653 / 0.075646 (-0.064993) \| 0.208567 / 0.419271 (-0.210704) \| 0.035863 / 0.043533 (-0.007670) \| 0.248522 / 0.255139 (-0.006617) \| 0.270274 / 0.283200 (-0.012925) \| 0.019683 / 0.141683 (-0.122000) \| 1.136342 / 1.452155 (-0.315812) \| 1.206757 / 1.492716 (-0.285960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094682 / 0.018006 (0.076676) \| 0.304092 / 0.000490 (0.303602) \| 0.000220 / 0.000200 (0.000020) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018606 / 0.037411 (-0.018805) \| 0.060568 / 0.014526 (0.046042) \| 0.074067 / 0.176557 (-0.102490) \| 0.118979 / 0.737135 (-0.618156) \| 0.075676 / 0.296338 (-0.220663) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290452 / 0.215209 (0.075243) \| 2.848868 / 2.077655 (0.771213) \| 1.534932 / 1.504120 (0.030812) \| 1.386717 / 1.541195 (-0.154478) \| 1.416645 / 1.468490 (-0.051845) \| 0.569020 / 4.584777 (-4.015757) \| 2.421168 / 3.745712 (-1.324545) \| 2.781358 / 5.269862 (-2.488503) \| 1.758495 / 4.565676 (-2.807182) \| 0.063851 / 0.424275 (-0.360424) \| 0.004968 / 0.007607 (-0.002639) \| 0.339198 / 0.226044 (0.113154) \| 3.356392 / 2.268929 (1.087464) \| 1.858145 / 55.444624 (-53.586479) \| 1.589000 / 6.876477 (-5.287477) \| 1.569175 / 2.142072 (-0.572897) \| 0.650571 / 4.805227 (-4.154657) \| 0.120288 / 6.500664 (-6.380376) \| 0.042489 / 0.075469 (-0.032980) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939963 / 1.841788 (-0.901824) \| 11.493612 / 8.074308 (3.419304) \| 10.353780 / 10.191392 (0.162388) \| 0.141945 / 0.680424 (-0.538479) \| 0.014397 / 0.534201 (-0.519804) \| 0.286971 / 0.579283 (-0.292312) \| 0.266787 / 0.434364 (-0.167577) \| 0.330385 / 0.540337 (-0.209952) \| 0.438542 / 1.386936 (-0.948394) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005360 / 0.011353 (-0.005993) \| 0.003720 / 0.011008 (-0.007288) \| 0.048790 / 0.038508 (0.010282) \| 0.050256 / 0.023109 (0.027147) \| 0.275445 / 0.275898 (-0.000453) \| 0.297725 / 0.323480 (-0.025755) \| 0.004077 / 0.007986 (-0.003909) \| 0.002759 / 0.004328 (-0.001569) \| 0.047653 / 0.004250 (0.043403) \| 0.040205 / 0.037052 (0.003153) \| 0.281028 / 0.258489 (0.022539) \| 0.304682 / 0.293841 (0.010841) \| 0.030158 / 0.128546 (-0.098388) \| 0.010957 / 0.075646 (-0.064689) \| 0.058193 / 0.419271 (-0.361079) \| 0.033277 / 0.043533 (-0.010256) \| 0.279501 / 0.255139 (0.024362) \| 0.295381 / 0.283200 (0.012181) \| 0.017889 / 0.141683 (-0.123794) \| 1.121354 / 1.452155 (-0.330801) \| 1.225702 / 1.492716 (-0.267014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093385 / 0.018006 (0.075378) \| 0.304642 / 0.000490 (0.304152) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021456 / 0.037411 (-0.015955) \| 0.068536 / 0.014526 (0.054010) \| 0.080867 / 0.176557 (-0.095689) \| 0.119093 / 0.737135 (-0.618042) \| 0.081875 / 0.296338 (-0.214464) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304434 / 0.215209 (0.089225) \| 2.990303 / 2.077655 (0.912649) \| 1.616959 / 1.504120 (0.112839) \| 1.493256 / 1.541195 (-0.047939) \| 1.542857 / 1.468490 (0.074367) \| 0.575517 / 4.584777 (-4.009260) \| 2.455165 / 3.745712 (-1.290547) \| 2.810089 / 5.269862 (-2.459773) \| 1.756502 / 4.565676 (-2.809175) \| 0.064801 / 0.424275 (-0.359475) \| 0.004969 / 0.007607 (-0.002638) \| 0.360227 / 0.226044 (0.134183) \| 3.575029 / 2.268929 (1.306100) \| 1.989955 / 55.444624 (-53.454669) \| 1.705306 / 6.876477 (-5.171171) \| 1.688523 / 2.142072 (-0.453550) \| 0.663266 / 4.805227 (-4.141962) \| 0.121852 / 6.500664 (-6.378812) \| 0.041853 / 0.075469 (-0.033616) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983535 / 1.841788 (-0.858252) \| 11.827656 / 8.074308 (3.753348) \| 10.663265 / 10.191392 (0.471873) \| 0.145942 / 0.680424 (-0.534482) \| 0.016004 / 0.534201 (-0.518197) \| 0.288907 / 0.579283 (-0.290376) \| 0.279100 / 0.434364 (-0.155264) \| 0.328061 / 0.540337 (-0.212276) \| 0.570253 / 1.386936 (-0.816683) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b52cbc18919869460557e15028e7f489eae8afc7 \"CML watermark\")\n" ]	2023-12-05T12:37:50Z	2023-12-05T12:44:59Z	2023-12-05T12:38:50Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6471.diff", "html_url": "https://github.com/huggingface/datasets/pull/6471", "merged_at": "2023-12-05T12:38:50Z", "patch_url": "https://github.com/huggingface/datasets/pull/6471.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6471" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6471/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6470	6,470	If an image in a dataset is corrupted, we get unescapable error	{ "avatar_url": "https://avatars.githubusercontent.com/u/14337872?v=4", "events_url": "https://api.github.com/users/chigozienri/events{/privacy}", "followers_url": "https://api.github.com/users/chigozienri/followers", "following_url": "https://api.github.com/users/chigozienri/following{/other_user}", "gists_url": "https://api.github.com/users/chigozienri/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/chigozienri", "id": 14337872, "login": "chigozienri", "node_id": "MDQ6VXNlcjE0MzM3ODcy", "organizations_url": "https://api.github.com/users/chigozienri/orgs", "received_events_url": "https://api.github.com/users/chigozienri/received_events", "repos_url": "https://api.github.com/users/chigozienri/repos", "site_admin": false, "starred_url": "https://api.github.com/users/chigozienri/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/chigozienri/subscriptions", "type": "User", "url": "https://api.github.com/users/chigozienri", "user_view_type": "public" }	[]	open	false	[]	2023-12-04T20:58:49Z	2023-12-04T20:58:49Z	null	NONE	null	null	### Describe the bug Example discussed in detail here: https://huggingface.co/datasets/sasha/birdsnap/discussions/1 ### Steps to reproduce the bug ``` from datasets import load_dataset, VerificationMode dataset = load_dataset( 'sasha/birdsnap', split="train", verification_mode=VerificationMode.ALL_CHECKS, streaming=True # I recommend using streaming=True when reproducing, as this dataset is large ) for idx, row in enumerate(dataset): # Iterating to 9287 took 7 minutes for me # If you already have the data locally cached and set streaming=False, you see the same error just by with dataset[9287] pass # error at 9287 OSError: image file is truncated (45 bytes not processed) # note that we can't avoid the error using a try/except + continue inside the loop ``` ### Expected behavior Able to escape errors in casting to Image() without killing the whole loop ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.31 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6470/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6469	6,469	Don't expand_info in HF glob	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.", "Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004998 / 0.011353 (-0.006355) \| 0.003523 / 0.011008 (-0.007486) \| 0.064932 / 0.038508 (0.026424) \| 0.050107 / 0.023109 (0.026998) \| 0.253715 / 0.275898 (-0.022183) \| 0.275364 / 0.323480 (-0.048116) \| 0.003902 / 0.007986 (-0.004084) \| 0.002716 / 0.004328 (-0.001612) \| 0.048458 / 0.004250 (0.044208) \| 0.037802 / 0.037052 (0.000750) \| 0.262328 / 0.258489 (0.003839) \| 0.285911 / 0.293841 (-0.007930) \| 0.027112 / 0.128546 (-0.101435) \| 0.010780 / 0.075646 (-0.064867) \| 0.206447 / 0.419271 (-0.212824) \| 0.035771 / 0.043533 (-0.007761) \| 0.255031 / 0.255139 (-0.000108) \| 0.270530 / 0.283200 (-0.012670) \| 0.017152 / 0.141683 (-0.124530) \| 1.094734 / 1.452155 (-0.357421) \| 1.163480 / 1.492716 (-0.329237) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092944 / 0.018006 (0.074938) \| 0.301042 / 0.000490 (0.300553) \| 0.000238 / 0.000200 (0.000038) \| 0.000049 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019090 / 0.037411 (-0.018321) \| 0.061046 / 0.014526 (0.046520) \| 0.073330 / 0.176557 (-0.103227) \| 0.121124 / 0.737135 (-0.616012) \| 0.080544 / 0.296338 (-0.215795) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.323866 / 0.215209 (0.108657) \| 2.797727 / 2.077655 (0.720072) \| 1.502994 / 1.504120 (-0.001126) \| 1.376177 / 1.541195 (-0.165018) \| 1.422741 / 1.468490 (-0.045749) \| 0.562990 / 4.584777 (-4.021786) \| 2.431781 / 3.745712 (-1.313931) \| 2.783226 / 5.269862 (-2.486635) \| 1.788055 / 4.565676 (-2.777621) \| 0.064206 / 0.424275 (-0.360069) \| 0.004989 / 0.007607 (-0.002618) \| 0.338282 / 0.226044 (0.112237) \| 3.356226 / 2.268929 (1.087297) \| 1.855644 / 55.444624 (-53.588980) \| 1.580876 / 6.876477 (-5.295601) \| 1.617418 / 2.142072 (-0.524655) \| 0.636816 / 4.805227 (-4.168411) \| 0.117680 / 6.500664 (-6.382985) \| 0.042560 / 0.075469 (-0.032909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956410 / 1.841788 (-0.885377) \| 11.764886 / 8.074308 (3.690578) \| 10.535801 / 10.191392 (0.344409) \| 0.137797 / 0.680424 (-0.542627) \| 0.014368 / 0.534201 (-0.519833) \| 0.286213 / 0.579283 (-0.293070) \| 0.267093 / 0.434364 (-0.167271) \| 0.334802 / 0.540337 (-0.205535) \| 0.441866 / 1.386936 (-0.945070) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005348 / 0.011353 (-0.006005) \| 0.003551 / 0.011008 (-0.007458) \| 0.049226 / 0.038508 (0.010718) \| 0.052072 / 0.023109 (0.028963) \| 0.268025 / 0.275898 (-0.007873) \| 0.289968 / 0.323480 (-0.033512) \| 0.004034 / 0.007986 (-0.003952) \| 0.002675 / 0.004328 (-0.001653) \| 0.048099 / 0.004250 (0.043848) \| 0.040141 / 0.037052 (0.003089) \| 0.272974 / 0.258489 (0.014485) \| 0.296097 / 0.293841 (0.002256) \| 0.028972 / 0.128546 (-0.099575) \| 0.010689 / 0.075646 (-0.064957) \| 0.057853 / 0.419271 (-0.361418) \| 0.032488 / 0.043533 (-0.011045) \| 0.272018 / 0.255139 (0.016879) \| 0.287179 / 0.283200 (0.003980) \| 0.018446 / 0.141683 (-0.123237) \| 1.140346 / 1.452155 (-0.311809) \| 1.247743 / 1.492716 (-0.244974) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091987 / 0.018006 (0.073980) \| 0.300527 / 0.000490 (0.300037) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021390 / 0.037411 (-0.016021) \| 0.068768 / 0.014526 (0.054242) \| 0.080798 / 0.176557 (-0.095759) \| 0.119081 / 0.737135 (-0.618054) \| 0.082461 / 0.296338 (-0.213878) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286631 / 0.215209 (0.071422) \| 2.804633 / 2.077655 (0.726978) \| 1.574122 / 1.504120 (0.070002) \| 1.459994 / 1.541195 (-0.081201) \| 1.499739 / 1.468490 (0.031249) \| 0.579595 / 4.584777 (-4.005182) \| 2.426407 / 3.745712 (-1.319306) \| 2.917994 / 5.269862 (-2.351868) \| 1.846439 / 4.565676 (-2.719238) \| 0.063274 / 0.424275 (-0.361001) \| 0.005028 / 0.007607 (-0.002579) \| 0.341114 / 0.226044 (0.115070) \| 3.402677 / 2.268929 (1.133748) \| 1.940980 / 55.444624 (-53.503645) \| 1.651902 / 6.876477 (-5.224575) \| 1.677037 / 2.142072 (-0.465036) \| 0.651576 / 4.805227 (-4.153651) \| 0.116398 / 6.500664 (-6.384266) \| 0.041060 / 0.075469 (-0.034409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973278 / 1.841788 (-0.868509) \| 12.248332 / 8.074308 (4.174024) \| 10.830627 / 10.191392 (0.639235) \| 0.143146 / 0.680424 (-0.537278) \| 0.016249 / 0.534201 (-0.517952) \| 0.298563 / 0.579283 (-0.280720) \| 0.278643 / 0.434364 (-0.155721) \| 0.338206 / 0.540337 (-0.202132) \| 0.589485 / 1.386936 (-0.797451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da29ac32c57e079199c173e4404342cc105ed774 \"CML watermark\")\n" ]	2023-12-04T12:00:37Z	2023-12-15T13:18:37Z	2023-12-15T13:12:30Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6469.diff", "html_url": "https://github.com/huggingface/datasets/pull/6469", "merged_at": "2023-12-15T13:12:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/6469.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6469" }	Finally fix https://github.com/huggingface/datasets/issues/5537	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6469/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6468	6,468	Use auth to get parquet export	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005076 / 0.011353 (-0.006277) \| 0.003510 / 0.011008 (-0.007499) \| 0.062939 / 0.038508 (0.024431) \| 0.049191 / 0.023109 (0.026082) \| 0.259088 / 0.275898 (-0.016810) \| 0.273523 / 0.323480 (-0.049957) \| 0.003902 / 0.007986 (-0.004083) \| 0.002699 / 0.004328 (-0.001630) \| 0.049077 / 0.004250 (0.044827) \| 0.037174 / 0.037052 (0.000121) \| 0.256467 / 0.258489 (-0.002022) \| 0.291235 / 0.293841 (-0.002606) \| 0.028119 / 0.128546 (-0.100427) \| 0.010404 / 0.075646 (-0.065243) \| 0.205825 / 0.419271 (-0.213446) \| 0.035741 / 0.043533 (-0.007792) \| 0.253219 / 0.255139 (-0.001920) \| 0.274986 / 0.283200 (-0.008214) \| 0.018379 / 0.141683 (-0.123304) \| 1.131139 / 1.452155 (-0.321016) \| 1.175875 / 1.492716 (-0.316841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090717 / 0.018006 (0.072710) \| 0.299285 / 0.000490 (0.298796) \| 0.000217 / 0.000200 (0.000017) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018678 / 0.037411 (-0.018733) \| 0.060558 / 0.014526 (0.046032) \| 0.073828 / 0.176557 (-0.102728) \| 0.119302 / 0.737135 (-0.617833) \| 0.075261 / 0.296338 (-0.221078) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277018 / 0.215209 (0.061809) \| 2.713255 / 2.077655 (0.635601) \| 1.427512 / 1.504120 (-0.076608) \| 1.311374 / 1.541195 (-0.229821) \| 1.348756 / 1.468490 (-0.119734) \| 0.561777 / 4.584777 (-4.023000) \| 2.393578 / 3.745712 (-1.352134) \| 2.798109 / 5.269862 (-2.471753) \| 1.754808 / 4.565676 (-2.810869) \| 0.062302 / 0.424275 (-0.361973) \| 0.004948 / 0.007607 (-0.002659) \| 0.328468 / 0.226044 (0.102423) \| 3.246558 / 2.268929 (0.977629) \| 1.786816 / 55.444624 (-53.657808) \| 1.482937 / 6.876477 (-5.393540) \| 1.516109 / 2.142072 (-0.625963) \| 0.634457 / 4.805227 (-4.170770) \| 0.116505 / 6.500664 (-6.384159) \| 0.042162 / 0.075469 (-0.033308) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935312 / 1.841788 (-0.906476) \| 11.540599 / 8.074308 (3.466291) \| 10.512593 / 10.191392 (0.321201) \| 0.129638 / 0.680424 (-0.550786) \| 0.013994 / 0.534201 (-0.520207) \| 0.291490 / 0.579283 (-0.287793) \| 0.263641 / 0.434364 (-0.170722) \| 0.328718 / 0.540337 (-0.211619) \| 0.437598 / 1.386936 (-0.949338) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005192 / 0.011353 (-0.006161) \| 0.003454 / 0.011008 (-0.007554) \| 0.049448 / 0.038508 (0.010940) \| 0.050968 / 0.023109 (0.027859) \| 0.273702 / 0.275898 (-0.002196) \| 0.296934 / 0.323480 (-0.026545) \| 0.004066 / 0.007986 (-0.003920) \| 0.002611 / 0.004328 (-0.001718) \| 0.048284 / 0.004250 (0.044034) \| 0.041399 / 0.037052 (0.004346) \| 0.283000 / 0.258489 (0.024511) \| 0.302553 / 0.293841 (0.008712) \| 0.029086 / 0.128546 (-0.099460) \| 0.010510 / 0.075646 (-0.065137) \| 0.058097 / 0.419271 (-0.361175) \| 0.032992 / 0.043533 (-0.010541) \| 0.271752 / 0.255139 (0.016613) \| 0.293535 / 0.283200 (0.010335) \| 0.016958 / 0.141683 (-0.124725) \| 1.130126 / 1.452155 (-0.322028) \| 1.187228 / 1.492716 (-0.305488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092321 / 0.018006 (0.074315) \| 0.302599 / 0.000490 (0.302109) \| 0.000215 / 0.000200 (0.000015) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021837 / 0.037411 (-0.015574) \| 0.071148 / 0.014526 (0.056622) \| 0.082448 / 0.176557 (-0.094108) \| 0.128083 / 0.737135 (-0.609053) \| 0.090864 / 0.296338 (-0.205474) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296248 / 0.215209 (0.081039) \| 2.881130 / 2.077655 (0.803476) \| 1.580360 / 1.504120 (0.076240) \| 1.454642 / 1.541195 (-0.086553) \| 1.461453 / 1.468490 (-0.007037) \| 0.567500 / 4.584777 (-4.017277) \| 2.493708 / 3.745712 (-1.252004) \| 2.756623 / 5.269862 (-2.513239) \| 1.771319 / 4.565676 (-2.794358) \| 0.062287 / 0.424275 (-0.361988) \| 0.004917 / 0.007607 (-0.002691) \| 0.348034 / 0.226044 (0.121990) \| 3.426938 / 2.268929 (1.158010) \| 1.954190 / 55.444624 (-53.490435) \| 1.660870 / 6.876477 (-5.215607) \| 1.675118 / 2.142072 (-0.466955) \| 0.636843 / 4.805227 (-4.168384) \| 0.115028 / 6.500664 (-6.385636) \| 0.040702 / 0.075469 (-0.034767) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988076 / 1.841788 (-0.853711) \| 11.890867 / 8.074308 (3.816559) \| 10.621169 / 10.191392 (0.429777) \| 0.131568 / 0.680424 (-0.548856) \| 0.014994 / 0.534201 (-0.519207) \| 0.288900 / 0.579283 (-0.290384) \| 0.272092 / 0.434364 (-0.162272) \| 0.329397 / 0.540337 (-0.210940) \| 0.569337 / 1.386936 (-0.817599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n" ]	2023-12-04T11:18:27Z	2023-12-04T17:21:22Z	2023-12-04T17:15:11Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6468.diff", "html_url": "https://github.com/huggingface/datasets/pull/6468", "merged_at": "2023-12-04T17:15:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/6468.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6468" }	added `token` to the `_datasets_server` functions	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": 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https://github.com/huggingface/datasets/issues/6467	6,467	New version release request	{ "avatar_url": "https://avatars.githubusercontent.com/u/36994684?v=4", "events_url": "https://api.github.com/users/LZHgrla/events{/privacy}", "followers_url": "https://api.github.com/users/LZHgrla/followers", "following_url": "https://api.github.com/users/LZHgrla/following{/other_user}", "gists_url": "https://api.github.com/users/LZHgrla/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LZHgrla", "id": 36994684, "login": "LZHgrla", "node_id": "MDQ6VXNlcjM2OTk0Njg0", "organizations_url": "https://api.github.com/users/LZHgrla/orgs", "received_events_url": "https://api.github.com/users/LZHgrla/received_events", "repos_url": "https://api.github.com/users/LZHgrla/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LZHgrla/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LZHgrla/subscriptions", "type": "User", "url": "https://api.github.com/users/LZHgrla", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	[ "We will publish it soon (we usually do it in intervals of 1-2 months, so probably next week)", "Thanks!" ]	2023-12-04T07:08:26Z	2023-12-04T15:42:22Z	2023-12-04T15:42:22Z	CONTRIBUTOR	null	null	### Feature request Hi! I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0. To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us? Thanks very much! ### Motivation . ### Your contribution .	{ "avatar_url": "https://avatars.githubusercontent.com/u/36994684?v=4", "events_url": "https://api.github.com/users/LZHgrla/events{/privacy}", "followers_url": "https://api.github.com/users/LZHgrla/followers", "following_url": "https://api.github.com/users/LZHgrla/following{/other_user}", "gists_url": "https://api.github.com/users/LZHgrla/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LZHgrla", "id": 36994684, "login": "LZHgrla", "node_id": "MDQ6VXNlcjM2OTk0Njg0", "organizations_url": "https://api.github.com/users/LZHgrla/orgs", "received_events_url": "https://api.github.com/users/LZHgrla/received_events", "repos_url": "https://api.github.com/users/LZHgrla/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LZHgrla/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LZHgrla/subscriptions", "type": "User", "url": "https://api.github.com/users/LZHgrla", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6467/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6466	6,466	Can't align optional features of struct	{ "avatar_url": "https://avatars.githubusercontent.com/u/8976546?v=4", "events_url": "https://api.github.com/users/Dref360/events{/privacy}", "followers_url": "https://api.github.com/users/Dref360/followers", "following_url": "https://api.github.com/users/Dref360/following{/other_user}", "gists_url": "https://api.github.com/users/Dref360/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Dref360", "id": 8976546, "login": "Dref360", "node_id": "MDQ6VXNlcjg5NzY1NDY=", "organizations_url": "https://api.github.com/users/Dref360/orgs", "received_events_url": "https://api.github.com/users/Dref360/received_events", "repos_url": "https://api.github.com/users/Dref360/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Dref360/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Dref360/subscriptions", "type": "User", "url": "https://api.github.com/users/Dref360", "user_view_type": "public" }	[]	closed	false	[ "Friendly bump, I would be happy to work on this issue once I get the go-ahead from the dev team. ", "Thanks for the PR!\r\n\r\nI'm struggling with this as well and would love to see this PR merged. My case is slightly different, with keys completely missing rather than being `None`:\r\n\r\n```\r\nds = Dataset.from_dict({'speaker': [{'name': 'Ben'}]})\r\nds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})\r\nprint(concatenate_datasets([ds, ds2]).features)\r\nprint(concatenate_datasets([ds, ds2]).to_dict())\r\n```\r\n\r\nI would expect this to work as well because other Dataset functions already handle this situation well. For example, this works just as expected:\r\n\r\n```\r\nds = Dataset.from_dict({'n': [1,2]})\r\nds_mapped = ds.map(lambda x: {\r\n 'speaker': {'name': 'Ben'} if x['n'] == 1 else {'name': 'Fred', 'email': 'abc@aol.com'}\r\n})\r\nprint(ds_mapped)\r\n```", "@vova-cyberhaven can you check with the new release if it fixes your issue? " ]	2023-12-03T15:57:07Z	2024-02-15T15:19:33Z	2024-02-08T14:38:34Z	CONTRIBUTOR	null	null	### Describe the bug Hello! I'm currently experiencing an issue where I can't concatenate datasets if an inner field of a Feature is Optional. I have a column named `speaker`, and this holds some information about a speaker. ```python @dataclass class Speaker: name: str email: Optional[str] ``` If I have two datasets, one happens to have `email` always None, then I get `The features can't be aligned because the key email of features` ### Steps to reproduce the bug You can run the following script: ```python ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]}) concatenate_datasets([ds, ds2]) >>>The features can't be aligned because the key speaker of features {'speaker': {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)}} has unexpected type - {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)} (expected either {'email': Value(dtype='null', id=None), 'name': Value(dtype='string', id=None)} or Value("null"). ``` ### Expected behavior I think this should work; if two top-level columns were in the same situation it would properly cast to `string`. ```python ds = Dataset.from_dict({'email': [None, None]}) ds2 = Dataset.from_dict({'email': ['abc@aol.com', 'one@yahoo.com']}) concatenate_datasets([ds, ds2]) >>> # Works! ``` ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35 - Python version: 3.9.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 - `fsspec` version: 2023.6.0 I would be happy to fix this issue.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6466/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6465	6,465	`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/3391297?v=4", "events_url": "https://api.github.com/users/mnoukhov/events{/privacy}", "followers_url": "https://api.github.com/users/mnoukhov/followers", "following_url": "https://api.github.com/users/mnoukhov/following{/other_user}", "gists_url": "https://api.github.com/users/mnoukhov/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mnoukhov", "id": 3391297, "login": "mnoukhov", "node_id": "MDQ6VXNlcjMzOTEyOTc=", "organizations_url": "https://api.github.com/users/mnoukhov/orgs", "received_events_url": "https://api.github.com/users/mnoukhov/received_events", "repos_url": "https://api.github.com/users/mnoukhov/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mnoukhov/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mnoukhov/subscriptions", "type": "User", "url": "https://api.github.com/users/mnoukhov", "user_view_type": "public" }	[]	open	false	[ "Hi, thanks for reporting! https://github.com/huggingface/datasets/pull/6459 will fix this.", "I meet a similar problem as using loading scripts. I have to set download_mode='force_redownload' to load the latest script." ]	2023-12-02T21:35:17Z	2024-08-20T08:32:11Z	null	NONE	null	null	### Describe the bug When a dataset is updated on the hub, using `load_dataset` will load the locally cached dataset instead of re-downloading the updated dataset ### Steps to reproduce the bug Here is a minimal example script to 1. create an initial dataset and upload 2. download it so it is stored in cache 3. change the dataset and re-upload 4. redownload ```python import time from datasets import Dataset, DatasetDict, DownloadMode, load_dataset username = "YOUR_USERNAME_HERE" initial = Dataset.from_dict({"foo": [1, 2, 3]}) print(f"Intial {initial['foo']}") initial_ds = DatasetDict({"train": initial}) initial_ds.push_to_hub("test") time.sleep(1) download = load_dataset(f"{username}/test", split="train") changed = download.map(lambda x: {"foo": x["foo"] + 1}) print(f"Changed {changed['foo']}") changed.push_to_hub("test") time.sleep(1) download_again = load_dataset(f"{username}/test", split="train") print(f"Download Changed {download_again['foo']}") # >>> gives the out-dated [1,2,3] when it should be changed [2,3,4] ``` The redownloaded dataset should be the changed dataset but it is actually the cached, initial dataset. Force-redownloading gives the correct dataset ```python download_again_force = load_dataset(f"{username}/test", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) print(f"Force Download Changed {download_again_force['foo']}") # >>> [2,3,4] ``` ### Expected behavior I assumed there should be some sort of hashing that should check for changes in the dataset and re-download if the hashes don't match ### Environment info - `datasets` version: 2.15.0 │ - Platform: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17 │ - Python version: 3.8.17 │ - `huggingface_hub` version: 0.19.4 │ - PyArrow version: 13.0.0 │ - Pandas version: 2.0.3 │ - `fsspec` version: 2023.6.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6465/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6464	6,464	Add concurrent loading of shards to datasets.load_from_disk	{ "avatar_url": "https://avatars.githubusercontent.com/u/51880718?v=4", "events_url": "https://api.github.com/users/kkoutini/events{/privacy}", "followers_url": "https://api.github.com/users/kkoutini/followers", "following_url": "https://api.github.com/users/kkoutini/following{/other_user}", "gists_url": "https://api.github.com/users/kkoutini/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kkoutini", "id": 51880718, "login": "kkoutini", "node_id": "MDQ6VXNlcjUxODgwNzE4", "organizations_url": "https://api.github.com/users/kkoutini/orgs", "received_events_url": "https://api.github.com/users/kkoutini/received_events", "repos_url": "https://api.github.com/users/kkoutini/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kkoutini/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kkoutini/subscriptions", "type": "User", "url": "https://api.github.com/users/kkoutini", "user_view_type": "public" }	[]	closed	false	[ "If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code\r\n\r\nAlso you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This parameter is kinda legacy.\r\n\r\nHave you been able to run the benchmark on a fresh node ? The speed up doesn't seem that big in your first report", "I got some fresh nodes with the 32 threads I'm loading the dataset with around 315 seconds (without any preloading). Sequentially, it used to take around 1865 seconds. \r\nOk I'll roll back the changes and switch to `tqdm.contrib.concurrent.thread_map` without the `num_proc` parameter. ", "I switched to `tqdm.contrib.concurrent.thread_map` the code looks much simpler now.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20 \r\n\r\nThe CI fail is unrelated to this PR - I'm trying to fix it on `main` right now", "> Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20\r\n> \r\n> The CI fail is unrelated to this PR - I'm trying to fix it on `main` right now\r\n\r\nThank you! I'll let him know :)", "great work guys! letting you know here too", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005268 / 0.011353 (-0.006085) \| 0.003520 / 0.011008 (-0.007488) \| 0.063247 / 0.038508 (0.024739) \| 0.032337 / 0.023109 (0.009228) \| 0.243251 / 0.275898 (-0.032647) \| 0.265816 / 0.323480 (-0.057664) \| 0.002960 / 0.007986 (-0.005025) \| 0.002733 / 0.004328 (-0.001595) \| 0.048965 / 0.004250 (0.044715) \| 0.044341 / 0.037052 (0.007289) \| 0.260352 / 0.258489 (0.001863) \| 0.288546 / 0.293841 (-0.005295) \| 0.027903 / 0.128546 (-0.100643) \| 0.010897 / 0.075646 (-0.064749) \| 0.210852 / 0.419271 (-0.208419) \| 0.036302 / 0.043533 (-0.007231) \| 0.247440 / 0.255139 (-0.007699) \| 0.263024 / 0.283200 (-0.020176) \| 0.017732 / 0.141683 (-0.123951) \| 1.144206 / 1.452155 (-0.307949) \| 1.206135 / 1.492716 (-0.286581) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098404 / 0.018006 (0.080398) \| 0.310268 / 0.000490 (0.309778) \| 0.000231 / 0.000200 (0.000031) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018342 / 0.037411 (-0.019070) \| 0.060620 / 0.014526 (0.046094) \| 0.074248 / 0.176557 (-0.102308) \| 0.121025 / 0.737135 (-0.616110) \| 0.075331 / 0.296338 (-0.221008) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293721 / 0.215209 (0.078512) \| 2.854259 / 2.077655 (0.776605) \| 1.520735 / 1.504120 (0.016615) \| 1.393490 / 1.541195 (-0.147705) \| 1.494905 / 1.468490 (0.026415) \| 0.573812 / 4.584777 (-4.010965) \| 2.418383 / 3.745712 (-1.327329) \| 2.803916 / 5.269862 (-2.465945) \| 1.741646 / 4.565676 (-2.824030) \| 0.063341 / 0.424275 (-0.360934) \| 0.004950 / 0.007607 (-0.002658) \| 0.341758 / 0.226044 (0.115714) \| 3.392918 / 2.268929 (1.123989) \| 1.867037 / 55.444624 (-53.577587) \| 1.571381 / 6.876477 (-5.305096) \| 1.582883 / 2.142072 (-0.559190) \| 0.663660 / 4.805227 (-4.141567) \| 0.119587 / 6.500664 (-6.381077) \| 0.042071 / 0.075469 (-0.033398) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940976 / 1.841788 (-0.900811) \| 11.841958 / 8.074308 (3.767650) \| 10.510954 / 10.191392 (0.319562) \| 0.131927 / 0.680424 (-0.548497) \| 0.015373 / 0.534201 (-0.518828) \| 0.294245 / 0.579283 (-0.285038) \| 0.269355 / 0.434364 (-0.165009) \| 0.330173 / 0.540337 (-0.210165) \| 0.436809 / 1.386936 (-0.950127) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005609 / 0.011353 (-0.005744) \| 0.003800 / 0.011008 (-0.007208) \| 0.055693 / 0.038508 (0.017185) \| 0.032606 / 0.023109 (0.009497) \| 0.302372 / 0.275898 (0.026474) \| 0.370530 / 0.323480 (0.047050) \| 0.004291 / 0.007986 (-0.003694) \| 0.002783 / 0.004328 (-0.001546) \| 0.049351 / 0.004250 (0.045101) \| 0.048186 / 0.037052 (0.011133) \| 0.290022 / 0.258489 (0.031533) \| 0.323358 / 0.293841 (0.029517) \| 0.053929 / 0.128546 (-0.074617) \| 0.011251 / 0.075646 (-0.064395) \| 0.058885 / 0.419271 (-0.360387) \| 0.033833 / 0.043533 (-0.009699) \| 0.283546 / 0.255139 (0.028407) \| 0.292416 / 0.283200 (0.009216) \| 0.017682 / 0.141683 (-0.124001) \| 1.141791 / 1.452155 (-0.310364) \| 1.202540 / 1.492716 (-0.290177) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101240 / 0.018006 (0.083233) \| 0.313274 / 0.000490 (0.312784) \| 0.000255 / 0.000200 (0.000055) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023144 / 0.037411 (-0.014268) \| 0.078418 / 0.014526 (0.063892) \| 0.089716 / 0.176557 (-0.086840) \| 0.129065 / 0.737135 (-0.608070) \| 0.090976 / 0.296338 (-0.205362) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294585 / 0.215209 (0.079376) \| 2.921350 / 2.077655 (0.843695) \| 1.600977 / 1.504120 (0.096857) \| 1.483218 / 1.541195 (-0.057977) \| 1.533599 / 1.468490 (0.065109) \| 0.580064 / 4.584777 (-4.004712) \| 2.463501 / 3.745712 (-1.282211) \| 2.905853 / 5.269862 (-2.364009) \| 1.799701 / 4.565676 (-2.765975) \| 0.065057 / 0.424275 (-0.359218) \| 0.005080 / 0.007607 (-0.002527) \| 0.352292 / 0.226044 (0.126248) \| 3.429664 / 2.268929 (1.160735) \| 1.970752 / 55.444624 (-53.473872) \| 1.697151 / 6.876477 (-5.179326) \| 1.751678 / 2.142072 (-0.390394) \| 0.679264 / 4.805227 (-4.125963) \| 0.118197 / 6.500664 (-6.382467) \| 0.041834 / 0.075469 (-0.033635) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985756 / 1.841788 (-0.856032) \| 13.335160 / 8.074308 (5.260852) \| 11.524807 / 10.191392 (1.333415) \| 0.134892 / 0.680424 (-0.545532) \| 0.016855 / 0.534201 (-0.517346) \| 0.294599 / 0.579283 (-0.284685) \| 0.285988 / 0.434364 (-0.148376) \| 0.331423 / 0.540337 (-0.208914) \| 0.418765 / 1.386936 (-0.968171) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#65434e449b6bb6c57121d9518d92abe9a97e0bb0 \"CML watermark\")\n" ]	2023-12-01T13:13:53Z	2024-01-26T15:17:43Z	2024-01-26T15:10:26Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6464.diff", "html_url": "https://github.com/huggingface/datasets/pull/6464", "merged_at": "2024-01-26T15:10:26Z", "patch_url": "https://github.com/huggingface/datasets/pull/6464.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6464" }	In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads. - Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252). - I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config. - I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`. ### Tests on luster file system (on a shared partial node): Loading 1231 shards of ~2GBs. The files were pre-loaded in another process before the script runs (couldn't get a fresh node). ```python import logging from time import perf_counter import datasets logger = datasets.logging.get_logger(__name__) datasets.logging.set_verbosity_info() logging.basicConfig(level=logging.DEBUG, format="%(message)s") class catchtime: # context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time def __init__(self, debug_print="Time", logger=logger): self.debug_print = debug_print self.logger = logger def __enter__(self): self.start = perf_counter() return self def __exit__(self, type, value, traceback): self.time = perf_counter() - self.start readout = f"{self.debug_print}: {self.time:.3f} seconds" self.logger.info(readout) dataset_path="" # warmup with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=16 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=32 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=32) # num_proc=1 with catchtime("Loading in conseq", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=1) ``` #### Run 1 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%\|██████████\| 1231/1231 [01:28<00:00, 13.96shards/s] Loading in parallel: 88.690 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%\|██████████\| 1231/1231 [01:48<00:00, 11.31shards/s] Loading in parallel: 109.339 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%\|██████████\| 1231/1231 [01:06<00:00, 18.56shards/s] Loading in parallel: 66.931 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%\|██████████\| 1231/1231 [05:09<00:00, 3.98shards/s] Loading in conseq: 309.792 seconds ``` #### Run 2 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%\|██████████\| 1231/1231 [01:38<00:00, 12.53shards/s] Loading in parallel: 98.831 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%\|██████████\| 1231/1231 [02:01<00:00, 10.16shards/s] Loading in parallel: 121.669 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%\|██████████\| 1231/1231 [01:07<00:00, 18.18shards/s] Loading in parallel: 68.192 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%\|██████████\| 1231/1231 [05:19<00:00, 3.86shards/s] Loading in conseq: 319.759 seconds ``` #### Run 3 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%\|██████████\| 1231/1231 [01:36<00:00, 12.74shards/s] Loading in parallel: 96.936 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%\|██████████\| 1231/1231 [02:00<00:00, 10.24shards/s] Loading in parallel: 120.761 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%\|██████████\| 1231/1231 [01:08<00:00, 18.04shards/s] Loading in parallel: 68.666 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%\|██████████\| 1231/1231 [05:35<00:00, 3.67shards/s] Loading in conseq: 335.777 seconds ``` fix #2252	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6464/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6463	6,463	Disable benchmarks in PRs	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "It's a way to detect regressions in performance sensitive methods like map, and find the commit that lead to the regression", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005357 / 0.011353 (-0.005996) \| 0.003295 / 0.011008 (-0.007713) \| 0.062354 / 0.038508 (0.023846) \| 0.054207 / 0.023109 (0.031098) \| 0.240030 / 0.275898 (-0.035869) \| 0.267863 / 0.323480 (-0.055617) \| 0.002925 / 0.007986 (-0.005061) \| 0.002634 / 0.004328 (-0.001695) \| 0.047952 / 0.004250 (0.043702) \| 0.038424 / 0.037052 (0.001372) \| 0.248059 / 0.258489 (-0.010430) \| 0.271923 / 0.293841 (-0.021918) \| 0.027513 / 0.128546 (-0.101034) \| 0.010344 / 0.075646 (-0.065302) \| 0.210864 / 0.419271 (-0.208407) \| 0.035911 / 0.043533 (-0.007622) \| 0.245166 / 0.255139 (-0.009973) \| 0.260914 / 0.283200 (-0.022285) \| 0.016709 / 0.141683 (-0.124974) \| 1.098324 / 1.452155 (-0.353830) \| 1.162638 / 1.492716 (-0.330079) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094419 / 0.018006 (0.076413) \| 0.303209 / 0.000490 (0.302719) \| 0.000214 / 0.000200 (0.000014) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018350 / 0.037411 (-0.019061) \| 0.060625 / 0.014526 (0.046099) \| 0.072545 / 0.176557 (-0.104012) \| 0.120905 / 0.737135 (-0.616231) \| 0.073858 / 0.296338 (-0.222480) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282011 / 0.215209 (0.066802) \| 2.758741 / 2.077655 (0.681086) \| 1.431691 / 1.504120 (-0.072429) \| 1.315883 / 1.541195 (-0.225312) \| 1.344235 / 1.468490 (-0.124255) \| 0.562117 / 4.584777 (-4.022660) \| 2.385641 / 3.745712 (-1.360071) \| 2.785402 / 5.269862 (-2.484460) \| 1.753912 / 4.565676 (-2.811764) \| 0.064054 / 0.424275 (-0.360221) \| 0.005050 / 0.007607 (-0.002557) \| 0.336452 / 0.226044 (0.110407) \| 3.302481 / 2.268929 (1.033553) \| 1.794105 / 55.444624 (-53.650519) \| 1.519346 / 6.876477 (-5.357131) \| 1.514911 / 2.142072 (-0.627161) \| 0.655779 / 4.805227 (-4.149449) \| 0.117913 / 6.500664 (-6.382751) \| 0.042229 / 0.075469 (-0.033240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935196 / 1.841788 (-0.906591) \| 11.490113 / 8.074308 (3.415805) \| 10.542446 / 10.191392 (0.351054) \| 0.129614 / 0.680424 (-0.550810) \| 0.014919 / 0.534201 (-0.519282) \| 0.288448 / 0.579283 (-0.290835) \| 0.266929 / 0.434364 (-0.167435) \| 0.328830 / 0.540337 (-0.211507) \| 0.475510 / 1.386936 (-0.911426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005469 / 0.011353 (-0.005884) \| 0.003798 / 0.011008 (-0.007210) \| 0.049129 / 0.038508 (0.010621) \| 0.055490 / 0.023109 (0.032380) \| 0.265828 / 0.275898 (-0.010070) \| 0.286031 / 0.323480 (-0.037448) \| 0.004075 / 0.007986 (-0.003910) \| 0.002668 / 0.004328 (-0.001660) \| 0.047823 / 0.004250 (0.043573) \| 0.041946 / 0.037052 (0.004894) \| 0.270359 / 0.258489 (0.011869) \| 0.294287 / 0.293841 (0.000446) \| 0.029643 / 0.128546 (-0.098903) \| 0.010523 / 0.075646 (-0.065123) \| 0.057370 / 0.419271 (-0.361902) \| 0.033149 / 0.043533 (-0.010384) \| 0.264408 / 0.255139 (0.009269) \| 0.280413 / 0.283200 (-0.002787) \| 0.018313 / 0.141683 (-0.123370) \| 1.105982 / 1.452155 (-0.346173) \| 1.182486 / 1.492716 (-0.310230) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092643 / 0.018006 (0.074637) \| 0.301320 / 0.000490 (0.300831) \| 0.000221 / 0.000200 (0.000021) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021253 / 0.037411 (-0.016158) \| 0.068052 / 0.014526 (0.053527) \| 0.080821 / 0.176557 (-0.095736) \| 0.119320 / 0.737135 (-0.617816) \| 0.081952 / 0.296338 (-0.214387) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288536 / 0.215209 (0.073327) \| 2.819900 / 2.077655 (0.742245) \| 1.545210 / 1.504120 (0.041090) \| 1.422047 / 1.541195 (-0.119147) \| 1.439158 / 1.468490 (-0.029332) \| 0.564910 / 4.584777 (-4.019867) \| 2.430474 / 3.745712 (-1.315238) \| 2.763979 / 5.269862 (-2.505882) \| 1.732203 / 4.565676 (-2.833474) \| 0.062692 / 0.424275 (-0.361583) \| 0.004936 / 0.007607 (-0.002671) \| 0.341626 / 0.226044 (0.115582) \| 3.366623 / 2.268929 (1.097694) \| 1.917198 / 55.444624 (-53.527426) \| 1.637635 / 6.876477 (-5.238842) \| 1.625953 / 2.142072 (-0.516119) \| 0.634936 / 4.805227 (-4.170291) \| 0.115336 / 6.500664 (-6.385328) \| 0.040946 / 0.075469 (-0.034524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964865 / 1.841788 (-0.876922) \| 12.077233 / 8.074308 (4.002925) \| 10.664120 / 10.191392 (0.472728) \| 0.132084 / 0.680424 (-0.548340) \| 0.015931 / 0.534201 (-0.518270) \| 0.289181 / 0.579283 (-0.290102) \| 0.276943 / 0.434364 (-0.157420) \| 0.324884 / 0.540337 (-0.215453) \| 0.552570 / 1.386936 (-0.834366) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ac3f2b3f6d867673e41a0253f9e1ad48db68a8e \"CML watermark\")\n" ]	2023-12-01T11:35:30Z	2023-12-01T12:09:09Z	2023-12-01T12:03:04Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6463.diff", "html_url": "https://github.com/huggingface/datasets/pull/6463", "merged_at": "2023-12-01T12:03:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/6463.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6463" }	In order to keep PR pages less spammy / more readable. Having the benchmarks on commits on `main` is enough imo	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6463/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6462	6,462	Missing DatasetNotFoundError	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005594 / 0.011353 (-0.005759) \| 0.003672 / 0.011008 (-0.007337) \| 0.062796 / 0.038508 (0.024288) \| 0.059432 / 0.023109 (0.036323) \| 0.253976 / 0.275898 (-0.021922) \| 0.281155 / 0.323480 (-0.042325) \| 0.003023 / 0.007986 (-0.004962) \| 0.003320 / 0.004328 (-0.001008) \| 0.049059 / 0.004250 (0.044809) \| 0.040252 / 0.037052 (0.003200) \| 0.259526 / 0.258489 (0.001037) \| 0.318798 / 0.293841 (0.024957) \| 0.027883 / 0.128546 (-0.100663) \| 0.010883 / 0.075646 (-0.064763) \| 0.206948 / 0.419271 (-0.212323) \| 0.036335 / 0.043533 (-0.007198) \| 0.253209 / 0.255139 (-0.001930) \| 0.275173 / 0.283200 (-0.008026) \| 0.020365 / 0.141683 (-0.121318) \| 1.121630 / 1.452155 (-0.330524) \| 1.174680 / 1.492716 (-0.318036) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098372 / 0.018006 (0.080366) \| 0.309949 / 0.000490 (0.309460) \| 0.000225 / 0.000200 (0.000025) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019495 / 0.037411 (-0.017916) \| 0.062321 / 0.014526 (0.047795) \| 0.074525 / 0.176557 (-0.102031) \| 0.121832 / 0.737135 (-0.615303) \| 0.077612 / 0.296338 (-0.218727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288156 / 0.215209 (0.072947) \| 2.816411 / 2.077655 (0.738756) \| 1.497926 / 1.504120 (-0.006193) \| 1.378137 / 1.541195 (-0.163058) \| 1.446466 / 1.468490 (-0.022024) \| 0.566195 / 4.584777 (-4.018582) \| 2.391933 / 3.745712 (-1.353780) \| 2.929290 / 5.269862 (-2.340572) \| 1.828215 / 4.565676 (-2.737462) \| 0.063312 / 0.424275 (-0.360963) \| 0.005199 / 0.007607 (-0.002408) \| 0.342883 / 0.226044 (0.116838) \| 3.378388 / 2.268929 (1.109459) \| 1.865710 / 55.444624 (-53.578915) \| 1.573442 / 6.876477 (-5.303035) \| 1.631228 / 2.142072 (-0.510845) \| 0.651614 / 4.805227 (-4.153613) \| 0.118177 / 6.500664 (-6.382487) \| 0.043303 / 0.075469 (-0.032166) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950694 / 1.841788 (-0.891094) \| 12.559851 / 8.074308 (4.485543) \| 10.751123 / 10.191392 (0.559731) \| 0.143107 / 0.680424 (-0.537317) \| 0.014469 / 0.534201 (-0.519732) \| 0.289531 / 0.579283 (-0.289752) \| 0.267316 / 0.434364 (-0.167047) \| 0.327748 / 0.540337 (-0.212590) \| 0.437758 / 1.386936 (-0.949178) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005669 / 0.011353 (-0.005684) \| 0.003831 / 0.011008 (-0.007177) \| 0.049096 / 0.038508 (0.010588) \| 0.061408 / 0.023109 (0.038299) \| 0.274571 / 0.275898 (-0.001327) \| 0.299978 / 0.323480 (-0.023501) \| 0.004216 / 0.007986 (-0.003769) \| 0.002848 / 0.004328 (-0.001480) \| 0.048755 / 0.004250 (0.044504) \| 0.042576 / 0.037052 (0.005524) \| 0.276781 / 0.258489 (0.018292) \| 0.300903 / 0.293841 (0.007062) \| 0.030243 / 0.128546 (-0.098303) \| 0.010967 / 0.075646 (-0.064679) \| 0.057879 / 0.419271 (-0.361392) \| 0.033206 / 0.043533 (-0.010327) \| 0.277620 / 0.255139 (0.022481) \| 0.296263 / 0.283200 (0.013064) \| 0.019022 / 0.141683 (-0.122660) \| 1.125615 / 1.452155 (-0.326539) \| 1.278016 / 1.492716 (-0.214700) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096836 / 0.018006 (0.078830) \| 0.307491 / 0.000490 (0.307001) \| 0.000230 / 0.000200 (0.000030) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021552 / 0.037411 (-0.015859) \| 0.071099 / 0.014526 (0.056573) \| 0.082432 / 0.176557 (-0.094124) \| 0.121826 / 0.737135 (-0.615310) \| 0.084902 / 0.296338 (-0.211437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.328113 / 0.215209 (0.112904) \| 2.989613 / 2.077655 (0.911959) \| 1.604904 / 1.504120 (0.100784) \| 1.485459 / 1.541195 (-0.055735) \| 1.524829 / 1.468490 (0.056339) \| 0.580589 / 4.584777 (-4.004188) \| 2.440087 / 3.745712 (-1.305625) \| 2.944697 / 5.269862 (-2.325164) \| 1.832728 / 4.565676 (-2.732949) \| 0.064423 / 0.424275 (-0.359852) \| 0.004991 / 0.007607 (-0.002616) \| 0.357878 / 0.226044 (0.131834) \| 3.515415 / 2.268929 (1.246487) \| 1.964492 / 55.444624 (-53.480132) \| 1.684058 / 6.876477 (-5.192418) \| 1.730294 / 2.142072 (-0.411778) \| 0.661228 / 4.805227 (-4.143999) \| 0.122894 / 6.500664 (-6.377770) \| 0.041776 / 0.075469 (-0.033693) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969849 / 1.841788 (-0.871939) \| 12.897067 / 8.074308 (4.822758) \| 10.908200 / 10.191392 (0.716808) \| 0.141139 / 0.680424 (-0.539285) \| 0.015377 / 0.534201 (-0.518824) \| 0.288625 / 0.579283 (-0.290658) \| 0.279020 / 0.434364 (-0.155344) \| 0.328386 / 0.540337 (-0.211951) \| 0.590833 / 1.386936 (-0.796103) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39ea60eaabb05d8ee38c072f375816cf87fce1a9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004986 / 0.011353 (-0.006367) \| 0.003070 / 0.011008 (-0.007938) \| 0.062433 / 0.038508 (0.023925) \| 0.050639 / 0.023109 (0.027530) \| 0.241807 / 0.275898 (-0.034091) \| 0.262517 / 0.323480 (-0.060963) \| 0.003826 / 0.007986 (-0.004160) \| 0.002602 / 0.004328 (-0.001727) \| 0.048508 / 0.004250 (0.044257) \| 0.037276 / 0.037052 (0.000224) \| 0.245757 / 0.258489 (-0.012732) \| 0.272969 / 0.293841 (-0.020871) \| 0.027139 / 0.128546 (-0.101407) \| 0.010265 / 0.075646 (-0.065381) \| 0.207279 / 0.419271 (-0.211992) \| 0.035312 / 0.043533 (-0.008221) \| 0.247535 / 0.255139 (-0.007604) \| 0.260668 / 0.283200 (-0.022532) \| 0.016496 / 0.141683 (-0.125187) \| 1.137510 / 1.452155 (-0.314645) \| 1.167870 / 1.492716 (-0.324847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091743 / 0.018006 (0.073736) \| 0.298649 / 0.000490 (0.298159) \| 0.000208 / 0.000200 (0.000009) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019053 / 0.037411 (-0.018359) \| 0.060300 / 0.014526 (0.045774) \| 0.072154 / 0.176557 (-0.104402) \| 0.120293 / 0.737135 (-0.616842) \| 0.073923 / 0.296338 (-0.222415) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283058 / 0.215209 (0.067849) \| 2.769503 / 2.077655 (0.691849) \| 1.457016 / 1.504120 (-0.047104) \| 1.335753 / 1.541195 (-0.205441) \| 1.325986 / 1.468490 (-0.142504) \| 0.562553 / 4.584777 (-4.022224) \| 2.406144 / 3.745712 (-1.339568) \| 2.778063 / 5.269862 (-2.491799) \| 1.782199 / 4.565676 (-2.783477) \| 0.062490 / 0.424275 (-0.361785) \| 0.004912 / 0.007607 (-0.002695) \| 0.338500 / 0.226044 (0.112456) \| 3.309746 / 2.268929 (1.040818) \| 1.819693 / 55.444624 (-53.624931) \| 1.510295 / 6.876477 (-5.366182) \| 1.578402 / 2.142072 (-0.563671) \| 0.637517 / 4.805227 (-4.167710) \| 0.117018 / 6.500664 (-6.383647) \| 0.048149 / 0.075469 (-0.027320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939424 / 1.841788 (-0.902364) \| 11.494891 / 8.074308 (3.420583) \| 10.115194 / 10.191392 (-0.076198) \| 0.126751 / 0.680424 (-0.553673) \| 0.013567 / 0.534201 (-0.520634) \| 0.282501 / 0.579283 (-0.296782) \| 0.260594 / 0.434364 (-0.173770) \| 0.325940 / 0.540337 (-0.214397) \| 0.426186 / 1.386936 (-0.960750) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005405 / 0.011353 (-0.005948) \| 0.003557 / 0.011008 (-0.007451) \| 0.051139 / 0.038508 (0.012631) \| 0.053446 / 0.023109 (0.030337) \| 0.268051 / 0.275898 (-0.007847) \| 0.292343 / 0.323480 (-0.031136) \| 0.004716 / 0.007986 (-0.003269) \| 0.002677 / 0.004328 (-0.001651) \| 0.047634 / 0.004250 (0.043384) \| 0.041062 / 0.037052 (0.004009) \| 0.269225 / 0.258489 (0.010736) \| 0.297462 / 0.293841 (0.003621) \| 0.029292 / 0.128546 (-0.099254) \| 0.010947 / 0.075646 (-0.064699) \| 0.057845 / 0.419271 (-0.361426) \| 0.032793 / 0.043533 (-0.010740) \| 0.265308 / 0.255139 (0.010169) \| 0.288242 / 0.283200 (0.005043) \| 0.018311 / 0.141683 (-0.123372) \| 1.140957 / 1.452155 (-0.311197) \| 1.204883 / 1.492716 (-0.287833) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091375 / 0.018006 (0.073368) \| 0.285922 / 0.000490 (0.285432) \| 0.000238 / 0.000200 (0.000038) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021277 / 0.037411 (-0.016134) \| 0.068853 / 0.014526 (0.054328) \| 0.081002 / 0.176557 (-0.095555) \| 0.120998 / 0.737135 (-0.616138) \| 0.082741 / 0.296338 (-0.213598) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299398 / 0.215209 (0.084189) \| 2.909622 / 2.077655 (0.831967) \| 1.624381 / 1.504120 (0.120261) \| 1.501683 / 1.541195 (-0.039512) \| 1.523045 / 1.468490 (0.054555) \| 0.548960 / 4.584777 (-4.035817) \| 2.413297 / 3.745712 (-1.332415) \| 2.817852 / 5.269862 (-2.452010) \| 1.754407 / 4.565676 (-2.811270) \| 0.061912 / 0.424275 (-0.362363) \| 0.004880 / 0.007607 (-0.002727) \| 0.353989 / 0.226044 (0.127944) \| 3.496147 / 2.268929 (1.227219) \| 2.003026 / 55.444624 (-53.441598) \| 1.702013 / 6.876477 (-5.174463) \| 1.680935 / 2.142072 (-0.461137) \| 0.630183 / 4.805227 (-4.175044) \| 0.113786 / 6.500664 (-6.386878) \| 0.040061 / 0.075469 (-0.035408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957218 / 1.841788 (-0.884569) \| 11.914469 / 8.074308 (3.840160) \| 10.488896 / 10.191392 (0.297504) \| 0.129292 / 0.680424 (-0.551132) \| 0.016603 / 0.534201 (-0.517598) \| 0.287367 / 0.579283 (-0.291916) \| 0.271332 / 0.434364 (-0.163032) \| 0.325577 / 0.540337 (-0.214761) \| 0.560553 / 1.386936 (-0.826383) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d31e434bbeafdf6a70cb80539342d8fe5f5fd27 \"CML watermark\")\n" ]	2023-11-30T18:09:43Z	2023-11-30T18:36:40Z	2023-11-30T18:30:30Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6462.diff", "html_url": "https://github.com/huggingface/datasets/pull/6462", "merged_at": "2023-11-30T18:30:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/6462.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6462" }	continuation of https://github.com/huggingface/datasets/pull/6431 this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6462/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6461	6,461	Fix shard retry mechanism in `push_to_hub`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "@Wauplin Maybe `504` should be added to the `retry_on_status_codes` tuple [here](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300) to guard against https://github.com/huggingface/datasets/issues/3872", "We could but I'm not sure to have witness a 504 on S3 before. The issue reported in https://github.com/huggingface/datasets/issues/3872 is a 504 on the `/upload` endpoint on the Hub and this is not an endpoint that is retried on [this line](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300).", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005110 / 0.011353 (-0.006243) \| 0.003307 / 0.011008 (-0.007701) \| 0.062601 / 0.038508 (0.024093) \| 0.049644 / 0.023109 (0.026534) \| 0.243195 / 0.275898 (-0.032703) \| 0.273543 / 0.323480 (-0.049936) \| 0.003862 / 0.007986 (-0.004123) \| 0.002624 / 0.004328 (-0.001705) \| 0.048273 / 0.004250 (0.044023) \| 0.037820 / 0.037052 (0.000768) \| 0.249134 / 0.258489 (-0.009355) \| 0.319359 / 0.293841 (0.025518) \| 0.027816 / 0.128546 (-0.100730) \| 0.010422 / 0.075646 (-0.065225) \| 0.206607 / 0.419271 (-0.212665) \| 0.035719 / 0.043533 (-0.007814) \| 0.250300 / 0.255139 (-0.004839) \| 0.290377 / 0.283200 (0.007177) \| 0.018459 / 0.141683 (-0.123224) \| 1.114664 / 1.452155 (-0.337490) \| 1.171429 / 1.492716 (-0.321288) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091483 / 0.018006 (0.073477) \| 0.302770 / 0.000490 (0.302281) \| 0.000203 / 0.000200 (0.000003) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018870 / 0.037411 (-0.018541) \| 0.062692 / 0.014526 (0.048166) \| 0.075381 / 0.176557 (-0.101176) \| 0.122338 / 0.737135 (-0.614797) \| 0.075608 / 0.296338 (-0.220730) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288115 / 0.215209 (0.072906) \| 2.816183 / 2.077655 (0.738528) \| 1.535601 / 1.504120 (0.031481) \| 1.409546 / 1.541195 (-0.131648) \| 1.438569 / 1.468490 (-0.029921) \| 0.561797 / 4.584777 (-4.022980) \| 2.373921 / 3.745712 (-1.371791) \| 2.739437 / 5.269862 (-2.530424) \| 1.750921 / 4.565676 (-2.814755) \| 0.062114 / 0.424275 (-0.362161) \| 0.004965 / 0.007607 (-0.002642) \| 0.348614 / 0.226044 (0.122569) \| 3.519631 / 2.268929 (1.250703) \| 1.910797 / 55.444624 (-53.533827) \| 1.610541 / 6.876477 (-5.265936) \| 1.617972 / 2.142072 (-0.524100) \| 0.639421 / 4.805227 (-4.165806) \| 0.117371 / 6.500664 (-6.383293) \| 0.041851 / 0.075469 (-0.033618) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945563 / 1.841788 (-0.896224) \| 11.362399 / 8.074308 (3.288090) \| 10.468468 / 10.191392 (0.277075) \| 0.128925 / 0.680424 (-0.551499) \| 0.013892 / 0.534201 (-0.520309) \| 0.285487 / 0.579283 (-0.293796) \| 0.269295 / 0.434364 (-0.165069) \| 0.324843 / 0.540337 (-0.215495) \| 0.438452 / 1.386936 (-0.948484) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005303 / 0.011353 (-0.006050) \| 0.003162 / 0.011008 (-0.007846) \| 0.048177 / 0.038508 (0.009669) \| 0.048708 / 0.023109 (0.025599) \| 0.271663 / 0.275898 (-0.004235) \| 0.289948 / 0.323480 (-0.033532) \| 0.003955 / 0.007986 (-0.004030) \| 0.002616 / 0.004328 (-0.001713) \| 0.047510 / 0.004250 (0.043260) \| 0.039938 / 0.037052 (0.002886) \| 0.277449 / 0.258489 (0.018960) \| 0.300315 / 0.293841 (0.006474) \| 0.029263 / 0.128546 (-0.099283) \| 0.010403 / 0.075646 (-0.065244) \| 0.056682 / 0.419271 (-0.362590) \| 0.032757 / 0.043533 (-0.010776) \| 0.273291 / 0.255139 (0.018152) \| 0.289023 / 0.283200 (0.005824) \| 0.017843 / 0.141683 (-0.123840) \| 1.124762 / 1.452155 (-0.327393) \| 1.176646 / 1.492716 (-0.316070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004568 / 0.018006 (-0.013438) \| 0.300715 / 0.000490 (0.300225) \| 0.000212 / 0.000200 (0.000012) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021528 / 0.037411 (-0.015883) \| 0.068317 / 0.014526 (0.053792) \| 0.081358 / 0.176557 (-0.095199) \| 0.119297 / 0.737135 (-0.617838) \| 0.082445 / 0.296338 (-0.213893) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289681 / 0.215209 (0.074472) \| 2.843862 / 2.077655 (0.766208) \| 1.574257 / 1.504120 (0.070137) \| 1.454026 / 1.541195 (-0.087169) \| 1.478379 / 1.468490 (0.009889) \| 0.558259 / 4.584777 (-4.026518) \| 2.513261 / 3.745712 (-1.232451) \| 2.759751 / 5.269862 (-2.510111) \| 1.730335 / 4.565676 (-2.835341) \| 0.063805 / 0.424275 (-0.360470) \| 0.004991 / 0.007607 (-0.002616) \| 0.346586 / 0.226044 (0.120542) \| 3.369163 / 2.268929 (1.100234) \| 1.934734 / 55.444624 (-53.509890) \| 1.658864 / 6.876477 (-5.217613) \| 1.645621 / 2.142072 (-0.496452) \| 0.636633 / 4.805227 (-4.168594) \| 0.116839 / 6.500664 (-6.383825) \| 0.040863 / 0.075469 (-0.034606) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960925 / 1.841788 (-0.880863) \| 11.769189 / 8.074308 (3.694881) \| 10.713662 / 10.191392 (0.522270) \| 0.140510 / 0.680424 (-0.539914) \| 0.015424 / 0.534201 (-0.518777) \| 0.288039 / 0.579283 (-0.291244) \| 0.277623 / 0.434364 (-0.156741) \| 0.322622 / 0.540337 (-0.217716) \| 0.539805 / 1.386936 (-0.847131) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07ad81c15bd3b954defe779fc37ba5f432f5ff2a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005501 / 0.011353 (-0.005852) \| 0.003754 / 0.011008 (-0.007254) \| 0.062628 / 0.038508 (0.024120) \| 0.059951 / 0.023109 (0.036842) \| 0.254851 / 0.275898 (-0.021047) \| 0.272133 / 0.323480 (-0.051347) \| 0.003962 / 0.007986 (-0.004024) \| 0.002759 / 0.004328 (-0.001569) \| 0.048412 / 0.004250 (0.044161) \| 0.039349 / 0.037052 (0.002297) \| 0.253093 / 0.258489 (-0.005397) \| 0.287048 / 0.293841 (-0.006793) \| 0.027197 / 0.128546 (-0.101349) \| 0.010828 / 0.075646 (-0.064819) \| 0.206371 / 0.419271 (-0.212901) \| 0.035881 / 0.043533 (-0.007652) \| 0.254905 / 0.255139 (-0.000234) \| 0.273819 / 0.283200 (-0.009381) \| 0.018041 / 0.141683 (-0.123642) \| 1.103970 / 1.452155 (-0.348185) \| 1.166340 / 1.492716 (-0.326377) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093196 / 0.018006 (0.075190) \| 0.302690 / 0.000490 (0.302200) \| 0.000219 / 0.000200 (0.000019) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019552 / 0.037411 (-0.017860) \| 0.062337 / 0.014526 (0.047811) \| 0.074070 / 0.176557 (-0.102486) \| 0.120998 / 0.737135 (-0.616137) \| 0.076265 / 0.296338 (-0.220074) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272637 / 0.215209 (0.057427) \| 2.693350 / 2.077655 (0.615696) \| 1.398020 / 1.504120 (-0.106100) \| 1.285706 / 1.541195 (-0.255488) \| 1.342810 / 1.468490 (-0.125680) \| 0.565378 / 4.584777 (-4.019399) \| 2.390131 / 3.745712 (-1.355581) \| 2.892137 / 5.269862 (-2.377725) \| 1.819840 / 4.565676 (-2.745836) \| 0.062789 / 0.424275 (-0.361486) \| 0.004920 / 0.007607 (-0.002687) \| 0.329281 / 0.226044 (0.103237) \| 3.261664 / 2.268929 (0.992735) \| 1.775102 / 55.444624 (-53.669523) \| 1.514341 / 6.876477 (-5.362136) \| 1.530805 / 2.142072 (-0.611267) \| 0.641009 / 4.805227 (-4.164218) \| 0.118626 / 6.500664 (-6.382038) \| 0.042732 / 0.075469 (-0.032737) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933179 / 1.841788 (-0.908609) \| 12.085247 / 8.074308 (4.010939) \| 10.541596 / 10.191392 (0.350204) \| 0.140141 / 0.680424 (-0.540283) \| 0.014646 / 0.534201 (-0.519555) \| 0.289640 / 0.579283 (-0.289643) \| 0.281042 / 0.434364 (-0.153322) \| 0.326462 / 0.540337 (-0.213876) \| 0.441981 / 1.386936 (-0.944955) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005259 / 0.011353 (-0.006094) \| 0.003766 / 0.011008 (-0.007242) \| 0.048782 / 0.038508 (0.010273) \| 0.064946 / 0.023109 (0.041836) \| 0.264529 / 0.275898 (-0.011369) \| 0.289675 / 0.323480 (-0.033805) \| 0.004057 / 0.007986 (-0.003928) \| 0.002805 / 0.004328 (-0.001523) \| 0.047709 / 0.004250 (0.043459) \| 0.041149 / 0.037052 (0.004096) \| 0.271254 / 0.258489 (0.012765) \| 0.296685 / 0.293841 (0.002844) \| 0.029486 / 0.128546 (-0.099060) \| 0.010608 / 0.075646 (-0.065038) \| 0.056392 / 0.419271 (-0.362879) \| 0.033181 / 0.043533 (-0.010352) \| 0.267029 / 0.255139 (0.011890) \| 0.284987 / 0.283200 (0.001787) \| 0.018045 / 0.141683 (-0.123637) \| 1.137358 / 1.452155 (-0.314796) \| 1.184007 / 1.492716 (-0.308709) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004603 / 0.018006 (-0.013403) \| 0.303901 / 0.000490 (0.303411) \| 0.000225 / 0.000200 (0.000025) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021957 / 0.037411 (-0.015454) \| 0.069427 / 0.014526 (0.054901) \| 0.082394 / 0.176557 (-0.094163) \| 0.120745 / 0.737135 (-0.616390) \| 0.084571 / 0.296338 (-0.211767) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292832 / 0.215209 (0.077623) \| 2.824295 / 2.077655 (0.746640) \| 1.563273 / 1.504120 (0.059153) \| 1.440202 / 1.541195 (-0.100992) \| 1.489810 / 1.468490 (0.021320) \| 0.561120 / 4.584777 (-4.023657) \| 2.439045 / 3.745712 (-1.306667) \| 2.867139 / 5.269862 (-2.402722) \| 1.793812 / 4.565676 (-2.771865) \| 0.062797 / 0.424275 (-0.361478) \| 0.005033 / 0.007607 (-0.002574) \| 0.343648 / 0.226044 (0.117604) \| 3.432285 / 2.268929 (1.163357) \| 1.918175 / 55.444624 (-53.526449) \| 1.637245 / 6.876477 (-5.239232) \| 1.709246 / 2.142072 (-0.432826) \| 0.634744 / 4.805227 (-4.170483) \| 0.115782 / 6.500664 (-6.384882) \| 0.041228 / 0.075469 (-0.034241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962369 / 1.841788 (-0.879418) \| 12.750819 / 8.074308 (4.676511) \| 10.927356 / 10.191392 (0.735964) \| 0.143454 / 0.680424 (-0.536970) \| 0.015348 / 0.534201 (-0.518853) \| 0.291207 / 0.579283 (-0.288076) \| 0.276924 / 0.434364 (-0.157440) \| 0.327287 / 0.540337 (-0.213050) \| 0.577439 / 1.386936 (-0.809497) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#544ad95f6b6da7fee44a2bc838e15a5e0156c946 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005070 / 0.011353 (-0.006283) \| 0.003475 / 0.011008 (-0.007533) \| 0.061985 / 0.038508 (0.023477) \| 0.048539 / 0.023109 (0.025430) \| 0.229935 / 0.275898 (-0.045963) \| 0.255247 / 0.323480 (-0.068233) \| 0.003919 / 0.007986 (-0.004066) \| 0.002664 / 0.004328 (-0.001664) \| 0.048892 / 0.004250 (0.044642) \| 0.037381 / 0.037052 (0.000328) \| 0.238517 / 0.258489 (-0.019972) \| 0.284069 / 0.293841 (-0.009772) \| 0.027513 / 0.128546 (-0.101033) \| 0.010778 / 0.075646 (-0.064868) \| 0.205004 / 0.419271 (-0.214268) \| 0.035553 / 0.043533 (-0.007980) \| 0.230117 / 0.255139 (-0.025022) \| 0.251150 / 0.283200 (-0.032050) \| 0.017951 / 0.141683 (-0.123732) \| 1.145548 / 1.452155 (-0.306607) \| 1.191659 / 1.492716 (-0.301057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092335 / 0.018006 (0.074329) \| 0.300264 / 0.000490 (0.299774) \| 0.000206 / 0.000200 (0.000006) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018608 / 0.037411 (-0.018804) \| 0.060376 / 0.014526 (0.045850) \| 0.073551 / 0.176557 (-0.103006) \| 0.118840 / 0.737135 (-0.618295) \| 0.074447 / 0.296338 (-0.221892) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287033 / 0.215209 (0.071824) \| 2.770958 / 2.077655 (0.693303) \| 1.443986 / 1.504120 (-0.060134) \| 1.314627 / 1.541195 (-0.226567) \| 1.342287 / 1.468490 (-0.126203) \| 0.559607 / 4.584777 (-4.025170) \| 2.409678 / 3.745712 (-1.336034) \| 2.772566 / 5.269862 (-2.497295) \| 1.743511 / 4.565676 (-2.822165) \| 0.062277 / 0.424275 (-0.361998) \| 0.004952 / 0.007607 (-0.002655) \| 0.330581 / 0.226044 (0.104537) \| 3.280385 / 2.268929 (1.011456) \| 1.809599 / 55.444624 (-53.635025) \| 1.532186 / 6.876477 (-5.344290) \| 1.529689 / 2.142072 (-0.612383) \| 0.645213 / 4.805227 (-4.160014) \| 0.117564 / 6.500664 (-6.383100) \| 0.041657 / 0.075469 (-0.033812) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943912 / 1.841788 (-0.897876) \| 11.414317 / 8.074308 (3.340009) \| 10.394915 / 10.191392 (0.203523) \| 0.129271 / 0.680424 (-0.551153) \| 0.013934 / 0.534201 (-0.520267) \| 0.288217 / 0.579283 (-0.291066) \| 0.267171 / 0.434364 (-0.167193) \| 0.327112 / 0.540337 (-0.213225) \| 0.446680 / 1.386936 (-0.940256) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005200 / 0.011353 (-0.006152) \| 0.003453 / 0.011008 (-0.007555) \| 0.048736 / 0.038508 (0.010228) \| 0.051073 / 0.023109 (0.027964) \| 0.276591 / 0.275898 (0.000693) \| 0.294495 / 0.323480 (-0.028985) \| 0.004069 / 0.007986 (-0.003917) \| 0.002945 / 0.004328 (-0.001383) \| 0.047090 / 0.004250 (0.042839) \| 0.040445 / 0.037052 (0.003393) \| 0.278464 / 0.258489 (0.019975) \| 0.304020 / 0.293841 (0.010179) \| 0.028811 / 0.128546 (-0.099736) \| 0.010388 / 0.075646 (-0.065259) \| 0.057214 / 0.419271 (-0.362057) \| 0.032588 / 0.043533 (-0.010945) \| 0.277694 / 0.255139 (0.022555) \| 0.294979 / 0.283200 (0.011779) \| 0.018384 / 0.141683 (-0.123299) \| 1.162332 / 1.452155 (-0.289822) \| 1.188355 / 1.492716 (-0.304361) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090501 / 0.018006 (0.072495) \| 0.303122 / 0.000490 (0.302632) \| 0.000222 / 0.000200 (0.000022) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022536 / 0.037411 (-0.014876) \| 0.068452 / 0.014526 (0.053926) \| 0.080932 / 0.176557 (-0.095625) \| 0.119185 / 0.737135 (-0.617950) \| 0.081513 / 0.296338 (-0.214825) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291522 / 0.215209 (0.076313) \| 2.849467 / 2.077655 (0.771812) \| 1.597395 / 1.504120 (0.093275) \| 1.512872 / 1.541195 (-0.028323) \| 1.488144 / 1.468490 (0.019654) \| 0.572436 / 4.584777 (-4.012341) \| 2.440129 / 3.745712 (-1.305583) \| 2.788045 / 5.269862 (-2.481817) \| 1.754246 / 4.565676 (-2.811430) \| 0.066706 / 0.424275 (-0.357569) \| 0.005035 / 0.007607 (-0.002573) \| 0.336621 / 0.226044 (0.110576) \| 3.322820 / 2.268929 (1.053891) \| 1.940494 / 55.444624 (-53.504130) \| 1.670022 / 6.876477 (-5.206454) \| 1.666353 / 2.142072 (-0.475720) \| 0.646180 / 4.805227 (-4.159047) \| 0.116676 / 6.500664 (-6.383988) \| 0.040559 / 0.075469 (-0.034910) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971396 / 1.841788 (-0.870392) \| 11.782426 / 8.074308 (3.708118) \| 10.672034 / 10.191392 (0.480642) \| 0.137658 / 0.680424 (-0.542766) \| 0.016210 / 0.534201 (-0.517991) \| 0.288302 / 0.579283 (-0.290981) \| 0.280775 / 0.434364 (-0.153589) \| 0.326962 / 0.540337 (-0.213375) \| 0.558511 / 1.386936 (-0.828425) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76020180407d7ea9a0b535758d8d1b241fd19d8c \"CML watermark\")\n" ]	2023-11-30T14:57:14Z	2023-12-01T17:57:39Z	2023-12-01T17:51:33Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6461.diff", "html_url": "https://github.com/huggingface/datasets/pull/6461", "merged_at": "2023-12-01T17:51:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/6461.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6461" }	When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that. Fix https://github.com/huggingface/datasets/issues/6392	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6461/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6460	6,460	jsonlines files don't load with `load_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/41377532?v=4", "events_url": "https://api.github.com/users/serenalotreck/events{/privacy}", "followers_url": "https://api.github.com/users/serenalotreck/followers", "following_url": "https://api.github.com/users/serenalotreck/following{/other_user}", "gists_url": "https://api.github.com/users/serenalotreck/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/serenalotreck", "id": 41377532, "login": "serenalotreck", "node_id": "MDQ6VXNlcjQxMzc3NTMy", "organizations_url": "https://api.github.com/users/serenalotreck/orgs", "received_events_url": "https://api.github.com/users/serenalotreck/received_events", "repos_url": "https://api.github.com/users/serenalotreck/repos", "site_admin": false, "starred_url": "https://api.github.com/users/serenalotreck/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/serenalotreck/subscriptions", "type": "User", "url": "https://api.github.com/users/serenalotreck", "user_view_type": "public" }	[]	closed	false	[ "Hi @serenalotreck,\r\n\r\nWe use Apache Arrow `pyarrow` to read jsonlines and it throws an error when trying to load your data files:\r\n```python\r\nIn [1]: import pyarrow as pa\r\n\r\nIn [2]: data = pa.json.read_json(\"train.jsonl\")\r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-14-e9b104832528> in <module>\r\n----> 1 data = pa.json.read_json(\"train.jsonl\")\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0\r\n```\r\n\r\nI think it has to do with the data structure of the fields \"ner\" (and also \"relations\"):\r\n```json\r\n\"ner\": [\r\n [\r\n [0, 4, \"Biochemical_process\"], \r\n [15, 16, \"Protein\"]\r\n ], \r\n```\r\nArrow interprets this data structure as an array, an arrays contain just a single data type: \r\n- when reading sequentially, it finds first the `0` and infers that the data is of type `number`;\r\n- when it finds the string `\"Biochemical_process\"`, it cannot cast it to number and throws the `ArrowInvalid` error\r\n\r\nOne solution could be to change the data structure of your data files. Any other ideas, @huggingface/datasets ?", "Hi @albertvillanova, \r\n\r\nThanks for the explanation! To the best of my knowledge, arrays in a json [can contain multiple data types](https://docs.actian.com/ingres/11.2/index.html#page/SQLRef/Data_Types.htm), and I'm able to read these files with the `jsonlines` package. Is the requirement for arrays to only have one data type specific to PyArrow?\r\n\r\nI'd prefer to keep the data structure as is, since it's a specific input requirement for the models this data was generated for. Any thoughts on how to enable the use of `load_dataset` with this dataset would be great!", "Hi again @serenalotreck,\r\n\r\nYes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n\r\nAs this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n\r\nLet's continue the discussion there! :hugs: ", "> Hi again @serenalotreck,\r\n> \r\n> Yes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n> \r\n> As this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n> \r\n> Let's continue the discussion there! 🤗\r\n\r\nThis is really terrible. My JSONL format data is very simple, but I still report this error\r\n![image](https://github.com/huggingface/datasets/assets/58240629/e3fed922-ced4-406c-b5bc-90a4b891c4ee)\r\nThe error message is as follows：\r\n File \"pyarrow/_json.pyx\", line 290, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column(/inputs) changed from string to number in row 208\r\n" ]	2023-11-29T21:20:11Z	2023-12-29T02:58:29Z	2023-12-05T13:30:53Z	NONE	null	null	### Describe the bug While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`. ### Steps to reproduce the bug Code: ``` from datasets import load_dataset dset = load_dataset('slotreck/pickle') ``` Traceback: ``` Downloading readme: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 925/925 [00:00<00:00, 3.11MB/s] Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 589k/589k [00:00<00:00, 18.9MB/s] Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 104k/104k [00:00<00:00, 4.61MB/s] Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 170k/170k [00:00<00:00, 7.71MB/s] Downloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 3.77it/s] Extracting data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 523.92it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, kw) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables raise e File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset storage_options=storage_options, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare download_and_prepare_kwargs, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split gen_kwargs=gen_kwargs, job_id=job_id, _prepare_split_args File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior For the dataset to be loaded without error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core - Python version: 3.7.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 8.0.0 - Pandas version: 1.3.5	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6460/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6459	6,459	Retrieve cached datasets that were pushed to hub when offline	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005292 / 0.011353 (-0.006061) \| 0.003811 / 0.011008 (-0.007197) \| 0.064912 / 0.038508 (0.026404) \| 0.061199 / 0.023109 (0.038090) \| 0.242953 / 0.275898 (-0.032945) \| 0.271789 / 0.323480 (-0.051691) \| 0.003994 / 0.007986 (-0.003991) \| 0.002723 / 0.004328 (-0.001606) \| 0.049952 / 0.004250 (0.045701) \| 0.039489 / 0.037052 (0.002437) \| 0.261143 / 0.258489 (0.002654) \| 0.288800 / 0.293841 (-0.005041) \| 0.028130 / 0.128546 (-0.100416) \| 0.010724 / 0.075646 (-0.064922) \| 0.208218 / 0.419271 (-0.211054) \| 0.036224 / 0.043533 (-0.007309) \| 0.247189 / 0.255139 (-0.007950) \| 0.274702 / 0.283200 (-0.008498) \| 0.019714 / 0.141683 (-0.121969) \| 1.134853 / 1.452155 (-0.317301) \| 1.192655 / 1.492716 (-0.300062) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096391 / 0.018006 (0.078385) \| 0.303802 / 0.000490 (0.303312) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019530 / 0.037411 (-0.017881) \| 0.061588 / 0.014526 (0.047062) \| 0.075122 / 0.176557 (-0.101434) \| 0.120980 / 0.737135 (-0.616155) \| 0.075807 / 0.296338 (-0.220532) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281672 / 0.215209 (0.066463) \| 2.779884 / 2.077655 (0.702229) \| 1.502026 / 1.504120 (-0.002094) \| 1.369474 / 1.541195 (-0.171721) \| 1.402694 / 1.468490 (-0.065796) \| 0.559120 / 4.584777 (-4.025657) \| 2.355320 / 3.745712 (-1.390393) \| 2.823987 / 5.269862 (-2.445875) \| 1.763888 / 4.565676 (-2.801788) \| 0.061715 / 0.424275 (-0.362560) \| 0.005015 / 0.007607 (-0.002592) \| 0.342669 / 0.226044 (0.116625) \| 3.360651 / 2.268929 (1.091722) \| 1.887277 / 55.444624 (-53.557348) \| 1.555613 / 6.876477 (-5.320864) \| 1.614126 / 2.142072 (-0.527946) \| 0.643797 / 4.805227 (-4.161430) \| 0.118365 / 6.500664 (-6.382299) \| 0.042596 / 0.075469 (-0.032873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951383 / 1.841788 (-0.890405) \| 13.169812 / 8.074308 (5.095504) \| 10.772460 / 10.191392 (0.581068) \| 0.133248 / 0.680424 (-0.547176) \| 0.014597 / 0.534201 (-0.519604) \| 0.289758 / 0.579283 (-0.289525) \| 0.266324 / 0.434364 (-0.168040) \| 0.334811 / 0.540337 (-0.205526) \| 0.445566 / 1.386936 (-0.941370) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005668 / 0.011353 (-0.005684) \| 0.003583 / 0.011008 (-0.007425) \| 0.050681 / 0.038508 (0.012173) \| 0.063244 / 0.023109 (0.040135) \| 0.279624 / 0.275898 (0.003726) \| 0.308030 / 0.323480 (-0.015450) \| 0.004160 / 0.007986 (-0.003826) \| 0.002633 / 0.004328 (-0.001696) \| 0.048475 / 0.004250 (0.044225) \| 0.043106 / 0.037052 (0.006054) \| 0.283678 / 0.258489 (0.025189) \| 0.309730 / 0.293841 (0.015889) \| 0.030290 / 0.128546 (-0.098256) \| 0.011112 / 0.075646 (-0.064534) \| 0.058234 / 0.419271 (-0.361038) \| 0.033553 / 0.043533 (-0.009979) \| 0.279902 / 0.255139 (0.024763) \| 0.298041 / 0.283200 (0.014841) \| 0.019367 / 0.141683 (-0.122316) \| 1.142438 / 1.452155 (-0.309717) \| 1.197305 / 1.492716 (-0.295411) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090875 / 0.018006 (0.072869) \| 0.301174 / 0.000490 (0.300685) \| 0.000216 / 0.000200 (0.000016) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021544 / 0.037411 (-0.015867) \| 0.071371 / 0.014526 (0.056846) \| 0.080821 / 0.176557 (-0.095736) \| 0.120054 / 0.737135 (-0.617082) \| 0.082611 / 0.296338 (-0.213728) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293787 / 0.215209 (0.078578) \| 2.862610 / 2.077655 (0.784955) \| 1.597282 / 1.504120 (0.093162) \| 1.485094 / 1.541195 (-0.056101) \| 1.507384 / 1.468490 (0.038893) \| 0.558470 / 4.584777 (-4.026307) \| 2.414137 / 3.745712 (-1.331575) \| 2.863342 / 5.269862 (-2.406520) \| 1.776973 / 4.565676 (-2.788704) \| 0.062296 / 0.424275 (-0.361979) \| 0.004954 / 0.007607 (-0.002653) \| 0.346037 / 0.226044 (0.119993) \| 3.441864 / 2.268929 (1.172935) \| 1.969842 / 55.444624 (-53.474783) \| 1.714878 / 6.876477 (-5.161599) \| 1.738141 / 2.142072 (-0.403931) \| 0.645929 / 4.805227 (-4.159298) \| 0.117332 / 6.500664 (-6.383332) \| 0.041963 / 0.075469 (-0.033507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983229 / 1.841788 (-0.858559) \| 13.186932 / 8.074308 (5.112624) \| 11.220549 / 10.191392 (1.029157) \| 0.142105 / 0.680424 (-0.538319) \| 0.015210 / 0.534201 (-0.518991) \| 0.290055 / 0.579283 (-0.289228) \| 0.274513 / 0.434364 (-0.159851) \| 0.346834 / 0.540337 (-0.193504) \| 0.575897 / 1.386936 (-0.811039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d3c0694d0c47a64a3cab5d468b4d9575ad7b1d96 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6459). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005308 / 0.011353 (-0.006045) \| 0.003135 / 0.011008 (-0.007873) \| 0.061820 / 0.038508 (0.023312) \| 0.052005 / 0.023109 (0.028895) \| 0.233507 / 0.275898 (-0.042391) \| 0.257790 / 0.323480 (-0.065690) \| 0.002848 / 0.007986 (-0.005138) \| 0.002645 / 0.004328 (-0.001683) \| 0.048379 / 0.004250 (0.044128) \| 0.038320 / 0.037052 (0.001268) \| 0.245470 / 0.258489 (-0.013019) \| 0.274854 / 0.293841 (-0.018987) \| 0.027335 / 0.128546 (-0.101211) \| 0.010349 / 0.075646 (-0.065297) \| 0.205872 / 0.419271 (-0.213400) \| 0.035896 / 0.043533 (-0.007637) \| 0.241645 / 0.255139 (-0.013494) \| 0.260033 / 0.283200 (-0.023167) \| 0.020325 / 0.141683 (-0.121358) \| 1.116768 / 1.452155 (-0.335387) \| 1.188067 / 1.492716 (-0.304649) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092622 / 0.018006 (0.074616) \| 0.302663 / 0.000490 (0.302173) \| 0.000227 / 0.000200 (0.000027) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018633 / 0.037411 (-0.018778) \| 0.060117 / 0.014526 (0.045592) \| 0.072713 / 0.176557 (-0.103844) \| 0.119955 / 0.737135 (-0.617180) \| 0.074698 / 0.296338 (-0.221640) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277157 / 0.215209 (0.061948) \| 2.699650 / 2.077655 (0.621995) \| 1.413625 / 1.504120 (-0.090494) \| 1.295900 / 1.541195 (-0.245295) \| 1.306280 / 1.468490 (-0.162210) \| 0.555354 / 4.584777 (-4.029423) \| 2.386866 / 3.745712 (-1.358847) \| 2.794069 / 5.269862 (-2.475793) \| 1.736275 / 4.565676 (-2.829401) \| 0.061812 / 0.424275 (-0.362464) \| 0.004957 / 0.007607 (-0.002650) \| 0.334533 / 0.226044 (0.108488) \| 3.251096 / 2.268929 (0.982168) \| 1.768193 / 55.444624 (-53.676431) \| 1.473752 / 6.876477 (-5.402724) \| 1.476320 / 2.142072 (-0.665753) \| 0.642485 / 4.805227 (-4.162742) \| 0.116986 / 6.500664 (-6.383678) \| 0.042083 / 0.075469 (-0.033386) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941364 / 1.841788 (-0.900424) \| 11.587408 / 8.074308 (3.513100) \| 10.500198 / 10.191392 (0.308806) \| 0.129126 / 0.680424 (-0.551298) \| 0.015206 / 0.534201 (-0.518995) \| 0.286580 / 0.579283 (-0.292703) \| 0.263566 / 0.434364 (-0.170798) \| 0.331662 / 0.540337 (-0.208676) \| 0.431423 / 1.386936 (-0.955513) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005151 / 0.011353 (-0.006202) \| 0.003425 / 0.011008 (-0.007583) \| 0.049301 / 0.038508 (0.010793) \| 0.052005 / 0.023109 (0.028895) \| 0.289594 / 0.275898 (0.013696) \| 0.312630 / 0.323480 (-0.010849) \| 0.003988 / 0.007986 (-0.003998) \| 0.002705 / 0.004328 (-0.001624) \| 0.048529 / 0.004250 (0.044279) \| 0.039645 / 0.037052 (0.002592) \| 0.293430 / 0.258489 (0.034941) \| 0.311697 / 0.293841 (0.017856) \| 0.029044 / 0.128546 (-0.099502) \| 0.010282 / 0.075646 (-0.065364) \| 0.057641 / 0.419271 (-0.361630) \| 0.032733 / 0.043533 (-0.010800) \| 0.293553 / 0.255139 (0.038414) \| 0.308850 / 0.283200 (0.025651) \| 0.018452 / 0.141683 (-0.123231) \| 1.147931 / 1.452155 (-0.304224) \| 1.173093 / 1.492716 (-0.319623) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100862 / 0.018006 (0.082856) \| 0.309286 / 0.000490 (0.308796) \| 0.000223 / 0.000200 (0.000023) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021365 / 0.037411 (-0.016046) \| 0.068987 / 0.014526 (0.054461) \| 0.081092 / 0.176557 (-0.095465) \| 0.119852 / 0.737135 (-0.617283) \| 0.082850 / 0.296338 (-0.213489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288477 / 0.215209 (0.073268) \| 2.833766 / 2.077655 (0.756111) \| 1.576670 / 1.504120 (0.072550) \| 1.431643 / 1.541195 (-0.109552) \| 1.442132 / 1.468490 (-0.026358) \| 0.556079 / 4.584777 (-4.028698) \| 2.465042 / 3.745712 (-1.280670) \| 2.786329 / 5.269862 (-2.483532) \| 1.779428 / 4.565676 (-2.786249) \| 0.062278 / 0.424275 (-0.361997) \| 0.004867 / 0.007607 (-0.002740) \| 0.348444 / 0.226044 (0.122399) \| 3.389824 / 2.268929 (1.120896) \| 1.919141 / 55.444624 (-53.525484) \| 1.635411 / 6.876477 (-5.241066) \| 1.654869 / 2.142072 (-0.487204) \| 0.634467 / 4.805227 (-4.170761) \| 0.114330 / 6.500664 (-6.386334) \| 0.039900 / 0.075469 (-0.035569) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970851 / 1.841788 (-0.870937) \| 11.951660 / 8.074308 (3.877352) \| 10.571115 / 10.191392 (0.379723) \| 0.131040 / 0.680424 (-0.549384) \| 0.015299 / 0.534201 (-0.518902) \| 0.287851 / 0.579283 (-0.291432) \| 0.278366 / 0.434364 (-0.155998) \| 0.326468 / 0.540337 (-0.213870) \| 0.552288 / 1.386936 (-0.834648) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8214ff2a9f706427669a6c2a01ccabffa5bf0d2b \"CML watermark\")\n" ]	2023-11-29T16:56:15Z	2024-03-25T13:55:42Z	2024-03-25T13:55:42Z	MEMBER	true	{ "diff_url": "https://github.com/huggingface/datasets/pull/6459.diff", "html_url": "https://github.com/huggingface/datasets/pull/6459", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6459.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6459" }	I drafted the logic to retrieve a no-script dataset in the cache. For example it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset from /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp///0b3caccda1725efb(last modified on Wed Nov 29 16:50:27 2023) since it couldn't be found locally at lhoestq/tmp. DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` fix https://github.com/huggingface/datasets/issues/3547 ## Implementation details (EDITED) I continued in https://github.com/huggingface/datasets/pull/6493, see the changes there TODO: - [x] tests - [ ] compatible with https://github.com/huggingface/datasets/pull/6458	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6459/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6458	6,458	Lazy data files resolution	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005097 / 0.011353 (-0.006256) \| 0.003523 / 0.011008 (-0.007485) \| 0.062827 / 0.038508 (0.024319) \| 0.051677 / 0.023109 (0.028568) \| 0.248919 / 0.275898 (-0.026980) \| 0.275892 / 0.323480 (-0.047588) \| 0.003908 / 0.007986 (-0.004077) \| 0.002622 / 0.004328 (-0.001706) \| 0.048634 / 0.004250 (0.044383) \| 0.037903 / 0.037052 (0.000850) \| 0.255754 / 0.258489 (-0.002735) \| 0.283343 / 0.293841 (-0.010498) \| 0.027886 / 0.128546 (-0.100660) \| 0.010849 / 0.075646 (-0.064797) \| 0.208255 / 0.419271 (-0.211017) \| 0.035664 / 0.043533 (-0.007869) \| 0.254661 / 0.255139 (-0.000478) \| 0.274366 / 0.283200 (-0.008834) \| 0.017240 / 0.141683 (-0.124443) \| 1.092952 / 1.452155 (-0.359203) \| 1.148373 / 1.492716 (-0.344344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091592 / 0.018006 (0.073586) \| 0.301926 / 0.000490 (0.301436) \| 0.000207 / 0.000200 (0.000007) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018525 / 0.037411 (-0.018887) \| 0.060539 / 0.014526 (0.046014) \| 0.073812 / 0.176557 (-0.102745) \| 0.120655 / 0.737135 (-0.616480) \| 0.076931 / 0.296338 (-0.219407) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282797 / 0.215209 (0.067588) \| 2.746573 / 2.077655 (0.668918) \| 1.477652 / 1.504120 (-0.026468) \| 1.349922 / 1.541195 (-0.191273) \| 1.374347 / 1.468490 (-0.094143) \| 0.574096 / 4.584777 (-4.010681) \| 2.383317 / 3.745712 (-1.362395) \| 2.809320 / 5.269862 (-2.460541) \| 1.758947 / 4.565676 (-2.806729) \| 0.064029 / 0.424275 (-0.360246) \| 0.004936 / 0.007607 (-0.002672) \| 0.331403 / 0.226044 (0.105358) \| 3.260908 / 2.268929 (0.991980) \| 1.817670 / 55.444624 (-53.626954) \| 1.525863 / 6.876477 (-5.350613) \| 1.542017 / 2.142072 (-0.600055) \| 0.638900 / 4.805227 (-4.166327) \| 0.119485 / 6.500664 (-6.381179) \| 0.042588 / 0.075469 (-0.032881) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951583 / 1.841788 (-0.890205) \| 11.621917 / 8.074308 (3.547609) \| 10.511062 / 10.191392 (0.319670) \| 0.130137 / 0.680424 (-0.550287) \| 0.014048 / 0.534201 (-0.520153) \| 0.290621 / 0.579283 (-0.288662) \| 0.271665 / 0.434364 (-0.162699) \| 0.331260 / 0.540337 (-0.209077) \| 0.441621 / 1.386936 (-0.945316) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005272 / 0.011353 (-0.006081) \| 0.003656 / 0.011008 (-0.007352) \| 0.049245 / 0.038508 (0.010737) \| 0.054130 / 0.023109 (0.031021) \| 0.274775 / 0.275898 (-0.001123) \| 0.296664 / 0.323480 (-0.026816) \| 0.004870 / 0.007986 (-0.003115) \| 0.002728 / 0.004328 (-0.001601) \| 0.048087 / 0.004250 (0.043837) \| 0.041448 / 0.037052 (0.004396) \| 0.279110 / 0.258489 (0.020621) \| 0.303660 / 0.293841 (0.009819) \| 0.029767 / 0.128546 (-0.098779) \| 0.010799 / 0.075646 (-0.064848) \| 0.058650 / 0.419271 (-0.360622) \| 0.033088 / 0.043533 (-0.010445) \| 0.274456 / 0.255139 (0.019317) \| 0.290206 / 0.283200 (0.007007) \| 0.017259 / 0.141683 (-0.124424) \| 1.176501 / 1.452155 (-0.275654) \| 1.197552 / 1.492716 (-0.295165) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092865 / 0.018006 (0.074859) \| 0.302437 / 0.000490 (0.301947) \| 0.000209 / 0.000200 (0.000009) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021211 / 0.037411 (-0.016200) \| 0.068858 / 0.014526 (0.054332) \| 0.081783 / 0.176557 (-0.094773) \| 0.120472 / 0.737135 (-0.616663) \| 0.083900 / 0.296338 (-0.212438) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295157 / 0.215209 (0.079948) \| 2.910979 / 2.077655 (0.833324) \| 1.575772 / 1.504120 (0.071652) \| 1.456955 / 1.541195 (-0.084239) \| 1.468982 / 1.468490 (0.000492) \| 0.560309 / 4.584777 (-4.024468) \| 2.460171 / 3.745712 (-1.285541) \| 2.805713 / 5.269862 (-2.464149) \| 1.754074 / 4.565676 (-2.811603) \| 0.063333 / 0.424275 (-0.360942) \| 0.004940 / 0.007607 (-0.002667) \| 0.346141 / 0.226044 (0.120097) \| 3.463431 / 2.268929 (1.194502) \| 1.929135 / 55.444624 (-53.515490) \| 1.660191 / 6.876477 (-5.216286) \| 1.668327 / 2.142072 (-0.473746) \| 0.644183 / 4.805227 (-4.161044) \| 0.115738 / 6.500664 (-6.384926) \| 0.041347 / 0.075469 (-0.034122) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961565 / 1.841788 (-0.880222) \| 12.232589 / 8.074308 (4.158281) \| 10.778774 / 10.191392 (0.587382) \| 0.132709 / 0.680424 (-0.547715) \| 0.015964 / 0.534201 (-0.518237) \| 0.286944 / 0.579283 (-0.292340) \| 0.279740 / 0.434364 (-0.154624) \| 0.333024 / 0.540337 (-0.207314) \| 0.438819 / 1.386936 (-0.948117) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51002cb0325772adaf46d6f3ce01d41c01b51079 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6458). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005317 / 0.011353 (-0.006036) \| 0.003936 / 0.011008 (-0.007072) \| 0.063122 / 0.038508 (0.024614) \| 0.061274 / 0.023109 (0.038165) \| 0.251764 / 0.275898 (-0.024134) \| 0.274849 / 0.323480 (-0.048631) \| 0.004059 / 0.007986 (-0.003927) \| 0.002874 / 0.004328 (-0.001455) \| 0.048716 / 0.004250 (0.044465) \| 0.038281 / 0.037052 (0.001228) \| 0.265224 / 0.258489 (0.006735) \| 0.285962 / 0.293841 (-0.007878) \| 0.028522 / 0.128546 (-0.100024) \| 0.011150 / 0.075646 (-0.064496) \| 0.208362 / 0.419271 (-0.210910) \| 0.038900 / 0.043533 (-0.004633) \| 0.254113 / 0.255139 (-0.001026) \| 0.276721 / 0.283200 (-0.006478) \| 0.018372 / 0.141683 (-0.123311) \| 1.121336 / 1.452155 (-0.330818) \| 1.189548 / 1.492716 (-0.303168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097633 / 0.018006 (0.079627) \| 0.304443 / 0.000490 (0.303953) \| 0.000218 / 0.000200 (0.000018) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021757 / 0.037411 (-0.015654) \| 0.061978 / 0.014526 (0.047453) \| 0.076296 / 0.176557 (-0.100260) \| 0.122320 / 0.737135 (-0.614816) \| 0.076738 / 0.296338 (-0.219601) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284328 / 0.215209 (0.069119) \| 2.793071 / 2.077655 (0.715417) \| 1.504768 / 1.504120 (0.000648) \| 1.386083 / 1.541195 (-0.155111) \| 1.457593 / 1.468490 (-0.010897) \| 0.575887 / 4.584777 (-4.008890) \| 2.419396 / 3.745712 (-1.326316) \| 2.931305 / 5.269862 (-2.338556) \| 1.840759 / 4.565676 (-2.724917) \| 0.063801 / 0.424275 (-0.360474) \| 0.004966 / 0.007607 (-0.002641) \| 0.341612 / 0.226044 (0.115568) \| 3.402842 / 2.268929 (1.133913) \| 1.860521 / 55.444624 (-53.584103) \| 1.603156 / 6.876477 (-5.273321) \| 1.665835 / 2.142072 (-0.476237) \| 0.655299 / 4.805227 (-4.149929) \| 0.124527 / 6.500664 (-6.376137) \| 0.044021 / 0.075469 (-0.031449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972068 / 1.841788 (-0.869720) \| 12.393202 / 8.074308 (4.318894) \| 10.420876 / 10.191392 (0.229484) \| 0.140684 / 0.680424 (-0.539740) \| 0.014442 / 0.534201 (-0.519759) \| 0.288182 / 0.579283 (-0.291101) \| 0.265029 / 0.434364 (-0.169334) \| 0.327133 / 0.540337 (-0.213204) \| 0.443403 / 1.386936 (-0.943533) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005559 / 0.011353 (-0.005794) \| 0.004046 / 0.011008 (-0.006962) \| 0.048991 / 0.038508 (0.010483) \| 0.059576 / 0.023109 (0.036467) \| 0.273596 / 0.275898 (-0.002302) \| 0.296658 / 0.323480 (-0.026822) \| 0.004089 / 0.007986 (-0.003897) \| 0.002777 / 0.004328 (-0.001551) \| 0.048216 / 0.004250 (0.043966) \| 0.043200 / 0.037052 (0.006148) \| 0.276815 / 0.258489 (0.018326) \| 0.300570 / 0.293841 (0.006729) \| 0.030250 / 0.128546 (-0.098296) \| 0.011322 / 0.075646 (-0.064324) \| 0.057843 / 0.419271 (-0.361429) \| 0.033366 / 0.043533 (-0.010167) \| 0.275636 / 0.255139 (0.020497) \| 0.293750 / 0.283200 (0.010550) \| 0.018551 / 0.141683 (-0.123132) \| 1.160919 / 1.452155 (-0.291236) \| 1.214519 / 1.492716 (-0.278197) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100074 / 0.018006 (0.082068) \| 0.308434 / 0.000490 (0.307944) \| 0.000232 / 0.000200 (0.000032) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022600 / 0.037411 (-0.014811) \| 0.070506 / 0.014526 (0.055980) \| 0.081185 / 0.176557 (-0.095371) \| 0.120688 / 0.737135 (-0.616448) \| 0.082897 / 0.296338 (-0.213441) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306661 / 0.215209 (0.091452) \| 2.989656 / 2.077655 (0.912001) \| 1.618868 / 1.504120 (0.114749) \| 1.485045 / 1.541195 (-0.056149) \| 1.549359 / 1.468490 (0.080869) \| 0.593596 / 4.584777 (-3.991181) \| 2.466215 / 3.745712 (-1.279497) \| 2.956570 / 5.269862 (-2.313292) \| 1.823160 / 4.565676 (-2.742516) \| 0.063442 / 0.424275 (-0.360833) \| 0.004928 / 0.007607 (-0.002679) \| 0.358464 / 0.226044 (0.132419) \| 3.566345 / 2.268929 (1.297417) \| 2.006784 / 55.444624 (-53.437840) \| 1.687091 / 6.876477 (-5.189386) \| 1.729464 / 2.142072 (-0.412609) \| 0.655656 / 4.805227 (-4.149572) \| 0.119044 / 6.500664 (-6.381620) \| 0.042782 / 0.075469 (-0.032687) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974937 / 1.841788 (-0.866850) \| 12.992888 / 8.074308 (4.918580) \| 10.893713 / 10.191392 (0.702321) \| 0.133853 / 0.680424 (-0.546570) \| 0.016055 / 0.534201 (-0.518145) \| 0.289342 / 0.579283 (-0.289941) \| 0.286094 / 0.434364 (-0.148270) \| 0.328670 / 0.540337 (-0.211667) \| 0.444605 / 1.386936 (-0.942331) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a5bb38bcc71ea21f2d7304aab374fdb81ded463 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005705 / 0.011353 (-0.005648) \| 0.003519 / 0.011008 (-0.007489) \| 0.062009 / 0.038508 (0.023501) \| 0.053481 / 0.023109 (0.030372) \| 0.262669 / 0.275898 (-0.013229) \| 0.280290 / 0.323480 (-0.043189) \| 0.002957 / 0.007986 (-0.005029) \| 0.002587 / 0.004328 (-0.001741) \| 0.047876 / 0.004250 (0.043626) \| 0.038868 / 0.037052 (0.001815) \| 0.267854 / 0.258489 (0.009365) \| 0.290430 / 0.293841 (-0.003411) \| 0.028120 / 0.128546 (-0.100427) \| 0.011042 / 0.075646 (-0.064605) \| 0.206113 / 0.419271 (-0.213158) \| 0.036039 / 0.043533 (-0.007494) \| 0.257715 / 0.255139 (0.002576) \| 0.281279 / 0.283200 (-0.001921) \| 0.019790 / 0.141683 (-0.121893) \| 1.114472 / 1.452155 (-0.337683) \| 1.192219 / 1.492716 (-0.300497) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091049 / 0.018006 (0.073043) \| 0.300846 / 0.000490 (0.300356) \| 0.000208 / 0.000200 (0.000008) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018569 / 0.037411 (-0.018843) \| 0.060075 / 0.014526 (0.045549) \| 0.073877 / 0.176557 (-0.102680) \| 0.120337 / 0.737135 (-0.616799) \| 0.075454 / 0.296338 (-0.220884) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290084 / 0.215209 (0.074875) \| 2.805712 / 2.077655 (0.728057) \| 1.459393 / 1.504120 (-0.044727) \| 1.327356 / 1.541195 (-0.213838) \| 1.384734 / 1.468490 (-0.083756) \| 0.574532 / 4.584777 (-4.010245) \| 2.419696 / 3.745712 (-1.326016) \| 2.805449 / 5.269862 (-2.464412) \| 1.764127 / 4.565676 (-2.801549) \| 0.063256 / 0.424275 (-0.361020) \| 0.004954 / 0.007607 (-0.002653) \| 0.344246 / 0.226044 (0.118202) \| 3.396050 / 2.268929 (1.127121) \| 1.807621 / 55.444624 (-53.637004) \| 1.536627 / 6.876477 (-5.339850) \| 1.552450 / 2.142072 (-0.589623) \| 0.651156 / 4.805227 (-4.154071) \| 0.119358 / 6.500664 (-6.381306) \| 0.042810 / 0.075469 (-0.032660) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930646 / 1.841788 (-0.911142) \| 11.830454 / 8.074308 (3.756146) \| 10.615315 / 10.191392 (0.423923) \| 0.130617 / 0.680424 (-0.549807) \| 0.014081 / 0.534201 (-0.520120) \| 0.285027 / 0.579283 (-0.294256) \| 0.267296 / 0.434364 (-0.167068) \| 0.331478 / 0.540337 (-0.208859) \| 0.442676 / 1.386936 (-0.944260) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005340 / 0.011353 (-0.006013) \| 0.003745 / 0.011008 (-0.007264) \| 0.049011 / 0.038508 (0.010503) \| 0.051342 / 0.023109 (0.028233) \| 0.272482 / 0.275898 (-0.003416) \| 0.292816 / 0.323480 (-0.030663) \| 0.003977 / 0.007986 (-0.004008) \| 0.002642 / 0.004328 (-0.001687) \| 0.048213 / 0.004250 (0.043963) \| 0.040341 / 0.037052 (0.003289) \| 0.275176 / 0.258489 (0.016687) \| 0.301098 / 0.293841 (0.007257) \| 0.029052 / 0.128546 (-0.099495) \| 0.010796 / 0.075646 (-0.064850) \| 0.057654 / 0.419271 (-0.361618) \| 0.032914 / 0.043533 (-0.010619) \| 0.271235 / 0.255139 (0.016096) \| 0.289883 / 0.283200 (0.006684) \| 0.018548 / 0.141683 (-0.123135) \| 1.134072 / 1.452155 (-0.318083) \| 1.208228 / 1.492716 (-0.284488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094524 / 0.018006 (0.076518) \| 0.310162 / 0.000490 (0.309672) \| 0.000237 / 0.000200 (0.000037) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021090 / 0.037411 (-0.016321) \| 0.068351 / 0.014526 (0.053825) \| 0.082370 / 0.176557 (-0.094186) \| 0.121648 / 0.737135 (-0.615487) \| 0.083433 / 0.296338 (-0.212906) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294616 / 0.215209 (0.079407) \| 2.894194 / 2.077655 (0.816539) \| 1.619739 / 1.504120 (0.115619) \| 1.492466 / 1.541195 (-0.048729) \| 1.511662 / 1.468490 (0.043172) \| 0.557179 / 4.584777 (-4.027597) \| 2.400669 / 3.745712 (-1.345043) \| 2.781363 / 5.269862 (-2.488499) \| 1.769144 / 4.565676 (-2.796533) \| 0.063996 / 0.424275 (-0.360279) \| 0.004922 / 0.007607 (-0.002685) \| 0.354483 / 0.226044 (0.128438) \| 3.474795 / 2.268929 (1.205867) \| 1.985743 / 55.444624 (-53.458881) \| 1.693173 / 6.876477 (-5.183303) \| 1.695857 / 2.142072 (-0.446216) \| 0.654800 / 4.805227 (-4.150427) \| 0.117316 / 6.500664 (-6.383348) \| 0.040708 / 0.075469 (-0.034761) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977678 / 1.841788 (-0.864109) \| 12.214098 / 8.074308 (4.139790) \| 10.741857 / 10.191392 (0.550465) \| 0.130308 / 0.680424 (-0.550116) \| 0.015053 / 0.534201 (-0.519148) \| 0.295496 / 0.579283 (-0.283787) \| 0.276348 / 0.434364 (-0.158015) \| 0.326568 / 0.540337 (-0.213769) \| 0.441902 / 1.386936 (-0.945034) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#214a3e6dcb66e9c1a8ff586553e8eee0f1c70710 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005218 / 0.011353 (-0.006135) \| 0.003270 / 0.011008 (-0.007738) \| 0.062380 / 0.038508 (0.023872) \| 0.052896 / 0.023109 (0.029787) \| 0.233060 / 0.275898 (-0.042838) \| 0.259194 / 0.323480 (-0.064286) \| 0.002880 / 0.007986 (-0.005106) \| 0.002643 / 0.004328 (-0.001686) \| 0.048084 / 0.004250 (0.043833) \| 0.038807 / 0.037052 (0.001755) \| 0.244925 / 0.258489 (-0.013564) \| 0.269619 / 0.293841 (-0.024222) \| 0.026901 / 0.128546 (-0.101646) \| 0.010150 / 0.075646 (-0.065497) \| 0.206854 / 0.419271 (-0.212417) \| 0.035618 / 0.043533 (-0.007915) \| 0.239577 / 0.255139 (-0.015562) \| 0.259684 / 0.283200 (-0.023516) \| 0.019823 / 0.141683 (-0.121860) \| 1.074472 / 1.452155 (-0.377682) \| 1.142911 / 1.492716 (-0.349805) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092616 / 0.018006 (0.074610) \| 0.301974 / 0.000490 (0.301485) \| 0.000201 / 0.000200 (0.000002) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018864 / 0.037411 (-0.018548) \| 0.061007 / 0.014526 (0.046481) \| 0.073228 / 0.176557 (-0.103328) \| 0.120719 / 0.737135 (-0.616416) \| 0.075686 / 0.296338 (-0.220653) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281404 / 0.215209 (0.066195) \| 2.777671 / 2.077655 (0.700017) \| 1.464689 / 1.504120 (-0.039431) \| 1.345357 / 1.541195 (-0.195838) \| 1.384273 / 1.468490 (-0.084217) \| 0.560298 / 4.584777 (-4.024479) \| 2.389877 / 3.745712 (-1.355835) \| 2.755564 / 5.269862 (-2.514297) \| 1.737754 / 4.565676 (-2.827922) \| 0.063025 / 0.424275 (-0.361251) \| 0.004975 / 0.007607 (-0.002632) \| 0.346741 / 0.226044 (0.120697) \| 3.321918 / 2.268929 (1.052989) \| 1.815700 / 55.444624 (-53.628924) \| 1.547333 / 6.876477 (-5.329144) \| 1.564809 / 2.142072 (-0.577263) \| 0.638645 / 4.805227 (-4.166582) \| 0.118157 / 6.500664 (-6.382507) \| 0.041605 / 0.075469 (-0.033864) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942515 / 1.841788 (-0.899273) \| 11.400386 / 8.074308 (3.326078) \| 10.208763 / 10.191392 (0.017370) \| 0.138144 / 0.680424 (-0.542280) \| 0.014354 / 0.534201 (-0.519847) \| 0.288289 / 0.579283 (-0.290994) \| 0.265973 / 0.434364 (-0.168391) \| 0.327703 / 0.540337 (-0.212634) \| 0.435474 / 1.386936 (-0.951462) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005163 / 0.011353 (-0.006190) \| 0.003307 / 0.011008 (-0.007701) \| 0.048885 / 0.038508 (0.010377) \| 0.049044 / 0.023109 (0.025935) \| 0.261408 / 0.275898 (-0.014490) \| 0.284625 / 0.323480 (-0.038855) \| 0.003970 / 0.007986 (-0.004015) \| 0.002754 / 0.004328 (-0.001575) \| 0.048271 / 0.004250 (0.044021) \| 0.039849 / 0.037052 (0.002797) \| 0.266898 / 0.258489 (0.008409) \| 0.291445 / 0.293841 (-0.002396) \| 0.028477 / 0.128546 (-0.100069) \| 0.010656 / 0.075646 (-0.064990) \| 0.057732 / 0.419271 (-0.361539) \| 0.033298 / 0.043533 (-0.010235) \| 0.297773 / 0.255139 (0.042634) \| 0.281894 / 0.283200 (-0.001305) \| 0.018595 / 0.141683 (-0.123088) \| 1.168849 / 1.452155 (-0.283306) \| 1.183493 / 1.492716 (-0.309224) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092683 / 0.018006 (0.074677) \| 0.300387 / 0.000490 (0.299897) \| 0.000221 / 0.000200 (0.000021) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021356 / 0.037411 (-0.016055) \| 0.068095 / 0.014526 (0.053569) \| 0.079806 / 0.176557 (-0.096750) \| 0.118965 / 0.737135 (-0.618170) \| 0.082066 / 0.296338 (-0.214273) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293105 / 0.215209 (0.077896) \| 2.842800 / 2.077655 (0.765146) \| 1.572052 / 1.504120 (0.067932) \| 1.450156 / 1.541195 (-0.091038) \| 1.464227 / 1.468490 (-0.004263) \| 0.561215 / 4.584777 (-4.023562) \| 2.456117 / 3.745712 (-1.289596) \| 2.739766 / 5.269862 (-2.530095) \| 1.730354 / 4.565676 (-2.835323) \| 0.062636 / 0.424275 (-0.361639) \| 0.004933 / 0.007607 (-0.002674) \| 0.345800 / 0.226044 (0.119756) \| 3.415858 / 2.268929 (1.146929) \| 1.937288 / 55.444624 (-53.507336) \| 1.661975 / 6.876477 (-5.214502) \| 1.660347 / 2.142072 (-0.481726) \| 0.642780 / 4.805227 (-4.162448) \| 0.116643 / 6.500664 (-6.384021) \| 0.041282 / 0.075469 (-0.034187) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976629 / 1.841788 (-0.865159) \| 11.900319 / 8.074308 (3.826011) \| 10.574198 / 10.191392 (0.382806) \| 0.129689 / 0.680424 (-0.550735) \| 0.015390 / 0.534201 (-0.518811) \| 0.286543 / 0.579283 (-0.292741) \| 0.277676 / 0.434364 (-0.156688) \| 0.325053 / 0.540337 (-0.215284) \| 0.439663 / 1.386936 (-0.947274) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a9674e17156ff10124632ba705125288de7442 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005382 / 0.011353 (-0.005971) \| 0.003606 / 0.011008 (-0.007402) \| 0.063234 / 0.038508 (0.024726) \| 0.053738 / 0.023109 (0.030629) \| 0.250405 / 0.275898 (-0.025493) \| 0.272244 / 0.323480 (-0.051236) \| 0.002896 / 0.007986 (-0.005090) \| 0.002684 / 0.004328 (-0.001644) \| 0.048394 / 0.004250 (0.044143) \| 0.039017 / 0.037052 (0.001964) \| 0.259554 / 0.258489 (0.001065) \| 0.287215 / 0.293841 (-0.006626) \| 0.028290 / 0.128546 (-0.100257) \| 0.011482 / 0.075646 (-0.064164) \| 0.214264 / 0.419271 (-0.205007) \| 0.036257 / 0.043533 (-0.007276) \| 0.252873 / 0.255139 (-0.002266) \| 0.271269 / 0.283200 (-0.011931) \| 0.017173 / 0.141683 (-0.124510) \| 1.137474 / 1.452155 (-0.314681) \| 1.161499 / 1.492716 (-0.331217) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092424 / 0.018006 (0.074418) \| 0.283703 / 0.000490 (0.283213) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018307 / 0.037411 (-0.019105) \| 0.060780 / 0.014526 (0.046254) \| 0.073984 / 0.176557 (-0.102573) \| 0.120824 / 0.737135 (-0.616311) \| 0.074724 / 0.296338 (-0.221615) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297682 / 0.215209 (0.082473) \| 2.853267 / 2.077655 (0.775612) \| 1.567643 / 1.504120 (0.063523) \| 1.437218 / 1.541195 (-0.103976) \| 1.467187 / 1.468490 (-0.001304) \| 0.560552 / 4.584777 (-4.024225) \| 2.387848 / 3.745712 (-1.357864) \| 2.718946 / 5.269862 (-2.550916) \| 1.724107 / 4.565676 (-2.841570) \| 0.061923 / 0.424275 (-0.362352) \| 0.004828 / 0.007607 (-0.002779) \| 0.353916 / 0.226044 (0.127871) \| 3.404477 / 2.268929 (1.135548) \| 1.906078 / 55.444624 (-53.538546) \| 1.629686 / 6.876477 (-5.246791) \| 1.640839 / 2.142072 (-0.501233) \| 0.641082 / 4.805227 (-4.164145) \| 0.118078 / 6.500664 (-6.382586) \| 0.041881 / 0.075469 (-0.033588) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936062 / 1.841788 (-0.905726) \| 11.397678 / 8.074308 (3.323370) \| 10.385159 / 10.191392 (0.193766) \| 0.127337 / 0.680424 (-0.553087) \| 0.013562 / 0.534201 (-0.520639) \| 0.290817 / 0.579283 (-0.288466) \| 0.259377 / 0.434364 (-0.174987) \| 0.324829 / 0.540337 (-0.215508) \| 0.434344 / 1.386936 (-0.952592) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005134 / 0.011353 (-0.006219) \| 0.003404 / 0.011008 (-0.007604) \| 0.048281 / 0.038508 (0.009772) \| 0.050952 / 0.023109 (0.027842) \| 0.277553 / 0.275898 (0.001655) \| 0.298855 / 0.323480 (-0.024625) \| 0.003928 / 0.007986 (-0.004058) \| 0.002642 / 0.004328 (-0.001687) \| 0.047374 / 0.004250 (0.043123) \| 0.039883 / 0.037052 (0.002831) \| 0.279808 / 0.258489 (0.021318) \| 0.301604 / 0.293841 (0.007763) \| 0.028708 / 0.128546 (-0.099838) \| 0.010949 / 0.075646 (-0.064697) \| 0.057090 / 0.419271 (-0.362181) \| 0.032438 / 0.043533 (-0.011095) \| 0.274690 / 0.255139 (0.019551) \| 0.290912 / 0.283200 (0.007712) \| 0.017556 / 0.141683 (-0.124127) \| 1.111091 / 1.452155 (-0.341064) \| 1.166063 / 1.492716 (-0.326653) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090557 / 0.018006 (0.072551) \| 0.298661 / 0.000490 (0.298171) \| 0.000228 / 0.000200 (0.000028) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021712 / 0.037411 (-0.015699) \| 0.068682 / 0.014526 (0.054156) \| 0.080108 / 0.176557 (-0.096449) \| 0.119480 / 0.737135 (-0.617655) \| 0.082703 / 0.296338 (-0.213636) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294095 / 0.215209 (0.078886) \| 2.884758 / 2.077655 (0.807103) \| 1.598312 / 1.504120 (0.094192) \| 1.480050 / 1.541195 (-0.061145) \| 1.488611 / 1.468490 (0.020121) \| 0.556052 / 4.584777 (-4.028724) \| 2.435484 / 3.745712 (-1.310228) \| 2.741592 / 5.269862 (-2.528270) \| 1.706223 / 4.565676 (-2.859454) \| 0.062214 / 0.424275 (-0.362061) \| 0.004901 / 0.007607 (-0.002706) \| 0.346301 / 0.226044 (0.120257) \| 3.474516 / 2.268929 (1.205587) \| 1.995205 / 55.444624 (-53.449419) \| 1.726349 / 6.876477 (-5.150128) \| 1.659600 / 2.142072 (-0.482472) \| 0.643560 / 4.805227 (-4.161667) \| 0.115222 / 6.500664 (-6.385442) \| 0.041137 / 0.075469 (-0.034332) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974566 / 1.841788 (-0.867221) \| 11.872479 / 8.074308 (3.798171) \| 10.496919 / 10.191392 (0.305527) \| 0.129087 / 0.680424 (-0.551337) \| 0.014627 / 0.534201 (-0.519574) \| 0.289070 / 0.579283 (-0.290213) \| 0.269609 / 0.434364 (-0.164755) \| 0.327785 / 0.540337 (-0.212553) \| 0.444634 / 1.386936 (-0.942302) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e0960ea165a9481b1ff6eed31771475120cb38 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005080 / 0.011353 (-0.006273) \| 0.003782 / 0.011008 (-0.007226) \| 0.062816 / 0.038508 (0.024308) \| 0.056338 / 0.023109 (0.033229) \| 0.251317 / 0.275898 (-0.024581) \| 0.269414 / 0.323480 (-0.054066) \| 0.003984 / 0.007986 (-0.004001) \| 0.002749 / 0.004328 (-0.001580) \| 0.048126 / 0.004250 (0.043876) \| 0.038516 / 0.037052 (0.001464) \| 0.253809 / 0.258489 (-0.004680) \| 0.283309 / 0.293841 (-0.010532) \| 0.027015 / 0.128546 (-0.101531) \| 0.010610 / 0.075646 (-0.065037) \| 0.213024 / 0.419271 (-0.206247) \| 0.035734 / 0.043533 (-0.007799) \| 0.247909 / 0.255139 (-0.007230) \| 0.263539 / 0.283200 (-0.019660) \| 0.018408 / 0.141683 (-0.123275) \| 1.104366 / 1.452155 (-0.347789) \| 1.169668 / 1.492716 (-0.323048) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.114366 / 0.018006 (0.096360) \| 0.317674 / 0.000490 (0.317184) \| 0.000227 / 0.000200 (0.000027) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018955 / 0.037411 (-0.018457) \| 0.060716 / 0.014526 (0.046190) \| 0.072963 / 0.176557 (-0.103593) \| 0.121671 / 0.737135 (-0.615464) \| 0.073785 / 0.296338 (-0.222554) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292349 / 0.215209 (0.077140) \| 2.832049 / 2.077655 (0.754394) \| 1.504488 / 1.504120 (0.000368) \| 1.403418 / 1.541195 (-0.137777) \| 1.449223 / 1.468490 (-0.019267) \| 0.563846 / 4.584777 (-4.020931) \| 2.376726 / 3.745712 (-1.368986) \| 2.823304 / 5.269862 (-2.446558) \| 1.774858 / 4.565676 (-2.790818) \| 0.063229 / 0.424275 (-0.361046) \| 0.004923 / 0.007607 (-0.002684) \| 0.347240 / 0.226044 (0.121195) \| 3.486563 / 2.268929 (1.217634) \| 1.890516 / 55.444624 (-53.554109) \| 1.570620 / 6.876477 (-5.305857) \| 1.600842 / 2.142072 (-0.541231) \| 0.644287 / 4.805227 (-4.160940) \| 0.116931 / 6.500664 (-6.383733) \| 0.042068 / 0.075469 (-0.033401) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935662 / 1.841788 (-0.906126) \| 11.950247 / 8.074308 (3.875939) \| 10.636225 / 10.191392 (0.444833) \| 0.139137 / 0.680424 (-0.541287) \| 0.014473 / 0.534201 (-0.519728) \| 0.294213 / 0.579283 (-0.285070) \| 0.273413 / 0.434364 (-0.160951) \| 0.325930 / 0.540337 (-0.214407) \| 0.444265 / 1.386936 (-0.942671) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005448 / 0.011353 (-0.005904) \| 0.003155 / 0.011008 (-0.007853) \| 0.048626 / 0.038508 (0.010117) \| 0.057427 / 0.023109 (0.034318) \| 0.270412 / 0.275898 (-0.005486) \| 0.290816 / 0.323480 (-0.032664) \| 0.004744 / 0.007986 (-0.003241) \| 0.002776 / 0.004328 (-0.001552) \| 0.047953 / 0.004250 (0.043703) \| 0.041126 / 0.037052 (0.004073) \| 0.276046 / 0.258489 (0.017557) \| 0.297548 / 0.293841 (0.003707) \| 0.029308 / 0.128546 (-0.099238) \| 0.010516 / 0.075646 (-0.065131) \| 0.056982 / 0.419271 (-0.362290) \| 0.032922 / 0.043533 (-0.010611) \| 0.271342 / 0.255139 (0.016203) \| 0.288963 / 0.283200 (0.005763) \| 0.019048 / 0.141683 (-0.122635) \| 1.130453 / 1.452155 (-0.321702) \| 1.206462 / 1.492716 (-0.286254) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099249 / 0.018006 (0.081242) \| 0.312409 / 0.000490 (0.311919) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021992 / 0.037411 (-0.015419) \| 0.068377 / 0.014526 (0.053851) \| 0.080749 / 0.176557 (-0.095807) \| 0.120534 / 0.737135 (-0.616602) \| 0.082549 / 0.296338 (-0.213790) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299634 / 0.215209 (0.084425) \| 2.943496 / 2.077655 (0.865841) \| 1.602842 / 1.504120 (0.098722) \| 1.462140 / 1.541195 (-0.079055) \| 1.511082 / 1.468490 (0.042592) \| 0.574148 / 4.584777 (-4.010629) \| 2.492158 / 3.745712 (-1.253554) \| 2.921695 / 5.269862 (-2.348166) \| 1.812416 / 4.565676 (-2.753260) \| 0.064145 / 0.424275 (-0.360130) \| 0.005133 / 0.007607 (-0.002475) \| 0.357935 / 0.226044 (0.131891) \| 3.543728 / 2.268929 (1.274800) \| 1.948676 / 55.444624 (-53.495948) \| 1.664960 / 6.876477 (-5.211517) \| 1.678703 / 2.142072 (-0.463370) \| 0.645867 / 4.805227 (-4.159360) \| 0.117671 / 6.500664 (-6.382993) \| 0.040887 / 0.075469 (-0.034582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979127 / 1.841788 (-0.862661) \| 12.363904 / 8.074308 (4.289596) \| 10.673725 / 10.191392 (0.482333) \| 0.143358 / 0.680424 (-0.537066) \| 0.015375 / 0.534201 (-0.518825) \| 0.287590 / 0.579283 (-0.291694) \| 0.284742 / 0.434364 (-0.149622) \| 0.326901 / 0.540337 (-0.213437) \| 0.443962 / 1.386936 (-0.942974) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68099ca55294bfc12a34781835dd73c533a764bd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004994 / 0.011353 (-0.006359) \| 0.003368 / 0.011008 (-0.007640) \| 0.062803 / 0.038508 (0.024295) \| 0.050778 / 0.023109 (0.027669) \| 0.255955 / 0.275898 (-0.019943) \| 0.278215 / 0.323480 (-0.045265) \| 0.003801 / 0.007986 (-0.004184) \| 0.002703 / 0.004328 (-0.001626) \| 0.048369 / 0.004250 (0.044119) \| 0.037795 / 0.037052 (0.000743) \| 0.255634 / 0.258489 (-0.002855) \| 0.284226 / 0.293841 (-0.009615) \| 0.027252 / 0.128546 (-0.101294) \| 0.010686 / 0.075646 (-0.064961) \| 0.206139 / 0.419271 (-0.213133) \| 0.035543 / 0.043533 (-0.007990) \| 0.257167 / 0.255139 (0.002028) \| 0.277784 / 0.283200 (-0.005416) \| 0.016938 / 0.141683 (-0.124745) \| 1.108595 / 1.452155 (-0.343560) \| 1.188542 / 1.492716 (-0.304175) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090938 / 0.018006 (0.072932) \| 0.298463 / 0.000490 (0.297973) \| 0.000203 / 0.000200 (0.000003) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027762 / 0.037411 (-0.009649) \| 0.060539 / 0.014526 (0.046014) \| 0.075986 / 0.176557 (-0.100570) \| 0.133851 / 0.737135 (-0.603285) \| 0.074669 / 0.296338 (-0.221670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285614 / 0.215209 (0.070405) \| 2.810529 / 2.077655 (0.732874) \| 1.537092 / 1.504120 (0.032973) \| 1.412211 / 1.541195 (-0.128983) \| 1.446395 / 1.468490 (-0.022095) \| 0.559008 / 4.584777 (-4.025769) \| 2.343445 / 3.745712 (-1.402267) \| 2.748113 / 5.269862 (-2.521748) \| 1.733593 / 4.565676 (-2.832083) \| 0.061720 / 0.424275 (-0.362555) \| 0.004930 / 0.007607 (-0.002677) \| 0.330646 / 0.226044 (0.104602) \| 3.314999 / 2.268929 (1.046071) \| 1.854527 / 55.444624 (-53.590098) \| 1.605819 / 6.876477 (-5.270657) \| 1.591406 / 2.142072 (-0.550667) \| 0.624239 / 4.805227 (-4.180988) \| 0.115352 / 6.500664 (-6.385312) \| 0.041600 / 0.075469 (-0.033869) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933179 / 1.841788 (-0.908608) \| 11.456372 / 8.074308 (3.382064) \| 10.578042 / 10.191392 (0.386650) \| 0.128045 / 0.680424 (-0.552379) \| 0.014212 / 0.534201 (-0.519989) \| 0.284795 / 0.579283 (-0.294488) \| 0.266210 / 0.434364 (-0.168153) \| 0.344468 / 0.540337 (-0.195869) \| 0.434414 / 1.386936 (-0.952522) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005142 / 0.011353 (-0.006211) \| 0.003607 / 0.011008 (-0.007401) \| 0.048770 / 0.038508 (0.010262) \| 0.051147 / 0.023109 (0.028038) \| 0.277329 / 0.275898 (0.001430) \| 0.300863 / 0.323480 (-0.022617) \| 0.004005 / 0.007986 (-0.003980) \| 0.002624 / 0.004328 (-0.001705) \| 0.047740 / 0.004250 (0.043489) \| 0.040811 / 0.037052 (0.003759) \| 0.280020 / 0.258489 (0.021531) \| 0.303758 / 0.293841 (0.009918) \| 0.028273 / 0.128546 (-0.100274) \| 0.010379 / 0.075646 (-0.065267) \| 0.057503 / 0.419271 (-0.361768) \| 0.032717 / 0.043533 (-0.010816) \| 0.277560 / 0.255139 (0.022421) \| 0.300622 / 0.283200 (0.017422) \| 0.018142 / 0.141683 (-0.123541) \| 1.121890 / 1.452155 (-0.330265) \| 1.251481 / 1.492716 (-0.241235) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091523 / 0.018006 (0.073517) \| 0.300173 / 0.000490 (0.299683) \| 0.000216 / 0.000200 (0.000016) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026386 / 0.037411 (-0.011025) \| 0.078710 / 0.014526 (0.064184) \| 0.090594 / 0.176557 (-0.085962) \| 0.130623 / 0.737135 (-0.606512) \| 0.092637 / 0.296338 (-0.203701) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299427 / 0.215209 (0.084218) \| 2.929463 / 2.077655 (0.851808) \| 1.608905 / 1.504120 (0.104785) \| 1.490863 / 1.541195 (-0.050331) \| 1.484286 / 1.468490 (0.015796) \| 0.568208 / 4.584777 (-4.016569) \| 2.447081 / 3.745712 (-1.298632) \| 2.801287 / 5.269862 (-2.468574) \| 1.744449 / 4.565676 (-2.821227) \| 0.064222 / 0.424275 (-0.360053) \| 0.004959 / 0.007607 (-0.002648) \| 0.350207 / 0.226044 (0.124162) \| 3.471944 / 2.268929 (1.203016) \| 1.951715 / 55.444624 (-53.492909) \| 1.668764 / 6.876477 (-5.207713) \| 1.675322 / 2.142072 (-0.466751) \| 0.642217 / 4.805227 (-4.163011) \| 0.116776 / 6.500664 (-6.383888) \| 0.040812 / 0.075469 (-0.034658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996478 / 1.841788 (-0.845310) \| 12.090647 / 8.074308 (4.016339) \| 10.723688 / 10.191392 (0.532296) \| 0.141770 / 0.680424 (-0.538653) \| 0.015578 / 0.534201 (-0.518623) \| 0.288236 / 0.579283 (-0.291047) \| 0.278542 / 0.434364 (-0.155822) \| 0.327411 / 0.540337 (-0.212927) \| 0.450309 / 1.386936 (-0.936627) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5dd4698f483d37afe243db0ffae774cbd34a4af4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004967 / 0.011353 (-0.006385) \| 0.003382 / 0.011008 (-0.007627) \| 0.063436 / 0.038508 (0.024928) \| 0.050769 / 0.023109 (0.027659) \| 0.254214 / 0.275898 (-0.021684) \| 0.272076 / 0.323480 (-0.051404) \| 0.003815 / 0.007986 (-0.004170) \| 0.002618 / 0.004328 (-0.001711) \| 0.049021 / 0.004250 (0.044771) \| 0.037329 / 0.037052 (0.000277) \| 0.261112 / 0.258489 (0.002623) \| 0.284133 / 0.293841 (-0.009708) \| 0.026828 / 0.128546 (-0.101719) \| 0.010757 / 0.075646 (-0.064889) \| 0.208047 / 0.419271 (-0.211225) \| 0.035061 / 0.043533 (-0.008472) \| 0.250896 / 0.255139 (-0.004243) \| 0.273038 / 0.283200 (-0.010162) \| 0.016559 / 0.141683 (-0.125124) \| 1.128899 / 1.452155 (-0.323255) \| 1.188857 / 1.492716 (-0.303860) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100121 / 0.018006 (0.082114) \| 0.298427 / 0.000490 (0.297937) \| 0.000218 / 0.000200 (0.000018) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018369 / 0.037411 (-0.019042) \| 0.060425 / 0.014526 (0.045899) \| 0.073501 / 0.176557 (-0.103055) \| 0.120254 / 0.737135 (-0.616881) \| 0.074889 / 0.296338 (-0.221450) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287153 / 0.215209 (0.071944) \| 2.797036 / 2.077655 (0.719382) \| 1.446216 / 1.504120 (-0.057904) \| 1.336015 / 1.541195 (-0.205179) \| 1.369841 / 1.468490 (-0.098650) \| 0.559424 / 4.584777 (-4.025353) \| 2.361344 / 3.745712 (-1.384368) \| 2.766619 / 5.269862 (-2.503243) \| 1.747235 / 4.565676 (-2.818441) \| 0.066243 / 0.424275 (-0.358032) \| 0.004974 / 0.007607 (-0.002633) \| 0.333565 / 0.226044 (0.107520) \| 3.319877 / 2.268929 (1.050948) \| 1.798024 / 55.444624 (-53.646601) \| 1.495896 / 6.876477 (-5.380580) \| 1.529243 / 2.142072 (-0.612830) \| 0.636609 / 4.805227 (-4.168618) \| 0.116151 / 6.500664 (-6.384514) \| 0.041779 / 0.075469 (-0.033690) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.952176 / 1.841788 (-0.889611) \| 11.559160 / 8.074308 (3.484852) \| 10.556771 / 10.191392 (0.365379) \| 0.127118 / 0.680424 (-0.553306) \| 0.014142 / 0.534201 (-0.520059) \| 0.286585 / 0.579283 (-0.292698) \| 0.260233 / 0.434364 (-0.174131) \| 0.324012 / 0.540337 (-0.216326) \| 0.435131 / 1.386936 (-0.951805) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005171 / 0.011353 (-0.006182) \| 0.003402 / 0.011008 (-0.007607) \| 0.048826 / 0.038508 (0.010318) \| 0.050455 / 0.023109 (0.027346) \| 0.272120 / 0.275898 (-0.003778) \| 0.290404 / 0.323480 (-0.033076) \| 0.003986 / 0.007986 (-0.003999) \| 0.002569 / 0.004328 (-0.001760) \| 0.047845 / 0.004250 (0.043595) \| 0.040203 / 0.037052 (0.003150) \| 0.278263 / 0.258489 (0.019774) \| 0.299255 / 0.293841 (0.005414) \| 0.028643 / 0.128546 (-0.099903) \| 0.010584 / 0.075646 (-0.065062) \| 0.056921 / 0.419271 (-0.362351) \| 0.032362 / 0.043533 (-0.011171) \| 0.274010 / 0.255139 (0.018871) \| 0.288601 / 0.283200 (0.005401) \| 0.017856 / 0.141683 (-0.123827) \| 1.154112 / 1.452155 (-0.298043) \| 1.216288 / 1.492716 (-0.276428) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091399 / 0.018006 (0.073392) \| 0.299966 / 0.000490 (0.299477) \| 0.000218 / 0.000200 (0.000018) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021728 / 0.037411 (-0.015683) \| 0.068285 / 0.014526 (0.053759) \| 0.081767 / 0.176557 (-0.094789) \| 0.120000 / 0.737135 (-0.617135) \| 0.082149 / 0.296338 (-0.214189) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289625 / 0.215209 (0.074416) \| 2.835114 / 2.077655 (0.757460) \| 1.583207 / 1.504120 (0.079087) \| 1.465251 / 1.541195 (-0.075944) \| 1.480691 / 1.468490 (0.012200) \| 0.569103 / 4.584777 (-4.015674) \| 2.416981 / 3.745712 (-1.328731) \| 2.761746 / 5.269862 (-2.508115) \| 1.720055 / 4.565676 (-2.845621) \| 0.063349 / 0.424275 (-0.360926) \| 0.004931 / 0.007607 (-0.002676) \| 0.343658 / 0.226044 (0.117614) \| 3.362996 / 2.268929 (1.094068) \| 1.948088 / 55.444624 (-53.496536) \| 1.659504 / 6.876477 (-5.216973) \| 1.660359 / 2.142072 (-0.481713) \| 0.647871 / 4.805227 (-4.157356) \| 0.117395 / 6.500664 (-6.383269) \| 0.041049 / 0.075469 (-0.034420) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953971 / 1.841788 (-0.887817) \| 12.076998 / 8.074308 (4.002690) \| 10.549021 / 10.191392 (0.357629) \| 0.130026 / 0.680424 (-0.550398) \| 0.015697 / 0.534201 (-0.518504) \| 0.287125 / 0.579283 (-0.292158) \| 0.298402 / 0.434364 (-0.135962) \| 0.326005 / 0.540337 (-0.214332) \| 0.444065 / 1.386936 (-0.942871) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf86d48792f585bf802bb2ff70e0d9c3a4de4bcf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005053 / 0.011353 (-0.006300) \| 0.003537 / 0.011008 (-0.007472) \| 0.062923 / 0.038508 (0.024415) \| 0.053796 / 0.023109 (0.030687) \| 0.242523 / 0.275898 (-0.033375) \| 0.264014 / 0.323480 (-0.059466) \| 0.002879 / 0.007986 (-0.005106) \| 0.003273 / 0.004328 (-0.001055) \| 0.048735 / 0.004250 (0.044484) \| 0.037541 / 0.037052 (0.000488) \| 0.248587 / 0.258489 (-0.009902) \| 0.275531 / 0.293841 (-0.018310) \| 0.027215 / 0.128546 (-0.101331) \| 0.010466 / 0.075646 (-0.065180) \| 0.206508 / 0.419271 (-0.212763) \| 0.035606 / 0.043533 (-0.007927) \| 0.251044 / 0.255139 (-0.004095) \| 0.267183 / 0.283200 (-0.016016) \| 0.018357 / 0.141683 (-0.123326) \| 1.083513 / 1.452155 (-0.368642) \| 1.152988 / 1.492716 (-0.339728) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091749 / 0.018006 (0.073742) \| 0.299946 / 0.000490 (0.299456) \| 0.000212 / 0.000200 (0.000013) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018300 / 0.037411 (-0.019111) \| 0.060691 / 0.014526 (0.046166) \| 0.072998 / 0.176557 (-0.103559) \| 0.120581 / 0.737135 (-0.616554) \| 0.073912 / 0.296338 (-0.222427) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277602 / 0.215209 (0.062393) \| 2.719181 / 2.077655 (0.641526) \| 1.450894 / 1.504120 (-0.053226) \| 1.314344 / 1.541195 (-0.226851) \| 1.351996 / 1.468490 (-0.116494) \| 0.586231 / 4.584777 (-3.998546) \| 2.349746 / 3.745712 (-1.395967) \| 2.810060 / 5.269862 (-2.459802) \| 1.761362 / 4.565676 (-2.804314) \| 0.062535 / 0.424275 (-0.361740) \| 0.004918 / 0.007607 (-0.002689) \| 0.336091 / 0.226044 (0.110047) \| 3.238139 / 2.268929 (0.969211) \| 1.769734 / 55.444624 (-53.674890) \| 1.505332 / 6.876477 (-5.371145) \| 1.527875 / 2.142072 (-0.614198) \| 0.640194 / 4.805227 (-4.165033) \| 0.116567 / 6.500664 (-6.384097) \| 0.042464 / 0.075469 (-0.033005) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930919 / 1.841788 (-0.910869) \| 11.462498 / 8.074308 (3.388190) \| 10.575359 / 10.191392 (0.383967) \| 0.130567 / 0.680424 (-0.549857) \| 0.014203 / 0.534201 (-0.519998) \| 0.286944 / 0.579283 (-0.292339) \| 0.264706 / 0.434364 (-0.169658) \| 0.324820 / 0.540337 (-0.215517) \| 0.434579 / 1.386936 (-0.952357) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005164 / 0.011353 (-0.006189) \| 0.003442 / 0.011008 (-0.007567) \| 0.050146 / 0.038508 (0.011638) \| 0.050800 / 0.023109 (0.027691) \| 0.263405 / 0.275898 (-0.012493) \| 0.284876 / 0.323480 (-0.038604) \| 0.004011 / 0.007986 (-0.003975) \| 0.002602 / 0.004328 (-0.001726) \| 0.046742 / 0.004250 (0.042491) \| 0.040393 / 0.037052 (0.003341) \| 0.265052 / 0.258489 (0.006563) \| 0.294217 / 0.293841 (0.000377) \| 0.028429 / 0.128546 (-0.100118) \| 0.010418 / 0.075646 (-0.065228) \| 0.057285 / 0.419271 (-0.361987) \| 0.032137 / 0.043533 (-0.011396) \| 0.265867 / 0.255139 (0.010728) \| 0.284764 / 0.283200 (0.001564) \| 0.017448 / 0.141683 (-0.124235) \| 1.172830 / 1.452155 (-0.279325) \| 1.223982 / 1.492716 (-0.268735) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091859 / 0.018006 (0.073853) \| 0.285421 / 0.000490 (0.284931) \| 0.000220 / 0.000200 (0.000020) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021620 / 0.037411 (-0.015792) \| 0.069058 / 0.014526 (0.054532) \| 0.082560 / 0.176557 (-0.093997) \| 0.119511 / 0.737135 (-0.617624) \| 0.082318 / 0.296338 (-0.214021) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291499 / 0.215209 (0.076290) \| 2.863352 / 2.077655 (0.785698) \| 1.557242 / 1.504120 (0.053122) \| 1.430170 / 1.541195 (-0.111024) \| 1.432850 / 1.468490 (-0.035640) \| 0.559716 / 4.584777 (-4.025061) \| 2.385405 / 3.745712 (-1.360307) \| 2.748938 / 5.269862 (-2.520924) \| 1.740802 / 4.565676 (-2.824874) \| 0.061811 / 0.424275 (-0.362465) \| 0.005174 / 0.007607 (-0.002433) \| 0.348687 / 0.226044 (0.122642) \| 3.420120 / 2.268929 (1.151191) \| 1.918278 / 55.444624 (-53.526346) \| 1.631559 / 6.876477 (-5.244918) \| 1.635850 / 2.142072 (-0.506222) \| 0.644144 / 4.805227 (-4.161083) \| 0.115823 / 6.500664 (-6.384841) \| 0.041255 / 0.075469 (-0.034214) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960066 / 1.841788 (-0.881722) \| 12.011372 / 8.074308 (3.937064) \| 10.580532 / 10.191392 (0.389140) \| 0.134763 / 0.680424 (-0.545661) \| 0.017027 / 0.534201 (-0.517174) \| 0.290484 / 0.579283 (-0.288799) \| 0.285171 / 0.434364 (-0.149193) \| 0.322453 / 0.540337 (-0.217884) \| 0.438088 / 1.386936 (-0.948848) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3fc42882a2d84d7482c27063f1e19539e99b9d3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005212 / 0.011353 (-0.006141) \| 0.003440 / 0.011008 (-0.007568) \| 0.063612 / 0.038508 (0.025104) \| 0.049070 / 0.023109 (0.025961) \| 0.269748 / 0.275898 (-0.006150) \| 0.283270 / 0.323480 (-0.040210) \| 0.002892 / 0.007986 (-0.005094) \| 0.002693 / 0.004328 (-0.001635) \| 0.049710 / 0.004250 (0.045459) \| 0.036707 / 0.037052 (-0.000345) \| 0.299035 / 0.258489 (0.040546) \| 0.296443 / 0.293841 (0.002602) \| 0.028095 / 0.128546 (-0.100451) \| 0.010682 / 0.075646 (-0.064964) \| 0.213914 / 0.419271 (-0.205358) \| 0.036210 / 0.043533 (-0.007323) \| 0.235720 / 0.255139 (-0.019419) \| 0.252687 / 0.283200 (-0.030512) \| 0.016985 / 0.141683 (-0.124698) \| 1.099024 / 1.452155 (-0.353130) \| 1.162970 / 1.492716 (-0.329746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093114 / 0.018006 (0.075108) \| 0.305168 / 0.000490 (0.304678) \| 0.000216 / 0.000200 (0.000016) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018370 / 0.037411 (-0.019041) \| 0.060534 / 0.014526 (0.046008) \| 0.073960 / 0.176557 (-0.102596) \| 0.120325 / 0.737135 (-0.616810) \| 0.073754 / 0.296338 (-0.222585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284244 / 0.215209 (0.069035) \| 2.756854 / 2.077655 (0.679199) \| 1.477304 / 1.504120 (-0.026816) \| 1.374635 / 1.541195 (-0.166560) \| 1.383284 / 1.468490 (-0.085206) \| 0.564656 / 4.584777 (-4.020121) \| 2.361719 / 3.745712 (-1.383993) \| 2.794822 / 5.269862 (-2.475039) \| 1.742981 / 4.565676 (-2.822696) \| 0.063443 / 0.424275 (-0.360832) \| 0.004952 / 0.007607 (-0.002655) \| 0.342058 / 0.226044 (0.116014) \| 3.351093 / 2.268929 (1.082164) \| 1.857375 / 55.444624 (-53.587250) \| 1.541680 / 6.876477 (-5.334797) \| 1.580147 / 2.142072 (-0.561926) \| 0.645216 / 4.805227 (-4.160012) \| 0.118768 / 6.500664 (-6.381896) \| 0.042115 / 0.075469 (-0.033354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.925845 / 1.841788 (-0.915943) \| 11.444147 / 8.074308 (3.369839) \| 10.291297 / 10.191392 (0.099905) \| 0.128129 / 0.680424 (-0.552295) \| 0.013774 / 0.534201 (-0.520427) \| 0.289278 / 0.579283 (-0.290005) \| 0.262353 / 0.434364 (-0.172011) \| 0.328517 / 0.540337 (-0.211820) \| 0.436050 / 1.386936 (-0.950886) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005666 / 0.011353 (-0.005687) \| 0.003691 / 0.011008 (-0.007318) \| 0.049361 / 0.038508 (0.010853) \| 0.054245 / 0.023109 (0.031136) \| 0.274433 / 0.275898 (-0.001465) \| 0.285648 / 0.323480 (-0.037832) \| 0.004080 / 0.007986 (-0.003906) \| 0.002666 / 0.004328 (-0.001663) \| 0.047539 / 0.004250 (0.043288) \| 0.041001 / 0.037052 (0.003948) \| 0.296018 / 0.258489 (0.037529) \| 0.294542 / 0.293841 (0.000701) \| 0.030546 / 0.128546 (-0.098001) \| 0.010556 / 0.075646 (-0.065090) \| 0.058146 / 0.419271 (-0.361126) \| 0.033407 / 0.043533 (-0.010126) \| 0.263977 / 0.255139 (0.008838) \| 0.286228 / 0.283200 (0.003028) \| 0.018088 / 0.141683 (-0.123595) \| 1.121295 / 1.452155 (-0.330860) \| 1.182183 / 1.492716 (-0.310533) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.104540 / 0.018006 (0.086534) \| 0.303494 / 0.000490 (0.303004) \| 0.000222 / 0.000200 (0.000022) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021274 / 0.037411 (-0.016137) \| 0.070146 / 0.014526 (0.055621) \| 0.080343 / 0.176557 (-0.096213) \| 0.120017 / 0.737135 (-0.617119) \| 0.081303 / 0.296338 (-0.215036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294390 / 0.215209 (0.079181) \| 2.883366 / 2.077655 (0.805711) \| 1.564629 / 1.504120 (0.060509) \| 1.432633 / 1.541195 (-0.108562) \| 1.438786 / 1.468490 (-0.029704) \| 0.569663 / 4.584777 (-4.015114) \| 2.448691 / 3.745712 (-1.297021) \| 2.817010 / 5.269862 (-2.452851) \| 1.757274 / 4.565676 (-2.808402) \| 0.064147 / 0.424275 (-0.360129) \| 0.004910 / 0.007607 (-0.002697) \| 0.344062 / 0.226044 (0.118018) \| 3.394223 / 2.268929 (1.125294) \| 1.927139 / 55.444624 (-53.517485) \| 1.624983 / 6.876477 (-5.251494) \| 1.629076 / 2.142072 (-0.512996) \| 0.654239 / 4.805227 (-4.150988) \| 0.117309 / 6.500664 (-6.383355) \| 0.041067 / 0.075469 (-0.034402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993184 / 1.841788 (-0.848604) \| 11.969985 / 8.074308 (3.895677) \| 10.363356 / 10.191392 (0.171964) \| 0.130708 / 0.680424 (-0.549716) \| 0.015577 / 0.534201 (-0.518624) \| 0.289579 / 0.579283 (-0.289704) \| 0.274875 / 0.434364 (-0.159488) \| 0.326736 / 0.540337 (-0.213601) \| 0.442770 / 1.386936 (-0.944166) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#796a47e388a5c5711a95bd649648608c18219ac5 \"CML watermark\")\n", "Getting the same windows error as in my other PR. I couldn't reproduce on my windows machine though 🧐 ", "`DataFilesList` is a list so we expect to be able to get its length with zero cost, which wouldn't be the case if we make it lazy no ? ", "But we don't call `len` on it, do we? And I couldn't find an instance of `DataFilesList` being used in GitHub's public repos.", "`DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns", "Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)", "> `DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns\r\n\r\nWould be interesting to know how often these scripts call `len` or do random access on `DataFilesList`.\r\n\r\nStill, I think we should opt for a solution that makes more sense for us. To avoid the breaking change, we can define a `BuilderConfig.data_files` property that resolves this iterable. \r\n\r\n> Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)\r\n\r\nThe `BuilderConfig.data_files` property suggested above should address this, no? \r\n\r\nI think we should be more careful not to make our API needlessly complex because of the YAML README feature. And if this can't be avoided, we should probably refactor the builder API.", "> The BuilderConfig.data_files property suggested above should address this, no?\r\n\r\nThat works indeed ! let me try something", "Implementing lazy DataFilesList and .data_files brings more complexity (less readable, more bad side effects) so I think the current solution is the best one", "I opened https://github.com/huggingface/datasets/pull/6493 to continue this and fix conflicts with https://github.com/huggingface/datasets/pull/6459" ]	2023-11-29T13:18:44Z	2024-02-08T14:41:35Z	2024-02-08T14:41:35Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6458.diff", "html_url": "https://github.com/huggingface/datasets/pull/6458", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6458.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6458" }	Related to discussion at https://github.com/huggingface/datasets/pull/6255 this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6458/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6457	6,457	`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'	{ "avatar_url": "https://avatars.githubusercontent.com/u/79070834?v=4", "events_url": "https://api.github.com/users/wasertech/events{/privacy}", "followers_url": "https://api.github.com/users/wasertech/followers", "following_url": "https://api.github.com/users/wasertech/following{/other_user}", "gists_url": "https://api.github.com/users/wasertech/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/wasertech", "id": 79070834, "login": "wasertech", "node_id": "MDQ6VXNlcjc5MDcwODM0", "organizations_url": "https://api.github.com/users/wasertech/orgs", "received_events_url": "https://api.github.com/users/wasertech/received_events", "repos_url": "https://api.github.com/users/wasertech/repos", "site_admin": false, "starred_url": "https://api.github.com/users/wasertech/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wasertech/subscriptions", "type": "User", "url": "https://api.github.com/users/wasertech", "user_view_type": "public" }	[]	closed	false	[ "Updating `fsspec>=2023.10.0` did solve the issue.", "May be it should be pinned somewhere?", "> Maybe this should go in datasets directly... anyways you can easily fix this error by updating datasets>=2.15.1.dev0.\r\n\r\n@lhoestq @mariosasko for what I understand this is a bug fixed in `datasets` already, right? No need to do anything in `huggingface_hub`?", "I've opened a PR with a fix in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/1875", "Thanks! PR is merged and will be shipped in next release of `huggingface_hub`." ]	2023-11-29T01:57:36Z	2023-11-29T15:39:03Z	2023-11-29T02:02:38Z	NONE	null	null	### Describe the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Steps to reproduce the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Expected behavior Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Environment info Please see https://github.com/huggingface/huggingface_hub/issues/1872	{ "avatar_url": "https://avatars.githubusercontent.com/u/79070834?v=4", "events_url": "https://api.github.com/users/wasertech/events{/privacy}", "followers_url": "https://api.github.com/users/wasertech/followers", "following_url": "https://api.github.com/users/wasertech/following{/other_user}", "gists_url": "https://api.github.com/users/wasertech/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/wasertech", "id": 79070834, "login": "wasertech", "node_id": "MDQ6VXNlcjc5MDcwODM0", "organizations_url": "https://api.github.com/users/wasertech/orgs", "received_events_url": "https://api.github.com/users/wasertech/received_events", "repos_url": "https://api.github.com/users/wasertech/repos", "site_admin": false, "starred_url": "https://api.github.com/users/wasertech/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wasertech/subscriptions", "type": "User", "url": "https://api.github.com/users/wasertech", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6457/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6456	6,456	Don't require trust_remote_code in inspect_dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005705 / 0.011353 (-0.005648) \| 0.003536 / 0.011008 (-0.007473) \| 0.062852 / 0.038508 (0.024343) \| 0.053902 / 0.023109 (0.030793) \| 0.239465 / 0.275898 (-0.036433) \| 0.270829 / 0.323480 (-0.052651) \| 0.004052 / 0.007986 (-0.003934) \| 0.002775 / 0.004328 (-0.001554) \| 0.048475 / 0.004250 (0.044225) \| 0.039430 / 0.037052 (0.002377) \| 0.244318 / 0.258489 (-0.014171) \| 0.277539 / 0.293841 (-0.016302) \| 0.027637 / 0.128546 (-0.100909) \| 0.010875 / 0.075646 (-0.064771) \| 0.208839 / 0.419271 (-0.210432) \| 0.036984 / 0.043533 (-0.006549) \| 0.246355 / 0.255139 (-0.008784) \| 0.271200 / 0.283200 (-0.011999) \| 0.020636 / 0.141683 (-0.121047) \| 1.078472 / 1.452155 (-0.373683) \| 1.155701 / 1.492716 (-0.337015) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100971 / 0.018006 (0.082965) \| 0.310996 / 0.000490 (0.310507) \| 0.000218 / 0.000200 (0.000018) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019300 / 0.037411 (-0.018111) \| 0.060625 / 0.014526 (0.046099) \| 0.073778 / 0.176557 (-0.102778) \| 0.120280 / 0.737135 (-0.616855) \| 0.075288 / 0.296338 (-0.221051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289838 / 0.215209 (0.074629) \| 2.859492 / 2.077655 (0.781837) \| 1.528478 / 1.504120 (0.024358) \| 1.417911 / 1.541195 (-0.123283) \| 1.444227 / 1.468490 (-0.024263) \| 0.566799 / 4.584777 (-4.017978) \| 2.402526 / 3.745712 (-1.343186) \| 2.805241 / 5.269862 (-2.464620) \| 1.798572 / 4.565676 (-2.767104) \| 0.062920 / 0.424275 (-0.361355) \| 0.004995 / 0.007607 (-0.002612) \| 0.340688 / 0.226044 (0.114644) \| 3.347967 / 2.268929 (1.079039) \| 1.898464 / 55.444624 (-53.546160) \| 1.604784 / 6.876477 (-5.271693) \| 1.648864 / 2.142072 (-0.493209) \| 0.642242 / 4.805227 (-4.162985) \| 0.117567 / 6.500664 (-6.383097) \| 0.041911 / 0.075469 (-0.033558) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949099 / 1.841788 (-0.892689) \| 12.367323 / 8.074308 (4.293015) \| 10.694238 / 10.191392 (0.502846) \| 0.143424 / 0.680424 (-0.537000) \| 0.014569 / 0.534201 (-0.519632) \| 0.289127 / 0.579283 (-0.290156) \| 0.270490 / 0.434364 (-0.163874) \| 0.326470 / 0.540337 (-0.213867) \| 0.432223 / 1.386936 (-0.954713) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005380 / 0.011353 (-0.005973) \| 0.003582 / 0.011008 (-0.007426) \| 0.049341 / 0.038508 (0.010833) \| 0.053274 / 0.023109 (0.030165) \| 0.284319 / 0.275898 (0.008421) \| 0.334248 / 0.323480 (0.010768) \| 0.004032 / 0.007986 (-0.003953) \| 0.002682 / 0.004328 (-0.001646) \| 0.048317 / 0.004250 (0.044067) \| 0.040157 / 0.037052 (0.003105) \| 0.284594 / 0.258489 (0.026105) \| 0.341567 / 0.293841 (0.047726) \| 0.029639 / 0.128546 (-0.098908) \| 0.010780 / 0.075646 (-0.064867) \| 0.057990 / 0.419271 (-0.361282) \| 0.032730 / 0.043533 (-0.010803) \| 0.290328 / 0.255139 (0.035189) \| 0.298563 / 0.283200 (0.015363) \| 0.018546 / 0.141683 (-0.123137) \| 1.143157 / 1.452155 (-0.308998) \| 1.191391 / 1.492716 (-0.301326) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093802 / 0.018006 (0.075796) \| 0.312771 / 0.000490 (0.312282) \| 0.000221 / 0.000200 (0.000021) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021867 / 0.037411 (-0.015544) \| 0.069064 / 0.014526 (0.054538) \| 0.082270 / 0.176557 (-0.094287) \| 0.120222 / 0.737135 (-0.616913) \| 0.084628 / 0.296338 (-0.211710) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295505 / 0.215209 (0.080296) \| 2.891105 / 2.077655 (0.813450) \| 1.619480 / 1.504120 (0.115360) \| 1.498290 / 1.541195 (-0.042905) \| 1.547896 / 1.468490 (0.079406) \| 0.575188 / 4.584777 (-4.009589) \| 2.434426 / 3.745712 (-1.311286) \| 2.899286 / 5.269862 (-2.370576) \| 1.806085 / 4.565676 (-2.759591) \| 0.063660 / 0.424275 (-0.360616) \| 0.004933 / 0.007607 (-0.002674) \| 0.348274 / 0.226044 (0.122229) \| 3.447900 / 2.268929 (1.178971) \| 1.956237 / 55.444624 (-53.488387) \| 1.680416 / 6.876477 (-5.196061) \| 1.732307 / 2.142072 (-0.409766) \| 0.668428 / 4.805227 (-4.136799) \| 0.119161 / 6.500664 (-6.381503) \| 0.041694 / 0.075469 (-0.033775) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973730 / 1.841788 (-0.868058) \| 12.082452 / 8.074308 (4.008144) \| 10.624836 / 10.191392 (0.433444) \| 0.144027 / 0.680424 (-0.536397) \| 0.014830 / 0.534201 (-0.519370) \| 0.289946 / 0.579283 (-0.289337) \| 0.281939 / 0.434364 (-0.152424) \| 0.325639 / 0.540337 (-0.214699) \| 0.551690 / 1.386936 (-0.835246) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e1cf8526c9216b08b5431695d9f8e0eec64cc5f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005279 / 0.011353 (-0.006074) \| 0.003506 / 0.011008 (-0.007502) \| 0.062579 / 0.038508 (0.024071) \| 0.052809 / 0.023109 (0.029700) \| 0.274693 / 0.275898 (-0.001205) \| 0.283917 / 0.323480 (-0.039563) \| 0.003950 / 0.007986 (-0.004036) \| 0.002772 / 0.004328 (-0.001557) \| 0.048127 / 0.004250 (0.043877) \| 0.037771 / 0.037052 (0.000719) \| 0.280595 / 0.258489 (0.022106) \| 0.292310 / 0.293841 (-0.001531) \| 0.027890 / 0.128546 (-0.100656) \| 0.010771 / 0.075646 (-0.064875) \| 0.207285 / 0.419271 (-0.211987) \| 0.036179 / 0.043533 (-0.007354) \| 0.253617 / 0.255139 (-0.001522) \| 0.276107 / 0.283200 (-0.007093) \| 0.018253 / 0.141683 (-0.123430) \| 1.112219 / 1.452155 (-0.339936) \| 1.166756 / 1.492716 (-0.325960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095159 / 0.018006 (0.077152) \| 0.306097 / 0.000490 (0.305608) \| 0.000219 / 0.000200 (0.000019) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019056 / 0.037411 (-0.018355) \| 0.060445 / 0.014526 (0.045919) \| 0.073553 / 0.176557 (-0.103004) \| 0.120306 / 0.737135 (-0.616829) \| 0.075613 / 0.296338 (-0.220725) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277839 / 0.215209 (0.062630) \| 2.761037 / 2.077655 (0.683382) \| 1.508524 / 1.504120 (0.004404) \| 1.368994 / 1.541195 (-0.172201) \| 1.415961 / 1.468490 (-0.052529) \| 0.570490 / 4.584777 (-4.014287) \| 2.356355 / 3.745712 (-1.389357) \| 2.806626 / 5.269862 (-2.463235) \| 1.757849 / 4.565676 (-2.807827) \| 0.063504 / 0.424275 (-0.360771) \| 0.005021 / 0.007607 (-0.002586) \| 0.338880 / 0.226044 (0.112836) \| 3.290947 / 2.268929 (1.022018) \| 1.818238 / 55.444624 (-53.626386) \| 1.529970 / 6.876477 (-5.346507) \| 1.557085 / 2.142072 (-0.584987) \| 0.645352 / 4.805227 (-4.159876) \| 0.123066 / 6.500664 (-6.377598) \| 0.043387 / 0.075469 (-0.032082) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974512 / 1.841788 (-0.867276) \| 11.976411 / 8.074308 (3.902103) \| 10.361084 / 10.191392 (0.169692) \| 0.127171 / 0.680424 (-0.553253) \| 0.014091 / 0.534201 (-0.520110) \| 0.288608 / 0.579283 (-0.290675) \| 0.261886 / 0.434364 (-0.172478) \| 0.331632 / 0.540337 (-0.208705) \| 0.437002 / 1.386936 (-0.949934) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005129 / 0.011353 (-0.006224) \| 0.003490 / 0.011008 (-0.007518) \| 0.049005 / 0.038508 (0.010497) \| 0.054077 / 0.023109 (0.030968) \| 0.276653 / 0.275898 (0.000755) \| 0.298752 / 0.323480 (-0.024728) \| 0.003979 / 0.007986 (-0.004007) \| 0.002625 / 0.004328 (-0.001703) \| 0.047951 / 0.004250 (0.043701) \| 0.040969 / 0.037052 (0.003916) \| 0.279879 / 0.258489 (0.021390) \| 0.306244 / 0.293841 (0.012403) \| 0.029025 / 0.128546 (-0.099522) \| 0.010450 / 0.075646 (-0.065197) \| 0.056846 / 0.419271 (-0.362426) \| 0.033476 / 0.043533 (-0.010057) \| 0.273340 / 0.255139 (0.018201) \| 0.294783 / 0.283200 (0.011584) \| 0.019105 / 0.141683 (-0.122578) \| 1.126389 / 1.452155 (-0.325766) \| 1.183369 / 1.492716 (-0.309348) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094995 / 0.018006 (0.076989) \| 0.306984 / 0.000490 (0.306495) \| 0.000224 / 0.000200 (0.000024) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021880 / 0.037411 (-0.015532) \| 0.069674 / 0.014526 (0.055148) \| 0.082191 / 0.176557 (-0.094366) \| 0.120956 / 0.737135 (-0.616179) \| 0.083843 / 0.296338 (-0.212495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295139 / 0.215209 (0.079929) \| 2.860520 / 2.077655 (0.782865) \| 1.578892 / 1.504120 (0.074772) \| 1.451003 / 1.541195 (-0.090192) \| 1.483099 / 1.468490 (0.014609) \| 0.550491 / 4.584777 (-4.034286) \| 2.430352 / 3.745712 (-1.315360) \| 2.874468 / 5.269862 (-2.395393) \| 1.741474 / 4.565676 (-2.824202) \| 0.062563 / 0.424275 (-0.361712) \| 0.004962 / 0.007607 (-0.002645) \| 0.343747 / 0.226044 (0.117703) \| 3.419046 / 2.268929 (1.150118) \| 1.943774 / 55.444624 (-53.500851) \| 1.650989 / 6.876477 (-5.225488) \| 1.704083 / 2.142072 (-0.437990) \| 0.645447 / 4.805227 (-4.159780) \| 0.125105 / 6.500664 (-6.375559) \| 0.041319 / 0.075469 (-0.034150) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959708 / 1.841788 (-0.882079) \| 12.235906 / 8.074308 (4.161598) \| 10.575402 / 10.191392 (0.384010) \| 0.143619 / 0.680424 (-0.536805) \| 0.015517 / 0.534201 (-0.518684) \| 0.285231 / 0.579283 (-0.294052) \| 0.281549 / 0.434364 (-0.152815) \| 0.326649 / 0.540337 (-0.213689) \| 0.565706 / 1.386936 (-0.821230) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb6985bc33277a3ece7f28c74ca742ba84655b0c \"CML watermark\")\n" ]	2023-11-28T19:47:07Z	2023-11-30T10:40:23Z	2023-11-30T10:34:12Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6456.diff", "html_url": "https://github.com/huggingface/datasets/pull/6456", "merged_at": "2023-11-30T10:34:12Z", "patch_url": "https://github.com/huggingface/datasets/pull/6456.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6456" }	don't require `trust_remote_code` in (deprecated) `inspect_dataset` (it defeats its purpose) (not super important but we might as well keep it until the next major release) this is needed to fix the tests in https://github.com/huggingface/datasets/pull/6448	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6456/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6454	6,454	Refactor `dill` logic	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005490 / 0.011353 (-0.005863) \| 0.003554 / 0.011008 (-0.007454) \| 0.062183 / 0.038508 (0.023675) \| 0.053093 / 0.023109 (0.029984) \| 0.245370 / 0.275898 (-0.030528) \| 0.271637 / 0.323480 (-0.051842) \| 0.002997 / 0.007986 (-0.004989) \| 0.002811 / 0.004328 (-0.001517) \| 0.047874 / 0.004250 (0.043623) \| 0.039673 / 0.037052 (0.002620) \| 0.253219 / 0.258489 (-0.005271) \| 0.280438 / 0.293841 (-0.013403) \| 0.028393 / 0.128546 (-0.100153) \| 0.010914 / 0.075646 (-0.064732) \| 0.207491 / 0.419271 (-0.211781) \| 0.037565 / 0.043533 (-0.005968) \| 0.252382 / 0.255139 (-0.002757) \| 0.272204 / 0.283200 (-0.010995) \| 0.019007 / 0.141683 (-0.122676) \| 1.099767 / 1.452155 (-0.352388) \| 1.173220 / 1.492716 (-0.319496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098777 / 0.018006 (0.080771) \| 0.325912 / 0.000490 (0.325422) \| 0.000214 / 0.000200 (0.000014) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018815 / 0.037411 (-0.018596) \| 0.070031 / 0.014526 (0.055506) \| 0.075395 / 0.176557 (-0.101162) \| 0.122633 / 0.737135 (-0.614502) \| 0.077621 / 0.296338 (-0.218718) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290830 / 0.215209 (0.075621) \| 2.869214 / 2.077655 (0.791559) \| 1.507337 / 1.504120 (0.003217) \| 1.351391 / 1.541195 (-0.189804) \| 1.386642 / 1.468490 (-0.081848) \| 0.570318 / 4.584777 (-4.014459) \| 2.423442 / 3.745712 (-1.322270) \| 2.897812 / 5.269862 (-2.372050) \| 1.796458 / 4.565676 (-2.769219) \| 0.063649 / 0.424275 (-0.360626) \| 0.005038 / 0.007607 (-0.002570) \| 0.357819 / 0.226044 (0.131774) \| 3.535478 / 2.268929 (1.266549) \| 1.831764 / 55.444624 (-53.612861) \| 1.545035 / 6.876477 (-5.331442) \| 1.585919 / 2.142072 (-0.556154) \| 0.643333 / 4.805227 (-4.161894) \| 0.120319 / 6.500664 (-6.380345) \| 0.043031 / 0.075469 (-0.032438) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981155 / 1.841788 (-0.860633) \| 12.136069 / 8.074308 (4.061760) \| 10.579923 / 10.191392 (0.388531) \| 0.152963 / 0.680424 (-0.527461) \| 0.014783 / 0.534201 (-0.519418) \| 0.289177 / 0.579283 (-0.290106) \| 0.271784 / 0.434364 (-0.162580) \| 0.322381 / 0.540337 (-0.217956) \| 0.420034 / 1.386936 (-0.966902) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005315 / 0.011353 (-0.006038) \| 0.003584 / 0.011008 (-0.007424) \| 0.048596 / 0.038508 (0.010088) \| 0.055940 / 0.023109 (0.032830) \| 0.277687 / 0.275898 (0.001789) \| 0.301545 / 0.323480 (-0.021935) \| 0.004150 / 0.007986 (-0.003836) \| 0.002699 / 0.004328 (-0.001629) \| 0.047661 / 0.004250 (0.043410) \| 0.040618 / 0.037052 (0.003565) \| 0.279173 / 0.258489 (0.020684) \| 0.306105 / 0.293841 (0.012264) \| 0.030099 / 0.128546 (-0.098447) \| 0.010784 / 0.075646 (-0.064862) \| 0.057418 / 0.419271 (-0.361853) \| 0.032632 / 0.043533 (-0.010901) \| 0.276064 / 0.255139 (0.020925) \| 0.307194 / 0.283200 (0.023995) \| 0.017416 / 0.141683 (-0.124267) \| 1.107749 / 1.452155 (-0.344406) \| 1.161104 / 1.492716 (-0.331612) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102395 / 0.018006 (0.084389) \| 0.316933 / 0.000490 (0.316443) \| 0.000246 / 0.000200 (0.000046) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022833 / 0.037411 (-0.014579) \| 0.069372 / 0.014526 (0.054846) \| 0.082139 / 0.176557 (-0.094418) \| 0.121666 / 0.737135 (-0.615469) \| 0.084039 / 0.296338 (-0.212300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298775 / 0.215209 (0.083566) \| 2.973898 / 2.077655 (0.896244) \| 1.614436 / 1.504120 (0.110316) \| 1.476112 / 1.541195 (-0.065083) \| 1.502031 / 1.468490 (0.033541) \| 0.580626 / 4.584777 (-4.004151) \| 2.493428 / 3.745712 (-1.252285) \| 2.931050 / 5.269862 (-2.338811) \| 1.823603 / 4.565676 (-2.742073) \| 0.064736 / 0.424275 (-0.359539) \| 0.004963 / 0.007607 (-0.002644) \| 0.355096 / 0.226044 (0.129052) \| 3.522801 / 2.268929 (1.253872) \| 1.968690 / 55.444624 (-53.475935) \| 1.698624 / 6.876477 (-5.177853) \| 1.714166 / 2.142072 (-0.427906) \| 0.681734 / 4.805227 (-4.123493) \| 0.118940 / 6.500664 (-6.381724) \| 0.041960 / 0.075469 (-0.033509) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985311 / 1.841788 (-0.856476) \| 12.785393 / 8.074308 (4.711085) \| 11.289459 / 10.191392 (1.098067) \| 0.145297 / 0.680424 (-0.535127) \| 0.016125 / 0.534201 (-0.518076) \| 0.289445 / 0.579283 (-0.289838) \| 0.278974 / 0.434364 (-0.155390) \| 0.322456 / 0.540337 (-0.217881) \| 0.418218 / 1.386936 (-0.968718) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#66cef090c55d3561412468d94cb545b47fb000fb \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005142 / 0.011353 (-0.006211) \| 0.004180 / 0.011008 (-0.006829) \| 0.062647 / 0.038508 (0.024139) \| 0.055072 / 0.023109 (0.031962) \| 0.254681 / 0.275898 (-0.021217) \| 0.282650 / 0.323480 (-0.040830) \| 0.003950 / 0.007986 (-0.004035) \| 0.002862 / 0.004328 (-0.001466) \| 0.048420 / 0.004250 (0.044170) \| 0.038447 / 0.037052 (0.001394) \| 0.258160 / 0.258489 (-0.000329) \| 0.288596 / 0.293841 (-0.005245) \| 0.027898 / 0.128546 (-0.100648) \| 0.011165 / 0.075646 (-0.064482) \| 0.206844 / 0.419271 (-0.212427) \| 0.036312 / 0.043533 (-0.007221) \| 0.257957 / 0.255139 (0.002819) \| 0.277387 / 0.283200 (-0.005812) \| 0.018205 / 0.141683 (-0.123478) \| 1.109870 / 1.452155 (-0.342284) \| 1.175005 / 1.492716 (-0.317712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096692 / 0.018006 (0.078686) \| 0.307463 / 0.000490 (0.306973) \| 0.000218 / 0.000200 (0.000018) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018602 / 0.037411 (-0.018809) \| 0.061489 / 0.014526 (0.046964) \| 0.072936 / 0.176557 (-0.103620) \| 0.119863 / 0.737135 (-0.617272) \| 0.073983 / 0.296338 (-0.222355) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291444 / 0.215209 (0.076235) \| 2.849024 / 2.077655 (0.771369) \| 1.533121 / 1.504120 (0.029001) \| 1.402148 / 1.541195 (-0.139046) \| 1.406397 / 1.468490 (-0.062094) \| 0.564241 / 4.584777 (-4.020536) \| 2.402052 / 3.745712 (-1.343660) \| 2.772639 / 5.269862 (-2.497223) \| 1.732342 / 4.565676 (-2.833334) \| 0.062361 / 0.424275 (-0.361914) \| 0.004945 / 0.007607 (-0.002662) \| 0.355841 / 0.226044 (0.129797) \| 3.426931 / 2.268929 (1.158003) \| 1.865412 / 55.444624 (-53.579212) \| 1.592628 / 6.876477 (-5.283849) \| 1.662364 / 2.142072 (-0.479708) \| 0.653278 / 4.805227 (-4.151949) \| 0.118626 / 6.500664 (-6.382038) \| 0.042961 / 0.075469 (-0.032508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956279 / 1.841788 (-0.885509) \| 11.635540 / 8.074308 (3.561232) \| 10.719590 / 10.191392 (0.528198) \| 0.130015 / 0.680424 (-0.550409) \| 0.014424 / 0.534201 (-0.519777) \| 0.288135 / 0.579283 (-0.291148) \| 0.270819 / 0.434364 (-0.163545) \| 0.320238 / 0.540337 (-0.220099) \| 0.421044 / 1.386936 (-0.965892) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005201 / 0.011353 (-0.006152) \| 0.003467 / 0.011008 (-0.007541) \| 0.048939 / 0.038508 (0.010431) \| 0.051841 / 0.023109 (0.028732) \| 0.273708 / 0.275898 (-0.002190) \| 0.293491 / 0.323480 (-0.029988) \| 0.004830 / 0.007986 (-0.003156) \| 0.002696 / 0.004328 (-0.001632) \| 0.047727 / 0.004250 (0.043476) \| 0.041319 / 0.037052 (0.004266) \| 0.273837 / 0.258489 (0.015348) \| 0.309860 / 0.293841 (0.016019) \| 0.029054 / 0.128546 (-0.099492) \| 0.010410 / 0.075646 (-0.065237) \| 0.058139 / 0.419271 (-0.361133) \| 0.032682 / 0.043533 (-0.010850) \| 0.273244 / 0.255139 (0.018105) \| 0.291579 / 0.283200 (0.008380) \| 0.018262 / 0.141683 (-0.123421) \| 1.144590 / 1.452155 (-0.307565) \| 1.202474 / 1.492716 (-0.290243) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097110 / 0.018006 (0.079104) \| 0.307344 / 0.000490 (0.306854) \| 0.000229 / 0.000200 (0.000029) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022263 / 0.037411 (-0.015148) \| 0.070140 / 0.014526 (0.055614) \| 0.081251 / 0.176557 (-0.095306) \| 0.120839 / 0.737135 (-0.616297) \| 0.083312 / 0.296338 (-0.213026) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297381 / 0.215209 (0.082172) \| 2.895530 / 2.077655 (0.817875) \| 1.608442 / 1.504120 (0.104322) \| 1.476237 / 1.541195 (-0.064958) \| 1.491306 / 1.468490 (0.022816) \| 0.567272 / 4.584777 (-4.017505) \| 2.463543 / 3.745712 (-1.282170) \| 2.814764 / 5.269862 (-2.455098) \| 1.725845 / 4.565676 (-2.839831) \| 0.064149 / 0.424275 (-0.360126) \| 0.004953 / 0.007607 (-0.002654) \| 0.359629 / 0.226044 (0.133585) \| 3.482414 / 2.268929 (1.213486) \| 1.949897 / 55.444624 (-53.494727) \| 1.677383 / 6.876477 (-5.199094) \| 1.683655 / 2.142072 (-0.458418) \| 0.645671 / 4.805227 (-4.159557) \| 0.115612 / 6.500664 (-6.385053) \| 0.041013 / 0.075469 (-0.034456) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967843 / 1.841788 (-0.873945) \| 12.376877 / 8.074308 (4.302569) \| 10.988174 / 10.191392 (0.796782) \| 0.134660 / 0.680424 (-0.545764) \| 0.015801 / 0.534201 (-0.518400) \| 0.288699 / 0.579283 (-0.290584) \| 0.284887 / 0.434364 (-0.149477) \| 0.322000 / 0.540337 (-0.218337) \| 0.412360 / 1.386936 (-0.974576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#148454d48b7c36507a283217c7c0e3bcc0539f75 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005407 / 0.011353 (-0.005946) \| 0.003496 / 0.011008 (-0.007512) \| 0.062730 / 0.038508 (0.024222) \| 0.051882 / 0.023109 (0.028773) \| 0.244766 / 0.275898 (-0.031132) \| 0.257963 / 0.323480 (-0.065516) \| 0.002894 / 0.007986 (-0.005092) \| 0.002567 / 0.004328 (-0.001761) \| 0.048756 / 0.004250 (0.044506) \| 0.039024 / 0.037052 (0.001971) \| 0.247303 / 0.258489 (-0.011186) \| 0.278341 / 0.293841 (-0.015500) \| 0.026725 / 0.128546 (-0.101821) \| 0.010577 / 0.075646 (-0.065069) \| 0.210483 / 0.419271 (-0.208789) \| 0.035230 / 0.043533 (-0.008303) \| 0.246125 / 0.255139 (-0.009014) \| 0.264039 / 0.283200 (-0.019160) \| 0.019881 / 0.141683 (-0.121802) \| 1.113475 / 1.452155 (-0.338679) \| 1.149606 / 1.492716 (-0.343110) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092946 / 0.018006 (0.074940) \| 0.299985 / 0.000490 (0.299495) \| 0.000215 / 0.000200 (0.000016) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018421 / 0.037411 (-0.018991) \| 0.060531 / 0.014526 (0.046005) \| 0.074459 / 0.176557 (-0.102098) \| 0.120369 / 0.737135 (-0.616766) \| 0.075505 / 0.296338 (-0.220833) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289497 / 0.215209 (0.074288) \| 2.783139 / 2.077655 (0.705485) \| 1.482533 / 1.504120 (-0.021587) \| 1.371013 / 1.541195 (-0.170182) \| 1.379114 / 1.468490 (-0.089376) \| 0.563953 / 4.584777 (-4.020824) \| 2.389996 / 3.745712 (-1.355716) \| 2.788067 / 5.269862 (-2.481795) \| 1.751772 / 4.565676 (-2.813904) \| 0.062680 / 0.424275 (-0.361595) \| 0.004901 / 0.007607 (-0.002706) \| 0.365193 / 0.226044 (0.139149) \| 3.389181 / 2.268929 (1.120252) \| 1.861659 / 55.444624 (-53.582965) \| 1.558899 / 6.876477 (-5.317577) \| 1.591079 / 2.142072 (-0.550993) \| 0.648300 / 4.805227 (-4.156927) \| 0.117486 / 6.500664 (-6.383178) \| 0.041961 / 0.075469 (-0.033508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944391 / 1.841788 (-0.897396) \| 11.500823 / 8.074308 (3.426515) \| 10.580430 / 10.191392 (0.389038) \| 0.142845 / 0.680424 (-0.537579) \| 0.014305 / 0.534201 (-0.519896) \| 0.290723 / 0.579283 (-0.288560) \| 0.266206 / 0.434364 (-0.168158) \| 0.325482 / 0.540337 (-0.214856) \| 0.416224 / 1.386936 (-0.970712) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005363 / 0.011353 (-0.005990) \| 0.003548 / 0.011008 (-0.007460) \| 0.048704 / 0.038508 (0.010196) \| 0.051025 / 0.023109 (0.027916) \| 0.273037 / 0.275898 (-0.002861) \| 0.297148 / 0.323480 (-0.026332) \| 0.003985 / 0.007986 (-0.004001) \| 0.002739 / 0.004328 (-0.001590) \| 0.048108 / 0.004250 (0.043857) \| 0.040244 / 0.037052 (0.003191) \| 0.277825 / 0.258489 (0.019336) \| 0.303704 / 0.293841 (0.009863) \| 0.029460 / 0.128546 (-0.099086) \| 0.010428 / 0.075646 (-0.065218) \| 0.057022 / 0.419271 (-0.362249) \| 0.032711 / 0.043533 (-0.010822) \| 0.274462 / 0.255139 (0.019323) \| 0.293499 / 0.283200 (0.010299) \| 0.018266 / 0.141683 (-0.123417) \| 1.158049 / 1.452155 (-0.294106) \| 1.170097 / 1.492716 (-0.322620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093412 / 0.018006 (0.075406) \| 0.301538 / 0.000490 (0.301049) \| 0.000222 / 0.000200 (0.000022) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021698 / 0.037411 (-0.015713) \| 0.068735 / 0.014526 (0.054209) \| 0.083010 / 0.176557 (-0.093546) \| 0.127491 / 0.737135 (-0.609644) \| 0.083005 / 0.296338 (-0.213333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298299 / 0.215209 (0.083090) \| 2.894209 / 2.077655 (0.816554) \| 1.597455 / 1.504120 (0.093335) \| 1.472953 / 1.541195 (-0.068241) \| 1.491553 / 1.468490 (0.023063) \| 0.556566 / 4.584777 (-4.028211) \| 2.419429 / 3.745712 (-1.326283) \| 2.788706 / 5.269862 (-2.481156) \| 1.759888 / 4.565676 (-2.805789) \| 0.062535 / 0.424275 (-0.361740) \| 0.004959 / 0.007607 (-0.002648) \| 0.345226 / 0.226044 (0.119182) \| 3.438539 / 2.268929 (1.169611) \| 1.943842 / 55.444624 (-53.500782) \| 1.661080 / 6.876477 (-5.215397) \| 1.687632 / 2.142072 (-0.454440) \| 0.639971 / 4.805227 (-4.165256) \| 0.116012 / 6.500664 (-6.384652) \| 0.041723 / 0.075469 (-0.033746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965143 / 1.841788 (-0.876645) \| 12.086547 / 8.074308 (4.012238) \| 10.708787 / 10.191392 (0.517395) \| 0.129506 / 0.680424 (-0.550918) \| 0.015254 / 0.534201 (-0.518947) \| 0.288326 / 0.579283 (-0.290957) \| 0.271976 / 0.434364 (-0.162388) \| 0.328402 / 0.540337 (-0.211936) \| 0.418102 / 1.386936 (-0.968834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b6f13ede3dccedf335bb2d8ff04db306dc710a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005375 / 0.011353 (-0.005978) \| 0.003530 / 0.011008 (-0.007478) \| 0.062521 / 0.038508 (0.024013) \| 0.051514 / 0.023109 (0.028405) \| 0.241623 / 0.275898 (-0.034275) \| 0.269054 / 0.323480 (-0.054426) \| 0.002877 / 0.007986 (-0.005109) \| 0.002724 / 0.004328 (-0.001605) \| 0.049045 / 0.004250 (0.044794) \| 0.038560 / 0.037052 (0.001507) \| 0.248437 / 0.258489 (-0.010052) \| 0.276762 / 0.293841 (-0.017079) \| 0.027522 / 0.128546 (-0.101024) \| 0.010817 / 0.075646 (-0.064829) \| 0.208686 / 0.419271 (-0.210585) \| 0.035818 / 0.043533 (-0.007715) \| 0.249398 / 0.255139 (-0.005741) \| 0.268288 / 0.283200 (-0.014911) \| 0.019039 / 0.141683 (-0.122644) \| 1.135115 / 1.452155 (-0.317040) \| 1.195531 / 1.492716 (-0.297185) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093126 / 0.018006 (0.075120) \| 0.301028 / 0.000490 (0.300539) \| 0.000222 / 0.000200 (0.000023) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018385 / 0.037411 (-0.019027) \| 0.060902 / 0.014526 (0.046376) \| 0.073168 / 0.176557 (-0.103389) \| 0.119216 / 0.737135 (-0.617919) \| 0.074225 / 0.296338 (-0.222114) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283749 / 0.215209 (0.068540) \| 2.741609 / 2.077655 (0.663954) \| 1.483439 / 1.504120 (-0.020681) \| 1.352896 / 1.541195 (-0.188299) \| 1.378824 / 1.468490 (-0.089667) \| 0.548731 / 4.584777 (-4.036046) \| 2.342717 / 3.745712 (-1.402995) \| 2.791592 / 5.269862 (-2.478269) \| 1.740605 / 4.565676 (-2.825071) \| 0.062059 / 0.424275 (-0.362216) \| 0.005028 / 0.007607 (-0.002579) \| 0.339205 / 0.226044 (0.113161) \| 3.353386 / 2.268929 (1.084458) \| 1.785717 / 55.444624 (-53.658907) \| 1.523390 / 6.876477 (-5.353086) \| 1.556999 / 2.142072 (-0.585073) \| 0.636745 / 4.805227 (-4.168483) \| 0.115821 / 6.500664 (-6.384843) \| 0.042200 / 0.075469 (-0.033269) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948678 / 1.841788 (-0.893110) \| 11.588670 / 8.074308 (3.514362) \| 10.897130 / 10.191392 (0.705738) \| 0.140068 / 0.680424 (-0.540356) \| 0.014565 / 0.534201 (-0.519636) \| 0.286336 / 0.579283 (-0.292947) \| 0.265292 / 0.434364 (-0.169072) \| 0.324146 / 0.540337 (-0.216192) \| 0.413463 / 1.386936 (-0.973473) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005187 / 0.011353 (-0.006165) \| 0.003471 / 0.011008 (-0.007537) \| 0.048968 / 0.038508 (0.010460) \| 0.051285 / 0.023109 (0.028176) \| 0.283286 / 0.275898 (0.007388) \| 0.307046 / 0.323480 (-0.016434) \| 0.004017 / 0.007986 (-0.003969) \| 0.002655 / 0.004328 (-0.001673) \| 0.047762 / 0.004250 (0.043512) \| 0.039855 / 0.037052 (0.002803) \| 0.283101 / 0.258489 (0.024612) \| 0.312905 / 0.293841 (0.019064) \| 0.028188 / 0.128546 (-0.100358) \| 0.010849 / 0.075646 (-0.064797) \| 0.058112 / 0.419271 (-0.361159) \| 0.032163 / 0.043533 (-0.011369) \| 0.280825 / 0.255139 (0.025686) \| 0.300946 / 0.283200 (0.017747) \| 0.017409 / 0.141683 (-0.124274) \| 1.127360 / 1.452155 (-0.324795) \| 1.180409 / 1.492716 (-0.312307) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093186 / 0.018006 (0.075180) \| 0.300827 / 0.000490 (0.300338) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021560 / 0.037411 (-0.015851) \| 0.069158 / 0.014526 (0.054632) \| 0.080953 / 0.176557 (-0.095603) \| 0.119071 / 0.737135 (-0.618064) \| 0.082817 / 0.296338 (-0.213521) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307259 / 0.215209 (0.092050) \| 2.996058 / 2.077655 (0.918404) \| 1.627406 / 1.504120 (0.123286) \| 1.500715 / 1.541195 (-0.040480) \| 1.524278 / 1.468490 (0.055788) \| 0.569711 / 4.584777 (-4.015066) \| 2.436132 / 3.745712 (-1.309580) \| 2.796995 / 5.269862 (-2.472866) \| 1.760701 / 4.565676 (-2.804975) \| 0.063521 / 0.424275 (-0.360754) \| 0.004909 / 0.007607 (-0.002698) \| 0.359129 / 0.226044 (0.133085) \| 3.567278 / 2.268929 (1.298349) \| 2.013821 / 55.444624 (-53.430804) \| 1.708021 / 6.876477 (-5.168456) \| 1.738959 / 2.142072 (-0.403114) \| 0.648620 / 4.805227 (-4.156607) \| 0.122016 / 6.500664 (-6.378648) \| 0.041802 / 0.075469 (-0.033667) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985208 / 1.841788 (-0.856579) \| 12.307785 / 8.074308 (4.233477) \| 10.587262 / 10.191392 (0.395870) \| 0.130468 / 0.680424 (-0.549956) \| 0.014912 / 0.534201 (-0.519289) \| 0.293822 / 0.579283 (-0.285461) \| 0.283021 / 0.434364 (-0.151343) \| 0.329560 / 0.540337 (-0.210777) \| 0.424741 / 1.386936 (-0.962195) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04426d9c8e0aa5c97af2826064287f8cab6bece0 \"CML watermark\")\n" ]	2023-11-27T20:01:25Z	2023-11-28T16:29:58Z	2023-11-28T16:29:31Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6454.diff", "html_url": "https://github.com/huggingface/datasets/pull/6454", "merged_at": "2023-11-28T16:29:31Z", "patch_url": "https://github.com/huggingface/datasets/pull/6454.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6454" }	Refactor the `dill` logic to make it easier to maintain (and fix some issues along the way) It makes the following improvements to the serialization API: * consistent order of a `dict`'s keys * support for hashing `torch.compile`-ed modules and functions * deprecates `datasets.fingerprint.hashregister` as the `hashregister`-ed reducers are never invoked anyways (does not support nested data as `pickle`/`dill` do) ~~TODO: optimize hashing of `pa.Table` and `datasets.table.Table`~~ The `pa_array.to_string` approach is faster for large arrays because it outputs the first 10 and last 10 elements (by default). The problem is that this can produce identical hashes for non-identical arrays if their differing elements get ellipsed... Fix https://github.com/huggingface/datasets/issues/6440, fix https://github.com/huggingface/datasets/issues/5839	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6454/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6453	6,453	Update hub-docs reference	{ "avatar_url": "https://avatars.githubusercontent.com/u/11827707?v=4", "events_url": "https://api.github.com/users/mishig25/events{/privacy}", "followers_url": "https://api.github.com/users/mishig25/followers", "following_url": "https://api.github.com/users/mishig25/following{/other_user}", "gists_url": "https://api.github.com/users/mishig25/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mishig25", "id": 11827707, "login": "mishig25", "node_id": "MDQ6VXNlcjExODI3NzA3", "organizations_url": "https://api.github.com/users/mishig25/orgs", "received_events_url": "https://api.github.com/users/mishig25/received_events", "repos_url": "https://api.github.com/users/mishig25/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mishig25/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mishig25/subscriptions", "type": "User", "url": "https://api.github.com/users/mishig25", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005119 / 0.011353 (-0.006234) \| 0.003469 / 0.011008 (-0.007540) \| 0.061791 / 0.038508 (0.023283) \| 0.051655 / 0.023109 (0.028545) \| 0.241157 / 0.275898 (-0.034741) \| 0.265930 / 0.323480 (-0.057549) \| 0.003851 / 0.007986 (-0.004134) \| 0.002412 / 0.004328 (-0.001916) \| 0.047498 / 0.004250 (0.043247) \| 0.037328 / 0.037052 (0.000276) \| 0.250418 / 0.258489 (-0.008071) \| 0.277842 / 0.293841 (-0.015999) \| 0.027626 / 0.128546 (-0.100920) \| 0.009947 / 0.075646 (-0.065699) \| 0.204549 / 0.419271 (-0.214722) \| 0.037546 / 0.043533 (-0.005987) \| 0.245383 / 0.255139 (-0.009756) \| 0.263486 / 0.283200 (-0.019713) \| 0.017792 / 0.141683 (-0.123891) \| 1.158900 / 1.452155 (-0.293255) \| 1.194060 / 1.492716 (-0.298657) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090607 / 0.018006 (0.072601) \| 0.299909 / 0.000490 (0.299419) \| 0.000206 / 0.000200 (0.000006) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018814 / 0.037411 (-0.018597) \| 0.062068 / 0.014526 (0.047542) \| 0.087221 / 0.176557 (-0.089336) \| 0.119594 / 0.737135 (-0.617541) \| 0.075485 / 0.296338 (-0.220853) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286093 / 0.215209 (0.070884) \| 2.767396 / 2.077655 (0.689741) \| 1.500472 / 1.504120 (-0.003648) \| 1.389514 / 1.541195 (-0.151680) \| 1.438933 / 1.468490 (-0.029557) \| 0.562545 / 4.584777 (-4.022232) \| 2.383330 / 3.745712 (-1.362382) \| 2.799215 / 5.269862 (-2.470647) \| 1.732618 / 4.565676 (-2.833058) \| 0.061282 / 0.424275 (-0.362993) \| 0.005007 / 0.007607 (-0.002601) \| 0.339769 / 0.226044 (0.113725) \| 3.337146 / 2.268929 (1.068218) \| 1.890789 / 55.444624 (-53.553836) \| 1.593555 / 6.876477 (-5.282922) \| 1.660016 / 2.142072 (-0.482057) \| 0.632452 / 4.805227 (-4.172775) \| 0.115503 / 6.500664 (-6.385161) \| 0.041590 / 0.075469 (-0.033880) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941966 / 1.841788 (-0.899822) \| 11.470271 / 8.074308 (3.395963) \| 10.579454 / 10.191392 (0.388062) \| 0.140970 / 0.680424 (-0.539454) \| 0.014057 / 0.534201 (-0.520144) \| 0.289326 / 0.579283 (-0.289957) \| 0.265366 / 0.434364 (-0.168998) \| 0.324612 / 0.540337 (-0.215726) \| 0.415832 / 1.386936 (-0.971104) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005208 / 0.011353 (-0.006145) \| 0.003199 / 0.011008 (-0.007809) \| 0.048299 / 0.038508 (0.009791) \| 0.050727 / 0.023109 (0.027618) \| 0.274897 / 0.275898 (-0.001001) \| 0.298328 / 0.323480 (-0.025152) \| 0.003989 / 0.007986 (-0.003997) \| 0.002439 / 0.004328 (-0.001890) \| 0.047308 / 0.004250 (0.043058) \| 0.039726 / 0.037052 (0.002673) \| 0.276279 / 0.258489 (0.017790) \| 0.303679 / 0.293841 (0.009838) \| 0.028943 / 0.128546 (-0.099603) \| 0.010223 / 0.075646 (-0.065423) \| 0.056694 / 0.419271 (-0.362577) \| 0.032283 / 0.043533 (-0.011250) \| 0.275344 / 0.255139 (0.020205) \| 0.296358 / 0.283200 (0.013158) \| 0.017481 / 0.141683 (-0.124201) \| 1.131063 / 1.452155 (-0.321092) \| 1.181146 / 1.492716 (-0.311570) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092259 / 0.018006 (0.074253) \| 0.299381 / 0.000490 (0.298891) \| 0.000216 / 0.000200 (0.000016) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021693 / 0.037411 (-0.015718) \| 0.070441 / 0.014526 (0.055916) \| 0.080648 / 0.176557 (-0.095908) \| 0.119002 / 0.737135 (-0.618133) \| 0.081412 / 0.296338 (-0.214926) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296475 / 0.215209 (0.081266) \| 2.905098 / 2.077655 (0.827443) \| 1.596321 / 1.504120 (0.092201) \| 1.472640 / 1.541195 (-0.068555) \| 1.484453 / 1.468490 (0.015963) \| 0.565229 / 4.584777 (-4.019548) \| 2.390631 / 3.745712 (-1.355081) \| 2.765125 / 5.269862 (-2.504737) \| 1.738993 / 4.565676 (-2.826683) \| 0.063034 / 0.424275 (-0.361241) \| 0.004891 / 0.007607 (-0.002716) \| 0.350678 / 0.226044 (0.124633) \| 3.530919 / 2.268929 (1.261990) \| 1.943758 / 55.444624 (-53.500867) \| 1.665553 / 6.876477 (-5.210924) \| 1.656990 / 2.142072 (-0.485083) \| 0.647027 / 4.805227 (-4.158201) \| 0.116771 / 6.500664 (-6.383893) \| 0.041012 / 0.075469 (-0.034457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.034226 / 1.841788 (-0.807561) \| 12.036726 / 8.074308 (3.962418) \| 10.934239 / 10.191392 (0.742847) \| 0.130142 / 0.680424 (-0.550281) \| 0.015537 / 0.534201 (-0.518664) \| 0.286020 / 0.579283 (-0.293263) \| 0.276739 / 0.434364 (-0.157625) \| 0.326284 / 0.540337 (-0.214054) \| 0.413392 / 1.386936 (-0.973544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4787c0022c8b59c15256021478b444a6c51fa984 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005400 / 0.011353 (-0.005953) \| 0.003415 / 0.011008 (-0.007593) \| 0.062416 / 0.038508 (0.023908) \| 0.055962 / 0.023109 (0.032853) \| 0.234725 / 0.275898 (-0.041173) \| 0.261775 / 0.323480 (-0.061705) \| 0.002868 / 0.007986 (-0.005118) \| 0.002426 / 0.004328 (-0.001902) \| 0.047989 / 0.004250 (0.043738) \| 0.039214 / 0.037052 (0.002162) \| 0.246068 / 0.258489 (-0.012421) \| 0.270245 / 0.293841 (-0.023596) \| 0.027558 / 0.128546 (-0.100988) \| 0.010256 / 0.075646 (-0.065390) \| 0.210988 / 0.419271 (-0.208283) \| 0.035684 / 0.043533 (-0.007849) \| 0.245254 / 0.255139 (-0.009885) \| 0.255476 / 0.283200 (-0.027724) \| 0.018495 / 0.141683 (-0.123188) \| 1.115458 / 1.452155 (-0.336697) \| 1.166149 / 1.492716 (-0.326567) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092736 / 0.018006 (0.074730) \| 0.301040 / 0.000490 (0.300550) \| 0.000213 / 0.000200 (0.000013) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018607 / 0.037411 (-0.018805) \| 0.062189 / 0.014526 (0.047664) \| 0.073782 / 0.176557 (-0.102775) \| 0.119895 / 0.737135 (-0.617240) \| 0.074907 / 0.296338 (-0.221431) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283986 / 0.215209 (0.068777) \| 2.824498 / 2.077655 (0.746844) \| 1.505848 / 1.504120 (0.001728) \| 1.358879 / 1.541195 (-0.182316) \| 1.357087 / 1.468490 (-0.111403) \| 0.574307 / 4.584777 (-4.010470) \| 2.416478 / 3.745712 (-1.329234) \| 2.772909 / 5.269862 (-2.496953) \| 1.750395 / 4.565676 (-2.815282) \| 0.062465 / 0.424275 (-0.361810) \| 0.004983 / 0.007607 (-0.002624) \| 0.344490 / 0.226044 (0.118445) \| 3.405062 / 2.268929 (1.136134) \| 1.854972 / 55.444624 (-53.589653) \| 1.572789 / 6.876477 (-5.303687) \| 1.586109 / 2.142072 (-0.555963) \| 0.647431 / 4.805227 (-4.157797) \| 0.123079 / 6.500664 (-6.377585) \| 0.042766 / 0.075469 (-0.032703) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950493 / 1.841788 (-0.891295) \| 11.814821 / 8.074308 (3.740513) \| 10.494768 / 10.191392 (0.303376) \| 0.131322 / 0.680424 (-0.549102) \| 0.015253 / 0.534201 (-0.518948) \| 0.287405 / 0.579283 (-0.291878) \| 0.269664 / 0.434364 (-0.164699) \| 0.322700 / 0.540337 (-0.217637) \| 0.424103 / 1.386936 (-0.962833) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005264 / 0.011353 (-0.006088) \| 0.003304 / 0.011008 (-0.007704) \| 0.048531 / 0.038508 (0.010023) \| 0.052752 / 0.023109 (0.029643) \| 0.274435 / 0.275898 (-0.001463) \| 0.297500 / 0.323480 (-0.025980) \| 0.003977 / 0.007986 (-0.004009) \| 0.002444 / 0.004328 (-0.001884) \| 0.048464 / 0.004250 (0.044214) \| 0.040192 / 0.037052 (0.003139) \| 0.278256 / 0.258489 (0.019767) \| 0.303627 / 0.293841 (0.009786) \| 0.028709 / 0.128546 (-0.099837) \| 0.010530 / 0.075646 (-0.065117) \| 0.057427 / 0.419271 (-0.361844) \| 0.032539 / 0.043533 (-0.010994) \| 0.272237 / 0.255139 (0.017098) \| 0.295288 / 0.283200 (0.012088) \| 0.018820 / 0.141683 (-0.122862) \| 1.116100 / 1.452155 (-0.336055) \| 1.180124 / 1.492716 (-0.312592) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092651 / 0.018006 (0.074644) \| 0.301481 / 0.000490 (0.300991) \| 0.000217 / 0.000200 (0.000017) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022461 / 0.037411 (-0.014951) \| 0.070623 / 0.014526 (0.056097) \| 0.082642 / 0.176557 (-0.093915) \| 0.120021 / 0.737135 (-0.617114) \| 0.083387 / 0.296338 (-0.212952) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291451 / 0.215209 (0.076242) \| 2.865602 / 2.077655 (0.787947) \| 1.592051 / 1.504120 (0.087931) \| 1.463521 / 1.541195 (-0.077673) \| 1.498899 / 1.468490 (0.030409) \| 0.570854 / 4.584777 (-4.013923) \| 2.410002 / 3.745712 (-1.335710) \| 2.768028 / 5.269862 (-2.501834) \| 1.740463 / 4.565676 (-2.825214) \| 0.063801 / 0.424275 (-0.360474) \| 0.005019 / 0.007607 (-0.002588) \| 0.348353 / 0.226044 (0.122309) \| 3.425793 / 2.268929 (1.156864) \| 1.957294 / 55.444624 (-53.487331) \| 1.696121 / 6.876477 (-5.180355) \| 1.691544 / 2.142072 (-0.450528) \| 0.645528 / 4.805227 (-4.159700) \| 0.118876 / 6.500664 (-6.381788) \| 0.041001 / 0.075469 (-0.034469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983805 / 1.841788 (-0.857983) \| 12.085909 / 8.074308 (4.011600) \| 10.835395 / 10.191392 (0.644003) \| 0.141971 / 0.680424 (-0.538453) \| 0.015534 / 0.534201 (-0.518667) \| 0.289289 / 0.579283 (-0.289994) \| 0.276316 / 0.434364 (-0.158048) \| 0.354577 / 0.540337 (-0.185761) \| 0.421824 / 1.386936 (-0.965112) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#27d1fe52857c6a25a29cac63a296405136b2797c \"CML watermark\")\n" ]	2023-11-27T09:57:20Z	2023-11-27T10:23:44Z	2023-11-27T10:17:34Z	NONE	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6453.diff", "html_url": "https://github.com/huggingface/datasets/pull/6453", "merged_at": "2023-11-27T10:17:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/6453.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6453" }	Follow up to huggingface/huggingface.js#296	{ "avatar_url": "https://avatars.githubusercontent.com/u/11827707?v=4", "events_url": "https://api.github.com/users/mishig25/events{/privacy}", "followers_url": "https://api.github.com/users/mishig25/followers", "following_url": "https://api.github.com/users/mishig25/following{/other_user}", "gists_url": "https://api.github.com/users/mishig25/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mishig25", "id": 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https://github.com/huggingface/datasets/pull/6452	6,452	Praveen_repo_pull_req	{ "avatar_url": "https://avatars.githubusercontent.com/u/151713216?v=4", "events_url": "https://api.github.com/users/Praveenhh/events{/privacy}", "followers_url": "https://api.github.com/users/Praveenhh/followers", "following_url": "https://api.github.com/users/Praveenhh/following{/other_user}", "gists_url": "https://api.github.com/users/Praveenhh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Praveenhh", "id": 151713216, "login": "Praveenhh", "node_id": "U_kgDOCQr1wA", "organizations_url": "https://api.github.com/users/Praveenhh/orgs", "received_events_url": "https://api.github.com/users/Praveenhh/received_events", "repos_url": "https://api.github.com/users/Praveenhh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Praveenhh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Praveenhh/subscriptions", "type": "User", "url": "https://api.github.com/users/Praveenhh", "user_view_type": "public" }	[]	closed	false	[]	2023-11-27T07:07:50Z	2023-11-27T09:28:00Z	2023-11-27T09:28:00Z	NONE	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6452.diff", "html_url": "https://github.com/huggingface/datasets/pull/6452", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6452.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6452" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6452/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6451	6,451	Unable to read "marsyas/gtzan" data	{ "avatar_url": "https://avatars.githubusercontent.com/u/32300890?v=4", "events_url": "https://api.github.com/users/gerald-wrona/events{/privacy}", "followers_url": "https://api.github.com/users/gerald-wrona/followers", "following_url": "https://api.github.com/users/gerald-wrona/following{/other_user}", "gists_url": "https://api.github.com/users/gerald-wrona/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/gerald-wrona", "id": 32300890, "login": "gerald-wrona", "node_id": "MDQ6VXNlcjMyMzAwODkw", "organizations_url": "https://api.github.com/users/gerald-wrona/orgs", "received_events_url": "https://api.github.com/users/gerald-wrona/received_events", "repos_url": "https://api.github.com/users/gerald-wrona/repos", "site_admin": false, "starred_url": "https://api.github.com/users/gerald-wrona/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/gerald-wrona/subscriptions", "type": "User", "url": "https://api.github.com/users/gerald-wrona", "user_view_type": "public" }	[]	closed	false	[ "Hi! We've merged a [PR](https://huggingface.co/datasets/marsyas/gtzan/discussions/1) that fixes the script's path logic on Windows.", "I have transferred the discussion to the corresponding dataset: https://huggingface.co/datasets/marsyas/gtzan/discussions/2\r\n\r\nLet's continue there.", "@mariosasko @albertvillanova \r\n\r\nThank you both very much for the speedy resolution :)" ]	2023-11-25T15:13:17Z	2023-12-01T12:53:46Z	2023-11-27T09:36:25Z	NONE	null	null	Hi, this is my code and the error: ``` from datasets import load_dataset gtzan = load_dataset("marsyas/gtzan", "all") ``` [error_trace.txt](https://github.com/huggingface/datasets/files/13464397/error_trace.txt) [audio_yml.txt](https://github.com/huggingface/datasets/files/13464410/audio_yml.txt) Python 3.11.5 Jupyter Notebook 6.5.4 Windows 10 I'm able to download and work with other datasets, but not this one. For example, both these below work fine: ``` from datasets import load_dataset dataset = load_dataset("facebook/voxpopuli", "pl", split="train", streaming=True) minds = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` Thanks for your help https://huggingface.co/datasets/marsyas/gtzan/tree/main	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6451/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6450	6,450	Support multiple image/audio columns in ImageFolder/AudioFolder	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "cfd3d7", "default": true, "description": "This issue or pull request already exists", "id": 1935892865, "name": "duplicate", "node_id": "MDU6TGFiZWwxOTM1ODkyODY1", "url": "https://api.github.com/repos/huggingface/datasets/labels/duplicate" }, { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	[ "A duplicate of https://github.com/huggingface/datasets/issues/5760" ]	2023-11-24T10:34:09Z	2023-11-28T11:07:17Z	2023-11-24T17:24:38Z	COLLABORATOR	null	null	### Feature request Have a metadata.csv file with multiple columns that point to relative image or audio files. ### Motivation Currently, ImageFolder allows one column, called `file_name`, pointing to relative image files. On the same model, AudioFolder allows one column, called `file_name`, pointing to relative audio files. But it's not possible to have two image columns, or to have two audio column, or to have one audio column and one image column. ### Your contribution no specific contribution	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6450/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6449	6,449	Fix metadata file resolution when inferred pattern is `**`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005551 / 0.011353 (-0.005802) \| 0.003297 / 0.011008 (-0.007711) \| 0.062524 / 0.038508 (0.024016) \| 0.058467 / 0.023109 (0.035358) \| 0.255703 / 0.275898 (-0.020195) \| 0.281420 / 0.323480 (-0.042060) \| 0.003857 / 0.007986 (-0.004129) \| 0.002460 / 0.004328 (-0.001868) \| 0.047762 / 0.004250 (0.043512) \| 0.038757 / 0.037052 (0.001705) \| 0.259937 / 0.258489 (0.001448) \| 0.290050 / 0.293841 (-0.003791) \| 0.028433 / 0.128546 (-0.100113) \| 0.010422 / 0.075646 (-0.065224) \| 0.207135 / 0.419271 (-0.212136) \| 0.036004 / 0.043533 (-0.007529) \| 0.268137 / 0.255139 (0.012998) \| 0.275020 / 0.283200 (-0.008179) \| 0.018301 / 0.141683 (-0.123382) \| 1.095479 / 1.452155 (-0.356676) \| 1.145452 / 1.492716 (-0.347265) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092046 / 0.018006 (0.074040) \| 0.299784 / 0.000490 (0.299294) \| 0.000214 / 0.000200 (0.000014) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019071 / 0.037411 (-0.018340) \| 0.072836 / 0.014526 (0.058310) \| 0.073974 / 0.176557 (-0.102583) \| 0.120903 / 0.737135 (-0.616232) \| 0.075740 / 0.296338 (-0.220599) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276365 / 0.215209 (0.061156) \| 2.671217 / 2.077655 (0.593563) \| 1.438862 / 1.504120 (-0.065258) \| 1.327348 / 1.541195 (-0.213847) \| 1.349514 / 1.468490 (-0.118976) \| 0.548793 / 4.584777 (-4.035984) \| 2.364458 / 3.745712 (-1.381255) \| 2.716205 / 5.269862 (-2.553657) \| 1.735714 / 4.565676 (-2.829963) \| 0.061140 / 0.424275 (-0.363135) \| 0.004926 / 0.007607 (-0.002681) \| 0.330449 / 0.226044 (0.104404) \| 3.255243 / 2.268929 (0.986315) \| 1.824254 / 55.444624 (-53.620371) \| 1.540262 / 6.876477 (-5.336215) \| 1.535632 / 2.142072 (-0.606441) \| 0.635224 / 4.805227 (-4.170003) \| 0.116230 / 6.500664 (-6.384435) \| 0.042706 / 0.075469 (-0.032763) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948796 / 1.841788 (-0.892992) \| 11.448403 / 8.074308 (3.374095) \| 10.523862 / 10.191392 (0.332470) \| 0.129694 / 0.680424 (-0.550730) \| 0.014146 / 0.534201 (-0.520055) \| 0.285706 / 0.579283 (-0.293577) \| 0.262572 / 0.434364 (-0.171792) \| 0.321251 / 0.540337 (-0.219087) \| 0.417130 / 1.386936 (-0.969806) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005266 / 0.011353 (-0.006086) \| 0.003339 / 0.011008 (-0.007670) \| 0.048411 / 0.038508 (0.009903) \| 0.053951 / 0.023109 (0.030842) \| 0.271228 / 0.275898 (-0.004670) \| 0.290066 / 0.323480 (-0.033414) \| 0.004087 / 0.007986 (-0.003898) \| 0.002446 / 0.004328 (-0.001882) \| 0.047049 / 0.004250 (0.042798) \| 0.040866 / 0.037052 (0.003813) \| 0.273711 / 0.258489 (0.015222) \| 0.298192 / 0.293841 (0.004351) \| 0.029025 / 0.128546 (-0.099521) \| 0.010479 / 0.075646 (-0.065167) \| 0.056941 / 0.419271 (-0.362330) \| 0.032914 / 0.043533 (-0.010619) \| 0.270432 / 0.255139 (0.015293) \| 0.291274 / 0.283200 (0.008074) \| 0.018602 / 0.141683 (-0.123081) \| 1.136707 / 1.452155 (-0.315447) \| 1.184704 / 1.492716 (-0.308012) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090041 / 0.018006 (0.072035) \| 0.300185 / 0.000490 (0.299696) \| 0.000221 / 0.000200 (0.000022) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022074 / 0.037411 (-0.015337) \| 0.070763 / 0.014526 (0.056237) \| 0.082141 / 0.176557 (-0.094415) \| 0.120286 / 0.737135 (-0.616850) \| 0.082680 / 0.296338 (-0.213659) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292223 / 0.215209 (0.077014) \| 2.856711 / 2.077655 (0.779056) \| 1.581194 / 1.504120 (0.077075) \| 1.496567 / 1.541195 (-0.044628) \| 1.485256 / 1.468490 (0.016766) \| 0.550633 / 4.584777 (-4.034144) \| 2.420281 / 3.745712 (-1.325431) \| 2.764373 / 5.269862 (-2.505489) \| 1.735958 / 4.565676 (-2.829719) \| 0.062562 / 0.424275 (-0.361714) \| 0.004918 / 0.007607 (-0.002689) \| 0.346038 / 0.226044 (0.119994) \| 3.443478 / 2.268929 (1.174550) \| 1.949366 / 55.444624 (-53.495259) \| 1.686140 / 6.876477 (-5.190337) \| 1.683038 / 2.142072 (-0.459034) \| 0.629270 / 4.805227 (-4.175958) \| 0.114947 / 6.500664 (-6.385717) \| 0.040635 / 0.075469 (-0.034834) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969746 / 1.841788 (-0.872041) \| 11.922662 / 8.074308 (3.848354) \| 10.441432 / 10.191392 (0.250040) \| 0.128950 / 0.680424 (-0.551473) \| 0.015964 / 0.534201 (-0.518237) \| 0.289176 / 0.579283 (-0.290107) \| 0.279203 / 0.434364 (-0.155161) \| 0.323833 / 0.540337 (-0.216505) \| 0.540297 / 1.386936 (-0.846639) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ed759d0f5aea6d166caa0532aa17c209bb3af79 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005288 / 0.011353 (-0.006065) \| 0.003383 / 0.011008 (-0.007625) \| 0.061926 / 0.038508 (0.023418) \| 0.049080 / 0.023109 (0.025971) \| 0.244852 / 0.275898 (-0.031046) \| 0.263957 / 0.323480 (-0.059523) \| 0.002810 / 0.007986 (-0.005175) \| 0.002384 / 0.004328 (-0.001945) \| 0.047807 / 0.004250 (0.043556) \| 0.038374 / 0.037052 (0.001321) \| 0.244414 / 0.258489 (-0.014075) \| 0.272257 / 0.293841 (-0.021584) \| 0.027356 / 0.128546 (-0.101190) \| 0.010235 / 0.075646 (-0.065411) \| 0.214896 / 0.419271 (-0.204375) \| 0.035604 / 0.043533 (-0.007929) \| 0.246584 / 0.255139 (-0.008555) \| 0.263281 / 0.283200 (-0.019918) \| 0.019689 / 0.141683 (-0.121994) \| 1.114100 / 1.452155 (-0.338054) \| 1.177644 / 1.492716 (-0.315073) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088892 / 0.018006 (0.070886) \| 0.298128 / 0.000490 (0.297639) \| 0.000199 / 0.000200 (-0.000001) \| 0.000046 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019337 / 0.037411 (-0.018075) \| 0.062096 / 0.014526 (0.047570) \| 0.073019 / 0.176557 (-0.103537) \| 0.118801 / 0.737135 (-0.618334) \| 0.074779 / 0.296338 (-0.221559) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289892 / 0.215209 (0.074683) \| 2.824131 / 2.077655 (0.746476) \| 1.466351 / 1.504120 (-0.037768) \| 1.339528 / 1.541195 (-0.201667) \| 1.369257 / 1.468490 (-0.099233) \| 0.561175 / 4.584777 (-4.023602) \| 2.394174 / 3.745712 (-1.351538) \| 2.749668 / 5.269862 (-2.520193) \| 1.747146 / 4.565676 (-2.818530) \| 0.063054 / 0.424275 (-0.361221) \| 0.004970 / 0.007607 (-0.002637) \| 0.342985 / 0.226044 (0.116941) \| 3.334894 / 2.268929 (1.065966) \| 1.838459 / 55.444624 (-53.606165) \| 1.579755 / 6.876477 (-5.296722) \| 1.560200 / 2.142072 (-0.581872) \| 0.642643 / 4.805227 (-4.162585) \| 0.117741 / 6.500664 (-6.382923) \| 0.042440 / 0.075469 (-0.033029) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937476 / 1.841788 (-0.904312) \| 11.403556 / 8.074308 (3.329248) \| 10.317207 / 10.191392 (0.125815) \| 0.145277 / 0.680424 (-0.535147) \| 0.015297 / 0.534201 (-0.518904) \| 0.287511 / 0.579283 (-0.291772) \| 0.263516 / 0.434364 (-0.170848) \| 0.320803 / 0.540337 (-0.219534) \| 0.415580 / 1.386936 (-0.971356) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005239 / 0.011353 (-0.006114) \| 0.003506 / 0.011008 (-0.007502) \| 0.048635 / 0.038508 (0.010127) \| 0.052067 / 0.023109 (0.028957) \| 0.277526 / 0.275898 (0.001628) \| 0.300536 / 0.323480 (-0.022944) \| 0.003982 / 0.007986 (-0.004004) \| 0.002413 / 0.004328 (-0.001915) \| 0.046523 / 0.004250 (0.042273) \| 0.039383 / 0.037052 (0.002331) \| 0.281208 / 0.258489 (0.022719) \| 0.306199 / 0.293841 (0.012359) \| 0.028646 / 0.128546 (-0.099900) \| 0.010664 / 0.075646 (-0.064982) \| 0.057393 / 0.419271 (-0.361879) \| 0.032171 / 0.043533 (-0.011362) \| 0.277576 / 0.255139 (0.022437) \| 0.296039 / 0.283200 (0.012840) \| 0.017519 / 0.141683 (-0.124164) \| 1.153172 / 1.452155 (-0.298982) \| 1.180274 / 1.492716 (-0.312442) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088287 / 0.018006 (0.070280) \| 0.297922 / 0.000490 (0.297433) \| 0.000216 / 0.000200 (0.000016) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021936 / 0.037411 (-0.015475) \| 0.070181 / 0.014526 (0.055655) \| 0.082068 / 0.176557 (-0.094488) \| 0.119327 / 0.737135 (-0.617808) \| 0.083642 / 0.296338 (-0.212697) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299449 / 0.215209 (0.084240) \| 2.914362 / 2.077655 (0.836707) \| 1.611906 / 1.504120 (0.107786) \| 1.488805 / 1.541195 (-0.052390) \| 1.536010 / 1.468490 (0.067520) \| 0.566772 / 4.584777 (-4.018004) \| 2.397897 / 3.745712 (-1.347815) \| 2.786048 / 5.269862 (-2.483814) \| 1.745153 / 4.565676 (-2.820523) \| 0.063870 / 0.424275 (-0.360405) \| 0.004968 / 0.007607 (-0.002640) \| 0.344455 / 0.226044 (0.118410) \| 3.465772 / 2.268929 (1.196844) \| 1.965761 / 55.444624 (-53.478863) \| 1.687960 / 6.876477 (-5.188516) \| 1.713987 / 2.142072 (-0.428085) \| 0.643760 / 4.805227 (-4.161467) \| 0.117623 / 6.500664 (-6.383042) \| 0.041086 / 0.075469 (-0.034383) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985129 / 1.841788 (-0.856659) \| 11.986676 / 8.074308 (3.912368) \| 10.493440 / 10.191392 (0.302048) \| 0.130070 / 0.680424 (-0.550353) \| 0.015293 / 0.534201 (-0.518908) \| 0.285683 / 0.579283 (-0.293600) \| 0.275656 / 0.434364 (-0.158708) \| 0.328704 / 0.540337 (-0.211633) \| 0.537249 / 1.386936 (-0.849687) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7ee58f322082d3af5f11863d1f809444910827a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005170 / 0.011353 (-0.006183) \| 0.003267 / 0.011008 (-0.007741) \| 0.061992 / 0.038508 (0.023484) \| 0.053414 / 0.023109 (0.030305) \| 0.245678 / 0.275898 (-0.030220) \| 0.261320 / 0.323480 (-0.062160) \| 0.003887 / 0.007986 (-0.004099) \| 0.002543 / 0.004328 (-0.001786) \| 0.048496 / 0.004250 (0.044246) \| 0.037392 / 0.037052 (0.000340) \| 0.243728 / 0.258489 (-0.014761) \| 0.272524 / 0.293841 (-0.021317) \| 0.027578 / 0.128546 (-0.100968) \| 0.010530 / 0.075646 (-0.065116) \| 0.206014 / 0.419271 (-0.213257) \| 0.035987 / 0.043533 (-0.007546) \| 0.243544 / 0.255139 (-0.011595) \| 0.263872 / 0.283200 (-0.019327) \| 0.017867 / 0.141683 (-0.123816) \| 1.105159 / 1.452155 (-0.346996) \| 1.186640 / 1.492716 (-0.306076) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092888 / 0.018006 (0.074882) \| 0.302024 / 0.000490 (0.301534) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019329 / 0.037411 (-0.018083) \| 0.062135 / 0.014526 (0.047609) \| 0.075125 / 0.176557 (-0.101431) \| 0.120743 / 0.737135 (-0.616393) \| 0.078687 / 0.296338 (-0.217652) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279449 / 0.215209 (0.064240) \| 2.727310 / 2.077655 (0.649656) \| 1.442710 / 1.504120 (-0.061410) \| 1.315271 / 1.541195 (-0.225923) \| 1.360435 / 1.468490 (-0.108055) \| 0.567720 / 4.584777 (-4.017057) \| 2.397049 / 3.745712 (-1.348663) \| 2.891180 / 5.269862 (-2.378682) \| 1.774179 / 4.565676 (-2.791497) \| 0.063155 / 0.424275 (-0.361120) \| 0.004963 / 0.007607 (-0.002644) \| 0.337526 / 0.226044 (0.111482) \| 3.266016 / 2.268929 (0.997088) \| 1.808819 / 55.444624 (-53.635806) \| 1.525326 / 6.876477 (-5.351151) \| 1.566937 / 2.142072 (-0.575135) \| 0.654226 / 4.805227 (-4.151001) \| 0.118968 / 6.500664 (-6.381696) \| 0.042666 / 0.075469 (-0.032803) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940792 / 1.841788 (-0.900996) \| 11.736380 / 8.074308 (3.662072) \| 10.709538 / 10.191392 (0.518146) \| 0.141390 / 0.680424 (-0.539034) \| 0.014204 / 0.534201 (-0.519996) \| 0.284842 / 0.579283 (-0.294441) \| 0.266315 / 0.434364 (-0.168049) \| 0.331619 / 0.540337 (-0.208718) \| 0.416446 / 1.386936 (-0.970491) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005298 / 0.011353 (-0.006055) \| 0.003507 / 0.011008 (-0.007501) \| 0.048315 / 0.038508 (0.009807) \| 0.054855 / 0.023109 (0.031746) \| 0.271558 / 0.275898 (-0.004340) \| 0.316851 / 0.323480 (-0.006628) \| 0.004054 / 0.007986 (-0.003932) \| 0.002433 / 0.004328 (-0.001896) \| 0.046442 / 0.004250 (0.042191) \| 0.040853 / 0.037052 (0.003801) \| 0.272537 / 0.258489 (0.014048) \| 0.293736 / 0.293841 (-0.000105) \| 0.029112 / 0.128546 (-0.099434) \| 0.010573 / 0.075646 (-0.065074) \| 0.056501 / 0.419271 (-0.362771) \| 0.032541 / 0.043533 (-0.010992) \| 0.271004 / 0.255139 (0.015865) \| 0.289276 / 0.283200 (0.006076) \| 0.018618 / 0.141683 (-0.123065) \| 1.149435 / 1.452155 (-0.302719) \| 1.205113 / 1.492716 (-0.287604) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094726 / 0.018006 (0.076720) \| 0.304347 / 0.000490 (0.303857) \| 0.000217 / 0.000200 (0.000017) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021374 / 0.037411 (-0.016037) \| 0.070574 / 0.014526 (0.056049) \| 0.081749 / 0.176557 (-0.094807) \| 0.119829 / 0.737135 (-0.617306) \| 0.082602 / 0.296338 (-0.213737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293378 / 0.215209 (0.078169) \| 2.893607 / 2.077655 (0.815952) \| 1.577734 / 1.504120 (0.073614) \| 1.453670 / 1.541195 (-0.087525) \| 1.467354 / 1.468490 (-0.001136) \| 0.563415 / 4.584777 (-4.021362) \| 2.438330 / 3.745712 (-1.307382) \| 2.761822 / 5.269862 (-2.508040) \| 1.730944 / 4.565676 (-2.834732) \| 0.062251 / 0.424275 (-0.362024) \| 0.004969 / 0.007607 (-0.002638) \| 0.371238 / 0.226044 (0.145194) \| 3.399831 / 2.268929 (1.130903) \| 1.936156 / 55.444624 (-53.508469) \| 1.649716 / 6.876477 (-5.226761) \| 1.669107 / 2.142072 (-0.472965) \| 0.633696 / 4.805227 (-4.171531) \| 0.115857 / 6.500664 (-6.384807) \| 0.041012 / 0.075469 (-0.034457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964777 / 1.841788 (-0.877010) \| 12.037613 / 8.074308 (3.963305) \| 10.579241 / 10.191392 (0.387849) \| 0.130932 / 0.680424 (-0.549492) \| 0.015621 / 0.534201 (-0.518580) \| 0.286898 / 0.579283 (-0.292385) \| 0.281139 / 0.434364 (-0.153225) \| 0.325240 / 0.540337 (-0.215097) \| 0.554302 / 1.386936 (-0.832635) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48d2378944a47987f96562ee856167aef1e78522 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005258 / 0.011353 (-0.006095) \| 0.003863 / 0.011008 (-0.007145) \| 0.064585 / 0.038508 (0.026077) \| 0.058013 / 0.023109 (0.034904) \| 0.249042 / 0.275898 (-0.026856) \| 0.273434 / 0.323480 (-0.050046) \| 0.004779 / 0.007986 (-0.003207) \| 0.002550 / 0.004328 (-0.001778) \| 0.048290 / 0.004250 (0.044040) \| 0.038777 / 0.037052 (0.001725) \| 0.253039 / 0.258489 (-0.005450) \| 0.285365 / 0.293841 (-0.008476) \| 0.028053 / 0.128546 (-0.100494) \| 0.010521 / 0.075646 (-0.065125) \| 0.210954 / 0.419271 (-0.208317) \| 0.035720 / 0.043533 (-0.007813) \| 0.252540 / 0.255139 (-0.002599) \| 0.264786 / 0.283200 (-0.018414) \| 0.018692 / 0.141683 (-0.122990) \| 1.108971 / 1.452155 (-0.343183) \| 1.201004 / 1.492716 (-0.291712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095936 / 0.018006 (0.077930) \| 0.302979 / 0.000490 (0.302489) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018859 / 0.037411 (-0.018552) \| 0.062559 / 0.014526 (0.048034) \| 0.073545 / 0.176557 (-0.103012) \| 0.120780 / 0.737135 (-0.616355) \| 0.074998 / 0.296338 (-0.221340) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276728 / 0.215209 (0.061519) \| 2.715310 / 2.077655 (0.637655) \| 1.444927 / 1.504120 (-0.059193) \| 1.323867 / 1.541195 (-0.217328) \| 1.364962 / 1.468490 (-0.103528) \| 0.556792 / 4.584777 (-4.027985) \| 2.409151 / 3.745712 (-1.336561) \| 2.811836 / 5.269862 (-2.458026) \| 1.777369 / 4.565676 (-2.788308) \| 0.061398 / 0.424275 (-0.362877) \| 0.004924 / 0.007607 (-0.002683) \| 0.341228 / 0.226044 (0.115183) \| 3.369570 / 2.268929 (1.100641) \| 1.858151 / 55.444624 (-53.586474) \| 1.587352 / 6.876477 (-5.289125) \| 1.625004 / 2.142072 (-0.517068) \| 0.635317 / 4.805227 (-4.169910) \| 0.117197 / 6.500664 (-6.383467) \| 0.042672 / 0.075469 (-0.032797) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940419 / 1.841788 (-0.901368) \| 12.156882 / 8.074308 (4.082574) \| 10.646780 / 10.191392 (0.455388) \| 0.129279 / 0.680424 (-0.551144) \| 0.013967 / 0.534201 (-0.520234) \| 0.287956 / 0.579283 (-0.291327) \| 0.265250 / 0.434364 (-0.169114) \| 0.323357 / 0.540337 (-0.216980) \| 0.412045 / 1.386936 (-0.974891) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005264 / 0.011353 (-0.006089) \| 0.003575 / 0.011008 (-0.007433) \| 0.049249 / 0.038508 (0.010741) \| 0.057069 / 0.023109 (0.033959) \| 0.327547 / 0.275898 (0.051649) \| 0.299027 / 0.323480 (-0.024453) \| 0.004768 / 0.007986 (-0.003217) \| 0.002522 / 0.004328 (-0.001807) \| 0.048020 / 0.004250 (0.043770) \| 0.041328 / 0.037052 (0.004275) \| 0.281385 / 0.258489 (0.022895) \| 0.304957 / 0.293841 (0.011116) \| 0.031371 / 0.128546 (-0.097175) \| 0.010523 / 0.075646 (-0.065124) \| 0.057073 / 0.419271 (-0.362198) \| 0.032913 / 0.043533 (-0.010620) \| 0.284963 / 0.255139 (0.029824) \| 0.291997 / 0.283200 (0.008798) \| 0.018325 / 0.141683 (-0.123357) \| 1.126681 / 1.452155 (-0.325473) \| 1.183011 / 1.492716 (-0.309705) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092544 / 0.018006 (0.074538) \| 0.299841 / 0.000490 (0.299351) \| 0.000221 / 0.000200 (0.000021) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022279 / 0.037411 (-0.015133) \| 0.072515 / 0.014526 (0.057989) \| 0.083068 / 0.176557 (-0.093488) \| 0.120600 / 0.737135 (-0.616536) \| 0.083574 / 0.296338 (-0.212765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293393 / 0.215209 (0.078184) \| 2.865420 / 2.077655 (0.787765) \| 1.562419 / 1.504120 (0.058299) \| 1.440846 / 1.541195 (-0.100349) \| 1.471993 / 1.468490 (0.003503) \| 0.572510 / 4.584777 (-4.012267) \| 2.427417 / 3.745712 (-1.318295) \| 2.895347 / 5.269862 (-2.374515) \| 1.790578 / 4.565676 (-2.775098) \| 0.064489 / 0.424275 (-0.359786) \| 0.005044 / 0.007607 (-0.002564) \| 0.340774 / 0.226044 (0.114730) \| 3.391414 / 2.268929 (1.122486) \| 1.939980 / 55.444624 (-53.504644) \| 1.658514 / 6.876477 (-5.217963) \| 1.741406 / 2.142072 (-0.400667) \| 0.649033 / 4.805227 (-4.156194) \| 0.117587 / 6.500664 (-6.383077) \| 0.042042 / 0.075469 (-0.033427) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980490 / 1.841788 (-0.861298) \| 12.664045 / 8.074308 (4.589737) \| 10.944437 / 10.191392 (0.753045) \| 0.142059 / 0.680424 (-0.538365) \| 0.015914 / 0.534201 (-0.518287) \| 0.288826 / 0.579283 (-0.290457) \| 0.282351 / 0.434364 (-0.152013) \| 0.325302 / 0.540337 (-0.215035) \| 0.416900 / 1.386936 (-0.970036) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005591 / 0.011353 (-0.005762) \| 0.003445 / 0.011008 (-0.007563) \| 0.064290 / 0.038508 (0.025782) \| 0.053046 / 0.023109 (0.029936) \| 0.229101 / 0.275898 (-0.046797) \| 0.255515 / 0.323480 (-0.067964) \| 0.002912 / 0.007986 (-0.005073) \| 0.002466 / 0.004328 (-0.001863) \| 0.049348 / 0.004250 (0.045098) \| 0.039492 / 0.037052 (0.002440) \| 0.236301 / 0.258489 (-0.022188) \| 0.270109 / 0.293841 (-0.023732) \| 0.027506 / 0.128546 (-0.101040) \| 0.010381 / 0.075646 (-0.065265) \| 0.209999 / 0.419271 (-0.209273) \| 0.035827 / 0.043533 (-0.007705) \| 0.237231 / 0.255139 (-0.017908) \| 0.254345 / 0.283200 (-0.028854) \| 0.019689 / 0.141683 (-0.121994) \| 1.096103 / 1.452155 (-0.356052) \| 1.172393 / 1.492716 (-0.320323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101749 / 0.018006 (0.083743) \| 0.310913 / 0.000490 (0.310424) \| 0.000217 / 0.000200 (0.000017) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018743 / 0.037411 (-0.018669) \| 0.064190 / 0.014526 (0.049664) \| 0.074575 / 0.176557 (-0.101982) \| 0.124143 / 0.737135 (-0.612993) \| 0.077415 / 0.296338 (-0.218924) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286175 / 0.215209 (0.070965) \| 2.781169 / 2.077655 (0.703515) \| 1.495130 / 1.504120 (-0.008990) \| 1.379136 / 1.541195 (-0.162059) \| 1.397548 / 1.468490 (-0.070942) \| 0.564467 / 4.584777 (-4.020310) \| 2.408896 / 3.745712 (-1.336816) \| 2.857771 / 5.269862 (-2.412091) \| 1.776531 / 4.565676 (-2.789145) \| 0.062700 / 0.424275 (-0.361575) \| 0.004965 / 0.007607 (-0.002642) \| 0.344026 / 0.226044 (0.117982) \| 3.390829 / 2.268929 (1.121900) \| 1.875258 / 55.444624 (-53.569366) \| 1.602435 / 6.876477 (-5.274042) \| 1.613619 / 2.142072 (-0.528454) \| 0.639421 / 4.805227 (-4.165806) \| 0.117697 / 6.500664 (-6.382967) \| 0.042878 / 0.075469 (-0.032591) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957694 / 1.841788 (-0.884094) \| 11.888917 / 8.074308 (3.814609) \| 10.643389 / 10.191392 (0.451997) \| 0.143358 / 0.680424 (-0.537066) \| 0.014382 / 0.534201 (-0.519819) \| 0.288731 / 0.579283 (-0.290552) \| 0.270040 / 0.434364 (-0.164324) \| 0.323586 / 0.540337 (-0.216751) \| 0.415743 / 1.386936 (-0.971193) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005228 / 0.011353 (-0.006125) \| 0.003445 / 0.011008 (-0.007563) \| 0.051072 / 0.038508 (0.012563) \| 0.053087 / 0.023109 (0.029978) \| 0.273116 / 0.275898 (-0.002782) \| 0.298633 / 0.323480 (-0.024847) \| 0.004067 / 0.007986 (-0.003919) \| 0.002537 / 0.004328 (-0.001791) \| 0.049326 / 0.004250 (0.045075) \| 0.041011 / 0.037052 (0.003959) \| 0.277748 / 0.258489 (0.019258) \| 0.304152 / 0.293841 (0.010311) \| 0.029012 / 0.128546 (-0.099534) \| 0.010589 / 0.075646 (-0.065057) \| 0.057564 / 0.419271 (-0.361707) \| 0.032785 / 0.043533 (-0.010747) \| 0.272508 / 0.255139 (0.017369) \| 0.294127 / 0.283200 (0.010927) \| 0.018466 / 0.141683 (-0.123217) \| 1.129341 / 1.452155 (-0.322814) \| 1.194631 / 1.492716 (-0.298086) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098558 / 0.018006 (0.080552) \| 0.312353 / 0.000490 (0.311863) \| 0.000269 / 0.000200 (0.000069) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022148 / 0.037411 (-0.015263) \| 0.070601 / 0.014526 (0.056075) \| 0.081780 / 0.176557 (-0.094777) \| 0.121993 / 0.737135 (-0.615142) \| 0.084263 / 0.296338 (-0.212076) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300501 / 0.215209 (0.085292) \| 2.927534 / 2.077655 (0.849879) \| 1.595527 / 1.504120 (0.091407) \| 1.475607 / 1.541195 (-0.065587) \| 1.496707 / 1.468490 (0.028217) \| 0.559051 / 4.584777 (-4.025726) \| 2.427126 / 3.745712 (-1.318586) \| 2.820908 / 5.269862 (-2.448953) \| 1.757492 / 4.565676 (-2.808185) \| 0.062391 / 0.424275 (-0.361884) \| 0.004950 / 0.007607 (-0.002657) \| 0.351204 / 0.226044 (0.125160) \| 3.485068 / 2.268929 (1.216139) \| 1.976418 / 55.444624 (-53.468207) \| 1.682715 / 6.876477 (-5.193761) \| 1.703457 / 2.142072 (-0.438616) \| 0.643476 / 4.805227 (-4.161751) \| 0.116321 / 6.500664 (-6.384343) \| 0.040776 / 0.075469 (-0.034694) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974152 / 1.841788 (-0.867635) \| 12.390170 / 8.074308 (4.315862) \| 10.866283 / 10.191392 (0.674891) \| 0.145049 / 0.680424 (-0.535375) \| 0.016404 / 0.534201 (-0.517797) \| 0.288799 / 0.579283 (-0.290484) \| 0.285917 / 0.434364 (-0.148447) \| 0.328455 / 0.540337 (-0.211883) \| 0.417286 / 1.386936 (-0.969650) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n" ]	2023-11-23T17:35:02Z	2023-11-27T10:02:56Z	2023-11-24T17:13:02Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6449.diff", "html_url": "https://github.com/huggingface/datasets/pull/6449", "merged_at": "2023-11-24T17:13:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/6449.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6449" }	Refetch metadata files in case they were dropped by `filter_extensions` in the previous step. Fix #6442	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6449/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6448	6,448	Use parquet export if possible	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005177 / 0.011353 (-0.006176) \| 0.003002 / 0.011008 (-0.008006) \| 0.061915 / 0.038508 (0.023407) \| 0.052065 / 0.023109 (0.028956) \| 0.246114 / 0.275898 (-0.029784) \| 0.273974 / 0.323480 (-0.049506) \| 0.002983 / 0.007986 (-0.005003) \| 0.002444 / 0.004328 (-0.001885) \| 0.048424 / 0.004250 (0.044174) \| 0.039609 / 0.037052 (0.002557) \| 0.257771 / 0.258489 (-0.000718) \| 0.286228 / 0.293841 (-0.007613) \| 0.023925 / 0.128546 (-0.104621) \| 0.007248 / 0.075646 (-0.068398) \| 0.202205 / 0.419271 (-0.217067) \| 0.037124 / 0.043533 (-0.006409) \| 0.254872 / 0.255139 (-0.000267) \| 0.275252 / 0.283200 (-0.007947) \| 0.019251 / 0.141683 (-0.122432) \| 1.074921 / 1.452155 (-0.377234) \| 1.146515 / 1.492716 (-0.346202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091998 / 0.018006 (0.073992) \| 0.299146 / 0.000490 (0.298656) \| 0.000240 / 0.000200 (0.000040) \| 0.000054 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019266 / 0.037411 (-0.018145) \| 0.062560 / 0.014526 (0.048034) \| 0.075012 / 0.176557 (-0.101544) \| 0.120077 / 0.737135 (-0.617058) \| 0.077851 / 0.296338 (-0.218488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290629 / 0.215209 (0.075420) \| 2.823847 / 2.077655 (0.746192) \| 1.516966 / 1.504120 (0.012846) \| 1.393383 / 1.541195 (-0.147812) \| 1.427688 / 1.468490 (-0.040802) \| 0.407456 / 4.584777 (-4.177321) \| 2.378280 / 3.745712 (-1.367433) \| 2.689800 / 5.269862 (-2.580061) \| 1.588037 / 4.565676 (-2.977640) \| 0.045837 / 0.424275 (-0.378438) \| 0.004884 / 0.007607 (-0.002724) \| 0.340464 / 0.226044 (0.114420) \| 3.377158 / 2.268929 (1.108230) \| 1.897854 / 55.444624 (-53.546771) \| 1.588285 / 6.876477 (-5.288191) \| 1.651708 / 2.142072 (-0.490364) \| 0.482018 / 4.805227 (-4.323209) \| 0.101583 / 6.500664 (-6.399081) \| 0.042306 / 0.075469 (-0.033163) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948659 / 1.841788 (-0.893128) \| 11.809778 / 8.074308 (3.735470) \| 10.481896 / 10.191392 (0.290504) \| 0.143538 / 0.680424 (-0.536885) \| 0.014105 / 0.534201 (-0.520096) \| 0.272278 / 0.579283 (-0.307005) \| 0.264241 / 0.434364 (-0.170123) \| 0.307187 / 0.540337 (-0.233150) \| 0.401270 / 1.386936 (-0.985666) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004831 / 0.011353 (-0.006521) \| 0.002896 / 0.011008 (-0.008112) \| 0.047479 / 0.038508 (0.008971) \| 0.050665 / 0.023109 (0.027555) \| 0.275243 / 0.275898 (-0.000655) \| 0.296547 / 0.323480 (-0.026933) \| 0.004022 / 0.007986 (-0.003963) \| 0.002425 / 0.004328 (-0.001904) \| 0.047086 / 0.004250 (0.042836) \| 0.039611 / 0.037052 (0.002558) \| 0.275272 / 0.258489 (0.016783) \| 0.302429 / 0.293841 (0.008588) \| 0.024308 / 0.128546 (-0.104238) \| 0.007167 / 0.075646 (-0.068479) \| 0.052825 / 0.419271 (-0.366446) \| 0.032319 / 0.043533 (-0.011213) \| 0.273334 / 0.255139 (0.018195) \| 0.291161 / 0.283200 (0.007961) \| 0.017918 / 0.141683 (-0.123764) \| 1.110005 / 1.452155 (-0.342150) \| 1.176616 / 1.492716 (-0.316100) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092478 / 0.018006 (0.074471) \| 0.311431 / 0.000490 (0.310942) \| 0.000237 / 0.000200 (0.000037) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021979 / 0.037411 (-0.015432) \| 0.080617 / 0.014526 (0.066091) \| 0.081534 / 0.176557 (-0.095023) \| 0.121073 / 0.737135 (-0.616062) \| 0.083235 / 0.296338 (-0.213104) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289527 / 0.215209 (0.074318) \| 2.839668 / 2.077655 (0.762013) \| 1.601737 / 1.504120 (0.097617) \| 1.496028 / 1.541195 (-0.045167) \| 1.511933 / 1.468490 (0.043443) \| 0.399819 / 4.584777 (-4.184958) \| 2.394147 / 3.745712 (-1.351565) \| 2.520767 / 5.269862 (-2.749095) \| 1.589496 / 4.565676 (-2.976180) \| 0.046673 / 0.424275 (-0.377602) \| 0.004858 / 0.007607 (-0.002749) \| 0.357986 / 0.226044 (0.131941) \| 3.376217 / 2.268929 (1.107289) \| 1.981853 / 55.444624 (-53.462771) \| 1.682240 / 6.876477 (-5.194236) \| 1.830643 / 2.142072 (-0.311429) \| 0.478286 / 4.805227 (-4.326941) \| 0.099589 / 6.500664 (-6.401075) \| 0.041173 / 0.075469 (-0.034296) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985160 / 1.841788 (-0.856628) \| 12.312963 / 8.074308 (4.238655) \| 10.577225 / 10.191392 (0.385833) \| 0.130167 / 0.680424 (-0.550257) \| 0.016657 / 0.534201 (-0.517544) \| 0.271330 / 0.579283 (-0.307953) \| 0.276979 / 0.434364 (-0.157385) \| 0.304904 / 0.540337 (-0.235434) \| 0.412090 / 1.386936 (-0.974846) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1adc80151e892122ecb60f4e0b4572b136b2dd47 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6448). All of your documentation changes will be reflected on that endpoint.", "hooray! very excited about this", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005039 / 0.011353 (-0.006314) \| 0.003577 / 0.011008 (-0.007431) \| 0.062892 / 0.038508 (0.024384) \| 0.056334 / 0.023109 (0.033225) \| 0.252281 / 0.275898 (-0.023617) \| 0.274945 / 0.323480 (-0.048535) \| 0.003906 / 0.007986 (-0.004080) \| 0.002483 / 0.004328 (-0.001845) \| 0.049006 / 0.004250 (0.044756) \| 0.038375 / 0.037052 (0.001323) \| 0.257376 / 0.258489 (-0.001113) \| 0.292512 / 0.293841 (-0.001328) \| 0.027134 / 0.128546 (-0.101412) \| 0.010579 / 0.075646 (-0.065068) \| 0.212021 / 0.419271 (-0.207250) \| 0.035851 / 0.043533 (-0.007682) \| 0.258076 / 0.255139 (0.002937) \| 0.271758 / 0.283200 (-0.011442) \| 0.018222 / 0.141683 (-0.123461) \| 1.120481 / 1.452155 (-0.331674) \| 1.187007 / 1.492716 (-0.305710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094986 / 0.018006 (0.076980) \| 0.302121 / 0.000490 (0.301631) \| 0.000211 / 0.000200 (0.000011) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019260 / 0.037411 (-0.018152) \| 0.062909 / 0.014526 (0.048383) \| 0.075644 / 0.176557 (-0.100912) \| 0.120966 / 0.737135 (-0.616170) \| 0.076678 / 0.296338 (-0.219661) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286754 / 0.215209 (0.071545) \| 2.797467 / 2.077655 (0.719812) \| 1.436798 / 1.504120 (-0.067322) \| 1.315032 / 1.541195 (-0.226163) \| 1.367841 / 1.468490 (-0.100649) \| 0.578917 / 4.584777 (-4.005860) \| 2.439773 / 3.745712 (-1.305939) \| 2.932779 / 5.269862 (-2.337082) \| 1.843895 / 4.565676 (-2.721782) \| 0.063351 / 0.424275 (-0.360925) \| 0.004998 / 0.007607 (-0.002610) \| 0.347385 / 0.226044 (0.121340) \| 3.449969 / 2.268929 (1.181040) \| 1.857734 / 55.444624 (-53.586890) \| 1.541341 / 6.876477 (-5.335136) \| 1.574915 / 2.142072 (-0.567158) \| 0.660178 / 4.805227 (-4.145049) \| 0.117686 / 6.500664 (-6.382978) \| 0.042602 / 0.075469 (-0.032867) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937735 / 1.841788 (-0.904052) \| 11.962091 / 8.074308 (3.887783) \| 10.401715 / 10.191392 (0.210323) \| 0.142200 / 0.680424 (-0.538224) \| 0.014137 / 0.534201 (-0.520064) \| 0.289853 / 0.579283 (-0.289430) \| 0.267100 / 0.434364 (-0.167264) \| 0.323401 / 0.540337 (-0.216936) \| 0.418665 / 1.386936 (-0.968271) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005480 / 0.011353 (-0.005873) \| 0.003401 / 0.011008 (-0.007607) \| 0.049304 / 0.038508 (0.010796) \| 0.062043 / 0.023109 (0.038934) \| 0.270571 / 0.275898 (-0.005327) \| 0.295226 / 0.323480 (-0.028254) \| 0.004152 / 0.007986 (-0.003834) \| 0.002511 / 0.004328 (-0.001817) \| 0.048480 / 0.004250 (0.044229) \| 0.043964 / 0.037052 (0.006912) \| 0.273545 / 0.258489 (0.015056) \| 0.295152 / 0.293841 (0.001311) \| 0.029224 / 0.128546 (-0.099322) \| 0.010629 / 0.075646 (-0.065018) \| 0.057433 / 0.419271 (-0.361839) \| 0.033115 / 0.043533 (-0.010418) \| 0.269893 / 0.255139 (0.014754) \| 0.288658 / 0.283200 (0.005459) \| 0.018216 / 0.141683 (-0.123467) \| 1.123039 / 1.452155 (-0.329116) \| 1.182892 / 1.492716 (-0.309825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095948 / 0.018006 (0.077942) \| 0.305811 / 0.000490 (0.305321) \| 0.000221 / 0.000200 (0.000021) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022996 / 0.037411 (-0.014415) \| 0.073836 / 0.014526 (0.059310) \| 0.082658 / 0.176557 (-0.093899) \| 0.121970 / 0.737135 (-0.615166) \| 0.086096 / 0.296338 (-0.210242) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291032 / 0.215209 (0.075823) \| 2.864613 / 2.077655 (0.786958) \| 1.567530 / 1.504120 (0.063410) \| 1.460291 / 1.541195 (-0.080903) \| 1.527066 / 1.468490 (0.058576) \| 0.571160 / 4.584777 (-4.013617) \| 2.465261 / 3.745712 (-1.280451) \| 2.915547 / 5.269862 (-2.354314) \| 1.835822 / 4.565676 (-2.729855) \| 0.064328 / 0.424275 (-0.359947) \| 0.005061 / 0.007607 (-0.002546) \| 0.357105 / 0.226044 (0.131061) \| 3.491363 / 2.268929 (1.222435) \| 1.943213 / 55.444624 (-53.501412) \| 1.675778 / 6.876477 (-5.200699) \| 1.719016 / 2.142072 (-0.423057) \| 0.658993 / 4.805227 (-4.146235) \| 0.122320 / 6.500664 (-6.378344) \| 0.049030 / 0.075469 (-0.026439) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964762 / 1.841788 (-0.877025) \| 12.367251 / 8.074308 (4.292943) \| 10.886213 / 10.191392 (0.694821) \| 0.141533 / 0.680424 (-0.538891) \| 0.015646 / 0.534201 (-0.518555) \| 0.288583 / 0.579283 (-0.290700) \| 0.280353 / 0.434364 (-0.154010) \| 0.329095 / 0.540337 (-0.211242) \| 0.565118 / 1.386936 (-0.821818) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#493bf695dc3ee6cc81bfd0aae6a38f70547bb752 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006475 / 0.011353 (-0.004878) \| 0.004080 / 0.011008 (-0.006928) \| 0.066479 / 0.038508 (0.027971) \| 0.073270 / 0.023109 (0.050161) \| 0.244412 / 0.275898 (-0.031486) \| 0.273778 / 0.323480 (-0.049702) \| 0.003186 / 0.007986 (-0.004800) \| 0.003419 / 0.004328 (-0.000910) \| 0.049743 / 0.004250 (0.045492) \| 0.043581 / 0.037052 (0.006529) \| 0.248215 / 0.258489 (-0.010274) \| 0.280873 / 0.293841 (-0.012967) \| 0.029282 / 0.128546 (-0.099264) \| 0.011241 / 0.075646 (-0.064405) \| 0.215031 / 0.419271 (-0.204241) \| 0.038764 / 0.043533 (-0.004769) \| 0.259363 / 0.255139 (0.004224) \| 0.279253 / 0.283200 (-0.003946) \| 0.019524 / 0.141683 (-0.122159) \| 1.104735 / 1.452155 (-0.347420) \| 1.159823 / 1.492716 (-0.332894) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.108383 / 0.018006 (0.090377) \| 0.332904 / 0.000490 (0.332415) \| 0.000222 / 0.000200 (0.000022) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020693 / 0.037411 (-0.016719) \| 0.071764 / 0.014526 (0.057238) \| 0.077073 / 0.176557 (-0.099484) \| 0.124604 / 0.737135 (-0.612532) \| 0.078057 / 0.296338 (-0.218282) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291014 / 0.215209 (0.075805) \| 2.865885 / 2.077655 (0.788231) \| 1.506141 / 1.504120 (0.002021) \| 1.435924 / 1.541195 (-0.105271) \| 1.461994 / 1.468490 (-0.006497) \| 0.571779 / 4.584777 (-4.012998) \| 2.461950 / 3.745712 (-1.283762) \| 3.079771 / 5.269862 (-2.190091) \| 1.933337 / 4.565676 (-2.632339) \| 0.063405 / 0.424275 (-0.360870) \| 0.005203 / 0.007607 (-0.002404) \| 0.345077 / 0.226044 (0.119032) \| 3.487189 / 2.268929 (1.218261) \| 1.903733 / 55.444624 (-53.540891) \| 1.705596 / 6.876477 (-5.170880) \| 1.718849 / 2.142072 (-0.423223) \| 0.658745 / 4.805227 (-4.146482) \| 0.120847 / 6.500664 (-6.379817) \| 0.045670 / 0.075469 (-0.029799) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965969 / 1.841788 (-0.875819) \| 13.520489 / 8.074308 (5.446181) \| 12.322363 / 10.191392 (2.130971) \| 0.146605 / 0.680424 (-0.533819) \| 0.015061 / 0.534201 (-0.519140) \| 0.298125 / 0.579283 (-0.281159) \| 0.276864 / 0.434364 (-0.157500) \| 0.326787 / 0.540337 (-0.213550) \| 0.436897 / 1.386936 (-0.950039) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005862 / 0.011353 (-0.005491) \| 0.003716 / 0.011008 (-0.007292) \| 0.052849 / 0.038508 (0.014341) \| 0.072114 / 0.023109 (0.049005) \| 0.277800 / 0.275898 (0.001902) \| 0.325321 / 0.323480 (0.001841) \| 0.004428 / 0.007986 (-0.003557) \| 0.002527 / 0.004328 (-0.001801) \| 0.048847 / 0.004250 (0.044596) \| 0.047355 / 0.037052 (0.010303) \| 0.279331 / 0.258489 (0.020842) \| 0.310477 / 0.293841 (0.016636) \| 0.029661 / 0.128546 (-0.098886) \| 0.010812 / 0.075646 (-0.064834) \| 0.059803 / 0.419271 (-0.359469) \| 0.033554 / 0.043533 (-0.009978) \| 0.276890 / 0.255139 (0.021751) \| 0.308911 / 0.283200 (0.025712) \| 0.020752 / 0.141683 (-0.120931) \| 1.120896 / 1.452155 (-0.331259) \| 1.186428 / 1.492716 (-0.306288) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.106551 / 0.018006 (0.088545) \| 0.354455 / 0.000490 (0.353966) \| 0.000353 / 0.000200 (0.000153) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023488 / 0.037411 (-0.013923) \| 0.080548 / 0.014526 (0.066022) \| 0.084431 / 0.176557 (-0.092126) \| 0.140698 / 0.737135 (-0.596438) \| 0.085692 / 0.296338 (-0.210647) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314253 / 0.215209 (0.099044) \| 2.993236 / 2.077655 (0.915582) \| 1.639013 / 1.504120 (0.134893) \| 1.543966 / 1.541195 (0.002771) \| 1.567732 / 1.468490 (0.099242) \| 0.565857 / 4.584777 (-4.018920) \| 2.545339 / 3.745712 (-1.200373) \| 3.134546 / 5.269862 (-2.135316) \| 1.940350 / 4.565676 (-2.625326) \| 0.063847 / 0.424275 (-0.360429) \| 0.005079 / 0.007607 (-0.002528) \| 0.365762 / 0.226044 (0.139718) \| 3.610921 / 2.268929 (1.341993) \| 2.035151 / 55.444624 (-53.409473) \| 1.773409 / 6.876477 (-5.103068) \| 1.790332 / 2.142072 (-0.351741) \| 0.683019 / 4.805227 (-4.122209) \| 0.119566 / 6.500664 (-6.381099) \| 0.043578 / 0.075469 (-0.031891) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996568 / 1.841788 (-0.845219) \| 14.094366 / 8.074308 (6.020058) \| 12.433600 / 10.191392 (2.242208) \| 0.139835 / 0.680424 (-0.540589) \| 0.016454 / 0.534201 (-0.517747) \| 0.294073 / 0.579283 (-0.285210) \| 0.309032 / 0.434364 (-0.125332) \| 0.330699 / 0.540337 (-0.209638) \| 0.619392 / 1.386936 (-0.767544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#026fbce1c93a30188b6d0646bb975da8f56e2a2f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005389 / 0.011353 (-0.005964) \| 0.003209 / 0.011008 (-0.007799) \| 0.061610 / 0.038508 (0.023102) \| 0.049781 / 0.023109 (0.026672) \| 0.240208 / 0.275898 (-0.035690) \| 0.263307 / 0.323480 (-0.060173) \| 0.002908 / 0.007986 (-0.005078) \| 0.002375 / 0.004328 (-0.001953) \| 0.047462 / 0.004250 (0.043212) \| 0.038643 / 0.037052 (0.001591) \| 0.246287 / 0.258489 (-0.012202) \| 0.278715 / 0.293841 (-0.015126) \| 0.027507 / 0.128546 (-0.101039) \| 0.010168 / 0.075646 (-0.065479) \| 0.204131 / 0.419271 (-0.215140) \| 0.035452 / 0.043533 (-0.008081) \| 0.251721 / 0.255139 (-0.003418) \| 0.266642 / 0.283200 (-0.016558) \| 0.017741 / 0.141683 (-0.123942) \| 1.094672 / 1.452155 (-0.357482) \| 1.162715 / 1.492716 (-0.330002) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092154 / 0.018006 (0.074148) \| 0.301376 / 0.000490 (0.300886) \| 0.000217 / 0.000200 (0.000017) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018534 / 0.037411 (-0.018877) \| 0.061995 / 0.014526 (0.047469) \| 0.072654 / 0.176557 (-0.103903) \| 0.119501 / 0.737135 (-0.617635) \| 0.073756 / 0.296338 (-0.222583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280066 / 0.215209 (0.064857) \| 2.744207 / 2.077655 (0.666553) \| 1.483367 / 1.504120 (-0.020753) \| 1.386173 / 1.541195 (-0.155022) \| 1.381833 / 1.468490 (-0.086657) \| 0.552780 / 4.584777 (-4.031997) \| 2.395541 / 3.745712 (-1.350171) \| 2.747507 / 5.269862 (-2.522355) \| 1.735074 / 4.565676 (-2.830602) \| 0.062096 / 0.424275 (-0.362179) \| 0.004905 / 0.007607 (-0.002702) \| 0.338327 / 0.226044 (0.112283) \| 3.365391 / 2.268929 (1.096462) \| 1.839663 / 55.444624 (-53.604961) \| 1.577535 / 6.876477 (-5.298942) \| 1.558054 / 2.142072 (-0.584018) \| 0.636520 / 4.805227 (-4.168708) \| 0.116182 / 6.500664 (-6.384482) \| 0.042078 / 0.075469 (-0.033391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938512 / 1.841788 (-0.903276) \| 11.455749 / 8.074308 (3.381441) \| 10.510985 / 10.191392 (0.319593) \| 0.140865 / 0.680424 (-0.539559) \| 0.014073 / 0.534201 (-0.520128) \| 0.294747 / 0.579283 (-0.284536) \| 0.266147 / 0.434364 (-0.168217) \| 0.325354 / 0.540337 (-0.214984) \| 0.422182 / 1.386936 (-0.964754) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005231 / 0.011353 (-0.006122) \| 0.003032 / 0.011008 (-0.007977) \| 0.049608 / 0.038508 (0.011099) \| 0.051441 / 0.023109 (0.028332) \| 0.273812 / 0.275898 (-0.002086) \| 0.294318 / 0.323480 (-0.029162) \| 0.003958 / 0.007986 (-0.004028) \| 0.002384 / 0.004328 (-0.001944) \| 0.047942 / 0.004250 (0.043691) \| 0.039179 / 0.037052 (0.002127) \| 0.277504 / 0.258489 (0.019014) \| 0.299713 / 0.293841 (0.005872) \| 0.028989 / 0.128546 (-0.099557) \| 0.010267 / 0.075646 (-0.065379) \| 0.058318 / 0.419271 (-0.360954) \| 0.032214 / 0.043533 (-0.011318) \| 0.277964 / 0.255139 (0.022825) \| 0.293055 / 0.283200 (0.009856) \| 0.018532 / 0.141683 (-0.123151) \| 1.128620 / 1.452155 (-0.323535) \| 1.187365 / 1.492716 (-0.305351) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092137 / 0.018006 (0.074130) \| 0.299726 / 0.000490 (0.299236) \| 0.000222 / 0.000200 (0.000022) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021342 / 0.037411 (-0.016070) \| 0.069943 / 0.014526 (0.055417) \| 0.079862 / 0.176557 (-0.096694) \| 0.118917 / 0.737135 (-0.618218) \| 0.081861 / 0.296338 (-0.214477) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295883 / 0.215209 (0.080674) \| 2.881640 / 2.077655 (0.803986) \| 1.597705 / 1.504120 (0.093585) \| 1.473220 / 1.541195 (-0.067975) \| 1.501006 / 1.468490 (0.032516) \| 0.559409 / 4.584777 (-4.025368) \| 2.442709 / 3.745712 (-1.303003) \| 2.742139 / 5.269862 (-2.527723) \| 1.726002 / 4.565676 (-2.839674) \| 0.062436 / 0.424275 (-0.361840) \| 0.004896 / 0.007607 (-0.002711) \| 0.349203 / 0.226044 (0.123159) \| 3.435175 / 2.268929 (1.166247) \| 1.954888 / 55.444624 (-53.489737) \| 1.666233 / 6.876477 (-5.210243) \| 1.680852 / 2.142072 (-0.461221) \| 0.644271 / 4.805227 (-4.160956) \| 0.115160 / 6.500664 (-6.385504) \| 0.040681 / 0.075469 (-0.034788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963810 / 1.841788 (-0.877977) \| 11.860860 / 8.074308 (3.786552) \| 10.541703 / 10.191392 (0.350311) \| 0.131532 / 0.680424 (-0.548892) \| 0.016790 / 0.534201 (-0.517411) \| 0.286695 / 0.579283 (-0.292588) \| 0.279628 / 0.434364 (-0.154735) \| 0.324622 / 0.540337 (-0.215715) \| 0.535507 / 1.386936 (-0.851429) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11217347e4bcfe1aaf794d164a5dd9f085b2f682 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005672 / 0.011353 (-0.005681) \| 0.003411 / 0.011008 (-0.007597) \| 0.062528 / 0.038508 (0.024020) \| 0.055209 / 0.023109 (0.032100) \| 0.248366 / 0.275898 (-0.027532) \| 0.279522 / 0.323480 (-0.043957) \| 0.002907 / 0.007986 (-0.005079) \| 0.002369 / 0.004328 (-0.001959) \| 0.047982 / 0.004250 (0.043731) \| 0.039009 / 0.037052 (0.001956) \| 0.256422 / 0.258489 (-0.002067) \| 0.288530 / 0.293841 (-0.005311) \| 0.028164 / 0.128546 (-0.100382) \| 0.010448 / 0.075646 (-0.065198) \| 0.208863 / 0.419271 (-0.210408) \| 0.036291 / 0.043533 (-0.007242) \| 0.251642 / 0.255139 (-0.003497) \| 0.275589 / 0.283200 (-0.007610) \| 0.019839 / 0.141683 (-0.121844) \| 1.092800 / 1.452155 (-0.359355) \| 1.147950 / 1.492716 (-0.344766) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094920 / 0.018006 (0.076914) \| 0.303049 / 0.000490 (0.302559) \| 0.000199 / 0.000200 (-0.000001) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018820 / 0.037411 (-0.018591) \| 0.063319 / 0.014526 (0.048793) \| 0.073644 / 0.176557 (-0.102912) \| 0.120045 / 0.737135 (-0.617091) \| 0.076219 / 0.296338 (-0.220119) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283897 / 0.215209 (0.068688) \| 2.822836 / 2.077655 (0.745182) \| 1.490505 / 1.504120 (-0.013615) \| 1.359777 / 1.541195 (-0.181418) \| 1.420536 / 1.468490 (-0.047954) \| 0.562308 / 4.584777 (-4.022469) \| 2.419249 / 3.745712 (-1.326463) \| 2.827620 / 5.269862 (-2.442241) \| 1.783171 / 4.565676 (-2.782505) \| 0.063206 / 0.424275 (-0.361069) \| 0.004966 / 0.007607 (-0.002641) \| 0.339647 / 0.226044 (0.113602) \| 3.378157 / 2.268929 (1.109229) \| 1.873221 / 55.444624 (-53.571403) \| 1.606367 / 6.876477 (-5.270109) \| 1.624976 / 2.142072 (-0.517096) \| 0.652653 / 4.805227 (-4.152574) \| 0.117997 / 6.500664 (-6.382667) \| 0.041955 / 0.075469 (-0.033514) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961420 / 1.841788 (-0.880368) \| 11.807624 / 8.074308 (3.733316) \| 10.668249 / 10.191392 (0.476857) \| 0.141855 / 0.680424 (-0.538569) \| 0.014451 / 0.534201 (-0.519750) \| 0.289706 / 0.579283 (-0.289577) \| 0.268392 / 0.434364 (-0.165972) \| 0.323435 / 0.540337 (-0.216903) \| 0.420667 / 1.386936 (-0.966269) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005382 / 0.011353 (-0.005971) \| 0.003361 / 0.011008 (-0.007647) \| 0.048420 / 0.038508 (0.009912) \| 0.053702 / 0.023109 (0.030593) \| 0.286976 / 0.275898 (0.011078) \| 0.296708 / 0.323480 (-0.026772) \| 0.004013 / 0.007986 (-0.003972) \| 0.002444 / 0.004328 (-0.001884) \| 0.047797 / 0.004250 (0.043547) \| 0.042361 / 0.037052 (0.005309) \| 0.277543 / 0.258489 (0.019054) \| 0.300736 / 0.293841 (0.006896) \| 0.029894 / 0.128546 (-0.098653) \| 0.014119 / 0.075646 (-0.061527) \| 0.057636 / 0.419271 (-0.361636) \| 0.032533 / 0.043533 (-0.010999) \| 0.280963 / 0.255139 (0.025824) \| 0.291305 / 0.283200 (0.008106) \| 0.018391 / 0.141683 (-0.123292) \| 1.140042 / 1.452155 (-0.312113) \| 1.179485 / 1.492716 (-0.313231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094668 / 0.018006 (0.076661) \| 0.301677 / 0.000490 (0.301187) \| 0.000245 / 0.000200 (0.000045) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021376 / 0.037411 (-0.016036) \| 0.070628 / 0.014526 (0.056102) \| 0.082249 / 0.176557 (-0.094308) \| 0.120423 / 0.737135 (-0.616712) \| 0.083792 / 0.296338 (-0.212546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298884 / 0.215209 (0.083675) \| 2.931849 / 2.077655 (0.854194) \| 1.591888 / 1.504120 (0.087768) \| 1.455781 / 1.541195 (-0.085414) \| 1.500312 / 1.468490 (0.031822) \| 0.558466 / 4.584777 (-4.026311) \| 2.450449 / 3.745712 (-1.295263) \| 2.842768 / 5.269862 (-2.427094) \| 1.755614 / 4.565676 (-2.810062) \| 0.063200 / 0.424275 (-0.361075) \| 0.005022 / 0.007607 (-0.002585) \| 0.358282 / 0.226044 (0.132238) \| 3.575392 / 2.268929 (1.306464) \| 1.960258 / 55.444624 (-53.484366) \| 1.675518 / 6.876477 (-5.200959) \| 1.696630 / 2.142072 (-0.445442) \| 0.647185 / 4.805227 (-4.158042) \| 0.117038 / 6.500664 (-6.383626) \| 0.041622 / 0.075469 (-0.033848) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962503 / 1.841788 (-0.879285) \| 12.194950 / 8.074308 (4.120642) \| 10.662233 / 10.191392 (0.470841) \| 0.131618 / 0.680424 (-0.548806) \| 0.016000 / 0.534201 (-0.518201) \| 0.291546 / 0.579283 (-0.287737) \| 0.279537 / 0.434364 (-0.154827) \| 0.328716 / 0.540337 (-0.211622) \| 0.547565 / 1.386936 (-0.839371) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de8f5f09f60613d47b5d7eb901752321c7b6a49 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005209 / 0.011353 (-0.006144) \| 0.003017 / 0.011008 (-0.007991) \| 0.062017 / 0.038508 (0.023509) \| 0.048268 / 0.023109 (0.025158) \| 0.246384 / 0.275898 (-0.029514) \| 0.270441 / 0.323480 (-0.053039) \| 0.002763 / 0.007986 (-0.005222) \| 0.003140 / 0.004328 (-0.001188) \| 0.048720 / 0.004250 (0.044470) \| 0.038175 / 0.037052 (0.001123) \| 0.254184 / 0.258489 (-0.004306) \| 0.275515 / 0.293841 (-0.018326) \| 0.027309 / 0.128546 (-0.101238) \| 0.010507 / 0.075646 (-0.065140) \| 0.210315 / 0.419271 (-0.208956) \| 0.035203 / 0.043533 (-0.008329) \| 0.253015 / 0.255139 (-0.002124) \| 0.271465 / 0.283200 (-0.011734) \| 0.019543 / 0.141683 (-0.122140) \| 1.119242 / 1.452155 (-0.332913) \| 1.149359 / 1.492716 (-0.343357) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088935 / 0.018006 (0.070928) \| 0.293922 / 0.000490 (0.293432) \| 0.000202 / 0.000200 (0.000002) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018174 / 0.037411 (-0.019237) \| 0.060215 / 0.014526 (0.045689) \| 0.072868 / 0.176557 (-0.103689) \| 0.117998 / 0.737135 (-0.619137) \| 0.074159 / 0.296338 (-0.222179) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289229 / 0.215209 (0.074020) \| 2.840414 / 2.077655 (0.762759) \| 1.468357 / 1.504120 (-0.035763) \| 1.347714 / 1.541195 (-0.193481) \| 1.363704 / 1.468490 (-0.104786) \| 0.572059 / 4.584777 (-4.012718) \| 2.400631 / 3.745712 (-1.345081) \| 2.755779 / 5.269862 (-2.514083) \| 1.740937 / 4.565676 (-2.824739) \| 0.063473 / 0.424275 (-0.360802) \| 0.005012 / 0.007607 (-0.002595) \| 0.336057 / 0.226044 (0.110012) \| 3.382126 / 2.268929 (1.113197) \| 1.807838 / 55.444624 (-53.636786) \| 1.534594 / 6.876477 (-5.341883) \| 1.529951 / 2.142072 (-0.612121) \| 0.636661 / 4.805227 (-4.168566) \| 0.117090 / 6.500664 (-6.383574) \| 0.042310 / 0.075469 (-0.033160) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.924440 / 1.841788 (-0.917347) \| 11.120517 / 8.074308 (3.046209) \| 10.177210 / 10.191392 (-0.014182) \| 0.139060 / 0.680424 (-0.541364) \| 0.013818 / 0.534201 (-0.520383) \| 0.285634 / 0.579283 (-0.293649) \| 0.268657 / 0.434364 (-0.165706) \| 0.325842 / 0.540337 (-0.214496) \| 0.439902 / 1.386936 (-0.947034) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005202 / 0.011353 (-0.006150) \| 0.003002 / 0.011008 (-0.008006) \| 0.048729 / 0.038508 (0.010221) \| 0.048178 / 0.023109 (0.025069) \| 0.288573 / 0.275898 (0.012675) \| 0.311122 / 0.323480 (-0.012358) \| 0.003953 / 0.007986 (-0.004033) \| 0.002544 / 0.004328 (-0.001785) \| 0.047762 / 0.004250 (0.043511) \| 0.039711 / 0.037052 (0.002658) \| 0.308389 / 0.258489 (0.049900) \| 0.321913 / 0.293841 (0.028072) \| 0.029166 / 0.128546 (-0.099380) \| 0.010697 / 0.075646 (-0.064950) \| 0.057758 / 0.419271 (-0.361514) \| 0.032743 / 0.043533 (-0.010789) \| 0.290933 / 0.255139 (0.035794) \| 0.309404 / 0.283200 (0.026205) \| 0.017691 / 0.141683 (-0.123992) \| 1.157713 / 1.452155 (-0.294442) \| 1.210485 / 1.492716 (-0.282231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088959 / 0.018006 (0.070953) \| 0.298531 / 0.000490 (0.298041) \| 0.000221 / 0.000200 (0.000021) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021129 / 0.037411 (-0.016283) \| 0.068419 / 0.014526 (0.053893) \| 0.079328 / 0.176557 (-0.097228) \| 0.118603 / 0.737135 (-0.618532) \| 0.080489 / 0.296338 (-0.215850) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292464 / 0.215209 (0.077254) \| 2.898221 / 2.077655 (0.820566) \| 1.600868 / 1.504120 (0.096748) \| 1.485128 / 1.541195 (-0.056067) \| 1.493091 / 1.468490 (0.024600) \| 0.576117 / 4.584777 (-4.008660) \| 2.450440 / 3.745712 (-1.295273) \| 2.746026 / 5.269862 (-2.523836) \| 1.722555 / 4.565676 (-2.843122) \| 0.062869 / 0.424275 (-0.361406) \| 0.004918 / 0.007607 (-0.002689) \| 0.348470 / 0.226044 (0.122425) \| 3.420267 / 2.268929 (1.151339) \| 1.942973 / 55.444624 (-53.501651) \| 1.667684 / 6.876477 (-5.208793) \| 1.669618 / 2.142072 (-0.472454) \| 0.630275 / 4.805227 (-4.174952) \| 0.115072 / 6.500664 (-6.385592) \| 0.040430 / 0.075469 (-0.035039) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989827 / 1.841788 (-0.851961) \| 11.578068 / 8.074308 (3.503760) \| 10.636060 / 10.191392 (0.444668) \| 0.131943 / 0.680424 (-0.548481) \| 0.015915 / 0.534201 (-0.518286) \| 0.287277 / 0.579283 (-0.292006) \| 0.279451 / 0.434364 (-0.154913) \| 0.325485 / 0.540337 (-0.214852) \| 0.544635 / 1.386936 (-0.842301) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22579be6c73867ac1a3c03e925abaf4872f8437 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005144 / 0.011353 (-0.006209) \| 0.003686 / 0.011008 (-0.007322) \| 0.064003 / 0.038508 (0.025495) \| 0.058962 / 0.023109 (0.035853) \| 0.233753 / 0.275898 (-0.042145) \| 0.255802 / 0.323480 (-0.067677) \| 0.003871 / 0.007986 (-0.004115) \| 0.002609 / 0.004328 (-0.001719) \| 0.048675 / 0.004250 (0.044425) \| 0.037550 / 0.037052 (0.000498) \| 0.240658 / 0.258489 (-0.017831) \| 0.272303 / 0.293841 (-0.021538) \| 0.027455 / 0.128546 (-0.101091) \| 0.010706 / 0.075646 (-0.064941) \| 0.210878 / 0.419271 (-0.208393) \| 0.035763 / 0.043533 (-0.007770) \| 0.239937 / 0.255139 (-0.015202) \| 0.262520 / 0.283200 (-0.020680) \| 0.017676 / 0.141683 (-0.124006) \| 1.095036 / 1.452155 (-0.357118) \| 1.178318 / 1.492716 (-0.314399) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095310 / 0.018006 (0.077304) \| 0.307485 / 0.000490 (0.306995) \| 0.000212 / 0.000200 (0.000013) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018630 / 0.037411 (-0.018781) \| 0.060461 / 0.014526 (0.045936) \| 0.073117 / 0.176557 (-0.103440) \| 0.119737 / 0.737135 (-0.617399) \| 0.073909 / 0.296338 (-0.222430) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280938 / 0.215209 (0.065729) \| 2.755333 / 2.077655 (0.677679) \| 1.468153 / 1.504120 (-0.035967) \| 1.350247 / 1.541195 (-0.190948) \| 1.379834 / 1.468490 (-0.088656) \| 0.564027 / 4.584777 (-4.020750) \| 2.387794 / 3.745712 (-1.357918) \| 2.768529 / 5.269862 (-2.501333) \| 1.761994 / 4.565676 (-2.803682) \| 0.062079 / 0.424275 (-0.362196) \| 0.005018 / 0.007607 (-0.002589) \| 0.337576 / 0.226044 (0.111532) \| 3.345347 / 2.268929 (1.076418) \| 1.821950 / 55.444624 (-53.622674) \| 1.545471 / 6.876477 (-5.331006) \| 1.534941 / 2.142072 (-0.607131) \| 0.626560 / 4.805227 (-4.178668) \| 0.116227 / 6.500664 (-6.384437) \| 0.041722 / 0.075469 (-0.033747) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950480 / 1.841788 (-0.891307) \| 11.616355 / 8.074308 (3.542047) \| 10.426687 / 10.191392 (0.235295) \| 0.129967 / 0.680424 (-0.550457) \| 0.013977 / 0.534201 (-0.520224) \| 0.287150 / 0.579283 (-0.292133) \| 0.264028 / 0.434364 (-0.170336) \| 0.325061 / 0.540337 (-0.215277) \| 0.441281 / 1.386936 (-0.945655) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005436 / 0.011353 (-0.005917) \| 0.003567 / 0.011008 (-0.007441) \| 0.055275 / 0.038508 (0.016767) \| 0.053216 / 0.023109 (0.030107) \| 0.272826 / 0.275898 (-0.003072) \| 0.298399 / 0.323480 (-0.025081) \| 0.004803 / 0.007986 (-0.003183) \| 0.002681 / 0.004328 (-0.001648) \| 0.048704 / 0.004250 (0.044453) \| 0.040048 / 0.037052 (0.002996) \| 0.278200 / 0.258489 (0.019711) \| 0.331167 / 0.293841 (0.037326) \| 0.029282 / 0.128546 (-0.099265) \| 0.010766 / 0.075646 (-0.064881) \| 0.057370 / 0.419271 (-0.361902) \| 0.032674 / 0.043533 (-0.010859) \| 0.269430 / 0.255139 (0.014291) \| 0.288256 / 0.283200 (0.005056) \| 0.019340 / 0.141683 (-0.122343) \| 1.118058 / 1.452155 (-0.334097) \| 1.157811 / 1.492716 (-0.334906) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094091 / 0.018006 (0.076085) \| 0.301833 / 0.000490 (0.301343) \| 0.000216 / 0.000200 (0.000016) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021327 / 0.037411 (-0.016085) \| 0.068636 / 0.014526 (0.054110) \| 0.080246 / 0.176557 (-0.096311) \| 0.120524 / 0.737135 (-0.616611) \| 0.082226 / 0.296338 (-0.214113) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293579 / 0.215209 (0.078370) \| 2.880281 / 2.077655 (0.802626) \| 1.594647 / 1.504120 (0.090528) \| 1.477152 / 1.541195 (-0.064043) \| 1.498122 / 1.468490 (0.029632) \| 0.555073 / 4.584777 (-4.029704) \| 2.446743 / 3.745712 (-1.298970) \| 2.794971 / 5.269862 (-2.474890) \| 1.749730 / 4.565676 (-2.815947) \| 0.062537 / 0.424275 (-0.361738) \| 0.004908 / 0.007607 (-0.002699) \| 0.350772 / 0.226044 (0.124727) \| 3.486535 / 2.268929 (1.217607) \| 1.957414 / 55.444624 (-53.487210) \| 1.669169 / 6.876477 (-5.207308) \| 1.682396 / 2.142072 (-0.459676) \| 0.627379 / 4.805227 (-4.177848) \| 0.117218 / 6.500664 (-6.383446) \| 0.041000 / 0.075469 (-0.034469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.958248 / 1.841788 (-0.883539) \| 12.022677 / 8.074308 (3.948369) \| 10.331661 / 10.191392 (0.140269) \| 0.129765 / 0.680424 (-0.550659) \| 0.015073 / 0.534201 (-0.519128) \| 0.287212 / 0.579283 (-0.292071) \| 0.278310 / 0.434364 (-0.156054) \| 0.328155 / 0.540337 (-0.212183) \| 0.564990 / 1.386936 (-0.821946) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c16e56371e50adae771288945e3389cb81a31fd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005576 / 0.011353 (-0.005777) \| 0.003430 / 0.011008 (-0.007578) \| 0.062714 / 0.038508 (0.024206) \| 0.051240 / 0.023109 (0.028131) \| 0.236637 / 0.275898 (-0.039261) \| 0.262660 / 0.323480 (-0.060820) \| 0.002924 / 0.007986 (-0.005061) \| 0.002712 / 0.004328 (-0.001616) \| 0.048680 / 0.004250 (0.044430) \| 0.038997 / 0.037052 (0.001945) \| 0.241426 / 0.258489 (-0.017063) \| 0.270652 / 0.293841 (-0.023189) \| 0.027355 / 0.128546 (-0.101192) \| 0.010640 / 0.075646 (-0.065006) \| 0.207754 / 0.419271 (-0.211517) \| 0.035921 / 0.043533 (-0.007612) \| 0.247645 / 0.255139 (-0.007494) \| 0.262933 / 0.283200 (-0.020266) \| 0.019658 / 0.141683 (-0.122025) \| 1.112576 / 1.452155 (-0.339578) \| 1.177362 / 1.492716 (-0.315354) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098100 / 0.018006 (0.080093) \| 0.310170 / 0.000490 (0.309680) \| 0.000220 / 0.000200 (0.000020) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019626 / 0.037411 (-0.017785) \| 0.065468 / 0.014526 (0.050942) \| 0.074767 / 0.176557 (-0.101789) \| 0.123619 / 0.737135 (-0.613516) \| 0.077159 / 0.296338 (-0.219179) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288585 / 0.215209 (0.073376) \| 2.771254 / 2.077655 (0.693599) \| 1.457091 / 1.504120 (-0.047029) \| 1.324341 / 1.541195 (-0.216854) \| 1.361960 / 1.468490 (-0.106530) \| 0.574197 / 4.584777 (-4.010580) \| 2.391440 / 3.745712 (-1.354273) \| 2.935060 / 5.269862 (-2.334802) \| 1.802792 / 4.565676 (-2.762884) \| 0.063530 / 0.424275 (-0.360745) \| 0.005129 / 0.007607 (-0.002478) \| 0.345977 / 0.226044 (0.119933) \| 3.368042 / 2.268929 (1.099113) \| 1.789575 / 55.444624 (-53.655050) \| 1.509165 / 6.876477 (-5.367312) \| 1.579792 / 2.142072 (-0.562280) \| 0.652136 / 4.805227 (-4.153091) \| 0.117014 / 6.500664 (-6.383650) \| 0.042385 / 0.075469 (-0.033084) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963967 / 1.841788 (-0.877821) \| 11.847856 / 8.074308 (3.773548) \| 10.584088 / 10.191392 (0.392696) \| 0.143953 / 0.680424 (-0.536471) \| 0.014355 / 0.534201 (-0.519846) \| 0.286936 / 0.579283 (-0.292347) \| 0.269039 / 0.434364 (-0.165325) \| 0.324531 / 0.540337 (-0.215807) \| 0.443187 / 1.386936 (-0.943749) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005448 / 0.011353 (-0.005905) \| 0.003742 / 0.011008 (-0.007266) \| 0.048808 / 0.038508 (0.010300) \| 0.055409 / 0.023109 (0.032300) \| 0.271574 / 0.275898 (-0.004324) \| 0.295599 / 0.323480 (-0.027881) \| 0.004208 / 0.007986 (-0.003778) \| 0.002683 / 0.004328 (-0.001645) \| 0.048813 / 0.004250 (0.044562) \| 0.043672 / 0.037052 (0.006620) \| 0.282173 / 0.258489 (0.023684) \| 0.295447 / 0.293841 (0.001606) \| 0.030461 / 0.128546 (-0.098086) \| 0.010988 / 0.075646 (-0.064658) \| 0.057050 / 0.419271 (-0.362221) \| 0.033329 / 0.043533 (-0.010203) \| 0.269700 / 0.255139 (0.014561) \| 0.287099 / 0.283200 (0.003899) \| 0.018203 / 0.141683 (-0.123480) \| 1.142584 / 1.452155 (-0.309571) \| 1.181848 / 1.492716 (-0.310869) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096958 / 0.018006 (0.078952) \| 0.310563 / 0.000490 (0.310074) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022213 / 0.037411 (-0.015199) \| 0.072054 / 0.014526 (0.057528) \| 0.086393 / 0.176557 (-0.090163) \| 0.122431 / 0.737135 (-0.614704) \| 0.085298 / 0.296338 (-0.211041) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290823 / 0.215209 (0.075614) \| 2.838026 / 2.077655 (0.760371) \| 1.541425 / 1.504120 (0.037305) \| 1.431903 / 1.541195 (-0.109292) \| 1.476567 / 1.468490 (0.008077) \| 0.557856 / 4.584777 (-4.026920) \| 2.449101 / 3.745712 (-1.296611) \| 2.924633 / 5.269862 (-2.345229) \| 1.824420 / 4.565676 (-2.741256) \| 0.063735 / 0.424275 (-0.360540) \| 0.005025 / 0.007607 (-0.002582) \| 0.349458 / 0.226044 (0.123413) \| 3.468627 / 2.268929 (1.199699) \| 1.925173 / 55.444624 (-53.519451) \| 1.655038 / 6.876477 (-5.221439) \| 1.698612 / 2.142072 (-0.443460) \| 0.643623 / 4.805227 (-4.161604) \| 0.116128 / 6.500664 (-6.384536) \| 0.042283 / 0.075469 (-0.033186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963029 / 1.841788 (-0.878758) \| 13.273985 / 8.074308 (5.199677) \| 11.400884 / 10.191392 (1.209492) \| 0.152635 / 0.680424 (-0.527788) \| 0.016442 / 0.534201 (-0.517759) \| 0.289272 / 0.579283 (-0.290012) \| 0.285286 / 0.434364 (-0.149078) \| 0.330028 / 0.540337 (-0.210310) \| 0.596500 / 1.386936 (-0.790436) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c427c4b1dcf84c898ae62dc521bf446bb35e0e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005124 / 0.011353 (-0.006229) \| 0.003832 / 0.011008 (-0.007176) \| 0.062806 / 0.038508 (0.024298) \| 0.053137 / 0.023109 (0.030028) \| 0.241155 / 0.275898 (-0.034743) \| 0.260521 / 0.323480 (-0.062959) \| 0.004005 / 0.007986 (-0.003981) \| 0.002754 / 0.004328 (-0.001575) \| 0.048934 / 0.004250 (0.044684) \| 0.039438 / 0.037052 (0.002385) \| 0.242534 / 0.258489 (-0.015955) \| 0.275498 / 0.293841 (-0.018343) \| 0.027338 / 0.128546 (-0.101208) \| 0.010809 / 0.075646 (-0.064837) \| 0.206986 / 0.419271 (-0.212285) \| 0.035614 / 0.043533 (-0.007919) \| 0.245780 / 0.255139 (-0.009359) \| 0.259793 / 0.283200 (-0.023407) \| 0.018108 / 0.141683 (-0.123575) \| 1.103412 / 1.452155 (-0.348742) \| 1.162940 / 1.492716 (-0.329776) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092463 / 0.018006 (0.074457) \| 0.299516 / 0.000490 (0.299026) \| 0.000210 / 0.000200 (0.000010) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018261 / 0.037411 (-0.019150) \| 0.060178 / 0.014526 (0.045652) \| 0.073043 / 0.176557 (-0.103513) \| 0.120541 / 0.737135 (-0.616594) \| 0.074972 / 0.296338 (-0.221367) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287288 / 0.215209 (0.072078) \| 2.814915 / 2.077655 (0.737260) \| 1.520221 / 1.504120 (0.016101) \| 1.396045 / 1.541195 (-0.145149) \| 1.419662 / 1.468490 (-0.048828) \| 0.589247 / 4.584777 (-3.995530) \| 2.411101 / 3.745712 (-1.334611) \| 2.777709 / 5.269862 (-2.492153) \| 1.750386 / 4.565676 (-2.815291) \| 0.063734 / 0.424275 (-0.360541) \| 0.005021 / 0.007607 (-0.002586) \| 0.338817 / 0.226044 (0.112773) \| 3.371218 / 2.268929 (1.102289) \| 1.892691 / 55.444624 (-53.551934) \| 1.599039 / 6.876477 (-5.277438) \| 1.574726 / 2.142072 (-0.567346) \| 0.665623 / 4.805227 (-4.139604) \| 0.118628 / 6.500664 (-6.382036) \| 0.041803 / 0.075469 (-0.033666) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948696 / 1.841788 (-0.893092) \| 11.502916 / 8.074308 (3.428608) \| 10.301174 / 10.191392 (0.109782) \| 0.141752 / 0.680424 (-0.538672) \| 0.014064 / 0.534201 (-0.520137) \| 0.286701 / 0.579283 (-0.292583) \| 0.265805 / 0.434364 (-0.168559) \| 0.328420 / 0.540337 (-0.211917) \| 0.433619 / 1.386936 (-0.953317) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005262 / 0.011353 (-0.006091) \| 0.003361 / 0.011008 (-0.007648) \| 0.049525 / 0.038508 (0.011016) \| 0.048950 / 0.023109 (0.025841) \| 0.273617 / 0.275898 (-0.002281) \| 0.296614 / 0.323480 (-0.026866) \| 0.004014 / 0.007986 (-0.003971) \| 0.002630 / 0.004328 (-0.001698) \| 0.048203 / 0.004250 (0.043952) \| 0.040912 / 0.037052 (0.003860) \| 0.279736 / 0.258489 (0.021247) \| 0.301671 / 0.293841 (0.007830) \| 0.028546 / 0.128546 (-0.100000) \| 0.010440 / 0.075646 (-0.065206) \| 0.057869 / 0.419271 (-0.361402) \| 0.032876 / 0.043533 (-0.010657) \| 0.277649 / 0.255139 (0.022510) \| 0.296565 / 0.283200 (0.013365) \| 0.017558 / 0.141683 (-0.124125) \| 1.155005 / 1.452155 (-0.297149) \| 1.204827 / 1.492716 (-0.287889) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093248 / 0.018006 (0.075242) \| 0.302721 / 0.000490 (0.302231) \| 0.000218 / 0.000200 (0.000018) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021882 / 0.037411 (-0.015530) \| 0.068259 / 0.014526 (0.053733) \| 0.080982 / 0.176557 (-0.095574) \| 0.119386 / 0.737135 (-0.617750) \| 0.081745 / 0.296338 (-0.214593) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297812 / 0.215209 (0.082603) \| 2.909938 / 2.077655 (0.832283) \| 1.603736 / 1.504120 (0.099616) \| 1.482989 / 1.541195 (-0.058206) \| 1.495107 / 1.468490 (0.026617) \| 0.562275 / 4.584777 (-4.022502) \| 2.424812 / 3.745712 (-1.320901) \| 2.759127 / 5.269862 (-2.510735) \| 1.733283 / 4.565676 (-2.832394) \| 0.063144 / 0.424275 (-0.361131) \| 0.004949 / 0.007607 (-0.002658) \| 0.352756 / 0.226044 (0.126711) \| 3.496028 / 2.268929 (1.227100) \| 1.982804 / 55.444624 (-53.461820) \| 1.689787 / 6.876477 (-5.186690) \| 1.672699 / 2.142072 (-0.469373) \| 0.660169 / 4.805227 (-4.145059) \| 0.116535 / 6.500664 (-6.384129) \| 0.040616 / 0.075469 (-0.034853) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975055 / 1.841788 (-0.866733) \| 11.919295 / 8.074308 (3.844986) \| 10.779188 / 10.191392 (0.587796) \| 0.143106 / 0.680424 (-0.537318) \| 0.015159 / 0.534201 (-0.519041) \| 0.289734 / 0.579283 (-0.289549) \| 0.278637 / 0.434364 (-0.155727) \| 0.328159 / 0.540337 (-0.212178) \| 0.570560 / 1.386936 (-0.816376) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#241500208da5fef64ad6ddc1cc5ab2be18f2f76d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005155 / 0.011353 (-0.006198) \| 0.003589 / 0.011008 (-0.007419) \| 0.064440 / 0.038508 (0.025932) \| 0.051020 / 0.023109 (0.027911) \| 0.246099 / 0.275898 (-0.029799) \| 0.273383 / 0.323480 (-0.050097) \| 0.003984 / 0.007986 (-0.004002) \| 0.002791 / 0.004328 (-0.001537) \| 0.049076 / 0.004250 (0.044826) \| 0.037975 / 0.037052 (0.000922) \| 0.253709 / 0.258489 (-0.004780) \| 0.281730 / 0.293841 (-0.012111) \| 0.028060 / 0.128546 (-0.100486) \| 0.010808 / 0.075646 (-0.064838) \| 0.206663 / 0.419271 (-0.212609) \| 0.035989 / 0.043533 (-0.007544) \| 0.252635 / 0.255139 (-0.002504) \| 0.280042 / 0.283200 (-0.003158) \| 0.016982 / 0.141683 (-0.124700) \| 1.098679 / 1.452155 (-0.353475) \| 1.157051 / 1.492716 (-0.335666) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098238 / 0.018006 (0.080232) \| 0.311990 / 0.000490 (0.311501) \| 0.000229 / 0.000200 (0.000029) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018270 / 0.037411 (-0.019141) \| 0.062711 / 0.014526 (0.048186) \| 0.074381 / 0.176557 (-0.102175) \| 0.119946 / 0.737135 (-0.617189) \| 0.075013 / 0.296338 (-0.221325) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282106 / 0.215209 (0.066897) \| 2.752653 / 2.077655 (0.674999) \| 1.488771 / 1.504120 (-0.015349) \| 1.372552 / 1.541195 (-0.168643) \| 1.390270 / 1.468490 (-0.078220) \| 0.558928 / 4.584777 (-4.025849) \| 2.411821 / 3.745712 (-1.333891) \| 2.771441 / 5.269862 (-2.498421) \| 1.747507 / 4.565676 (-2.818169) \| 0.061360 / 0.424275 (-0.362915) \| 0.004956 / 0.007607 (-0.002652) \| 0.332330 / 0.226044 (0.106286) \| 3.301405 / 2.268929 (1.032476) \| 1.786726 / 55.444624 (-53.657899) \| 1.529974 / 6.876477 (-5.346502) \| 1.538412 / 2.142072 (-0.603660) \| 0.637590 / 4.805227 (-4.167637) \| 0.117215 / 6.500664 (-6.383449) \| 0.042186 / 0.075469 (-0.033283) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945574 / 1.841788 (-0.896213) \| 11.616152 / 8.074308 (3.541844) \| 10.365114 / 10.191392 (0.173722) \| 0.130358 / 0.680424 (-0.550066) \| 0.013587 / 0.534201 (-0.520614) \| 0.306024 / 0.579283 (-0.273259) \| 0.270577 / 0.434364 (-0.163787) \| 0.340768 / 0.540337 (-0.199569) \| 0.460841 / 1.386936 (-0.926095) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005254 / 0.011353 (-0.006099) \| 0.003137 / 0.011008 (-0.007871) \| 0.048302 / 0.038508 (0.009794) \| 0.051952 / 0.023109 (0.028843) \| 0.269078 / 0.275898 (-0.006820) \| 0.292044 / 0.323480 (-0.031436) \| 0.003985 / 0.007986 (-0.004000) \| 0.002597 / 0.004328 (-0.001732) \| 0.049998 / 0.004250 (0.045747) \| 0.040227 / 0.037052 (0.003174) \| 0.274714 / 0.258489 (0.016225) \| 0.298160 / 0.293841 (0.004319) \| 0.028857 / 0.128546 (-0.099690) \| 0.010545 / 0.075646 (-0.065101) \| 0.057234 / 0.419271 (-0.362038) \| 0.032515 / 0.043533 (-0.011018) \| 0.271526 / 0.255139 (0.016387) \| 0.288556 / 0.283200 (0.005356) \| 0.018155 / 0.141683 (-0.123527) \| 1.201906 / 1.452155 (-0.250248) \| 1.220068 / 1.492716 (-0.272648) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100098 / 0.018006 (0.082092) \| 0.311081 / 0.000490 (0.310591) \| 0.000231 / 0.000200 (0.000032) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022349 / 0.037411 (-0.015062) \| 0.069698 / 0.014526 (0.055172) \| 0.081334 / 0.176557 (-0.095222) \| 0.120847 / 0.737135 (-0.616289) \| 0.082091 / 0.296338 (-0.214248) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293810 / 0.215209 (0.078601) \| 2.844191 / 2.077655 (0.766536) \| 1.594494 / 1.504120 (0.090374) \| 1.486531 / 1.541195 (-0.054664) \| 1.506307 / 1.468490 (0.037817) \| 0.560247 / 4.584777 (-4.024530) \| 2.478309 / 3.745712 (-1.267403) \| 2.759024 / 5.269862 (-2.510837) \| 1.733063 / 4.565676 (-2.832613) \| 0.061838 / 0.424275 (-0.362438) \| 0.004869 / 0.007607 (-0.002738) \| 0.347267 / 0.226044 (0.121222) \| 3.407737 / 2.268929 (1.138808) \| 1.944420 / 55.444624 (-53.500204) \| 1.660060 / 6.876477 (-5.216417) \| 1.704219 / 2.142072 (-0.437854) \| 0.646969 / 4.805227 (-4.158258) \| 0.115750 / 6.500664 (-6.384914) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972537 / 1.841788 (-0.869251) \| 12.013530 / 8.074308 (3.939222) \| 10.650215 / 10.191392 (0.458823) \| 0.132877 / 0.680424 (-0.547547) \| 0.016828 / 0.534201 (-0.517372) \| 0.288321 / 0.579283 (-0.290962) \| 0.284203 / 0.434364 (-0.150161) \| 0.324016 / 0.540337 (-0.216321) \| 0.575403 / 1.386936 (-0.811533) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17ec1a7a610adba3db44f316a930b979872d4ef7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005925 / 0.011353 (-0.005427) \| 0.005138 / 0.011008 (-0.005870) \| 0.069865 / 0.038508 (0.031356) \| 0.067181 / 0.023109 (0.044072) \| 0.309642 / 0.275898 (0.033743) \| 0.302919 / 0.323480 (-0.020561) \| 0.003365 / 0.007986 (-0.004620) \| 0.003148 / 0.004328 (-0.001180) \| 0.054102 / 0.004250 (0.049852) \| 0.044196 / 0.037052 (0.007143) \| 0.306882 / 0.258489 (0.048393) \| 0.315153 / 0.293841 (0.021313) \| 0.030458 / 0.128546 (-0.098089) \| 0.011773 / 0.075646 (-0.063874) \| 0.235075 / 0.419271 (-0.184196) \| 0.040840 / 0.043533 (-0.002693) \| 0.279897 / 0.255139 (0.024758) \| 0.316334 / 0.283200 (0.033135) \| 0.020128 / 0.141683 (-0.121555) \| 1.237327 / 1.452155 (-0.214828) \| 1.290386 / 1.492716 (-0.202331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.118540 / 0.018006 (0.100534) \| 0.363282 / 0.000490 (0.362792) \| 0.000266 / 0.000200 (0.000066) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021435 / 0.037411 (-0.015977) \| 0.068124 / 0.014526 (0.053598) \| 0.082747 / 0.176557 (-0.093809) \| 0.137179 / 0.737135 (-0.599956) \| 0.084815 / 0.296338 (-0.211523) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307836 / 0.215209 (0.092626) \| 2.983444 / 2.077655 (0.905790) \| 1.616430 / 1.504120 (0.112310) \| 1.466843 / 1.541195 (-0.074351) \| 1.512440 / 1.468490 (0.043950) \| 0.652311 / 4.584777 (-3.932466) \| 2.676420 / 3.745712 (-1.069292) \| 3.265747 / 5.269862 (-2.004115) \| 2.028586 / 4.565676 (-2.537090) \| 0.071997 / 0.424275 (-0.352278) \| 0.007068 / 0.007607 (-0.000539) \| 0.367199 / 0.226044 (0.141155) \| 3.617970 / 2.268929 (1.349042) \| 1.991345 / 55.444624 (-53.453280) \| 1.670015 / 6.876477 (-5.206462) \| 1.720515 / 2.142072 (-0.421557) \| 0.724649 / 4.805227 (-4.080579) \| 0.134888 / 6.500664 (-6.365776) \| 0.048325 / 0.075469 (-0.027144) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.051058 / 1.841788 (-0.790730) \| 13.772809 / 8.074308 (5.698501) \| 11.813879 / 10.191392 (1.622487) \| 0.160065 / 0.680424 (-0.520359) \| 0.016256 / 0.534201 (-0.517945) \| 0.320393 / 0.579283 (-0.258890) \| 0.314462 / 0.434364 (-0.119901) \| 0.371911 / 0.540337 (-0.168427) \| 0.506864 / 1.386936 (-0.880072) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005857 / 0.011353 (-0.005496) \| 0.004077 / 0.011008 (-0.006931) \| 0.056033 / 0.038508 (0.017525) \| 0.067622 / 0.023109 (0.044513) \| 0.298956 / 0.275898 (0.023058) \| 0.323484 / 0.323480 (0.000004) \| 0.004825 / 0.007986 (-0.003160) \| 0.003120 / 0.004328 (-0.001208) \| 0.055227 / 0.004250 (0.050976) \| 0.048439 / 0.037052 (0.011387) \| 0.303207 / 0.258489 (0.044718) \| 0.329478 / 0.293841 (0.035637) \| 0.032516 / 0.128546 (-0.096031) \| 0.012260 / 0.075646 (-0.063386) \| 0.065037 / 0.419271 (-0.354234) \| 0.038799 / 0.043533 (-0.004734) \| 0.299102 / 0.255139 (0.043963) \| 0.318248 / 0.283200 (0.035048) \| 0.020190 / 0.141683 (-0.121493) \| 1.263479 / 1.452155 (-0.188676) \| 1.329788 / 1.492716 (-0.162928) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.119801 / 0.018006 (0.101794) \| 0.359618 / 0.000490 (0.359129) \| 0.000260 / 0.000200 (0.000060) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026876 / 0.037411 (-0.010535) \| 0.080637 / 0.014526 (0.066111) \| 0.092260 / 0.176557 (-0.084297) \| 0.137260 / 0.737135 (-0.599875) \| 0.093309 / 0.296338 (-0.203029) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.329327 / 0.215209 (0.114118) \| 3.193014 / 2.077655 (1.115359) \| 1.755838 / 1.504120 (0.251718) \| 1.612279 / 1.541195 (0.071084) \| 1.631958 / 1.468490 (0.163468) \| 0.630886 / 4.584777 (-3.953891) \| 2.739731 / 3.745712 (-1.005981) \| 3.186745 / 5.269862 (-2.083117) \| 1.987125 / 4.565676 (-2.578552) \| 0.070694 / 0.424275 (-0.353581) \| 0.006461 / 0.007607 (-0.001146) \| 0.386367 / 0.226044 (0.160323) \| 3.815837 / 2.268929 (1.546908) \| 2.155904 / 55.444624 (-53.288720) \| 1.832575 / 6.876477 (-5.043902) \| 1.842097 / 2.142072 (-0.299975) \| 0.716394 / 4.805227 (-4.088833) \| 0.130796 / 6.500664 (-6.369869) \| 0.045674 / 0.075469 (-0.029795) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.109117 / 1.841788 (-0.732671) \| 14.116582 / 8.074308 (6.042274) \| 11.926356 / 10.191392 (1.734964) \| 0.150543 / 0.680424 (-0.529881) \| 0.017426 / 0.534201 (-0.516775) \| 0.323058 / 0.579283 (-0.256225) \| 0.330228 / 0.434364 (-0.104136) \| 0.372533 / 0.540337 (-0.167804) \| 0.661348 / 1.386936 (-0.725588) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04ffd22a30ecc7545234559edd9d23c85c6d84d9 \"CML watermark\")\n", "Thanks for the review, I took your comments into account !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005477 / 0.011353 (-0.005876) \| 0.003509 / 0.011008 (-0.007499) \| 0.062884 / 0.038508 (0.024376) \| 0.051042 / 0.023109 (0.027933) \| 0.285180 / 0.275898 (0.009282) \| 0.315353 / 0.323480 (-0.008127) \| 0.002943 / 0.007986 (-0.005043) \| 0.003286 / 0.004328 (-0.001042) \| 0.048885 / 0.004250 (0.044635) \| 0.038591 / 0.037052 (0.001539) \| 0.288527 / 0.258489 (0.030038) \| 0.316102 / 0.293841 (0.022261) \| 0.028252 / 0.128546 (-0.100295) \| 0.010622 / 0.075646 (-0.065024) \| 0.205573 / 0.419271 (-0.213699) \| 0.035764 / 0.043533 (-0.007769) \| 0.285729 / 0.255139 (0.030590) \| 0.304578 / 0.283200 (0.021378) \| 0.019862 / 0.141683 (-0.121821) \| 1.102866 / 1.452155 (-0.349288) \| 1.175161 / 1.492716 (-0.317555) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095253 / 0.018006 (0.077246) \| 0.302290 / 0.000490 (0.301800) \| 0.000243 / 0.000200 (0.000043) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018680 / 0.037411 (-0.018731) \| 0.060375 / 0.014526 (0.045849) \| 0.074033 / 0.176557 (-0.102524) \| 0.120290 / 0.737135 (-0.616845) \| 0.075350 / 0.296338 (-0.220989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277617 / 0.215209 (0.062408) \| 2.718201 / 2.077655 (0.640546) \| 1.462952 / 1.504120 (-0.041168) \| 1.339199 / 1.541195 (-0.201996) \| 1.375805 / 1.468490 (-0.092685) \| 0.559956 / 4.584777 (-4.024821) \| 2.373865 / 3.745712 (-1.371847) \| 2.795732 / 5.269862 (-2.474129) \| 1.755490 / 4.565676 (-2.810186) \| 0.062002 / 0.424275 (-0.362273) \| 0.004935 / 0.007607 (-0.002672) \| 0.334786 / 0.226044 (0.108741) \| 3.237499 / 2.268929 (0.968571) \| 1.787561 / 55.444624 (-53.657064) \| 1.513300 / 6.876477 (-5.363176) \| 1.549797 / 2.142072 (-0.592275) \| 0.643587 / 4.805227 (-4.161640) \| 0.117275 / 6.500664 (-6.383389) \| 0.042184 / 0.075469 (-0.033285) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933366 / 1.841788 (-0.908421) \| 11.792282 / 8.074308 (3.717973) \| 10.466608 / 10.191392 (0.275216) \| 0.142148 / 0.680424 (-0.538275) \| 0.014084 / 0.534201 (-0.520117) \| 0.287233 / 0.579283 (-0.292050) \| 0.266022 / 0.434364 (-0.168342) \| 0.326854 / 0.540337 (-0.213483) \| 0.451348 / 1.386936 (-0.935588) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005384 / 0.011353 (-0.005969) \| 0.003562 / 0.011008 (-0.007446) \| 0.049014 / 0.038508 (0.010506) \| 0.057480 / 0.023109 (0.034371) \| 0.274456 / 0.275898 (-0.001442) \| 0.298387 / 0.323480 (-0.025093) \| 0.003909 / 0.007986 (-0.004076) \| 0.002646 / 0.004328 (-0.001683) \| 0.048374 / 0.004250 (0.044124) \| 0.040907 / 0.037052 (0.003854) \| 0.278267 / 0.258489 (0.019778) \| 0.299862 / 0.293841 (0.006021) \| 0.029108 / 0.128546 (-0.099439) \| 0.010752 / 0.075646 (-0.064894) \| 0.057523 / 0.419271 (-0.361749) \| 0.032692 / 0.043533 (-0.010841) \| 0.276288 / 0.255139 (0.021149) \| 0.291572 / 0.283200 (0.008372) \| 0.017818 / 0.141683 (-0.123865) \| 1.129517 / 1.452155 (-0.322638) \| 1.186630 / 1.492716 (-0.306086) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093405 / 0.018006 (0.075399) \| 0.301254 / 0.000490 (0.300764) \| 0.000225 / 0.000200 (0.000025) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021793 / 0.037411 (-0.015618) \| 0.069033 / 0.014526 (0.054508) \| 0.083502 / 0.176557 (-0.093055) \| 0.122149 / 0.737135 (-0.614986) \| 0.083801 / 0.296338 (-0.212537) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299149 / 0.215209 (0.083940) \| 2.936550 / 2.077655 (0.858895) \| 1.595766 / 1.504120 (0.091647) \| 1.487117 / 1.541195 (-0.054078) \| 1.494606 / 1.468490 (0.026116) \| 0.569346 / 4.584777 (-4.015431) \| 2.445642 / 3.745712 (-1.300070) \| 2.805696 / 5.269862 (-2.464165) \| 1.743796 / 4.565676 (-2.821881) \| 0.062695 / 0.424275 (-0.361580) \| 0.004885 / 0.007607 (-0.002723) \| 0.354186 / 0.226044 (0.128142) \| 3.487926 / 2.268929 (1.218997) \| 1.965703 / 55.444624 (-53.478922) \| 1.682284 / 6.876477 (-5.194193) \| 1.705586 / 2.142072 (-0.436487) \| 0.655099 / 4.805227 (-4.150128) \| 0.116441 / 6.500664 (-6.384223) \| 0.040851 / 0.075469 (-0.034618) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967361 / 1.841788 (-0.874427) \| 12.037718 / 8.074308 (3.963409) \| 10.599761 / 10.191392 (0.408369) \| 0.143127 / 0.680424 (-0.537297) \| 0.015063 / 0.534201 (-0.519138) \| 0.286894 / 0.579283 (-0.292389) \| 0.301505 / 0.434364 (-0.132859) \| 0.324339 / 0.540337 (-0.215999) \| 0.591782 / 1.386936 (-0.795154) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96ff08d4aa6dbafc8a10a9d03dfabe236378bcd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005337 / 0.011353 (-0.006015) \| 0.004074 / 0.011008 (-0.006934) \| 0.062653 / 0.038508 (0.024145) \| 0.054295 / 0.023109 (0.031186) \| 0.248284 / 0.275898 (-0.027614) \| 0.271604 / 0.323480 (-0.051876) \| 0.003931 / 0.007986 (-0.004055) \| 0.002907 / 0.004328 (-0.001422) \| 0.047991 / 0.004250 (0.043740) \| 0.042842 / 0.037052 (0.005790) \| 0.253648 / 0.258489 (-0.004841) \| 0.282546 / 0.293841 (-0.011295) \| 0.028005 / 0.128546 (-0.100541) \| 0.010734 / 0.075646 (-0.064912) \| 0.210023 / 0.419271 (-0.209248) \| 0.035940 / 0.043533 (-0.007592) \| 0.250766 / 0.255139 (-0.004373) \| 0.267644 / 0.283200 (-0.015556) \| 0.020451 / 0.141683 (-0.121232) \| 1.114972 / 1.452155 (-0.337183) \| 1.159823 / 1.492716 (-0.332893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095527 / 0.018006 (0.077521) \| 0.303321 / 0.000490 (0.302831) \| 0.000216 / 0.000200 (0.000016) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018725 / 0.037411 (-0.018686) \| 0.062537 / 0.014526 (0.048011) \| 0.073091 / 0.176557 (-0.103466) \| 0.119570 / 0.737135 (-0.617565) \| 0.074863 / 0.296338 (-0.221476) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284936 / 0.215209 (0.069727) \| 2.802498 / 2.077655 (0.724843) \| 1.493316 / 1.504120 (-0.010804) \| 1.372319 / 1.541195 (-0.168875) \| 1.403657 / 1.468490 (-0.064833) \| 0.569303 / 4.584777 (-4.015474) \| 2.402498 / 3.745712 (-1.343214) \| 2.834778 / 5.269862 (-2.435084) \| 1.791312 / 4.565676 (-2.774365) \| 0.062526 / 0.424275 (-0.361749) \| 0.004947 / 0.007607 (-0.002660) \| 0.345141 / 0.226044 (0.119097) \| 3.371863 / 2.268929 (1.102934) \| 1.846023 / 55.444624 (-53.598602) \| 1.596368 / 6.876477 (-5.280109) \| 1.615902 / 2.142072 (-0.526170) \| 0.644333 / 4.805227 (-4.160894) \| 0.119460 / 6.500664 (-6.381204) \| 0.049122 / 0.075469 (-0.026347) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951839 / 1.841788 (-0.889948) \| 11.677074 / 8.074308 (3.602766) \| 10.562586 / 10.191392 (0.371194) \| 0.143633 / 0.680424 (-0.536791) \| 0.014157 / 0.534201 (-0.520044) \| 0.289141 / 0.579283 (-0.290142) \| 0.264719 / 0.434364 (-0.169645) \| 0.327862 / 0.540337 (-0.212476) \| 0.451215 / 1.386936 (-0.935721) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005343 / 0.011353 (-0.006010) \| 0.003522 / 0.011008 (-0.007486) \| 0.049354 / 0.038508 (0.010846) \| 0.051441 / 0.023109 (0.028332) \| 0.259350 / 0.275898 (-0.016548) \| 0.288946 / 0.323480 (-0.034534) \| 0.004052 / 0.007986 (-0.003934) \| 0.002690 / 0.004328 (-0.001639) \| 0.049996 / 0.004250 (0.045746) \| 0.040224 / 0.037052 (0.003171) \| 0.264588 / 0.258489 (0.006099) \| 0.296474 / 0.293841 (0.002633) \| 0.028868 / 0.128546 (-0.099679) \| 0.010917 / 0.075646 (-0.064730) \| 0.057866 / 0.419271 (-0.361405) \| 0.032610 / 0.043533 (-0.010923) \| 0.260657 / 0.255139 (0.005518) \| 0.276947 / 0.283200 (-0.006253) \| 0.018877 / 0.141683 (-0.122806) \| 1.126205 / 1.452155 (-0.325949) \| 1.206173 / 1.492716 (-0.286543) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094464 / 0.018006 (0.076458) \| 0.304473 / 0.000490 (0.303984) \| 0.000231 / 0.000200 (0.000031) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021472 / 0.037411 (-0.015939) \| 0.070864 / 0.014526 (0.056338) \| 0.086607 / 0.176557 (-0.089950) \| 0.120679 / 0.737135 (-0.616456) \| 0.084271 / 0.296338 (-0.212068) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296448 / 0.215209 (0.081239) \| 2.893996 / 2.077655 (0.816341) \| 1.573409 / 1.504120 (0.069289) \| 1.438799 / 1.541195 (-0.102396) \| 1.461241 / 1.468490 (-0.007249) \| 0.566737 / 4.584777 (-4.018040) \| 2.425709 / 3.745712 (-1.320003) \| 2.826764 / 5.269862 (-2.443098) \| 1.785330 / 4.565676 (-2.780347) \| 0.063721 / 0.424275 (-0.360554) \| 0.005158 / 0.007607 (-0.002449) \| 0.354961 / 0.226044 (0.128916) \| 3.457499 / 2.268929 (1.188570) \| 1.931374 / 55.444624 (-53.513251) \| 1.646515 / 6.876477 (-5.229962) \| 1.629891 / 2.142072 (-0.512182) \| 0.648922 / 4.805227 (-4.156305) \| 0.114953 / 6.500664 (-6.385711) \| 0.040997 / 0.075469 (-0.034472) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951049 / 1.841788 (-0.890739) \| 12.258298 / 8.074308 (4.183990) \| 10.663309 / 10.191392 (0.471917) \| 0.142933 / 0.680424 (-0.537491) \| 0.015927 / 0.534201 (-0.518273) \| 0.286914 / 0.579283 (-0.292369) \| 0.286600 / 0.434364 (-0.147764) \| 0.324464 / 0.540337 (-0.215874) \| 0.575075 / 1.386936 (-0.811861) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed47b9d5e9c6aa03a0aa07d8abfd3fa8241da353 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005298 / 0.011353 (-0.006055) \| 0.003645 / 0.011008 (-0.007363) \| 0.061629 / 0.038508 (0.023121) \| 0.052322 / 0.023109 (0.029212) \| 0.242579 / 0.275898 (-0.033319) \| 0.263525 / 0.323480 (-0.059955) \| 0.002794 / 0.007986 (-0.005192) \| 0.002152 / 0.004328 (-0.002177) \| 0.048301 / 0.004250 (0.044050) \| 0.038177 / 0.037052 (0.001125) \| 0.247724 / 0.258489 (-0.010765) \| 0.274455 / 0.293841 (-0.019386) \| 0.026992 / 0.128546 (-0.101555) \| 0.010110 / 0.075646 (-0.065536) \| 0.205662 / 0.419271 (-0.213609) \| 0.034901 / 0.043533 (-0.008632) \| 0.241920 / 0.255139 (-0.013219) \| 0.262048 / 0.283200 (-0.021152) \| 0.019111 / 0.141683 (-0.122572) \| 1.127600 / 1.452155 (-0.324555) \| 1.193931 / 1.492716 (-0.298786) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090321 / 0.018006 (0.072315) \| 0.299046 / 0.000490 (0.298556) \| 0.000197 / 0.000200 (-0.000003) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018278 / 0.037411 (-0.019133) \| 0.060114 / 0.014526 (0.045588) \| 0.073602 / 0.176557 (-0.102954) \| 0.119676 / 0.737135 (-0.617459) \| 0.074786 / 0.296338 (-0.221552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280385 / 0.215209 (0.065176) \| 2.764259 / 2.077655 (0.686604) \| 1.501027 / 1.504120 (-0.003093) \| 1.376900 / 1.541195 (-0.164295) \| 1.390587 / 1.468490 (-0.077903) \| 0.555180 / 4.584777 (-4.029597) \| 2.354307 / 3.745712 (-1.391405) \| 2.755862 / 5.269862 (-2.514000) \| 1.714771 / 4.565676 (-2.850906) \| 0.062507 / 0.424275 (-0.361768) \| 0.004974 / 0.007607 (-0.002633) \| 0.333900 / 0.226044 (0.107856) \| 3.266922 / 2.268929 (0.997994) \| 1.805401 / 55.444624 (-53.639223) \| 1.526970 / 6.876477 (-5.349507) \| 1.539425 / 2.142072 (-0.602647) \| 0.629364 / 4.805227 (-4.175863) \| 0.114929 / 6.500664 (-6.385735) \| 0.041258 / 0.075469 (-0.034211) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968601 / 1.841788 (-0.873187) \| 11.260937 / 8.074308 (3.186629) \| 10.393839 / 10.191392 (0.202447) \| 0.127988 / 0.680424 (-0.552436) \| 0.014564 / 0.534201 (-0.519637) \| 0.286560 / 0.579283 (-0.292723) \| 0.260493 / 0.434364 (-0.173871) \| 0.330949 / 0.540337 (-0.209388) \| 0.435798 / 1.386936 (-0.951138) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005232 / 0.011353 (-0.006121) \| 0.003030 / 0.011008 (-0.007978) \| 0.048513 / 0.038508 (0.010005) \| 0.049501 / 0.023109 (0.026392) \| 0.270545 / 0.275898 (-0.005353) \| 0.289128 / 0.323480 (-0.034352) \| 0.003925 / 0.007986 (-0.004061) \| 0.002568 / 0.004328 (-0.001761) \| 0.047692 / 0.004250 (0.043442) \| 0.039854 / 0.037052 (0.002802) \| 0.272654 / 0.258489 (0.014165) \| 0.296275 / 0.293841 (0.002434) \| 0.029027 / 0.128546 (-0.099519) \| 0.010335 / 0.075646 (-0.065311) \| 0.056726 / 0.419271 (-0.362546) \| 0.033257 / 0.043533 (-0.010275) \| 0.272672 / 0.255139 (0.017533) \| 0.286298 / 0.283200 (0.003098) \| 0.017877 / 0.141683 (-0.123806) \| 1.150322 / 1.452155 (-0.301833) \| 1.221031 / 1.492716 (-0.271685) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102838 / 0.018006 (0.084832) \| 0.298810 / 0.000490 (0.298320) \| 0.000207 / 0.000200 (0.000007) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021232 / 0.037411 (-0.016180) \| 0.067949 / 0.014526 (0.053423) \| 0.116487 / 0.176557 (-0.060070) \| 0.124035 / 0.737135 (-0.613100) \| 0.081075 / 0.296338 (-0.215263) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289098 / 0.215209 (0.073889) \| 2.844476 / 2.077655 (0.766821) \| 1.609576 / 1.504120 (0.105456) \| 1.480453 / 1.541195 (-0.060742) \| 1.489672 / 1.468490 (0.021182) \| 0.589661 / 4.584777 (-3.995116) \| 2.453804 / 3.745712 (-1.291908) \| 2.722381 / 5.269862 (-2.547480) \| 1.720251 / 4.565676 (-2.845425) \| 0.066085 / 0.424275 (-0.358190) \| 0.004943 / 0.007607 (-0.002664) \| 0.355149 / 0.226044 (0.129104) \| 3.444323 / 2.268929 (1.175395) \| 1.971157 / 55.444624 (-53.473467) \| 1.683029 / 6.876477 (-5.193448) \| 1.672798 / 2.142072 (-0.469274) \| 0.644812 / 4.805227 (-4.160416) \| 0.115098 / 6.500664 (-6.385566) \| 0.039883 / 0.075469 (-0.035586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960454 / 1.841788 (-0.881334) \| 11.604732 / 8.074308 (3.530424) \| 10.405481 / 10.191392 (0.214089) \| 0.129146 / 0.680424 (-0.551278) \| 0.014945 / 0.534201 (-0.519256) \| 0.286239 / 0.579283 (-0.293044) \| 0.281041 / 0.434364 (-0.153323) \| 0.320448 / 0.540337 (-0.219890) \| 0.554304 / 1.386936 (-0.832632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2cfb7859b029654829c4dfee230812ddab1f104 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005510 / 0.011353 (-0.005843) \| 0.003575 / 0.011008 (-0.007433) \| 0.062232 / 0.038508 (0.023724) \| 0.051115 / 0.023109 (0.028006) \| 0.250709 / 0.275898 (-0.025189) \| 0.274837 / 0.323480 (-0.048642) \| 0.002972 / 0.007986 (-0.005014) \| 0.002708 / 0.004328 (-0.001621) \| 0.048088 / 0.004250 (0.043838) \| 0.038588 / 0.037052 (0.001535) \| 0.252550 / 0.258489 (-0.005939) \| 0.285238 / 0.293841 (-0.008603) \| 0.027867 / 0.128546 (-0.100679) \| 0.011000 / 0.075646 (-0.064646) \| 0.206918 / 0.419271 (-0.212354) \| 0.035711 / 0.043533 (-0.007822) \| 0.255306 / 0.255139 (0.000167) \| 0.298636 / 0.283200 (0.015436) \| 0.018222 / 0.141683 (-0.123461) \| 1.122276 / 1.452155 (-0.329879) \| 1.196471 / 1.492716 (-0.296245) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092072 / 0.018006 (0.074066) \| 0.301469 / 0.000490 (0.300979) \| 0.000225 / 0.000200 (0.000025) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018672 / 0.037411 (-0.018739) \| 0.060235 / 0.014526 (0.045709) \| 0.074036 / 0.176557 (-0.102521) \| 0.119578 / 0.737135 (-0.617557) \| 0.073605 / 0.296338 (-0.222734) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286474 / 0.215209 (0.071264) \| 2.779427 / 2.077655 (0.701772) \| 1.478746 / 1.504120 (-0.025373) \| 1.362692 / 1.541195 (-0.178503) \| 1.388194 / 1.468490 (-0.080296) \| 0.560707 / 4.584777 (-4.024070) \| 2.352846 / 3.745712 (-1.392866) \| 2.784400 / 5.269862 (-2.485461) \| 1.775642 / 4.565676 (-2.790035) \| 0.062324 / 0.424275 (-0.361951) \| 0.004938 / 0.007607 (-0.002669) \| 0.334149 / 0.226044 (0.108105) \| 3.319446 / 2.268929 (1.050517) \| 1.810369 / 55.444624 (-53.634255) \| 1.559462 / 6.876477 (-5.317014) \| 1.611199 / 2.142072 (-0.530873) \| 0.655984 / 4.805227 (-4.149244) \| 0.118508 / 6.500664 (-6.382156) \| 0.043661 / 0.075469 (-0.031808) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935046 / 1.841788 (-0.906742) \| 11.413501 / 8.074308 (3.339192) \| 10.392314 / 10.191392 (0.200922) \| 0.131507 / 0.680424 (-0.548917) \| 0.014827 / 0.534201 (-0.519374) \| 0.289069 / 0.579283 (-0.290214) \| 0.268288 / 0.434364 (-0.166076) \| 0.326843 / 0.540337 (-0.213495) \| 0.441283 / 1.386936 (-0.945653) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005375 / 0.011353 (-0.005978) \| 0.003549 / 0.011008 (-0.007459) \| 0.048996 / 0.038508 (0.010488) \| 0.051408 / 0.023109 (0.028298) \| 0.272265 / 0.275898 (-0.003633) \| 0.293228 / 0.323480 (-0.030252) \| 0.004147 / 0.007986 (-0.003839) \| 0.002673 / 0.004328 (-0.001655) \| 0.048116 / 0.004250 (0.043865) \| 0.039926 / 0.037052 (0.002874) \| 0.276987 / 0.258489 (0.018498) \| 0.302955 / 0.293841 (0.009115) \| 0.029488 / 0.128546 (-0.099058) \| 0.010797 / 0.075646 (-0.064849) \| 0.057552 / 0.419271 (-0.361720) \| 0.032827 / 0.043533 (-0.010706) \| 0.270888 / 0.255139 (0.015749) \| 0.289136 / 0.283200 (0.005937) \| 0.018815 / 0.141683 (-0.122868) \| 1.148624 / 1.452155 (-0.303530) \| 1.191184 / 1.492716 (-0.301532) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091712 / 0.018006 (0.073706) \| 0.311198 / 0.000490 (0.310708) \| 0.000226 / 0.000200 (0.000026) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022097 / 0.037411 (-0.015314) \| 0.070641 / 0.014526 (0.056116) \| 0.080084 / 0.176557 (-0.096472) \| 0.118998 / 0.737135 (-0.618137) \| 0.081827 / 0.296338 (-0.214512) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298599 / 0.215209 (0.083390) \| 2.884759 / 2.077655 (0.807105) \| 1.630794 / 1.504120 (0.126674) \| 1.454309 / 1.541195 (-0.086886) \| 1.466795 / 1.468490 (-0.001695) \| 0.565405 / 4.584777 (-4.019372) \| 2.460883 / 3.745712 (-1.284829) \| 2.764193 / 5.269862 (-2.505668) \| 1.734270 / 4.565676 (-2.831407) \| 0.063408 / 0.424275 (-0.360867) \| 0.004887 / 0.007607 (-0.002720) \| 0.347762 / 0.226044 (0.121717) \| 3.458385 / 2.268929 (1.189457) \| 1.965434 / 55.444624 (-53.479190) \| 1.671047 / 6.876477 (-5.205430) \| 1.665642 / 2.142072 (-0.476430) \| 0.640665 / 4.805227 (-4.164562) \| 0.116025 / 6.500664 (-6.384639) \| 0.040147 / 0.075469 (-0.035322) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982194 / 1.841788 (-0.859593) \| 11.983487 / 8.074308 (3.909179) \| 10.660605 / 10.191392 (0.469213) \| 0.140647 / 0.680424 (-0.539777) \| 0.015870 / 0.534201 (-0.518331) \| 0.287032 / 0.579283 (-0.292251) \| 0.276629 / 0.434364 (-0.157735) \| 0.331171 / 0.540337 (-0.209166) \| 0.575346 / 1.386936 (-0.811590) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56433c2f6a42d5fcc5acb46c6275911c29afc371 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005014 / 0.011353 (-0.006339) \| 0.003434 / 0.011008 (-0.007574) \| 0.063283 / 0.038508 (0.024775) \| 0.048068 / 0.023109 (0.024959) \| 0.239521 / 0.275898 (-0.036377) \| 0.265294 / 0.323480 (-0.058186) \| 0.003790 / 0.007986 (-0.004196) \| 0.002577 / 0.004328 (-0.001751) \| 0.048618 / 0.004250 (0.044368) \| 0.037427 / 0.037052 (0.000375) \| 0.245263 / 0.258489 (-0.013226) \| 0.276618 / 0.293841 (-0.017223) \| 0.026615 / 0.128546 (-0.101931) \| 0.010378 / 0.075646 (-0.065268) \| 0.205670 / 0.419271 (-0.213601) \| 0.035076 / 0.043533 (-0.008457) \| 0.245062 / 0.255139 (-0.010077) \| 0.264584 / 0.283200 (-0.018616) \| 0.017760 / 0.141683 (-0.123922) \| 1.148061 / 1.452155 (-0.304094) \| 1.192762 / 1.492716 (-0.299955) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090870 / 0.018006 (0.072864) \| 0.305458 / 0.000490 (0.304968) \| 0.000207 / 0.000200 (0.000007) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018597 / 0.037411 (-0.018814) \| 0.060349 / 0.014526 (0.045823) \| 0.074854 / 0.176557 (-0.101702) \| 0.123243 / 0.737135 (-0.613892) \| 0.075843 / 0.296338 (-0.220496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275855 / 0.215209 (0.060645) \| 2.723965 / 2.077655 (0.646311) \| 1.436010 / 1.504120 (-0.068110) \| 1.323495 / 1.541195 (-0.217700) \| 1.356234 / 1.468490 (-0.112256) \| 0.564388 / 4.584777 (-4.020389) \| 2.390180 / 3.745712 (-1.355532) \| 2.782863 / 5.269862 (-2.486998) \| 1.765048 / 4.565676 (-2.800628) \| 0.062680 / 0.424275 (-0.361595) \| 0.004929 / 0.007607 (-0.002678) \| 0.337578 / 0.226044 (0.111533) \| 3.316780 / 2.268929 (1.047851) \| 1.803829 / 55.444624 (-53.640795) \| 1.524585 / 6.876477 (-5.351891) \| 1.549695 / 2.142072 (-0.592377) \| 0.638053 / 4.805227 (-4.167174) \| 0.116983 / 6.500664 (-6.383681) \| 0.042251 / 0.075469 (-0.033218) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946978 / 1.841788 (-0.894810) \| 11.809483 / 8.074308 (3.735175) \| 10.459974 / 10.191392 (0.268582) \| 0.130015 / 0.680424 (-0.550409) \| 0.013843 / 0.534201 (-0.520358) \| 0.286972 / 0.579283 (-0.292311) \| 0.268904 / 0.434364 (-0.165460) \| 0.325591 / 0.540337 (-0.214746) \| 0.439233 / 1.386936 (-0.947703) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005804 / 0.011353 (-0.005549) \| 0.003431 / 0.011008 (-0.007577) \| 0.049041 / 0.038508 (0.010533) \| 0.054758 / 0.023109 (0.031649) \| 0.262330 / 0.275898 (-0.013568) \| 0.288872 / 0.323480 (-0.034608) \| 0.004016 / 0.007986 (-0.003970) \| 0.002606 / 0.004328 (-0.001722) \| 0.047878 / 0.004250 (0.043628) \| 0.045066 / 0.037052 (0.008013) \| 0.266310 / 0.258489 (0.007820) \| 0.290072 / 0.293841 (-0.003768) \| 0.028738 / 0.128546 (-0.099809) \| 0.010667 / 0.075646 (-0.064979) \| 0.057300 / 0.419271 (-0.361972) \| 0.032715 / 0.043533 (-0.010818) \| 0.264043 / 0.255139 (0.008904) \| 0.278652 / 0.283200 (-0.004547) \| 0.017873 / 0.141683 (-0.123810) \| 1.125981 / 1.452155 (-0.326174) \| 1.168548 / 1.492716 (-0.324168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090997 / 0.018006 (0.072991) \| 0.300807 / 0.000490 (0.300317) \| 0.000223 / 0.000200 (0.000023) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021510 / 0.037411 (-0.015901) \| 0.068251 / 0.014526 (0.053725) \| 0.082073 / 0.176557 (-0.094484) \| 0.120071 / 0.737135 (-0.617064) \| 0.082245 / 0.296338 (-0.214093) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290601 / 0.215209 (0.075392) \| 2.871855 / 2.077655 (0.794200) \| 1.558239 / 1.504120 (0.054119) \| 1.447767 / 1.541195 (-0.093427) \| 1.446851 / 1.468490 (-0.021639) \| 0.573990 / 4.584777 (-4.010787) \| 2.439859 / 3.745712 (-1.305853) \| 2.795899 / 5.269862 (-2.473963) \| 1.746751 / 4.565676 (-2.818926) \| 0.062100 / 0.424275 (-0.362175) \| 0.004948 / 0.007607 (-0.002659) \| 0.344281 / 0.226044 (0.118236) \| 3.427499 / 2.268929 (1.158570) \| 1.940348 / 55.444624 (-53.504276) \| 1.660926 / 6.876477 (-5.215551) \| 1.669485 / 2.142072 (-0.472588) \| 0.634034 / 4.805227 (-4.171193) \| 0.114748 / 6.500664 (-6.385916) \| 0.041617 / 0.075469 (-0.033852) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966411 / 1.841788 (-0.875376) \| 12.040753 / 8.074308 (3.966445) \| 10.506542 / 10.191392 (0.315150) \| 0.129659 / 0.680424 (-0.550764) \| 0.015691 / 0.534201 (-0.518510) \| 0.286911 / 0.579283 (-0.292372) \| 0.273588 / 0.434364 (-0.160776) \| 0.333642 / 0.540337 (-0.206695) \| 0.568550 / 1.386936 (-0.818386) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b38ed4705263df92ae06d89baab0932ae10065e0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005023 / 0.011353 (-0.006330) \| 0.003492 / 0.011008 (-0.007516) \| 0.062808 / 0.038508 (0.024300) \| 0.051649 / 0.023109 (0.028540) \| 0.246871 / 0.275898 (-0.029027) \| 0.273430 / 0.323480 (-0.050050) \| 0.003851 / 0.007986 (-0.004135) \| 0.002643 / 0.004328 (-0.001686) \| 0.048499 / 0.004250 (0.044248) \| 0.037713 / 0.037052 (0.000661) \| 0.256431 / 0.258489 (-0.002058) \| 0.306956 / 0.293841 (0.013116) \| 0.027116 / 0.128546 (-0.101430) \| 0.010769 / 0.075646 (-0.064877) \| 0.206218 / 0.419271 (-0.213053) \| 0.035592 / 0.043533 (-0.007941) \| 0.249629 / 0.255139 (-0.005510) \| 0.268438 / 0.283200 (-0.014761) \| 0.018557 / 0.141683 (-0.123125) \| 1.123988 / 1.452155 (-0.328167) \| 1.158196 / 1.492716 (-0.334520) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090221 / 0.018006 (0.072215) \| 0.300892 / 0.000490 (0.300402) \| 0.000209 / 0.000200 (0.000009) \| 0.000046 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018305 / 0.037411 (-0.019106) \| 0.060294 / 0.014526 (0.045769) \| 0.073330 / 0.176557 (-0.103227) \| 0.119620 / 0.737135 (-0.617515) \| 0.074611 / 0.296338 (-0.221727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285347 / 0.215209 (0.070138) \| 2.795144 / 2.077655 (0.717490) \| 1.468321 / 1.504120 (-0.035799) \| 1.343848 / 1.541195 (-0.197347) \| 1.388998 / 1.468490 (-0.079492) \| 0.559609 / 4.584777 (-4.025168) \| 2.355056 / 3.745712 (-1.390656) \| 2.798763 / 5.269862 (-2.471099) \| 1.764371 / 4.565676 (-2.801305) \| 0.062563 / 0.424275 (-0.361712) \| 0.005101 / 0.007607 (-0.002506) \| 0.339205 / 0.226044 (0.113161) \| 3.336729 / 2.268929 (1.067800) \| 1.801987 / 55.444624 (-53.642637) \| 1.526720 / 6.876477 (-5.349757) \| 1.539324 / 2.142072 (-0.602749) \| 0.635805 / 4.805227 (-4.169422) \| 0.138762 / 6.500664 (-6.361902) \| 0.042092 / 0.075469 (-0.033377) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.928755 / 1.841788 (-0.913032) \| 11.468224 / 8.074308 (3.393916) \| 10.784568 / 10.191392 (0.593176) \| 0.130332 / 0.680424 (-0.550092) \| 0.014203 / 0.534201 (-0.519998) \| 0.287125 / 0.579283 (-0.292158) \| 0.263921 / 0.434364 (-0.170443) \| 0.327824 / 0.540337 (-0.212513) \| 0.434679 / 1.386936 (-0.952257) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005194 / 0.011353 (-0.006159) \| 0.003411 / 0.011008 (-0.007598) \| 0.050122 / 0.038508 (0.011614) \| 0.049378 / 0.023109 (0.026269) \| 0.272980 / 0.275898 (-0.002918) \| 0.298047 / 0.323480 (-0.025433) \| 0.003945 / 0.007986 (-0.004041) \| 0.002633 / 0.004328 (-0.001696) \| 0.048935 / 0.004250 (0.044685) \| 0.040157 / 0.037052 (0.003104) \| 0.277056 / 0.258489 (0.018567) \| 0.299824 / 0.293841 (0.005983) \| 0.028997 / 0.128546 (-0.099550) \| 0.010868 / 0.075646 (-0.064779) \| 0.057895 / 0.419271 (-0.361377) \| 0.033522 / 0.043533 (-0.010010) \| 0.274912 / 0.255139 (0.019773) \| 0.288902 / 0.283200 (0.005702) \| 0.018016 / 0.141683 (-0.123667) \| 1.116669 / 1.452155 (-0.335485) \| 1.175007 / 1.492716 (-0.317710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090169 / 0.018006 (0.072163) \| 0.310577 / 0.000490 (0.310087) \| 0.000215 / 0.000200 (0.000015) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020448 / 0.037411 (-0.016963) \| 0.068216 / 0.014526 (0.053690) \| 0.081798 / 0.176557 (-0.094759) \| 0.119151 / 0.737135 (-0.617985) \| 0.085197 / 0.296338 (-0.211142) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294957 / 0.215209 (0.079748) \| 2.874065 / 2.077655 (0.796410) \| 1.590963 / 1.504120 (0.086843) \| 1.459596 / 1.541195 (-0.081599) \| 1.467931 / 1.468490 (-0.000559) \| 0.562832 / 4.584777 (-4.021944) \| 2.426384 / 3.745712 (-1.319328) \| 2.767749 / 5.269862 (-2.502112) \| 1.746702 / 4.565676 (-2.818975) \| 0.063353 / 0.424275 (-0.360922) \| 0.005073 / 0.007607 (-0.002534) \| 0.348258 / 0.226044 (0.122213) \| 3.390351 / 2.268929 (1.121423) \| 1.950092 / 55.444624 (-53.494532) \| 1.671227 / 6.876477 (-5.205250) \| 1.683349 / 2.142072 (-0.458723) \| 0.637613 / 4.805227 (-4.167614) \| 0.115172 / 6.500664 (-6.385492) \| 0.040202 / 0.075469 (-0.035267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963085 / 1.841788 (-0.878702) \| 11.895384 / 8.074308 (3.821076) \| 10.609906 / 10.191392 (0.418513) \| 0.130865 / 0.680424 (-0.549559) \| 0.016020 / 0.534201 (-0.518181) \| 0.287540 / 0.579283 (-0.291743) \| 0.278204 / 0.434364 (-0.156160) \| 0.326007 / 0.540337 (-0.214330) \| 0.590881 / 1.386936 (-0.796055) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c291e330a7d460ff09d867377de1d4c53fd5394c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005266 / 0.011353 (-0.006087) \| 0.003751 / 0.011008 (-0.007257) \| 0.063835 / 0.038508 (0.025327) \| 0.052688 / 0.023109 (0.029579) \| 0.261957 / 0.275898 (-0.013941) \| 0.284264 / 0.323480 (-0.039216) \| 0.003958 / 0.007986 (-0.004027) \| 0.002696 / 0.004328 (-0.001633) \| 0.052791 / 0.004250 (0.048540) \| 0.038294 / 0.037052 (0.001242) \| 0.259488 / 0.258489 (0.000999) \| 0.298368 / 0.293841 (0.004528) \| 0.028309 / 0.128546 (-0.100237) \| 0.010819 / 0.075646 (-0.064827) \| 0.208221 / 0.419271 (-0.211050) \| 0.036373 / 0.043533 (-0.007160) \| 0.257000 / 0.255139 (0.001861) \| 0.273108 / 0.283200 (-0.010092) \| 0.019674 / 0.141683 (-0.122009) \| 1.119196 / 1.452155 (-0.332958) \| 1.161613 / 1.492716 (-0.331104) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093408 / 0.018006 (0.075401) \| 0.302278 / 0.000490 (0.301788) \| 0.000212 / 0.000200 (0.000012) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019417 / 0.037411 (-0.017995) \| 0.060847 / 0.014526 (0.046321) \| 0.075399 / 0.176557 (-0.101158) \| 0.121233 / 0.737135 (-0.615902) \| 0.076916 / 0.296338 (-0.219422) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281265 / 0.215209 (0.066056) \| 2.651726 / 2.077655 (0.574072) \| 1.457726 / 1.504120 (-0.046394) \| 1.339250 / 1.541195 (-0.201945) \| 1.398529 / 1.468490 (-0.069961) \| 0.566574 / 4.584777 (-4.018203) \| 2.431576 / 3.745712 (-1.314136) \| 2.845884 / 5.269862 (-2.423977) \| 1.798051 / 4.565676 (-2.767626) \| 0.063619 / 0.424275 (-0.360656) \| 0.005286 / 0.007607 (-0.002321) \| 0.332834 / 0.226044 (0.106789) \| 3.293222 / 2.268929 (1.024293) \| 1.837810 / 55.444624 (-53.606815) \| 1.568511 / 6.876477 (-5.307966) \| 1.627518 / 2.142072 (-0.514555) \| 0.643520 / 4.805227 (-4.161708) \| 0.118482 / 6.500664 (-6.382182) \| 0.049563 / 0.075469 (-0.025906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947767 / 1.841788 (-0.894021) \| 11.994999 / 8.074308 (3.920691) \| 10.662651 / 10.191392 (0.471259) \| 0.142070 / 0.680424 (-0.538354) \| 0.014276 / 0.534201 (-0.519925) \| 0.288455 / 0.579283 (-0.290828) \| 0.266335 / 0.434364 (-0.168029) \| 0.328455 / 0.540337 (-0.211883) \| 0.440740 / 1.386936 (-0.946196) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005636 / 0.011353 (-0.005717) \| 0.003664 / 0.011008 (-0.007344) \| 0.050340 / 0.038508 (0.011832) \| 0.062795 / 0.023109 (0.039685) \| 0.280874 / 0.275898 (0.004976) \| 0.314056 / 0.323480 (-0.009424) \| 0.004089 / 0.007986 (-0.003897) \| 0.002780 / 0.004328 (-0.001548) \| 0.048468 / 0.004250 (0.044218) \| 0.042924 / 0.037052 (0.005871) \| 0.281381 / 0.258489 (0.022892) \| 0.308232 / 0.293841 (0.014391) \| 0.030294 / 0.128546 (-0.098252) \| 0.011098 / 0.075646 (-0.064548) \| 0.057535 / 0.419271 (-0.361736) \| 0.034217 / 0.043533 (-0.009316) \| 0.283022 / 0.255139 (0.027883) \| 0.298425 / 0.283200 (0.015225) \| 0.019285 / 0.141683 (-0.122398) \| 1.117722 / 1.452155 (-0.334433) \| 1.185878 / 1.492716 (-0.306839) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094915 / 0.018006 (0.076909) \| 0.311782 / 0.000490 (0.311293) \| 0.000217 / 0.000200 (0.000017) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022652 / 0.037411 (-0.014759) \| 0.069766 / 0.014526 (0.055240) \| 0.084495 / 0.176557 (-0.092061) \| 0.121295 / 0.737135 (-0.615841) \| 0.082447 / 0.296338 (-0.213891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294286 / 0.215209 (0.079077) \| 2.863694 / 2.077655 (0.786039) \| 1.578338 / 1.504120 (0.074219) \| 1.478737 / 1.541195 (-0.062458) \| 1.528569 / 1.468490 (0.060079) \| 0.576944 / 4.584777 (-4.007833) \| 2.438730 / 3.745712 (-1.306982) \| 2.956138 / 5.269862 (-2.313723) \| 1.844484 / 4.565676 (-2.721192) \| 0.065980 / 0.424275 (-0.358295) \| 0.004998 / 0.007607 (-0.002609) \| 0.352063 / 0.226044 (0.126019) \| 3.456355 / 2.268929 (1.187426) \| 1.971582 / 55.444624 (-53.473042) \| 1.684536 / 6.876477 (-5.191940) \| 1.726823 / 2.142072 (-0.415250) \| 0.660235 / 4.805227 (-4.144992) \| 0.119029 / 6.500664 (-6.381635) \| 0.042497 / 0.075469 (-0.032972) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971817 / 1.841788 (-0.869970) \| 12.900324 / 8.074308 (4.826015) \| 10.957495 / 10.191392 (0.766103) \| 0.133705 / 0.680424 (-0.546718) \| 0.015669 / 0.534201 (-0.518532) \| 0.287340 / 0.579283 (-0.291943) \| 0.280380 / 0.434364 (-0.153984) \| 0.330369 / 0.540337 (-0.209969) \| 0.581793 / 1.386936 (-0.805143) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2af5efae1985499d6a0a1b6ab4120337eebf776 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005038 / 0.011353 (-0.006315) \| 0.003737 / 0.011008 (-0.007272) \| 0.063118 / 0.038508 (0.024610) \| 0.050120 / 0.023109 (0.027011) \| 0.240722 / 0.275898 (-0.035176) \| 0.263128 / 0.323480 (-0.060352) \| 0.003839 / 0.007986 (-0.004147) \| 0.002718 / 0.004328 (-0.001610) \| 0.047869 / 0.004250 (0.043618) \| 0.038092 / 0.037052 (0.001040) \| 0.245759 / 0.258489 (-0.012730) \| 0.277728 / 0.293841 (-0.016113) \| 0.027466 / 0.128546 (-0.101081) \| 0.011767 / 0.075646 (-0.063879) \| 0.205505 / 0.419271 (-0.213766) \| 0.035429 / 0.043533 (-0.008104) \| 0.241665 / 0.255139 (-0.013474) \| 0.260908 / 0.283200 (-0.022292) \| 0.017133 / 0.141683 (-0.124550) \| 1.107725 / 1.452155 (-0.344429) \| 1.169707 / 1.492716 (-0.323009) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094112 / 0.018006 (0.076106) \| 0.302596 / 0.000490 (0.302106) \| 0.000237 / 0.000200 (0.000037) \| 0.000041 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017923 / 0.037411 (-0.019488) \| 0.060356 / 0.014526 (0.045830) \| 0.073708 / 0.176557 (-0.102849) \| 0.119952 / 0.737135 (-0.617183) \| 0.075350 / 0.296338 (-0.220989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289253 / 0.215209 (0.074044) \| 2.800772 / 2.077655 (0.723117) \| 1.538368 / 1.504120 (0.034248) \| 1.401037 / 1.541195 (-0.140158) \| 1.427170 / 1.468490 (-0.041320) \| 0.560497 / 4.584777 (-4.024280) \| 2.417844 / 3.745712 (-1.327868) \| 2.798377 / 5.269862 (-2.471484) \| 1.756517 / 4.565676 (-2.809160) \| 0.063897 / 0.424275 (-0.360378) \| 0.005323 / 0.007607 (-0.002284) \| 0.339881 / 0.226044 (0.113836) \| 3.354858 / 2.268929 (1.085929) \| 1.877233 / 55.444624 (-53.567391) \| 1.578713 / 6.876477 (-5.297764) \| 1.631898 / 2.142072 (-0.510175) \| 0.640303 / 4.805227 (-4.164924) \| 0.116731 / 6.500664 (-6.383933) \| 0.041978 / 0.075469 (-0.033491) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963259 / 1.841788 (-0.878529) \| 11.983646 / 8.074308 (3.909338) \| 10.561596 / 10.191392 (0.370204) \| 0.135863 / 0.680424 (-0.544561) \| 0.015607 / 0.534201 (-0.518594) \| 0.295164 / 0.579283 (-0.284119) \| 0.283366 / 0.434364 (-0.150998) \| 0.341848 / 0.540337 (-0.198489) \| 0.448359 / 1.386936 (-0.938577) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005366 / 0.011353 (-0.005987) \| 0.003621 / 0.011008 (-0.007387) \| 0.048615 / 0.038508 (0.010107) \| 0.053950 / 0.023109 (0.030841) \| 0.273112 / 0.275898 (-0.002786) \| 0.295655 / 0.323480 (-0.027825) \| 0.004066 / 0.007986 (-0.003920) \| 0.002700 / 0.004328 (-0.001628) \| 0.047899 / 0.004250 (0.043648) \| 0.041633 / 0.037052 (0.004581) \| 0.277760 / 0.258489 (0.019271) \| 0.302068 / 0.293841 (0.008227) \| 0.028879 / 0.128546 (-0.099668) \| 0.010756 / 0.075646 (-0.064891) \| 0.057190 / 0.419271 (-0.362082) \| 0.032555 / 0.043533 (-0.010978) \| 0.272045 / 0.255139 (0.016906) \| 0.289330 / 0.283200 (0.006130) \| 0.018466 / 0.141683 (-0.123216) \| 1.180435 / 1.452155 (-0.271720) \| 1.192228 / 1.492716 (-0.300488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094871 / 0.018006 (0.076864) \| 0.302552 / 0.000490 (0.302062) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022008 / 0.037411 (-0.015403) \| 0.068528 / 0.014526 (0.054002) \| 0.081735 / 0.176557 (-0.094821) \| 0.120990 / 0.737135 (-0.616145) \| 0.083155 / 0.296338 (-0.213184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305030 / 0.215209 (0.089821) \| 3.009812 / 2.077655 (0.932158) \| 1.677773 / 1.504120 (0.173654) \| 1.552280 / 1.541195 (0.011085) \| 1.606248 / 1.468490 (0.137758) \| 0.557093 / 4.584777 (-4.027684) \| 2.418292 / 3.745712 (-1.327420) \| 2.813049 / 5.269862 (-2.456813) \| 1.764507 / 4.565676 (-2.801169) \| 0.065089 / 0.424275 (-0.359186) \| 0.004944 / 0.007607 (-0.002663) \| 0.360672 / 0.226044 (0.134628) \| 3.525850 / 2.268929 (1.256921) \| 2.030091 / 55.444624 (-53.414533) \| 1.754669 / 6.876477 (-5.121807) \| 1.772673 / 2.142072 (-0.369399) \| 0.642904 / 4.805227 (-4.162324) \| 0.116018 / 6.500664 (-6.384646) \| 0.041308 / 0.075469 (-0.034161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986386 / 1.841788 (-0.855401) \| 12.291623 / 8.074308 (4.217315) \| 10.655932 / 10.191392 (0.464540) \| 0.141736 / 0.680424 (-0.538688) \| 0.016669 / 0.534201 (-0.517532) \| 0.286875 / 0.579283 (-0.292408) \| 0.281898 / 0.434364 (-0.152466) \| 0.325206 / 0.540337 (-0.215132) \| 0.577607 / 1.386936 (-0.809329) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cf33502493fb9760ea8cc8e51622bf94d0c9e31 \"CML watermark\")\n", "Alright tests are passing (except one on temp dir cleanup windows but I don't think it's related to this PR ?)\r\n\r\n```\r\nFAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\\\Users\\\\RUNNER~1\\\\AppData\\\\Local\\\\Temp\\\\tmpqy3f2ft_\\\\hf-internal-testing___dataset_with_script\\\\default\\\\0.0.0\\\\c240e2be3370bdbd\\\\dataset_with_script-train.arrow'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005072 / 0.011353 (-0.006281) \| 0.003449 / 0.011008 (-0.007559) \| 0.062630 / 0.038508 (0.024122) \| 0.054276 / 0.023109 (0.031167) \| 0.253345 / 0.275898 (-0.022553) \| 0.273460 / 0.323480 (-0.050020) \| 0.003859 / 0.007986 (-0.004127) \| 0.002646 / 0.004328 (-0.001683) \| 0.048289 / 0.004250 (0.044038) \| 0.037943 / 0.037052 (0.000891) \| 0.256569 / 0.258489 (-0.001920) \| 0.287809 / 0.293841 (-0.006032) \| 0.027675 / 0.128546 (-0.100872) \| 0.010554 / 0.075646 (-0.065092) \| 0.205157 / 0.419271 (-0.214115) \| 0.035464 / 0.043533 (-0.008069) \| 0.254300 / 0.255139 (-0.000839) \| 0.272907 / 0.283200 (-0.010292) \| 0.018146 / 0.141683 (-0.123537) \| 1.110528 / 1.452155 (-0.341626) \| 1.170156 / 1.492716 (-0.322560) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093151 / 0.018006 (0.075144) \| 0.302087 / 0.000490 (0.301598) \| 0.000216 / 0.000200 (0.000016) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018744 / 0.037411 (-0.018667) \| 0.059843 / 0.014526 (0.045317) \| 0.073165 / 0.176557 (-0.103391) \| 0.120464 / 0.737135 (-0.616671) \| 0.074992 / 0.296338 (-0.221347) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285103 / 0.215209 (0.069894) \| 2.820254 / 2.077655 (0.742600) \| 1.505336 / 1.504120 (0.001216) \| 1.368631 / 1.541195 (-0.172564) \| 1.404140 / 1.468490 (-0.064350) \| 0.563906 / 4.584777 (-4.020871) \| 2.411871 / 3.745712 (-1.333841) \| 2.788390 / 5.269862 (-2.481471) \| 1.749788 / 4.565676 (-2.815888) \| 0.062171 / 0.424275 (-0.362104) \| 0.004918 / 0.007607 (-0.002689) \| 0.339615 / 0.226044 (0.113571) \| 3.337789 / 2.268929 (1.068861) \| 1.808445 / 55.444624 (-53.636180) \| 1.541015 / 6.876477 (-5.335462) \| 1.572389 / 2.142072 (-0.569683) \| 0.641739 / 4.805227 (-4.163488) \| 0.115844 / 6.500664 (-6.384820) \| 0.042504 / 0.075469 (-0.032965) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942463 / 1.841788 (-0.899325) \| 11.602364 / 8.074308 (3.528056) \| 10.628921 / 10.191392 (0.437529) \| 0.136154 / 0.680424 (-0.544270) \| 0.013842 / 0.534201 (-0.520359) \| 0.287085 / 0.579283 (-0.292198) \| 0.269860 / 0.434364 (-0.164503) \| 0.329525 / 0.540337 (-0.210812) \| 0.441287 / 1.386936 (-0.945649) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005215 / 0.011353 (-0.006138) \| 0.003549 / 0.011008 (-0.007460) \| 0.049199 / 0.038508 (0.010691) \| 0.051655 / 0.023109 (0.028545) \| 0.272150 / 0.275898 (-0.003748) \| 0.291978 / 0.323480 (-0.031502) \| 0.003985 / 0.007986 (-0.004001) \| 0.002668 / 0.004328 (-0.001661) \| 0.048524 / 0.004250 (0.044274) \| 0.039824 / 0.037052 (0.002772) \| 0.275566 / 0.258489 (0.017077) \| 0.298076 / 0.293841 (0.004235) \| 0.029508 / 0.128546 (-0.099038) \| 0.010673 / 0.075646 (-0.064973) \| 0.057327 / 0.419271 (-0.361944) \| 0.032590 / 0.043533 (-0.010943) \| 0.273295 / 0.255139 (0.018156) \| 0.289127 / 0.283200 (0.005928) \| 0.017694 / 0.141683 (-0.123989) \| 1.134502 / 1.452155 (-0.317653) \| 1.185603 / 1.492716 (-0.307114) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098403 / 0.018006 (0.080396) \| 0.302735 / 0.000490 (0.302245) \| 0.000228 / 0.000200 (0.000028) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025192 / 0.037411 (-0.012219) \| 0.068149 / 0.014526 (0.053623) \| 0.082220 / 0.176557 (-0.094336) \| 0.119491 / 0.737135 (-0.617645) \| 0.082484 / 0.296338 (-0.213855) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295339 / 0.215209 (0.080130) \| 2.868411 / 2.077655 (0.790757) \| 1.590665 / 1.504120 (0.086545) \| 1.465995 / 1.541195 (-0.075200) \| 1.489205 / 1.468490 (0.020715) \| 0.562503 / 4.584777 (-4.022274) \| 2.480100 / 3.745712 (-1.265613) \| 2.774216 / 5.269862 (-2.495646) \| 1.733129 / 4.565676 (-2.832548) \| 0.062698 / 0.424275 (-0.361577) \| 0.004910 / 0.007607 (-0.002697) \| 0.354766 / 0.226044 (0.128722) \| 3.435541 / 2.268929 (1.166613) \| 1.953357 / 55.444624 (-53.491267) \| 1.673584 / 6.876477 (-5.202893) \| 1.677749 / 2.142072 (-0.464323) \| 0.632601 / 4.805227 (-4.172626) \| 0.114875 / 6.500664 (-6.385789) \| 0.040577 / 0.075469 (-0.034892) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967003 / 1.841788 (-0.874785) \| 11.964490 / 8.074308 (3.890181) \| 10.493812 / 10.191392 (0.302420) \| 0.132177 / 0.680424 (-0.548247) \| 0.015149 / 0.534201 (-0.519052) \| 0.289011 / 0.579283 (-0.290272) \| 0.285479 / 0.434364 (-0.148885) \| 0.327090 / 0.540337 (-0.213248) \| 0.571747 / 1.386936 (-0.815189) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c9b4cb7ee4720415261216d72051e2a3320fe41 \"CML watermark\")\n" ]	2023-11-23T17:31:57Z	2023-12-01T17:57:17Z	2023-12-01T17:50:59Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6448.diff", "html_url": "https://github.com/huggingface/datasets/pull/6448", "merged_at": "2023-12-01T17:50:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/6448.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6448" }	The idea is to make this code work for datasets with scripts if they have a Parquet export ```python ds = load_dataset("squad", trust_remote_code=False) ``` And more generally, it means we use the Parquet export whenever it's possible (it's safer and faster than dataset scripts). I also added a `config.USE_PARQUET_EXPORT` variable to use in the datasets-server parquet conversion job - [x] Needs https://github.com/huggingface/datasets/pull/6429 to be merged first cc @severo I use the /parquet and /info endpoints from datasets-server	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 2, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6448/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6447	6,447	Support one dataset loader per config when using YAML	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	[]	2023-11-23T13:03:07Z	2023-11-23T13:03:07Z	null	COLLABORATOR	null	null	### Feature request See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1 I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc. ### Motivation It would be more flexible for the users ### Your contribution No specific contribution	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6447/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6446	6,446	Speech Commands v2 dataset doesn't match AST-v2 config	{ "avatar_url": "https://avatars.githubusercontent.com/u/18024303?v=4", "events_url": "https://api.github.com/users/vymao/events{/privacy}", "followers_url": "https://api.github.com/users/vymao/followers", "following_url": "https://api.github.com/users/vymao/following{/other_user}", "gists_url": "https://api.github.com/users/vymao/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vymao", "id": 18024303, "login": "vymao", "node_id": "MDQ6VXNlcjE4MDI0MzAz", "organizations_url": "https://api.github.com/users/vymao/orgs", "received_events_url": "https://api.github.com/users/vymao/received_events", "repos_url": "https://api.github.com/users/vymao/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vymao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vymao/subscriptions", "type": "User", "url": "https://api.github.com/users/vymao", "user_view_type": "public" }	[]	closed	false	[ "You can use `.align_labels_with_mapping` on the dataset to align the labels with the model config.\r\n\r\nRegarding the number of labels, only the special `_silence_` label corresponding to noise is missing, which is consistent with the model paper (reports training on 35 labels). You can run a `.filter` to drop it.\r\n\r\nPS: You should create a discussion on a model/dataset repo (on the Hub) for these kinds of questions", "Thanks, will keep that in mind. But I tried running `dataset_aligned = dataset.align_labels_with_mapping(model.config.id2label, 'label')`, and received this error: \r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in align_labels_with_mapping\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in <dictcomp>\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\nAttributeError: 'int' object has no attribute 'lower'\r\n```\r\nMy guess is that the dataset `label` column is purely an int ID, and I'm not sure there's a way to identify which class label the ID belongs to in the dataset easily.", "Replacing `model.config.id2label` with `model.config.label2id` should fix the issue.\r\n\r\nSo, the full code to align the labels with the model config is as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoFeatureExtractor, AutoModelForAudioClassification\r\n\r\n# extractor = AutoFeatureExtractor.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\nmodel = AutoModelForAudioClassification.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\n\r\nds = load_dataset(\"speech_commands\", \"v0.02\")\r\nds = ds.filter(lambda label: label != ds[\"train\"].features[\"label\"].str2int(\"_silence_\"), input_columns=\"label\")\r\nds = ds.align_labels_with_mapping(model.config.label2id, \"label\")\r\n```" ]	2023-11-22T20:46:36Z	2023-11-28T14:46:08Z	2023-11-28T14:46:08Z	NONE	null	null	### Describe the bug [According](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) to `MIT/ast-finetuned-speech-commands-v2`, the model was trained on the Speech Commands v2 dataset. However, while the model config says the model should have 35 class labels, the dataset itself has 36 class labels. Moreover, the class labels themselves don't match between the model config and the dataset. It is difficult to reproduce the data used to fine tune `MIT/ast-finetuned-speech-commands-v2`. ### Steps to reproduce the bug ``` >>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-speech-commands-v2") >>> model.config.id2label {0: 'backward', 1: 'follow', 2: 'five', 3: 'bed', 4: 'zero', 5: 'on', 6: 'learn', 7: 'two', 8: 'house', 9: 'tree', 10: 'dog', 11: 'stop', 12: 'seven', 13: 'eight', 14: 'down', 15: 'six', 16: 'forward', 17: 'cat', 18: 'right', 19: 'visual', 20: 'four', 21: 'wow', 22: 'no', 23: 'nine', 24: 'off', 25: 'three', 26: 'left', 27: 'marvin', 28: 'yes', 29: 'up', 30: 'sheila', 31: 'happy', 32: 'bird', 33: 'go', 34: 'one'} >>> dataset = load_dataset("speech_commands", "v0.02", split="test") >>> torch.unique(torch.Tensor(dataset['label'])) tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35.]) ``` If you try to explore the [dataset itself](https://huggingface.co/datasets/speech_commands/viewer/v0.02/test), you can see that the id to label does not match what is provided by `model.config.id2label`. ### Expected behavior The labels should match completely and there should be the same number of label classes between the model config and the dataset itself. ### Environment info datasets = 2.14.6, transformers = 4.33.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6446/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6445	6,445	Use `filelock` package for file locking	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005431 / 0.011353 (-0.005922) \| 0.003255 / 0.011008 (-0.007753) \| 0.062867 / 0.038508 (0.024359) \| 0.051917 / 0.023109 (0.028808) \| 0.254229 / 0.275898 (-0.021669) \| 0.276949 / 0.323480 (-0.046531) \| 0.002868 / 0.007986 (-0.005117) \| 0.002539 / 0.004328 (-0.001789) \| 0.048366 / 0.004250 (0.044115) \| 0.038497 / 0.037052 (0.001445) \| 0.252158 / 0.258489 (-0.006332) \| 0.288868 / 0.293841 (-0.004973) \| 0.027956 / 0.128546 (-0.100591) \| 0.010500 / 0.075646 (-0.065147) \| 0.209263 / 0.419271 (-0.210008) \| 0.035415 / 0.043533 (-0.008118) \| 0.253104 / 0.255139 (-0.002035) \| 0.274646 / 0.283200 (-0.008554) \| 0.019923 / 0.141683 (-0.121760) \| 1.081870 / 1.452155 (-0.370285) \| 1.157159 / 1.492716 (-0.335557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097420 / 0.018006 (0.079414) \| 0.315021 / 0.000490 (0.314531) \| 0.000218 / 0.000200 (0.000018) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018826 / 0.037411 (-0.018585) \| 0.061921 / 0.014526 (0.047395) \| 0.086825 / 0.176557 (-0.089731) \| 0.120606 / 0.737135 (-0.616529) \| 0.074344 / 0.296338 (-0.221994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283238 / 0.215209 (0.068028) \| 2.771817 / 2.077655 (0.694162) \| 1.500194 / 1.504120 (-0.003926) \| 1.379286 / 1.541195 (-0.161908) \| 1.447747 / 1.468490 (-0.020743) \| 0.587176 / 4.584777 (-3.997601) \| 2.411260 / 3.745712 (-1.334452) \| 2.897682 / 5.269862 (-2.372180) \| 1.821720 / 4.565676 (-2.743957) \| 0.063299 / 0.424275 (-0.360976) \| 0.004969 / 0.007607 (-0.002639) \| 0.346417 / 0.226044 (0.120373) \| 3.432936 / 2.268929 (1.164007) \| 1.898662 / 55.444624 (-53.545963) \| 1.624339 / 6.876477 (-5.252138) \| 1.641653 / 2.142072 (-0.500419) \| 0.655773 / 4.805227 (-4.149454) \| 0.118588 / 6.500664 (-6.382076) \| 0.043919 / 0.075469 (-0.031551) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949466 / 1.841788 (-0.892322) \| 12.378025 / 8.074308 (4.303717) \| 10.750942 / 10.191392 (0.559550) \| 0.146575 / 0.680424 (-0.533849) \| 0.015453 / 0.534201 (-0.518748) \| 0.290608 / 0.579283 (-0.288676) \| 0.273000 / 0.434364 (-0.161364) \| 0.328019 / 0.540337 (-0.212318) \| 0.417396 / 1.386936 (-0.969540) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005363 / 0.011353 (-0.005990) \| 0.003421 / 0.011008 (-0.007587) \| 0.049429 / 0.038508 (0.010920) \| 0.052774 / 0.023109 (0.029664) \| 0.274058 / 0.275898 (-0.001840) \| 0.297307 / 0.323480 (-0.026173) \| 0.004000 / 0.007986 (-0.003986) \| 0.002463 / 0.004328 (-0.001866) \| 0.048824 / 0.004250 (0.044574) \| 0.041064 / 0.037052 (0.004012) \| 0.279066 / 0.258489 (0.020577) \| 0.302420 / 0.293841 (0.008579) \| 0.029665 / 0.128546 (-0.098881) \| 0.010628 / 0.075646 (-0.065018) \| 0.057678 / 0.419271 (-0.361594) \| 0.032731 / 0.043533 (-0.010802) \| 0.274662 / 0.255139 (0.019523) \| 0.291878 / 0.283200 (0.008678) \| 0.018820 / 0.141683 (-0.122863) \| 1.124042 / 1.452155 (-0.328112) \| 1.175020 / 1.492716 (-0.317697) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099419 / 0.018006 (0.081413) \| 0.311511 / 0.000490 (0.311022) \| 0.000228 / 0.000200 (0.000028) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022478 / 0.037411 (-0.014933) \| 0.071955 / 0.014526 (0.057429) \| 0.081423 / 0.176557 (-0.095134) \| 0.119574 / 0.737135 (-0.617561) \| 0.084724 / 0.296338 (-0.211615) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295537 / 0.215209 (0.080328) \| 2.893855 / 2.077655 (0.816201) \| 1.602065 / 1.504120 (0.097945) \| 1.478193 / 1.541195 (-0.063002) \| 1.508250 / 1.468490 (0.039760) \| 0.566140 / 4.584777 (-4.018637) \| 2.455474 / 3.745712 (-1.290238) \| 2.849525 / 5.269862 (-2.420337) \| 1.763830 / 4.565676 (-2.801846) \| 0.062375 / 0.424275 (-0.361900) \| 0.004992 / 0.007607 (-0.002615) \| 0.346068 / 0.226044 (0.120023) \| 3.452421 / 2.268929 (1.183492) \| 1.970346 / 55.444624 (-53.474278) \| 1.690865 / 6.876477 (-5.185612) \| 1.705358 / 2.142072 (-0.436714) \| 0.644261 / 4.805227 (-4.160967) \| 0.120596 / 6.500664 (-6.380068) \| 0.042699 / 0.075469 (-0.032770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980506 / 1.841788 (-0.861281) \| 12.401901 / 8.074308 (4.327593) \| 11.169413 / 10.191392 (0.978021) \| 0.142540 / 0.680424 (-0.537884) \| 0.015730 / 0.534201 (-0.518471) \| 0.288871 / 0.579283 (-0.290412) \| 0.287487 / 0.434364 (-0.146877) \| 0.325133 / 0.540337 (-0.215204) \| 0.417979 / 1.386936 (-0.968957) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#965685891db0d06979490aaebab72d5dc628e42b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005062 / 0.011353 (-0.006291) \| 0.003024 / 0.011008 (-0.007984) \| 0.061801 / 0.038508 (0.023293) \| 0.048934 / 0.023109 (0.025825) \| 0.248024 / 0.275898 (-0.027874) \| 0.265665 / 0.323480 (-0.057815) \| 0.003885 / 0.007986 (-0.004100) \| 0.002371 / 0.004328 (-0.001957) \| 0.047895 / 0.004250 (0.043644) \| 0.039015 / 0.037052 (0.001963) \| 0.252320 / 0.258489 (-0.006169) \| 0.286533 / 0.293841 (-0.007308) \| 0.027694 / 0.128546 (-0.100852) \| 0.010254 / 0.075646 (-0.065392) \| 0.206586 / 0.419271 (-0.212685) \| 0.035681 / 0.043533 (-0.007852) \| 0.251645 / 0.255139 (-0.003494) \| 0.285462 / 0.283200 (0.002262) \| 0.017326 / 0.141683 (-0.124357) \| 1.086927 / 1.452155 (-0.365228) \| 1.153172 / 1.492716 (-0.339545) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093020 / 0.018006 (0.075014) \| 0.300018 / 0.000490 (0.299528) \| 0.000208 / 0.000200 (0.000008) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018828 / 0.037411 (-0.018584) \| 0.062569 / 0.014526 (0.048043) \| 0.074130 / 0.176557 (-0.102427) \| 0.119304 / 0.737135 (-0.617832) \| 0.076409 / 0.296338 (-0.219930) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285938 / 0.215209 (0.070729) \| 2.780662 / 2.077655 (0.703007) \| 1.522401 / 1.504120 (0.018281) \| 1.392475 / 1.541195 (-0.148720) \| 1.412517 / 1.468490 (-0.055973) \| 0.562768 / 4.584777 (-4.022009) \| 2.421406 / 3.745712 (-1.324306) \| 2.786271 / 5.269862 (-2.483591) \| 1.737193 / 4.565676 (-2.828484) \| 0.062775 / 0.424275 (-0.361500) \| 0.004908 / 0.007607 (-0.002699) \| 0.345070 / 0.226044 (0.119026) \| 3.383700 / 2.268929 (1.114771) \| 1.795974 / 55.444624 (-53.648651) \| 1.527656 / 6.876477 (-5.348820) \| 1.514035 / 2.142072 (-0.628037) \| 0.647652 / 4.805227 (-4.157575) \| 0.120121 / 6.500664 (-6.380543) \| 0.042259 / 0.075469 (-0.033210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948951 / 1.841788 (-0.892837) \| 11.514971 / 8.074308 (3.440663) \| 10.722668 / 10.191392 (0.531276) \| 0.143034 / 0.680424 (-0.537390) \| 0.014800 / 0.534201 (-0.519401) \| 0.286189 / 0.579283 (-0.293094) \| 0.270735 / 0.434364 (-0.163629) \| 0.323907 / 0.540337 (-0.216430) \| 0.417569 / 1.386936 (-0.969367) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005670 / 0.011353 (-0.005683) \| 0.003238 / 0.011008 (-0.007770) \| 0.048520 / 0.038508 (0.010012) \| 0.051341 / 0.023109 (0.028232) \| 0.273883 / 0.275898 (-0.002015) \| 0.295165 / 0.323480 (-0.028315) \| 0.004755 / 0.007986 (-0.003231) \| 0.002471 / 0.004328 (-0.001857) \| 0.047487 / 0.004250 (0.043237) \| 0.040225 / 0.037052 (0.003172) \| 0.276758 / 0.258489 (0.018269) \| 0.301182 / 0.293841 (0.007341) \| 0.029749 / 0.128546 (-0.098797) \| 0.010340 / 0.075646 (-0.065306) \| 0.057193 / 0.419271 (-0.362079) \| 0.033067 / 0.043533 (-0.010466) \| 0.272716 / 0.255139 (0.017577) \| 0.292301 / 0.283200 (0.009101) \| 0.019075 / 0.141683 (-0.122608) \| 1.101778 / 1.452155 (-0.350376) \| 1.173573 / 1.492716 (-0.319143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091008 / 0.018006 (0.073002) \| 0.300749 / 0.000490 (0.300259) \| 0.000218 / 0.000200 (0.000018) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021760 / 0.037411 (-0.015651) \| 0.071407 / 0.014526 (0.056881) \| 0.081151 / 0.176557 (-0.095406) \| 0.120140 / 0.737135 (-0.616995) \| 0.082408 / 0.296338 (-0.213931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294828 / 0.215209 (0.079619) \| 2.880701 / 2.077655 (0.803047) \| 1.604187 / 1.504120 (0.100068) \| 1.479236 / 1.541195 (-0.061959) \| 1.498875 / 1.468490 (0.030385) \| 0.561950 / 4.584777 (-4.022827) \| 2.462531 / 3.745712 (-1.283181) \| 2.800905 / 5.269862 (-2.468957) \| 1.746535 / 4.565676 (-2.819141) \| 0.062732 / 0.424275 (-0.361544) \| 0.004932 / 0.007607 (-0.002675) \| 0.347125 / 0.226044 (0.121081) \| 3.431343 / 2.268929 (1.162415) \| 1.964999 / 55.444624 (-53.479625) \| 1.669709 / 6.876477 (-5.206768) \| 1.675148 / 2.142072 (-0.466924) \| 0.635436 / 4.805227 (-4.169792) \| 0.116598 / 6.500664 (-6.384066) \| 0.041447 / 0.075469 (-0.034022) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975751 / 1.841788 (-0.866037) \| 12.060246 / 8.074308 (3.985938) \| 10.871641 / 10.191392 (0.680249) \| 0.142936 / 0.680424 (-0.537488) \| 0.015779 / 0.534201 (-0.518422) \| 0.287120 / 0.579283 (-0.292163) \| 0.283963 / 0.434364 (-0.150401) \| 0.341231 / 0.540337 (-0.199107) \| 0.419518 / 1.386936 (-0.967418) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0943ff0072dcef473530d8a494f314048f3a3d51 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005105 / 0.011353 (-0.006248) \| 0.002855 / 0.011008 (-0.008153) \| 0.062044 / 0.038508 (0.023536) \| 0.052948 / 0.023109 (0.029839) \| 0.249841 / 0.275898 (-0.026057) \| 0.276687 / 0.323480 (-0.046792) \| 0.003792 / 0.007986 (-0.004194) \| 0.002385 / 0.004328 (-0.001943) \| 0.048648 / 0.004250 (0.044398) \| 0.038317 / 0.037052 (0.001264) \| 0.255235 / 0.258489 (-0.003254) \| 0.287870 / 0.293841 (-0.005971) \| 0.027429 / 0.128546 (-0.101117) \| 0.010182 / 0.075646 (-0.065464) \| 0.206980 / 0.419271 (-0.212291) \| 0.035444 / 0.043533 (-0.008089) \| 0.255073 / 0.255139 (-0.000066) \| 0.270636 / 0.283200 (-0.012563) \| 0.018003 / 0.141683 (-0.123680) \| 1.124691 / 1.452155 (-0.327463) \| 1.191872 / 1.492716 (-0.300844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088824 / 0.018006 (0.070818) \| 0.302771 / 0.000490 (0.302281) \| 0.000210 / 0.000200 (0.000010) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018102 / 0.037411 (-0.019310) \| 0.062131 / 0.014526 (0.047605) \| 0.073230 / 0.176557 (-0.103327) \| 0.119789 / 0.737135 (-0.617346) \| 0.074804 / 0.296338 (-0.221534) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293244 / 0.215209 (0.078035) \| 2.891401 / 2.077655 (0.813746) \| 1.504481 / 1.504120 (0.000361) \| 1.381251 / 1.541195 (-0.159944) \| 1.387245 / 1.468490 (-0.081245) \| 0.552732 / 4.584777 (-4.032045) \| 2.386439 / 3.745712 (-1.359273) \| 2.718918 / 5.269862 (-2.550944) \| 1.725401 / 4.565676 (-2.840275) \| 0.061946 / 0.424275 (-0.362329) \| 0.004957 / 0.007607 (-0.002650) \| 0.342776 / 0.226044 (0.116731) \| 3.418911 / 2.268929 (1.149983) \| 1.838283 / 55.444624 (-53.606341) \| 1.538013 / 6.876477 (-5.338464) \| 1.545144 / 2.142072 (-0.596928) \| 0.637857 / 4.805227 (-4.167370) \| 0.116451 / 6.500664 (-6.384213) \| 0.042228 / 0.075469 (-0.033241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943575 / 1.841788 (-0.898212) \| 11.492939 / 8.074308 (3.418631) \| 10.601605 / 10.191392 (0.410212) \| 0.139084 / 0.680424 (-0.541340) \| 0.013691 / 0.534201 (-0.520510) \| 0.286696 / 0.579283 (-0.292587) \| 0.259979 / 0.434364 (-0.174385) \| 0.322578 / 0.540337 (-0.217759) \| 0.411950 / 1.386936 (-0.974986) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005168 / 0.011353 (-0.006185) \| 0.003238 / 0.011008 (-0.007770) \| 0.049028 / 0.038508 (0.010520) \| 0.052930 / 0.023109 (0.029821) \| 0.274750 / 0.275898 (-0.001148) \| 0.294023 / 0.323480 (-0.029457) \| 0.003829 / 0.007986 (-0.004157) \| 0.002372 / 0.004328 (-0.001956) \| 0.048689 / 0.004250 (0.044439) \| 0.040056 / 0.037052 (0.003003) \| 0.280147 / 0.258489 (0.021658) \| 0.304871 / 0.293841 (0.011030) \| 0.028734 / 0.128546 (-0.099812) \| 0.010624 / 0.075646 (-0.065022) \| 0.058705 / 0.419271 (-0.360566) \| 0.032140 / 0.043533 (-0.011393) \| 0.276702 / 0.255139 (0.021563) \| 0.293186 / 0.283200 (0.009987) \| 0.018124 / 0.141683 (-0.123559) \| 1.139398 / 1.452155 (-0.312757) \| 1.174862 / 1.492716 (-0.317855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087627 / 0.018006 (0.069620) \| 0.298376 / 0.000490 (0.297886) \| 0.000238 / 0.000200 (0.000038) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021344 / 0.037411 (-0.016067) \| 0.070208 / 0.014526 (0.055682) \| 0.081177 / 0.176557 (-0.095380) \| 0.120170 / 0.737135 (-0.616965) \| 0.082472 / 0.296338 (-0.213866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293227 / 0.215209 (0.078018) \| 2.844619 / 2.077655 (0.766964) \| 1.586922 / 1.504120 (0.082803) \| 1.460256 / 1.541195 (-0.080938) \| 1.475955 / 1.468490 (0.007465) \| 0.553226 / 4.584777 (-4.031551) \| 2.418869 / 3.745712 (-1.326843) \| 2.709256 / 5.269862 (-2.560606) \| 1.705935 / 4.565676 (-2.859741) \| 0.062391 / 0.424275 (-0.361884) \| 0.004929 / 0.007607 (-0.002678) \| 0.350358 / 0.226044 (0.124313) \| 3.448824 / 2.268929 (1.179896) \| 1.929451 / 55.444624 (-53.515174) \| 1.669438 / 6.876477 (-5.207038) \| 1.660923 / 2.142072 (-0.481150) \| 0.633107 / 4.805227 (-4.172120) \| 0.114657 / 6.500664 (-6.386007) \| 0.041256 / 0.075469 (-0.034214) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968408 / 1.841788 (-0.873380) \| 11.749754 / 8.074308 (3.675446) \| 10.796670 / 10.191392 (0.605278) \| 0.128881 / 0.680424 (-0.551543) \| 0.015326 / 0.534201 (-0.518875) \| 0.286407 / 0.579283 (-0.292876) \| 0.276324 / 0.434364 (-0.158040) \| 0.326201 / 0.540337 (-0.214136) \| 0.419854 / 1.386936 (-0.967082) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1731d5a8cd103533ef6b438b4429ab51d3a6a0ce \"CML watermark\")\n" ]	2023-11-22T19:04:45Z	2023-11-23T18:47:30Z	2023-11-23T18:41:23Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6445.diff", "html_url": "https://github.com/huggingface/datasets/pull/6445", "merged_at": "2023-11-23T18:41:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6445.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6445" }	Use the `filelock` package instead of `datasets.utils.filelock` for file locking to be consistent with `huggingface_hub` and not to be responsible for improving the `filelock` capabilities 🙂. (Reverts https://github.com/huggingface/datasets/pull/859, but these `INFO` logs are not printed by default (anymore?), so this should be okay)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6445/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6444	6,444	Remove `Table.__getstate__` and `Table.__setstate__`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36994684?v=4", "events_url": "https://api.github.com/users/LZHgrla/events{/privacy}", "followers_url": "https://api.github.com/users/LZHgrla/followers", "following_url": "https://api.github.com/users/LZHgrla/following{/other_user}", "gists_url": "https://api.github.com/users/LZHgrla/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LZHgrla", "id": 36994684, "login": "LZHgrla", "node_id": "MDQ6VXNlcjM2OTk0Njg0", "organizations_url": "https://api.github.com/users/LZHgrla/orgs", "received_events_url": "https://api.github.com/users/LZHgrla/received_events", "repos_url": "https://api.github.com/users/LZHgrla/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LZHgrla/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LZHgrla/subscriptions", "type": "User", "url": "https://api.github.com/users/LZHgrla", "user_view_type": "public" }	[]	closed	false	[ "Thanks for working on this! The [issue](https://bugs.python.org/issue24658) with pickling objects larger than 4GB seems to be patched in Python 3.8 (the minimal supported version was 3.6 at the time of implementing this), so a simple solution would be removing the `Table.__setstate__` and `Table.__getstate__` overrides.", "@mariosasko \r\nCool!\r\nI removed these overrides, and it worked.\r\n\r\nAll modifications are committed. Ready for review!", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005251 / 0.011353 (-0.006102) \| 0.003804 / 0.011008 (-0.007204) \| 0.063143 / 0.038508 (0.024635) \| 0.059409 / 0.023109 (0.036300) \| 0.255319 / 0.275898 (-0.020579) \| 0.279194 / 0.323480 (-0.044285) \| 0.004643 / 0.007986 (-0.003343) \| 0.002560 / 0.004328 (-0.001768) \| 0.047490 / 0.004250 (0.043240) \| 0.039034 / 0.037052 (0.001982) \| 0.257352 / 0.258489 (-0.001137) \| 0.293029 / 0.293841 (-0.000812) \| 0.027548 / 0.128546 (-0.100998) \| 0.011307 / 0.075646 (-0.064339) \| 0.210325 / 0.419271 (-0.208946) \| 0.035161 / 0.043533 (-0.008372) \| 0.253491 / 0.255139 (-0.001648) \| 0.272085 / 0.283200 (-0.011115) \| 0.018924 / 0.141683 (-0.122759) \| 1.111148 / 1.452155 (-0.341007) \| 1.178076 / 1.492716 (-0.314641) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092447 / 0.018006 (0.074441) \| 0.303680 / 0.000490 (0.303190) \| 0.000208 / 0.000200 (0.000008) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019087 / 0.037411 (-0.018325) \| 0.062663 / 0.014526 (0.048137) \| 0.074651 / 0.176557 (-0.101905) \| 0.121334 / 0.737135 (-0.615802) \| 0.076703 / 0.296338 (-0.219636) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286505 / 0.215209 (0.071295) \| 2.804942 / 2.077655 (0.727287) \| 1.481930 / 1.504120 (-0.022190) \| 1.369485 / 1.541195 (-0.171710) \| 1.424467 / 1.468490 (-0.044023) \| 0.556810 / 4.584777 (-4.027967) \| 2.416338 / 3.745712 (-1.329374) \| 2.901869 / 5.269862 (-2.367992) \| 1.827007 / 4.565676 (-2.738669) \| 0.062252 / 0.424275 (-0.362024) \| 0.005076 / 0.007607 (-0.002531) \| 0.343850 / 0.226044 (0.117805) \| 3.377611 / 2.268929 (1.108683) \| 1.860214 / 55.444624 (-53.584410) \| 1.595146 / 6.876477 (-5.281331) \| 1.627234 / 2.142072 (-0.514838) \| 0.651027 / 4.805227 (-4.154200) \| 0.119214 / 6.500664 (-6.381450) \| 0.043342 / 0.075469 (-0.032127) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942863 / 1.841788 (-0.898924) \| 12.484633 / 8.074308 (4.410324) \| 10.560668 / 10.191392 (0.369276) \| 0.144647 / 0.680424 (-0.535777) \| 0.014734 / 0.534201 (-0.519466) \| 0.286575 / 0.579283 (-0.292708) \| 0.270913 / 0.434364 (-0.163451) \| 0.323792 / 0.540337 (-0.216545) \| 0.419186 / 1.386936 (-0.967750) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005315 / 0.011353 (-0.006038) \| 0.003548 / 0.011008 (-0.007460) \| 0.049271 / 0.038508 (0.010763) \| 0.055198 / 0.023109 (0.032089) \| 0.275940 / 0.275898 (0.000042) \| 0.307637 / 0.323480 (-0.015843) \| 0.003997 / 0.007986 (-0.003988) \| 0.002544 / 0.004328 (-0.001785) \| 0.050381 / 0.004250 (0.046130) \| 0.041158 / 0.037052 (0.004105) \| 0.281519 / 0.258489 (0.023030) \| 0.308085 / 0.293841 (0.014244) \| 0.030464 / 0.128546 (-0.098083) \| 0.010690 / 0.075646 (-0.064957) \| 0.057458 / 0.419271 (-0.361814) \| 0.032814 / 0.043533 (-0.010719) \| 0.282435 / 0.255139 (0.027296) \| 0.301342 / 0.283200 (0.018142) \| 0.017556 / 0.141683 (-0.124127) \| 1.159423 / 1.452155 (-0.292732) \| 1.177344 / 1.492716 (-0.315372) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091086 / 0.018006 (0.073079) \| 0.305316 / 0.000490 (0.304826) \| 0.000218 / 0.000200 (0.000019) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021832 / 0.037411 (-0.015579) \| 0.071055 / 0.014526 (0.056529) \| 0.082982 / 0.176557 (-0.093574) \| 0.119966 / 0.737135 (-0.617169) \| 0.083539 / 0.296338 (-0.212800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302501 / 0.215209 (0.087292) \| 2.936347 / 2.077655 (0.858692) \| 1.601658 / 1.504120 (0.097538) \| 1.467267 / 1.541195 (-0.073928) \| 1.514656 / 1.468490 (0.046166) \| 0.563934 / 4.584777 (-4.020843) \| 2.513715 / 3.745712 (-1.231997) \| 2.813014 / 5.269862 (-2.456847) \| 1.773243 / 4.565676 (-2.792433) \| 0.063208 / 0.424275 (-0.361067) \| 0.004979 / 0.007607 (-0.002628) \| 0.360694 / 0.226044 (0.134650) \| 3.520578 / 2.268929 (1.251650) \| 1.975369 / 55.444624 (-53.469255) \| 1.691257 / 6.876477 (-5.185220) \| 1.730872 / 2.142072 (-0.411200) \| 0.655366 / 4.805227 (-4.149861) \| 0.146043 / 6.500664 (-6.354621) \| 0.041386 / 0.075469 (-0.034083) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979840 / 1.841788 (-0.861948) \| 12.456924 / 8.074308 (4.382616) \| 10.938595 / 10.191392 (0.747203) \| 0.133853 / 0.680424 (-0.546571) \| 0.015744 / 0.534201 (-0.518457) \| 0.289585 / 0.579283 (-0.289698) \| 0.291143 / 0.434364 (-0.143221) \| 0.328109 / 0.540337 (-0.212228) \| 0.561897 / 1.386936 (-0.825039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05ec66cc1abc20bd13d02c681b7be372ae084a4f \"CML watermark\")\n" ]	2023-11-22T17:55:10Z	2023-11-23T15:19:43Z	2023-11-23T15:13:28Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6444.diff", "html_url": "https://github.com/huggingface/datasets/pull/6444", "merged_at": "2023-11-23T15:13:28Z", "patch_url": "https://github.com/huggingface/datasets/pull/6444.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6444" }	When using distributed training, the code of `os.remove(filename)` may be executed separately by each rank, leading to `FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmprxxxxxxx.arrow'` ```python from torch import distributed as dist if dist.get_rank() == 0: dataset = process_dataset(args, *kwargs) objects = [dataset] else: objects = [None] dist.broadcast_object_list(objects, src=0) dataset = objects[0] ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", 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https://github.com/huggingface/datasets/issues/6443	6,443	Trouble loading files defined in YAML explicitly	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	[ "There is a typo in one of the file names - `data/edf.csv` should be renamed to `data/def.csv` 🙂. ", "wow, I reviewed it twice to avoid being ashamed like that, but... I didn't notice the typo.\r\n\r\n---\r\n\r\nBesides this: do you think we would be able to improve the error message to make this clearer?", "Hi @severo 👋🏼\n\nI'd like to work on improving the error message when files listed in `data_files` inside the YAML config are not found in the dataset repository.\n\nMy plan is to:\n- Detect missing files explicitly by comparing listed paths in `data_files` with actual files in the repo\n- Update the raised error to clearly mention which files are missing, e.g.:\n `\"The following files were not found in the dataset repository: data/def.csv\"`\n\nLet me know if this direction makes sense — happy to start working on a PR!\n", "cc @lhoestq ", "Hi ! Doesn't it raise a `FileNotFoundError` when it's the case ? see in the source code:\n\nhttps://github.com/huggingface/datasets/blob/4a637bd963557bca42d3a2d8c54200fccb7ab91e/src/datasets/data_files.py#L379-L383", "Hi @lhoestq, thanks a lot for the pointer and for taking the time to review!\n\nYou're totally right — the FileNotFoundError does get raised from data_files.py when a file pattern isn't found.\n\nWhat I was hoping to improve is the clarity of the error message, especially when users define multiple paths in data_files via a YAML config. For example, instead of the generic:\n\n\"Unable to find 'data/def.csv'\"\n\nit could say something like:\n\n\"The following files listed in the data_files YAML config were not found in the dataset repository: data/def.csv\"\n\nThis could help users quickly catch typos or misnamed files in longer configs (like what happened in this issue 😅).\n\nIf that sounds like a helpful addition, I'd love to work on a small PR to enhance the error messaging.\n\nThanks again for all your help and feedback!" ]	2023-11-22T15:18:10Z	2025-06-23T13:46:46Z	null	COLLABORATOR	null	null	Look at https://huggingface.co/datasets/severo/doc-yaml-2 It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration ``` You can select multiple files per split using a list of paths: my_dataset_repository/ ├── README.md ├── data/ │ ├── abc.csv │ └── def.csv └── holdout/ └── ghi.csv --- configs: - config_name: default data_files: - split: train path: - "data/abc.csv" - "data/def.csv" - split: test path: "holdout/ghi.csv" --- ``` It raises the following error: ``` Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6443/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6442	6,442	Trouble loading image folder with additional features - metadata file ignored	{ "avatar_url": "https://avatars.githubusercontent.com/u/57615435?v=4", "events_url": "https://api.github.com/users/linoytsaban/events{/privacy}", "followers_url": "https://api.github.com/users/linoytsaban/followers", "following_url": "https://api.github.com/users/linoytsaban/following{/other_user}", "gists_url": "https://api.github.com/users/linoytsaban/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/linoytsaban", "id": 57615435, "login": "linoytsaban", "node_id": "MDQ6VXNlcjU3NjE1NDM1", "organizations_url": "https://api.github.com/users/linoytsaban/orgs", "received_events_url": "https://api.github.com/users/linoytsaban/received_events", "repos_url": "https://api.github.com/users/linoytsaban/repos", "site_admin": false, "starred_url": "https://api.github.com/users/linoytsaban/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/linoytsaban/subscriptions", "type": "User", "url": "https://api.github.com/users/linoytsaban", "user_view_type": "public" }	[]	closed	false	[ "I reproduced too:\r\n- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)\r\n- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)\r\n- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)" ]	2023-11-22T11:01:35Z	2023-11-24T17:13:03Z	2023-11-24T17:13:03Z	NONE	null	null	### Describe the bug Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. When loading a local image folder with captions using `datasets==2.13.0` ``` from datasets import load_dataset data = load_dataset(<image_folder_path>) data.column_names ``` yields `{'train': ['image', 'prompt']}` but when using `datasets==2.15.0` yeilds `{'train': ['image']}` Putting the images and `metadata.jsonl` file into a nested `train` folder or loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and yields `{'train': ['image', 'prompt']}` ### Steps to reproduce the bug 1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt" 2. run: ``` from datasets import load_dataset data = load_dataset("<image_folder_path>") data.column_names ``` ### Expected behavior `{'train': ['image', 'prompt']}` ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6442/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6441	6,441	Trouble Loading a Gated Dataset For User with Granted Permission	{ "avatar_url": "https://avatars.githubusercontent.com/u/124715309?v=4", "events_url": "https://api.github.com/users/e-trop/events{/privacy}", "followers_url": "https://api.github.com/users/e-trop/followers", "following_url": "https://api.github.com/users/e-trop/following{/other_user}", "gists_url": "https://api.github.com/users/e-trop/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/e-trop", "id": 124715309, "login": "e-trop", "node_id": "U_kgDOB28BLQ", "organizations_url": "https://api.github.com/users/e-trop/orgs", "received_events_url": "https://api.github.com/users/e-trop/received_events", "repos_url": "https://api.github.com/users/e-trop/repos", "site_admin": false, "starred_url": "https://api.github.com/users/e-trop/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/e-trop/subscriptions", "type": "User", "url": "https://api.github.com/users/e-trop", "user_view_type": "public" }	[]	closed	false	[ "> Also when they try to click the url link for the dataset they get a 404 error.\r\n\r\nThis seems to be a Hub error then (cc @SBrandeis)", "Could you report this to https://discuss.huggingface.co/c/hub/23, providing the URL of the dataset, or at least if the dataset is public or private?", "Thanks for the reply! I've created an issue on the hub's board here: https://discuss.huggingface.co/t/trouble-loading-a-gated-dataset-for-user-with-granted-permission/65565" ]	2023-11-21T19:24:36Z	2023-12-13T08:27:16Z	2023-12-13T08:27:16Z	NONE	null	null	### Describe the bug I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get `FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error. ### Steps to reproduce the bug 1. Grant access to gated dataset for specific users 2. Users accept invitation 3. Users login to hugging face hub using cli login 4. Users run load_dataset ### Expected behavior Dataset is loaded normally for users who were granted access to the gated dataset. ### Environment info datasets==2.15.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6441/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6440	6,440	`.map` not hashing under python 3.9	{ "avatar_url": "https://avatars.githubusercontent.com/u/9058204?v=4", "events_url": "https://api.github.com/users/changyeli/events{/privacy}", "followers_url": "https://api.github.com/users/changyeli/followers", "following_url": "https://api.github.com/users/changyeli/following{/other_user}", "gists_url": "https://api.github.com/users/changyeli/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/changyeli", "id": 9058204, "login": "changyeli", "node_id": "MDQ6VXNlcjkwNTgyMDQ=", "organizations_url": "https://api.github.com/users/changyeli/orgs", "received_events_url": "https://api.github.com/users/changyeli/received_events", "repos_url": "https://api.github.com/users/changyeli/repos", "site_admin": false, "starred_url": "https://api.github.com/users/changyeli/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/changyeli/subscriptions", "type": "User", "url": "https://api.github.com/users/changyeli", "user_view_type": "public" }	[]	closed	false	[ "Tried to upgrade Python to 3.11 - still get this message. A partial solution is to NOT use `num_proc` at all. It will be considerably longer to finish the job.", "Hi! The `model = torch.compile(model)` line is problematic for our hashing logic. We would have to merge https://github.com/huggingface/datasets/pull/5867 to support hashing `torch.compile`-ed models/functions. \r\n\r\nI've started refactoring the hashing logic and plan to incorporate a fix for `torch.compile` as part of it, so this should be addressed soon (probably this or next week). " ]	2023-11-21T15:14:54Z	2023-11-28T16:29:33Z	2023-11-28T16:29:33Z	NONE	null	null	### Describe the bug The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message: `Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ### Steps to reproduce the bug ```python def map_to_pred(batch): """ Perform inference on an audio batch Parameters: batch (dict): A dictionary containing audio data and other related information. Returns: dict: The input batch dictionary with added prediction and transcription fields. """ audio = batch['audio'] input_features = processor( audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features input_features = input_features.to('cuda') with torch.no_grad(): predicted_ids = model.generate(input_features) preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] batch['prediction'] = processor.tokenizer._normalize(preds) batch["transcription"] = processor.tokenizer._normalize(batch['transcription']) return batch MODEL_CARD = "openai/whisper-small" MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1] model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD) processor = AutoProcessor.from_pretrained( MODEL_CARD, language="english", task="transcribe") model = torch.compile(model) dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test") dt = dt.cast_column("audio", Audio(sampling_rate=16000)) result = coraal_dt.map(map_to_pred, num_proc=16) ``` ### Expected behavior Hashed and cached dataset starts inferencing ### Environment info - `transformers` version: 4.35.0 - Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - Safetensors version: 0.4.0 - Accelerate version: 0.24.1 - Accelerate config: not found - PyTorch version (GPU?): 2.1.0 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6440/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6439	6,439	Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding	{ "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/AntreasAntoniou", "id": 10792502, "login": "AntreasAntoniou", "node_id": "MDQ6VXNlcjEwNzkyNTAy", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "type": "User", "url": "https://api.github.com/users/AntreasAntoniou", "user_view_type": "public" }	[]	open	false	[]	2023-11-20T20:07:23Z	2023-11-20T20:07:37Z	null	NONE	null	null	### Describe the bug I am working with a dataset I am trying to publish. The path is Antreas/TALI. It's a fairly large dataset, and contains images, video, audio and text. I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. Find the script I am using below: ```python import multiprocessing as mp import pathlib from typing import Optional import datasets from rich import print from tqdm import tqdm def download_dataset_via_hub( dataset_name: str, dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), ): import huggingface_hub as hf_hub download_folder = hf_hub.snapshot_download( repo_id=dataset_name, repo_type="dataset", cache_dir=dataset_download_path, resume_download=True, max_workers=num_download_workers, ignore_patterns=[], ) return pathlib.Path(download_folder) / "data" def load_dataset_via_hub( dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), dataset_name: Optional[str] = None, ): from dataclasses import dataclass, field from datasets import ClassLabel, Features, Image, Sequence, Value dataset_path = download_dataset_via_hub( dataset_download_path=dataset_download_path, num_download_workers=num_download_workers, dataset_name=dataset_name, ) # Building a list of file paths for validation set train_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob(".parquet") if "train" in file.as_posix() ] val_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob(".parquet") if "val" in file.as_posix() ] test_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "test" in file.as_posix() ] print( f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set" ) data_files = { "test": test_files, "val": val_files, "train": train_files, } features = Features( { "image": Image( decode=True ), # Set `decode=True` if you want to decode the images, otherwise `decode=False` "image_url": Value("string"), "item_idx": Value("int64"), "wit_features": Sequence( { "attribution_passes_lang_id": Value("bool"), "caption_alt_text_description": Value("string"), "caption_reference_description": Value("string"), "caption_title_and_reference_description": Value("string"), "context_page_description": Value("string"), "context_section_description": Value("string"), "hierarchical_section_title": Value("string"), "is_main_image": Value("bool"), "language": Value("string"), "page_changed_recently": Value("bool"), "page_title": Value("string"), "page_url": Value("string"), "section_title": Value("string"), } ), "wit_idx": Value("int64"), "youtube_title_text": Value("string"), "youtube_description_text": Value("string"), "youtube_video_content": Value("binary"), "youtube_video_starting_time": Value("string"), "youtube_subtitle_text": Value("string"), "youtube_video_size": Value("int64"), "youtube_video_file_path": Value("string"), } ) dataset = datasets.load_dataset( "parquet" if dataset_name is None else dataset_name, data_files=data_files, features=features, num_proc=1, cache_dir=dataset_download_path / "cache", ) return dataset if __name__ == "__main__": dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/") dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[ "test" ] for sample in tqdm(dataset): print(list(sample.keys())) ``` Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! ### Steps to reproduce the bug 1. Run the code I provided to get a sense of how fast snapshot + manual is 2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 3. You should now have an appreciation of how long these things take. ### Expected behavior The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6439/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6438	6,438	Support GeoParquet	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	[ "Thank you, @severo ! I would be more than happy to help in any way I can. I am not familiar with this repo's codebase, but I would be eager to contribute. :)\r\n\r\nFor the preview in Datasets Hub, I think it makes sense to just display the geospatial column as text. If there were a dataset loader, though, I think it should be able to support the geospatial components. Geopandas is probably the most user-friendly interface for that. I'm not sure if it's currently relevant in the context of geoparquet, but I think the pyogrio driver is faster than fiona.\r\n\r\nBut the whole gdal dependency thing can be a real pain. If anything, it would need to be an optional dependency. Maybe it would be best if the loader tries importing relevant geospatial libraries, and in the event of an ImportError, falls back to text for the geometry column.\r\n\r\nPlease let me know if I can be of assistance, and thanks again for creating this Issue. :)", "Just hitting into this same issue too showing GeoParquet files in Datasets Viewer. I tried to implement a custom reader for GeoParquet in https://huggingface.co/datasets/weiji14/clay_vector_embeddings/discussions/1, but it seems like HuggingFace has disabled datasets with custom loading scripts from using the dataset viewer according to https://discuss.huggingface.co/t/dataset-repo-requires-arbitrary-python-code-execution/59346 :frowning_face: \r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/2f84d8ce-91c2-48cb-b72c-547ea8583892)\r\n\r\nI'm thinking now if there's a way to simply map files with GeoParquet extensions (.gpq, .geoparquet, etc) to use the Parquet reader. Maybe we could allowlist these geoparquet file extensions at https://github.com/huggingface/datasets/blame/0caf91285116ec910f409e82cc6e1f4cff7496e3/src/datasets/packaged_modules/__init__.py#L30-L51? Having the table columns show up would be a quick win.\r\n\r\nLonger term though, it would certainly be nice if the WKB geometry columns could be displayed in a nicer form. Geopandas' [read_parquet](https://geopandas.org/en/v0.14.1/docs/reference/api/geopandas.read_parquet.html) function is supposedly faster than `pyogrio.read_dataframe` according to https://github.com/geopandas/geopandas/discussions/2724#discussioncomment-4606048, but there's also [`pyogrio.raw.read_arrow`](https://pyogrio.readthedocs.io/en/latest/api.html#pyogrio.raw.read_arrow) now that can read into a `pyarrow.Table` directly.", "Update: It looks like renaming the GeoParquet file to have a file extension of `.parquet` works (see https://huggingface.co/datasets/weiji14/clay_vector_embeddings). HuggingFace's default parquet reader is able to read the GeoParquet file, though the geometry column is of an unknown type:\r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/9060c300-d595-4409-9ccb-5e0207396883)\r\n\r\nI've opened a quick PR at #6508 to allow files with a `.geoparquet` or `*.gpq` extension to be read using the default Parquet reader. Let's see how that goes :smile:", "@joshuasundance-swca, @weiji14, If I'm understanding this correctly, the code below wouldn't be recommended to due to dependency headaches? If that's the case, what solution would there be to see the geometry features for .gpq files in huggingfaceHub? \r\n\r\ncode for dataset_loader.py\r\n```\r\nimport geopandas as gpd\r\n# ... (other imports remain the same)\r\n\r\nclass ClayVectorEmbeddings(datasets.ArrowBasedBuilder):\r\n # ... (other parts of the class remain the same)\r\n\r\n def _info(self):\r\n # Read the GeoParquet file to get the schema for the 'geometry' feature\r\n gdf = gpd.read_file(\"path/to/your/geoparquet/file.gpq\") # Replace with your file path\r\n geometry_schema = str(gdf.geometry.dtype)\r\n\r\n return datasets.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=\"Clay Vector Embeddings in GeoParquet format.\",\r\n # This defines the different columns of the dataset and their types\r\n features=datasets.Features(\r\n {\r\n \"source_url\": datasets.Value(dtype=\"string\"),\r\n \"date\": datasets.Value(dtype=\"date32\"),\r\n \"embeddings\": datasets.Value(\"string\"),\r\n \"geometry\": datasets.Value(dtype=geometry_schema), # Use the schema read by GeoPandas\r\n # ... (other features)\r\n }\r\n ),\r\n )\r\n\r\n# ... (rest of the script remains the same)\r\n\r\n```", "Hi @mehrdad-es, I'm not sure if HuggingFace would be keen to add `geopandas` to HuggingFace Hub (maybe a question for @severo?). Having a geometry viewer would be an even bigger task, and if you're thinking of a map-viewer, it might involve some redesign of the website UI. Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.", "I've created https://github.com/huggingface/datasets-server/issues/2416 to discuss the possibility of supporting (vectorial) geospatial columns in the dataset viewer, or in the converted parquet files.\r\n\r\nAt the same time, it would be super interesting to see what is already possible to do with a Hugging Face dataset that hosts geospatial data. \r\n\r\n> Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.\r\n\r\nIt would be awesome to show this inside a [Space](https://huggingface.co/docs/hub/spaces)." ]	2023-11-20T11:54:58Z	2024-02-07T08:36:51Z	null	COLLABORATOR	null	null	### Feature request Support the GeoParquet format ### Motivation GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns. It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1). ### Your contribution I would be happy to help work on a PR (but I don't think I can do one on my own). Also, we have to define what we want to support: - load all the columns, but get the "geospatial" column in text-only mode for now - or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6438/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6437	6,437	Problem in training iterable dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/38107672?v=4", "events_url": "https://api.github.com/users/21Timothy/events{/privacy}", "followers_url": "https://api.github.com/users/21Timothy/followers", "following_url": "https://api.github.com/users/21Timothy/following{/other_user}", "gists_url": "https://api.github.com/users/21Timothy/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/21Timothy", "id": 38107672, "login": "21Timothy", "node_id": "MDQ6VXNlcjM4MTA3Njcy", "organizations_url": "https://api.github.com/users/21Timothy/orgs", "received_events_url": "https://api.github.com/users/21Timothy/received_events", "repos_url": "https://api.github.com/users/21Timothy/repos", "site_admin": false, "starred_url": "https://api.github.com/users/21Timothy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/21Timothy/subscriptions", "type": "User", "url": "https://api.github.com/users/21Timothy", "user_view_type": "public" }	[]	open	false	[ "Has anyone ever encountered this problem before?", "`split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n\r\nHowever if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.", "> `split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n> \r\n> However if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.\r\n\r\nThanks for your answer! I finally solve it by using the torch.distributed.algorithms.join.Join. I think maybe some rookie like me would face the same question the day after tomorrow hh.", "Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?", "> Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?\r\n\r\nI think it's none. I have tried barrier() to solve the problem but I failed. Maybe it's a tool for other situation.", "Hi @21Timothy @medbar @alex-hh would it make sense to improve `split_dataset_by_node()` with these changes ?\n\n1) make each node have the same number of samples - this way you don't need barrier / join context\n2) ensure each node only loads its own shards (no duplicate, no overlap, no skip)\n\nI also asked in https://github.com/huggingface/datasets/issues/6594 where other people were interested\n\nAnd I opened https://github.com/huggingface/datasets/pull/7854 for those who would like to try this out" ]	2023-11-20T03:04:02Z	2024-05-22T03:14:13Z	null	NONE	null	null	### Describe the bug I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow. ### Steps to reproduce the bug ``` def train(args, model, device, train_loader, optimizer, criterion, epoch, length): model.train() idx_length = 0 for batch_idx, data in enumerate(train_loader): s_time = time.time() X = data['X'] target = data['y'].reshape(-1, 28) X, target = X.to(device), target.to(device) optimizer.zero_grad() output = model(X) loss = criterion(output, target) loss.backward() optimizer.step() idx_length += 1 if batch_idx % args.log_interval == 0: # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( # epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), # 100. * batch_idx * len( # X) * torch.distributed.get_world_size() / length, loss.item())) print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format( epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), 100. * batch_idx * len( X) * torch.distributed.get_world_size() / length)) if args.dry_run: break print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now())) train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True) test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True) train_iterable_dataset = train_iterable_dataset.map(process_fn) test_iterable_dataset = test_iterable_dataset.map(process_fn) train_iterable_dataset = train_iterable_dataset.map(scale) test_iterable_dataset = test_iterable_dataset.map(scale) train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards) train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 3,#ngpus_per_node, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_iterable_dataset, train_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # train_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) test_loader = torch.utils.data.DataLoader(test_iterable_dataset, test_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # test_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) for epoch in range(1, args.epochs + 1): start_time = time.time() train_iterable_dataset.set_epoch(epoch) test_iterable_dataset.set_epoch(epoch) train(args, model, device, train_loader, optimizer, criterion, epoch, train_len) test(args, model, device, criterion2, test_loader) ``` And here’s the part of output: ``` Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078 Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373 Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375 Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703 Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961 Process 3 length: 5877 time: 2023-11-17 17:03:26.582317 Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test. 3 0 tensor(45508.8516, device='cuda:3') 3 100 tensor(45309.0469, device='cuda:3') 3 200 tensor(45675.3047, device='cuda:3') 3 300 tensor(45263.0273, device='cuda:3') Process 3 Reduce metrics. Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)] Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176 Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289 Process 1 length: 5888 time: 2023-11-17 17:20:48.578204 Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test. 1 0 tensor(45265.9141, device='cuda:1') ``` ### Expected behavior I'd like to know how to fix this problem. ### Environment info ``` torch==2.0 datasets==2.14.0 ```	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6437/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6436	6,436	TypeError: <lambda>() takes 0 positional arguments but 1 was given	{ "avatar_url": "https://avatars.githubusercontent.com/u/47111429?v=4", "events_url": "https://api.github.com/users/ahmadmustafaanis/events{/privacy}", "followers_url": "https://api.github.com/users/ahmadmustafaanis/followers", "following_url": "https://api.github.com/users/ahmadmustafaanis/following{/other_user}", "gists_url": "https://api.github.com/users/ahmadmustafaanis/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ahmadmustafaanis", "id": 47111429, "login": "ahmadmustafaanis", "node_id": "MDQ6VXNlcjQ3MTExNDI5", "organizations_url": "https://api.github.com/users/ahmadmustafaanis/orgs", "received_events_url": "https://api.github.com/users/ahmadmustafaanis/received_events", "repos_url": "https://api.github.com/users/ahmadmustafaanis/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ahmadmustafaanis/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ahmadmustafaanis/subscriptions", "type": "User", "url": "https://api.github.com/users/ahmadmustafaanis", "user_view_type": "public" }	[]	closed	false	[ "This looks like a problem with your environment rather than `datasets`.", "I meet the same problem,\r\nand originally use\r\n```python\r\nlocale.getpreferredencoding = lambda : \"UTF-8\"\r\n```\r\nand change to\r\n```\r\nlocale.getpreferredencoding = lambda x: \"UTF-8\"\r\n```\r\nand it works.", "> I meet the same problem, and originally use\n> \n> locale.getpreferredencoding = lambda : \"UTF-8\"\n> \n> and change to\n> \n> ```\n> locale.getpreferredencoding = lambda x: \"UTF-8\"\n> ```\n> \n> and it works.\n\nThanks, works for me too." ]	2023-11-19T13:10:20Z	2025-05-05T18:21:21Z	2023-11-29T16:28:34Z	NONE	null	null	### Describe the bug ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import Dataset 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(args, kwargs) 154 def get_file_type(args, *kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, args, *kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` or ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>() ----> 1 import datasets 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(args, *kwargs) 154 def get_file_type(args, *kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` ### Steps to reproduce the bug `import datasets` on colab ### Expected behavior work fine ### Environment info colab `!pip install datasets`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6436/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6435	6,435	Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method	{ "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kopyl", "id": 17604849, "login": "kopyl", "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "organizations_url": "https://api.github.com/users/kopyl/orgs", "received_events_url": "https://api.github.com/users/kopyl/received_events", "repos_url": "https://api.github.com/users/kopyl/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "type": "User", "url": "https://api.github.com/users/kopyl", "user_view_type": "public" }	[]	closed	false	[ "[This doc section](https://huggingface.co/docs/datasets/main/en/process#multiprocessing) explains how to modify the script to avoid this error.", "@mariosasko thank you very much, i'll check it", "@mariosasko no it does not\r\n\r\n`Dataset.filter() got an unexpected keyword argument 'with_rank'`" ]	2023-11-19T04:21:16Z	2024-01-27T17:14:20Z	2023-12-04T16:57:43Z	NONE	null	null	### Describe the bug 1. I ran dataset mapping with `num_proc=6` in it and got this error: `RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method` I can't actually find a way to run multi-GPU dataset mapping. Can you help? ### Steps to reproduce the bug 1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py ### Expected behavior Should work well ### Environment info 6x A100 SXM, Linux	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6435/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6434	6,434	Use `ruff` for formatting	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004293 / 0.011353 (-0.007060) \| 0.002953 / 0.011008 (-0.008055) \| 0.063712 / 0.038508 (0.025204) \| 0.029963 / 0.023109 (0.006854) \| 0.248574 / 0.275898 (-0.027324) \| 0.272757 / 0.323480 (-0.050723) \| 0.003878 / 0.007986 (-0.004108) \| 0.002456 / 0.004328 (-0.001872) \| 0.047959 / 0.004250 (0.043709) \| 0.043277 / 0.037052 (0.006224) \| 0.255071 / 0.258489 (-0.003418) \| 0.283934 / 0.293841 (-0.009907) \| 0.022870 / 0.128546 (-0.105676) \| 0.007224 / 0.075646 (-0.068422) \| 0.221595 / 0.419271 (-0.197677) \| 0.053468 / 0.043533 (0.009935) \| 0.249906 / 0.255139 (-0.005233) \| 0.274894 / 0.283200 (-0.008305) \| 0.017246 / 0.141683 (-0.124437) \| 1.112440 / 1.452155 (-0.339714) \| 1.167293 / 1.492716 (-0.325424) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092684 / 0.018006 (0.074677) \| 0.301721 / 0.000490 (0.301231) \| 0.000220 / 0.000200 (0.000020) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018289 / 0.037411 (-0.019122) \| 0.061898 / 0.014526 (0.047372) \| 0.072904 / 0.176557 (-0.103653) \| 0.118515 / 0.737135 (-0.618621) \| 0.074000 / 0.296338 (-0.222338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287044 / 0.215209 (0.071835) \| 2.818091 / 2.077655 (0.740436) \| 1.502401 / 1.504120 (-0.001719) \| 1.374688 / 1.541195 (-0.166506) \| 1.410254 / 1.468490 (-0.058236) \| 0.407519 / 4.584777 (-4.177258) \| 2.379199 / 3.745712 (-1.366513) \| 2.585745 / 5.269862 (-2.684117) \| 1.562336 / 4.565676 (-3.003341) \| 0.045977 / 0.424275 (-0.378299) \| 0.004809 / 0.007607 (-0.002798) \| 0.347942 / 0.226044 (0.121897) \| 3.383318 / 2.268929 (1.114390) \| 1.844784 / 55.444624 (-53.599841) \| 1.561949 / 6.876477 (-5.314528) \| 1.571082 / 2.142072 (-0.570990) \| 0.482469 / 4.805227 (-4.322758) \| 0.099357 / 6.500664 (-6.401307) \| 0.041039 / 0.075469 (-0.034430) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944236 / 1.841788 (-0.897551) \| 11.519623 / 8.074308 (3.445315) \| 10.353829 / 10.191392 (0.162437) \| 0.137530 / 0.680424 (-0.542894) \| 0.014454 / 0.534201 (-0.519747) \| 0.268657 / 0.579283 (-0.310626) \| 0.265165 / 0.434364 (-0.169199) \| 0.302626 / 0.540337 (-0.237712) \| 0.426923 / 1.386936 (-0.960013) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004711 / 0.011353 (-0.006641) \| 0.002504 / 0.011008 (-0.008504) \| 0.047671 / 0.038508 (0.009163) \| 0.051147 / 0.023109 (0.028037) \| 0.272848 / 0.275898 (-0.003050) \| 0.291705 / 0.323480 (-0.031775) \| 0.004002 / 0.007986 (-0.003984) \| 0.002382 / 0.004328 (-0.001947) \| 0.047583 / 0.004250 (0.043332) \| 0.038203 / 0.037052 (0.001150) \| 0.278536 / 0.258489 (0.020047) \| 0.305872 / 0.293841 (0.012031) \| 0.023890 / 0.128546 (-0.104657) \| 0.006954 / 0.075646 (-0.068693) \| 0.053716 / 0.419271 (-0.365556) \| 0.032158 / 0.043533 (-0.011375) \| 0.273939 / 0.255139 (0.018800) \| 0.290722 / 0.283200 (0.007522) \| 0.016946 / 0.141683 (-0.124737) \| 1.102726 / 1.452155 (-0.349429) \| 1.169356 / 1.492716 (-0.323360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092520 / 0.018006 (0.074514) \| 0.301949 / 0.000490 (0.301459) \| 0.000248 / 0.000200 (0.000048) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021013 / 0.037411 (-0.016399) \| 0.069965 / 0.014526 (0.055439) \| 0.080105 / 0.176557 (-0.096451) \| 0.119802 / 0.737135 (-0.617334) \| 0.081615 / 0.296338 (-0.214724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301170 / 0.215209 (0.085960) \| 2.884817 / 2.077655 (0.807162) \| 1.596376 / 1.504120 (0.092256) \| 1.471205 / 1.541195 (-0.069990) \| 1.499061 / 1.468490 (0.030571) \| 0.407729 / 4.584777 (-4.177048) \| 2.432824 / 3.745712 (-1.312888) \| 2.561905 / 5.269862 (-2.707957) \| 1.535364 / 4.565676 (-3.030313) \| 0.046592 / 0.424275 (-0.377683) \| 0.004773 / 0.007607 (-0.002834) \| 0.350872 / 0.226044 (0.124828) \| 3.474874 / 2.268929 (1.205945) \| 1.963114 / 55.444624 (-53.481510) \| 1.688213 / 6.876477 (-5.188263) \| 1.686325 / 2.142072 (-0.455748) \| 0.487151 / 4.805227 (-4.318076) \| 0.104253 / 6.500664 (-6.396411) \| 0.043499 / 0.075469 (-0.031970) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980395 / 1.841788 (-0.861393) \| 11.907393 / 8.074308 (3.833085) \| 10.983688 / 10.191392 (0.792296) \| 0.142875 / 0.680424 (-0.537549) \| 0.015375 / 0.534201 (-0.518826) \| 0.270043 / 0.579283 (-0.309240) \| 0.295092 / 0.434364 (-0.139272) \| 0.309466 / 0.540337 (-0.230871) \| 0.409812 / 1.386936 (-0.977124) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17f97ca8ec66f6664d3e9b7ceb84fe3ca49a9c18 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004703 / 0.011353 (-0.006650) \| 0.002767 / 0.011008 (-0.008241) \| 0.063162 / 0.038508 (0.024654) \| 0.052241 / 0.023109 (0.029132) \| 0.237138 / 0.275898 (-0.038760) \| 0.262793 / 0.323480 (-0.060687) \| 0.003873 / 0.007986 (-0.004113) \| 0.002433 / 0.004328 (-0.001896) \| 0.048647 / 0.004250 (0.044397) \| 0.037887 / 0.037052 (0.000834) \| 0.244939 / 0.258489 (-0.013551) \| 0.304015 / 0.293841 (0.010174) \| 0.022859 / 0.128546 (-0.105688) \| 0.006763 / 0.075646 (-0.068883) \| 0.202728 / 0.419271 (-0.216544) \| 0.035369 / 0.043533 (-0.008164) \| 0.240785 / 0.255139 (-0.014354) \| 0.255109 / 0.283200 (-0.028091) \| 0.017951 / 0.141683 (-0.123732) \| 1.096103 / 1.452155 (-0.356052) \| 1.167662 / 1.492716 (-0.325054) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092285 / 0.018006 (0.074279) \| 0.300201 / 0.000490 (0.299711) \| 0.000222 / 0.000200 (0.000022) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018271 / 0.037411 (-0.019140) \| 0.062306 / 0.014526 (0.047780) \| 0.072615 / 0.176557 (-0.103942) \| 0.119357 / 0.737135 (-0.617779) \| 0.073365 / 0.296338 (-0.222974) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278763 / 0.215209 (0.063554) \| 2.714943 / 2.077655 (0.637288) \| 1.426318 / 1.504120 (-0.077802) \| 1.313296 / 1.541195 (-0.227898) \| 1.330920 / 1.468490 (-0.137570) \| 0.391466 / 4.584777 (-4.193311) \| 2.380521 / 3.745712 (-1.365191) \| 2.545042 / 5.269862 (-2.724819) \| 1.549696 / 4.565676 (-3.015980) \| 0.044661 / 0.424275 (-0.379614) \| 0.005269 / 0.007607 (-0.002338) \| 0.331112 / 0.226044 (0.105068) \| 3.241120 / 2.268929 (0.972192) \| 1.783771 / 55.444624 (-53.660853) \| 1.506205 / 6.876477 (-5.370272) \| 1.521062 / 2.142072 (-0.621010) \| 0.462339 / 4.805227 (-4.342888) \| 0.097646 / 6.500664 (-6.403018) \| 0.041365 / 0.075469 (-0.034104) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939653 / 1.841788 (-0.902135) \| 11.415472 / 8.074308 (3.341164) \| 10.338961 / 10.191392 (0.147569) \| 0.128543 / 0.680424 (-0.551881) \| 0.013997 / 0.534201 (-0.520204) \| 0.270034 / 0.579283 (-0.309249) \| 0.266766 / 0.434364 (-0.167598) \| 0.305290 / 0.540337 (-0.235047) \| 0.395969 / 1.386936 (-0.990967) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004869 / 0.011353 (-0.006484) \| 0.002445 / 0.011008 (-0.008563) \| 0.051256 / 0.038508 (0.012748) \| 0.050871 / 0.023109 (0.027761) \| 0.271044 / 0.275898 (-0.004854) \| 0.294138 / 0.323480 (-0.029342) \| 0.003974 / 0.007986 (-0.004012) \| 0.002423 / 0.004328 (-0.001906) \| 0.048277 / 0.004250 (0.044027) \| 0.039685 / 0.037052 (0.002632) \| 0.277092 / 0.258489 (0.018603) \| 0.302097 / 0.293841 (0.008256) \| 0.024515 / 0.128546 (-0.104031) \| 0.006892 / 0.075646 (-0.068754) \| 0.053528 / 0.419271 (-0.365744) \| 0.032243 / 0.043533 (-0.011290) \| 0.272098 / 0.255139 (0.016959) \| 0.291678 / 0.283200 (0.008479) \| 0.018368 / 0.141683 (-0.123315) \| 1.160151 / 1.452155 (-0.292004) \| 1.193643 / 1.492716 (-0.299073) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096669 / 0.018006 (0.078663) \| 0.299043 / 0.000490 (0.298553) \| 0.000227 / 0.000200 (0.000027) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021557 / 0.037411 (-0.015855) \| 0.069875 / 0.014526 (0.055349) \| 0.080952 / 0.176557 (-0.095605) \| 0.119509 / 0.737135 (-0.617626) \| 0.082030 / 0.296338 (-0.214308) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303062 / 0.215209 (0.087853) \| 2.943823 / 2.077655 (0.866169) \| 1.607816 / 1.504120 (0.103696) \| 1.479773 / 1.541195 (-0.061422) \| 1.482663 / 1.468490 (0.014173) \| 0.411923 / 4.584777 (-4.172854) \| 2.450138 / 3.745712 (-1.295574) \| 2.466111 / 5.269862 (-2.803751) \| 1.543852 / 4.565676 (-3.021825) \| 0.046256 / 0.424275 (-0.378019) \| 0.004787 / 0.007607 (-0.002820) \| 0.353673 / 0.226044 (0.127628) \| 3.528218 / 2.268929 (1.259289) \| 1.984663 / 55.444624 (-53.459962) \| 1.675785 / 6.876477 (-5.200691) \| 1.775646 / 2.142072 (-0.366426) \| 0.483277 / 4.805227 (-4.321950) \| 0.097781 / 6.500664 (-6.402883) \| 0.040291 / 0.075469 (-0.035178) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975458 / 1.841788 (-0.866330) \| 11.961966 / 8.074308 (3.887658) \| 10.558559 / 10.191392 (0.367167) \| 0.131372 / 0.680424 (-0.549052) \| 0.016156 / 0.534201 (-0.518045) \| 0.269254 / 0.579283 (-0.310029) \| 0.274896 / 0.434364 (-0.159468) \| 0.304672 / 0.540337 (-0.235665) \| 0.517652 / 1.386936 (-0.869284) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1a1e7416892dcb71097b47120bc9b26b3d90f06a \"CML watermark\")\n" ]	2023-11-17T16:53:22Z	2023-11-21T14:19:21Z	2023-11-21T14:13:13Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6434.diff", "html_url": "https://github.com/huggingface/datasets/pull/6434", "merged_at": "2023-11-21T14:13:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6434.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6434" }	Use `ruff` instead of `black` for formatting to be consistent with `transformers` ([PR](https://github.com/huggingface/transformers/pull/27144)) and `huggingface_hub` ([PR 1](https://github.com/huggingface/huggingface_hub/pull/1783) and [PR 2](https://github.com/huggingface/huggingface_hub/pull/1789)).	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6434/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6433	6,433	Better `tqdm` wrapper	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005070 / 0.011353 (-0.006283) \| 0.003251 / 0.011008 (-0.007757) \| 0.061528 / 0.038508 (0.023020) \| 0.055386 / 0.023109 (0.032276) \| 0.248536 / 0.275898 (-0.027362) \| 0.272346 / 0.323480 (-0.051134) \| 0.003875 / 0.007986 (-0.004111) \| 0.002396 / 0.004328 (-0.001933) \| 0.047659 / 0.004250 (0.043409) \| 0.037448 / 0.037052 (0.000396) \| 0.251101 / 0.258489 (-0.007388) \| 0.282353 / 0.293841 (-0.011488) \| 0.027784 / 0.128546 (-0.100762) \| 0.010534 / 0.075646 (-0.065113) \| 0.206025 / 0.419271 (-0.213246) \| 0.035410 / 0.043533 (-0.008123) \| 0.250626 / 0.255139 (-0.004513) \| 0.266801 / 0.283200 (-0.016399) \| 0.017704 / 0.141683 (-0.123979) \| 1.089970 / 1.452155 (-0.362185) \| 1.171683 / 1.492716 (-0.321033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092700 / 0.018006 (0.074694) \| 0.301314 / 0.000490 (0.300824) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018385 / 0.037411 (-0.019026) \| 0.062364 / 0.014526 (0.047838) \| 0.075887 / 0.176557 (-0.100670) \| 0.119484 / 0.737135 (-0.617651) \| 0.074490 / 0.296338 (-0.221849) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283893 / 0.215209 (0.068684) \| 2.746772 / 2.077655 (0.669118) \| 1.486568 / 1.504120 (-0.017552) \| 1.376451 / 1.541195 (-0.164744) \| 1.377928 / 1.468490 (-0.090562) \| 0.572393 / 4.584777 (-4.012384) \| 2.383282 / 3.745712 (-1.362430) \| 2.791614 / 5.269862 (-2.478248) \| 1.753373 / 4.565676 (-2.812303) \| 0.063539 / 0.424275 (-0.360736) \| 0.005014 / 0.007607 (-0.002593) \| 0.341300 / 0.226044 (0.115256) \| 3.376960 / 2.268929 (1.108032) \| 1.914162 / 55.444624 (-53.530462) \| 1.590188 / 6.876477 (-5.286289) \| 1.618420 / 2.142072 (-0.523652) \| 0.648723 / 4.805227 (-4.156504) \| 0.117745 / 6.500664 (-6.382919) \| 0.048858 / 0.075469 (-0.026611) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944422 / 1.841788 (-0.897366) \| 11.603590 / 8.074308 (3.529282) \| 10.707000 / 10.191392 (0.515608) \| 0.130779 / 0.680424 (-0.549645) \| 0.015126 / 0.534201 (-0.519075) \| 0.284869 / 0.579283 (-0.294414) \| 0.266778 / 0.434364 (-0.167585) \| 0.320646 / 0.540337 (-0.219691) \| 0.417167 / 1.386936 (-0.969769) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005384 / 0.011353 (-0.005969) \| 0.003311 / 0.011008 (-0.007698) \| 0.049933 / 0.038508 (0.011425) \| 0.052791 / 0.023109 (0.029681) \| 0.277061 / 0.275898 (0.001162) \| 0.302149 / 0.323480 (-0.021331) \| 0.004006 / 0.007986 (-0.003979) \| 0.002394 / 0.004328 (-0.001934) \| 0.049020 / 0.004250 (0.044770) \| 0.040168 / 0.037052 (0.003116) \| 0.278625 / 0.258489 (0.020136) \| 0.308641 / 0.293841 (0.014800) \| 0.029808 / 0.128546 (-0.098738) \| 0.010873 / 0.075646 (-0.064774) \| 0.058040 / 0.419271 (-0.361231) \| 0.032706 / 0.043533 (-0.010827) \| 0.277254 / 0.255139 (0.022115) \| 0.295208 / 0.283200 (0.012008) \| 0.017769 / 0.141683 (-0.123914) \| 1.126416 / 1.452155 (-0.325739) \| 1.169046 / 1.492716 (-0.323670) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094776 / 0.018006 (0.076770) \| 0.306262 / 0.000490 (0.305772) \| 0.000223 / 0.000200 (0.000023) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022279 / 0.037411 (-0.015132) \| 0.086784 / 0.014526 (0.072258) \| 0.082268 / 0.176557 (-0.094289) \| 0.120131 / 0.737135 (-0.617004) \| 0.082862 / 0.296338 (-0.213476) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300565 / 0.215209 (0.085356) \| 2.923424 / 2.077655 (0.845769) \| 1.594836 / 1.504120 (0.090716) \| 1.504323 / 1.541195 (-0.036872) \| 1.498495 / 1.468490 (0.030005) \| 0.570969 / 4.584777 (-4.013808) \| 2.476966 / 3.745712 (-1.268746) \| 2.785190 / 5.269862 (-2.484672) \| 1.749839 / 4.565676 (-2.815837) \| 0.062809 / 0.424275 (-0.361466) \| 0.004908 / 0.007607 (-0.002699) \| 0.361513 / 0.226044 (0.135469) \| 3.587135 / 2.268929 (1.318207) \| 1.952030 / 55.444624 (-53.492595) \| 1.661552 / 6.876477 (-5.214925) \| 1.678673 / 2.142072 (-0.463399) \| 0.645083 / 4.805227 (-4.160144) \| 0.117098 / 6.500664 (-6.383566) \| 0.041630 / 0.075469 (-0.033839) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987883 / 1.841788 (-0.853904) \| 12.300764 / 8.074308 (4.226456) \| 10.962068 / 10.191392 (0.770675) \| 0.143200 / 0.680424 (-0.537224) \| 0.015743 / 0.534201 (-0.518458) \| 0.289733 / 0.579283 (-0.289550) \| 0.276384 / 0.434364 (-0.157979) \| 0.328542 / 0.540337 (-0.211795) \| 0.552175 / 1.386936 (-0.834761) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81a65a57cf9fd0abdf85b664a144c9a06cb2896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005110 / 0.011353 (-0.006243) \| 0.003311 / 0.011008 (-0.007697) \| 0.061962 / 0.038508 (0.023454) \| 0.050250 / 0.023109 (0.027140) \| 0.245313 / 0.275898 (-0.030585) \| 0.268748 / 0.323480 (-0.054732) \| 0.004693 / 0.007986 (-0.003293) \| 0.002465 / 0.004328 (-0.001863) \| 0.047698 / 0.004250 (0.043447) \| 0.037314 / 0.037052 (0.000262) \| 0.250370 / 0.258489 (-0.008119) \| 0.286023 / 0.293841 (-0.007818) \| 0.027891 / 0.128546 (-0.100655) \| 0.010574 / 0.075646 (-0.065072) \| 0.204895 / 0.419271 (-0.214376) \| 0.036014 / 0.043533 (-0.007519) \| 0.250959 / 0.255139 (-0.004180) \| 0.266710 / 0.283200 (-0.016489) \| 0.018492 / 0.141683 (-0.123191) \| 1.115340 / 1.452155 (-0.336815) \| 1.176488 / 1.492716 (-0.316229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099409 / 0.018006 (0.081402) \| 0.310151 / 0.000490 (0.309661) \| 0.000223 / 0.000200 (0.000023) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018132 / 0.037411 (-0.019279) \| 0.061820 / 0.014526 (0.047294) \| 0.074960 / 0.176557 (-0.101596) \| 0.119793 / 0.737135 (-0.617342) \| 0.074132 / 0.296338 (-0.222206) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286388 / 0.215209 (0.071179) \| 2.830791 / 2.077655 (0.753137) \| 1.514588 / 1.504120 (0.010468) \| 1.376514 / 1.541195 (-0.164681) \| 1.405080 / 1.468490 (-0.063410) \| 0.555297 / 4.584777 (-4.029480) \| 2.364838 / 3.745712 (-1.380874) \| 2.806050 / 5.269862 (-2.463812) \| 1.756114 / 4.565676 (-2.809562) \| 0.062254 / 0.424275 (-0.362022) \| 0.005020 / 0.007607 (-0.002588) \| 0.346272 / 0.226044 (0.120227) \| 3.453195 / 2.268929 (1.184266) \| 1.837810 / 55.444624 (-53.606814) \| 1.577984 / 6.876477 (-5.298493) \| 1.560821 / 2.142072 (-0.581251) \| 0.633930 / 4.805227 (-4.171297) \| 0.116414 / 6.500664 (-6.384250) \| 0.042007 / 0.075469 (-0.033462) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941322 / 1.841788 (-0.900466) \| 11.740927 / 8.074308 (3.666618) \| 10.450543 / 10.191392 (0.259151) \| 0.128820 / 0.680424 (-0.551604) \| 0.014856 / 0.534201 (-0.519345) \| 0.285636 / 0.579283 (-0.293647) \| 0.270051 / 0.434364 (-0.164313) \| 0.321244 / 0.540337 (-0.219093) \| 0.415486 / 1.386936 (-0.971450) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005333 / 0.011353 (-0.006020) \| 0.003370 / 0.011008 (-0.007638) \| 0.049046 / 0.038508 (0.010538) \| 0.055767 / 0.023109 (0.032658) \| 0.273463 / 0.275898 (-0.002435) \| 0.292909 / 0.323480 (-0.030571) \| 0.004102 / 0.007986 (-0.003883) \| 0.002460 / 0.004328 (-0.001868) \| 0.048025 / 0.004250 (0.043775) \| 0.040342 / 0.037052 (0.003290) \| 0.275114 / 0.258489 (0.016625) \| 0.295988 / 0.293841 (0.002147) \| 0.029461 / 0.128546 (-0.099085) \| 0.010654 / 0.075646 (-0.064992) \| 0.057196 / 0.419271 (-0.362076) \| 0.033238 / 0.043533 (-0.010295) \| 0.275885 / 0.255139 (0.020746) \| 0.288566 / 0.283200 (0.005366) \| 0.018058 / 0.141683 (-0.123625) \| 1.130513 / 1.452155 (-0.321642) \| 1.173608 / 1.492716 (-0.319108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097751 / 0.018006 (0.079745) \| 0.312106 / 0.000490 (0.311616) \| 0.000232 / 0.000200 (0.000032) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021201 / 0.037411 (-0.016211) \| 0.070150 / 0.014526 (0.055624) \| 0.081073 / 0.176557 (-0.095484) \| 0.119520 / 0.737135 (-0.617615) \| 0.084479 / 0.296338 (-0.211859) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292322 / 0.215209 (0.077113) \| 2.844070 / 2.077655 (0.766415) \| 1.581838 / 1.504120 (0.077718) \| 1.462665 / 1.541195 (-0.078530) \| 1.483013 / 1.468490 (0.014523) \| 0.558705 / 4.584777 (-4.026072) \| 2.422368 / 3.745712 (-1.323344) \| 2.772274 / 5.269862 (-2.497587) \| 1.725901 / 4.565676 (-2.839775) \| 0.062993 / 0.424275 (-0.361282) \| 0.004982 / 0.007607 (-0.002625) \| 0.344336 / 0.226044 (0.118292) \| 3.425230 / 2.268929 (1.156302) \| 1.947199 / 55.444624 (-53.497425) \| 1.670362 / 6.876477 (-5.206115) \| 1.674112 / 2.142072 (-0.467961) \| 0.633857 / 4.805227 (-4.171370) \| 0.114837 / 6.500664 (-6.385827) \| 0.042558 / 0.075469 (-0.032911) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979474 / 1.841788 (-0.862314) \| 12.110856 / 8.074308 (4.036548) \| 10.605998 / 10.191392 (0.414606) \| 0.130769 / 0.680424 (-0.549654) \| 0.016057 / 0.534201 (-0.518144) \| 0.296448 / 0.579283 (-0.282835) \| 0.278078 / 0.434364 (-0.156286) \| 0.320809 / 0.540337 (-0.219528) \| 0.570756 / 1.386936 (-0.816180) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeb9727cc680a8f8172a012920bf84f285fec5a0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005181 / 0.011353 (-0.006172) \| 0.003434 / 0.011008 (-0.007574) \| 0.062333 / 0.038508 (0.023825) \| 0.058544 / 0.023109 (0.035435) \| 0.233794 / 0.275898 (-0.042104) \| 0.258774 / 0.323480 (-0.064706) \| 0.003869 / 0.007986 (-0.004117) \| 0.002478 / 0.004328 (-0.001850) \| 0.047871 / 0.004250 (0.043620) \| 0.037997 / 0.037052 (0.000945) \| 0.241269 / 0.258489 (-0.017220) \| 0.270103 / 0.293841 (-0.023738) \| 0.027710 / 0.128546 (-0.100836) \| 0.010683 / 0.075646 (-0.064963) \| 0.213204 / 0.419271 (-0.206067) \| 0.036156 / 0.043533 (-0.007377) \| 0.240061 / 0.255139 (-0.015078) \| 0.253627 / 0.283200 (-0.029573) \| 0.017880 / 0.141683 (-0.123803) \| 1.102965 / 1.452155 (-0.349189) \| 1.176919 / 1.492716 (-0.315797) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093206 / 0.018006 (0.075200) \| 0.300960 / 0.000490 (0.300470) \| 0.000214 / 0.000200 (0.000014) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019417 / 0.037411 (-0.017994) \| 0.061948 / 0.014526 (0.047422) \| 0.073560 / 0.176557 (-0.102997) \| 0.120682 / 0.737135 (-0.616453) \| 0.074925 / 0.296338 (-0.221413) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280157 / 0.215209 (0.064948) \| 2.760648 / 2.077655 (0.682994) \| 1.482129 / 1.504120 (-0.021991) \| 1.364091 / 1.541195 (-0.177104) \| 1.415680 / 1.468490 (-0.052810) \| 0.564697 / 4.584777 (-4.020080) \| 2.364080 / 3.745712 (-1.381633) \| 2.794018 / 5.269862 (-2.475844) \| 1.752157 / 4.565676 (-2.813520) \| 0.062234 / 0.424275 (-0.362041) \| 0.004927 / 0.007607 (-0.002680) \| 0.337835 / 0.226044 (0.111790) \| 3.313819 / 2.268929 (1.044891) \| 1.834095 / 55.444624 (-53.610530) \| 1.559964 / 6.876477 (-5.316513) \| 1.598489 / 2.142072 (-0.543584) \| 0.636829 / 4.805227 (-4.168399) \| 0.116436 / 6.500664 (-6.384228) \| 0.042506 / 0.075469 (-0.032963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951508 / 1.841788 (-0.890280) \| 11.599532 / 8.074308 (3.525224) \| 10.492355 / 10.191392 (0.300963) \| 0.151582 / 0.680424 (-0.528842) \| 0.014356 / 0.534201 (-0.519845) \| 0.288448 / 0.579283 (-0.290835) \| 0.265607 / 0.434364 (-0.168757) \| 0.324455 / 0.540337 (-0.215883) \| 0.416718 / 1.386936 (-0.970218) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005489 / 0.011353 (-0.005864) \| 0.003481 / 0.011008 (-0.007527) \| 0.048952 / 0.038508 (0.010444) \| 0.054650 / 0.023109 (0.031540) \| 0.280853 / 0.275898 (0.004955) \| 0.298089 / 0.323480 (-0.025391) \| 0.004762 / 0.007986 (-0.003224) \| 0.002500 / 0.004328 (-0.001828) \| 0.048503 / 0.004250 (0.044253) \| 0.042048 / 0.037052 (0.004995) \| 0.281729 / 0.258489 (0.023240) \| 0.303625 / 0.293841 (0.009785) \| 0.028887 / 0.128546 (-0.099659) \| 0.010687 / 0.075646 (-0.064960) \| 0.058093 / 0.419271 (-0.361178) \| 0.032366 / 0.043533 (-0.011167) \| 0.281987 / 0.255139 (0.026848) \| 0.295554 / 0.283200 (0.012355) \| 0.019242 / 0.141683 (-0.122441) \| 1.127760 / 1.452155 (-0.324395) \| 1.166868 / 1.492716 (-0.325848) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092367 / 0.018006 (0.074361) \| 0.300195 / 0.000490 (0.299706) \| 0.000222 / 0.000200 (0.000022) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022062 / 0.037411 (-0.015349) \| 0.069955 / 0.014526 (0.055429) \| 0.081224 / 0.176557 (-0.095333) \| 0.120183 / 0.737135 (-0.616953) \| 0.082846 / 0.296338 (-0.213492) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295880 / 0.215209 (0.080671) \| 2.902508 / 2.077655 (0.824853) \| 1.616311 / 1.504120 (0.112191) \| 1.491320 / 1.541195 (-0.049875) \| 1.517333 / 1.468490 (0.048843) \| 0.566824 / 4.584777 (-4.017953) \| 2.428397 / 3.745712 (-1.317315) \| 2.807241 / 5.269862 (-2.462620) \| 1.786364 / 4.565676 (-2.779312) \| 0.065253 / 0.424275 (-0.359022) \| 0.004971 / 0.007607 (-0.002636) \| 0.350095 / 0.226044 (0.124051) \| 3.422226 / 2.268929 (1.153297) \| 1.972955 / 55.444624 (-53.471670) \| 1.686142 / 6.876477 (-5.190335) \| 1.694539 / 2.142072 (-0.447533) \| 0.645709 / 4.805227 (-4.159518) \| 0.117774 / 6.500664 (-6.382890) \| 0.041679 / 0.075469 (-0.033790) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976835 / 1.841788 (-0.864952) \| 12.358039 / 8.074308 (4.283730) \| 10.774304 / 10.191392 (0.582912) \| 0.130442 / 0.680424 (-0.549982) \| 0.016071 / 0.534201 (-0.518130) \| 0.289911 / 0.579283 (-0.289372) \| 0.280693 / 0.434364 (-0.153671) \| 0.325598 / 0.540337 (-0.214739) \| 0.549618 / 1.386936 (-0.837318) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1570235228b67a15dce1ed535e905edd7442117f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005176 / 0.011353 (-0.006177) \| 0.003297 / 0.011008 (-0.007711) \| 0.061673 / 0.038508 (0.023165) \| 0.052174 / 0.023109 (0.029065) \| 0.245897 / 0.275898 (-0.030001) \| 0.273102 / 0.323480 (-0.050377) \| 0.003870 / 0.007986 (-0.004115) \| 0.002385 / 0.004328 (-0.001943) \| 0.047675 / 0.004250 (0.043424) \| 0.037722 / 0.037052 (0.000670) \| 0.250780 / 0.258489 (-0.007709) \| 0.279464 / 0.293841 (-0.014376) \| 0.028107 / 0.128546 (-0.100439) \| 0.010460 / 0.075646 (-0.065187) \| 0.205522 / 0.419271 (-0.213750) \| 0.035781 / 0.043533 (-0.007752) \| 0.246526 / 0.255139 (-0.008613) \| 0.263919 / 0.283200 (-0.019281) \| 0.018634 / 0.141683 (-0.123049) \| 1.103845 / 1.452155 (-0.348310) \| 1.175536 / 1.492716 (-0.317181) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091696 / 0.018006 (0.073690) \| 0.301284 / 0.000490 (0.300794) \| 0.000213 / 0.000200 (0.000013) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019153 / 0.037411 (-0.018258) \| 0.063846 / 0.014526 (0.049320) \| 0.073635 / 0.176557 (-0.102922) \| 0.119625 / 0.737135 (-0.617511) \| 0.075161 / 0.296338 (-0.221177) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285637 / 0.215209 (0.070428) \| 2.751787 / 2.077655 (0.674132) \| 1.465098 / 1.504120 (-0.039022) \| 1.341676 / 1.541195 (-0.199519) \| 1.390636 / 1.468490 (-0.077854) \| 0.567663 / 4.584777 (-4.017114) \| 2.378183 / 3.745712 (-1.367529) \| 2.801830 / 5.269862 (-2.468032) \| 1.750125 / 4.565676 (-2.815551) \| 0.063705 / 0.424275 (-0.360570) \| 0.004967 / 0.007607 (-0.002640) \| 0.373302 / 0.226044 (0.147258) \| 3.301847 / 2.268929 (1.032918) \| 1.830117 / 55.444624 (-53.614508) \| 1.564360 / 6.876477 (-5.312117) \| 1.551766 / 2.142072 (-0.590306) \| 0.654424 / 4.805227 (-4.150803) \| 0.120656 / 6.500664 (-6.380008) \| 0.042383 / 0.075469 (-0.033086) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.931815 / 1.841788 (-0.909973) \| 11.755904 / 8.074308 (3.681596) \| 10.571707 / 10.191392 (0.380315) \| 0.131118 / 0.680424 (-0.549306) \| 0.015241 / 0.534201 (-0.518960) \| 0.287137 / 0.579283 (-0.292146) \| 0.265651 / 0.434364 (-0.168713) \| 0.329083 / 0.540337 (-0.211254) \| 0.417501 / 1.386936 (-0.969435) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005355 / 0.011353 (-0.005998) \| 0.003305 / 0.011008 (-0.007703) \| 0.048289 / 0.038508 (0.009781) \| 0.059223 / 0.023109 (0.036114) \| 0.267213 / 0.275898 (-0.008685) \| 0.290151 / 0.323480 (-0.033329) \| 0.004683 / 0.007986 (-0.003303) \| 0.002413 / 0.004328 (-0.001916) \| 0.047982 / 0.004250 (0.043732) \| 0.040943 / 0.037052 (0.003891) \| 0.270967 / 0.258489 (0.012478) \| 0.297644 / 0.293841 (0.003803) \| 0.029309 / 0.128546 (-0.099237) \| 0.010624 / 0.075646 (-0.065023) \| 0.057359 / 0.419271 (-0.361913) \| 0.032716 / 0.043533 (-0.010816) \| 0.268602 / 0.255139 (0.013463) \| 0.286016 / 0.283200 (0.002817) \| 0.018578 / 0.141683 (-0.123105) \| 1.120275 / 1.452155 (-0.331880) \| 1.195514 / 1.492716 (-0.297202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092590 / 0.018006 (0.074584) \| 0.302589 / 0.000490 (0.302099) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022439 / 0.037411 (-0.014972) \| 0.070914 / 0.014526 (0.056388) \| 0.084927 / 0.176557 (-0.091629) \| 0.123154 / 0.737135 (-0.613981) \| 0.085527 / 0.296338 (-0.210812) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292652 / 0.215209 (0.077443) \| 2.843736 / 2.077655 (0.766081) \| 1.561289 / 1.504120 (0.057169) \| 1.439500 / 1.541195 (-0.101695) \| 1.485074 / 1.468490 (0.016584) \| 0.570520 / 4.584777 (-4.014257) \| 2.436611 / 3.745712 (-1.309102) \| 2.925600 / 5.269862 (-2.344261) \| 1.796518 / 4.565676 (-2.769159) \| 0.065075 / 0.424275 (-0.359200) \| 0.004995 / 0.007607 (-0.002612) \| 0.349976 / 0.226044 (0.123932) \| 3.442535 / 2.268929 (1.173607) \| 1.919002 / 55.444624 (-53.525622) \| 1.659222 / 6.876477 (-5.217255) \| 1.648370 / 2.142072 (-0.493703) \| 0.643119 / 4.805227 (-4.162108) \| 0.118015 / 6.500664 (-6.382649) \| 0.041229 / 0.075469 (-0.034240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986226 / 1.841788 (-0.855562) \| 12.302487 / 8.074308 (4.228179) \| 10.528848 / 10.191392 (0.337456) \| 0.143911 / 0.680424 (-0.536513) \| 0.015265 / 0.534201 (-0.518936) \| 0.287692 / 0.579283 (-0.291591) \| 0.277011 / 0.434364 (-0.157353) \| 0.327650 / 0.540337 (-0.212688) \| 0.552951 / 1.386936 (-0.833985) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0af18e68664db94e863f0dcde4b0f3a7adcc80e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005234 / 0.011353 (-0.006119) \| 0.003324 / 0.011008 (-0.007684) \| 0.062429 / 0.038508 (0.023921) \| 0.051619 / 0.023109 (0.028510) \| 0.256850 / 0.275898 (-0.019048) \| 0.260566 / 0.323480 (-0.062914) \| 0.002914 / 0.007986 (-0.005071) \| 0.003093 / 0.004328 (-0.001235) \| 0.047947 / 0.004250 (0.043696) \| 0.038753 / 0.037052 (0.001701) \| 0.246810 / 0.258489 (-0.011679) \| 0.275128 / 0.293841 (-0.018713) \| 0.027171 / 0.128546 (-0.101375) \| 0.010290 / 0.075646 (-0.065356) \| 0.206069 / 0.419271 (-0.213203) \| 0.035514 / 0.043533 (-0.008019) \| 0.240645 / 0.255139 (-0.014494) \| 0.259693 / 0.283200 (-0.023507) \| 0.019722 / 0.141683 (-0.121961) \| 1.128534 / 1.452155 (-0.323620) \| 1.139602 / 1.492716 (-0.353115) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095837 / 0.018006 (0.077830) \| 0.304754 / 0.000490 (0.304264) \| 0.000204 / 0.000200 (0.000004) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018349 / 0.037411 (-0.019063) \| 0.062763 / 0.014526 (0.048237) \| 0.074443 / 0.176557 (-0.102113) \| 0.120607 / 0.737135 (-0.616528) \| 0.077721 / 0.296338 (-0.218617) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281852 / 0.215209 (0.066643) \| 2.770806 / 2.077655 (0.693151) \| 1.466255 / 1.504120 (-0.037864) \| 1.349611 / 1.541195 (-0.191584) \| 1.385463 / 1.468490 (-0.083027) \| 0.566489 / 4.584777 (-4.018288) \| 2.420932 / 3.745712 (-1.324780) \| 2.809397 / 5.269862 (-2.460464) \| 1.749734 / 4.565676 (-2.815942) \| 0.063407 / 0.424275 (-0.360868) \| 0.005038 / 0.007607 (-0.002569) \| 0.379121 / 0.226044 (0.153077) \| 3.500938 / 2.268929 (1.232010) \| 1.852207 / 55.444624 (-53.592417) \| 1.570474 / 6.876477 (-5.306002) \| 1.555222 / 2.142072 (-0.586850) \| 0.657198 / 4.805227 (-4.148030) \| 0.119835 / 6.500664 (-6.380829) \| 0.042453 / 0.075469 (-0.033016) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949953 / 1.841788 (-0.891835) \| 11.736811 / 8.074308 (3.662503) \| 10.558049 / 10.191392 (0.366657) \| 0.146230 / 0.680424 (-0.534194) \| 0.014922 / 0.534201 (-0.519279) \| 0.289100 / 0.579283 (-0.290183) \| 0.267130 / 0.434364 (-0.167234) \| 0.320055 / 0.540337 (-0.220282) \| 0.417244 / 1.386936 (-0.969692) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005309 / 0.011353 (-0.006044) \| 0.003329 / 0.011008 (-0.007679) \| 0.048576 / 0.038508 (0.010068) \| 0.055219 / 0.023109 (0.032110) \| 0.271522 / 0.275898 (-0.004376) \| 0.294435 / 0.323480 (-0.029045) \| 0.004018 / 0.007986 (-0.003968) \| 0.002456 / 0.004328 (-0.001873) \| 0.047939 / 0.004250 (0.043689) \| 0.041195 / 0.037052 (0.004143) \| 0.274819 / 0.258489 (0.016330) \| 0.299407 / 0.293841 (0.005566) \| 0.029145 / 0.128546 (-0.099401) \| 0.010680 / 0.075646 (-0.064966) \| 0.057238 / 0.419271 (-0.362034) \| 0.032722 / 0.043533 (-0.010810) \| 0.272066 / 0.255139 (0.016927) \| 0.289223 / 0.283200 (0.006023) \| 0.017826 / 0.141683 (-0.123857) \| 1.119079 / 1.452155 (-0.333076) \| 1.179109 / 1.492716 (-0.313608) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095662 / 0.018006 (0.077656) \| 0.307652 / 0.000490 (0.307162) \| 0.000213 / 0.000200 (0.000013) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022263 / 0.037411 (-0.015149) \| 0.070224 / 0.014526 (0.055698) \| 0.081477 / 0.176557 (-0.095079) \| 0.120763 / 0.737135 (-0.616372) \| 0.083152 / 0.296338 (-0.213187) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295780 / 0.215209 (0.080571) \| 2.926623 / 2.077655 (0.848968) \| 1.605901 / 1.504120 (0.101781) \| 1.482874 / 1.541195 (-0.058321) \| 1.501467 / 1.468490 (0.032977) \| 0.569566 / 4.584777 (-4.015211) \| 2.474948 / 3.745712 (-1.270764) \| 2.831877 / 5.269862 (-2.437985) \| 1.761229 / 4.565676 (-2.804448) \| 0.064129 / 0.424275 (-0.360147) \| 0.004964 / 0.007607 (-0.002643) \| 0.350081 / 0.226044 (0.124037) \| 3.446766 / 2.268929 (1.177837) \| 1.974998 / 55.444624 (-53.469627) \| 1.683381 / 6.876477 (-5.193095) \| 1.711543 / 2.142072 (-0.430530) \| 0.648695 / 4.805227 (-4.156532) \| 0.118224 / 6.500664 (-6.382440) \| 0.040895 / 0.075469 (-0.034574) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960208 / 1.841788 (-0.881580) \| 12.164941 / 8.074308 (4.090633) \| 10.860573 / 10.191392 (0.669181) \| 0.133525 / 0.680424 (-0.546899) \| 0.015643 / 0.534201 (-0.518558) \| 0.290898 / 0.579283 (-0.288386) \| 0.289612 / 0.434364 (-0.144752) \| 0.325836 / 0.540337 (-0.214501) \| 0.565592 / 1.386936 (-0.821344) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d19a315920c6d4293f8226273d99bf3de5c1d4e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006097 / 0.011353 (-0.005256) \| 0.004386 / 0.011008 (-0.006622) \| 0.064481 / 0.038508 (0.025973) \| 0.059983 / 0.023109 (0.036873) \| 0.268177 / 0.275898 (-0.007721) \| 0.296207 / 0.323480 (-0.027273) \| 0.002986 / 0.007986 (-0.005000) \| 0.002923 / 0.004328 (-0.001406) \| 0.048798 / 0.004250 (0.044547) \| 0.039945 / 0.037052 (0.002893) \| 0.271234 / 0.258489 (0.012745) \| 0.295461 / 0.293841 (0.001620) \| 0.028771 / 0.128546 (-0.099775) \| 0.011104 / 0.075646 (-0.064542) \| 0.207471 / 0.419271 (-0.211800) \| 0.036955 / 0.043533 (-0.006578) \| 0.254761 / 0.255139 (-0.000378) \| 0.275933 / 0.283200 (-0.007267) \| 0.021232 / 0.141683 (-0.120451) \| 1.170771 / 1.452155 (-0.281384) \| 1.188900 / 1.492716 (-0.303816) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092328 / 0.018006 (0.074322) \| 0.302591 / 0.000490 (0.302102) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019207 / 0.037411 (-0.018204) \| 0.070247 / 0.014526 (0.055721) \| 0.074963 / 0.176557 (-0.101593) \| 0.124301 / 0.737135 (-0.612834) \| 0.077356 / 0.296338 (-0.218982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283321 / 0.215209 (0.068112) \| 2.800448 / 2.077655 (0.722793) \| 1.510278 / 1.504120 (0.006158) \| 1.390353 / 1.541195 (-0.150842) \| 1.387881 / 1.468490 (-0.080609) \| 0.563927 / 4.584777 (-4.020850) \| 2.387753 / 3.745712 (-1.357959) \| 2.776655 / 5.269862 (-2.493207) \| 1.767383 / 4.565676 (-2.798293) \| 0.064864 / 0.424275 (-0.359411) \| 0.004999 / 0.007607 (-0.002608) \| 0.351173 / 0.226044 (0.125129) \| 3.459446 / 2.268929 (1.190517) \| 1.873078 / 55.444624 (-53.571547) \| 1.602831 / 6.876477 (-5.273646) \| 1.595612 / 2.142072 (-0.546460) \| 0.648786 / 4.805227 (-4.156441) \| 0.118720 / 6.500664 (-6.381944) \| 0.042821 / 0.075469 (-0.032649) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970738 / 1.841788 (-0.871049) \| 12.273548 / 8.074308 (4.199240) \| 11.191375 / 10.191392 (0.999983) \| 0.131903 / 0.680424 (-0.548521) \| 0.014512 / 0.534201 (-0.519689) \| 0.289382 / 0.579283 (-0.289901) \| 0.269449 / 0.434364 (-0.164915) \| 0.327557 / 0.540337 (-0.212781) \| 0.427052 / 1.386936 (-0.959884) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005472 / 0.011353 (-0.005881) \| 0.003380 / 0.011008 (-0.007628) \| 0.050677 / 0.038508 (0.012169) \| 0.059606 / 0.023109 (0.036497) \| 0.275798 / 0.275898 (-0.000100) \| 0.303733 / 0.323480 (-0.019747) \| 0.004187 / 0.007986 (-0.003799) \| 0.002657 / 0.004328 (-0.001672) \| 0.048713 / 0.004250 (0.044463) \| 0.043501 / 0.037052 (0.006449) \| 0.278845 / 0.258489 (0.020356) \| 0.305322 / 0.293841 (0.011481) \| 0.030665 / 0.128546 (-0.097881) \| 0.010600 / 0.075646 (-0.065047) \| 0.058923 / 0.419271 (-0.360349) \| 0.032936 / 0.043533 (-0.010596) \| 0.272835 / 0.255139 (0.017696) \| 0.293975 / 0.283200 (0.010775) \| 0.018193 / 0.141683 (-0.123490) \| 1.144903 / 1.452155 (-0.307251) \| 1.192220 / 1.492716 (-0.300497) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094519 / 0.018006 (0.076513) \| 0.305591 / 0.000490 (0.305101) \| 0.000221 / 0.000200 (0.000021) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022108 / 0.037411 (-0.015303) \| 0.070184 / 0.014526 (0.055658) \| 0.081640 / 0.176557 (-0.094916) \| 0.124661 / 0.737135 (-0.612474) \| 0.082229 / 0.296338 (-0.214110) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303710 / 0.215209 (0.088501) \| 2.966478 / 2.077655 (0.888824) \| 1.646066 / 1.504120 (0.141946) \| 1.551454 / 1.541195 (0.010259) \| 1.557995 / 1.468490 (0.089505) \| 0.577723 / 4.584777 (-4.007054) \| 2.510321 / 3.745712 (-1.235391) \| 2.951343 / 5.269862 (-2.318519) \| 1.857550 / 4.565676 (-2.708127) \| 0.064079 / 0.424275 (-0.360196) \| 0.004971 / 0.007607 (-0.002636) \| 0.359022 / 0.226044 (0.132978) \| 3.628716 / 2.268929 (1.359788) \| 2.011380 / 55.444624 (-53.433245) \| 1.710407 / 6.876477 (-5.166070) \| 1.756235 / 2.142072 (-0.385838) \| 0.659185 / 4.805227 (-4.146042) \| 0.120245 / 6.500664 (-6.380419) \| 0.042751 / 0.075469 (-0.032718) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.026794 / 1.841788 (-0.814993) \| 12.695125 / 8.074308 (4.620816) \| 10.864908 / 10.191392 (0.673516) \| 0.136128 / 0.680424 (-0.544295) \| 0.016824 / 0.534201 (-0.517377) \| 0.289717 / 0.579283 (-0.289567) \| 0.282919 / 0.434364 (-0.151445) \| 0.323345 / 0.540337 (-0.216992) \| 0.556375 / 1.386936 (-0.830561) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52207295162f734235b71428d13e6a42c6fdc370 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005407 / 0.011353 (-0.005946) \| 0.003464 / 0.011008 (-0.007544) \| 0.062084 / 0.038508 (0.023576) \| 0.052582 / 0.023109 (0.029472) \| 0.251239 / 0.275898 (-0.024659) \| 0.276675 / 0.323480 (-0.046805) \| 0.002894 / 0.007986 (-0.005092) \| 0.003850 / 0.004328 (-0.000479) \| 0.047789 / 0.004250 (0.043538) \| 0.038955 / 0.037052 (0.001903) \| 0.258333 / 0.258489 (-0.000156) \| 0.290103 / 0.293841 (-0.003738) \| 0.027291 / 0.128546 (-0.101256) \| 0.010575 / 0.075646 (-0.065071) \| 0.207208 / 0.419271 (-0.212063) \| 0.035848 / 0.043533 (-0.007685) \| 0.253918 / 0.255139 (-0.001221) \| 0.269870 / 0.283200 (-0.013330) \| 0.019830 / 0.141683 (-0.121853) \| 1.085332 / 1.452155 (-0.366823) \| 1.171385 / 1.492716 (-0.321331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094956 / 0.018006 (0.076950) \| 0.301104 / 0.000490 (0.300614) \| 0.000204 / 0.000200 (0.000004) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019045 / 0.037411 (-0.018367) \| 0.070815 / 0.014526 (0.056289) \| 0.073763 / 0.176557 (-0.102794) \| 0.120668 / 0.737135 (-0.616467) \| 0.075197 / 0.296338 (-0.221141) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286072 / 0.215209 (0.070863) \| 2.762868 / 2.077655 (0.685213) \| 1.504481 / 1.504120 (0.000361) \| 1.390301 / 1.541195 (-0.150894) \| 1.449571 / 1.468490 (-0.018919) \| 0.555598 / 4.584777 (-4.029179) \| 2.404975 / 3.745712 (-1.340737) \| 2.864359 / 5.269862 (-2.405503) \| 1.764913 / 4.565676 (-2.800763) \| 0.062956 / 0.424275 (-0.361320) \| 0.005116 / 0.007607 (-0.002491) \| 0.344027 / 0.226044 (0.117983) \| 3.426781 / 2.268929 (1.157852) \| 1.891040 / 55.444624 (-53.553584) \| 1.599972 / 6.876477 (-5.276505) \| 1.603464 / 2.142072 (-0.538608) \| 0.638136 / 4.805227 (-4.167091) \| 0.117808 / 6.500664 (-6.382857) \| 0.043740 / 0.075469 (-0.031730) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.934654 / 1.841788 (-0.907133) \| 12.243698 / 8.074308 (4.169390) \| 10.566791 / 10.191392 (0.375399) \| 0.130440 / 0.680424 (-0.549983) \| 0.014019 / 0.534201 (-0.520182) \| 0.285453 / 0.579283 (-0.293831) \| 0.266121 / 0.434364 (-0.168243) \| 0.325962 / 0.540337 (-0.214375) \| 0.422181 / 1.386936 (-0.964755) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005151 / 0.011353 (-0.006202) \| 0.003704 / 0.011008 (-0.007304) \| 0.049483 / 0.038508 (0.010975) \| 0.055147 / 0.023109 (0.032038) \| 0.277589 / 0.275898 (0.001691) \| 0.301274 / 0.323480 (-0.022206) \| 0.004031 / 0.007986 (-0.003955) \| 0.002568 / 0.004328 (-0.001760) \| 0.048830 / 0.004250 (0.044580) \| 0.040391 / 0.037052 (0.003339) \| 0.281031 / 0.258489 (0.022541) \| 0.304263 / 0.293841 (0.010422) \| 0.029237 / 0.128546 (-0.099309) \| 0.010598 / 0.075646 (-0.065048) \| 0.058089 / 0.419271 (-0.361182) \| 0.032529 / 0.043533 (-0.011004) \| 0.275761 / 0.255139 (0.020622) \| 0.294427 / 0.283200 (0.011227) \| 0.017227 / 0.141683 (-0.124456) \| 1.138036 / 1.452155 (-0.314119) \| 1.201946 / 1.492716 (-0.290770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094241 / 0.018006 (0.076234) \| 0.301622 / 0.000490 (0.301132) \| 0.000229 / 0.000200 (0.000029) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022731 / 0.037411 (-0.014680) \| 0.071217 / 0.014526 (0.056691) \| 0.082619 / 0.176557 (-0.093937) \| 0.123308 / 0.737135 (-0.613827) \| 0.083552 / 0.296338 (-0.212787) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295770 / 0.215209 (0.080561) \| 2.886069 / 2.077655 (0.808414) \| 1.597686 / 1.504120 (0.093566) \| 1.458612 / 1.541195 (-0.082583) \| 1.501171 / 1.468490 (0.032680) \| 0.575653 / 4.584777 (-4.009124) \| 2.444021 / 3.745712 (-1.301691) \| 2.860192 / 5.269862 (-2.409669) \| 1.758896 / 4.565676 (-2.806780) \| 0.063334 / 0.424275 (-0.360941) \| 0.004913 / 0.007607 (-0.002694) \| 0.341828 / 0.226044 (0.115783) \| 3.420310 / 2.268929 (1.151381) \| 1.996099 / 55.444624 (-53.448525) \| 1.680112 / 6.876477 (-5.196365) \| 1.693418 / 2.142072 (-0.448654) \| 0.697321 / 4.805227 (-4.107906) \| 0.120474 / 6.500664 (-6.380190) \| 0.042192 / 0.075469 (-0.033277) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975876 / 1.841788 (-0.865912) \| 12.174933 / 8.074308 (4.100625) \| 10.400906 / 10.191392 (0.209514) \| 0.162244 / 0.680424 (-0.518180) \| 0.016443 / 0.534201 (-0.517758) \| 0.293430 / 0.579283 (-0.285853) \| 0.285664 / 0.434364 (-0.148700) \| 0.332322 / 0.540337 (-0.208015) \| 0.609815 / 1.386936 (-0.777121) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2c417d087d232b5abf9054ffb10305cc06c5440 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005155 / 0.011353 (-0.006198) \| 0.003226 / 0.011008 (-0.007782) \| 0.062651 / 0.038508 (0.024143) \| 0.051314 / 0.023109 (0.028205) \| 0.246075 / 0.275898 (-0.029823) \| 0.266859 / 0.323480 (-0.056621) \| 0.003895 / 0.007986 (-0.004091) \| 0.002462 / 0.004328 (-0.001866) \| 0.048097 / 0.004250 (0.043846) \| 0.037313 / 0.037052 (0.000261) \| 0.253208 / 0.258489 (-0.005281) \| 0.280255 / 0.293841 (-0.013585) \| 0.027052 / 0.128546 (-0.101494) \| 0.010276 / 0.075646 (-0.065370) \| 0.205663 / 0.419271 (-0.213608) \| 0.035111 / 0.043533 (-0.008422) \| 0.253757 / 0.255139 (-0.001382) \| 0.265466 / 0.283200 (-0.017733) \| 0.017873 / 0.141683 (-0.123810) \| 1.118906 / 1.452155 (-0.333249) \| 1.176384 / 1.492716 (-0.316332) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094921 / 0.018006 (0.076914) \| 0.300459 / 0.000490 (0.299970) \| 0.000214 / 0.000200 (0.000014) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018430 / 0.037411 (-0.018981) \| 0.062690 / 0.014526 (0.048165) \| 0.074215 / 0.176557 (-0.102342) \| 0.119969 / 0.737135 (-0.617166) \| 0.075846 / 0.296338 (-0.220493) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273492 / 0.215209 (0.058283) \| 2.667937 / 2.077655 (0.590282) \| 1.405912 / 1.504120 (-0.098208) \| 1.269041 / 1.541195 (-0.272153) \| 1.313461 / 1.468490 (-0.155029) \| 0.554633 / 4.584777 (-4.030144) \| 2.325552 / 3.745712 (-1.420160) \| 2.825580 / 5.269862 (-2.444282) \| 1.745432 / 4.565676 (-2.820245) \| 0.062497 / 0.424275 (-0.361778) \| 0.004935 / 0.007607 (-0.002673) \| 0.337045 / 0.226044 (0.111001) \| 3.246360 / 2.268929 (0.977432) \| 1.775329 / 55.444624 (-53.669296) \| 1.491812 / 6.876477 (-5.384665) \| 1.499783 / 2.142072 (-0.642290) \| 0.636768 / 4.805227 (-4.168459) \| 0.116471 / 6.500664 (-6.384193) \| 0.041838 / 0.075469 (-0.033631) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937388 / 1.841788 (-0.904400) \| 11.950930 / 8.074308 (3.876622) \| 10.532062 / 10.191392 (0.340670) \| 0.129490 / 0.680424 (-0.550934) \| 0.013907 / 0.534201 (-0.520294) \| 0.287503 / 0.579283 (-0.291780) \| 0.270548 / 0.434364 (-0.163816) \| 0.324321 / 0.540337 (-0.216016) \| 0.427639 / 1.386936 (-0.959297) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005272 / 0.011353 (-0.006081) \| 0.003413 / 0.011008 (-0.007595) \| 0.049800 / 0.038508 (0.011292) \| 0.055978 / 0.023109 (0.032868) \| 0.274365 / 0.275898 (-0.001533) \| 0.293414 / 0.323480 (-0.030066) \| 0.003994 / 0.007986 (-0.003992) \| 0.002480 / 0.004328 (-0.001848) \| 0.048787 / 0.004250 (0.044537) \| 0.040520 / 0.037052 (0.003468) \| 0.276198 / 0.258489 (0.017709) \| 0.301085 / 0.293841 (0.007244) \| 0.028352 / 0.128546 (-0.100194) \| 0.010631 / 0.075646 (-0.065015) \| 0.057103 / 0.419271 (-0.362168) \| 0.032277 / 0.043533 (-0.011256) \| 0.274472 / 0.255139 (0.019333) \| 0.289953 / 0.283200 (0.006754) \| 0.018048 / 0.141683 (-0.123635) \| 1.120329 / 1.452155 (-0.331826) \| 1.175784 / 1.492716 (-0.316932) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102519 / 0.018006 (0.084512) \| 0.322030 / 0.000490 (0.321540) \| 0.000234 / 0.000200 (0.000034) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023084 / 0.037411 (-0.014327) \| 0.069592 / 0.014526 (0.055066) \| 0.081293 / 0.176557 (-0.095264) \| 0.119546 / 0.737135 (-0.617589) \| 0.083249 / 0.296338 (-0.213090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294997 / 0.215209 (0.079788) \| 2.925517 / 2.077655 (0.847863) \| 1.607824 / 1.504120 (0.103705) \| 1.469586 / 1.541195 (-0.071608) \| 1.492350 / 1.468490 (0.023860) \| 0.561351 / 4.584777 (-4.023426) \| 2.446741 / 3.745712 (-1.298972) \| 2.842588 / 5.269862 (-2.427273) \| 1.789189 / 4.565676 (-2.776487) \| 0.064064 / 0.424275 (-0.360211) \| 0.005011 / 0.007607 (-0.002597) \| 0.351059 / 0.226044 (0.125015) \| 3.485277 / 2.268929 (1.216348) \| 1.981821 / 55.444624 (-53.462803) \| 1.671846 / 6.876477 (-5.204631) \| 1.702014 / 2.142072 (-0.440058) \| 0.645205 / 4.805227 (-4.160023) \| 0.117358 / 6.500664 (-6.383306) \| 0.041633 / 0.075469 (-0.033836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963281 / 1.841788 (-0.878506) \| 12.141256 / 8.074308 (4.066947) \| 10.595207 / 10.191392 (0.403815) \| 0.130401 / 0.680424 (-0.550023) \| 0.015490 / 0.534201 (-0.518710) \| 0.284201 / 0.579283 (-0.295082) \| 0.280244 / 0.434364 (-0.154120) \| 0.323545 / 0.540337 (-0.216792) \| 0.561246 / 1.386936 (-0.825690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3193829cf0dd9888c42bd7640a71d9d656cba2a \"CML watermark\")\n" ]	2023-11-17T15:45:15Z	2023-11-22T16:48:18Z	2023-11-22T16:42:08Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6433.diff", "html_url": "https://github.com/huggingface/datasets/pull/6433", "merged_at": "2023-11-22T16:42:08Z", "patch_url": "https://github.com/huggingface/datasets/pull/6433.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6433" }	This PR aligns the `tqdm` logic with `huggingface_hub` (without introducing breaking changes), as the current one is error-prone. Additionally, it improves the doc page about the `datasets`' utilities, and the handling of local `fsspec` paths in `cached_path`. Fix #6409	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6433/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6432	6,432	load_dataset does not load all of the data in my input file	{ "avatar_url": "https://avatars.githubusercontent.com/u/121301001?v=4", "events_url": "https://api.github.com/users/demongolem-biz2/events{/privacy}", "followers_url": "https://api.github.com/users/demongolem-biz2/followers", "following_url": "https://api.github.com/users/demongolem-biz2/following{/other_user}", "gists_url": "https://api.github.com/users/demongolem-biz2/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/demongolem-biz2", "id": 121301001, "login": "demongolem-biz2", "node_id": "U_kgDOBzroCQ", "organizations_url": "https://api.github.com/users/demongolem-biz2/orgs", "received_events_url": "https://api.github.com/users/demongolem-biz2/received_events", "repos_url": "https://api.github.com/users/demongolem-biz2/repos", "site_admin": false, "starred_url": "https://api.github.com/users/demongolem-biz2/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/demongolem-biz2/subscriptions", "type": "User", "url": "https://api.github.com/users/demongolem-biz2", "user_view_type": "public" }	[]	open	false	[ "You should use `datasets.load_dataset` instead of `nlp.load_dataset`, as the `nlp` package is outdated.\r\n\r\nIf switching to `datasets.load_dataset` doesn't fix the issue, sharing the JSON file (feel free to replace the data with dummy data) would be nice so that we can reproduce it ourselves." ]	2023-11-17T14:28:50Z	2023-11-22T17:34:58Z	null	NONE	null	null	### Describe the bug I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements. ### Steps to reproduce the bug train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN) valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION) logger.info(len(train_dataset)) logger.info(len(valid_dataset)) Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files. ### Expected behavior I see all 127 elements in my dataset when performing len ### Environment info Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6432/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6431	6,431	Create DatasetNotFoundError and DataFilesNotFoundError	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004459 / 0.011353 (-0.006894) \| 0.002883 / 0.011008 (-0.008125) \| 0.062434 / 0.038508 (0.023925) \| 0.030353 / 0.023109 (0.007244) \| 0.256696 / 0.275898 (-0.019202) \| 0.280557 / 0.323480 (-0.042923) \| 0.003903 / 0.007986 (-0.004083) \| 0.002424 / 0.004328 (-0.001905) \| 0.048509 / 0.004250 (0.044259) \| 0.043583 / 0.037052 (0.006531) \| 0.253900 / 0.258489 (-0.004590) \| 0.309146 / 0.293841 (0.015305) \| 0.023253 / 0.128546 (-0.105294) \| 0.007073 / 0.075646 (-0.068573) \| 0.204118 / 0.419271 (-0.215154) \| 0.056429 / 0.043533 (0.012897) \| 0.247331 / 0.255139 (-0.007808) \| 0.271581 / 0.283200 (-0.011619) \| 0.017021 / 0.141683 (-0.124662) \| 1.115057 / 1.452155 (-0.337098) \| 1.209947 / 1.492716 (-0.282770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093141 / 0.018006 (0.075134) \| 0.295987 / 0.000490 (0.295497) \| 0.000221 / 0.000200 (0.000021) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019182 / 0.037411 (-0.018230) \| 0.062049 / 0.014526 (0.047523) \| 0.073824 / 0.176557 (-0.102733) \| 0.120175 / 0.737135 (-0.616960) \| 0.074700 / 0.296338 (-0.221639) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280036 / 0.215209 (0.064827) \| 2.731512 / 2.077655 (0.653857) \| 1.414606 / 1.504120 (-0.089514) \| 1.302433 / 1.541195 (-0.238761) \| 1.313012 / 1.468490 (-0.155478) \| 0.399722 / 4.584777 (-4.185055) \| 2.371249 / 3.745712 (-1.374463) \| 2.582520 / 5.269862 (-2.687342) \| 1.558505 / 4.565676 (-3.007171) \| 0.045765 / 0.424275 (-0.378510) \| 0.004748 / 0.007607 (-0.002859) \| 0.327623 / 0.226044 (0.101578) \| 3.258742 / 2.268929 (0.989814) \| 1.756798 / 55.444624 (-53.687826) \| 1.494551 / 6.876477 (-5.381925) \| 1.518161 / 2.142072 (-0.623911) \| 0.468560 / 4.805227 (-4.336667) \| 0.101034 / 6.500664 (-6.399630) \| 0.048259 / 0.075469 (-0.027210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938146 / 1.841788 (-0.903642) \| 11.636387 / 8.074308 (3.562078) \| 10.638909 / 10.191392 (0.447517) \| 0.128340 / 0.680424 (-0.552084) \| 0.015194 / 0.534201 (-0.519007) \| 0.275961 / 0.579283 (-0.303322) \| 0.264629 / 0.434364 (-0.169735) \| 0.308580 / 0.540337 (-0.231758) \| 0.433658 / 1.386936 (-0.953278) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002801 / 0.011008 (-0.008208) \| 0.048101 / 0.038508 (0.009593) \| 0.056406 / 0.023109 (0.033296) \| 0.274966 / 0.275898 (-0.000932) \| 0.298310 / 0.323480 (-0.025170) \| 0.004115 / 0.007986 (-0.003871) \| 0.002437 / 0.004328 (-0.001891) \| 0.047921 / 0.004250 (0.043671) \| 0.038812 / 0.037052 (0.001760) \| 0.279594 / 0.258489 (0.021105) \| 0.313703 / 0.293841 (0.019862) \| 0.024485 / 0.128546 (-0.104061) \| 0.007095 / 0.075646 (-0.068551) \| 0.053398 / 0.419271 (-0.365874) \| 0.032306 / 0.043533 (-0.011227) \| 0.278014 / 0.255139 (0.022875) \| 0.301156 / 0.283200 (0.017956) \| 0.017353 / 0.141683 (-0.124330) \| 1.150168 / 1.452155 (-0.301987) \| 1.190822 / 1.492716 (-0.301894) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092162 / 0.018006 (0.074156) \| 0.301031 / 0.000490 (0.300541) \| 0.000244 / 0.000200 (0.000044) \| 0.000062 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020918 / 0.037411 (-0.016494) \| 0.072030 / 0.014526 (0.057504) \| 0.081813 / 0.176557 (-0.094743) \| 0.120233 / 0.737135 (-0.616903) \| 0.082874 / 0.296338 (-0.213465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291659 / 0.215209 (0.076450) \| 2.841978 / 2.077655 (0.764323) \| 1.594207 / 1.504120 (0.090087) \| 1.473941 / 1.541195 (-0.067254) \| 1.514393 / 1.468490 (0.045903) \| 0.393393 / 4.584777 (-4.191384) \| 2.443663 / 3.745712 (-1.302050) \| 2.545747 / 5.269862 (-2.724114) \| 1.521130 / 4.565676 (-3.044546) \| 0.046246 / 0.424275 (-0.378030) \| 0.004826 / 0.007607 (-0.002781) \| 0.340909 / 0.226044 (0.114865) \| 3.319474 / 2.268929 (1.050546) \| 1.933110 / 55.444624 (-53.511515) \| 1.662463 / 6.876477 (-5.214014) \| 1.670331 / 2.142072 (-0.471742) \| 0.458062 / 4.805227 (-4.347165) \| 0.098397 / 6.500664 (-6.402267) \| 0.041339 / 0.075469 (-0.034130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973718 / 1.841788 (-0.868070) \| 12.095266 / 8.074308 (4.020957) \| 10.761212 / 10.191392 (0.569820) \| 0.142352 / 0.680424 (-0.538072) \| 0.015423 / 0.534201 (-0.518778) \| 0.270912 / 0.579283 (-0.308371) \| 0.276618 / 0.434364 (-0.157746) \| 0.309120 / 0.540337 (-0.231217) \| 0.415330 / 1.386936 (-0.971606) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4ba6f0e2641056774c01f62984aef5de5d68f1 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004676 / 0.011353 (-0.006677) \| 0.003101 / 0.011008 (-0.007907) \| 0.062260 / 0.038508 (0.023752) \| 0.030012 / 0.023109 (0.006903) \| 0.253704 / 0.275898 (-0.022194) \| 0.276404 / 0.323480 (-0.047075) \| 0.004060 / 0.007986 (-0.003926) \| 0.002467 / 0.004328 (-0.001861) \| 0.047921 / 0.004250 (0.043670) \| 0.045760 / 0.037052 (0.008708) \| 0.254529 / 0.258489 (-0.003960) \| 0.286283 / 0.293841 (-0.007558) \| 0.023301 / 0.128546 (-0.105246) \| 0.007407 / 0.075646 (-0.068239) \| 0.204541 / 0.419271 (-0.214730) \| 0.056387 / 0.043533 (0.012854) \| 0.252120 / 0.255139 (-0.003019) \| 0.275795 / 0.283200 (-0.007404) \| 0.018648 / 0.141683 (-0.123034) \| 1.113484 / 1.452155 (-0.338671) \| 1.168685 / 1.492716 (-0.324031) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098286 / 0.018006 (0.080280) \| 0.304619 / 0.000490 (0.304129) \| 0.000225 / 0.000200 (0.000025) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019183 / 0.037411 (-0.018229) \| 0.062183 / 0.014526 (0.047657) \| 0.074288 / 0.176557 (-0.102269) \| 0.120576 / 0.737135 (-0.616560) \| 0.074833 / 0.296338 (-0.221505) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280512 / 0.215209 (0.065303) \| 2.770052 / 2.077655 (0.692397) \| 1.471234 / 1.504120 (-0.032886) \| 1.352080 / 1.541195 (-0.189114) \| 1.374518 / 1.468490 (-0.093973) \| 0.407108 / 4.584777 (-4.177669) \| 2.400581 / 3.745712 (-1.345131) \| 2.677507 / 5.269862 (-2.592355) \| 1.578042 / 4.565676 (-2.987635) \| 0.048539 / 0.424275 (-0.375736) \| 0.004905 / 0.007607 (-0.002703) \| 0.346676 / 0.226044 (0.120631) \| 3.367732 / 2.268929 (1.098803) \| 1.844405 / 55.444624 (-53.600220) \| 1.576883 / 6.876477 (-5.299594) \| 1.666986 / 2.142072 (-0.475086) \| 0.495872 / 4.805227 (-4.309355) \| 0.103142 / 6.500664 (-6.397522) \| 0.044037 / 0.075469 (-0.031432) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980865 / 1.841788 (-0.860923) \| 12.268525 / 8.074308 (4.194217) \| 10.756554 / 10.191392 (0.565162) \| 0.129954 / 0.680424 (-0.550470) \| 0.013864 / 0.534201 (-0.520337) \| 0.267653 / 0.579283 (-0.311630) \| 0.265120 / 0.434364 (-0.169244) \| 0.309050 / 0.540337 (-0.231288) \| 0.423877 / 1.386936 (-0.963059) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005074 / 0.011353 (-0.006279) \| 0.003001 / 0.011008 (-0.008007) \| 0.048271 / 0.038508 (0.009763) \| 0.061206 / 0.023109 (0.038097) \| 0.279268 / 0.275898 (0.003370) \| 0.302592 / 0.323480 (-0.020888) \| 0.004177 / 0.007986 (-0.003809) \| 0.002452 / 0.004328 (-0.001876) \| 0.048259 / 0.004250 (0.044009) \| 0.040032 / 0.037052 (0.002979) \| 0.281398 / 0.258489 (0.022909) \| 0.314121 / 0.293841 (0.020280) \| 0.025137 / 0.128546 (-0.103409) \| 0.007230 / 0.075646 (-0.068416) \| 0.054537 / 0.419271 (-0.364735) \| 0.033266 / 0.043533 (-0.010267) \| 0.277305 / 0.255139 (0.022166) \| 0.295993 / 0.283200 (0.012794) \| 0.019278 / 0.141683 (-0.122405) \| 1.131700 / 1.452155 (-0.320454) \| 1.183848 / 1.492716 (-0.308868) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092258 / 0.018006 (0.074251) \| 0.310668 / 0.000490 (0.310178) \| 0.000219 / 0.000200 (0.000019) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021838 / 0.037411 (-0.015574) \| 0.071382 / 0.014526 (0.056857) \| 0.081389 / 0.176557 (-0.095168) \| 0.120389 / 0.737135 (-0.616746) \| 0.084135 / 0.296338 (-0.212203) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291676 / 0.215209 (0.076467) \| 2.840623 / 2.077655 (0.762968) \| 1.565748 / 1.504120 (0.061628) \| 1.452529 / 1.541195 (-0.088666) \| 1.490633 / 1.468490 (0.022143) \| 0.402878 / 4.584777 (-4.181899) \| 2.486192 / 3.745712 (-1.259520) \| 2.520563 / 5.269862 (-2.749299) \| 1.518550 / 4.565676 (-3.047127) \| 0.047423 / 0.424275 (-0.376852) \| 0.004823 / 0.007607 (-0.002784) \| 0.353122 / 0.226044 (0.127078) \| 3.452136 / 2.268929 (1.183208) \| 1.973798 / 55.444624 (-53.470827) \| 1.669569 / 6.876477 (-5.206907) \| 1.654910 / 2.142072 (-0.487163) \| 0.486746 / 4.805227 (-4.318481) \| 0.097260 / 6.500664 (-6.403404) \| 0.040608 / 0.075469 (-0.034861) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989705 / 1.841788 (-0.852083) \| 12.114386 / 8.074308 (4.040077) \| 11.284551 / 10.191392 (1.093159) \| 0.141408 / 0.680424 (-0.539016) \| 0.015275 / 0.534201 (-0.518926) \| 0.267407 / 0.579283 (-0.311877) \| 0.281007 / 0.434364 (-0.153357) \| 0.309617 / 0.540337 (-0.230720) \| 0.414033 / 1.386936 (-0.972903) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f3e3feec9d7d4d36111401787eb7b5fd51836 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004888 / 0.011353 (-0.006465) \| 0.002775 / 0.011008 (-0.008233) \| 0.062000 / 0.038508 (0.023492) \| 0.050694 / 0.023109 (0.027584) \| 0.257063 / 0.275898 (-0.018835) \| 0.282743 / 0.323480 (-0.040736) \| 0.002862 / 0.007986 (-0.005124) \| 0.002305 / 0.004328 (-0.002023) \| 0.049549 / 0.004250 (0.045299) \| 0.038754 / 0.037052 (0.001701) \| 0.264047 / 0.258489 (0.005558) \| 0.310162 / 0.293841 (0.016321) \| 0.022901 / 0.128546 (-0.105645) \| 0.006894 / 0.075646 (-0.068752) \| 0.202467 / 0.419271 (-0.216805) \| 0.035901 / 0.043533 (-0.007631) \| 0.262344 / 0.255139 (0.007205) \| 0.285563 / 0.283200 (0.002364) \| 0.017070 / 0.141683 (-0.124613) \| 1.113972 / 1.452155 (-0.338182) \| 1.176261 / 1.492716 (-0.316455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092912 / 0.018006 (0.074906) \| 0.302610 / 0.000490 (0.302120) \| 0.000204 / 0.000200 (0.000005) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018232 / 0.037411 (-0.019179) \| 0.062367 / 0.014526 (0.047841) \| 0.074570 / 0.176557 (-0.101987) \| 0.120468 / 0.737135 (-0.616668) \| 0.075187 / 0.296338 (-0.221151) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279760 / 0.215209 (0.064551) \| 2.715372 / 2.077655 (0.637717) \| 1.461636 / 1.504120 (-0.042484) \| 1.324220 / 1.541195 (-0.216975) \| 1.350724 / 1.468490 (-0.117766) \| 0.395648 / 4.584777 (-4.189129) \| 2.376548 / 3.745712 (-1.369164) \| 2.594662 / 5.269862 (-2.675200) \| 1.553528 / 4.565676 (-3.012148) \| 0.047875 / 0.424275 (-0.376400) \| 0.005287 / 0.007607 (-0.002321) \| 0.334734 / 0.226044 (0.108689) \| 3.294753 / 2.268929 (1.025825) \| 1.797901 / 55.444624 (-53.646724) \| 1.510907 / 6.876477 (-5.365570) \| 1.536070 / 2.142072 (-0.606003) \| 0.474672 / 4.805227 (-4.330555) \| 0.099323 / 6.500664 (-6.401341) \| 0.041703 / 0.075469 (-0.033766) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947441 / 1.841788 (-0.894347) \| 11.451378 / 8.074308 (3.377070) \| 10.283213 / 10.191392 (0.091821) \| 0.131032 / 0.680424 (-0.549392) \| 0.014423 / 0.534201 (-0.519777) \| 0.272568 / 0.579283 (-0.306715) \| 0.267127 / 0.434364 (-0.167237) \| 0.307361 / 0.540337 (-0.232976) \| 0.403858 / 1.386936 (-0.983078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004836 / 0.011353 (-0.006517) \| 0.002544 / 0.011008 (-0.008464) \| 0.047979 / 0.038508 (0.009471) \| 0.052211 / 0.023109 (0.029102) \| 0.273394 / 0.275898 (-0.002504) \| 0.291202 / 0.323480 (-0.032277) \| 0.004094 / 0.007986 (-0.003891) \| 0.002415 / 0.004328 (-0.001914) \| 0.048057 / 0.004250 (0.043807) \| 0.039756 / 0.037052 (0.002703) \| 0.277301 / 0.258489 (0.018812) \| 0.297626 / 0.293841 (0.003785) \| 0.024641 / 0.128546 (-0.103905) \| 0.006957 / 0.075646 (-0.068690) \| 0.053574 / 0.419271 (-0.365697) \| 0.036532 / 0.043533 (-0.007001) \| 0.273753 / 0.255139 (0.018614) \| 0.294254 / 0.283200 (0.011054) \| 0.022252 / 0.141683 (-0.119431) \| 1.128609 / 1.452155 (-0.323546) \| 1.217322 / 1.492716 (-0.275394) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091050 / 0.018006 (0.073044) \| 0.300089 / 0.000490 (0.299600) \| 0.000215 / 0.000200 (0.000015) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021423 / 0.037411 (-0.015988) \| 0.069892 / 0.014526 (0.055366) \| 0.081125 / 0.176557 (-0.095432) \| 0.118725 / 0.737135 (-0.618411) \| 0.081357 / 0.296338 (-0.214981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295046 / 0.215209 (0.079837) \| 2.868813 / 2.077655 (0.791159) \| 1.579613 / 1.504120 (0.075493) \| 1.449308 / 1.541195 (-0.091887) \| 1.478804 / 1.468490 (0.010314) \| 0.416916 / 4.584777 (-4.167861) \| 2.461093 / 3.745712 (-1.284619) \| 2.449792 / 5.269862 (-2.820070) \| 1.573930 / 4.565676 (-2.991746) \| 0.046808 / 0.424275 (-0.377467) \| 0.004811 / 0.007607 (-0.002796) \| 0.352805 / 0.226044 (0.126761) \| 3.495034 / 2.268929 (1.226105) \| 1.952019 / 55.444624 (-53.492606) \| 1.642607 / 6.876477 (-5.233869) \| 1.775235 / 2.142072 (-0.366837) \| 0.482196 / 4.805227 (-4.323032) \| 0.099562 / 6.500664 (-6.401102) \| 0.040709 / 0.075469 (-0.034760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972750 / 1.841788 (-0.869038) \| 11.905172 / 8.074308 (3.830864) \| 10.613847 / 10.191392 (0.422455) \| 0.129892 / 0.680424 (-0.550532) \| 0.015611 / 0.534201 (-0.518590) \| 0.271884 / 0.579283 (-0.307400) \| 0.275270 / 0.434364 (-0.159094) \| 0.303213 / 0.540337 (-0.237125) \| 0.402338 / 1.386936 (-0.984598) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf8fa7ad7609ad34d4cc689f529ea606dd2560e0 \"CML watermark\")\n", "I think this PR can be merged.", "you already have an approval, feel free to merge!\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004826 / 0.011353 (-0.006527) \| 0.002979 / 0.011008 (-0.008029) \| 0.062055 / 0.038508 (0.023547) \| 0.056574 / 0.023109 (0.033465) \| 0.244342 / 0.275898 (-0.031556) \| 0.278040 / 0.323480 (-0.045439) \| 0.004020 / 0.007986 (-0.003965) \| 0.002474 / 0.004328 (-0.001855) \| 0.048451 / 0.004250 (0.044200) \| 0.038633 / 0.037052 (0.001580) \| 0.251389 / 0.258489 (-0.007100) \| 0.282739 / 0.293841 (-0.011102) \| 0.023298 / 0.128546 (-0.105248) \| 0.007513 / 0.075646 (-0.068134) \| 0.203014 / 0.419271 (-0.216257) \| 0.036216 / 0.043533 (-0.007317) \| 0.250988 / 0.255139 (-0.004151) \| 0.281228 / 0.283200 (-0.001972) \| 0.018259 / 0.141683 (-0.123424) \| 1.121200 / 1.452155 (-0.330955) \| 1.184298 / 1.492716 (-0.308419) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093730 / 0.018006 (0.075724) \| 0.301716 / 0.000490 (0.301226) \| 0.000223 / 0.000200 (0.000023) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019238 / 0.037411 (-0.018173) \| 0.064329 / 0.014526 (0.049803) \| 0.075657 / 0.176557 (-0.100899) \| 0.122616 / 0.737135 (-0.614519) \| 0.077459 / 0.296338 (-0.218880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280153 / 0.215209 (0.064944) \| 2.715488 / 2.077655 (0.637833) \| 1.449666 / 1.504120 (-0.054454) \| 1.331903 / 1.541195 (-0.209292) \| 1.396200 / 1.468490 (-0.072290) \| 0.398861 / 4.584777 (-4.185916) \| 2.402814 / 3.745712 (-1.342898) \| 2.664033 / 5.269862 (-2.605829) \| 1.619589 / 4.565676 (-2.946088) \| 0.044798 / 0.424275 (-0.379477) \| 0.004989 / 0.007607 (-0.002618) \| 0.336822 / 0.226044 (0.110777) \| 3.245604 / 2.268929 (0.976676) \| 1.815633 / 55.444624 (-53.628991) \| 1.557975 / 6.876477 (-5.318501) \| 1.603655 / 2.142072 (-0.538417) \| 0.462980 / 4.805227 (-4.342247) \| 0.098340 / 6.500664 (-6.402324) \| 0.042750 / 0.075469 (-0.032719) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973785 / 1.841788 (-0.868003) \| 12.379356 / 8.074308 (4.305048) \| 10.540164 / 10.191392 (0.348772) \| 0.144803 / 0.680424 (-0.535621) \| 0.013875 / 0.534201 (-0.520326) \| 0.270192 / 0.579283 (-0.309091) \| 0.264614 / 0.434364 (-0.169750) \| 0.313454 / 0.540337 (-0.226883) \| 0.402310 / 1.386936 (-0.984626) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004987 / 0.011353 (-0.006366) \| 0.003017 / 0.011008 (-0.007992) \| 0.048592 / 0.038508 (0.010084) \| 0.059370 / 0.023109 (0.036261) \| 0.277536 / 0.275898 (0.001638) \| 0.300592 / 0.323480 (-0.022888) \| 0.004870 / 0.007986 (-0.003115) \| 0.002452 / 0.004328 (-0.001876) \| 0.047972 / 0.004250 (0.043721) \| 0.042336 / 0.037052 (0.005283) \| 0.277570 / 0.258489 (0.019081) \| 0.304739 / 0.293841 (0.010898) \| 0.025313 / 0.128546 (-0.103233) \| 0.007219 / 0.075646 (-0.068427) \| 0.053967 / 0.419271 (-0.365304) \| 0.033314 / 0.043533 (-0.010219) \| 0.273908 / 0.255139 (0.018769) \| 0.291913 / 0.283200 (0.008713) \| 0.019440 / 0.141683 (-0.122243) \| 1.111047 / 1.452155 (-0.341107) \| 1.191276 / 1.492716 (-0.301440) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093985 / 0.018006 (0.075979) \| 0.303105 / 0.000490 (0.302615) \| 0.000235 / 0.000200 (0.000035) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022226 / 0.037411 (-0.015186) \| 0.072151 / 0.014526 (0.057625) \| 0.081700 / 0.176557 (-0.094857) \| 0.121407 / 0.737135 (-0.615729) \| 0.083217 / 0.296338 (-0.213121) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297286 / 0.215209 (0.082077) \| 2.913392 / 2.077655 (0.835738) \| 1.591758 / 1.504120 (0.087638) \| 1.463339 / 1.541195 (-0.077856) \| 1.495095 / 1.468490 (0.026605) \| 0.414341 / 4.584777 (-4.170436) \| 2.412438 / 3.745712 (-1.333275) \| 2.611452 / 5.269862 (-2.658410) \| 1.658545 / 4.565676 (-2.907132) \| 0.047269 / 0.424275 (-0.377007) \| 0.004872 / 0.007607 (-0.002735) \| 0.350746 / 0.226044 (0.124701) \| 3.491482 / 2.268929 (1.222554) \| 1.999009 / 55.444624 (-53.445616) \| 1.672862 / 6.876477 (-5.203615) \| 1.863095 / 2.142072 (-0.278977) \| 0.484746 / 4.805227 (-4.320481) \| 0.100774 / 6.500664 (-6.399890) \| 0.042519 / 0.075469 (-0.032950) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984497 / 1.841788 (-0.857291) \| 12.972576 / 8.074308 (4.898268) \| 10.886021 / 10.191392 (0.694629) \| 0.141639 / 0.680424 (-0.538785) \| 0.015726 / 0.534201 (-0.518475) \| 0.284160 / 0.579283 (-0.295123) \| 0.291437 / 0.434364 (-0.142927) \| 0.314121 / 0.540337 (-0.226217) \| 0.420439 / 1.386936 (-0.966497) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ad7c7767b9cda62113c207f0ff42506a8f27c0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004881 / 0.011353 (-0.006472) \| 0.002550 / 0.011008 (-0.008458) \| 0.062171 / 0.038508 (0.023663) \| 0.055341 / 0.023109 (0.032232) \| 0.243132 / 0.275898 (-0.032766) \| 0.265174 / 0.323480 (-0.058306) \| 0.002934 / 0.007986 (-0.005052) \| 0.002233 / 0.004328 (-0.002096) \| 0.049302 / 0.004250 (0.045052) \| 0.039491 / 0.037052 (0.002439) \| 0.252776 / 0.258489 (-0.005713) \| 0.280923 / 0.293841 (-0.012918) \| 0.022585 / 0.128546 (-0.105962) \| 0.006888 / 0.075646 (-0.068759) \| 0.202751 / 0.419271 (-0.216521) \| 0.035250 / 0.043533 (-0.008283) \| 0.251745 / 0.255139 (-0.003394) \| 0.267431 / 0.283200 (-0.015768) \| 0.019486 / 0.141683 (-0.122197) \| 1.161783 / 1.452155 (-0.290372) \| 1.194254 / 1.492716 (-0.298463) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097772 / 0.018006 (0.079766) \| 0.309137 / 0.000490 (0.308647) \| 0.000225 / 0.000200 (0.000025) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018719 / 0.037411 (-0.018693) \| 0.062211 / 0.014526 (0.047686) \| 0.074291 / 0.176557 (-0.102266) \| 0.119436 / 0.737135 (-0.617699) \| 0.075519 / 0.296338 (-0.220820) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279778 / 0.215209 (0.064569) \| 2.730678 / 2.077655 (0.653023) \| 1.413922 / 1.504120 (-0.090198) \| 1.286747 / 1.541195 (-0.254447) \| 1.299835 / 1.468490 (-0.168656) \| 0.392516 / 4.584777 (-4.192261) \| 2.381816 / 3.745712 (-1.363896) \| 2.616944 / 5.269862 (-2.652918) \| 1.606152 / 4.565676 (-2.959525) \| 0.044867 / 0.424275 (-0.379408) \| 0.004915 / 0.007607 (-0.002692) \| 0.334078 / 0.226044 (0.108034) \| 3.388096 / 2.268929 (1.119167) \| 1.756666 / 55.444624 (-53.687958) \| 1.497211 / 6.876477 (-5.379266) \| 1.496787 / 2.142072 (-0.645285) \| 0.469145 / 4.805227 (-4.336082) \| 0.097821 / 6.500664 (-6.402843) \| 0.041850 / 0.075469 (-0.033619) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956878 / 1.841788 (-0.884910) \| 11.520184 / 8.074308 (3.445875) \| 10.659216 / 10.191392 (0.467824) \| 0.143687 / 0.680424 (-0.536737) \| 0.014118 / 0.534201 (-0.520083) \| 0.270990 / 0.579283 (-0.308293) \| 0.270057 / 0.434364 (-0.164306) \| 0.311109 / 0.540337 (-0.229229) \| 0.407042 / 1.386936 (-0.979894) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004816 / 0.011353 (-0.006537) \| 0.002898 / 0.011008 (-0.008110) \| 0.048540 / 0.038508 (0.010032) \| 0.055286 / 0.023109 (0.032176) \| 0.279086 / 0.275898 (0.003187) \| 0.298950 / 0.323480 (-0.024529) \| 0.004090 / 0.007986 (-0.003896) \| 0.002497 / 0.004328 (-0.001832) \| 0.049160 / 0.004250 (0.044910) \| 0.040612 / 0.037052 (0.003560) \| 0.287832 / 0.258489 (0.029343) \| 0.305617 / 0.293841 (0.011776) \| 0.023936 / 0.128546 (-0.104610) \| 0.007565 / 0.075646 (-0.068081) \| 0.054037 / 0.419271 (-0.365235) \| 0.032389 / 0.043533 (-0.011144) \| 0.283031 / 0.255139 (0.027892) \| 0.295411 / 0.283200 (0.012212) \| 0.018466 / 0.141683 (-0.123217) \| 1.134660 / 1.452155 (-0.317495) \| 1.196212 / 1.492716 (-0.296504) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099961 / 0.018006 (0.081955) \| 0.310831 / 0.000490 (0.310342) \| 0.000238 / 0.000200 (0.000038) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021566 / 0.037411 (-0.015845) \| 0.070255 / 0.014526 (0.055729) \| 0.081221 / 0.176557 (-0.095336) \| 0.119404 / 0.737135 (-0.617732) \| 0.083005 / 0.296338 (-0.213333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302788 / 0.215209 (0.087579) \| 2.928876 / 2.077655 (0.851221) \| 1.601221 / 1.504120 (0.097101) \| 1.485147 / 1.541195 (-0.056047) \| 1.508698 / 1.468490 (0.040207) \| 0.402783 / 4.584777 (-4.181994) \| 2.432151 / 3.745712 (-1.313561) \| 2.476848 / 5.269862 (-2.793013) \| 1.585487 / 4.565676 (-2.980189) \| 0.045965 / 0.424275 (-0.378310) \| 0.004818 / 0.007607 (-0.002789) \| 0.354847 / 0.226044 (0.128803) \| 3.500670 / 2.268929 (1.231742) \| 1.951904 / 55.444624 (-53.492720) \| 1.675152 / 6.876477 (-5.201325) \| 1.795971 / 2.142072 (-0.346101) \| 0.470625 / 4.805227 (-4.334602) \| 0.126080 / 6.500664 (-6.374584) \| 0.040506 / 0.075469 (-0.034963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985251 / 1.841788 (-0.856536) \| 12.316710 / 8.074308 (4.242402) \| 10.674437 / 10.191392 (0.483045) \| 0.133622 / 0.680424 (-0.546802) \| 0.016756 / 0.534201 (-0.517445) \| 0.269318 / 0.579283 (-0.309965) \| 0.282258 / 0.434364 (-0.152106) \| 0.309941 / 0.540337 (-0.230396) \| 0.403189 / 1.386936 (-0.983747) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08ceb927025575c453228cab31291b74043dba1a \"CML watermark\")\n", "I am merging this PR because we need it by `datasets-server`.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004935 / 0.011353 (-0.006418) \| 0.002643 / 0.011008 (-0.008365) \| 0.064449 / 0.038508 (0.025941) \| 0.053110 / 0.023109 (0.030001) \| 0.261576 / 0.275898 (-0.014322) \| 0.270866 / 0.323480 (-0.052614) \| 0.002895 / 0.007986 (-0.005091) \| 0.002349 / 0.004328 (-0.001979) \| 0.047620 / 0.004250 (0.043370) \| 0.038699 / 0.037052 (0.001647) \| 0.246663 / 0.258489 (-0.011826) \| 0.282021 / 0.293841 (-0.011820) \| 0.022807 / 0.128546 (-0.105739) \| 0.007242 / 0.075646 (-0.068404) \| 0.204236 / 0.419271 (-0.215035) \| 0.035429 / 0.043533 (-0.008104) \| 0.241684 / 0.255139 (-0.013455) \| 0.262343 / 0.283200 (-0.020857) \| 0.020036 / 0.141683 (-0.121647) \| 1.112687 / 1.452155 (-0.339467) \| 1.167086 / 1.492716 (-0.325630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.107059 / 0.018006 (0.089053) \| 0.301036 / 0.000490 (0.300546) \| 0.000224 / 0.000200 (0.000024) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018464 / 0.037411 (-0.018947) \| 0.063822 / 0.014526 (0.049296) \| 0.073562 / 0.176557 (-0.102994) \| 0.120136 / 0.737135 (-0.616999) \| 0.074934 / 0.296338 (-0.221405) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275474 / 0.215209 (0.060265) \| 2.714239 / 2.077655 (0.636584) \| 1.455535 / 1.504120 (-0.048585) \| 1.336530 / 1.541195 (-0.204665) \| 1.359607 / 1.468490 (-0.108883) \| 0.396303 / 4.584777 (-4.188474) \| 2.366076 / 3.745712 (-1.379636) \| 2.600755 / 5.269862 (-2.669107) \| 1.572382 / 4.565676 (-2.993294) \| 0.045795 / 0.424275 (-0.378480) \| 0.004932 / 0.007607 (-0.002675) \| 0.332175 / 0.226044 (0.106130) \| 3.257843 / 2.268929 (0.988915) \| 1.799021 / 55.444624 (-53.645603) \| 1.532813 / 6.876477 (-5.343663) \| 1.552279 / 2.142072 (-0.589794) \| 0.471369 / 4.805227 (-4.333858) \| 0.098931 / 6.500664 (-6.401733) \| 0.042735 / 0.075469 (-0.032734) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960779 / 1.841788 (-0.881009) \| 11.741631 / 8.074308 (3.667322) \| 10.355721 / 10.191392 (0.164329) \| 0.129025 / 0.680424 (-0.551399) \| 0.013794 / 0.534201 (-0.520407) \| 0.267268 / 0.579283 (-0.312015) \| 0.265582 / 0.434364 (-0.168782) \| 0.306242 / 0.540337 (-0.234095) \| 0.400367 / 1.386936 (-0.986569) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004966 / 0.011353 (-0.006387) \| 0.002846 / 0.011008 (-0.008163) \| 0.049104 / 0.038508 (0.010596) \| 0.055436 / 0.023109 (0.032327) \| 0.273892 / 0.275898 (-0.002006) \| 0.300207 / 0.323480 (-0.023273) \| 0.004017 / 0.007986 (-0.003969) \| 0.002465 / 0.004328 (-0.001863) \| 0.048088 / 0.004250 (0.043837) \| 0.040037 / 0.037052 (0.002984) \| 0.279918 / 0.258489 (0.021429) \| 0.305378 / 0.293841 (0.011537) \| 0.024326 / 0.128546 (-0.104220) \| 0.006992 / 0.075646 (-0.068654) \| 0.053545 / 0.419271 (-0.365726) \| 0.032312 / 0.043533 (-0.011221) \| 0.272899 / 0.255139 (0.017760) \| 0.289683 / 0.283200 (0.006483) \| 0.019121 / 0.141683 (-0.122562) \| 1.133296 / 1.452155 (-0.318858) \| 1.220989 / 1.492716 (-0.271728) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093193 / 0.018006 (0.075187) \| 0.307658 / 0.000490 (0.307168) \| 0.000224 / 0.000200 (0.000024) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022906 / 0.037411 (-0.014506) \| 0.080931 / 0.014526 (0.066405) \| 0.081442 / 0.176557 (-0.095115) \| 0.121150 / 0.737135 (-0.615986) \| 0.083387 / 0.296338 (-0.212952) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294979 / 0.215209 (0.079770) \| 2.900090 / 2.077655 (0.822435) \| 1.610061 / 1.504120 (0.105941) \| 1.455118 / 1.541195 (-0.086077) \| 1.456599 / 1.468490 (-0.011891) \| 0.397919 / 4.584777 (-4.186858) \| 2.421010 / 3.745712 (-1.324702) \| 2.486527 / 5.269862 (-2.783334) \| 1.573854 / 4.565676 (-2.991822) \| 0.046199 / 0.424275 (-0.378076) \| 0.004888 / 0.007607 (-0.002719) \| 0.342183 / 0.226044 (0.116139) \| 3.392068 / 2.268929 (1.123140) \| 1.963688 / 55.444624 (-53.480936) \| 1.667611 / 6.876477 (-5.208866) \| 1.833706 / 2.142072 (-0.308367) \| 0.509421 / 4.805227 (-4.295806) \| 0.099669 / 6.500664 (-6.400995) \| 0.041004 / 0.075469 (-0.034465) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956314 / 1.841788 (-0.885474) \| 12.190194 / 8.074308 (4.115886) \| 10.417839 / 10.191392 (0.226447) \| 0.144139 / 0.680424 (-0.536285) \| 0.015841 / 0.534201 (-0.518359) \| 0.270436 / 0.579283 (-0.308847) \| 0.273952 / 0.434364 (-0.160412) \| 0.303018 / 0.540337 (-0.237319) \| 0.410163 / 1.386936 (-0.976773) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa8558fc7fe1f9f7675c7c5d21a14d1a19598296 \"CML watermark\")\n" ]	2023-11-16T16:02:55Z	2023-11-22T15:18:51Z	2023-11-22T15:12:33Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6431.diff", "html_url": "https://github.com/huggingface/datasets/pull/6431", "merged_at": "2023-11-22T15:12:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/6431.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6431" }	Create `DatasetNotFoundError` and `DataFilesNotFoundError`. Fix #6397. CC: @severo	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6431/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6429	6,429	Add trust_remote_code argument	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004947 / 0.011353 (-0.006405) \| 0.002961 / 0.011008 (-0.008047) \| 0.063474 / 0.038508 (0.024966) \| 0.030162 / 0.023109 (0.007053) \| 0.232388 / 0.275898 (-0.043511) \| 0.257654 / 0.323480 (-0.065826) \| 0.002969 / 0.007986 (-0.005017) \| 0.002336 / 0.004328 (-0.001993) \| 0.049724 / 0.004250 (0.045473) \| 0.045608 / 0.037052 (0.008555) \| 0.236079 / 0.258489 (-0.022410) \| 0.267809 / 0.293841 (-0.026032) \| 0.023805 / 0.128546 (-0.104741) \| 0.007177 / 0.075646 (-0.068470) \| 0.202167 / 0.419271 (-0.217104) \| 0.056181 / 0.043533 (0.012648) \| 0.256464 / 0.255139 (0.001325) \| 0.271908 / 0.283200 (-0.011292) \| 0.020211 / 0.141683 (-0.121472) \| 1.114112 / 1.452155 (-0.338042) \| 1.174879 / 1.492716 (-0.317837) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093457 / 0.018006 (0.075451) \| 0.307643 / 0.000490 (0.307154) \| 0.000212 / 0.000200 (0.000012) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018635 / 0.037411 (-0.018777) \| 0.062099 / 0.014526 (0.047573) \| 0.073619 / 0.176557 (-0.102938) \| 0.119986 / 0.737135 (-0.617149) \| 0.075439 / 0.296338 (-0.220899) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280142 / 0.215209 (0.064933) \| 2.733790 / 2.077655 (0.656136) \| 1.457633 / 1.504120 (-0.046487) \| 1.336288 / 1.541195 (-0.204907) \| 1.363191 / 1.468490 (-0.105299) \| 0.399331 / 4.584777 (-4.185446) \| 2.343099 / 3.745712 (-1.402614) \| 2.617059 / 5.269862 (-2.652802) \| 1.575912 / 4.565676 (-2.989765) \| 0.045621 / 0.424275 (-0.378655) \| 0.004825 / 0.007607 (-0.002782) \| 0.346669 / 0.226044 (0.120625) \| 3.225982 / 2.268929 (0.957054) \| 1.787067 / 55.444624 (-53.657557) \| 1.503883 / 6.876477 (-5.372593) \| 1.527593 / 2.142072 (-0.614479) \| 0.466806 / 4.805227 (-4.338421) \| 0.098537 / 6.500664 (-6.402127) \| 0.042028 / 0.075469 (-0.033441) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945040 / 1.841788 (-0.896748) \| 11.970022 / 8.074308 (3.895714) \| 10.261176 / 10.191392 (0.069784) \| 0.138231 / 0.680424 (-0.542193) \| 0.013933 / 0.534201 (-0.520268) \| 0.270640 / 0.579283 (-0.308643) \| 0.263185 / 0.434364 (-0.171178) \| 0.306686 / 0.540337 (-0.233651) \| 0.423164 / 1.386936 (-0.963772) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004765 / 0.011353 (-0.006588) \| 0.003158 / 0.011008 (-0.007850) \| 0.047813 / 0.038508 (0.009305) \| 0.053363 / 0.023109 (0.030254) \| 0.278570 / 0.275898 (0.002671) \| 0.291500 / 0.323480 (-0.031980) \| 0.003987 / 0.007986 (-0.003998) \| 0.002430 / 0.004328 (-0.001898) \| 0.048059 / 0.004250 (0.043809) \| 0.038595 / 0.037052 (0.001542) \| 0.276383 / 0.258489 (0.017894) \| 0.304234 / 0.293841 (0.010393) \| 0.024402 / 0.128546 (-0.104144) \| 0.007303 / 0.075646 (-0.068343) \| 0.055091 / 0.419271 (-0.364180) \| 0.032735 / 0.043533 (-0.010797) \| 0.270905 / 0.255139 (0.015766) \| 0.287181 / 0.283200 (0.003981) \| 0.018919 / 0.141683 (-0.122764) \| 1.153814 / 1.452155 (-0.298341) \| 1.197009 / 1.492716 (-0.295707) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093743 / 0.018006 (0.075737) \| 0.302877 / 0.000490 (0.302387) \| 0.000223 / 0.000200 (0.000023) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021279 / 0.037411 (-0.016133) \| 0.070886 / 0.014526 (0.056360) \| 0.081628 / 0.176557 (-0.094928) \| 0.119721 / 0.737135 (-0.617414) \| 0.083093 / 0.296338 (-0.213245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297788 / 0.215209 (0.082579) \| 2.915235 / 2.077655 (0.837580) \| 1.587580 / 1.504120 (0.083460) \| 1.461699 / 1.541195 (-0.079495) \| 1.520609 / 1.468490 (0.052119) \| 0.398363 / 4.584777 (-4.186413) \| 2.408415 / 3.745712 (-1.337297) \| 2.552776 / 5.269862 (-2.717086) \| 1.508219 / 4.565676 (-3.057457) \| 0.045884 / 0.424275 (-0.378391) \| 0.004842 / 0.007607 (-0.002765) \| 0.341376 / 0.226044 (0.115331) \| 3.420192 / 2.268929 (1.151264) \| 1.974938 / 55.444624 (-53.469686) \| 1.678283 / 6.876477 (-5.198194) \| 1.702439 / 2.142072 (-0.439633) \| 0.467056 / 4.805227 (-4.338172) \| 0.098684 / 6.500664 (-6.401980) \| 0.041052 / 0.075469 (-0.034417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990145 / 1.841788 (-0.851643) \| 12.143198 / 8.074308 (4.068890) \| 10.911039 / 10.191392 (0.719647) \| 0.130384 / 0.680424 (-0.550040) \| 0.015602 / 0.534201 (-0.518599) \| 0.270799 / 0.579283 (-0.308484) \| 0.279060 / 0.434364 (-0.155304) \| 0.315108 / 0.540337 (-0.225230) \| 0.413576 / 1.386936 (-0.973360) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d99b8225e28cca88ed9c2d9b1d8e0342762c4ece \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004911 / 0.011353 (-0.006442) \| 0.002808 / 0.011008 (-0.008200) \| 0.061367 / 0.038508 (0.022859) \| 0.050154 / 0.023109 (0.027045) \| 0.250403 / 0.275898 (-0.025495) \| 0.273831 / 0.323480 (-0.049649) \| 0.002914 / 0.007986 (-0.005071) \| 0.002493 / 0.004328 (-0.001836) \| 0.048288 / 0.004250 (0.044037) \| 0.039219 / 0.037052 (0.002167) \| 0.260043 / 0.258489 (0.001554) \| 0.288177 / 0.293841 (-0.005664) \| 0.023123 / 0.128546 (-0.105423) \| 0.006981 / 0.075646 (-0.068666) \| 0.201306 / 0.419271 (-0.217965) \| 0.035670 / 0.043533 (-0.007863) \| 0.255237 / 0.255139 (0.000098) \| 0.283701 / 0.283200 (0.000502) \| 0.019349 / 0.141683 (-0.122334) \| 1.100963 / 1.452155 (-0.351192) \| 1.152725 / 1.492716 (-0.339992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.106350 / 0.018006 (0.088344) \| 0.300577 / 0.000490 (0.300087) \| 0.000206 / 0.000200 (0.000006) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019028 / 0.037411 (-0.018384) \| 0.062643 / 0.014526 (0.048118) \| 0.072771 / 0.176557 (-0.103786) \| 0.119873 / 0.737135 (-0.617263) \| 0.074470 / 0.296338 (-0.221869) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287032 / 0.215209 (0.071823) \| 2.826134 / 2.077655 (0.748480) \| 1.507362 / 1.504120 (0.003242) \| 1.382929 / 1.541195 (-0.158266) \| 1.385361 / 1.468490 (-0.083129) \| 0.412081 / 4.584777 (-4.172696) \| 2.384289 / 3.745712 (-1.361423) \| 2.551316 / 5.269862 (-2.718546) \| 1.562954 / 4.565676 (-3.002722) \| 0.046669 / 0.424275 (-0.377606) \| 0.004804 / 0.007607 (-0.002803) \| 0.337751 / 0.226044 (0.111707) \| 3.378894 / 2.268929 (1.109965) \| 1.848817 / 55.444624 (-53.595807) \| 1.564560 / 6.876477 (-5.311917) \| 1.579577 / 2.142072 (-0.562496) \| 0.484531 / 4.805227 (-4.320697) \| 0.101157 / 6.500664 (-6.399507) \| 0.042272 / 0.075469 (-0.033197) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948289 / 1.841788 (-0.893498) \| 11.490877 / 8.074308 (3.416569) \| 10.492787 / 10.191392 (0.301395) \| 0.128575 / 0.680424 (-0.551849) \| 0.013716 / 0.534201 (-0.520485) \| 0.271075 / 0.579283 (-0.308208) \| 0.269749 / 0.434364 (-0.164615) \| 0.306378 / 0.540337 (-0.233959) \| 0.400204 / 1.386936 (-0.986732) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004821 / 0.011353 (-0.006532) \| 0.002773 / 0.011008 (-0.008235) \| 0.048934 / 0.038508 (0.010426) \| 0.049490 / 0.023109 (0.026380) \| 0.271107 / 0.275898 (-0.004791) \| 0.291472 / 0.323480 (-0.032008) \| 0.004734 / 0.007986 (-0.003252) \| 0.002437 / 0.004328 (-0.001892) \| 0.048840 / 0.004250 (0.044590) \| 0.039757 / 0.037052 (0.002704) \| 0.276037 / 0.258489 (0.017548) \| 0.298220 / 0.293841 (0.004379) \| 0.024595 / 0.128546 (-0.103952) \| 0.007320 / 0.075646 (-0.068327) \| 0.054693 / 0.419271 (-0.364578) \| 0.032672 / 0.043533 (-0.010861) \| 0.271555 / 0.255139 (0.016416) \| 0.287685 / 0.283200 (0.004485) \| 0.017159 / 0.141683 (-0.124524) \| 1.118496 / 1.452155 (-0.333659) \| 1.177389 / 1.492716 (-0.315327) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090469 / 0.018006 (0.072463) \| 0.306014 / 0.000490 (0.305525) \| 0.000218 / 0.000200 (0.000018) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021452 / 0.037411 (-0.015960) \| 0.070014 / 0.014526 (0.055488) \| 0.081917 / 0.176557 (-0.094639) \| 0.120615 / 0.737135 (-0.616520) \| 0.081745 / 0.296338 (-0.214593) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294049 / 0.215209 (0.078840) \| 2.886802 / 2.077655 (0.809147) \| 1.607817 / 1.504120 (0.103697) \| 1.474172 / 1.541195 (-0.067023) \| 1.474744 / 1.468490 (0.006254) \| 0.398178 / 4.584777 (-4.186599) \| 2.455908 / 3.745712 (-1.289804) \| 2.463003 / 5.269862 (-2.806858) \| 1.560402 / 4.565676 (-3.005275) \| 0.046208 / 0.424275 (-0.378067) \| 0.004862 / 0.007607 (-0.002745) \| 0.350862 / 0.226044 (0.124817) \| 3.463958 / 2.268929 (1.195030) \| 1.934696 / 55.444624 (-53.509928) \| 1.660090 / 6.876477 (-5.216387) \| 1.770920 / 2.142072 (-0.371153) \| 0.468409 / 4.805227 (-4.336819) \| 0.096812 / 6.500664 (-6.403852) \| 0.040580 / 0.075469 (-0.034889) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978102 / 1.841788 (-0.863686) \| 11.943265 / 8.074308 (3.868957) \| 10.684995 / 10.191392 (0.493603) \| 0.131554 / 0.680424 (-0.548870) \| 0.015608 / 0.534201 (-0.518593) \| 0.271449 / 0.579283 (-0.307834) \| 0.282485 / 0.434364 (-0.151879) \| 0.302376 / 0.540337 (-0.237962) \| 0.524908 / 1.386936 (-0.862028) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bb0b21e37a57257a7d428f8744c862ca92c0c7e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004926 / 0.011353 (-0.006427) \| 0.003020 / 0.011008 (-0.007988) \| 0.061899 / 0.038508 (0.023391) \| 0.063836 / 0.023109 (0.040726) \| 0.239252 / 0.275898 (-0.036646) \| 0.268320 / 0.323480 (-0.055160) \| 0.003939 / 0.007986 (-0.004046) \| 0.002557 / 0.004328 (-0.001772) \| 0.048469 / 0.004250 (0.044219) \| 0.038707 / 0.037052 (0.001655) \| 0.247563 / 0.258489 (-0.010926) \| 0.281171 / 0.293841 (-0.012670) \| 0.023564 / 0.128546 (-0.104983) \| 0.007699 / 0.075646 (-0.067948) \| 0.207561 / 0.419271 (-0.211710) \| 0.036362 / 0.043533 (-0.007171) \| 0.248324 / 0.255139 (-0.006814) \| 0.269673 / 0.283200 (-0.013527) \| 0.018841 / 0.141683 (-0.122842) \| 1.123407 / 1.452155 (-0.328748) \| 1.170422 / 1.492716 (-0.322295) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096278 / 0.018006 (0.078272) \| 0.311477 / 0.000490 (0.310988) \| 0.000217 / 0.000200 (0.000017) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019470 / 0.037411 (-0.017942) \| 0.071888 / 0.014526 (0.057362) \| 0.074264 / 0.176557 (-0.102292) \| 0.124413 / 0.737135 (-0.612723) \| 0.075602 / 0.296338 (-0.220737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284755 / 0.215209 (0.069546) \| 2.770789 / 2.077655 (0.693135) \| 1.478276 / 1.504120 (-0.025843) \| 1.375287 / 1.541195 (-0.165907) \| 1.398032 / 1.468490 (-0.070458) \| 0.420457 / 4.584777 (-4.164320) \| 2.445929 / 3.745712 (-1.299783) \| 2.819548 / 5.269862 (-2.450313) \| 1.628506 / 4.565676 (-2.937171) \| 0.047687 / 0.424275 (-0.376588) \| 0.004861 / 0.007607 (-0.002746) \| 0.340173 / 0.226044 (0.114129) \| 3.340703 / 2.268929 (1.071774) \| 1.882803 / 55.444624 (-53.561821) \| 1.587206 / 6.876477 (-5.289271) \| 1.645298 / 2.142072 (-0.496774) \| 0.490957 / 4.805227 (-4.314270) \| 0.102779 / 6.500664 (-6.397885) \| 0.048372 / 0.075469 (-0.027098) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.958311 / 1.841788 (-0.883477) \| 12.354981 / 8.074308 (4.280673) \| 10.864826 / 10.191392 (0.673434) \| 0.149053 / 0.680424 (-0.531371) \| 0.015078 / 0.534201 (-0.519123) \| 0.270117 / 0.579283 (-0.309166) \| 0.274495 / 0.434364 (-0.159869) \| 0.307584 / 0.540337 (-0.232753) \| 0.405603 / 1.386936 (-0.981333) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004996 / 0.011353 (-0.006357) \| 0.002995 / 0.011008 (-0.008014) \| 0.047897 / 0.038508 (0.009389) \| 0.056413 / 0.023109 (0.033303) \| 0.277669 / 0.275898 (0.001771) \| 0.300679 / 0.323480 (-0.022801) \| 0.004094 / 0.007986 (-0.003892) \| 0.002519 / 0.004328 (-0.001810) \| 0.049536 / 0.004250 (0.045285) \| 0.042341 / 0.037052 (0.005288) \| 0.281533 / 0.258489 (0.023044) \| 0.306771 / 0.293841 (0.012930) \| 0.025379 / 0.128546 (-0.103167) \| 0.007495 / 0.075646 (-0.068152) \| 0.054453 / 0.419271 (-0.364818) \| 0.032616 / 0.043533 (-0.010917) \| 0.277844 / 0.255139 (0.022705) \| 0.296265 / 0.283200 (0.013065) \| 0.019462 / 0.141683 (-0.122221) \| 1.115841 / 1.452155 (-0.336313) \| 1.169662 / 1.492716 (-0.323054) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095459 / 0.018006 (0.077453) \| 0.301590 / 0.000490 (0.301100) \| 0.000230 / 0.000200 (0.000030) \| 0.000061 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022182 / 0.037411 (-0.015229) \| 0.085367 / 0.014526 (0.070842) \| 0.084006 / 0.176557 (-0.092550) \| 0.121260 / 0.737135 (-0.615876) \| 0.084137 / 0.296338 (-0.212202) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310335 / 0.215209 (0.095126) \| 3.002531 / 2.077655 (0.924876) \| 1.642282 / 1.504120 (0.138162) \| 1.573044 / 1.541195 (0.031849) \| 1.572076 / 1.468490 (0.103586) \| 0.422037 / 4.584777 (-4.162740) \| 2.495295 / 3.745712 (-1.250417) \| 2.523707 / 5.269862 (-2.746155) \| 1.725824 / 4.565676 (-2.839853) \| 0.047814 / 0.424275 (-0.376461) \| 0.004868 / 0.007607 (-0.002739) \| 0.352833 / 0.226044 (0.126789) \| 3.477241 / 2.268929 (1.208313) \| 1.983888 / 55.444624 (-53.460736) \| 1.696883 / 6.876477 (-5.179594) \| 1.831665 / 2.142072 (-0.310407) \| 0.502976 / 4.805227 (-4.302251) \| 0.101264 / 6.500664 (-6.399400) \| 0.041779 / 0.075469 (-0.033690) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981629 / 1.841788 (-0.860159) \| 12.550634 / 8.074308 (4.476326) \| 11.113382 / 10.191392 (0.921990) \| 0.136565 / 0.680424 (-0.543859) \| 0.016742 / 0.534201 (-0.517459) \| 0.274316 / 0.579283 (-0.304967) \| 0.284687 / 0.434364 (-0.149676) \| 0.309966 / 0.540337 (-0.230372) \| 0.557990 / 1.386936 (-0.828946) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0c30facb87af83107a645eeffcd18c0775afe11 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004980 / 0.011353 (-0.006373) \| 0.002786 / 0.011008 (-0.008222) \| 0.062460 / 0.038508 (0.023952) \| 0.051811 / 0.023109 (0.028702) \| 0.231734 / 0.275898 (-0.044164) \| 0.254075 / 0.323480 (-0.069405) \| 0.002884 / 0.007986 (-0.005102) \| 0.002317 / 0.004328 (-0.002011) \| 0.049044 / 0.004250 (0.044793) \| 0.038984 / 0.037052 (0.001931) \| 0.241193 / 0.258489 (-0.017296) \| 0.272091 / 0.293841 (-0.021750) \| 0.023098 / 0.128546 (-0.105448) \| 0.007190 / 0.075646 (-0.068456) \| 0.201409 / 0.419271 (-0.217863) \| 0.036100 / 0.043533 (-0.007433) \| 0.238185 / 0.255139 (-0.016954) \| 0.257127 / 0.283200 (-0.026072) \| 0.019542 / 0.141683 (-0.122141) \| 1.127925 / 1.452155 (-0.324230) \| 1.174354 / 1.492716 (-0.318362) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099608 / 0.018006 (0.081601) \| 0.315046 / 0.000490 (0.314556) \| 0.000282 / 0.000200 (0.000082) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018710 / 0.037411 (-0.018701) \| 0.062557 / 0.014526 (0.048031) \| 0.074021 / 0.176557 (-0.102536) \| 0.119670 / 0.737135 (-0.617465) \| 0.076491 / 0.296338 (-0.219847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282940 / 0.215209 (0.067731) \| 2.788542 / 2.077655 (0.710887) \| 1.496039 / 1.504120 (-0.008080) \| 1.367542 / 1.541195 (-0.173653) \| 1.393705 / 1.468490 (-0.074785) \| 0.405910 / 4.584777 (-4.178867) \| 2.422544 / 3.745712 (-1.323168) \| 2.602822 / 5.269862 (-2.667039) \| 1.586853 / 4.565676 (-2.978823) \| 0.045440 / 0.424275 (-0.378836) \| 0.004792 / 0.007607 (-0.002815) \| 0.342059 / 0.226044 (0.116015) \| 3.366880 / 2.268929 (1.097952) \| 1.810566 / 55.444624 (-53.634058) \| 1.527112 / 6.876477 (-5.349364) \| 1.548906 / 2.142072 (-0.593166) \| 0.479491 / 4.805227 (-4.325736) \| 0.099807 / 6.500664 (-6.400857) \| 0.041951 / 0.075469 (-0.033518) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953723 / 1.841788 (-0.888065) \| 11.837240 / 8.074308 (3.762932) \| 10.562979 / 10.191392 (0.371587) \| 0.145064 / 0.680424 (-0.535360) \| 0.014285 / 0.534201 (-0.519916) \| 0.270605 / 0.579283 (-0.308678) \| 0.264086 / 0.434364 (-0.170278) \| 0.308000 / 0.540337 (-0.232337) \| 0.403916 / 1.386936 (-0.983020) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004796 / 0.011353 (-0.006557) \| 0.002997 / 0.011008 (-0.008011) \| 0.048702 / 0.038508 (0.010193) \| 0.053377 / 0.023109 (0.030267) \| 0.271852 / 0.275898 (-0.004046) \| 0.293366 / 0.323480 (-0.030114) \| 0.004041 / 0.007986 (-0.003945) \| 0.002459 / 0.004328 (-0.001869) \| 0.048197 / 0.004250 (0.043947) \| 0.040094 / 0.037052 (0.003042) \| 0.275837 / 0.258489 (0.017348) \| 0.301174 / 0.293841 (0.007333) \| 0.024433 / 0.128546 (-0.104113) \| 0.007203 / 0.075646 (-0.068444) \| 0.054080 / 0.419271 (-0.365192) \| 0.033237 / 0.043533 (-0.010295) \| 0.271177 / 0.255139 (0.016038) \| 0.293062 / 0.283200 (0.009862) \| 0.018399 / 0.141683 (-0.123284) \| 1.149527 / 1.452155 (-0.302628) \| 1.202717 / 1.492716 (-0.290000) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093168 / 0.018006 (0.075162) \| 0.290536 / 0.000490 (0.290046) \| 0.000290 / 0.000200 (0.000090) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021191 / 0.037411 (-0.016221) \| 0.069990 / 0.014526 (0.055465) \| 0.080636 / 0.176557 (-0.095920) \| 0.120151 / 0.737135 (-0.616984) \| 0.082944 / 0.296338 (-0.213395) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289673 / 0.215209 (0.074463) \| 2.828419 / 2.077655 (0.750764) \| 1.590741 / 1.504120 (0.086621) \| 1.480969 / 1.541195 (-0.060226) \| 1.512761 / 1.468490 (0.044271) \| 0.398328 / 4.584777 (-4.186449) \| 2.441134 / 3.745712 (-1.304578) \| 2.487606 / 5.269862 (-2.782256) \| 1.586604 / 4.565676 (-2.979073) \| 0.045578 / 0.424275 (-0.378697) \| 0.004842 / 0.007607 (-0.002766) \| 0.344556 / 0.226044 (0.118512) \| 3.395982 / 2.268929 (1.127053) \| 1.963354 / 55.444624 (-53.481271) \| 1.680496 / 6.876477 (-5.195980) \| 1.827916 / 2.142072 (-0.314157) \| 0.476203 / 4.805227 (-4.329024) \| 0.098016 / 6.500664 (-6.402648) \| 0.041234 / 0.075469 (-0.034235) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977820 / 1.841788 (-0.863968) \| 12.139614 / 8.074308 (4.065306) \| 10.643071 / 10.191392 (0.451679) \| 0.130928 / 0.680424 (-0.549496) \| 0.015341 / 0.534201 (-0.518860) \| 0.271304 / 0.579283 (-0.307979) \| 0.284671 / 0.434364 (-0.149693) \| 0.306210 / 0.540337 (-0.234128) \| 0.546498 / 1.386936 (-0.840438) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bf7408a171db4a744d1760a9e32ba21deb8d41d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004748 / 0.011353 (-0.006605) \| 0.002942 / 0.011008 (-0.008066) \| 0.061298 / 0.038508 (0.022790) \| 0.052873 / 0.023109 (0.029764) \| 0.250573 / 0.275898 (-0.025325) \| 0.270636 / 0.323480 (-0.052844) \| 0.002925 / 0.007986 (-0.005061) \| 0.003126 / 0.004328 (-0.001203) \| 0.047340 / 0.004250 (0.043090) \| 0.038662 / 0.037052 (0.001609) \| 0.252151 / 0.258489 (-0.006338) \| 0.284700 / 0.293841 (-0.009141) \| 0.025145 / 0.128546 (-0.103402) \| 0.007075 / 0.075646 (-0.068572) \| 0.200501 / 0.419271 (-0.218771) \| 0.035623 / 0.043533 (-0.007910) \| 0.249657 / 0.255139 (-0.005482) \| 0.272384 / 0.283200 (-0.010815) \| 0.018331 / 0.141683 (-0.123351) \| 1.095064 / 1.452155 (-0.357091) \| 1.145304 / 1.492716 (-0.347412) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092548 / 0.018006 (0.074542) \| 0.299338 / 0.000490 (0.298848) \| 0.000212 / 0.000200 (0.000012) \| 0.000046 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018723 / 0.037411 (-0.018688) \| 0.062226 / 0.014526 (0.047700) \| 0.072840 / 0.176557 (-0.103717) \| 0.120073 / 0.737135 (-0.617063) \| 0.074536 / 0.296338 (-0.221802) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284862 / 0.215209 (0.069653) \| 2.791842 / 2.077655 (0.714188) \| 1.506481 / 1.504120 (0.002361) \| 1.368952 / 1.541195 (-0.172243) \| 1.372555 / 1.468490 (-0.095935) \| 0.408292 / 4.584777 (-4.176485) \| 2.381155 / 3.745712 (-1.364558) \| 2.613617 / 5.269862 (-2.656244) \| 1.575892 / 4.565676 (-2.989785) \| 0.047526 / 0.424275 (-0.376749) \| 0.004792 / 0.007607 (-0.002815) \| 0.344818 / 0.226044 (0.118773) \| 3.344965 / 2.268929 (1.076036) \| 1.883659 / 55.444624 (-53.560965) \| 1.596039 / 6.876477 (-5.280437) \| 1.584410 / 2.142072 (-0.557662) \| 0.486672 / 4.805227 (-4.318555) \| 0.101464 / 6.500664 (-6.399200) \| 0.041824 / 0.075469 (-0.033645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930491 / 1.841788 (-0.911296) \| 11.636526 / 8.074308 (3.562218) \| 10.371829 / 10.191392 (0.180437) \| 0.138181 / 0.680424 (-0.542243) \| 0.014307 / 0.534201 (-0.519894) \| 0.268322 / 0.579283 (-0.310961) \| 0.264173 / 0.434364 (-0.170191) \| 0.303649 / 0.540337 (-0.236688) \| 0.399958 / 1.386936 (-0.986978) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004802 / 0.011353 (-0.006551) \| 0.002861 / 0.011008 (-0.008147) \| 0.048843 / 0.038508 (0.010335) \| 0.053887 / 0.023109 (0.030778) \| 0.278690 / 0.275898 (0.002792) \| 0.302729 / 0.323480 (-0.020751) \| 0.003929 / 0.007986 (-0.004057) \| 0.002376 / 0.004328 (-0.001953) \| 0.048146 / 0.004250 (0.043896) \| 0.039842 / 0.037052 (0.002790) \| 0.281595 / 0.258489 (0.023106) \| 0.305813 / 0.293841 (0.011972) \| 0.024214 / 0.128546 (-0.104333) \| 0.007201 / 0.075646 (-0.068446) \| 0.053604 / 0.419271 (-0.365667) \| 0.032841 / 0.043533 (-0.010691) \| 0.276168 / 0.255139 (0.021029) \| 0.293869 / 0.283200 (0.010669) \| 0.017550 / 0.141683 (-0.124132) \| 1.121508 / 1.452155 (-0.330647) \| 1.177694 / 1.492716 (-0.315022) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091805 / 0.018006 (0.073799) \| 0.299026 / 0.000490 (0.298536) \| 0.000219 / 0.000200 (0.000019) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021094 / 0.037411 (-0.016318) \| 0.069769 / 0.014526 (0.055243) \| 0.081191 / 0.176557 (-0.095366) \| 0.118884 / 0.737135 (-0.618252) \| 0.081955 / 0.296338 (-0.214383) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292159 / 0.215209 (0.076950) \| 2.874473 / 2.077655 (0.796819) \| 1.614695 / 1.504120 (0.110575) \| 1.492123 / 1.541195 (-0.049071) \| 1.505293 / 1.468490 (0.036803) \| 0.394498 / 4.584777 (-4.190279) \| 2.455539 / 3.745712 (-1.290173) \| 2.458184 / 5.269862 (-2.811677) \| 1.569108 / 4.565676 (-2.996569) \| 0.046576 / 0.424275 (-0.377699) \| 0.005093 / 0.007607 (-0.002514) \| 0.346142 / 0.226044 (0.120098) \| 3.398171 / 2.268929 (1.129242) \| 1.971953 / 55.444624 (-53.472672) \| 1.695275 / 6.876477 (-5.181201) \| 1.840511 / 2.142072 (-0.301562) \| 0.465932 / 4.805227 (-4.339295) \| 0.098578 / 6.500664 (-6.402086) \| 0.040456 / 0.075469 (-0.035013) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977636 / 1.841788 (-0.864152) \| 12.083585 / 8.074308 (4.009277) \| 10.509082 / 10.191392 (0.317690) \| 0.130717 / 0.680424 (-0.549707) \| 0.015958 / 0.534201 (-0.518243) \| 0.273504 / 0.579283 (-0.305780) \| 0.276498 / 0.434364 (-0.157866) \| 0.306139 / 0.540337 (-0.234199) \| 0.522521 / 1.386936 (-0.864415) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e17dd8acec9a958ba82a5f753276b842eaadf52 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004859 / 0.011353 (-0.006493) \| 0.002423 / 0.011008 (-0.008585) \| 0.060969 / 0.038508 (0.022461) \| 0.048758 / 0.023109 (0.025649) \| 0.245400 / 0.275898 (-0.030498) \| 0.263686 / 0.323480 (-0.059794) \| 0.002852 / 0.007986 (-0.005134) \| 0.002273 / 0.004328 (-0.002055) \| 0.047648 / 0.004250 (0.043398) \| 0.038310 / 0.037052 (0.001258) \| 0.249849 / 0.258489 (-0.008640) \| 0.279305 / 0.293841 (-0.014536) \| 0.022897 / 0.128546 (-0.105649) \| 0.006882 / 0.075646 (-0.068764) \| 0.202793 / 0.419271 (-0.216478) \| 0.034557 / 0.043533 (-0.008976) \| 0.252147 / 0.255139 (-0.002992) \| 0.267414 / 0.283200 (-0.015785) \| 0.019956 / 0.141683 (-0.121727) \| 1.106181 / 1.452155 (-0.345973) \| 1.158423 / 1.492716 (-0.334293) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.086848 / 0.018006 (0.068842) \| 0.295235 / 0.000490 (0.294745) \| 0.000211 / 0.000200 (0.000011) \| 0.000041 / 0.000054 (-0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018209 / 0.037411 (-0.019203) \| 0.061967 / 0.014526 (0.047441) \| 0.071551 / 0.176557 (-0.105005) \| 0.117525 / 0.737135 (-0.619611) \| 0.073401 / 0.296338 (-0.222937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272388 / 0.215209 (0.057179) \| 2.689797 / 2.077655 (0.612143) \| 1.440897 / 1.504120 (-0.063223) \| 1.334689 / 1.541195 (-0.206505) \| 1.356395 / 1.468490 (-0.112095) \| 0.387201 / 4.584777 (-4.197576) \| 2.342908 / 3.745712 (-1.402804) \| 2.480156 / 5.269862 (-2.789706) \| 1.512342 / 4.565676 (-3.053335) \| 0.042324 / 0.424275 (-0.381951) \| 0.004744 / 0.007607 (-0.002863) \| 0.323568 / 0.226044 (0.097523) \| 3.190021 / 2.268929 (0.921093) \| 1.765046 / 55.444624 (-53.679578) \| 1.513958 / 6.876477 (-5.362519) \| 1.504943 / 2.142072 (-0.637129) \| 0.452302 / 4.805227 (-4.352925) \| 0.094728 / 6.500664 (-6.405936) \| 0.038641 / 0.075469 (-0.036828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939721 / 1.841788 (-0.902067) \| 11.174180 / 8.074308 (3.099872) \| 10.046717 / 10.191392 (-0.144675) \| 0.124877 / 0.680424 (-0.555547) \| 0.013687 / 0.534201 (-0.520514) \| 0.261002 / 0.579283 (-0.318282) \| 0.267349 / 0.434364 (-0.167015) \| 0.306545 / 0.540337 (-0.233792) \| 0.389322 / 1.386936 (-0.997614) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004702 / 0.011353 (-0.006651) \| 0.002431 / 0.011008 (-0.008577) \| 0.046138 / 0.038508 (0.007630) \| 0.048356 / 0.023109 (0.025246) \| 0.272154 / 0.275898 (-0.003744) \| 0.292676 / 0.323480 (-0.030804) \| 0.003870 / 0.007986 (-0.004115) \| 0.002294 / 0.004328 (-0.002035) \| 0.048129 / 0.004250 (0.043879) \| 0.039026 / 0.037052 (0.001974) \| 0.273900 / 0.258489 (0.015411) \| 0.295927 / 0.293841 (0.002086) \| 0.024044 / 0.128546 (-0.104502) \| 0.006906 / 0.075646 (-0.068740) \| 0.053268 / 0.419271 (-0.366004) \| 0.032360 / 0.043533 (-0.011173) \| 0.273470 / 0.255139 (0.018331) \| 0.286207 / 0.283200 (0.003007) \| 0.017580 / 0.141683 (-0.124103) \| 1.091064 / 1.452155 (-0.361091) \| 1.159645 / 1.492716 (-0.333071) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087149 / 0.018006 (0.069143) \| 0.293489 / 0.000490 (0.293000) \| 0.000217 / 0.000200 (0.000017) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021779 / 0.037411 (-0.015632) \| 0.066453 / 0.014526 (0.051928) \| 0.078517 / 0.176557 (-0.098039) \| 0.117317 / 0.737135 (-0.619819) \| 0.079828 / 0.296338 (-0.216511) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287605 / 0.215209 (0.072396) \| 2.811094 / 2.077655 (0.733439) \| 1.572474 / 1.504120 (0.068354) \| 1.450294 / 1.541195 (-0.090900) \| 1.456052 / 1.468490 (-0.012438) \| 0.402095 / 4.584777 (-4.182682) \| 2.444709 / 3.745712 (-1.301003) \| 2.390837 / 5.269862 (-2.879024) \| 1.530519 / 4.565676 (-3.035157) \| 0.043520 / 0.424275 (-0.380755) \| 0.004788 / 0.007607 (-0.002819) \| 0.337436 / 0.226044 (0.111391) \| 3.326111 / 2.268929 (1.057182) \| 1.889273 / 55.444624 (-53.555352) \| 1.624423 / 6.876477 (-5.252054) \| 1.715766 / 2.142072 (-0.426307) \| 0.484570 / 4.805227 (-4.320657) \| 0.091691 / 6.500664 (-6.408973) \| 0.038278 / 0.075469 (-0.037191) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961708 / 1.841788 (-0.880079) \| 11.496471 / 8.074308 (3.422162) \| 10.211589 / 10.191392 (0.020197) \| 0.127584 / 0.680424 (-0.552840) \| 0.015178 / 0.534201 (-0.519023) \| 0.267290 / 0.579283 (-0.311993) \| 0.259305 / 0.434364 (-0.175059) \| 0.303433 / 0.540337 (-0.236905) \| 0.508016 / 1.386936 (-0.878920) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72880aa8a3e4b49438db72b13fb9a2541331820b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004558 / 0.011353 (-0.006795) \| 0.002563 / 0.011008 (-0.008445) \| 0.061314 / 0.038508 (0.022806) \| 0.049312 / 0.023109 (0.026203) \| 0.240988 / 0.275898 (-0.034910) \| 0.260548 / 0.323480 (-0.062932) \| 0.002817 / 0.007986 (-0.005169) \| 0.002904 / 0.004328 (-0.001425) \| 0.048515 / 0.004250 (0.044264) \| 0.037511 / 0.037052 (0.000459) \| 0.244880 / 0.258489 (-0.013609) \| 0.276118 / 0.293841 (-0.017723) \| 0.022636 / 0.128546 (-0.105910) \| 0.006694 / 0.075646 (-0.068953) \| 0.201336 / 0.419271 (-0.217936) \| 0.035228 / 0.043533 (-0.008305) \| 0.242424 / 0.255139 (-0.012715) \| 0.260178 / 0.283200 (-0.023022) \| 0.017836 / 0.141683 (-0.123847) \| 1.122296 / 1.452155 (-0.329859) \| 1.189024 / 1.492716 (-0.303692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090051 / 0.018006 (0.072045) \| 0.298562 / 0.000490 (0.298073) \| 0.000216 / 0.000200 (0.000016) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018228 / 0.037411 (-0.019184) \| 0.062379 / 0.014526 (0.047853) \| 0.073482 / 0.176557 (-0.103075) \| 0.120341 / 0.737135 (-0.616794) \| 0.073868 / 0.296338 (-0.222470) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280195 / 0.215209 (0.064986) \| 2.743333 / 2.077655 (0.665678) \| 1.470078 / 1.504120 (-0.034042) \| 1.335874 / 1.541195 (-0.205321) \| 1.342961 / 1.468490 (-0.125529) \| 0.409203 / 4.584777 (-4.175574) \| 2.392217 / 3.745712 (-1.353495) \| 2.544161 / 5.269862 (-2.725701) \| 1.544016 / 4.565676 (-3.021660) \| 0.059485 / 0.424275 (-0.364790) \| 0.004833 / 0.007607 (-0.002775) \| 0.335114 / 0.226044 (0.109070) \| 3.289009 / 2.268929 (1.020080) \| 1.854666 / 55.444624 (-53.589959) \| 1.566282 / 6.876477 (-5.310195) \| 1.561287 / 2.142072 (-0.580786) \| 0.484961 / 4.805227 (-4.320267) \| 0.099651 / 6.500664 (-6.401013) \| 0.041408 / 0.075469 (-0.034061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941743 / 1.841788 (-0.900044) \| 11.165692 / 8.074308 (3.091383) \| 10.236693 / 10.191392 (0.045301) \| 0.129694 / 0.680424 (-0.550730) \| 0.014879 / 0.534201 (-0.519322) \| 0.275120 / 0.579283 (-0.304163) \| 0.263822 / 0.434364 (-0.170542) \| 0.306429 / 0.540337 (-0.233909) \| 0.397611 / 1.386936 (-0.989325) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004714 / 0.011353 (-0.006639) \| 0.002430 / 0.011008 (-0.008578) \| 0.047644 / 0.038508 (0.009136) \| 0.049710 / 0.023109 (0.026601) \| 0.271950 / 0.275898 (-0.003948) \| 0.290996 / 0.323480 (-0.032483) \| 0.003888 / 0.007986 (-0.004097) \| 0.002367 / 0.004328 (-0.001962) \| 0.047623 / 0.004250 (0.043372) \| 0.039574 / 0.037052 (0.002522) \| 0.274540 / 0.258489 (0.016051) \| 0.298065 / 0.293841 (0.004224) \| 0.024677 / 0.128546 (-0.103869) \| 0.006844 / 0.075646 (-0.068802) \| 0.053180 / 0.419271 (-0.366091) \| 0.032391 / 0.043533 (-0.011141) \| 0.273222 / 0.255139 (0.018083) \| 0.290336 / 0.283200 (0.007136) \| 0.017911 / 0.141683 (-0.123772) \| 1.105879 / 1.452155 (-0.346276) \| 1.176979 / 1.492716 (-0.315737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089563 / 0.018006 (0.071557) \| 0.296392 / 0.000490 (0.295903) \| 0.000214 / 0.000200 (0.000014) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021588 / 0.037411 (-0.015824) \| 0.069951 / 0.014526 (0.055425) \| 0.080397 / 0.176557 (-0.096160) \| 0.118772 / 0.737135 (-0.618363) \| 0.080356 / 0.296338 (-0.215983) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288492 / 0.215209 (0.073283) \| 2.839553 / 2.077655 (0.761898) \| 1.597504 / 1.504120 (0.093384) \| 1.475001 / 1.541195 (-0.066193) \| 1.481561 / 1.468490 (0.013071) \| 0.411851 / 4.584777 (-4.172926) \| 2.397322 / 3.745712 (-1.348390) \| 2.444078 / 5.269862 (-2.825784) \| 1.557106 / 4.565676 (-3.008571) \| 0.047159 / 0.424275 (-0.377116) \| 0.004842 / 0.007607 (-0.002765) \| 0.346221 / 0.226044 (0.120177) \| 3.387900 / 2.268929 (1.118972) \| 1.962167 / 55.444624 (-53.482457) \| 1.675017 / 6.876477 (-5.201460) \| 1.788745 / 2.142072 (-0.353328) \| 0.488063 / 4.805227 (-4.317164) \| 0.098878 / 6.500664 (-6.401786) \| 0.040369 / 0.075469 (-0.035100) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977999 / 1.841788 (-0.863789) \| 11.671558 / 8.074308 (3.597250) \| 10.327847 / 10.191392 (0.136455) \| 0.129317 / 0.680424 (-0.551107) \| 0.015600 / 0.534201 (-0.518601) \| 0.267967 / 0.579283 (-0.311316) \| 0.273811 / 0.434364 (-0.160553) \| 0.301749 / 0.540337 (-0.238588) \| 0.515493 / 1.386936 (-0.871443) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5394939b0b3d124674f938e1f1cd9e8de3cbdbf7 \"CML watermark\")\n", "I added tests and docs @mariosasko @albertvillanova let le know what you think !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004867 / 0.011353 (-0.006486) \| 0.002952 / 0.011008 (-0.008056) \| 0.062008 / 0.038508 (0.023500) \| 0.055279 / 0.023109 (0.032170) \| 0.248160 / 0.275898 (-0.027738) \| 0.276173 / 0.323480 (-0.047307) \| 0.003945 / 0.007986 (-0.004041) \| 0.002371 / 0.004328 (-0.001958) \| 0.048385 / 0.004250 (0.044134) \| 0.038997 / 0.037052 (0.001945) \| 0.257465 / 0.258489 (-0.001024) \| 0.286920 / 0.293841 (-0.006921) \| 0.023031 / 0.128546 (-0.105515) \| 0.007075 / 0.075646 (-0.068571) \| 0.201897 / 0.419271 (-0.217375) \| 0.035637 / 0.043533 (-0.007896) \| 0.252050 / 0.255139 (-0.003089) \| 0.272580 / 0.283200 (-0.010620) \| 0.018578 / 0.141683 (-0.123105) \| 1.129427 / 1.452155 (-0.322727) \| 1.172182 / 1.492716 (-0.320534) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091806 / 0.018006 (0.073800) \| 0.298632 / 0.000490 (0.298143) \| 0.000202 / 0.000200 (0.000002) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019123 / 0.037411 (-0.018288) \| 0.062603 / 0.014526 (0.048077) \| 0.074352 / 0.176557 (-0.102205) \| 0.120431 / 0.737135 (-0.616704) \| 0.074622 / 0.296338 (-0.221717) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276019 / 0.215209 (0.060810) \| 2.701610 / 2.077655 (0.623955) \| 1.398388 / 1.504120 (-0.105732) \| 1.270902 / 1.541195 (-0.270292) \| 1.307992 / 1.468490 (-0.160499) \| 0.396350 / 4.584777 (-4.188427) \| 2.351064 / 3.745712 (-1.394648) \| 2.606229 / 5.269862 (-2.663632) \| 1.591075 / 4.565676 (-2.974601) \| 0.046429 / 0.424275 (-0.377846) \| 0.004832 / 0.007607 (-0.002775) \| 0.327887 / 0.226044 (0.101843) \| 3.277847 / 2.268929 (1.008918) \| 1.767210 / 55.444624 (-53.677414) \| 1.483997 / 6.876477 (-5.392479) \| 1.515689 / 2.142072 (-0.626383) \| 0.471326 / 4.805227 (-4.333902) \| 0.098821 / 6.500664 (-6.401843) \| 0.041914 / 0.075469 (-0.033555) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956278 / 1.841788 (-0.885510) \| 11.924373 / 8.074308 (3.850065) \| 10.493926 / 10.191392 (0.302534) \| 0.140214 / 0.680424 (-0.540210) \| 0.013679 / 0.534201 (-0.520522) \| 0.270304 / 0.579283 (-0.308979) \| 0.266518 / 0.434364 (-0.167846) \| 0.310113 / 0.540337 (-0.230224) \| 0.399811 / 1.386936 (-0.987125) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004793 / 0.011353 (-0.006560) \| 0.002879 / 0.011008 (-0.008130) \| 0.048632 / 0.038508 (0.010124) \| 0.051413 / 0.023109 (0.028304) \| 0.272704 / 0.275898 (-0.003194) \| 0.291541 / 0.323480 (-0.031939) \| 0.003913 / 0.007986 (-0.004072) \| 0.002387 / 0.004328 (-0.001941) \| 0.049045 / 0.004250 (0.044795) \| 0.040164 / 0.037052 (0.003112) \| 0.273052 / 0.258489 (0.014563) \| 0.300139 / 0.293841 (0.006298) \| 0.024225 / 0.128546 (-0.104321) \| 0.007060 / 0.075646 (-0.068587) \| 0.054360 / 0.419271 (-0.364911) \| 0.032882 / 0.043533 (-0.010650) \| 0.270295 / 0.255139 (0.015157) \| 0.312253 / 0.283200 (0.029054) \| 0.017413 / 0.141683 (-0.124270) \| 1.137306 / 1.452155 (-0.314849) \| 1.203705 / 1.492716 (-0.289011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091083 / 0.018006 (0.073077) \| 0.301607 / 0.000490 (0.301117) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021753 / 0.037411 (-0.015658) \| 0.069693 / 0.014526 (0.055167) \| 0.080481 / 0.176557 (-0.096075) \| 0.118581 / 0.737135 (-0.618555) \| 0.082231 / 0.296338 (-0.214108) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300014 / 0.215209 (0.084805) \| 2.885934 / 2.077655 (0.808279) \| 1.594120 / 1.504120 (0.090000) \| 1.472312 / 1.541195 (-0.068883) \| 1.491663 / 1.468490 (0.023173) \| 0.412946 / 4.584777 (-4.171831) \| 2.494168 / 3.745712 (-1.251544) \| 2.527987 / 5.269862 (-2.741875) \| 1.589187 / 4.565676 (-2.976490) \| 0.046594 / 0.424275 (-0.377681) \| 0.004810 / 0.007607 (-0.002797) \| 0.345496 / 0.226044 (0.119452) \| 3.428850 / 2.268929 (1.159921) \| 1.952696 / 55.444624 (-53.491929) \| 1.663285 / 6.876477 (-5.213191) \| 1.822187 / 2.142072 (-0.319885) \| 0.483798 / 4.805227 (-4.321430) \| 0.101403 / 6.500664 (-6.399261) \| 0.041773 / 0.075469 (-0.033696) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974247 / 1.841788 (-0.867541) \| 12.459980 / 8.074308 (4.385672) \| 10.354792 / 10.191392 (0.163400) \| 0.129083 / 0.680424 (-0.551341) \| 0.015225 / 0.534201 (-0.518976) \| 0.267673 / 0.579283 (-0.311610) \| 0.281011 / 0.434364 (-0.153352) \| 0.303054 / 0.540337 (-0.237283) \| 0.405719 / 1.386936 (-0.981217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33dc51fc1a8122b842bb7839ff0eda32f173c325 \"CML watermark\")\n", "I switched to using `deepmind/code_contests` in examples in the docs to avoid having to pass trust_remote_code, and remove the DEFAULT naming stuff :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005169 / 0.011353 (-0.006184) \| 0.003066 / 0.011008 (-0.007942) \| 0.068884 / 0.038508 (0.030376) \| 0.060345 / 0.023109 (0.037236) \| 0.243050 / 0.275898 (-0.032848) \| 0.265523 / 0.323480 (-0.057957) \| 0.002918 / 0.007986 (-0.005067) \| 0.002495 / 0.004328 (-0.001834) \| 0.051538 / 0.004250 (0.047288) \| 0.040010 / 0.037052 (0.002957) \| 0.249603 / 0.258489 (-0.008886) \| 0.287955 / 0.293841 (-0.005886) \| 0.024003 / 0.128546 (-0.104543) \| 0.007111 / 0.075646 (-0.068535) \| 0.205041 / 0.419271 (-0.214231) \| 0.036296 / 0.043533 (-0.007237) \| 0.246135 / 0.255139 (-0.009004) \| 0.268801 / 0.283200 (-0.014399) \| 0.018451 / 0.141683 (-0.123232) \| 1.130387 / 1.452155 (-0.321767) \| 1.162041 / 1.492716 (-0.330675) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096370 / 0.018006 (0.078364) \| 0.309867 / 0.000490 (0.309377) \| 0.000229 / 0.000200 (0.000029) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018688 / 0.037411 (-0.018723) \| 0.062859 / 0.014526 (0.048333) \| 0.076383 / 0.176557 (-0.100173) \| 0.120385 / 0.737135 (-0.616750) \| 0.080192 / 0.296338 (-0.216147) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282994 / 0.215209 (0.067785) \| 2.742341 / 2.077655 (0.664686) \| 1.432041 / 1.504120 (-0.072079) \| 1.303282 / 1.541195 (-0.237913) \| 1.347198 / 1.468490 (-0.121292) \| 0.399145 / 4.584777 (-4.185632) \| 2.359766 / 3.745712 (-1.385947) \| 2.753577 / 5.269862 (-2.516285) \| 1.639953 / 4.565676 (-2.925724) \| 0.047111 / 0.424275 (-0.377164) \| 0.004946 / 0.007607 (-0.002661) \| 0.338857 / 0.226044 (0.112813) \| 3.328709 / 2.268929 (1.059781) \| 1.794729 / 55.444624 (-53.649895) \| 1.508514 / 6.876477 (-5.367963) \| 1.550737 / 2.142072 (-0.591335) \| 0.484227 / 4.805227 (-4.321000) \| 0.101001 / 6.500664 (-6.399663) \| 0.042792 / 0.075469 (-0.032677) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956471 / 1.841788 (-0.885317) \| 12.031362 / 8.074308 (3.957054) \| 10.512914 / 10.191392 (0.321522) \| 0.141841 / 0.680424 (-0.538583) \| 0.014343 / 0.534201 (-0.519858) \| 0.273916 / 0.579283 (-0.305367) \| 0.266150 / 0.434364 (-0.168214) \| 0.312020 / 0.540337 (-0.228317) \| 0.410465 / 1.386936 (-0.976471) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004945 / 0.011353 (-0.006408) \| 0.003288 / 0.011008 (-0.007720) \| 0.048247 / 0.038508 (0.009739) \| 0.057892 / 0.023109 (0.034783) \| 0.269741 / 0.275898 (-0.006157) \| 0.293728 / 0.323480 (-0.029752) \| 0.004789 / 0.007986 (-0.003197) \| 0.002477 / 0.004328 (-0.001852) \| 0.047825 / 0.004250 (0.043575) \| 0.040780 / 0.037052 (0.003727) \| 0.273355 / 0.258489 (0.014865) \| 0.300057 / 0.293841 (0.006216) \| 0.024481 / 0.128546 (-0.104066) \| 0.007285 / 0.075646 (-0.068361) \| 0.053046 / 0.419271 (-0.366226) \| 0.032342 / 0.043533 (-0.011190) \| 0.272293 / 0.255139 (0.017154) \| 0.290842 / 0.283200 (0.007642) \| 0.017546 / 0.141683 (-0.124137) \| 1.155816 / 1.452155 (-0.296339) \| 1.195839 / 1.492716 (-0.296878) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094177 / 0.018006 (0.076170) \| 0.305122 / 0.000490 (0.304632) \| 0.000237 / 0.000200 (0.000037) \| 0.000063 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021817 / 0.037411 (-0.015595) \| 0.070711 / 0.014526 (0.056185) \| 0.084028 / 0.176557 (-0.092528) \| 0.120160 / 0.737135 (-0.616975) \| 0.083085 / 0.296338 (-0.213254) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289127 / 0.215209 (0.073918) \| 2.826365 / 2.077655 (0.748710) \| 1.582910 / 1.504120 (0.078790) \| 1.472796 / 1.541195 (-0.068399) \| 1.497491 / 1.468490 (0.029000) \| 0.412276 / 4.584777 (-4.172501) \| 2.430692 / 3.745712 (-1.315020) \| 2.556444 / 5.269862 (-2.713418) \| 1.625782 / 4.565676 (-2.939895) \| 0.047921 / 0.424275 (-0.376354) \| 0.004809 / 0.007607 (-0.002798) \| 0.345569 / 0.226044 (0.119524) \| 3.417785 / 2.268929 (1.148856) \| 1.959223 / 55.444624 (-53.485401) \| 1.672765 / 6.876477 (-5.203712) \| 1.852444 / 2.142072 (-0.289628) \| 0.489225 / 4.805227 (-4.316002) \| 0.100624 / 6.500664 (-6.400040) \| 0.041242 / 0.075469 (-0.034227) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971130 / 1.841788 (-0.870658) \| 12.652204 / 8.074308 (4.577896) \| 10.661821 / 10.191392 (0.470429) \| 0.147636 / 0.680424 (-0.532787) \| 0.015738 / 0.534201 (-0.518463) \| 0.272763 / 0.579283 (-0.306520) \| 0.282623 / 0.434364 (-0.151741) \| 0.341303 / 0.540337 (-0.199035) \| 0.412149 / 1.386936 (-0.974787) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9499908c97ceef1792f69b71e93e36602880a4ae \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004589 / 0.011353 (-0.006764) \| 0.002730 / 0.011008 (-0.008279) \| 0.061862 / 0.038508 (0.023353) \| 0.050945 / 0.023109 (0.027836) \| 0.240776 / 0.275898 (-0.035122) \| 0.266000 / 0.323480 (-0.057480) \| 0.003823 / 0.007986 (-0.004162) \| 0.002345 / 0.004328 (-0.001983) \| 0.047821 / 0.004250 (0.043571) \| 0.037813 / 0.037052 (0.000761) \| 0.251075 / 0.258489 (-0.007415) \| 0.279430 / 0.293841 (-0.014411) \| 0.022957 / 0.128546 (-0.105590) \| 0.007294 / 0.075646 (-0.068353) \| 0.206092 / 0.419271 (-0.213180) \| 0.035308 / 0.043533 (-0.008225) \| 0.247197 / 0.255139 (-0.007942) \| 0.264988 / 0.283200 (-0.018212) \| 0.017588 / 0.141683 (-0.124095) \| 1.093291 / 1.452155 (-0.358864) \| 1.165477 / 1.492716 (-0.327240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.104057 / 0.018006 (0.086051) \| 0.303424 / 0.000490 (0.302934) \| 0.000223 / 0.000200 (0.000023) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019040 / 0.037411 (-0.018371) \| 0.063161 / 0.014526 (0.048635) \| 0.085333 / 0.176557 (-0.091224) \| 0.155973 / 0.737135 (-0.581162) \| 0.077528 / 0.296338 (-0.218810) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276104 / 0.215209 (0.060895) \| 2.738174 / 2.077655 (0.660519) \| 1.479484 / 1.504120 (-0.024636) \| 1.354094 / 1.541195 (-0.187100) \| 1.385312 / 1.468490 (-0.083178) \| 0.401398 / 4.584777 (-4.183379) \| 2.368503 / 3.745712 (-1.377209) \| 2.586405 / 5.269862 (-2.683457) \| 1.573978 / 4.565676 (-2.991699) \| 0.046969 / 0.424275 (-0.377306) \| 0.004874 / 0.007607 (-0.002733) \| 0.334028 / 0.226044 (0.107984) \| 3.269645 / 2.268929 (1.000717) \| 1.834528 / 55.444624 (-53.610096) \| 1.559883 / 6.876477 (-5.316594) \| 1.581380 / 2.142072 (-0.560693) \| 0.479580 / 4.805227 (-4.325647) \| 0.099077 / 6.500664 (-6.401587) \| 0.041166 / 0.075469 (-0.034303) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.918810 / 1.841788 (-0.922978) \| 11.505017 / 8.074308 (3.430709) \| 10.331934 / 10.191392 (0.140542) \| 0.128079 / 0.680424 (-0.552345) \| 0.013716 / 0.534201 (-0.520485) \| 0.271567 / 0.579283 (-0.307716) \| 0.264846 / 0.434364 (-0.169518) \| 0.305245 / 0.540337 (-0.235092) \| 0.401391 / 1.386936 (-0.985546) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004860 / 0.011353 (-0.006493) \| 0.002854 / 0.011008 (-0.008155) \| 0.048327 / 0.038508 (0.009819) \| 0.051377 / 0.023109 (0.028268) \| 0.264344 / 0.275898 (-0.011554) \| 0.286800 / 0.323480 (-0.036680) \| 0.003969 / 0.007986 (-0.004016) \| 0.002415 / 0.004328 (-0.001914) \| 0.048498 / 0.004250 (0.044247) \| 0.040399 / 0.037052 (0.003347) \| 0.267254 / 0.258489 (0.008765) \| 0.292049 / 0.293841 (-0.001792) \| 0.024730 / 0.128546 (-0.103817) \| 0.007275 / 0.075646 (-0.068371) \| 0.053725 / 0.419271 (-0.365546) \| 0.033142 / 0.043533 (-0.010391) \| 0.265418 / 0.255139 (0.010279) \| 0.286242 / 0.283200 (0.003042) \| 0.017824 / 0.141683 (-0.123859) \| 1.135978 / 1.452155 (-0.316176) \| 1.192506 / 1.492716 (-0.300210) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091907 / 0.018006 (0.073900) \| 0.307152 / 0.000490 (0.306663) \| 0.000223 / 0.000200 (0.000023) \| 0.000046 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021909 / 0.037411 (-0.015502) \| 0.070676 / 0.014526 (0.056150) \| 0.081651 / 0.176557 (-0.094906) \| 0.120915 / 0.737135 (-0.616220) \| 0.085882 / 0.296338 (-0.210456) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288008 / 0.215209 (0.072799) \| 2.861352 / 2.077655 (0.783697) \| 1.539045 / 1.504120 (0.034925) \| 1.412175 / 1.541195 (-0.129019) \| 1.421236 / 1.468490 (-0.047254) \| 0.404921 / 4.584777 (-4.179856) \| 2.480211 / 3.745712 (-1.265501) \| 2.473083 / 5.269862 (-2.796779) \| 1.558894 / 4.565676 (-3.006783) \| 0.046692 / 0.424275 (-0.377584) \| 0.004802 / 0.007607 (-0.002805) \| 0.346046 / 0.226044 (0.120001) \| 3.464387 / 2.268929 (1.195459) \| 1.937298 / 55.444624 (-53.507326) \| 1.593701 / 6.876477 (-5.282776) \| 1.730688 / 2.142072 (-0.411385) \| 0.481069 / 4.805227 (-4.324158) \| 0.098991 / 6.500664 (-6.401673) \| 0.040491 / 0.075469 (-0.034978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967809 / 1.841788 (-0.873979) \| 11.952335 / 8.074308 (3.878027) \| 10.616711 / 10.191392 (0.425319) \| 0.128938 / 0.680424 (-0.551486) \| 0.015455 / 0.534201 (-0.518746) \| 0.272100 / 0.579283 (-0.307183) \| 0.278275 / 0.434364 (-0.156089) \| 0.309711 / 0.540337 (-0.230627) \| 0.411026 / 1.386936 (-0.975910) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#495bc04226a67983f523d12d42b680172f8d4893 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008470 / 0.011353 (-0.002883) \| 0.003201 / 0.011008 (-0.007808) \| 0.063193 / 0.038508 (0.024685) \| 0.064174 / 0.023109 (0.041064) \| 0.248316 / 0.275898 (-0.027582) \| 0.281598 / 0.323480 (-0.041882) \| 0.004076 / 0.007986 (-0.003909) \| 0.002397 / 0.004328 (-0.001932) \| 0.048834 / 0.004250 (0.044584) \| 0.056517 / 0.037052 (0.019465) \| 0.254164 / 0.258489 (-0.004326) \| 0.289800 / 0.293841 (-0.004041) \| 0.031092 / 0.128546 (-0.097454) \| 0.010885 / 0.075646 (-0.064762) \| 0.219198 / 0.419271 (-0.200073) \| 0.040087 / 0.043533 (-0.003446) \| 0.250900 / 0.255139 (-0.004239) \| 0.267787 / 0.283200 (-0.015413) \| 0.019666 / 0.141683 (-0.122017) \| 1.114960 / 1.452155 (-0.337194) \| 1.266675 / 1.492716 (-0.226041) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091429 / 0.018006 (0.073422) \| 0.301804 / 0.000490 (0.301314) \| 0.000212 / 0.000200 (0.000012) \| 0.000064 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021053 / 0.037411 (-0.016358) \| 0.062407 / 0.014526 (0.047881) \| 0.073166 / 0.176557 (-0.103391) \| 0.119642 / 0.737135 (-0.617493) \| 0.074771 / 0.296338 (-0.221567) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278582 / 0.215209 (0.063373) \| 2.773023 / 2.077655 (0.695368) \| 1.459977 / 1.504120 (-0.044143) \| 1.330453 / 1.541195 (-0.210742) \| 1.372797 / 1.468490 (-0.095693) \| 0.628845 / 4.584777 (-3.955932) \| 3.428779 / 3.745712 (-0.316933) \| 3.138967 / 5.269862 (-2.130895) \| 2.126891 / 4.565676 (-2.438785) \| 0.062340 / 0.424275 (-0.361935) \| 0.004939 / 0.007607 (-0.002668) \| 0.336058 / 0.226044 (0.110014) \| 3.463741 / 2.268929 (1.194813) \| 1.847504 / 55.444624 (-53.597120) \| 1.984173 / 6.876477 (-4.892304) \| 1.602962 / 2.142072 (-0.539110) \| 0.637683 / 4.805227 (-4.167545) \| 0.117898 / 6.500664 (-6.382766) \| 0.043308 / 0.075469 (-0.032161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.087773 / 1.841788 (-0.754014) \| 14.959526 / 8.074308 (6.885218) \| 10.886003 / 10.191392 (0.694611) \| 0.163385 / 0.680424 (-0.517039) \| 0.016679 / 0.534201 (-0.517522) \| 0.351913 / 0.579283 (-0.227370) \| 0.359007 / 0.434364 (-0.075357) \| 0.323824 / 0.540337 (-0.216513) \| 0.549268 / 1.386936 (-0.837668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005265 / 0.011353 (-0.006088) \| 0.003367 / 0.011008 (-0.007641) \| 0.062741 / 0.038508 (0.024233) \| 0.068463 / 0.023109 (0.045354) \| 0.258497 / 0.275898 (-0.017401) \| 0.355360 / 0.323480 (0.031880) \| 0.003910 / 0.007986 (-0.004075) \| 0.002399 / 0.004328 (-0.001929) \| 0.055564 / 0.004250 (0.051313) \| 0.039644 / 0.037052 (0.002591) \| 0.258313 / 0.258489 (-0.000176) \| 0.328927 / 0.293841 (0.035086) \| 0.035634 / 0.128546 (-0.092912) \| 0.010378 / 0.075646 (-0.065268) \| 0.073109 / 0.419271 (-0.346163) \| 0.039752 / 0.043533 (-0.003781) \| 0.258237 / 0.255139 (0.003098) \| 0.330329 / 0.283200 (0.047129) \| 0.023924 / 0.141683 (-0.117759) \| 1.198639 / 1.452155 (-0.253515) \| 1.202307 / 1.492716 (-0.290409) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091297 / 0.018006 (0.073290) \| 0.298729 / 0.000490 (0.298240) \| 0.000210 / 0.000200 (0.000010) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022381 / 0.037411 (-0.015030) \| 0.070226 / 0.014526 (0.055700) \| 0.080549 / 0.176557 (-0.096007) \| 0.119677 / 0.737135 (-0.617458) \| 0.082612 / 0.296338 (-0.213727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289270 / 0.215209 (0.074061) \| 2.853830 / 2.077655 (0.776175) \| 1.528938 / 1.504120 (0.024818) \| 1.398429 / 1.541195 (-0.142766) \| 1.472465 / 1.468490 (0.003975) \| 0.779015 / 4.584777 (-3.805762) \| 3.287724 / 3.745712 (-0.457988) \| 3.020908 / 5.269862 (-2.248953) \| 1.926094 / 4.565676 (-2.639583) \| 0.063163 / 0.424275 (-0.361112) \| 0.005175 / 0.007607 (-0.002432) \| 0.342884 / 0.226044 (0.116840) \| 3.476837 / 2.268929 (1.207908) \| 1.880683 / 55.444624 (-53.563942) \| 1.613845 / 6.876477 (-5.262632) \| 1.624734 / 2.142072 (-0.517338) \| 0.626220 / 4.805227 (-4.179007) \| 0.114976 / 6.500664 (-6.385689) \| 0.040670 / 0.075469 (-0.034799) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.116815 / 1.841788 (-0.724973) \| 15.388426 / 8.074308 (7.314118) \| 10.825276 / 10.191392 (0.633884) \| 0.172659 / 0.680424 (-0.507765) \| 0.015468 / 0.534201 (-0.518733) \| 0.285552 / 0.579283 (-0.293731) \| 0.346886 / 0.434364 (-0.087478) \| 0.348696 / 0.540337 (-0.191641) \| 0.729335 / 1.386936 (-0.657601) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7bbf346dc268b8084dee406b2a6e2b96d44bc3b \"CML watermark\")\n" ]	2023-11-16T12:12:54Z	2023-11-28T16:10:39Z	2023-11-28T16:03:43Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6429.diff", "html_url": "https://github.com/huggingface/datasets/pull/6429", "merged_at": "2023-11-28T16:03:43Z", "patch_url": "https://github.com/huggingface/datasets/pull/6429.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6429" }	Draft about adding `trust_remote_code` to `load_dataset`. ```python ds = load_dataset(..., trust_remote_code=True) # run remote code (current default) ``` It would default to `True` (current behavior) and in the next major release it will prompt the user to check the code before running it (we'll communicate on this before doing it of course). ```python # in the future ds = load_dataset(...) # prompt the user to check the code before running it (future default) ds = load_dataset(..., trust_remote_code=True) # run remote code ds = load_dataset(..., trust_remote_code=False) # disallow remote code ``` Related to https://github.com/huggingface/datasets/issues/6400 Will do a separate PR to use the parquet export when possible	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ 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https://github.com/huggingface/datasets/pull/6428	6,428	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6428). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004839 / 0.011353 (-0.006514) \| 0.002928 / 0.011008 (-0.008080) \| 0.061730 / 0.038508 (0.023221) \| 0.030523 / 0.023109 (0.007414) \| 0.252679 / 0.275898 (-0.023219) \| 0.281597 / 0.323480 (-0.041883) \| 0.003025 / 0.007986 (-0.004961) \| 0.002374 / 0.004328 (-0.001955) \| 0.048134 / 0.004250 (0.043884) \| 0.045843 / 0.037052 (0.008791) \| 0.256274 / 0.258489 (-0.002215) \| 0.288704 / 0.293841 (-0.005137) \| 0.023486 / 0.128546 (-0.105060) \| 0.007186 / 0.075646 (-0.068461) \| 0.202519 / 0.419271 (-0.216753) \| 0.058192 / 0.043533 (0.014659) \| 0.256448 / 0.255139 (0.001309) \| 0.279417 / 0.283200 (-0.003783) \| 0.019942 / 0.141683 (-0.121740) \| 1.100954 / 1.452155 (-0.351201) \| 1.168183 / 1.492716 (-0.324533) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091314 / 0.018006 (0.073308) \| 0.298614 / 0.000490 (0.298124) \| 0.000232 / 0.000200 (0.000032) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018071 / 0.037411 (-0.019340) \| 0.062265 / 0.014526 (0.047740) \| 0.073228 / 0.176557 (-0.103328) \| 0.119163 / 0.737135 (-0.617972) \| 0.074717 / 0.296338 (-0.221622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273906 / 0.215209 (0.058697) \| 2.683995 / 2.077655 (0.606340) \| 1.418773 / 1.504120 (-0.085347) \| 1.310473 / 1.541195 (-0.230722) \| 1.303152 / 1.468490 (-0.165339) \| 0.390846 / 4.584777 (-4.193931) \| 2.346407 / 3.745712 (-1.399305) \| 2.582945 / 5.269862 (-2.686916) \| 1.569549 / 4.565676 (-2.996128) \| 0.044893 / 0.424275 (-0.379383) \| 0.004754 / 0.007607 (-0.002853) \| 0.323491 / 0.226044 (0.097447) \| 3.229736 / 2.268929 (0.960808) \| 1.783551 / 55.444624 (-53.661074) \| 1.499685 / 6.876477 (-5.376792) \| 1.515826 / 2.142072 (-0.626246) \| 0.475768 / 4.805227 (-4.329460) \| 0.099579 / 6.500664 (-6.401085) \| 0.042709 / 0.075469 (-0.032760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.926120 / 1.841788 (-0.915667) \| 11.597189 / 8.074308 (3.522881) \| 10.327055 / 10.191392 (0.135663) \| 0.127479 / 0.680424 (-0.552945) \| 0.014844 / 0.534201 (-0.519357) \| 0.261181 / 0.579283 (-0.318102) \| 0.258407 / 0.434364 (-0.175957) \| 0.303192 / 0.540337 (-0.237146) \| 0.416665 / 1.386936 (-0.970271) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004759 / 0.011353 (-0.006594) \| 0.002780 / 0.011008 (-0.008228) \| 0.047991 / 0.038508 (0.009483) \| 0.052263 / 0.023109 (0.029153) \| 0.261228 / 0.275898 (-0.014670) \| 0.287779 / 0.323480 (-0.035701) \| 0.003961 / 0.007986 (-0.004024) \| 0.002357 / 0.004328 (-0.001971) \| 0.047755 / 0.004250 (0.043505) \| 0.038066 / 0.037052 (0.001014) \| 0.269502 / 0.258489 (0.011013) \| 0.298348 / 0.293841 (0.004507) \| 0.024398 / 0.128546 (-0.104149) \| 0.007189 / 0.075646 (-0.068457) \| 0.053356 / 0.419271 (-0.365915) \| 0.032459 / 0.043533 (-0.011074) \| 0.266389 / 0.255139 (0.011250) \| 0.305367 / 0.283200 (0.022168) \| 0.017629 / 0.141683 (-0.124054) \| 1.145789 / 1.452155 (-0.306366) \| 1.204778 / 1.492716 (-0.287938) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091347 / 0.018006 (0.073341) \| 0.298671 / 0.000490 (0.298181) \| 0.000229 / 0.000200 (0.000029) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021374 / 0.037411 (-0.016037) \| 0.068869 / 0.014526 (0.054344) \| 0.080443 / 0.176557 (-0.096113) \| 0.118759 / 0.737135 (-0.618376) \| 0.081646 / 0.296338 (-0.214692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295274 / 0.215209 (0.080065) \| 2.889349 / 2.077655 (0.811695) \| 1.561020 / 1.504120 (0.056900) \| 1.425025 / 1.541195 (-0.116170) \| 1.495446 / 1.468490 (0.026956) \| 0.403825 / 4.584777 (-4.180952) \| 2.404905 / 3.745712 (-1.340807) \| 2.590104 / 5.269862 (-2.679758) \| 1.570559 / 4.565676 (-2.995118) \| 0.046342 / 0.424275 (-0.377933) \| 0.004799 / 0.007607 (-0.002809) \| 0.349981 / 0.226044 (0.123937) \| 3.437341 / 2.268929 (1.168412) \| 1.948155 / 55.444624 (-53.496469) \| 1.637765 / 6.876477 (-5.238711) \| 1.671521 / 2.142072 (-0.470551) \| 0.479500 / 4.805227 (-4.325727) \| 0.098305 / 6.500664 (-6.402359) \| 0.040864 / 0.075469 (-0.034605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979986 / 1.841788 (-0.861801) \| 12.169722 / 8.074308 (4.095413) \| 11.297345 / 10.191392 (1.105953) \| 0.129123 / 0.680424 (-0.551301) \| 0.015389 / 0.534201 (-0.518812) \| 0.270964 / 0.579283 (-0.308319) \| 0.269590 / 0.434364 (-0.164774) \| 0.310662 / 0.540337 (-0.229675) \| 0.406272 / 1.386936 (-0.980664) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31873f1e9acbe013e6d396d1ed5492db8cd59dd3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004620 / 0.011353 (-0.006733) \| 0.002971 / 0.011008 (-0.008038) \| 0.062864 / 0.038508 (0.024355) \| 0.028743 / 0.023109 (0.005634) \| 0.246729 / 0.275898 (-0.029169) \| 0.271165 / 0.323480 (-0.052315) \| 0.003930 / 0.007986 (-0.004056) \| 0.002422 / 0.004328 (-0.001906) \| 0.047430 / 0.004250 (0.043180) \| 0.044895 / 0.037052 (0.007843) \| 0.249128 / 0.258489 (-0.009361) \| 0.283384 / 0.293841 (-0.010457) \| 0.023288 / 0.128546 (-0.105259) \| 0.007241 / 0.075646 (-0.068405) \| 0.207551 / 0.419271 (-0.211720) \| 0.055008 / 0.043533 (0.011475) \| 0.252781 / 0.255139 (-0.002358) \| 0.296924 / 0.283200 (0.013724) \| 0.017860 / 0.141683 (-0.123822) \| 1.094597 / 1.452155 (-0.357558) \| 1.162314 / 1.492716 (-0.330402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091423 / 0.018006 (0.073417) \| 0.302833 / 0.000490 (0.302343) \| 0.000242 / 0.000200 (0.000042) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018143 / 0.037411 (-0.019268) \| 0.066371 / 0.014526 (0.051845) \| 0.072774 / 0.176557 (-0.103783) \| 0.119062 / 0.737135 (-0.618073) \| 0.102836 / 0.296338 (-0.193502) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280117 / 0.215209 (0.064908) \| 2.757955 / 2.077655 (0.680301) \| 1.494994 / 1.504120 (-0.009126) \| 1.375325 / 1.541195 (-0.165870) \| 1.384179 / 1.468490 (-0.084311) \| 0.399824 / 4.584777 (-4.184953) \| 2.368575 / 3.745712 (-1.377137) \| 2.574035 / 5.269862 (-2.695827) \| 1.548738 / 4.565676 (-3.016939) \| 0.045841 / 0.424275 (-0.378434) \| 0.004799 / 0.007607 (-0.002808) \| 0.331522 / 0.226044 (0.105478) \| 3.324471 / 2.268929 (1.055543) \| 1.838637 / 55.444624 (-53.605987) \| 1.562854 / 6.876477 (-5.313623) \| 1.581736 / 2.142072 (-0.560336) \| 0.468832 / 4.805227 (-4.336396) \| 0.099309 / 6.500664 (-6.401355) \| 0.042078 / 0.075469 (-0.033391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.928468 / 1.841788 (-0.913320) \| 11.331143 / 8.074308 (3.256835) \| 10.296213 / 10.191392 (0.104821) \| 0.138912 / 0.680424 (-0.541511) \| 0.014044 / 0.534201 (-0.520157) \| 0.267293 / 0.579283 (-0.311991) \| 0.267267 / 0.434364 (-0.167097) \| 0.306560 / 0.540337 (-0.233778) \| 0.423926 / 1.386936 (-0.963010) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004842 / 0.011353 (-0.006511) \| 0.002917 / 0.011008 (-0.008091) \| 0.048263 / 0.038508 (0.009755) \| 0.051453 / 0.023109 (0.028344) \| 0.278330 / 0.275898 (0.002432) \| 0.298569 / 0.323480 (-0.024911) \| 0.003936 / 0.007986 (-0.004049) \| 0.002479 / 0.004328 (-0.001850) \| 0.048281 / 0.004250 (0.044031) \| 0.038925 / 0.037052 (0.001872) \| 0.285258 / 0.258489 (0.026769) \| 0.313701 / 0.293841 (0.019860) \| 0.024916 / 0.128546 (-0.103630) \| 0.007142 / 0.075646 (-0.068504) \| 0.053634 / 0.419271 (-0.365638) \| 0.032842 / 0.043533 (-0.010690) \| 0.279373 / 0.255139 (0.024234) \| 0.295844 / 0.283200 (0.012644) \| 0.018142 / 0.141683 (-0.123541) \| 1.136960 / 1.452155 (-0.315195) \| 1.184438 / 1.492716 (-0.308278) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090271 / 0.018006 (0.072264) \| 0.299940 / 0.000490 (0.299450) \| 0.000234 / 0.000200 (0.000034) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021175 / 0.037411 (-0.016237) \| 0.070924 / 0.014526 (0.056398) \| 0.080584 / 0.176557 (-0.095972) \| 0.119278 / 0.737135 (-0.617857) \| 0.082361 / 0.296338 (-0.213977) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298312 / 0.215209 (0.083103) \| 2.895361 / 2.077655 (0.817706) \| 1.616120 / 1.504120 (0.112001) \| 1.484444 / 1.541195 (-0.056750) \| 1.541893 / 1.468490 (0.073403) \| 0.409968 / 4.584777 (-4.174809) \| 2.423639 / 3.745712 (-1.322073) \| 2.585122 / 5.269862 (-2.684740) \| 1.540343 / 4.565676 (-3.025333) \| 0.046604 / 0.424275 (-0.377671) \| 0.004742 / 0.007607 (-0.002865) \| 0.341659 / 0.226044 (0.115614) \| 3.409259 / 2.268929 (1.140330) \| 2.007068 / 55.444624 (-53.437556) \| 1.681348 / 6.876477 (-5.195129) \| 1.719253 / 2.142072 (-0.422819) \| 0.482301 / 4.805227 (-4.322926) \| 0.099619 / 6.500664 (-6.401045) \| 0.041247 / 0.075469 (-0.034222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971783 / 1.841788 (-0.870004) \| 12.208000 / 8.074308 (4.133692) \| 10.948230 / 10.191392 (0.756838) \| 0.131824 / 0.680424 (-0.548599) \| 0.015696 / 0.534201 (-0.518505) \| 0.272265 / 0.579283 (-0.307018) \| 0.276093 / 0.434364 (-0.158270) \| 0.305897 / 0.540337 (-0.234441) \| 0.411632 / 1.386936 (-0.975304) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bf75fe522c6fedd16d00b4a928f613dee11f73c \"CML watermark\")\n" ]	2023-11-16T08:12:55Z	2023-11-16T08:19:39Z	2023-11-16T08:13:28Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6428.diff", "html_url": "https://github.com/huggingface/datasets/pull/6428", "merged_at": "2023-11-16T08:13:28Z", "patch_url": "https://github.com/huggingface/datasets/pull/6428.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6428" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6428/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6427	6,427	Release: 2.15.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004331 / 0.011353 (-0.007022) \| 0.002573 / 0.011008 (-0.008435) \| 0.061002 / 0.038508 (0.022494) \| 0.029259 / 0.023109 (0.006149) \| 0.242983 / 0.275898 (-0.032915) \| 0.267629 / 0.323480 (-0.055851) \| 0.003906 / 0.007986 (-0.004080) \| 0.002383 / 0.004328 (-0.001946) \| 0.047574 / 0.004250 (0.043323) \| 0.042153 / 0.037052 (0.005101) \| 0.245821 / 0.258489 (-0.012668) \| 0.276479 / 0.293841 (-0.017362) \| 0.022498 / 0.128546 (-0.106049) \| 0.006775 / 0.075646 (-0.068871) \| 0.201795 / 0.419271 (-0.217477) \| 0.052443 / 0.043533 (0.008910) \| 0.248320 / 0.255139 (-0.006819) \| 0.261964 / 0.283200 (-0.021235) \| 0.016764 / 0.141683 (-0.124919) \| 1.118702 / 1.452155 (-0.333453) \| 1.203079 / 1.492716 (-0.289638) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088808 / 0.018006 (0.070801) \| 0.296526 / 0.000490 (0.296037) \| 0.000203 / 0.000200 (0.000003) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018816 / 0.037411 (-0.018595) \| 0.062295 / 0.014526 (0.047769) \| 0.075228 / 0.176557 (-0.101329) \| 0.119916 / 0.737135 (-0.617219) \| 0.077206 / 0.296338 (-0.219132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276723 / 0.215209 (0.061514) \| 2.711431 / 2.077655 (0.633776) \| 1.425590 / 1.504120 (-0.078530) \| 1.301383 / 1.541195 (-0.239812) \| 1.316314 / 1.468490 (-0.152176) \| 0.402709 / 4.584777 (-4.182068) \| 2.347229 / 3.745712 (-1.398483) \| 2.596937 / 5.269862 (-2.672925) \| 1.560658 / 4.565676 (-3.005018) \| 0.046162 / 0.424275 (-0.378113) \| 0.004760 / 0.007607 (-0.002848) \| 0.330522 / 0.226044 (0.104478) \| 3.244072 / 2.268929 (0.975143) \| 1.747603 / 55.444624 (-53.697021) \| 1.475534 / 6.876477 (-5.400943) \| 1.485135 / 2.142072 (-0.656938) \| 0.476794 / 4.805227 (-4.328433) \| 0.098496 / 6.500664 (-6.402168) \| 0.040740 / 0.075469 (-0.034729) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939020 / 1.841788 (-0.902768) \| 11.235187 / 8.074308 (3.160878) \| 10.194975 / 10.191392 (0.003583) \| 0.126241 / 0.680424 (-0.554182) \| 0.013990 / 0.534201 (-0.520211) \| 0.269149 / 0.579283 (-0.310134) \| 0.256950 / 0.434364 (-0.177414) \| 0.301282 / 0.540337 (-0.239056) \| 0.421490 / 1.386936 (-0.965446) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004956 / 0.011353 (-0.006397) \| 0.002478 / 0.011008 (-0.008530) \| 0.047773 / 0.038508 (0.009265) \| 0.050076 / 0.023109 (0.026967) \| 0.261915 / 0.275898 (-0.013983) \| 0.282553 / 0.323480 (-0.040927) \| 0.003881 / 0.007986 (-0.004105) \| 0.002329 / 0.004328 (-0.001999) \| 0.048091 / 0.004250 (0.043841) \| 0.038188 / 0.037052 (0.001135) \| 0.265502 / 0.258489 (0.007013) \| 0.292568 / 0.293841 (-0.001273) \| 0.024172 / 0.128546 (-0.104374) \| 0.006865 / 0.075646 (-0.068781) \| 0.053199 / 0.419271 (-0.366072) \| 0.032201 / 0.043533 (-0.011332) \| 0.265774 / 0.255139 (0.010635) \| 0.277954 / 0.283200 (-0.005245) \| 0.017798 / 0.141683 (-0.123885) \| 1.121503 / 1.452155 (-0.330652) \| 1.176319 / 1.492716 (-0.316398) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087027 / 0.018006 (0.069020) \| 0.296182 / 0.000490 (0.295693) \| 0.000216 / 0.000200 (0.000017) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020990 / 0.037411 (-0.016421) \| 0.069693 / 0.014526 (0.055168) \| 0.081098 / 0.176557 (-0.095459) \| 0.117760 / 0.737135 (-0.619375) \| 0.081493 / 0.296338 (-0.214845) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295078 / 0.215209 (0.079869) \| 2.876602 / 2.077655 (0.798947) \| 1.558011 / 1.504120 (0.053891) \| 1.426715 / 1.541195 (-0.114480) \| 1.443785 / 1.468490 (-0.024705) \| 0.400826 / 4.584777 (-4.183951) \| 2.378903 / 3.745712 (-1.366810) \| 2.473128 / 5.269862 (-2.796734) \| 1.500785 / 4.565676 (-3.064891) \| 0.045438 / 0.424275 (-0.378837) \| 0.004953 / 0.007607 (-0.002654) \| 0.348182 / 0.226044 (0.122137) \| 3.427751 / 2.268929 (1.158822) \| 1.925173 / 55.444624 (-53.519451) \| 1.633354 / 6.876477 (-5.243123) \| 1.651573 / 2.142072 (-0.490499) \| 0.473260 / 4.805227 (-4.331968) \| 0.097613 / 6.500664 (-6.403051) \| 0.040196 / 0.075469 (-0.035273) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951780 / 1.841788 (-0.890008) \| 11.709342 / 8.074308 (3.635034) \| 10.571831 / 10.191392 (0.380439) \| 0.134344 / 0.680424 (-0.546079) \| 0.022116 / 0.534201 (-0.512084) \| 0.269651 / 0.579283 (-0.309632) \| 0.272310 / 0.434364 (-0.162054) \| 0.306434 / 0.540337 (-0.233903) \| 0.408320 / 1.386936 (-0.978616) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ea64b77079cf76675421917472c05d06ace63fc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004402 / 0.011353 (-0.006951) \| 0.002732 / 0.011008 (-0.008277) \| 0.062799 / 0.038508 (0.024291) \| 0.029155 / 0.023109 (0.006046) \| 0.241925 / 0.275898 (-0.033973) \| 0.275694 / 0.323480 (-0.047786) \| 0.003989 / 0.007986 (-0.003997) \| 0.002528 / 0.004328 (-0.001801) \| 0.048410 / 0.004250 (0.044160) \| 0.043729 / 0.037052 (0.006677) \| 0.248843 / 0.258489 (-0.009646) \| 0.282980 / 0.293841 (-0.010860) \| 0.023828 / 0.128546 (-0.104718) \| 0.006972 / 0.075646 (-0.068675) \| 0.213222 / 0.419271 (-0.206049) \| 0.054883 / 0.043533 (0.011350) \| 0.251353 / 0.255139 (-0.003786) \| 0.269818 / 0.283200 (-0.013381) \| 0.016906 / 0.141683 (-0.124777) \| 1.114109 / 1.452155 (-0.338045) \| 1.162942 / 1.492716 (-0.329774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093724 / 0.018006 (0.075718) \| 0.301989 / 0.000490 (0.301499) \| 0.000213 / 0.000200 (0.000014) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018245 / 0.037411 (-0.019166) \| 0.062237 / 0.014526 (0.047712) \| 0.075644 / 0.176557 (-0.100913) \| 0.119655 / 0.737135 (-0.617480) \| 0.074525 / 0.296338 (-0.221814) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274534 / 0.215209 (0.059324) \| 2.683678 / 2.077655 (0.606024) \| 1.453306 / 1.504120 (-0.050814) \| 1.347630 / 1.541195 (-0.193564) \| 1.352875 / 1.468490 (-0.115615) \| 0.398425 / 4.584777 (-4.186352) \| 2.375738 / 3.745712 (-1.369974) \| 2.591573 / 5.269862 (-2.678289) \| 1.555527 / 4.565676 (-3.010150) \| 0.045656 / 0.424275 (-0.378619) \| 0.004898 / 0.007607 (-0.002709) \| 0.330591 / 0.226044 (0.104547) \| 3.247638 / 2.268929 (0.978710) \| 1.816676 / 55.444624 (-53.627948) \| 1.531754 / 6.876477 (-5.344723) \| 1.543196 / 2.142072 (-0.598877) \| 0.472489 / 4.805227 (-4.332739) \| 0.099311 / 6.500664 (-6.401353) \| 0.042139 / 0.075469 (-0.033330) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945472 / 1.841788 (-0.896316) \| 11.476550 / 8.074308 (3.402242) \| 10.281157 / 10.191392 (0.089765) \| 0.141062 / 0.680424 (-0.539362) \| 0.013634 / 0.534201 (-0.520567) \| 0.268778 / 0.579283 (-0.310505) \| 0.263542 / 0.434364 (-0.170822) \| 0.307918 / 0.540337 (-0.232420) \| 0.421231 / 1.386936 (-0.965705) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005090 / 0.011353 (-0.006263) \| 0.003135 / 0.011008 (-0.007873) \| 0.048058 / 0.038508 (0.009550) \| 0.052898 / 0.023109 (0.029789) \| 0.273233 / 0.275898 (-0.002665) \| 0.299516 / 0.323480 (-0.023964) \| 0.004126 / 0.007986 (-0.003860) \| 0.002331 / 0.004328 (-0.001997) \| 0.047627 / 0.004250 (0.043376) \| 0.039076 / 0.037052 (0.002023) \| 0.276625 / 0.258489 (0.018136) \| 0.308180 / 0.293841 (0.014340) \| 0.024929 / 0.128546 (-0.103618) \| 0.007396 / 0.075646 (-0.068251) \| 0.053408 / 0.419271 (-0.365863) \| 0.032896 / 0.043533 (-0.010637) \| 0.275412 / 0.255139 (0.020273) \| 0.292014 / 0.283200 (0.008814) \| 0.018336 / 0.141683 (-0.123347) \| 1.123565 / 1.452155 (-0.328589) \| 1.175382 / 1.492716 (-0.317334) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093799 / 0.018006 (0.075793) \| 0.304219 / 0.000490 (0.303729) \| 0.000231 / 0.000200 (0.000031) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021034 / 0.037411 (-0.016377) \| 0.069961 / 0.014526 (0.055435) \| 0.080311 / 0.176557 (-0.096246) \| 0.118603 / 0.737135 (-0.618532) \| 0.084003 / 0.296338 (-0.212335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305610 / 0.215209 (0.090401) \| 2.962027 / 2.077655 (0.884372) \| 1.598604 / 1.504120 (0.094484) \| 1.476227 / 1.541195 (-0.064967) \| 1.528960 / 1.468490 (0.060470) \| 0.404545 / 4.584777 (-4.180232) \| 2.423147 / 3.745712 (-1.322565) \| 2.516632 / 5.269862 (-2.753229) \| 1.529000 / 4.565676 (-3.036677) \| 0.045780 / 0.424275 (-0.378495) \| 0.004784 / 0.007607 (-0.002823) \| 0.358836 / 0.226044 (0.132792) \| 3.508782 / 2.268929 (1.239853) \| 1.954513 / 55.444624 (-53.490111) \| 1.672824 / 6.876477 (-5.203653) \| 1.683482 / 2.142072 (-0.458590) \| 0.479014 / 4.805227 (-4.326213) \| 0.098325 / 6.500664 (-6.402340) \| 0.040934 / 0.075469 (-0.034536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974770 / 1.841788 (-0.867017) \| 11.956137 / 8.074308 (3.881829) \| 10.956458 / 10.191392 (0.765066) \| 0.141800 / 0.680424 (-0.538624) \| 0.015439 / 0.534201 (-0.518762) \| 0.271783 / 0.579283 (-0.307500) \| 0.278058 / 0.434364 (-0.156306) \| 0.305823 / 0.540337 (-0.234514) \| 0.415677 / 1.386936 (-0.971259) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004483 / 0.011353 (-0.006870) \| 0.002560 / 0.011008 (-0.008448) \| 0.061428 / 0.038508 (0.022920) \| 0.029460 / 0.023109 (0.006351) \| 0.238971 / 0.275898 (-0.036927) \| 0.271768 / 0.323480 (-0.051712) \| 0.003970 / 0.007986 (-0.004016) \| 0.002408 / 0.004328 (-0.001921) \| 0.047755 / 0.004250 (0.043505) \| 0.043358 / 0.037052 (0.006306) \| 0.245543 / 0.258489 (-0.012946) \| 0.278230 / 0.293841 (-0.015611) \| 0.023819 / 0.128546 (-0.104727) \| 0.006856 / 0.075646 (-0.068790) \| 0.204603 / 0.419271 (-0.214668) \| 0.054521 / 0.043533 (0.010989) \| 0.246277 / 0.255139 (-0.008862) \| 0.271230 / 0.283200 (-0.011969) \| 0.017283 / 0.141683 (-0.124400) \| 1.088955 / 1.452155 (-0.363200) \| 1.245141 / 1.492716 (-0.247575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091534 / 0.018006 (0.073528) \| 0.299517 / 0.000490 (0.299027) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018105 / 0.037411 (-0.019306) \| 0.061860 / 0.014526 (0.047334) \| 0.074494 / 0.176557 (-0.102063) \| 0.120107 / 0.737135 (-0.617029) \| 0.073406 / 0.296338 (-0.222932) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278140 / 0.215209 (0.062931) \| 2.746208 / 2.077655 (0.668553) \| 1.476264 / 1.504120 (-0.027856) \| 1.356498 / 1.541195 (-0.184697) \| 1.362998 / 1.468490 (-0.105492) \| 0.401884 / 4.584777 (-4.182893) \| 2.409836 / 3.745712 (-1.335877) \| 2.579087 / 5.269862 (-2.690775) \| 1.545021 / 4.565676 (-3.020656) \| 0.046001 / 0.424275 (-0.378274) \| 0.004812 / 0.007607 (-0.002795) \| 0.339767 / 0.226044 (0.113722) \| 3.341599 / 2.268929 (1.072670) \| 1.821498 / 55.444624 (-53.623127) \| 1.559311 / 6.876477 (-5.317166) \| 1.570368 / 2.142072 (-0.571704) \| 0.472688 / 4.805227 (-4.332539) \| 0.099549 / 6.500664 (-6.401115) \| 0.041644 / 0.075469 (-0.033825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951988 / 1.841788 (-0.889799) \| 11.371459 / 8.074308 (3.297150) \| 10.229446 / 10.191392 (0.038054) \| 0.128105 / 0.680424 (-0.552319) \| 0.014418 / 0.534201 (-0.519783) \| 0.268615 / 0.579283 (-0.310668) \| 0.263956 / 0.434364 (-0.170407) \| 0.302213 / 0.540337 (-0.238125) \| 0.427224 / 1.386936 (-0.959712) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005150 / 0.011353 (-0.006203) \| 0.002557 / 0.011008 (-0.008451) \| 0.048092 / 0.038508 (0.009584) \| 0.050522 / 0.023109 (0.027413) \| 0.272195 / 0.275898 (-0.003703) \| 0.294191 / 0.323480 (-0.029289) \| 0.004098 / 0.007986 (-0.003887) \| 0.002350 / 0.004328 (-0.001978) \| 0.048682 / 0.004250 (0.044432) \| 0.038381 / 0.037052 (0.001328) \| 0.275530 / 0.258489 (0.017041) \| 0.303991 / 0.293841 (0.010150) \| 0.024734 / 0.128546 (-0.103812) \| 0.006926 / 0.075646 (-0.068720) \| 0.053683 / 0.419271 (-0.365588) \| 0.032675 / 0.043533 (-0.010858) \| 0.272816 / 0.255139 (0.017677) \| 0.291754 / 0.283200 (0.008554) \| 0.018290 / 0.141683 (-0.123392) \| 1.127696 / 1.452155 (-0.324459) \| 1.187080 / 1.492716 (-0.305636) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091224 / 0.018006 (0.073218) \| 0.288838 / 0.000490 (0.288348) \| 0.000226 / 0.000200 (0.000026) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021409 / 0.037411 (-0.016003) \| 0.069846 / 0.014526 (0.055320) \| 0.079962 / 0.176557 (-0.096594) \| 0.118575 / 0.737135 (-0.618560) \| 0.080223 / 0.296338 (-0.216115) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290835 / 0.215209 (0.075626) \| 2.831787 / 2.077655 (0.754133) \| 1.587728 / 1.504120 (0.083608) \| 1.461939 / 1.541195 (-0.079256) \| 1.495257 / 1.468490 (0.026767) \| 0.397653 / 4.584777 (-4.187124) \| 2.399903 / 3.745712 (-1.345809) \| 2.527615 / 5.269862 (-2.742247) \| 1.501555 / 4.565676 (-3.064121) \| 0.045742 / 0.424275 (-0.378533) \| 0.004797 / 0.007607 (-0.002811) \| 0.339139 / 0.226044 (0.113094) \| 3.358340 / 2.268929 (1.089412) \| 1.968955 / 55.444624 (-53.475670) \| 1.663598 / 6.876477 (-5.212879) \| 1.673995 / 2.142072 (-0.468078) \| 0.463444 / 4.805227 (-4.341783) \| 0.098008 / 6.500664 (-6.402656) \| 0.040836 / 0.075469 (-0.034633) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974033 / 1.841788 (-0.867755) \| 11.863206 / 8.074308 (3.788897) \| 10.892389 / 10.191392 (0.700997) \| 0.128884 / 0.680424 (-0.551540) \| 0.015319 / 0.534201 (-0.518882) \| 0.268931 / 0.579283 (-0.310353) \| 0.274148 / 0.434364 (-0.160216) \| 0.305407 / 0.540337 (-0.234930) \| 0.410574 / 1.386936 (-0.976362) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n" ]	2023-11-16T07:37:20Z	2023-11-16T08:12:12Z	2023-11-16T07:43:05Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6427.diff", "html_url": "https://github.com/huggingface/datasets/pull/6427", "merged_at": "2023-11-16T07:43:05Z", "patch_url": "https://github.com/huggingface/datasets/pull/6427.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6427" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6427/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6426	6,426	More robust temporary directory deletion	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6426). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004750 / 0.011353 (-0.006603) \| 0.002928 / 0.011008 (-0.008080) \| 0.061962 / 0.038508 (0.023454) \| 0.029878 / 0.023109 (0.006768) \| 0.233380 / 0.275898 (-0.042518) \| 0.262221 / 0.323480 (-0.061259) \| 0.002982 / 0.007986 (-0.005004) \| 0.003698 / 0.004328 (-0.000630) \| 0.048565 / 0.004250 (0.044314) \| 0.046107 / 0.037052 (0.009055) \| 0.240090 / 0.258489 (-0.018399) \| 0.267294 / 0.293841 (-0.026547) \| 0.023335 / 0.128546 (-0.105211) \| 0.007221 / 0.075646 (-0.068425) \| 0.200903 / 0.419271 (-0.218369) \| 0.059237 / 0.043533 (0.015705) \| 0.234929 / 0.255139 (-0.020210) \| 0.256326 / 0.283200 (-0.026874) \| 0.018549 / 0.141683 (-0.123134) \| 1.103519 / 1.452155 (-0.348635) \| 1.156573 / 1.492716 (-0.336143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091205 / 0.018006 (0.073199) \| 0.303533 / 0.000490 (0.303043) \| 0.000204 / 0.000200 (0.000004) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018572 / 0.037411 (-0.018839) \| 0.062323 / 0.014526 (0.047797) \| 0.074528 / 0.176557 (-0.102029) \| 0.120295 / 0.737135 (-0.616841) \| 0.076786 / 0.296338 (-0.219552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278814 / 0.215209 (0.063605) \| 2.745483 / 2.077655 (0.667829) \| 1.486073 / 1.504120 (-0.018047) \| 1.385334 / 1.541195 (-0.155861) \| 1.386351 / 1.468490 (-0.082139) \| 0.395545 / 4.584777 (-4.189232) \| 2.409468 / 3.745712 (-1.336244) \| 2.670702 / 5.269862 (-2.599159) \| 1.629245 / 4.565676 (-2.936432) \| 0.045990 / 0.424275 (-0.378286) \| 0.004782 / 0.007607 (-0.002825) \| 0.332912 / 0.226044 (0.106867) \| 3.249277 / 2.268929 (0.980349) \| 1.888690 / 55.444624 (-53.555934) \| 1.533462 / 6.876477 (-5.343015) \| 1.576045 / 2.142072 (-0.566027) \| 0.473090 / 4.805227 (-4.332138) \| 0.099448 / 6.500664 (-6.401216) \| 0.042613 / 0.075469 (-0.032857) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944229 / 1.841788 (-0.897559) \| 12.103621 / 8.074308 (4.029313) \| 10.643471 / 10.191392 (0.452079) \| 0.143004 / 0.680424 (-0.537420) \| 0.013872 / 0.534201 (-0.520329) \| 0.272026 / 0.579283 (-0.307257) \| 0.298701 / 0.434364 (-0.135663) \| 0.310299 / 0.540337 (-0.230038) \| 0.420934 / 1.386936 (-0.966002) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004904 / 0.011353 (-0.006449) \| 0.003064 / 0.011008 (-0.007945) \| 0.047982 / 0.038508 (0.009474) \| 0.056354 / 0.023109 (0.033245) \| 0.292893 / 0.275898 (0.016995) \| 0.348744 / 0.323480 (0.025264) \| 0.003988 / 0.007986 (-0.003997) \| 0.002431 / 0.004328 (-0.001898) \| 0.049108 / 0.004250 (0.044857) \| 0.039055 / 0.037052 (0.002002) \| 0.278129 / 0.258489 (0.019640) \| 0.318547 / 0.293841 (0.024706) \| 0.025040 / 0.128546 (-0.103507) \| 0.007166 / 0.075646 (-0.068480) \| 0.053967 / 0.419271 (-0.365305) \| 0.033128 / 0.043533 (-0.010405) \| 0.272849 / 0.255139 (0.017710) \| 0.312143 / 0.283200 (0.028943) \| 0.017942 / 0.141683 (-0.123741) \| 1.192297 / 1.452155 (-0.259857) \| 1.328102 / 1.492716 (-0.164615) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090903 / 0.018006 (0.072896) \| 0.301260 / 0.000490 (0.300770) \| 0.000215 / 0.000200 (0.000015) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021112 / 0.037411 (-0.016300) \| 0.070181 / 0.014526 (0.055656) \| 0.082431 / 0.176557 (-0.094126) \| 0.121973 / 0.737135 (-0.615163) \| 0.083617 / 0.296338 (-0.212721) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289587 / 0.215209 (0.074378) \| 2.877895 / 2.077655 (0.800240) \| 1.721417 / 1.504120 (0.217297) \| 1.536023 / 1.541195 (-0.005171) \| 1.550917 / 1.468490 (0.082427) \| 0.402978 / 4.584777 (-4.181799) \| 2.431767 / 3.745712 (-1.313946) \| 2.544419 / 5.269862 (-2.725442) \| 1.554562 / 4.565676 (-3.011115) \| 0.046260 / 0.424275 (-0.378015) \| 0.004923 / 0.007607 (-0.002684) \| 0.341584 / 0.226044 (0.115540) \| 3.362133 / 2.268929 (1.093205) \| 1.928741 / 55.444624 (-53.515884) \| 1.654798 / 6.876477 (-5.221679) \| 1.715111 / 2.142072 (-0.426962) \| 0.471029 / 4.805227 (-4.334198) \| 0.098912 / 6.500664 (-6.401752) \| 0.041018 / 0.075469 (-0.034451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992880 / 1.841788 (-0.848907) \| 12.083890 / 8.074308 (4.009582) \| 11.023833 / 10.191392 (0.832441) \| 0.139217 / 0.680424 (-0.541207) \| 0.015183 / 0.534201 (-0.519018) \| 0.271637 / 0.579283 (-0.307646) \| 0.278910 / 0.434364 (-0.155454) \| 0.306891 / 0.540337 (-0.233447) \| 0.424412 / 1.386936 (-0.962524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d51f37eb9996d4c52250ee6e987ccce0d74f2f4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004545 / 0.011353 (-0.006808) \| 0.002955 / 0.011008 (-0.008054) \| 0.062119 / 0.038508 (0.023611) \| 0.029357 / 0.023109 (0.006248) \| 0.240068 / 0.275898 (-0.035830) \| 0.273376 / 0.323480 (-0.050104) \| 0.003884 / 0.007986 (-0.004102) \| 0.002390 / 0.004328 (-0.001938) \| 0.048621 / 0.004250 (0.044371) \| 0.043867 / 0.037052 (0.006815) \| 0.247240 / 0.258489 (-0.011249) \| 0.279187 / 0.293841 (-0.014654) \| 0.023377 / 0.128546 (-0.105169) \| 0.007261 / 0.075646 (-0.068385) \| 0.201913 / 0.419271 (-0.217359) \| 0.057063 / 0.043533 (0.013530) \| 0.245698 / 0.255139 (-0.009441) \| 0.265644 / 0.283200 (-0.017556) \| 0.018077 / 0.141683 (-0.123606) \| 1.133225 / 1.452155 (-0.318930) \| 1.186380 / 1.492716 (-0.306336) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089639 / 0.018006 (0.071632) \| 0.298918 / 0.000490 (0.298428) \| 0.000198 / 0.000200 (-0.000002) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019037 / 0.037411 (-0.018374) \| 0.062580 / 0.014526 (0.048055) \| 0.072974 / 0.176557 (-0.103582) \| 0.119909 / 0.737135 (-0.617226) \| 0.075021 / 0.296338 (-0.221317) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276561 / 0.215209 (0.061352) \| 2.697281 / 2.077655 (0.619626) \| 1.419772 / 1.504120 (-0.084348) \| 1.302079 / 1.541195 (-0.239115) \| 1.329143 / 1.468490 (-0.139347) \| 0.395528 / 4.584777 (-4.189249) \| 2.365788 / 3.745712 (-1.379925) \| 2.583802 / 5.269862 (-2.686059) \| 1.561983 / 4.565676 (-3.003694) \| 0.045269 / 0.424275 (-0.379006) \| 0.004826 / 0.007607 (-0.002781) \| 0.331041 / 0.226044 (0.104996) \| 3.292523 / 2.268929 (1.023595) \| 1.797865 / 55.444624 (-53.646759) \| 1.509229 / 6.876477 (-5.367248) \| 1.498884 / 2.142072 (-0.643188) \| 0.458518 / 4.805227 (-4.346709) \| 0.098076 / 6.500664 (-6.402588) \| 0.042290 / 0.075469 (-0.033179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.922331 / 1.841788 (-0.919457) \| 11.605041 / 8.074308 (3.530732) \| 10.471664 / 10.191392 (0.280272) \| 0.130325 / 0.680424 (-0.550098) \| 0.014084 / 0.534201 (-0.520117) \| 0.278877 / 0.579283 (-0.300406) \| 0.263104 / 0.434364 (-0.171259) \| 0.306723 / 0.540337 (-0.233615) \| 0.416238 / 1.386936 (-0.970698) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005094 / 0.011353 (-0.006259) \| 0.002794 / 0.011008 (-0.008214) \| 0.048189 / 0.038508 (0.009680) \| 0.050409 / 0.023109 (0.027300) \| 0.272618 / 0.275898 (-0.003280) \| 0.293589 / 0.323480 (-0.029891) \| 0.003995 / 0.007986 (-0.003991) \| 0.002373 / 0.004328 (-0.001956) \| 0.048269 / 0.004250 (0.044018) \| 0.038751 / 0.037052 (0.001698) \| 0.273495 / 0.258489 (0.015006) \| 0.309244 / 0.293841 (0.015403) \| 0.024681 / 0.128546 (-0.103866) \| 0.007390 / 0.075646 (-0.068256) \| 0.053844 / 0.419271 (-0.365427) \| 0.032395 / 0.043533 (-0.011137) \| 0.271963 / 0.255139 (0.016824) \| 0.289557 / 0.283200 (0.006357) \| 0.018659 / 0.141683 (-0.123024) \| 1.154478 / 1.452155 (-0.297676) \| 1.199772 / 1.492716 (-0.292944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089771 / 0.018006 (0.071764) \| 0.299468 / 0.000490 (0.298978) \| 0.000219 / 0.000200 (0.000020) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021854 / 0.037411 (-0.015558) \| 0.070280 / 0.014526 (0.055754) \| 0.080956 / 0.176557 (-0.095600) \| 0.119430 / 0.737135 (-0.617705) \| 0.082778 / 0.296338 (-0.213561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304273 / 0.215209 (0.089064) \| 2.968264 / 2.077655 (0.890609) \| 1.592363 / 1.504120 (0.088243) \| 1.460795 / 1.541195 (-0.080400) \| 1.501545 / 1.468490 (0.033055) \| 0.411001 / 4.584777 (-4.173776) \| 2.464273 / 3.745712 (-1.281439) \| 2.524585 / 5.269862 (-2.745277) \| 1.537443 / 4.565676 (-3.028234) \| 0.046163 / 0.424275 (-0.378112) \| 0.004783 / 0.007607 (-0.002824) \| 0.354251 / 0.226044 (0.128206) \| 3.512087 / 2.268929 (1.243158) \| 1.968156 / 55.444624 (-53.476468) \| 1.664966 / 6.876477 (-5.211510) \| 1.685013 / 2.142072 (-0.457060) \| 0.485793 / 4.805227 (-4.319435) \| 0.099789 / 6.500664 (-6.400875) \| 0.040705 / 0.075469 (-0.034764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966570 / 1.841788 (-0.875218) \| 12.023188 / 8.074308 (3.948880) \| 11.122602 / 10.191392 (0.931210) \| 0.141002 / 0.680424 (-0.539422) \| 0.015955 / 0.534201 (-0.518246) \| 0.270293 / 0.579283 (-0.308990) \| 0.281839 / 0.434364 (-0.152525) \| 0.307279 / 0.540337 (-0.233058) \| 0.434687 / 1.386936 (-0.952249) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eaad71464e85c7358eaa36494227a43257ffcd8 \"CML watermark\")\n", "What would be the impact for non-windows users ?\r\n\r\nAlso I wonder if a gc.collect() after the `del` could help to remove the PermissionError ? Or register the dataset for deletion on copy/pickle maybe ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004973 / 0.011353 (-0.006380) \| 0.002753 / 0.011008 (-0.008256) \| 0.061489 / 0.038508 (0.022981) \| 0.051122 / 0.023109 (0.028012) \| 0.228783 / 0.275898 (-0.047115) \| 0.256982 / 0.323480 (-0.066498) \| 0.002873 / 0.007986 (-0.005112) \| 0.003544 / 0.004328 (-0.000784) \| 0.048721 / 0.004250 (0.044471) \| 0.039137 / 0.037052 (0.002085) \| 0.244988 / 0.258489 (-0.013501) \| 0.275230 / 0.293841 (-0.018611) \| 0.023034 / 0.128546 (-0.105513) \| 0.006988 / 0.075646 (-0.068658) \| 0.202780 / 0.419271 (-0.216492) \| 0.035325 / 0.043533 (-0.008207) \| 0.241722 / 0.255139 (-0.013417) \| 0.259671 / 0.283200 (-0.023528) \| 0.019875 / 0.141683 (-0.121808) \| 1.098667 / 1.452155 (-0.353488) \| 1.161444 / 1.492716 (-0.331272) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093591 / 0.018006 (0.075585) \| 0.298703 / 0.000490 (0.298213) \| 0.000219 / 0.000200 (0.000019) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018319 / 0.037411 (-0.019092) \| 0.062993 / 0.014526 (0.048467) \| 0.074313 / 0.176557 (-0.102244) \| 0.123089 / 0.737135 (-0.614046) \| 0.075177 / 0.296338 (-0.221162) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268584 / 0.215209 (0.053375) \| 2.633116 / 2.077655 (0.555461) \| 1.390743 / 1.504120 (-0.113377) \| 1.277385 / 1.541195 (-0.263810) \| 1.287934 / 1.468490 (-0.180556) \| 0.387934 / 4.584777 (-4.196843) \| 2.345819 / 3.745712 (-1.399893) \| 2.558169 / 5.269862 (-2.711693) \| 1.569812 / 4.565676 (-2.995865) \| 0.045297 / 0.424275 (-0.378978) \| 0.005238 / 0.007607 (-0.002369) \| 0.359704 / 0.226044 (0.133659) \| 3.204688 / 2.268929 (0.935759) \| 1.753321 / 55.444624 (-53.691303) \| 1.492223 / 6.876477 (-5.384254) \| 1.498207 / 2.142072 (-0.643865) \| 0.459830 / 4.805227 (-4.345397) \| 0.098194 / 6.500664 (-6.402470) \| 0.042632 / 0.075469 (-0.032837) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963020 / 1.841788 (-0.878768) \| 11.500470 / 8.074308 (3.426161) \| 10.451882 / 10.191392 (0.260490) \| 0.127706 / 0.680424 (-0.552718) \| 0.014084 / 0.534201 (-0.520117) \| 0.269728 / 0.579283 (-0.309555) \| 0.260283 / 0.434364 (-0.174080) \| 0.303717 / 0.540337 (-0.236620) \| 0.397028 / 1.386936 (-0.989908) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004823 / 0.011353 (-0.006529) \| 0.002751 / 0.011008 (-0.008257) \| 0.048719 / 0.038508 (0.010211) \| 0.051409 / 0.023109 (0.028300) \| 0.267139 / 0.275898 (-0.008759) \| 0.287659 / 0.323480 (-0.035821) \| 0.003959 / 0.007986 (-0.004027) \| 0.002376 / 0.004328 (-0.001953) \| 0.047942 / 0.004250 (0.043692) \| 0.039742 / 0.037052 (0.002690) \| 0.268348 / 0.258489 (0.009859) \| 0.297201 / 0.293841 (0.003360) \| 0.024226 / 0.128546 (-0.104320) \| 0.007103 / 0.075646 (-0.068544) \| 0.053310 / 0.419271 (-0.365961) \| 0.032716 / 0.043533 (-0.010816) \| 0.269469 / 0.255139 (0.014330) \| 0.287752 / 0.283200 (0.004553) \| 0.018191 / 0.141683 (-0.123492) \| 1.114086 / 1.452155 (-0.338069) \| 1.188054 / 1.492716 (-0.304662) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091072 / 0.018006 (0.073066) \| 0.300367 / 0.000490 (0.299877) \| 0.000218 / 0.000200 (0.000018) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020970 / 0.037411 (-0.016441) \| 0.070356 / 0.014526 (0.055830) \| 0.081339 / 0.176557 (-0.095218) \| 0.120741 / 0.737135 (-0.616394) \| 0.081677 / 0.296338 (-0.214662) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290405 / 0.215209 (0.075196) \| 2.863877 / 2.077655 (0.786222) \| 1.524603 / 1.504120 (0.020483) \| 1.397917 / 1.541195 (-0.143278) \| 1.402635 / 1.468490 (-0.065855) \| 0.405525 / 4.584777 (-4.179252) \| 2.432474 / 3.745712 (-1.313239) \| 2.446277 / 5.269862 (-2.823585) \| 1.550300 / 4.565676 (-3.015377) \| 0.046545 / 0.424275 (-0.377730) \| 0.004824 / 0.007607 (-0.002783) \| 0.343578 / 0.226044 (0.117534) \| 3.436850 / 2.268929 (1.167922) \| 1.897200 / 55.444624 (-53.547425) \| 1.625222 / 6.876477 (-5.251255) \| 1.730488 / 2.142072 (-0.411585) \| 0.482099 / 4.805227 (-4.323129) \| 0.097828 / 6.500664 (-6.402836) \| 0.040385 / 0.075469 (-0.035084) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950975 / 1.841788 (-0.890812) \| 11.875024 / 8.074308 (3.800715) \| 10.430301 / 10.191392 (0.238909) \| 0.130546 / 0.680424 (-0.549878) \| 0.015423 / 0.534201 (-0.518778) \| 0.269592 / 0.579283 (-0.309691) \| 0.282505 / 0.434364 (-0.151859) \| 0.305567 / 0.540337 (-0.234771) \| 0.522142 / 1.386936 (-0.864794) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c166692aa955528180dd4d55474a984f6044896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004983 / 0.011353 (-0.006369) \| 0.003346 / 0.011008 (-0.007662) \| 0.062233 / 0.038508 (0.023725) \| 0.050246 / 0.023109 (0.027137) \| 0.305738 / 0.275898 (0.029839) \| 0.321863 / 0.323480 (-0.001617) \| 0.003870 / 0.007986 (-0.004116) \| 0.002610 / 0.004328 (-0.001718) \| 0.047734 / 0.004250 (0.043483) \| 0.037611 / 0.037052 (0.000559) \| 0.299121 / 0.258489 (0.040632) \| 0.327370 / 0.293841 (0.033529) \| 0.027009 / 0.128546 (-0.101537) \| 0.010816 / 0.075646 (-0.064830) \| 0.204627 / 0.419271 (-0.214645) \| 0.035708 / 0.043533 (-0.007825) \| 0.291837 / 0.255139 (0.036698) \| 0.313646 / 0.283200 (0.030447) \| 0.017277 / 0.141683 (-0.124405) \| 1.097907 / 1.452155 (-0.354248) \| 1.163203 / 1.492716 (-0.329513) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091933 / 0.018006 (0.073926) \| 0.298787 / 0.000490 (0.298297) \| 0.000204 / 0.000200 (0.000004) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018349 / 0.037411 (-0.019062) \| 0.061520 / 0.014526 (0.046994) \| 0.073159 / 0.176557 (-0.103397) \| 0.118657 / 0.737135 (-0.618478) \| 0.073601 / 0.296338 (-0.222737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276297 / 0.215209 (0.061088) \| 2.725668 / 2.077655 (0.648013) \| 1.458079 / 1.504120 (-0.046041) \| 1.331236 / 1.541195 (-0.209959) \| 1.347919 / 1.468490 (-0.120571) \| 0.565954 / 4.584777 (-4.018823) \| 2.380883 / 3.745712 (-1.364829) \| 2.800533 / 5.269862 (-2.469329) \| 1.740534 / 4.565676 (-2.825142) \| 0.065617 / 0.424275 (-0.358658) \| 0.004907 / 0.007607 (-0.002700) \| 0.335973 / 0.226044 (0.109929) \| 3.337405 / 2.268929 (1.068476) \| 1.819852 / 55.444624 (-53.624772) \| 1.542724 / 6.876477 (-5.333752) \| 1.509508 / 2.142072 (-0.632565) \| 0.648618 / 4.805227 (-4.156609) \| 0.116812 / 6.500664 (-6.383852) \| 0.041561 / 0.075469 (-0.033909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943488 / 1.841788 (-0.898299) \| 11.184770 / 8.074308 (3.110462) \| 10.406311 / 10.191392 (0.214919) \| 0.129841 / 0.680424 (-0.550583) \| 0.013736 / 0.534201 (-0.520465) \| 0.287281 / 0.579283 (-0.292002) \| 0.267403 / 0.434364 (-0.166961) \| 0.325319 / 0.540337 (-0.215019) \| 0.454207 / 1.386936 (-0.932729) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005169 / 0.011353 (-0.006183) \| 0.003155 / 0.011008 (-0.007854) \| 0.048101 / 0.038508 (0.009593) \| 0.048726 / 0.023109 (0.025617) \| 0.275768 / 0.275898 (-0.000130) \| 0.291209 / 0.323480 (-0.032271) \| 0.003984 / 0.007986 (-0.004001) \| 0.002586 / 0.004328 (-0.001742) \| 0.047751 / 0.004250 (0.043500) \| 0.040176 / 0.037052 (0.003124) \| 0.279161 / 0.258489 (0.020672) \| 0.297371 / 0.293841 (0.003530) \| 0.028502 / 0.128546 (-0.100044) \| 0.010103 / 0.075646 (-0.065544) \| 0.056920 / 0.419271 (-0.362351) \| 0.032174 / 0.043533 (-0.011359) \| 0.271925 / 0.255139 (0.016786) \| 0.289572 / 0.283200 (0.006372) \| 0.017981 / 0.141683 (-0.123702) \| 1.192972 / 1.452155 (-0.259183) \| 1.223231 / 1.492716 (-0.269485) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091363 / 0.018006 (0.073356) \| 0.298106 / 0.000490 (0.297616) \| 0.000216 / 0.000200 (0.000016) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021509 / 0.037411 (-0.015902) \| 0.068377 / 0.014526 (0.053851) \| 0.079798 / 0.176557 (-0.096759) \| 0.120546 / 0.737135 (-0.616589) \| 0.080602 / 0.296338 (-0.215737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300809 / 0.215209 (0.085600) \| 2.921144 / 2.077655 (0.843489) \| 1.621096 / 1.504120 (0.116976) \| 1.504265 / 1.541195 (-0.036930) \| 1.508050 / 1.468490 (0.039560) \| 0.554291 / 4.584777 (-4.030486) \| 2.418798 / 3.745712 (-1.326914) \| 2.768088 / 5.269862 (-2.501773) \| 1.728267 / 4.565676 (-2.837410) \| 0.062943 / 0.424275 (-0.361332) \| 0.004891 / 0.007607 (-0.002716) \| 0.350298 / 0.226044 (0.124254) \| 3.442782 / 2.268929 (1.173853) \| 1.960163 / 55.444624 (-53.484461) \| 1.682000 / 6.876477 (-5.194477) \| 1.680311 / 2.142072 (-0.461761) \| 0.631201 / 4.805227 (-4.174026) \| 0.115211 / 6.500664 (-6.385453) \| 0.041279 / 0.075469 (-0.034190) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962478 / 1.841788 (-0.879310) \| 11.671463 / 8.074308 (3.597155) \| 10.640129 / 10.191392 (0.448737) \| 0.130649 / 0.680424 (-0.549775) \| 0.016169 / 0.534201 (-0.518032) \| 0.286894 / 0.579283 (-0.292389) \| 0.269319 / 0.434364 (-0.165045) \| 0.324512 / 0.540337 (-0.215825) \| 0.550874 / 1.386936 (-0.836062) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69f135121beb1616f1d7c7584b317d4e41e21275 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005078 / 0.011353 (-0.006275) \| 0.003950 / 0.011008 (-0.007058) \| 0.063345 / 0.038508 (0.024837) \| 0.054486 / 0.023109 (0.031377) \| 0.243213 / 0.275898 (-0.032685) \| 0.264079 / 0.323480 (-0.059401) \| 0.003922 / 0.007986 (-0.004064) \| 0.002631 / 0.004328 (-0.001698) \| 0.048660 / 0.004250 (0.044409) \| 0.037205 / 0.037052 (0.000153) \| 0.244577 / 0.258489 (-0.013912) \| 0.276025 / 0.293841 (-0.017816) \| 0.027134 / 0.128546 (-0.101412) \| 0.010921 / 0.075646 (-0.064726) \| 0.209792 / 0.419271 (-0.209479) \| 0.035999 / 0.043533 (-0.007534) \| 0.245671 / 0.255139 (-0.009468) \| 0.262807 / 0.283200 (-0.020393) \| 0.018173 / 0.141683 (-0.123510) \| 1.084417 / 1.452155 (-0.367738) \| 1.148284 / 1.492716 (-0.344432) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093128 / 0.018006 (0.075122) \| 0.301606 / 0.000490 (0.301117) \| 0.000221 / 0.000200 (0.000021) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018718 / 0.037411 (-0.018693) \| 0.060819 / 0.014526 (0.046293) \| 0.073050 / 0.176557 (-0.103507) \| 0.120043 / 0.737135 (-0.617092) \| 0.075374 / 0.296338 (-0.220965) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291080 / 0.215209 (0.075871) \| 2.808802 / 2.077655 (0.731148) \| 1.485686 / 1.504120 (-0.018434) \| 1.354356 / 1.541195 (-0.186839) \| 1.347863 / 1.468490 (-0.120627) \| 0.571501 / 4.584777 (-4.013276) \| 2.377960 / 3.745712 (-1.367752) \| 2.768023 / 5.269862 (-2.501839) \| 1.754360 / 4.565676 (-2.811316) \| 0.063115 / 0.424275 (-0.361160) \| 0.004941 / 0.007607 (-0.002666) \| 0.338281 / 0.226044 (0.112237) \| 3.340587 / 2.268929 (1.071658) \| 1.849479 / 55.444624 (-53.595145) \| 1.551846 / 6.876477 (-5.324631) \| 1.539090 / 2.142072 (-0.602983) \| 0.644522 / 4.805227 (-4.160705) \| 0.117398 / 6.500664 (-6.383266) \| 0.042239 / 0.075469 (-0.033230) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949496 / 1.841788 (-0.892291) \| 11.548352 / 8.074308 (3.474044) \| 10.478065 / 10.191392 (0.286673) \| 0.129534 / 0.680424 (-0.550890) \| 0.015378 / 0.534201 (-0.518822) \| 0.287221 / 0.579283 (-0.292062) \| 0.262944 / 0.434364 (-0.171419) \| 0.321727 / 0.540337 (-0.218611) \| 0.432354 / 1.386936 (-0.954582) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005256 / 0.011353 (-0.006097) \| 0.003491 / 0.011008 (-0.007517) \| 0.048647 / 0.038508 (0.010139) \| 0.054011 / 0.023109 (0.030901) \| 0.271786 / 0.275898 (-0.004112) \| 0.291964 / 0.323480 (-0.031516) \| 0.004035 / 0.007986 (-0.003950) \| 0.002671 / 0.004328 (-0.001657) \| 0.048108 / 0.004250 (0.043857) \| 0.040421 / 0.037052 (0.003368) \| 0.278594 / 0.258489 (0.020105) \| 0.300707 / 0.293841 (0.006867) \| 0.028924 / 0.128546 (-0.099623) \| 0.010600 / 0.075646 (-0.065047) \| 0.057649 / 0.419271 (-0.361623) \| 0.034221 / 0.043533 (-0.009312) \| 0.276692 / 0.255139 (0.021553) \| 0.293545 / 0.283200 (0.010345) \| 0.017908 / 0.141683 (-0.123775) \| 1.135108 / 1.452155 (-0.317047) \| 1.190823 / 1.492716 (-0.301893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095243 / 0.018006 (0.077237) \| 0.301885 / 0.000490 (0.301396) \| 0.000235 / 0.000200 (0.000035) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021561 / 0.037411 (-0.015850) \| 0.069054 / 0.014526 (0.054529) \| 0.080466 / 0.176557 (-0.096091) \| 0.121323 / 0.737135 (-0.615812) \| 0.081891 / 0.296338 (-0.214448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293957 / 0.215209 (0.078748) \| 2.869035 / 2.077655 (0.791380) \| 1.608837 / 1.504120 (0.104717) \| 1.440594 / 1.541195 (-0.100601) \| 1.464775 / 1.468490 (-0.003715) \| 0.565663 / 4.584777 (-4.019114) \| 2.439456 / 3.745712 (-1.306256) \| 2.794775 / 5.269862 (-2.475087) \| 1.750026 / 4.565676 (-2.815651) \| 0.063291 / 0.424275 (-0.360984) \| 0.004930 / 0.007607 (-0.002677) \| 0.347169 / 0.226044 (0.121125) \| 3.408260 / 2.268929 (1.139331) \| 1.920933 / 55.444624 (-53.523691) \| 1.648821 / 6.876477 (-5.227656) \| 1.639022 / 2.142072 (-0.503051) \| 0.642870 / 4.805227 (-4.162357) \| 0.117077 / 6.500664 (-6.383587) \| 0.040784 / 0.075469 (-0.034685) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993501 / 1.841788 (-0.848287) \| 12.012423 / 8.074308 (3.938115) \| 10.740932 / 10.191392 (0.549540) \| 0.132409 / 0.680424 (-0.548015) \| 0.015294 / 0.534201 (-0.518907) \| 0.287902 / 0.579283 (-0.291381) \| 0.281350 / 0.434364 (-0.153014) \| 0.329201 / 0.540337 (-0.211137) \| 0.553199 / 1.386936 (-0.833737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ecd3a22c5dec2133491a320515e12956512439eb \"CML watermark\")\n" ]	2023-11-15T19:06:42Z	2023-12-01T15:37:32Z	2023-12-01T15:31:19Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6426.diff", "html_url": "https://github.com/huggingface/datasets/pull/6426", "merged_at": "2023-12-01T15:31:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/6426.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6426" }	While fixing the Windows errors in #6362, I noticed that `PermissionError` can still easily be thrown on the session exit by the temporary cache directory's finalizer (we would also have to keep track of intermediate datasets, copies, etc.). ~~Due to the low usage of `datasets` on Windows, this PR takes a simpler approach to the issue than https://github.com/huggingface/datasets/pull/2403 - it tries to delete the temporary cache directory, and if this fails, logs a warning message about using a `delete-temp-cache` CLI command to delete it manually. The problematic references are freed after the session exits, so the CLI command should then succeed.~~ This PR implements `Dataset.__setstate__` to register datasets with temporary cache files for deletion.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6426/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6425	6,425	Fix deprecation warning when building conda package	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004811 / 0.011353 (-0.006542) \| 0.002478 / 0.011008 (-0.008530) \| 0.062241 / 0.038508 (0.023733) \| 0.031153 / 0.023109 (0.008044) \| 0.248896 / 0.275898 (-0.027002) \| 0.276860 / 0.323480 (-0.046620) \| 0.002934 / 0.007986 (-0.005052) \| 0.002428 / 0.004328 (-0.001901) \| 0.048507 / 0.004250 (0.044257) \| 0.044567 / 0.037052 (0.007515) \| 0.253570 / 0.258489 (-0.004919) \| 0.280762 / 0.293841 (-0.013079) \| 0.023549 / 0.128546 (-0.104997) \| 0.006985 / 0.075646 (-0.068661) \| 0.206227 / 0.419271 (-0.213044) \| 0.054027 / 0.043533 (0.010494) \| 0.257655 / 0.255139 (0.002516) \| 0.273498 / 0.283200 (-0.009702) \| 0.018997 / 0.141683 (-0.122685) \| 1.111732 / 1.452155 (-0.340422) \| 1.162078 / 1.492716 (-0.330639) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091816 / 0.018006 (0.073810) \| 0.299428 / 0.000490 (0.298938) \| 0.000211 / 0.000200 (0.000012) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018503 / 0.037411 (-0.018908) \| 0.062933 / 0.014526 (0.048407) \| 0.076349 / 0.176557 (-0.100208) \| 0.123291 / 0.737135 (-0.613844) \| 0.077491 / 0.296338 (-0.218847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280770 / 0.215209 (0.065561) \| 2.762185 / 2.077655 (0.684530) \| 1.429124 / 1.504120 (-0.074996) \| 1.303162 / 1.541195 (-0.238033) \| 1.307523 / 1.468490 (-0.160967) \| 0.405593 / 4.584777 (-4.179184) \| 2.396992 / 3.745712 (-1.348721) \| 2.550968 / 5.269862 (-2.718894) \| 1.557358 / 4.565676 (-3.008318) \| 0.046149 / 0.424275 (-0.378126) \| 0.004808 / 0.007607 (-0.002799) \| 0.341870 / 0.226044 (0.115825) \| 3.362478 / 2.268929 (1.093550) \| 1.786360 / 55.444624 (-53.658264) \| 1.483419 / 6.876477 (-5.393058) \| 1.493463 / 2.142072 (-0.648609) \| 0.470605 / 4.805227 (-4.334623) \| 0.098372 / 6.500664 (-6.402292) \| 0.041722 / 0.075469 (-0.033748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938148 / 1.841788 (-0.903640) \| 11.219184 / 8.074308 (3.144876) \| 10.454439 / 10.191392 (0.263047) \| 0.139645 / 0.680424 (-0.540778) \| 0.014453 / 0.534201 (-0.519748) \| 0.268975 / 0.579283 (-0.310308) \| 0.262060 / 0.434364 (-0.172304) \| 0.313652 / 0.540337 (-0.226686) \| 0.423992 / 1.386936 (-0.962944) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004829 / 0.011353 (-0.006524) \| 0.002426 / 0.011008 (-0.008582) \| 0.049064 / 0.038508 (0.010555) \| 0.049728 / 0.023109 (0.026619) \| 0.273263 / 0.275898 (-0.002635) \| 0.295645 / 0.323480 (-0.027835) \| 0.004156 / 0.007986 (-0.003830) \| 0.002397 / 0.004328 (-0.001932) \| 0.048902 / 0.004250 (0.044652) \| 0.038414 / 0.037052 (0.001362) \| 0.276176 / 0.258489 (0.017687) \| 0.306844 / 0.293841 (0.013003) \| 0.024546 / 0.128546 (-0.104000) \| 0.006946 / 0.075646 (-0.068701) \| 0.054024 / 0.419271 (-0.365247) \| 0.032444 / 0.043533 (-0.011089) \| 0.274125 / 0.255139 (0.018986) \| 0.293226 / 0.283200 (0.010027) \| 0.018003 / 0.141683 (-0.123680) \| 1.130402 / 1.452155 (-0.321752) \| 1.195969 / 1.492716 (-0.296748) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090043 / 0.018006 (0.072037) \| 0.298699 / 0.000490 (0.298209) \| 0.000214 / 0.000200 (0.000014) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021284 / 0.037411 (-0.016127) \| 0.069954 / 0.014526 (0.055428) \| 0.080445 / 0.176557 (-0.096111) \| 0.119461 / 0.737135 (-0.617674) \| 0.080632 / 0.296338 (-0.215706) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302246 / 0.215209 (0.087037) \| 2.991936 / 2.077655 (0.914281) \| 1.662969 / 1.504120 (0.158850) \| 1.533141 / 1.541195 (-0.008054) \| 1.583183 / 1.468490 (0.114693) \| 0.402864 / 4.584777 (-4.181913) \| 2.424119 / 3.745712 (-1.321593) \| 2.489558 / 5.269862 (-2.780303) \| 1.502196 / 4.565676 (-3.063481) \| 0.045980 / 0.424275 (-0.378295) \| 0.004768 / 0.007607 (-0.002839) \| 0.356089 / 0.226044 (0.130044) \| 3.481333 / 2.268929 (1.212404) \| 2.009713 / 55.444624 (-53.434912) \| 1.730021 / 6.876477 (-5.146455) \| 1.704656 / 2.142072 (-0.437416) \| 0.470832 / 4.805227 (-4.334395) \| 0.097473 / 6.500664 (-6.403191) \| 0.040437 / 0.075469 (-0.035032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981497 / 1.841788 (-0.860291) \| 11.827242 / 8.074308 (3.752933) \| 10.888324 / 10.191392 (0.696932) \| 0.129249 / 0.680424 (-0.551174) \| 0.015812 / 0.534201 (-0.518389) \| 0.269657 / 0.579283 (-0.309626) \| 0.275585 / 0.434364 (-0.158779) \| 0.305698 / 0.540337 (-0.234639) \| 0.411497 / 1.386936 (-0.975439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6425). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005402 / 0.011353 (-0.005951) \| 0.003955 / 0.011008 (-0.007053) \| 0.064096 / 0.038508 (0.025588) \| 0.062330 / 0.023109 (0.039221) \| 0.254729 / 0.275898 (-0.021169) \| 0.276259 / 0.323480 (-0.047221) \| 0.003052 / 0.007986 (-0.004934) \| 0.003474 / 0.004328 (-0.000854) \| 0.048938 / 0.004250 (0.044687) \| 0.038635 / 0.037052 (0.001583) \| 0.267953 / 0.258489 (0.009464) \| 0.293725 / 0.293841 (-0.000116) \| 0.028266 / 0.128546 (-0.100280) \| 0.011188 / 0.075646 (-0.064458) \| 0.221204 / 0.419271 (-0.198067) \| 0.036549 / 0.043533 (-0.006984) \| 0.252484 / 0.255139 (-0.002655) \| 0.273855 / 0.283200 (-0.009345) \| 0.017975 / 0.141683 (-0.123708) \| 1.112265 / 1.452155 (-0.339890) \| 1.185647 / 1.492716 (-0.307069) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096223 / 0.018006 (0.078217) \| 0.305010 / 0.000490 (0.304520) \| 0.000227 / 0.000200 (0.000027) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018924 / 0.037411 (-0.018488) \| 0.061910 / 0.014526 (0.047384) \| 0.073751 / 0.176557 (-0.102806) \| 0.120956 / 0.737135 (-0.616179) \| 0.075090 / 0.296338 (-0.221249) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293277 / 0.215209 (0.078068) \| 2.867468 / 2.077655 (0.789813) \| 1.518218 / 1.504120 (0.014098) \| 1.393741 / 1.541195 (-0.147454) \| 1.424979 / 1.468490 (-0.043511) \| 0.579766 / 4.584777 (-4.005011) \| 2.434951 / 3.745712 (-1.310761) \| 2.909924 / 5.269862 (-2.359937) \| 1.838123 / 4.565676 (-2.727554) \| 0.064260 / 0.424275 (-0.360015) \| 0.005169 / 0.007607 (-0.002438) \| 0.348228 / 0.226044 (0.122184) \| 3.447558 / 2.268929 (1.178629) \| 1.884988 / 55.444624 (-53.559636) \| 1.570921 / 6.876477 (-5.305556) \| 1.646341 / 2.142072 (-0.495732) \| 0.660189 / 4.805227 (-4.145038) \| 0.120026 / 6.500664 (-6.380638) \| 0.043715 / 0.075469 (-0.031754) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953253 / 1.841788 (-0.888535) \| 12.576112 / 8.074308 (4.501804) \| 11.132637 / 10.191392 (0.941245) \| 0.132870 / 0.680424 (-0.547553) \| 0.014720 / 0.534201 (-0.519481) \| 0.291866 / 0.579283 (-0.287417) \| 0.265456 / 0.434364 (-0.168908) \| 0.338629 / 0.540337 (-0.201709) \| 0.456323 / 1.386936 (-0.930613) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005644 / 0.011353 (-0.005709) \| 0.003624 / 0.011008 (-0.007384) \| 0.049043 / 0.038508 (0.010535) \| 0.059572 / 0.023109 (0.036463) \| 0.277159 / 0.275898 (0.001261) \| 0.303933 / 0.323480 (-0.019547) \| 0.004294 / 0.007986 (-0.003692) \| 0.002744 / 0.004328 (-0.001584) \| 0.048187 / 0.004250 (0.043937) \| 0.043655 / 0.037052 (0.006603) \| 0.282441 / 0.258489 (0.023952) \| 0.317130 / 0.293841 (0.023289) \| 0.030159 / 0.128546 (-0.098387) \| 0.011300 / 0.075646 (-0.064346) \| 0.057451 / 0.419271 (-0.361821) \| 0.033666 / 0.043533 (-0.009866) \| 0.274554 / 0.255139 (0.019415) \| 0.292470 / 0.283200 (0.009270) \| 0.018757 / 0.141683 (-0.122926) \| 1.170094 / 1.452155 (-0.282060) \| 1.244626 / 1.492716 (-0.248090) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094920 / 0.018006 (0.076914) \| 0.304156 / 0.000490 (0.303666) \| 0.000226 / 0.000200 (0.000026) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022297 / 0.037411 (-0.015115) \| 0.068908 / 0.014526 (0.054383) \| 0.081520 / 0.176557 (-0.095037) \| 0.122422 / 0.737135 (-0.614714) \| 0.082533 / 0.296338 (-0.213806) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296080 / 0.215209 (0.080871) \| 2.883120 / 2.077655 (0.805465) \| 1.607950 / 1.504120 (0.103830) \| 1.496191 / 1.541195 (-0.045004) \| 1.520549 / 1.468490 (0.052059) \| 0.562081 / 4.584777 (-4.022696) \| 2.453447 / 3.745712 (-1.292265) \| 2.943676 / 5.269862 (-2.326186) \| 1.820581 / 4.565676 (-2.745096) \| 0.064518 / 0.424275 (-0.359757) \| 0.005406 / 0.007607 (-0.002201) \| 0.349022 / 0.226044 (0.122978) \| 3.472117 / 2.268929 (1.203188) \| 2.006928 / 55.444624 (-53.437696) \| 1.704800 / 6.876477 (-5.171677) \| 1.719025 / 2.142072 (-0.423048) \| 0.643719 / 4.805227 (-4.161508) \| 0.117723 / 6.500664 (-6.382941) \| 0.043158 / 0.075469 (-0.032311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981229 / 1.841788 (-0.860559) \| 12.637620 / 8.074308 (4.563312) \| 10.848775 / 10.191392 (0.657383) \| 0.143981 / 0.680424 (-0.536443) \| 0.015950 / 0.534201 (-0.518251) \| 0.287542 / 0.579283 (-0.291741) \| 0.278989 / 0.434364 (-0.155375) \| 0.331786 / 0.540337 (-0.208552) \| 0.607238 / 1.386936 (-0.779698) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06fb2f9973962ee97d1af7888209819b8ba7de37 \"CML watermark\")\n" ]	2023-11-15T18:00:11Z	2023-12-13T14:22:30Z	2023-12-13T14:16:00Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6425.diff", "html_url": "https://github.com/huggingface/datasets/pull/6425", "merged_at": "2023-12-13T14:16:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6425.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6425" }	When building/releasing conda package, we get this deprecation warning: ``` /usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead. ``` This PR fixes the deprecation warning by using `conda build` instead.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6425/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6424	6,424	[docs] troubleshooting guide	{ "avatar_url": "https://avatars.githubusercontent.com/u/1065417?v=4", "events_url": "https://api.github.com/users/MKhalusova/events{/privacy}", "followers_url": "https://api.github.com/users/MKhalusova/followers", "following_url": "https://api.github.com/users/MKhalusova/following{/other_user}", "gists_url": "https://api.github.com/users/MKhalusova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MKhalusova", "id": 1065417, "login": "MKhalusova", "node_id": "MDQ6VXNlcjEwNjU0MTc=", "organizations_url": "https://api.github.com/users/MKhalusova/orgs", "received_events_url": "https://api.github.com/users/MKhalusova/received_events", "repos_url": "https://api.github.com/users/MKhalusova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MKhalusova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MKhalusova/subscriptions", "type": "User", "url": "https://api.github.com/users/MKhalusova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6424). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005323 / 0.011353 (-0.006030) \| 0.003560 / 0.011008 (-0.007448) \| 0.062572 / 0.038508 (0.024064) \| 0.049549 / 0.023109 (0.026440) \| 0.236522 / 0.275898 (-0.039376) \| 0.260601 / 0.323480 (-0.062879) \| 0.002887 / 0.007986 (-0.005099) \| 0.003225 / 0.004328 (-0.001103) \| 0.048210 / 0.004250 (0.043960) \| 0.038783 / 0.037052 (0.001731) \| 0.242506 / 0.258489 (-0.015983) \| 0.273906 / 0.293841 (-0.019935) \| 0.027202 / 0.128546 (-0.101344) \| 0.010577 / 0.075646 (-0.065069) \| 0.211669 / 0.419271 (-0.207603) \| 0.035727 / 0.043533 (-0.007806) \| 0.242303 / 0.255139 (-0.012836) \| 0.260468 / 0.283200 (-0.022732) \| 0.020109 / 0.141683 (-0.121573) \| 1.089603 / 1.452155 (-0.362552) \| 1.149899 / 1.492716 (-0.342817) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088768 / 0.018006 (0.070761) \| 0.300300 / 0.000490 (0.299810) \| 0.000212 / 0.000200 (0.000013) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018758 / 0.037411 (-0.018653) \| 0.060097 / 0.014526 (0.045571) \| 0.074060 / 0.176557 (-0.102496) \| 0.119977 / 0.737135 (-0.617158) \| 0.075298 / 0.296338 (-0.221040) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278640 / 0.215209 (0.063431) \| 2.715574 / 2.077655 (0.637919) \| 1.466644 / 1.504120 (-0.037476) \| 1.344470 / 1.541195 (-0.196725) \| 1.386984 / 1.468490 (-0.081506) \| 0.575796 / 4.584777 (-4.008981) \| 2.392324 / 3.745712 (-1.353388) \| 2.826284 / 5.269862 (-2.443578) \| 1.758997 / 4.565676 (-2.806679) \| 0.062474 / 0.424275 (-0.361801) \| 0.004930 / 0.007607 (-0.002678) \| 0.332595 / 0.226044 (0.106551) \| 3.240076 / 2.268929 (0.971147) \| 1.785283 / 55.444624 (-53.659341) \| 1.527594 / 6.876477 (-5.348882) \| 1.562840 / 2.142072 (-0.579233) \| 0.655474 / 4.805227 (-4.149754) \| 0.116682 / 6.500664 (-6.383983) \| 0.042664 / 0.075469 (-0.032805) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936306 / 1.841788 (-0.905481) \| 11.561239 / 8.074308 (3.486931) \| 10.341918 / 10.191392 (0.150526) \| 0.140602 / 0.680424 (-0.539822) \| 0.013857 / 0.534201 (-0.520344) \| 0.294241 / 0.579283 (-0.285042) \| 0.268359 / 0.434364 (-0.166005) \| 0.326344 / 0.540337 (-0.213993) \| 0.430936 / 1.386936 (-0.956000) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005197 / 0.011353 (-0.006156) \| 0.003543 / 0.011008 (-0.007465) \| 0.049051 / 0.038508 (0.010542) \| 0.052742 / 0.023109 (0.029633) \| 0.277032 / 0.275898 (0.001134) \| 0.300799 / 0.323480 (-0.022681) \| 0.003922 / 0.007986 (-0.004064) \| 0.002573 / 0.004328 (-0.001755) \| 0.047270 / 0.004250 (0.043019) \| 0.039782 / 0.037052 (0.002730) \| 0.282780 / 0.258489 (0.024291) \| 0.308858 / 0.293841 (0.015017) \| 0.028641 / 0.128546 (-0.099905) \| 0.010516 / 0.075646 (-0.065131) \| 0.056367 / 0.419271 (-0.362904) \| 0.032346 / 0.043533 (-0.011186) \| 0.277591 / 0.255139 (0.022452) \| 0.298539 / 0.283200 (0.015339) \| 0.018168 / 0.141683 (-0.123515) \| 1.104331 / 1.452155 (-0.347823) \| 1.187691 / 1.492716 (-0.305025) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089511 / 0.018006 (0.071505) \| 0.301309 / 0.000490 (0.300820) \| 0.000213 / 0.000200 (0.000013) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021466 / 0.037411 (-0.015945) \| 0.069917 / 0.014526 (0.055391) \| 0.081105 / 0.176557 (-0.095452) \| 0.119619 / 0.737135 (-0.617516) \| 0.083928 / 0.296338 (-0.212410) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296471 / 0.215209 (0.081262) \| 2.912139 / 2.077655 (0.834484) \| 1.588861 / 1.504120 (0.084741) \| 1.452148 / 1.541195 (-0.089047) \| 1.475388 / 1.468490 (0.006898) \| 0.555779 / 4.584777 (-4.028998) \| 2.425599 / 3.745712 (-1.320113) \| 2.792848 / 5.269862 (-2.477013) \| 1.718757 / 4.565676 (-2.846919) \| 0.077687 / 0.424275 (-0.346588) \| 0.007522 / 0.007607 (-0.000085) \| 0.348254 / 0.226044 (0.122210) \| 3.439315 / 2.268929 (1.170386) \| 1.925907 / 55.444624 (-53.518717) \| 1.646163 / 6.876477 (-5.230314) \| 1.662148 / 2.142072 (-0.479924) \| 0.637277 / 4.805227 (-4.167950) \| 0.116159 / 6.500664 (-6.384505) \| 0.041518 / 0.075469 (-0.033952) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966358 / 1.841788 (-0.875430) \| 12.125201 / 8.074308 (4.050892) \| 10.629939 / 10.191392 (0.438547) \| 0.132439 / 0.680424 (-0.547984) \| 0.015622 / 0.534201 (-0.518579) \| 0.288824 / 0.579283 (-0.290459) \| 0.277634 / 0.434364 (-0.156730) \| 0.327200 / 0.540337 (-0.213138) \| 0.549679 / 1.386936 (-0.837257) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0850f663f5498e0f296461e99a345dfd65e3358f \"CML watermark\")\n" ]	2023-11-15T17:28:14Z	2023-11-30T17:29:55Z	2023-11-30T17:23:46Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6424.diff", "html_url": "https://github.com/huggingface/datasets/pull/6424", "merged_at": "2023-11-30T17:23:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/6424.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6424" }	Hi all! This is a PR adding a troubleshooting guide for Datasets docs. I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are: - creating a dataset from a folder and not following the required format - authentication issues when using `push_to_hub` - `Too Many Requests` with `push_to_hub` - Pickling issues when using Dataset.from_generator() There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here.	{ "avatar_url": "https://avatars.githubusercontent.com/u/1065417?v=4", "events_url": "https://api.github.com/users/MKhalusova/events{/privacy}", "followers_url": "https://api.github.com/users/MKhalusova/followers", "following_url": "https://api.github.com/users/MKhalusova/following{/other_user}", "gists_url": "https://api.github.com/users/MKhalusova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MKhalusova", "id": 1065417, "login": "MKhalusova", "node_id": "MDQ6VXNlcjEwNjU0MTc=", "organizations_url": "https://api.github.com/users/MKhalusova/orgs", "received_events_url": "https://api.github.com/users/MKhalusova/received_events", "repos_url": "https://api.github.com/users/MKhalusova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MKhalusova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MKhalusova/subscriptions", "type": "User", "url": "https://api.github.com/users/MKhalusova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6424/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6423	6,423	Fix conda release by adding pyarrow-hotfix dependency	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004476 / 0.011353 (-0.006877) \| 0.002691 / 0.011008 (-0.008317) \| 0.061400 / 0.038508 (0.022892) \| 0.030096 / 0.023109 (0.006986) \| 0.279868 / 0.275898 (0.003970) \| 0.310320 / 0.323480 (-0.013159) \| 0.003873 / 0.007986 (-0.004112) \| 0.002394 / 0.004328 (-0.001935) \| 0.048307 / 0.004250 (0.044056) \| 0.043326 / 0.037052 (0.006273) \| 0.288256 / 0.258489 (0.029767) \| 0.311449 / 0.293841 (0.017609) \| 0.022970 / 0.128546 (-0.105576) \| 0.006714 / 0.075646 (-0.068932) \| 0.201656 / 0.419271 (-0.217615) \| 0.052811 / 0.043533 (0.009278) \| 0.285123 / 0.255139 (0.029984) \| 0.301495 / 0.283200 (0.018295) \| 0.017531 / 0.141683 (-0.124152) \| 1.097660 / 1.452155 (-0.354494) \| 1.161986 / 1.492716 (-0.330731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089223 / 0.018006 (0.071217) \| 0.297815 / 0.000490 (0.297326) \| 0.000205 / 0.000200 (0.000005) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018679 / 0.037411 (-0.018732) \| 0.062742 / 0.014526 (0.048216) \| 0.072869 / 0.176557 (-0.103687) \| 0.120730 / 0.737135 (-0.616406) \| 0.074526 / 0.296338 (-0.221813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299977 / 0.215209 (0.084768) \| 2.921029 / 2.077655 (0.843375) \| 1.632283 / 1.504120 (0.128163) \| 1.508008 / 1.541195 (-0.033187) \| 1.513967 / 1.468490 (0.045477) \| 0.403056 / 4.584777 (-4.181721) \| 2.340011 / 3.745712 (-1.405701) \| 2.552319 / 5.269862 (-2.717543) \| 1.549741 / 4.565676 (-3.015935) \| 0.046303 / 0.424275 (-0.377972) \| 0.004768 / 0.007607 (-0.002839) \| 0.356921 / 0.226044 (0.130877) \| 3.506410 / 2.268929 (1.237482) \| 1.975394 / 55.444624 (-53.469230) \| 1.688683 / 6.876477 (-5.187794) \| 1.715502 / 2.142072 (-0.426571) \| 0.471016 / 4.805227 (-4.334212) \| 0.099552 / 6.500664 (-6.401112) \| 0.042095 / 0.075469 (-0.033374) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.955784 / 1.841788 (-0.886004) \| 11.191802 / 8.074308 (3.117494) \| 10.127818 / 10.191392 (-0.063574) \| 0.141225 / 0.680424 (-0.539199) \| 0.014486 / 0.534201 (-0.519715) \| 0.267204 / 0.579283 (-0.312079) \| 0.289108 / 0.434364 (-0.145256) \| 0.309458 / 0.540337 (-0.230880) \| 0.422802 / 1.386936 (-0.964134) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002907 / 0.011008 (-0.008101) \| 0.047666 / 0.038508 (0.009158) \| 0.051183 / 0.023109 (0.028074) \| 0.266315 / 0.275898 (-0.009583) \| 0.286429 / 0.323480 (-0.037051) \| 0.003954 / 0.007986 (-0.004031) \| 0.002041 / 0.004328 (-0.002288) \| 0.047652 / 0.004250 (0.043401) \| 0.038211 / 0.037052 (0.001158) \| 0.272210 / 0.258489 (0.013721) \| 0.299425 / 0.293841 (0.005584) \| 0.024266 / 0.128546 (-0.104280) \| 0.006747 / 0.075646 (-0.068900) \| 0.052959 / 0.419271 (-0.366312) \| 0.032094 / 0.043533 (-0.011439) \| 0.265677 / 0.255139 (0.010538) \| 0.285373 / 0.283200 (0.002174) \| 0.017577 / 0.141683 (-0.124106) \| 1.114514 / 1.452155 (-0.337640) \| 1.212970 / 1.492716 (-0.279746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088347 / 0.018006 (0.070341) \| 0.296678 / 0.000490 (0.296188) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021159 / 0.037411 (-0.016253) \| 0.069886 / 0.014526 (0.055360) \| 0.079832 / 0.176557 (-0.096725) \| 0.115512 / 0.737135 (-0.621623) \| 0.081600 / 0.296338 (-0.214739) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292659 / 0.215209 (0.077450) \| 2.872556 / 2.077655 (0.794901) \| 1.573017 / 1.504120 (0.068897) \| 1.445122 / 1.541195 (-0.096072) \| 1.485584 / 1.468490 (0.017094) \| 0.388638 / 4.584777 (-4.196139) \| 2.434847 / 3.745712 (-1.310865) \| 2.518167 / 5.269862 (-2.751695) \| 1.503000 / 4.565676 (-3.062676) \| 0.045123 / 0.424275 (-0.379153) \| 0.004778 / 0.007607 (-0.002829) \| 0.347955 / 0.226044 (0.121910) \| 3.384819 / 2.268929 (1.115891) \| 1.920185 / 55.444624 (-53.524439) \| 1.646910 / 6.876477 (-5.229567) \| 1.638092 / 2.142072 (-0.503980) \| 0.450535 / 4.805227 (-4.354692) \| 0.095301 / 6.500664 (-6.405363) \| 0.040275 / 0.075469 (-0.035194) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956088 / 1.841788 (-0.885700) \| 11.776642 / 8.074308 (3.702334) \| 10.651063 / 10.191392 (0.459671) \| 0.127079 / 0.680424 (-0.553345) \| 0.015080 / 0.534201 (-0.519121) \| 0.273737 / 0.579283 (-0.305546) \| 0.271434 / 0.434364 (-0.162929) \| 0.308448 / 0.540337 (-0.231889) \| 0.412467 / 1.386936 (-0.974469) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#af014830363401a0166a2b8435ca2f863cb468d4 \"CML watermark\")\n", "Once this PR is merged, we should upload the missing version to conda.\r\n\r\n@lhoestq you did this in the past. If you tell me your approach (I see a tag called `VERSION`...), I could do it myself.", "Maybe open a PR against the 2.14 branch and update `release-conda.yml` like this ?\r\n\r\n```diff\r\n- on:\r\n- push:\r\n- tags:\r\n- - \"[0-9]+.[0-9]+.[0-9]+*\"\r\n+ on: push\r\n```\r\n\r\nand then set it back to normal after the release is done", "After having cherry-picked the commit in this PR, I have released the conda package. See: \r\n- https://github.com/huggingface/datasets/actions/runs/6880182419/job/18713812449\r\n- https://anaconda.org/HuggingFace/datasets/files?version=2.14.7\r\n\r\nI am merging this PR.\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004993 / 0.011353 (-0.006360) \| 0.002964 / 0.011008 (-0.008044) \| 0.062588 / 0.038508 (0.024080) \| 0.030794 / 0.023109 (0.007685) \| 0.234856 / 0.275898 (-0.041042) \| 0.264807 / 0.323480 (-0.058673) \| 0.003139 / 0.007986 (-0.004847) \| 0.002498 / 0.004328 (-0.001831) \| 0.048058 / 0.004250 (0.043807) \| 0.048349 / 0.037052 (0.011296) \| 0.238210 / 0.258489 (-0.020279) \| 0.278144 / 0.293841 (-0.015697) \| 0.023219 / 0.128546 (-0.105327) \| 0.007296 / 0.075646 (-0.068351) \| 0.203263 / 0.419271 (-0.216008) \| 0.058844 / 0.043533 (0.015311) \| 0.246330 / 0.255139 (-0.008809) \| 0.264550 / 0.283200 (-0.018649) \| 0.018580 / 0.141683 (-0.123103) \| 1.084163 / 1.452155 (-0.367992) \| 1.154891 / 1.492716 (-0.337825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092393 / 0.018006 (0.074387) \| 0.300545 / 0.000490 (0.300055) \| 0.000203 / 0.000200 (0.000003) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018648 / 0.037411 (-0.018763) \| 0.063151 / 0.014526 (0.048625) \| 0.074206 / 0.176557 (-0.102350) \| 0.120929 / 0.737135 (-0.616207) \| 0.075970 / 0.296338 (-0.220368) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278489 / 0.215209 (0.063279) \| 2.664804 / 2.077655 (0.587150) \| 1.433040 / 1.504120 (-0.071080) \| 1.321416 / 1.541195 (-0.219779) \| 1.320964 / 1.468490 (-0.147526) \| 0.401289 / 4.584777 (-4.183488) \| 2.365310 / 3.745712 (-1.380402) \| 2.635798 / 5.269862 (-2.634063) \| 1.584384 / 4.565676 (-2.981293) \| 0.045675 / 0.424275 (-0.378600) \| 0.004854 / 0.007607 (-0.002753) \| 0.337592 / 0.226044 (0.111548) \| 3.330462 / 2.268929 (1.061534) \| 1.794507 / 55.444624 (-53.650117) \| 1.531284 / 6.876477 (-5.345193) \| 1.507165 / 2.142072 (-0.634908) \| 0.478622 / 4.805227 (-4.326606) \| 0.099105 / 6.500664 (-6.401560) \| 0.041575 / 0.075469 (-0.033894) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941790 / 1.841788 (-0.899997) \| 11.609871 / 8.074308 (3.535563) \| 10.770869 / 10.191392 (0.579477) \| 0.138931 / 0.680424 (-0.541493) \| 0.014406 / 0.534201 (-0.519795) \| 0.269681 / 0.579283 (-0.309602) \| 0.260556 / 0.434364 (-0.173808) \| 0.308244 / 0.540337 (-0.232093) \| 0.428867 / 1.386936 (-0.958069) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.003263 / 0.011008 (-0.007745) \| 0.049143 / 0.038508 (0.010635) \| 0.052033 / 0.023109 (0.028924) \| 0.267815 / 0.275898 (-0.008083) \| 0.288733 / 0.323480 (-0.034747) \| 0.004159 / 0.007986 (-0.003826) \| 0.002407 / 0.004328 (-0.001921) \| 0.048978 / 0.004250 (0.044728) \| 0.038994 / 0.037052 (0.001942) \| 0.264028 / 0.258489 (0.005539) \| 0.303930 / 0.293841 (0.010090) \| 0.024283 / 0.128546 (-0.104263) \| 0.007201 / 0.075646 (-0.068446) \| 0.053810 / 0.419271 (-0.365461) \| 0.032611 / 0.043533 (-0.010922) \| 0.266730 / 0.255139 (0.011591) \| 0.281564 / 0.283200 (-0.001635) \| 0.018720 / 0.141683 (-0.122963) \| 1.140676 / 1.452155 (-0.311479) \| 1.206604 / 1.492716 (-0.286113) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.109390 / 0.018006 (0.091384) \| 0.313783 / 0.000490 (0.313294) \| 0.000228 / 0.000200 (0.000028) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021228 / 0.037411 (-0.016183) \| 0.070505 / 0.014526 (0.055979) \| 0.081961 / 0.176557 (-0.094595) \| 0.119943 / 0.737135 (-0.617193) \| 0.083582 / 0.296338 (-0.212757) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295702 / 0.215209 (0.080493) \| 2.886865 / 2.077655 (0.809210) \| 1.583206 / 1.504120 (0.079086) \| 1.451129 / 1.541195 (-0.090065) \| 1.486253 / 1.468490 (0.017763) \| 0.403207 / 4.584777 (-4.181570) \| 2.408889 / 3.745712 (-1.336824) \| 2.578480 / 5.269862 (-2.691381) \| 1.533066 / 4.565676 (-3.032610) \| 0.046075 / 0.424275 (-0.378200) \| 0.004877 / 0.007607 (-0.002730) \| 0.345995 / 0.226044 (0.119950) \| 3.377039 / 2.268929 (1.108110) \| 1.944614 / 55.444624 (-53.500010) \| 1.677691 / 6.876477 (-5.198786) \| 1.672828 / 2.142072 (-0.469244) \| 0.468426 / 4.805227 (-4.336802) \| 0.097290 / 6.500664 (-6.403374) \| 0.040695 / 0.075469 (-0.034774) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965778 / 1.841788 (-0.876010) \| 12.092639 / 8.074308 (4.018331) \| 11.210968 / 10.191392 (1.019576) \| 0.131212 / 0.680424 (-0.549212) \| 0.015865 / 0.534201 (-0.518336) \| 0.285702 / 0.579283 (-0.293581) \| 0.278319 / 0.434364 (-0.156045) \| 0.336063 / 0.540337 (-0.204275) \| 0.426265 / 1.386936 (-0.960671) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d122b3ddc67705cc2b622bcbd79de9ff943a5742 \"CML watermark\")\n" ]	2023-11-15T14:57:12Z	2023-11-15T17:15:33Z	2023-11-15T17:09:24Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6423.diff", "html_url": "https://github.com/huggingface/datasets/pull/6423", "merged_at": "2023-11-15T17:09:24Z", "patch_url": "https://github.com/huggingface/datasets/pull/6423.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6423" }	Fix conda release by adding pyarrow-hotfix dependency. Note that conda release failed in latest 2.14.7 release: https://github.com/huggingface/datasets/actions/runs/6874667214/job/18696761723 ``` Traceback (most recent call last): File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/test_tmp/run_test.py", line 2, in <module> import datasets File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 67, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_writer.py", line 27, in <module> from .features import Features, Image, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/__init__.py", line 18, in <module> from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, Sequence, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/features.py", line 34, in <module> import pyarrow_hotfix # noqa: F401 # to fix vulnerability on pyarrow<14.0.1 ^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'pyarrow_hotfix' ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6423/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6422	6,422	Allow to choose the `writer_batch_size` when using `save_to_disk`	{ "avatar_url": "https://avatars.githubusercontent.com/u/38216711?v=4", "events_url": "https://api.github.com/users/NathanGodey/events{/privacy}", "followers_url": "https://api.github.com/users/NathanGodey/followers", "following_url": "https://api.github.com/users/NathanGodey/following{/other_user}", "gists_url": "https://api.github.com/users/NathanGodey/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/NathanGodey", "id": 38216711, "login": "NathanGodey", "node_id": "MDQ6VXNlcjM4MjE2NzEx", "organizations_url": "https://api.github.com/users/NathanGodey/orgs", "received_events_url": "https://api.github.com/users/NathanGodey/received_events", "repos_url": "https://api.github.com/users/NathanGodey/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NathanGodey/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NathanGodey/subscriptions", "type": "User", "url": "https://api.github.com/users/NathanGodey", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	[ "We have a config variable that controls the batch size in `save_to_disk`:\r\n```python\r\nimport datasets\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE = <smaller_batch_size>\r\n...\r\nds.save_to_disk(...)\r\n```", "Thank you for your answer!\r\n\r\nFrom what I am reading in `https://github.com/huggingface/datasets/blob/2.14.5/src/datasets/arrow_dataset.py`, every function involved (`select`, `shard`, ...) has a default hardcoded batch size of 1000, as such:\r\n```python\r\ndef select(\r\n self,\r\n indices: Iterable,\r\n keep_in_memory: bool = False,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n...\r\n```\r\nThen, `ArrowWriter` is instantiated with the specified `writer_batch_size`. In `ArrowWriter`, `writer_batch_size` is set to `datasets.config.DEFAULT_MAX_BATCH_SIZE` if it is `None`(https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L345C14-L345C31). However, in our case, it is already set to 1000 by \"parent\" methods, so it won't happen.\r\n\r\nNevertheless, due to this: \r\n```python\r\ndef _save_to_disk_single(job_id: int, shard: \"Dataset\", fpath: str, storage_options: Optional[dict]):\r\n batch_size = config.DEFAULT_MAX_BATCH_SIZE\r\n...\r\n```\r\nit seems to work. I will use it as such, but it should maybe be added to documentation? And maybe improved in next versions?" ]	2023-11-15T11:18:34Z	2023-11-16T10:00:21Z	null	NONE	null	null	### Feature request Add an argument in `save_to_disk` regarding batch size, which would be passed to `shard` and other methods. ### Motivation The `Dataset.save_to_disk` method currently calls `shard` without passing a `writer_batch_size` argument, thus implicitly using the default value (1000). This can result in RAM saturation when using a lot of processes on long text sequences or other modalities, or for specific IO configs. ### Your contribution I would be glad to submit a PR, as long as it does not imply extensive tests refactoring.	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6422/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6421	6,421	Add pyarrow-hotfix to release docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d4c5f9", "default": false, "description": "Maintenance tasks", "id": 4296013012, "name": "maintenance", "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance" } ]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004755 / 0.011353 (-0.006598) \| 0.002683 / 0.011008 (-0.008325) \| 0.061701 / 0.038508 (0.023193) \| 0.030123 / 0.023109 (0.007013) \| 0.238186 / 0.275898 (-0.037712) \| 0.266570 / 0.323480 (-0.056910) \| 0.002898 / 0.007986 (-0.005088) \| 0.002381 / 0.004328 (-0.001948) \| 0.048033 / 0.004250 (0.043782) \| 0.044529 / 0.037052 (0.007477) \| 0.246728 / 0.258489 (-0.011761) \| 0.302066 / 0.293841 (0.008225) \| 0.024008 / 0.128546 (-0.104539) \| 0.006626 / 0.075646 (-0.069020) \| 0.202000 / 0.419271 (-0.217272) \| 0.056492 / 0.043533 (0.012959) \| 0.243417 / 0.255139 (-0.011722) \| 0.263947 / 0.283200 (-0.019253) \| 0.020481 / 0.141683 (-0.121202) \| 1.130635 / 1.452155 (-0.321520) \| 1.180570 / 1.492716 (-0.312146) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095541 / 0.018006 (0.077535) \| 0.306152 / 0.000490 (0.305662) \| 0.000217 / 0.000200 (0.000017) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018593 / 0.037411 (-0.018818) \| 0.063029 / 0.014526 (0.048503) \| 0.074312 / 0.176557 (-0.102245) \| 0.119882 / 0.737135 (-0.617254) \| 0.074066 / 0.296338 (-0.222273) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275409 / 0.215209 (0.060200) \| 2.727061 / 2.077655 (0.649407) \| 1.415632 / 1.504120 (-0.088488) \| 1.294922 / 1.541195 (-0.246273) \| 1.341636 / 1.468490 (-0.126854) \| 0.403250 / 4.584777 (-4.181527) \| 2.384657 / 3.745712 (-1.361055) \| 2.604131 / 5.269862 (-2.665731) \| 1.558888 / 4.565676 (-3.006789) \| 0.046008 / 0.424275 (-0.378267) \| 0.004819 / 0.007607 (-0.002789) \| 0.331046 / 0.226044 (0.105002) \| 3.340950 / 2.268929 (1.072021) \| 1.801077 / 55.444624 (-53.643548) \| 1.479162 / 6.876477 (-5.397315) \| 1.503713 / 2.142072 (-0.638359) \| 0.474931 / 4.805227 (-4.330296) \| 0.101869 / 6.500664 (-6.398795) \| 0.041946 / 0.075469 (-0.033523) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.955641 / 1.841788 (-0.886147) \| 11.441032 / 8.074308 (3.366724) \| 10.267731 / 10.191392 (0.076339) \| 0.128735 / 0.680424 (-0.551689) \| 0.013942 / 0.534201 (-0.520259) \| 0.266620 / 0.579283 (-0.312663) \| 0.262334 / 0.434364 (-0.172029) \| 0.302713 / 0.540337 (-0.237624) \| 0.430323 / 1.386936 (-0.956613) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004670 / 0.011353 (-0.006683) \| 0.002671 / 0.011008 (-0.008338) \| 0.048949 / 0.038508 (0.010441) \| 0.052520 / 0.023109 (0.029411) \| 0.272614 / 0.275898 (-0.003284) \| 0.292618 / 0.323480 (-0.030862) \| 0.004016 / 0.007986 (-0.003969) \| 0.002430 / 0.004328 (-0.001899) \| 0.048313 / 0.004250 (0.044063) \| 0.038647 / 0.037052 (0.001595) \| 0.279893 / 0.258489 (0.021404) \| 0.305371 / 0.293841 (0.011530) \| 0.023710 / 0.128546 (-0.104836) \| 0.006999 / 0.075646 (-0.068648) \| 0.053315 / 0.419271 (-0.365956) \| 0.032417 / 0.043533 (-0.011115) \| 0.272066 / 0.255139 (0.016927) \| 0.291717 / 0.283200 (0.008518) \| 0.018127 / 0.141683 (-0.123556) \| 1.173611 / 1.452155 (-0.278544) \| 1.183659 / 1.492716 (-0.309057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094831 / 0.018006 (0.076824) \| 0.304911 / 0.000490 (0.304421) \| 0.000225 / 0.000200 (0.000025) \| 0.000049 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020948 / 0.037411 (-0.016463) \| 0.070255 / 0.014526 (0.055729) \| 0.081371 / 0.176557 (-0.095186) \| 0.118932 / 0.737135 (-0.618203) \| 0.082207 / 0.296338 (-0.214132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294067 / 0.215209 (0.078858) \| 2.856981 / 2.077655 (0.779326) \| 1.598392 / 1.504120 (0.094273) \| 1.479093 / 1.541195 (-0.062102) \| 1.509495 / 1.468490 (0.041005) \| 0.396303 / 4.584777 (-4.188473) \| 2.429077 / 3.745712 (-1.316635) \| 2.525037 / 5.269862 (-2.744824) \| 1.503332 / 4.565676 (-3.062345) \| 0.046191 / 0.424275 (-0.378084) \| 0.004858 / 0.007607 (-0.002750) \| 0.349528 / 0.226044 (0.123484) \| 3.401451 / 2.268929 (1.132522) \| 1.989613 / 55.444624 (-53.455012) \| 1.664528 / 6.876477 (-5.211949) \| 1.669076 / 2.142072 (-0.472997) \| 0.467090 / 4.805227 (-4.338137) \| 0.098137 / 6.500664 (-6.402527) \| 0.040448 / 0.075469 (-0.035021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969578 / 1.841788 (-0.872210) \| 12.064705 / 8.074308 (3.990396) \| 10.991438 / 10.191392 (0.800046) \| 0.130149 / 0.680424 (-0.550275) \| 0.015357 / 0.534201 (-0.518844) \| 0.266567 / 0.579283 (-0.312717) \| 0.270619 / 0.434364 (-0.163744) \| 0.305978 / 0.540337 (-0.234359) \| 0.411164 / 1.386936 (-0.975772) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#86a2cf3174c55899535ee5f1707892a430ee53bc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009810 / 0.011353 (-0.001543) \| 0.005411 / 0.011008 (-0.005598) \| 0.111670 / 0.038508 (0.073162) \| 0.050288 / 0.023109 (0.027179) \| 0.415625 / 0.275898 (0.139727) \| 0.479382 / 0.323480 (0.155902) \| 0.005104 / 0.007986 (-0.002882) \| 0.007122 / 0.004328 (0.002793) \| 0.079626 / 0.004250 (0.075375) \| 0.079421 / 0.037052 (0.042369) \| 0.406722 / 0.258489 (0.148233) \| 0.461511 / 0.293841 (0.167670) \| 0.053812 / 0.128546 (-0.074734) \| 0.014315 / 0.075646 (-0.061331) \| 0.389636 / 0.419271 (-0.029636) \| 0.111859 / 0.043533 (0.068326) \| 0.411703 / 0.255139 (0.156564) \| 0.457072 / 0.283200 (0.173872) \| 0.039807 / 0.141683 (-0.101876) \| 1.744064 / 1.452155 (0.291909) \| 1.968321 / 1.492716 (0.475604) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.341839 / 0.018006 (0.323833) \| 0.628083 / 0.000490 (0.627593) \| 0.023787 / 0.000200 (0.023587) \| 0.000601 / 0.000054 (0.000547) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034170 / 0.037411 (-0.003241) \| 0.091159 / 0.014526 (0.076633) \| 0.108993 / 0.176557 (-0.067563) \| 0.186906 / 0.737135 (-0.550229) \| 0.109753 / 0.296338 (-0.186586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.684138 / 0.215209 (0.468929) \| 6.634852 / 2.077655 (4.557198) \| 3.102870 / 1.504120 (1.598750) \| 2.831023 / 1.541195 (1.289828) \| 2.831597 / 1.468490 (1.363107) \| 0.903584 / 4.584777 (-3.681193) \| 5.503341 / 3.745712 (1.757629) \| 4.970283 / 5.269862 (-0.299579) \| 3.139413 / 4.565676 (-1.426264) \| 0.109848 / 0.424275 (-0.314427) \| 0.008501 / 0.007607 (0.000894) \| 0.823815 / 0.226044 (0.597770) \| 7.963355 / 2.268929 (5.694426) \| 4.002010 / 55.444624 (-51.442614) \| 3.229390 / 6.876477 (-3.647087) \| 3.166413 / 2.142072 (1.024341) \| 1.030313 / 4.805227 (-3.774914) \| 0.219394 / 6.500664 (-6.281270) \| 0.077760 / 0.075469 (0.002291) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.580309 / 1.841788 (-0.261479) \| 24.279185 / 8.074308 (16.204877) \| 22.305293 / 10.191392 (12.113901) \| 0.235711 / 0.680424 (-0.444713) \| 0.030342 / 0.534201 (-0.503859) \| 0.498137 / 0.579283 (-0.081146) \| 0.619173 / 0.434364 (0.184809) \| 0.529904 / 0.540337 (-0.010434) \| 0.822547 / 1.386936 (-0.564389) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009375 / 0.011353 (-0.001978) \| 0.006009 / 0.011008 (-0.004999) \| 0.074080 / 0.038508 (0.035572) \| 0.089454 / 0.023109 (0.066345) \| 0.473458 / 0.275898 (0.197560) \| 0.462558 / 0.323480 (0.139078) \| 0.006415 / 0.007986 (-0.001571) \| 0.004777 / 0.004328 (0.000448) \| 0.076563 / 0.004250 (0.072313) \| 0.062793 / 0.037052 (0.025741) \| 0.455860 / 0.258489 (0.197371) \| 0.485281 / 0.293841 (0.191440) \| 0.052966 / 0.128546 (-0.075580) \| 0.021600 / 0.075646 (-0.054046) \| 0.090407 / 0.419271 (-0.328864) \| 0.063951 / 0.043533 (0.020418) \| 0.487561 / 0.255139 (0.232422) \| 0.479958 / 0.283200 (0.196758) \| 0.039263 / 0.141683 (-0.102420) \| 1.727215 / 1.452155 (0.275061) \| 1.962039 / 1.492716 (0.469323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296267 / 0.018006 (0.278261) \| 0.604982 / 0.000490 (0.604493) \| 0.007842 / 0.000200 (0.007642) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034317 / 0.037411 (-0.003094) \| 0.097796 / 0.014526 (0.083270) \| 0.126034 / 0.176557 (-0.050522) \| 0.180873 / 0.737135 (-0.556262) \| 0.125410 / 0.296338 (-0.170928) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.608278 / 0.215209 (0.393069) \| 6.154006 / 2.077655 (4.076351) \| 2.822342 / 1.504120 (1.318222) \| 2.568263 / 1.541195 (1.027068) \| 2.518545 / 1.468490 (1.050055) \| 0.863186 / 4.584777 (-3.721591) \| 5.367969 / 3.745712 (1.622257) \| 4.737691 / 5.269862 (-0.532170) \| 2.917620 / 4.565676 (-1.648056) \| 0.100731 / 0.424275 (-0.323544) \| 0.008611 / 0.007607 (0.001004) \| 0.735523 / 0.226044 (0.509479) \| 7.552790 / 2.268929 (5.283862) \| 3.821835 / 55.444624 (-51.622789) \| 2.878259 / 6.876477 (-3.998217) \| 2.957686 / 2.142072 (0.815613) \| 0.964630 / 4.805227 (-3.840598) \| 0.207098 / 6.500664 (-6.293566) \| 0.084215 / 0.075469 (0.008746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.711020 / 1.841788 (-0.130768) \| 24.034122 / 8.074308 (15.959814) \| 21.378504 / 10.191392 (11.187112) \| 0.233433 / 0.680424 (-0.446990) \| 0.037214 / 0.534201 (-0.496987) \| 0.511952 / 0.579283 (-0.067332) \| 0.591486 / 0.434364 (0.157123) \| 0.606549 / 0.540337 (0.066211) \| 0.833773 / 1.386936 (-0.553163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#671f9b32fc559a35996c1b9070fad1a2647a7fef \"CML watermark\")\n" ]	2023-11-15T10:06:44Z	2023-11-15T13:49:55Z	2023-11-15T13:38:22Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6421.diff", "html_url": "https://github.com/huggingface/datasets/pull/6421", "merged_at": "2023-11-15T13:38:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6421.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6421" }	Add `pyarrow-hotfix` to release docs.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": 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https://github.com/huggingface/datasets/pull/6420	6,420	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6420). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004536 / 0.011353 (-0.006816) \| 0.002979 / 0.011008 (-0.008030) \| 0.061984 / 0.038508 (0.023476) \| 0.029382 / 0.023109 (0.006273) \| 0.245237 / 0.275898 (-0.030661) \| 0.270571 / 0.323480 (-0.052909) \| 0.003956 / 0.007986 (-0.004029) \| 0.002453 / 0.004328 (-0.001876) \| 0.047967 / 0.004250 (0.043717) \| 0.043695 / 0.037052 (0.006643) \| 0.248457 / 0.258489 (-0.010032) \| 0.283293 / 0.293841 (-0.010548) \| 0.023603 / 0.128546 (-0.104943) \| 0.007225 / 0.075646 (-0.068422) \| 0.200533 / 0.419271 (-0.218739) \| 0.055310 / 0.043533 (0.011777) \| 0.245152 / 0.255139 (-0.009987) \| 0.267187 / 0.283200 (-0.016012) \| 0.018158 / 0.141683 (-0.123525) \| 1.126079 / 1.452155 (-0.326075) \| 1.185137 / 1.492716 (-0.307580) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092436 / 0.018006 (0.074430) \| 0.300132 / 0.000490 (0.299642) \| 0.000206 / 0.000200 (0.000006) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018476 / 0.037411 (-0.018935) \| 0.062827 / 0.014526 (0.048301) \| 0.074605 / 0.176557 (-0.101952) \| 0.119768 / 0.737135 (-0.617368) \| 0.076044 / 0.296338 (-0.220294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279717 / 0.215209 (0.064508) \| 2.752308 / 2.077655 (0.674654) \| 1.434954 / 1.504120 (-0.069166) \| 1.314700 / 1.541195 (-0.226495) \| 1.347689 / 1.468490 (-0.120802) \| 0.400332 / 4.584777 (-4.184445) \| 2.383024 / 3.745712 (-1.362689) \| 2.583130 / 5.269862 (-2.686732) \| 1.567670 / 4.565676 (-2.998007) \| 0.045446 / 0.424275 (-0.378829) \| 0.004813 / 0.007607 (-0.002794) \| 0.336191 / 0.226044 (0.110147) \| 3.319837 / 2.268929 (1.050909) \| 1.816808 / 55.444624 (-53.627817) \| 1.539052 / 6.876477 (-5.337424) \| 1.550765 / 2.142072 (-0.591307) \| 0.484253 / 4.805227 (-4.320974) \| 0.100494 / 6.500664 (-6.400170) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940857 / 1.841788 (-0.900931) \| 11.784946 / 8.074308 (3.710638) \| 10.397038 / 10.191392 (0.205646) \| 0.141458 / 0.680424 (-0.538965) \| 0.014193 / 0.534201 (-0.520008) \| 0.268304 / 0.579283 (-0.310979) \| 0.267059 / 0.434364 (-0.167305) \| 0.309389 / 0.540337 (-0.230949) \| 0.420628 / 1.386936 (-0.966308) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004776 / 0.011353 (-0.006577) \| 0.002941 / 0.011008 (-0.008067) \| 0.048659 / 0.038508 (0.010151) \| 0.053334 / 0.023109 (0.030225) \| 0.273342 / 0.275898 (-0.002556) \| 0.302278 / 0.323480 (-0.021202) \| 0.004001 / 0.007986 (-0.003984) \| 0.002414 / 0.004328 (-0.001914) \| 0.047504 / 0.004250 (0.043254) \| 0.038581 / 0.037052 (0.001529) \| 0.277768 / 0.258489 (0.019279) \| 0.306772 / 0.293841 (0.012931) \| 0.024146 / 0.128546 (-0.104400) \| 0.007233 / 0.075646 (-0.068413) \| 0.053308 / 0.419271 (-0.365964) \| 0.032617 / 0.043533 (-0.010916) \| 0.277390 / 0.255139 (0.022251) \| 0.296015 / 0.283200 (0.012816) \| 0.018733 / 0.141683 (-0.122950) \| 1.124895 / 1.452155 (-0.327260) \| 1.182579 / 1.492716 (-0.310137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093375 / 0.018006 (0.075369) \| 0.301555 / 0.000490 (0.301066) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021284 / 0.037411 (-0.016127) \| 0.070158 / 0.014526 (0.055632) \| 0.080187 / 0.176557 (-0.096370) \| 0.119282 / 0.737135 (-0.617854) \| 0.081672 / 0.296338 (-0.214666) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314396 / 0.215209 (0.099187) \| 2.975114 / 2.077655 (0.897459) \| 1.724658 / 1.504120 (0.220539) \| 1.604464 / 1.541195 (0.063269) \| 1.652736 / 1.468490 (0.184246) \| 0.395064 / 4.584777 (-4.189713) \| 2.412768 / 3.745712 (-1.332944) \| 2.564427 / 5.269862 (-2.705435) \| 1.507627 / 4.565676 (-3.058050) \| 0.045463 / 0.424275 (-0.378812) \| 0.004797 / 0.007607 (-0.002810) \| 0.383115 / 0.226044 (0.157071) \| 3.501976 / 2.268929 (1.233048) \| 2.087512 / 55.444624 (-53.357113) \| 1.793132 / 6.876477 (-5.083345) \| 1.804178 / 2.142072 (-0.337895) \| 0.468287 / 4.805227 (-4.336940) \| 0.097247 / 6.500664 (-6.403417) \| 0.041139 / 0.075469 (-0.034330) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976034 / 1.841788 (-0.865754) \| 12.431248 / 8.074308 (4.356940) \| 10.896064 / 10.191392 (0.704672) \| 0.129137 / 0.680424 (-0.551287) \| 0.015636 / 0.534201 (-0.518565) \| 0.268219 / 0.579283 (-0.311064) \| 0.278345 / 0.434364 (-0.156019) \| 0.302696 / 0.540337 (-0.237642) \| 0.408465 / 1.386936 (-0.978471) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51c53e94acd7a273c24899c045446df021314cd2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007703 / 0.011353 (-0.003650) \| 0.004614 / 0.011008 (-0.006394) \| 0.101425 / 0.038508 (0.062917) \| 0.040122 / 0.023109 (0.017013) \| 0.398890 / 0.275898 (0.122992) \| 0.424392 / 0.323480 (0.100912) \| 0.005411 / 0.007986 (-0.002575) \| 0.003747 / 0.004328 (-0.000582) \| 0.080494 / 0.004250 (0.076243) \| 0.059392 / 0.037052 (0.022340) \| 0.398025 / 0.258489 (0.139536) \| 0.454293 / 0.293841 (0.160452) \| 0.043662 / 0.128546 (-0.084884) \| 0.013726 / 0.075646 (-0.061920) \| 0.352910 / 0.419271 (-0.066362) \| 0.088572 / 0.043533 (0.045039) \| 0.401677 / 0.255139 (0.146538) \| 0.421774 / 0.283200 (0.138575) \| 0.033377 / 0.141683 (-0.108305) \| 1.728499 / 1.452155 (0.276344) \| 1.821557 / 1.492716 (0.328841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230744 / 0.018006 (0.212738) \| 0.496188 / 0.000490 (0.495698) \| 0.010315 / 0.000200 (0.010115) \| 0.000402 / 0.000054 (0.000348) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028859 / 0.037411 (-0.008552) \| 0.089688 / 0.014526 (0.075163) \| 0.111697 / 0.176557 (-0.064860) \| 0.183238 / 0.737135 (-0.553898) \| 0.112407 / 0.296338 (-0.183931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.558394 / 0.215209 (0.343185) \| 5.643048 / 2.077655 (3.565393) \| 2.454622 / 1.504120 (0.950502) \| 2.183338 / 1.541195 (0.642143) \| 2.324793 / 1.468490 (0.856303) \| 0.859482 / 4.584777 (-3.725295) \| 4.959346 / 3.745712 (1.213634) \| 4.599224 / 5.269862 (-0.670638) \| 2.764382 / 4.565676 (-1.801295) \| 0.089976 / 0.424275 (-0.334299) \| 0.008144 / 0.007607 (0.000537) \| 0.634675 / 0.226044 (0.408631) \| 6.555693 / 2.268929 (4.286765) \| 3.080252 / 55.444624 (-52.364373) \| 2.442715 / 6.876477 (-4.433762) \| 2.475126 / 2.142072 (0.333053) \| 0.986459 / 4.805227 (-3.818768) \| 0.193859 / 6.500664 (-6.306805) \| 0.063652 / 0.075469 (-0.011817) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.545318 / 1.841788 (-0.296469) \| 21.928751 / 8.074308 (13.854442) \| 20.598229 / 10.191392 (10.406837) \| 0.234046 / 0.680424 (-0.446377) \| 0.025947 / 0.534201 (-0.508254) \| 0.459773 / 0.579283 (-0.119510) \| 0.598026 / 0.434364 (0.163662) \| 0.555260 / 0.540337 (0.014922) \| 0.782767 / 1.386936 (-0.604169) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009322 / 0.011353 (-0.002030) \| 0.004650 / 0.011008 (-0.006358) \| 0.079326 / 0.038508 (0.040818) \| 0.079112 / 0.023109 (0.056003) \| 0.428708 / 0.275898 (0.152810) \| 0.481647 / 0.323480 (0.158168) \| 0.006419 / 0.007986 (-0.001566) \| 0.003878 / 0.004328 (-0.000450) \| 0.079013 / 0.004250 (0.074762) \| 0.058107 / 0.037052 (0.021055) \| 0.436967 / 0.258489 (0.178478) \| 0.501120 / 0.293841 (0.207279) \| 0.052972 / 0.128546 (-0.075574) \| 0.014414 / 0.075646 (-0.061232) \| 0.098587 / 0.419271 (-0.320685) \| 0.061626 / 0.043533 (0.018093) \| 0.451623 / 0.255139 (0.196484) \| 0.468893 / 0.283200 (0.185693) \| 0.032479 / 0.141683 (-0.109203) \| 1.911743 / 1.452155 (0.459588) \| 1.969024 / 1.492716 (0.476308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232015 / 0.018006 (0.214009) \| 0.508637 / 0.000490 (0.508147) \| 0.005470 / 0.000200 (0.005270) \| 0.000131 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035345 / 0.037411 (-0.002066) \| 0.106319 / 0.014526 (0.091794) \| 0.117205 / 0.176557 (-0.059352) \| 0.176527 / 0.737135 (-0.560608) \| 0.121566 / 0.296338 (-0.174773) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584920 / 0.215209 (0.369711) \| 5.745688 / 2.077655 (3.668034) \| 2.519875 / 1.504120 (1.015755) \| 2.197593 / 1.541195 (0.656398) \| 2.296670 / 1.468490 (0.828180) \| 0.831938 / 4.584777 (-3.752839) \| 5.130594 / 3.745712 (1.384882) \| 4.581385 / 5.269862 (-0.688476) \| 2.829516 / 4.565676 (-1.736161) \| 0.099015 / 0.424275 (-0.325260) \| 0.011468 / 0.007607 (0.003861) \| 0.702717 / 0.226044 (0.476672) \| 6.856099 / 2.268929 (4.587170) \| 3.372966 / 55.444624 (-52.071658) \| 2.567664 / 6.876477 (-4.308812) \| 2.699200 / 2.142072 (0.557127) \| 0.992316 / 4.805227 (-3.812911) \| 0.190463 / 6.500664 (-6.310201) \| 0.063305 / 0.075469 (-0.012165) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.591491 / 1.841788 (-0.250296) \| 21.696492 / 8.074308 (13.622184) \| 19.695404 / 10.191392 (9.504012) \| 0.222853 / 0.680424 (-0.457571) \| 0.032936 / 0.534201 (-0.501265) \| 0.431209 / 0.579283 (-0.148074) \| 0.543101 / 0.434364 (0.108737) \| 0.543427 / 0.540337 (0.003089) \| 0.742102 / 1.386936 (-0.644834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#534a227179265df9093230885613c95390325705 \"CML watermark\")\n" ]	2023-11-15T08:22:19Z	2023-11-15T08:33:36Z	2023-11-15T08:22:33Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6420.diff", "html_url": "https://github.com/huggingface/datasets/pull/6420", "merged_at": "2023-11-15T08:22:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/6420.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6420" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6420/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6419	6,419	Release: 2.14.7	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004943 / 0.011353 (-0.006410) \| 0.002900 / 0.011008 (-0.008109) \| 0.061495 / 0.038508 (0.022987) \| 0.053575 / 0.023109 (0.030466) \| 0.249318 / 0.275898 (-0.026580) \| 0.271773 / 0.323480 (-0.051706) \| 0.003074 / 0.007986 (-0.004911) \| 0.003738 / 0.004328 (-0.000590) \| 0.047624 / 0.004250 (0.043373) \| 0.045141 / 0.037052 (0.008089) \| 0.255467 / 0.258489 (-0.003022) \| 0.286577 / 0.293841 (-0.007264) \| 0.023113 / 0.128546 (-0.105433) \| 0.007189 / 0.075646 (-0.068458) \| 0.204441 / 0.419271 (-0.214830) \| 0.036829 / 0.043533 (-0.006704) \| 0.252474 / 0.255139 (-0.002665) \| 0.270960 / 0.283200 (-0.012239) \| 0.019666 / 0.141683 (-0.122017) \| 1.095139 / 1.452155 (-0.357015) \| 1.158659 / 1.492716 (-0.334057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091046 / 0.018006 (0.073040) \| 0.298346 / 0.000490 (0.297856) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018702 / 0.037411 (-0.018709) \| 0.062213 / 0.014526 (0.047687) \| 0.073364 / 0.176557 (-0.103193) \| 0.119841 / 0.737135 (-0.617294) \| 0.074070 / 0.296338 (-0.222268) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282388 / 0.215209 (0.067179) \| 2.792029 / 2.077655 (0.714375) \| 1.471483 / 1.504120 (-0.032637) \| 1.386236 / 1.541195 (-0.154959) \| 1.377489 / 1.468490 (-0.091001) \| 0.410335 / 4.584777 (-4.174442) \| 2.424866 / 3.745712 (-1.320846) \| 2.610609 / 5.269862 (-2.659253) \| 1.574636 / 4.565676 (-2.991041) \| 0.046716 / 0.424275 (-0.377559) \| 0.004768 / 0.007607 (-0.002839) \| 0.339831 / 0.226044 (0.113787) \| 3.297579 / 2.268929 (1.028651) \| 1.851410 / 55.444624 (-53.593214) \| 1.550048 / 6.876477 (-5.326428) \| 1.576647 / 2.142072 (-0.565425) \| 0.482538 / 4.805227 (-4.322689) \| 0.101381 / 6.500664 (-6.399283) \| 0.042066 / 0.075469 (-0.033403) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972664 / 1.841788 (-0.869123) \| 11.580700 / 8.074308 (3.506392) \| 10.586747 / 10.191392 (0.395355) \| 0.127844 / 0.680424 (-0.552580) \| 0.014270 / 0.534201 (-0.519931) \| 0.269678 / 0.579283 (-0.309605) \| 0.264022 / 0.434364 (-0.170342) \| 0.309395 / 0.540337 (-0.230942) \| 0.429228 / 1.386936 (-0.957708) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004815 / 0.011353 (-0.006538) \| 0.002890 / 0.011008 (-0.008119) \| 0.048039 / 0.038508 (0.009531) \| 0.053029 / 0.023109 (0.029920) \| 0.271346 / 0.275898 (-0.004552) \| 0.294488 / 0.323480 (-0.028992) \| 0.003983 / 0.007986 (-0.004003) \| 0.002439 / 0.004328 (-0.001889) \| 0.048250 / 0.004250 (0.044000) \| 0.038855 / 0.037052 (0.001803) \| 0.284723 / 0.258489 (0.026234) \| 0.303604 / 0.293841 (0.009763) \| 0.024384 / 0.128546 (-0.104163) \| 0.007021 / 0.075646 (-0.068625) \| 0.053850 / 0.419271 (-0.365422) \| 0.032177 / 0.043533 (-0.011356) \| 0.270039 / 0.255139 (0.014900) \| 0.289669 / 0.283200 (0.006469) \| 0.018840 / 0.141683 (-0.122842) \| 1.122191 / 1.452155 (-0.329963) \| 1.187083 / 1.492716 (-0.305634) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090609 / 0.018006 (0.072603) \| 0.298915 / 0.000490 (0.298425) \| 0.000216 / 0.000200 (0.000016) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020919 / 0.037411 (-0.016492) \| 0.070474 / 0.014526 (0.055948) \| 0.082421 / 0.176557 (-0.094135) \| 0.126967 / 0.737135 (-0.610168) \| 0.083447 / 0.296338 (-0.212892) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300153 / 0.215209 (0.084944) \| 2.958992 / 2.077655 (0.881337) \| 1.631228 / 1.504120 (0.127108) \| 1.497991 / 1.541195 (-0.043204) \| 1.536963 / 1.468490 (0.068473) \| 0.403047 / 4.584777 (-4.181730) \| 2.448782 / 3.745712 (-1.296930) \| 2.571954 / 5.269862 (-2.697908) \| 1.556346 / 4.565676 (-3.009331) \| 0.045992 / 0.424275 (-0.378283) \| 0.004785 / 0.007607 (-0.002822) \| 0.357448 / 0.226044 (0.131404) \| 3.558808 / 2.268929 (1.289880) \| 1.992624 / 55.444624 (-53.452001) \| 1.695027 / 6.876477 (-5.181450) \| 1.695183 / 2.142072 (-0.446889) \| 0.477001 / 4.805227 (-4.328226) \| 0.097485 / 6.500664 (-6.403179) \| 0.040530 / 0.075469 (-0.034939) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976342 / 1.841788 (-0.865445) \| 12.141698 / 8.074308 (4.067390) \| 10.881101 / 10.191392 (0.689709) \| 0.142443 / 0.680424 (-0.537981) \| 0.015583 / 0.534201 (-0.518618) \| 0.269727 / 0.579283 (-0.309556) \| 0.275890 / 0.434364 (-0.158474) \| 0.306351 / 0.540337 (-0.233987) \| 0.412003 / 1.386936 (-0.974933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c744261000fd684f54c54de8ac4f15a726092d7 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004946 / 0.011353 (-0.006407) \| 0.002863 / 0.011008 (-0.008146) \| 0.061888 / 0.038508 (0.023380) \| 0.050664 / 0.023109 (0.027554) \| 0.242635 / 0.275898 (-0.033263) \| 0.271741 / 0.323480 (-0.051739) \| 0.003023 / 0.007986 (-0.004963) \| 0.003088 / 0.004328 (-0.001241) \| 0.049286 / 0.004250 (0.045036) \| 0.044699 / 0.037052 (0.007647) \| 0.249581 / 0.258489 (-0.008908) \| 0.285633 / 0.293841 (-0.008208) \| 0.023048 / 0.128546 (-0.105499) \| 0.007235 / 0.075646 (-0.068412) \| 0.202989 / 0.419271 (-0.216282) \| 0.036357 / 0.043533 (-0.007175) \| 0.245980 / 0.255139 (-0.009159) \| 0.277486 / 0.283200 (-0.005713) \| 0.019215 / 0.141683 (-0.122468) \| 1.096456 / 1.452155 (-0.355699) \| 1.152196 / 1.492716 (-0.340520) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092026 / 0.018006 (0.074020) \| 0.303038 / 0.000490 (0.302549) \| 0.000209 / 0.000200 (0.000009) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018670 / 0.037411 (-0.018741) \| 0.061972 / 0.014526 (0.047446) \| 0.072963 / 0.176557 (-0.103594) \| 0.119984 / 0.737135 (-0.617151) \| 0.074074 / 0.296338 (-0.222265) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282444 / 0.215209 (0.067235) \| 2.754571 / 2.077655 (0.676916) \| 1.482635 / 1.504120 (-0.021485) \| 1.352039 / 1.541195 (-0.189155) \| 1.359333 / 1.468490 (-0.109157) \| 0.399690 / 4.584777 (-4.185087) \| 2.364844 / 3.745712 (-1.380868) \| 2.603942 / 5.269862 (-2.665919) \| 1.569512 / 4.565676 (-2.996164) \| 0.046074 / 0.424275 (-0.378201) \| 0.004745 / 0.007607 (-0.002862) \| 0.339066 / 0.226044 (0.113022) \| 3.363456 / 2.268929 (1.094527) \| 1.822213 / 55.444624 (-53.622411) \| 1.536622 / 6.876477 (-5.339854) \| 1.574772 / 2.142072 (-0.567300) \| 0.474418 / 4.805227 (-4.330809) \| 0.099572 / 6.500664 (-6.401092) \| 0.041824 / 0.075469 (-0.033645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956300 / 1.841788 (-0.885487) \| 11.648886 / 8.074308 (3.574578) \| 10.645700 / 10.191392 (0.454308) \| 0.138924 / 0.680424 (-0.541499) \| 0.013936 / 0.534201 (-0.520265) \| 0.270319 / 0.579283 (-0.308964) \| 0.269735 / 0.434364 (-0.164629) \| 0.309699 / 0.540337 (-0.230639) \| 0.429139 / 1.386936 (-0.957797) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004838 / 0.011353 (-0.006515) \| 0.002937 / 0.011008 (-0.008072) \| 0.048094 / 0.038508 (0.009586) \| 0.053131 / 0.023109 (0.030022) \| 0.271893 / 0.275898 (-0.004005) \| 0.291025 / 0.323480 (-0.032454) \| 0.004058 / 0.007986 (-0.003928) \| 0.002410 / 0.004328 (-0.001919) \| 0.047939 / 0.004250 (0.043689) \| 0.038996 / 0.037052 (0.001944) \| 0.274983 / 0.258489 (0.016494) \| 0.306175 / 0.293841 (0.012334) \| 0.024388 / 0.128546 (-0.104159) \| 0.007242 / 0.075646 (-0.068404) \| 0.054011 / 0.419271 (-0.365261) \| 0.032750 / 0.043533 (-0.010783) \| 0.271147 / 0.255139 (0.016008) \| 0.288163 / 0.283200 (0.004963) \| 0.018383 / 0.141683 (-0.123299) \| 1.116134 / 1.452155 (-0.336021) \| 1.185964 / 1.492716 (-0.306752) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093289 / 0.018006 (0.075283) \| 0.303058 / 0.000490 (0.302568) \| 0.000241 / 0.000200 (0.000041) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021422 / 0.037411 (-0.015990) \| 0.069974 / 0.014526 (0.055449) \| 0.081164 / 0.176557 (-0.095392) \| 0.119991 / 0.737135 (-0.617144) \| 0.082154 / 0.296338 (-0.214184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292298 / 0.215209 (0.077089) \| 2.851475 / 2.077655 (0.773821) \| 1.558283 / 1.504120 (0.054163) \| 1.432431 / 1.541195 (-0.108764) \| 1.479282 / 1.468490 (0.010792) \| 0.413124 / 4.584777 (-4.171653) \| 2.473005 / 3.745712 (-1.272707) \| 2.548779 / 5.269862 (-2.721082) \| 1.520776 / 4.565676 (-3.044900) \| 0.046476 / 0.424275 (-0.377799) \| 0.004814 / 0.007607 (-0.002794) \| 0.347036 / 0.226044 (0.120992) \| 3.424928 / 2.268929 (1.155999) \| 1.963274 / 55.444624 (-53.481351) \| 1.653794 / 6.876477 (-5.222683) \| 1.643874 / 2.142072 (-0.498198) \| 0.469086 / 4.805227 (-4.336141) \| 0.097417 / 6.500664 (-6.403247) \| 0.040468 / 0.075469 (-0.035002) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972783 / 1.841788 (-0.869005) \| 12.122994 / 8.074308 (4.048686) \| 10.876396 / 10.191392 (0.685004) \| 0.130573 / 0.680424 (-0.549850) \| 0.016693 / 0.534201 (-0.517508) \| 0.270952 / 0.579283 (-0.308331) \| 0.273834 / 0.434364 (-0.160530) \| 0.305049 / 0.540337 (-0.235289) \| 0.408776 / 1.386936 (-0.978160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4216e5d57ea07e6b1ed73a3ec2cf845c6e59f70 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004606 / 0.011353 (-0.006747) \| 0.002433 / 0.011008 (-0.008576) \| 0.061985 / 0.038508 (0.023477) \| 0.048853 / 0.023109 (0.025744) \| 0.244506 / 0.275898 (-0.031392) \| 0.270159 / 0.323480 (-0.053321) \| 0.003962 / 0.007986 (-0.004024) \| 0.002376 / 0.004328 (-0.001952) \| 0.048067 / 0.004250 (0.043817) \| 0.041864 / 0.037052 (0.004812) \| 0.249743 / 0.258489 (-0.008746) \| 0.287723 / 0.293841 (-0.006117) \| 0.022954 / 0.128546 (-0.105593) \| 0.006845 / 0.075646 (-0.068801) \| 0.206313 / 0.419271 (-0.212959) \| 0.035780 / 0.043533 (-0.007753) \| 0.244286 / 0.255139 (-0.010853) \| 0.270026 / 0.283200 (-0.013173) \| 0.018177 / 0.141683 (-0.123506) \| 1.083998 / 1.452155 (-0.368157) \| 1.156086 / 1.492716 (-0.336630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093754 / 0.018006 (0.075748) \| 0.302157 / 0.000490 (0.301667) \| 0.000215 / 0.000200 (0.000015) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018745 / 0.037411 (-0.018666) \| 0.061707 / 0.014526 (0.047181) \| 0.074356 / 0.176557 (-0.102200) \| 0.121643 / 0.737135 (-0.615492) \| 0.075885 / 0.296338 (-0.220454) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289156 / 0.215209 (0.073947) \| 2.881327 / 2.077655 (0.803672) \| 1.483568 / 1.504120 (-0.020552) \| 1.355933 / 1.541195 (-0.185262) \| 1.389693 / 1.468490 (-0.078797) \| 0.402834 / 4.584777 (-4.181943) \| 2.390634 / 3.745712 (-1.355078) \| 2.596761 / 5.269862 (-2.673101) \| 1.527602 / 4.565676 (-3.038074) \| 0.046434 / 0.424275 (-0.377841) \| 0.004783 / 0.007607 (-0.002824) \| 0.341017 / 0.226044 (0.114972) \| 3.429023 / 2.268929 (1.160095) \| 1.832988 / 55.444624 (-53.611637) \| 1.526510 / 6.876477 (-5.349967) \| 1.539382 / 2.142072 (-0.602690) \| 0.475734 / 4.805227 (-4.329493) \| 0.098710 / 6.500664 (-6.401954) \| 0.041136 / 0.075469 (-0.034333) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.922023 / 1.841788 (-0.919765) \| 11.428215 / 8.074308 (3.353907) \| 10.356668 / 10.191392 (0.165276) \| 0.139575 / 0.680424 (-0.540848) \| 0.014541 / 0.534201 (-0.519660) \| 0.271359 / 0.579283 (-0.307924) \| 0.266701 / 0.434364 (-0.167663) \| 0.309449 / 0.540337 (-0.230888) \| 0.422047 / 1.386936 (-0.964889) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004892 / 0.011353 (-0.006461) \| 0.002792 / 0.011008 (-0.008216) \| 0.048027 / 0.038508 (0.009519) \| 0.059256 / 0.023109 (0.036147) \| 0.270150 / 0.275898 (-0.005748) \| 0.294530 / 0.323480 (-0.028950) \| 0.004162 / 0.007986 (-0.003823) \| 0.002470 / 0.004328 (-0.001858) \| 0.047993 / 0.004250 (0.043743) \| 0.040380 / 0.037052 (0.003328) \| 0.275247 / 0.258489 (0.016758) \| 0.305684 / 0.293841 (0.011843) \| 0.025072 / 0.128546 (-0.103474) \| 0.007183 / 0.075646 (-0.068463) \| 0.054875 / 0.419271 (-0.364397) \| 0.033053 / 0.043533 (-0.010480) \| 0.271281 / 0.255139 (0.016142) \| 0.288057 / 0.283200 (0.004858) \| 0.018692 / 0.141683 (-0.122991) \| 1.125224 / 1.452155 (-0.326930) \| 1.171083 / 1.492716 (-0.321633) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103102 / 0.018006 (0.085096) \| 0.309099 / 0.000490 (0.308609) \| 0.000232 / 0.000200 (0.000032) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021532 / 0.037411 (-0.015879) \| 0.069927 / 0.014526 (0.055401) \| 0.080920 / 0.176557 (-0.095637) \| 0.122214 / 0.737135 (-0.614921) \| 0.082268 / 0.296338 (-0.214071) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298121 / 0.215209 (0.082912) \| 2.933000 / 2.077655 (0.855345) \| 1.608782 / 1.504120 (0.104662) \| 1.554083 / 1.541195 (0.012889) \| 1.552700 / 1.468490 (0.084209) \| 0.400576 / 4.584777 (-4.184201) \| 2.412914 / 3.745712 (-1.332798) \| 2.545706 / 5.269862 (-2.724155) \| 1.548797 / 4.565676 (-3.016879) \| 0.045553 / 0.424275 (-0.378722) \| 0.004751 / 0.007607 (-0.002857) \| 0.343002 / 0.226044 (0.116958) \| 3.402866 / 2.268929 (1.133937) \| 1.969910 / 55.444624 (-53.474715) \| 1.686639 / 6.876477 (-5.189838) \| 1.768474 / 2.142072 (-0.373599) \| 0.471299 / 4.805227 (-4.333928) \| 0.097696 / 6.500664 (-6.402968) \| 0.041693 / 0.075469 (-0.033776) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971380 / 1.841788 (-0.870408) \| 12.686033 / 8.074308 (4.611725) \| 11.370946 / 10.191392 (1.179554) \| 0.138377 / 0.680424 (-0.542047) \| 0.015623 / 0.534201 (-0.518578) \| 0.270935 / 0.579283 (-0.308348) \| 0.276235 / 0.434364 (-0.158129) \| 0.310196 / 0.540337 (-0.230141) \| 0.416908 / 1.386936 (-0.970028) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004581 / 0.011353 (-0.006772) \| 0.002468 / 0.011008 (-0.008541) \| 0.061420 / 0.038508 (0.022912) \| 0.047685 / 0.023109 (0.024575) \| 0.237756 / 0.275898 (-0.038142) \| 0.267548 / 0.323480 (-0.055932) \| 0.003899 / 0.007986 (-0.004086) \| 0.002338 / 0.004328 (-0.001990) \| 0.048794 / 0.004250 (0.044543) \| 0.042485 / 0.037052 (0.005433) \| 0.250165 / 0.258489 (-0.008324) \| 0.278791 / 0.293841 (-0.015050) \| 0.022371 / 0.128546 (-0.106175) \| 0.006923 / 0.075646 (-0.068723) \| 0.201401 / 0.419271 (-0.217870) \| 0.035867 / 0.043533 (-0.007665) \| 0.244628 / 0.255139 (-0.010511) \| 0.271137 / 0.283200 (-0.012063) \| 0.017257 / 0.141683 (-0.124426) \| 1.097261 / 1.452155 (-0.354894) \| 1.163314 / 1.492716 (-0.329402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089060 / 0.018006 (0.071054) \| 0.297489 / 0.000490 (0.296999) \| 0.000207 / 0.000200 (0.000007) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018583 / 0.037411 (-0.018828) \| 0.061974 / 0.014526 (0.047449) \| 0.073300 / 0.176557 (-0.103256) \| 0.118871 / 0.737135 (-0.618264) \| 0.075513 / 0.296338 (-0.220826) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285544 / 0.215209 (0.070335) \| 2.799871 / 2.077655 (0.722216) \| 1.479871 / 1.504120 (-0.024249) \| 1.351128 / 1.541195 (-0.190067) \| 1.377540 / 1.468490 (-0.090950) \| 0.393056 / 4.584777 (-4.191721) \| 2.341791 / 3.745712 (-1.403921) \| 2.546854 / 5.269862 (-2.723007) \| 1.547368 / 4.565676 (-3.018309) \| 0.046056 / 0.424275 (-0.378219) \| 0.004765 / 0.007607 (-0.002842) \| 0.336384 / 0.226044 (0.110339) \| 3.283277 / 2.268929 (1.014348) \| 1.784535 / 55.444624 (-53.660089) \| 1.557809 / 6.876477 (-5.318667) \| 1.581728 / 2.142072 (-0.560344) \| 0.470527 / 4.805227 (-4.334700) \| 0.098383 / 6.500664 (-6.402281) \| 0.041563 / 0.075469 (-0.033906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946924 / 1.841788 (-0.894863) \| 11.202775 / 8.074308 (3.128467) \| 10.249760 / 10.191392 (0.058368) \| 0.142337 / 0.680424 (-0.538087) \| 0.013784 / 0.534201 (-0.520417) \| 0.267237 / 0.579283 (-0.312046) \| 0.264142 / 0.434364 (-0.170222) \| 0.306343 / 0.540337 (-0.233994) \| 0.423681 / 1.386936 (-0.963255) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004786 / 0.011353 (-0.006567) \| 0.002398 / 0.011008 (-0.008610) \| 0.047325 / 0.038508 (0.008817) \| 0.050753 / 0.023109 (0.027644) \| 0.271132 / 0.275898 (-0.004766) \| 0.290854 / 0.323480 (-0.032626) \| 0.003953 / 0.007986 (-0.004033) \| 0.002238 / 0.004328 (-0.002090) \| 0.047463 / 0.004250 (0.043213) \| 0.038504 / 0.037052 (0.001451) \| 0.273182 / 0.258489 (0.014693) \| 0.303449 / 0.293841 (0.009608) \| 0.024069 / 0.128546 (-0.104477) \| 0.006712 / 0.075646 (-0.068934) \| 0.053032 / 0.419271 (-0.366239) \| 0.032221 / 0.043533 (-0.011312) \| 0.271770 / 0.255139 (0.016631) \| 0.287876 / 0.283200 (0.004677) \| 0.018040 / 0.141683 (-0.123643) \| 1.138749 / 1.452155 (-0.313405) \| 1.192048 / 1.492716 (-0.300668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089132 / 0.018006 (0.071126) \| 0.298636 / 0.000490 (0.298146) \| 0.000220 / 0.000200 (0.000020) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020808 / 0.037411 (-0.016603) \| 0.069506 / 0.014526 (0.054980) \| 0.079412 / 0.176557 (-0.097145) \| 0.118188 / 0.737135 (-0.618947) \| 0.083044 / 0.296338 (-0.213294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293502 / 0.215209 (0.078293) \| 2.863692 / 2.077655 (0.786037) \| 1.590877 / 1.504120 (0.086757) \| 1.483634 / 1.541195 (-0.057561) \| 1.502113 / 1.468490 (0.033623) \| 0.402170 / 4.584777 (-4.182607) \| 2.414188 / 3.745712 (-1.331524) \| 2.500146 / 5.269862 (-2.769716) \| 1.506977 / 4.565676 (-3.058699) \| 0.045849 / 0.424275 (-0.378426) \| 0.004755 / 0.007607 (-0.002852) \| 0.343073 / 0.226044 (0.117029) \| 3.354985 / 2.268929 (1.086056) \| 1.952594 / 55.444624 (-53.492030) \| 1.664084 / 6.876477 (-5.212392) \| 1.664203 / 2.142072 (-0.477869) \| 0.475858 / 4.805227 (-4.329370) \| 0.097539 / 6.500664 (-6.403125) \| 0.040201 / 0.075469 (-0.035268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980051 / 1.841788 (-0.861736) \| 11.615291 / 8.074308 (3.540983) \| 10.492092 / 10.191392 (0.300700) \| 0.130450 / 0.680424 (-0.549974) \| 0.015883 / 0.534201 (-0.518318) \| 0.267575 / 0.579283 (-0.311708) \| 0.276981 / 0.434364 (-0.157383) \| 0.310221 / 0.540337 (-0.230116) \| 0.417143 / 1.386936 (-0.969793) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004721 / 0.011353 (-0.006632) \| 0.002931 / 0.011008 (-0.008077) \| 0.061948 / 0.038508 (0.023440) \| 0.051066 / 0.023109 (0.027957) \| 0.245431 / 0.275898 (-0.030467) \| 0.295627 / 0.323480 (-0.027852) \| 0.003997 / 0.007986 (-0.003988) \| 0.002408 / 0.004328 (-0.001920) \| 0.048292 / 0.004250 (0.044041) \| 0.044716 / 0.037052 (0.007664) \| 0.255119 / 0.258489 (-0.003371) \| 0.287384 / 0.293841 (-0.006457) \| 0.022835 / 0.128546 (-0.105711) \| 0.007162 / 0.075646 (-0.068484) \| 0.201352 / 0.419271 (-0.217920) \| 0.036626 / 0.043533 (-0.006906) \| 0.249590 / 0.255139 (-0.005549) \| 0.270822 / 0.283200 (-0.012378) \| 0.018152 / 0.141683 (-0.123531) \| 1.097046 / 1.452155 (-0.355109) \| 1.160461 / 1.492716 (-0.332255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091712 / 0.018006 (0.073705) \| 0.299121 / 0.000490 (0.298631) \| 0.000244 / 0.000200 (0.000044) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018998 / 0.037411 (-0.018413) \| 0.062811 / 0.014526 (0.048285) \| 0.076348 / 0.176557 (-0.100209) \| 0.123898 / 0.737135 (-0.613238) \| 0.076249 / 0.296338 (-0.220090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282780 / 0.215209 (0.067571) \| 2.739028 / 2.077655 (0.661373) \| 1.472564 / 1.504120 (-0.031556) \| 1.347343 / 1.541195 (-0.193852) \| 1.387130 / 1.468490 (-0.081360) \| 0.403348 / 4.584777 (-4.181429) \| 2.369924 / 3.745712 (-1.375788) \| 2.612875 / 5.269862 (-2.656987) \| 1.588079 / 4.565676 (-2.977598) \| 0.045233 / 0.424275 (-0.379042) \| 0.004767 / 0.007607 (-0.002840) \| 0.336614 / 0.226044 (0.110570) \| 3.300485 / 2.268929 (1.031556) \| 1.834365 / 55.444624 (-53.610259) \| 1.559799 / 6.876477 (-5.316677) \| 1.601265 / 2.142072 (-0.540808) \| 0.468158 / 4.805227 (-4.337069) \| 0.099811 / 6.500664 (-6.400853) \| 0.042688 / 0.075469 (-0.032782) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.934097 / 1.841788 (-0.907691) \| 11.687713 / 8.074308 (3.613405) \| 10.412723 / 10.191392 (0.221331) \| 0.139276 / 0.680424 (-0.541148) \| 0.014042 / 0.534201 (-0.520159) \| 0.270306 / 0.579283 (-0.308978) \| 0.266609 / 0.434364 (-0.167755) \| 0.314179 / 0.540337 (-0.226158) \| 0.437744 / 1.386936 (-0.949192) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004893 / 0.011353 (-0.006460) \| 0.002952 / 0.011008 (-0.008056) \| 0.050441 / 0.038508 (0.011933) \| 0.051838 / 0.023109 (0.028729) \| 0.271163 / 0.275898 (-0.004735) \| 0.293031 / 0.323480 (-0.030449) \| 0.003976 / 0.007986 (-0.004010) \| 0.002396 / 0.004328 (-0.001933) \| 0.048103 / 0.004250 (0.043852) \| 0.038732 / 0.037052 (0.001680) \| 0.274276 / 0.258489 (0.015787) \| 0.305112 / 0.293841 (0.011271) \| 0.024112 / 0.128546 (-0.104434) \| 0.007203 / 0.075646 (-0.068443) \| 0.053502 / 0.419271 (-0.365770) \| 0.032360 / 0.043533 (-0.011173) \| 0.270154 / 0.255139 (0.015015) \| 0.286689 / 0.283200 (0.003489) \| 0.018285 / 0.141683 (-0.123397) \| 1.141421 / 1.452155 (-0.310734) \| 1.244062 / 1.492716 (-0.248654) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090960 / 0.018006 (0.072954) \| 0.286134 / 0.000490 (0.285644) \| 0.000207 / 0.000200 (0.000007) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020789 / 0.037411 (-0.016622) \| 0.070850 / 0.014526 (0.056324) \| 0.080750 / 0.176557 (-0.095807) \| 0.120046 / 0.737135 (-0.617089) \| 0.083630 / 0.296338 (-0.212708) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290654 / 0.215209 (0.075445) \| 2.846669 / 2.077655 (0.769014) \| 1.561752 / 1.504120 (0.057632) \| 1.442968 / 1.541195 (-0.098227) \| 1.503551 / 1.468490 (0.035061) \| 0.399731 / 4.584777 (-4.185046) \| 2.430099 / 3.745712 (-1.315613) \| 2.556169 / 5.269862 (-2.713692) \| 1.545591 / 4.565676 (-3.020085) \| 0.045967 / 0.424275 (-0.378309) \| 0.004851 / 0.007607 (-0.002756) \| 0.340167 / 0.226044 (0.114122) \| 3.392738 / 2.268929 (1.123809) \| 1.943577 / 55.444624 (-53.501047) \| 1.650057 / 6.876477 (-5.226420) \| 1.686872 / 2.142072 (-0.455201) \| 0.470305 / 4.805227 (-4.334923) \| 0.097296 / 6.500664 (-6.403368) \| 0.041399 / 0.075469 (-0.034070) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985660 / 1.841788 (-0.856128) \| 12.300826 / 8.074308 (4.226518) \| 10.972591 / 10.191392 (0.781199) \| 0.131512 / 0.680424 (-0.548912) \| 0.015742 / 0.534201 (-0.518459) \| 0.270630 / 0.579283 (-0.308653) \| 0.276039 / 0.434364 (-0.158325) \| 0.302288 / 0.540337 (-0.238050) \| 0.409415 / 1.386936 (-0.977521) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n" ]	2023-11-15T08:07:37Z	2023-11-15T17:35:30Z	2023-11-15T08:12:59Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6419.diff", "html_url": "https://github.com/huggingface/datasets/pull/6419", "merged_at": "2023-11-15T08:12:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/6419.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6419" }	Release 2.14.7.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", 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https://github.com/huggingface/datasets/pull/6418	6,418	Remove token value from warnings	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005135 / 0.011353 (-0.006218) \| 0.002950 / 0.011008 (-0.008058) \| 0.062316 / 0.038508 (0.023808) \| 0.030068 / 0.023109 (0.006959) \| 0.251998 / 0.275898 (-0.023900) \| 0.274806 / 0.323480 (-0.048674) \| 0.003067 / 0.007986 (-0.004919) \| 0.003082 / 0.004328 (-0.001247) \| 0.048503 / 0.004250 (0.044253) \| 0.045167 / 0.037052 (0.008114) \| 0.254277 / 0.258489 (-0.004212) \| 0.290528 / 0.293841 (-0.003313) \| 0.023666 / 0.128546 (-0.104880) \| 0.007049 / 0.075646 (-0.068597) \| 0.202367 / 0.419271 (-0.216905) \| 0.056291 / 0.043533 (0.012758) \| 0.251923 / 0.255139 (-0.003216) \| 0.273595 / 0.283200 (-0.009605) \| 0.019065 / 0.141683 (-0.122618) \| 1.100832 / 1.452155 (-0.351322) \| 1.266758 / 1.492716 (-0.225959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094311 / 0.018006 (0.076305) \| 0.303199 / 0.000490 (0.302709) \| 0.000238 / 0.000200 (0.000039) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019413 / 0.037411 (-0.017999) \| 0.062618 / 0.014526 (0.048092) \| 0.072850 / 0.176557 (-0.103707) \| 0.119124 / 0.737135 (-0.618012) \| 0.074044 / 0.296338 (-0.222294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273660 / 0.215209 (0.058451) \| 2.682371 / 2.077655 (0.604716) \| 1.426041 / 1.504120 (-0.078079) \| 1.317186 / 1.541195 (-0.224009) \| 1.332385 / 1.468490 (-0.136106) \| 0.394599 / 4.584777 (-4.190178) \| 2.368167 / 3.745712 (-1.377545) \| 2.683728 / 5.269862 (-2.586134) \| 1.668348 / 4.565676 (-2.897329) \| 0.046177 / 0.424275 (-0.378098) \| 0.004833 / 0.007607 (-0.002774) \| 0.331413 / 0.226044 (0.105369) \| 3.278984 / 2.268929 (1.010055) \| 1.797600 / 55.444624 (-53.647024) \| 1.492202 / 6.876477 (-5.384274) \| 1.536039 / 2.142072 (-0.606034) \| 0.470601 / 4.805227 (-4.334626) \| 0.100833 / 6.500664 (-6.399831) \| 0.042787 / 0.075469 (-0.032682) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959036 / 1.841788 (-0.882752) \| 11.632956 / 8.074308 (3.558648) \| 10.384574 / 10.191392 (0.193182) \| 0.127477 / 0.680424 (-0.552946) \| 0.014072 / 0.534201 (-0.520129) \| 0.269534 / 0.579283 (-0.309749) \| 0.259753 / 0.434364 (-0.174611) \| 0.313450 / 0.540337 (-0.226888) \| 0.431799 / 1.386936 (-0.955137) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004964 / 0.011353 (-0.006389) \| 0.002906 / 0.011008 (-0.008102) \| 0.048145 / 0.038508 (0.009637) \| 0.056457 / 0.023109 (0.033348) \| 0.274131 / 0.275898 (-0.001767) \| 0.298534 / 0.323480 (-0.024946) \| 0.004145 / 0.007986 (-0.003841) \| 0.002415 / 0.004328 (-0.001913) \| 0.048558 / 0.004250 (0.044308) \| 0.039031 / 0.037052 (0.001978) \| 0.278948 / 0.258489 (0.020459) \| 0.312358 / 0.293841 (0.018517) \| 0.024902 / 0.128546 (-0.103645) \| 0.007286 / 0.075646 (-0.068360) \| 0.053839 / 0.419271 (-0.365433) \| 0.032510 / 0.043533 (-0.011023) \| 0.272023 / 0.255139 (0.016884) \| 0.293420 / 0.283200 (0.010221) \| 0.018932 / 0.141683 (-0.122750) \| 1.122792 / 1.452155 (-0.329362) \| 1.167385 / 1.492716 (-0.325331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094574 / 0.018006 (0.076567) \| 0.303810 / 0.000490 (0.303321) \| 0.000227 / 0.000200 (0.000027) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021675 / 0.037411 (-0.015737) \| 0.070289 / 0.014526 (0.055763) \| 0.080345 / 0.176557 (-0.096211) \| 0.120220 / 0.737135 (-0.616915) \| 0.084080 / 0.296338 (-0.212259) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300134 / 0.215209 (0.084925) \| 2.945831 / 2.077655 (0.868176) \| 1.605303 / 1.504120 (0.101183) \| 1.480135 / 1.541195 (-0.061059) \| 1.526039 / 1.468490 (0.057549) \| 0.398264 / 4.584777 (-4.186512) \| 2.461391 / 3.745712 (-1.284321) \| 2.559929 / 5.269862 (-2.709933) \| 1.541391 / 4.565676 (-3.024286) \| 0.045319 / 0.424275 (-0.378957) \| 0.004834 / 0.007607 (-0.002773) \| 0.352186 / 0.226044 (0.126141) \| 3.500108 / 2.268929 (1.231180) \| 1.966394 / 55.444624 (-53.478230) \| 1.675500 / 6.876477 (-5.200977) \| 1.683134 / 2.142072 (-0.458938) \| 0.465085 / 4.805227 (-4.340142) \| 0.097235 / 6.500664 (-6.403429) \| 0.040764 / 0.075469 (-0.034705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982813 / 1.841788 (-0.858975) \| 12.382529 / 8.074308 (4.308221) \| 11.082660 / 10.191392 (0.891268) \| 0.129113 / 0.680424 (-0.551310) \| 0.015718 / 0.534201 (-0.518483) \| 0.272776 / 0.579283 (-0.306507) \| 0.275513 / 0.434364 (-0.158850) \| 0.304933 / 0.540337 (-0.235404) \| 0.414591 / 1.386936 (-0.972345) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8723b129a64928eba40baf70ffd462060ade9f97 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004400 / 0.011353 (-0.006953) \| 0.002580 / 0.011008 (-0.008428) \| 0.060975 / 0.038508 (0.022467) \| 0.029337 / 0.023109 (0.006228) \| 0.248643 / 0.275898 (-0.027255) \| 0.274476 / 0.323480 (-0.049004) \| 0.003925 / 0.007986 (-0.004061) \| 0.002332 / 0.004328 (-0.001997) \| 0.049501 / 0.004250 (0.045251) \| 0.042730 / 0.037052 (0.005678) \| 0.255823 / 0.258489 (-0.002666) \| 0.281748 / 0.293841 (-0.012093) \| 0.023118 / 0.128546 (-0.105428) \| 0.006957 / 0.075646 (-0.068690) \| 0.201630 / 0.419271 (-0.217641) \| 0.054258 / 0.043533 (0.010725) \| 0.252289 / 0.255139 (-0.002850) \| 0.267561 / 0.283200 (-0.015639) \| 0.016903 / 0.141683 (-0.124780) \| 1.104322 / 1.452155 (-0.347833) \| 1.160027 / 1.492716 (-0.332689) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096340 / 0.018006 (0.078333) \| 0.305187 / 0.000490 (0.304697) \| 0.000222 / 0.000200 (0.000022) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018733 / 0.037411 (-0.018678) \| 0.062382 / 0.014526 (0.047856) \| 0.072309 / 0.176557 (-0.104248) \| 0.119772 / 0.737135 (-0.617364) \| 0.074655 / 0.296338 (-0.221683) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286150 / 0.215209 (0.070941) \| 2.770328 / 2.077655 (0.692673) \| 1.494593 / 1.504120 (-0.009527) \| 1.358611 / 1.541195 (-0.182583) \| 1.396308 / 1.468490 (-0.072182) \| 0.394806 / 4.584777 (-4.189971) \| 2.349100 / 3.745712 (-1.396613) \| 2.600541 / 5.269862 (-2.669321) \| 1.568975 / 4.565676 (-2.996701) \| 0.046212 / 0.424275 (-0.378063) \| 0.004821 / 0.007607 (-0.002786) \| 0.332286 / 0.226044 (0.106242) \| 3.302643 / 2.268929 (1.033714) \| 1.838992 / 55.444624 (-53.605633) \| 1.571919 / 6.876477 (-5.304557) \| 1.574956 / 2.142072 (-0.567117) \| 0.464156 / 4.805227 (-4.341071) \| 0.097983 / 6.500664 (-6.402681) \| 0.042243 / 0.075469 (-0.033226) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941675 / 1.841788 (-0.900113) \| 11.450326 / 8.074308 (3.376017) \| 10.169943 / 10.191392 (-0.021449) \| 0.137879 / 0.680424 (-0.542545) \| 0.013765 / 0.534201 (-0.520436) \| 0.268633 / 0.579283 (-0.310650) \| 0.265083 / 0.434364 (-0.169281) \| 0.302099 / 0.540337 (-0.238238) \| 0.423033 / 1.386936 (-0.963903) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004998 / 0.011353 (-0.006355) \| 0.003174 / 0.011008 (-0.007834) \| 0.047924 / 0.038508 (0.009416) \| 0.057598 / 0.023109 (0.034489) \| 0.278823 / 0.275898 (0.002925) \| 0.334349 / 0.323480 (0.010869) \| 0.004053 / 0.007986 (-0.003932) \| 0.002554 / 0.004328 (-0.001774) \| 0.047797 / 0.004250 (0.043547) \| 0.039802 / 0.037052 (0.002749) \| 0.278295 / 0.258489 (0.019806) \| 0.319597 / 0.293841 (0.025757) \| 0.024802 / 0.128546 (-0.103744) \| 0.007362 / 0.075646 (-0.068284) \| 0.066983 / 0.419271 (-0.352288) \| 0.032707 / 0.043533 (-0.010826) \| 0.277350 / 0.255139 (0.022211) \| 0.296829 / 0.283200 (0.013629) \| 0.017902 / 0.141683 (-0.123781) \| 1.129765 / 1.452155 (-0.322390) \| 1.201940 / 1.492716 (-0.290777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095631 / 0.018006 (0.077625) \| 0.296999 / 0.000490 (0.296510) \| 0.000234 / 0.000200 (0.000034) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021547 / 0.037411 (-0.015865) \| 0.070003 / 0.014526 (0.055477) \| 0.083173 / 0.176557 (-0.093384) \| 0.121676 / 0.737135 (-0.615459) \| 0.082974 / 0.296338 (-0.213364) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298982 / 0.215209 (0.083773) \| 2.918666 / 2.077655 (0.841011) \| 1.582054 / 1.504120 (0.077934) \| 1.463804 / 1.541195 (-0.077391) \| 1.484384 / 1.468490 (0.015893) \| 0.399443 / 4.584777 (-4.185334) \| 2.393515 / 3.745712 (-1.352197) \| 2.533004 / 5.269862 (-2.736858) \| 1.490411 / 4.565676 (-3.075266) \| 0.045274 / 0.424275 (-0.379002) \| 0.004783 / 0.007607 (-0.002824) \| 0.350510 / 0.226044 (0.124465) \| 3.437927 / 2.268929 (1.168998) \| 1.940115 / 55.444624 (-53.504509) \| 1.662025 / 6.876477 (-5.214452) \| 1.640621 / 2.142072 (-0.501452) \| 0.464014 / 4.805227 (-4.341214) \| 0.095506 / 6.500664 (-6.405158) \| 0.040172 / 0.075469 (-0.035297) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975618 / 1.841788 (-0.866169) \| 12.561067 / 8.074308 (4.486759) \| 11.408037 / 10.191392 (1.216645) \| 0.130699 / 0.680424 (-0.549725) \| 0.016796 / 0.534201 (-0.517405) \| 0.271130 / 0.579283 (-0.308153) \| 0.283506 / 0.434364 (-0.150857) \| 0.304482 / 0.540337 (-0.235856) \| 0.413673 / 1.386936 (-0.973263) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#723038a73248dd12dc0673d2b341e9295c441ea3 \"CML watermark\")\n" ]	2023-11-14T17:34:06Z	2023-11-14T22:26:04Z	2023-11-14T22:19:45Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6418.diff", "html_url": "https://github.com/huggingface/datasets/pull/6418", "merged_at": "2023-11-14T22:19:45Z", "patch_url": "https://github.com/huggingface/datasets/pull/6418.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6418" }	Fix #6412	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": 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https://github.com/huggingface/datasets/issues/6417	6,417	Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error	{ "avatar_url": "https://avatars.githubusercontent.com/u/57496007?v=4", "events_url": "https://api.github.com/users/Davo00/events{/privacy}", "followers_url": "https://api.github.com/users/Davo00/followers", "following_url": "https://api.github.com/users/Davo00/following{/other_user}", "gists_url": "https://api.github.com/users/Davo00/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Davo00", "id": 57496007, "login": "Davo00", "node_id": "MDQ6VXNlcjU3NDk2MDA3", "organizations_url": "https://api.github.com/users/Davo00/orgs", "received_events_url": "https://api.github.com/users/Davo00/received_events", "repos_url": "https://api.github.com/users/Davo00/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Davo00/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Davo00/subscriptions", "type": "User", "url": "https://api.github.com/users/Davo00", "user_view_type": "public" }	[]	closed	false	[ "Very strange: `datasets-cli env`\r\n> \r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.9.0\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.13\r\n> - PyArrow version: 8.0.0\r\n> - Pandas version: 1.3.5\r\n\r\nAfter updating datasets and pyarrow on base environment, although I am using a different one called layoutLM\r\n\r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.14.6\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.18\r\n> - Huggingface_hub version: 0.17.3\r\n> - PyArrow version: 14.0.1\r\n> - Pandas version: 2.1.3", "Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.", "> Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.\r\n\r\nThanks for the info and the latest release, it seems this has also solved my issue. First run after the update worked and I am training right now :D\r\nWill close the Issu" ]	2023-11-14T16:53:20Z	2023-11-16T20:23:41Z	2023-11-16T20:23:41Z	NONE	null	null	### Describe the bug Arrow issues when running the example Notebook laptop locally on Mac with M1. Works on Google Collab. Notebook: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb Error: `ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.` Caused by: ``` # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) ``` ### Steps to reproduce the bug Run the notebook provided, locally. If possible also on M1. ### Expected behavior The cell where features are mapped to Array2D and Array3D should work without any issues. ### Environment info Tried with Python 3.9 and 3.10 conda envs. Running Mac M1. `pip show datasets` > Name: datasets Version: 2.14.6 Summary: HuggingFace community-driven open-source library of datasets `pip list` > Package Version > ------------------------- ------------ > accelerate 0.24.1 > aiohttp 3.8.6 > aiosignal 1.3.1 > anyio 3.5.0 > appnope 0.1.2 > argon2-cffi 21.3.0 > argon2-cffi-bindings 21.2.0 > asttokens 2.0.5 > async-timeout 4.0.3 > attrs 23.1.0 > backcall 0.2.0 > beautifulsoup4 4.12.2 > bleach 4.1.0 > certifi 2023.7.22 > cffi 1.15.1 > charset-normalizer 3.3.2 > comm 0.1.2 > datasets 2.14.6 > debugpy 1.6.7 > decorator 5.1.1 > defusedxml 0.7.1 > dill 0.3.7 > entrypoints 0.4 > exceptiongroup 1.0.4 > executing 0.8.3 > fastjsonschema 2.16.2 > filelock 3.13.1 > frozenlist 1.4.0 > fsspec 2023.10.0 > huggingface-hub 0.17.3 > idna 3.4 > importlib-metadata 6.0.0 > IProgress 0.4 > ipykernel 6.25.0 > ipython 8.15.0 > ipython-genutils 0.2.0 > jedi 0.18.1 > Jinja2 3.1.2 > joblib 1.3.2 > jsonschema 4.19.2 > jsonschema-specifications 2023.7.1 > jupyter_client 7.4.9 > jupyter_core 5.5.0 > jupyter-server 1.23.4 > jupyterlab-pygments 0.1.2 > MarkupSafe 2.1.1 > matplotlib-inline 0.1.6 > mistune 2.0.4 > mpmath 1.3.0 > multidict 6.0.4 > multiprocess 0.70.15 > nbclassic 1.0.0 > nbclient 0.8.0 > nbconvert 7.10.0 > nbformat 5.9.2 > nest-asyncio 1.5.6 > networkx 3.2.1 > notebook 6.5.4 > notebook_shim 0.2.3 > numpy 1.26.1 > packaging 23.1 > pandas 2.1.3 > pandocfilters 1.5.0 > parso 0.8.3 > pexpect 4.8.0 > pickleshare 0.7.5 > Pillow 10.1.0 > pip 23.3 > platformdirs 3.10.0 > prometheus-client 0.14.1 > prompt-toolkit 3.0.36 > psutil 5.9.0 > ptyprocess 0.7.0 > pure-eval 0.2.2 > pyarrow 14.0.1 > pycparser 2.21 > Pygments 2.15.1 > python-dateutil 2.8.2 > pytz 2023.3.post1 > PyYAML 6.0.1 > pyzmq 23.2.0 > referencing 0.30.2 > regex 2023.10.3 > requests 2.31.0 > rpds-py 0.10.6 > safetensors 0.4.0 > scikit-learn 1.3.2 > scipy 1.11.3 > Send2Trash 1.8.2 > seqeval 1.2.2 > setuptools 68.0.0 > six 1.16.0 > sniffio 1.2.0 > soupsieve 2.5 > stack-data 0.2.0 > sympy 1.12 > terminado 0.17.1 > threadpoolctl 3.2.0 > tinycss2 1.2.1 > tokenizers 0.14.1 > torch 2.1.0 > tornado 6.3.3 > tqdm 4.66.1 > traitlets 5.7.1 > transformers 4.36.0.dev0 > typing_extensions 4.7.1 > tzdata 2023.3 > urllib3 2.0.7 > wcwidth 0.2.5 > webencodings 0.5.1 > websocket-client 0.58.0 > wheel 0.41.2 > xxhash 3.4.1 > yarl 1.9.2 > zipp 3.11.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/57496007?v=4", "events_url": "https://api.github.com/users/Davo00/events{/privacy}", "followers_url": "https://api.github.com/users/Davo00/followers", "following_url": "https://api.github.com/users/Davo00/following{/other_user}", "gists_url": "https://api.github.com/users/Davo00/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Davo00", "id": 57496007, "login": "Davo00", "node_id": "MDQ6VXNlcjU3NDk2MDA3", "organizations_url": "https://api.github.com/users/Davo00/orgs", "received_events_url": "https://api.github.com/users/Davo00/received_events", "repos_url": "https://api.github.com/users/Davo00/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Davo00/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Davo00/subscriptions", "type": "User", "url": "https://api.github.com/users/Davo00", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6417/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6416	6,416	Rename audio_classificiation.py to audio_classification.py	{ "avatar_url": "https://avatars.githubusercontent.com/u/1595907?v=4", "events_url": "https://api.github.com/users/carlthome/events{/privacy}", "followers_url": "https://api.github.com/users/carlthome/followers", "following_url": "https://api.github.com/users/carlthome/following{/other_user}", "gists_url": "https://api.github.com/users/carlthome/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/carlthome", "id": 1595907, "login": "carlthome", "node_id": "MDQ6VXNlcjE1OTU5MDc=", "organizations_url": "https://api.github.com/users/carlthome/orgs", "received_events_url": "https://api.github.com/users/carlthome/received_events", "repos_url": "https://api.github.com/users/carlthome/repos", "site_admin": false, "starred_url": "https://api.github.com/users/carlthome/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/carlthome/subscriptions", "type": "User", "url": "https://api.github.com/users/carlthome", "user_view_type": "public" }	[]	closed	false	[ "Oh good catch. Can you also rename it in `src/datasets/tasks/__init__.py` ?", "Fixed! \r\n\r\n(I think, tough word to spell right TBH)", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004737 / 0.011353 (-0.006616) \| 0.002446 / 0.011008 (-0.008563) \| 0.060928 / 0.038508 (0.022420) \| 0.030479 / 0.023109 (0.007370) \| 0.238385 / 0.275898 (-0.037513) \| 0.265563 / 0.323480 (-0.057917) \| 0.002910 / 0.007986 (-0.005076) \| 0.002325 / 0.004328 (-0.002004) \| 0.047817 / 0.004250 (0.043566) \| 0.044243 / 0.037052 (0.007191) \| 0.245190 / 0.258489 (-0.013299) \| 0.275449 / 0.293841 (-0.018392) \| 0.023384 / 0.128546 (-0.105162) \| 0.006820 / 0.075646 (-0.068826) \| 0.201488 / 0.419271 (-0.217783) \| 0.057758 / 0.043533 (0.014225) \| 0.245279 / 0.255139 (-0.009860) \| 0.266094 / 0.283200 (-0.017106) \| 0.019254 / 0.141683 (-0.122429) \| 1.107497 / 1.452155 (-0.344658) \| 1.161412 / 1.492716 (-0.331304) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094909 / 0.018006 (0.076903) \| 0.305185 / 0.000490 (0.304695) \| 0.000221 / 0.000200 (0.000021) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018352 / 0.037411 (-0.019059) \| 0.062441 / 0.014526 (0.047915) \| 0.072386 / 0.176557 (-0.104171) \| 0.118836 / 0.737135 (-0.618299) \| 0.074514 / 0.296338 (-0.221824) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283632 / 0.215209 (0.068423) \| 2.751845 / 2.077655 (0.674190) \| 1.478620 / 1.504120 (-0.025499) \| 1.357221 / 1.541195 (-0.183974) \| 1.415297 / 1.468490 (-0.053194) \| 0.400093 / 4.584777 (-4.184684) \| 2.404607 / 3.745712 (-1.341105) \| 2.617572 / 5.269862 (-2.652289) \| 1.587622 / 4.565676 (-2.978055) \| 0.045997 / 0.424275 (-0.378278) \| 0.004872 / 0.007607 (-0.002735) \| 0.338901 / 0.226044 (0.112856) \| 3.371362 / 2.268929 (1.102434) \| 1.870469 / 55.444624 (-53.574155) \| 1.561670 / 6.876477 (-5.314807) \| 1.573186 / 2.142072 (-0.568886) \| 0.478735 / 4.805227 (-4.326492) \| 0.098743 / 6.500664 (-6.401921) \| 0.041780 / 0.075469 (-0.033689) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945422 / 1.841788 (-0.896366) \| 11.563464 / 8.074308 (3.489156) \| 10.368731 / 10.191392 (0.177339) \| 0.129910 / 0.680424 (-0.550513) \| 0.014014 / 0.534201 (-0.520187) \| 0.269036 / 0.579283 (-0.310247) \| 0.265516 / 0.434364 (-0.168848) \| 0.311082 / 0.540337 (-0.229255) \| 0.431510 / 1.386936 (-0.955426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006284) \| 0.002989 / 0.011008 (-0.008019) \| 0.048213 / 0.038508 (0.009705) \| 0.056133 / 0.023109 (0.033024) \| 0.283347 / 0.275898 (0.007449) \| 0.307505 / 0.323480 (-0.015975) \| 0.004041 / 0.007986 (-0.003944) \| 0.002477 / 0.004328 (-0.001852) \| 0.047771 / 0.004250 (0.043521) \| 0.039361 / 0.037052 (0.002309) \| 0.283764 / 0.258489 (0.025275) \| 0.320644 / 0.293841 (0.026803) \| 0.024972 / 0.128546 (-0.103575) \| 0.007599 / 0.075646 (-0.068048) \| 0.054732 / 0.419271 (-0.364539) \| 0.032774 / 0.043533 (-0.010759) \| 0.285594 / 0.255139 (0.030455) \| 0.301500 / 0.283200 (0.018300) \| 0.018181 / 0.141683 (-0.123501) \| 1.126311 / 1.452155 (-0.325843) \| 1.187147 / 1.492716 (-0.305569) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097397 / 0.018006 (0.079391) \| 0.315112 / 0.000490 (0.314622) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021529 / 0.037411 (-0.015882) \| 0.073208 / 0.014526 (0.058682) \| 0.081683 / 0.176557 (-0.094874) \| 0.120475 / 0.737135 (-0.616660) \| 0.083265 / 0.296338 (-0.213073) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289976 / 0.215209 (0.074767) \| 2.839860 / 2.077655 (0.762205) \| 1.592635 / 1.504120 (0.088515) \| 1.466722 / 1.541195 (-0.074472) \| 1.552850 / 1.468490 (0.084360) \| 0.418693 / 4.584777 (-4.166084) \| 2.526620 / 3.745712 (-1.219093) \| 2.706182 / 5.269862 (-2.563680) \| 1.618514 / 4.565676 (-2.947162) \| 0.046303 / 0.424275 (-0.377972) \| 0.004873 / 0.007607 (-0.002734) \| 0.345146 / 0.226044 (0.119102) \| 3.378448 / 2.268929 (1.109520) \| 1.986393 / 55.444624 (-53.458231) \| 1.681838 / 6.876477 (-5.194639) \| 1.738093 / 2.142072 (-0.403980) \| 0.484386 / 4.805227 (-4.320842) \| 0.100693 / 6.500664 (-6.399971) \| 0.043084 / 0.075469 (-0.032385) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976399 / 1.841788 (-0.865389) \| 13.122968 / 8.074308 (5.048660) \| 11.245031 / 10.191392 (1.053639) \| 0.134433 / 0.680424 (-0.545991) \| 0.017439 / 0.534201 (-0.516762) \| 0.274083 / 0.579283 (-0.305200) \| 0.287353 / 0.434364 (-0.147011) \| 0.309231 / 0.540337 (-0.231106) \| 0.418003 / 1.386936 (-0.968933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939f136f255eab68a5bf6441db2a395f8af78511 \"CML watermark\")\n" ]	2023-11-14T15:15:29Z	2023-11-15T11:59:32Z	2023-11-15T11:53:20Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6416.diff", "html_url": "https://github.com/huggingface/datasets/pull/6416", "merged_at": "2023-11-15T11:53:20Z", "patch_url": "https://github.com/huggingface/datasets/pull/6416.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6416" }	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6416/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6415	6,415	Fix multi gpu map example	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004537 / 0.011353 (-0.006816) \| 0.002844 / 0.011008 (-0.008164) \| 0.062506 / 0.038508 (0.023998) \| 0.029675 / 0.023109 (0.006566) \| 0.238080 / 0.275898 (-0.037818) \| 0.259858 / 0.323480 (-0.063622) \| 0.004015 / 0.007986 (-0.003970) \| 0.002432 / 0.004328 (-0.001897) \| 0.049477 / 0.004250 (0.045227) \| 0.045383 / 0.037052 (0.008331) \| 0.241934 / 0.258489 (-0.016555) \| 0.270759 / 0.293841 (-0.023082) \| 0.023207 / 0.128546 (-0.105339) \| 0.007107 / 0.075646 (-0.068539) \| 0.207626 / 0.419271 (-0.211645) \| 0.056706 / 0.043533 (0.013173) \| 0.239713 / 0.255139 (-0.015426) \| 0.256639 / 0.283200 (-0.026560) \| 0.017514 / 0.141683 (-0.124169) \| 1.105201 / 1.452155 (-0.346953) \| 1.173087 / 1.492716 (-0.319629) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093391 / 0.018006 (0.075384) \| 0.302673 / 0.000490 (0.302184) \| 0.000218 / 0.000200 (0.000018) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019447 / 0.037411 (-0.017965) \| 0.063349 / 0.014526 (0.048823) \| 0.075600 / 0.176557 (-0.100957) \| 0.121098 / 0.737135 (-0.616037) \| 0.075028 / 0.296338 (-0.221311) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291479 / 0.215209 (0.076270) \| 2.787231 / 2.077655 (0.709576) \| 1.480205 / 1.504120 (-0.023915) \| 1.417656 / 1.541195 (-0.123538) \| 1.394529 / 1.468490 (-0.073962) \| 0.408843 / 4.584777 (-4.175934) \| 2.398691 / 3.745712 (-1.347021) \| 2.635457 / 5.269862 (-2.634404) \| 1.591722 / 4.565676 (-2.973955) \| 0.048445 / 0.424275 (-0.375830) \| 0.004864 / 0.007607 (-0.002743) \| 0.349014 / 0.226044 (0.122969) \| 3.436962 / 2.268929 (1.168033) \| 1.839266 / 55.444624 (-53.605359) \| 1.535252 / 6.876477 (-5.341225) \| 1.581048 / 2.142072 (-0.561025) \| 0.491150 / 4.805227 (-4.314078) \| 0.101279 / 6.500664 (-6.399385) \| 0.041938 / 0.075469 (-0.033532) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946986 / 1.841788 (-0.894801) \| 11.766196 / 8.074308 (3.691888) \| 10.425615 / 10.191392 (0.234223) \| 0.129957 / 0.680424 (-0.550467) \| 0.014859 / 0.534201 (-0.519342) \| 0.268046 / 0.579283 (-0.311237) \| 0.263724 / 0.434364 (-0.170640) \| 0.311028 / 0.540337 (-0.229309) \| 0.434715 / 1.386936 (-0.952221) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004874 / 0.011353 (-0.006479) \| 0.002942 / 0.011008 (-0.008067) \| 0.048250 / 0.038508 (0.009742) \| 0.053726 / 0.023109 (0.030617) \| 0.268870 / 0.275898 (-0.007028) \| 0.289152 / 0.323480 (-0.034328) \| 0.003982 / 0.007986 (-0.004004) \| 0.002488 / 0.004328 (-0.001840) \| 0.047902 / 0.004250 (0.043652) \| 0.038732 / 0.037052 (0.001680) \| 0.271021 / 0.258489 (0.012532) \| 0.299967 / 0.293841 (0.006126) \| 0.024672 / 0.128546 (-0.103874) \| 0.007311 / 0.075646 (-0.068336) \| 0.053721 / 0.419271 (-0.365550) \| 0.032407 / 0.043533 (-0.011126) \| 0.266604 / 0.255139 (0.011465) \| 0.286816 / 0.283200 (0.003617) \| 0.018973 / 0.141683 (-0.122710) \| 1.122460 / 1.452155 (-0.329695) \| 1.177720 / 1.492716 (-0.314997) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093968 / 0.018006 (0.075962) \| 0.304010 / 0.000490 (0.303521) \| 0.000228 / 0.000200 (0.000028) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021203 / 0.037411 (-0.016208) \| 0.070318 / 0.014526 (0.055793) \| 0.081688 / 0.176557 (-0.094869) \| 0.120916 / 0.737135 (-0.616219) \| 0.083452 / 0.296338 (-0.212886) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293961 / 0.215209 (0.078752) \| 2.858514 / 2.077655 (0.780860) \| 1.556169 / 1.504120 (0.052049) \| 1.431523 / 1.541195 (-0.109671) \| 1.478145 / 1.468490 (0.009654) \| 0.408927 / 4.584777 (-4.175850) \| 2.440630 / 3.745712 (-1.305082) \| 2.586327 / 5.269862 (-2.683534) \| 1.529495 / 4.565676 (-3.036182) \| 0.047387 / 0.424275 (-0.376888) \| 0.004817 / 0.007607 (-0.002790) \| 0.345009 / 0.226044 (0.118965) \| 3.386313 / 2.268929 (1.117384) \| 1.922361 / 55.444624 (-53.522264) \| 1.640814 / 6.876477 (-5.235663) \| 1.657005 / 2.142072 (-0.485068) \| 0.483844 / 4.805227 (-4.321383) \| 0.099470 / 6.500664 (-6.401194) \| 0.040735 / 0.075469 (-0.034734) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986311 / 1.841788 (-0.855476) \| 12.327425 / 8.074308 (4.253117) \| 10.995135 / 10.191392 (0.803743) \| 0.146814 / 0.680424 (-0.533610) \| 0.015820 / 0.534201 (-0.518381) \| 0.272319 / 0.579283 (-0.306964) \| 0.274858 / 0.434364 (-0.159506) \| 0.305728 / 0.540337 (-0.234609) \| 0.421400 / 1.386936 (-0.965536) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#611a03d70378d6e48a19fac89e7616cf556b920a \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007995 / 0.011353 (-0.003358) \| 0.004596 / 0.011008 (-0.006412) \| 0.099818 / 0.038508 (0.061310) \| 0.053539 / 0.023109 (0.030429) \| 0.367757 / 0.275898 (0.091859) \| 0.409351 / 0.323480 (0.085871) \| 0.007423 / 0.007986 (-0.000563) \| 0.003770 / 0.004328 (-0.000558) \| 0.075635 / 0.004250 (0.071385) \| 0.078844 / 0.037052 (0.041791) \| 0.374523 / 0.258489 (0.116034) \| 0.423378 / 0.293841 (0.129537) \| 0.038901 / 0.128546 (-0.089645) \| 0.009985 / 0.075646 (-0.065661) \| 0.342793 / 0.419271 (-0.076479) \| 0.098045 / 0.043533 (0.054512) \| 0.368077 / 0.255139 (0.112938) \| 0.394251 / 0.283200 (0.111051) \| 0.030624 / 0.141683 (-0.111059) \| 1.782728 / 1.452155 (0.330574) \| 1.867571 / 1.492716 (0.374855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265550 / 0.018006 (0.247544) \| 0.504045 / 0.000490 (0.503555) \| 0.016523 / 0.000200 (0.016323) \| 0.000757 / 0.000054 (0.000702) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034239 / 0.037411 (-0.003172) \| 0.099953 / 0.014526 (0.085427) \| 0.113728 / 0.176557 (-0.062829) \| 0.180113 / 0.737135 (-0.557023) \| 0.114506 / 0.296338 (-0.181833) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507186 / 0.215209 (0.291977) \| 5.033590 / 2.077655 (2.955935) \| 2.480111 / 1.504120 (0.975991) \| 2.258966 / 1.541195 (0.717771) \| 2.316045 / 1.468490 (0.847555) \| 0.622482 / 4.584777 (-3.962295) \| 4.400909 / 3.745712 (0.655197) \| 4.012443 / 5.269862 (-1.257419) \| 2.408294 / 4.565676 (-2.157383) \| 0.067608 / 0.424275 (-0.356668) \| 0.008638 / 0.007607 (0.001031) \| 0.546558 / 0.226044 (0.320513) \| 5.472973 / 2.268929 (3.204044) \| 2.795147 / 55.444624 (-52.649477) \| 2.371153 / 6.876477 (-4.505324) \| 2.440883 / 2.142072 (0.298811) \| 0.682380 / 4.805227 (-4.122847) \| 0.156819 / 6.500664 (-6.343845) \| 0.071969 / 0.075469 (-0.003500) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.500200 / 1.841788 (-0.341588) \| 22.854103 / 8.074308 (14.779795) \| 16.691945 / 10.191392 (6.500553) \| 0.210945 / 0.680424 (-0.469479) \| 0.023234 / 0.534201 (-0.510967) \| 0.475641 / 0.579283 (-0.103642) \| 0.491553 / 0.434364 (0.057189) \| 0.549311 / 0.540337 (0.008974) \| 0.858498 / 1.386936 (-0.528439) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009020 / 0.011353 (-0.002333) \| 0.004768 / 0.011008 (-0.006240) \| 0.082841 / 0.038508 (0.044333) \| 0.095111 / 0.023109 (0.072002) \| 0.486050 / 0.275898 (0.210151) \| 0.527074 / 0.323480 (0.203594) \| 0.006622 / 0.007986 (-0.001364) \| 0.003961 / 0.004328 (-0.000367) \| 0.083361 / 0.004250 (0.079111) \| 0.068571 / 0.037052 (0.031518) \| 0.494575 / 0.258489 (0.236086) \| 0.545593 / 0.293841 (0.251752) \| 0.047671 / 0.128546 (-0.080875) \| 0.010715 / 0.075646 (-0.064932) \| 0.096239 / 0.419271 (-0.323033) \| 0.061556 / 0.043533 (0.018023) \| 0.484301 / 0.255139 (0.229162) \| 0.492189 / 0.283200 (0.208989) \| 0.029374 / 0.141683 (-0.112309) \| 1.911833 / 1.452155 (0.459678) \| 2.005744 / 1.492716 (0.513028) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265402 / 0.018006 (0.247396) \| 0.501034 / 0.000490 (0.500545) \| 0.004039 / 0.000200 (0.003839) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.041005 / 0.037411 (0.003594) \| 0.119204 / 0.014526 (0.104678) \| 0.134583 / 0.176557 (-0.041973) \| 0.195995 / 0.737135 (-0.541140) \| 0.133125 / 0.296338 (-0.163214) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.503012 / 0.215209 (0.287803) \| 5.021972 / 2.077655 (2.944318) \| 2.912987 / 1.504120 (1.408867) \| 2.707637 / 1.541195 (1.166442) \| 2.824065 / 1.468490 (1.355575) \| 0.664285 / 4.584777 (-3.920492) \| 4.341905 / 3.745712 (0.596193) \| 4.152839 / 5.269862 (-1.117022) \| 2.438138 / 4.565676 (-2.127539) \| 0.076169 / 0.424275 (-0.348106) \| 0.010471 / 0.007607 (0.002864) \| 0.680918 / 0.226044 (0.454874) \| 6.424209 / 2.268929 (4.155281) \| 3.285353 / 55.444624 (-52.159271) \| 2.865458 / 6.876477 (-4.011019) \| 2.946246 / 2.142072 (0.804173) \| 0.700051 / 4.805227 (-4.105176) \| 0.155299 / 6.500664 (-6.345365) \| 0.069372 / 0.075469 (-0.006097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.749517 / 1.841788 (-0.092271) \| 23.382582 / 8.074308 (15.308274) \| 17.708718 / 10.191392 (7.517326) \| 0.197042 / 0.680424 (-0.483382) \| 0.023874 / 0.534201 (-0.510327) \| 0.471631 / 0.579283 (-0.107652) \| 0.512649 / 0.434364 (0.078285) \| 0.614479 / 0.540337 (0.074142) \| 0.771859 / 1.386936 (-0.615077) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f084b2d85f5004ed969d2387027093b2d765a4f \"CML watermark\")\n", "Merging this one, but lmk if you have more comments for subsequent improvements @NielsRogge ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004874 / 0.011353 (-0.006479) \| 0.002866 / 0.011008 (-0.008142) \| 0.061761 / 0.038508 (0.023253) \| 0.052185 / 0.023109 (0.029076) \| 0.242264 / 0.275898 (-0.033634) \| 0.267816 / 0.323480 (-0.055664) \| 0.002844 / 0.007986 (-0.005142) \| 0.002349 / 0.004328 (-0.001979) \| 0.048393 / 0.004250 (0.044142) \| 0.038590 / 0.037052 (0.001538) \| 0.257483 / 0.258489 (-0.001006) \| 0.279704 / 0.293841 (-0.014137) \| 0.023125 / 0.128546 (-0.105421) \| 0.007044 / 0.075646 (-0.068602) \| 0.203606 / 0.419271 (-0.215665) \| 0.035489 / 0.043533 (-0.008044) \| 0.248419 / 0.255139 (-0.006719) \| 0.266357 / 0.283200 (-0.016843) \| 0.020178 / 0.141683 (-0.121505) \| 1.163674 / 1.452155 (-0.288481) \| 1.191340 / 1.492716 (-0.301376) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092972 / 0.018006 (0.074966) \| 0.295260 / 0.000490 (0.294770) \| 0.000214 / 0.000200 (0.000014) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018109 / 0.037411 (-0.019302) \| 0.061743 / 0.014526 (0.047217) \| 0.073965 / 0.176557 (-0.102592) \| 0.119493 / 0.737135 (-0.617642) \| 0.075646 / 0.296338 (-0.220692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275700 / 0.215209 (0.060491) \| 2.666846 / 2.077655 (0.589191) \| 1.401452 / 1.504120 (-0.102668) \| 1.276009 / 1.541195 (-0.265186) \| 1.309914 / 1.468490 (-0.158576) \| 0.396411 / 4.584777 (-4.188365) \| 2.347193 / 3.745712 (-1.398519) \| 2.568006 / 5.269862 (-2.701856) \| 1.564572 / 4.565676 (-3.001105) \| 0.045450 / 0.424275 (-0.378825) \| 0.004827 / 0.007607 (-0.002780) \| 0.333092 / 0.226044 (0.107048) \| 3.284295 / 2.268929 (1.015367) \| 1.809928 / 55.444624 (-53.634696) \| 1.486041 / 6.876477 (-5.390436) \| 1.528198 / 2.142072 (-0.613875) \| 0.470053 / 4.805227 (-4.335174) \| 0.098559 / 6.500664 (-6.402105) \| 0.041637 / 0.075469 (-0.033832) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948915 / 1.841788 (-0.892873) \| 11.513211 / 8.074308 (3.438903) \| 10.386419 / 10.191392 (0.195027) \| 0.129513 / 0.680424 (-0.550910) \| 0.021772 / 0.534201 (-0.512429) \| 0.295627 / 0.579283 (-0.283656) \| 0.261008 / 0.434364 (-0.173355) \| 0.305869 / 0.540337 (-0.234469) \| 0.399676 / 1.386936 (-0.987260) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004799 / 0.011353 (-0.006553) \| 0.002764 / 0.011008 (-0.008244) \| 0.048469 / 0.038508 (0.009961) \| 0.051346 / 0.023109 (0.028236) \| 0.274853 / 0.275898 (-0.001045) \| 0.300770 / 0.323480 (-0.022710) \| 0.003986 / 0.007986 (-0.003999) \| 0.002376 / 0.004328 (-0.001952) \| 0.048545 / 0.004250 (0.044294) \| 0.039854 / 0.037052 (0.002801) \| 0.280053 / 0.258489 (0.021564) \| 0.312797 / 0.293841 (0.018957) \| 0.024513 / 0.128546 (-0.104033) \| 0.006971 / 0.075646 (-0.068675) \| 0.053030 / 0.419271 (-0.366241) \| 0.035580 / 0.043533 (-0.007953) \| 0.276078 / 0.255139 (0.020939) \| 0.299345 / 0.283200 (0.016145) \| 0.020423 / 0.141683 (-0.121260) \| 1.103053 / 1.452155 (-0.349102) \| 1.179747 / 1.492716 (-0.312969) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093042 / 0.018006 (0.075036) \| 0.299421 / 0.000490 (0.298932) \| 0.000232 / 0.000200 (0.000033) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021966 / 0.037411 (-0.015445) \| 0.070978 / 0.014526 (0.056452) \| 0.083841 / 0.176557 (-0.092715) \| 0.121223 / 0.737135 (-0.615912) \| 0.082829 / 0.296338 (-0.213510) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289436 / 0.215209 (0.074227) \| 2.838074 / 2.077655 (0.760419) \| 1.597013 / 1.504120 (0.092893) \| 1.476888 / 1.541195 (-0.064307) \| 1.504582 / 1.468490 (0.036092) \| 0.398050 / 4.584777 (-4.186727) \| 2.434446 / 3.745712 (-1.311266) \| 2.493545 / 5.269862 (-2.776316) \| 1.584159 / 4.565676 (-2.981517) \| 0.046461 / 0.424275 (-0.377814) \| 0.004876 / 0.007607 (-0.002731) \| 0.344166 / 0.226044 (0.118122) \| 3.388530 / 2.268929 (1.119602) \| 1.939585 / 55.444624 (-53.505039) \| 1.672495 / 6.876477 (-5.203982) \| 1.811825 / 2.142072 (-0.330247) \| 0.470798 / 4.805227 (-4.334429) \| 0.097522 / 6.500664 (-6.403142) \| 0.040887 / 0.075469 (-0.034582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990081 / 1.841788 (-0.851707) \| 12.619827 / 8.074308 (4.545519) \| 10.748062 / 10.191392 (0.556670) \| 0.130409 / 0.680424 (-0.550015) \| 0.016624 / 0.534201 (-0.517577) \| 0.272381 / 0.579283 (-0.306902) \| 0.270597 / 0.434364 (-0.163767) \| 0.306458 / 0.540337 (-0.233879) \| 0.408700 / 1.386936 (-0.978236) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc44d2188a1baac50d28a6c8110d6e5497f409de \"CML watermark\")\n", "This is a little hard to follow — where is the documentation currently? I am trying to follow from snippets, here is what I have based on your convo in this thread:\r\n\r\n```>>> import os\r\n>>>\r\n>>> for i in range(torch.cuda.device_count()): # send model to every GPU\r\n... model.to(f\"cuda:{i}\")\r\n>>>\r\n>>> def gpu_computation(example, rank):\r\n... torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU\r\n... inputs = tokenizer(texts, truncation=True, return_tensors=\"pt\").to(f\"cuda:{rank}\")\r\n... outputs = model(*inputs)\r\n... .... \r\n```\r\n\r\nbut I'm getting device errors (data is on device 3, but it thinks model is on device 0, despite setting `torch.cuda.set_device`\r\n\r\nIs this correct? What version of Torch are you using for this? ", "Anyway, this didn't work for me:\r\n\r\n`torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU`\r\n\r\nbut substituting it for:\r\n\r\n`model.to(f\"cuda:{rank}\")`\r\n\r\n(`.to` doesn't make a million copies of the model on the device, which I was worried it would do... so you can use it in an inner process)\r\n\r\n(btw, versions: `torch==2.1.1`, `cuda=12.2`)", "Yeah for me this issue isn't resolved yet, we need a better code example", "Hi @alex2awesome, could you open a PR with your suggestion to improve this code snippet ?", "i'm happy to when i get it fully working, but i feel like there are some fundamentals that I'm not fully understanding...\r\n\r\nI've set it up twice now, for 2 GPU-processing pipelines. \r\n\r\nIn one pipelines, my memory usage is fine, it delivers me a huge speedup, and everything is great. In the second pipeline, I keep getting OOM errors when `num_proc > 1` that I don't get when `num_proc=1`. \r\n\r\nThere is a discussion here: https://github.com/pytorch/pytorch/issues/44156 about CUDA memory leaks in multiprocessing setups, and I haven't had the time to fully read the source code to `datasets.map` to understand whether the situations are parallel. Also, if they are, then I don't know what the solution is, not really knowing how it is implemented under the hood. In that discussion, one guy offers a work-around, but it doesn't look great.\r\n\r\nSo, I haven't fully tested out enough to see what the issue. If I feel comfortable over the next several days to generate a slimmed-down example that will generalize to real-world cases such as those I'm working with now, then I will contribute it.\r\n\r\n", "@lhoestq do you know how `datasets` does multiprocessing? Do we use:\r\nhttps://pytorch.org/docs/stable/multiprocessing.html#module-torch.multiprocessing?\r\n\r\nIf so, there are lots of points around memory usage, here:\r\nhttps://pytorch.org/docs/stable/notes/multiprocessing.html\r\n\r\nEDIT: ahh I see it is using python's native multiprocessing library: https://github.com/huggingface/datasets/blob/2.15.0/src/datasets/arrow_dataset.py#L3172-L3189", "After some more research and playing around, I can't pinpoint the source of my CUDA memory leak nor can I determine with confidence what works and what doesn't in this setup.\r\n\r\nI'm not really an expert on multiprocessing in general, but my gut is that the current set-up isn't ideal for multiprocessing and I'm not sure I would recommend users to do this. \r\n\r\nKinda unfortunate, because I don't see any great tools for distributed inference out there, and in theory, `datasets.map` could be the standard.\r\n\r\nAre either of you more experienced in this?", "Not sure about your GPU's OOM :/\r\n\r\nStill, I opened a PR with your suggestion here: https://github.com/huggingface/datasets/pull/6550", "I still get only 0 rank...\r\n\r\nHere is my code: https://pastebin.com/c6du8jaM\r\n\r\nfrom this ^ i just improt one function:\r\n\r\n\r\n```\r\nfrom test import map_train\r\nfrom multiprocess import set_start_method\r\n\r\n\r\nset_start_method(\"spawn\")\r\nmap_train()\r\n```\r\n\r\nAnd here is the traceback:\r\nhttps://pastebin.com/YijspwQK ", "Also this code from your docs is not valid (source: https://huggingface.co/docs/datasets/main/en/process#multiprocessing):\r\n```\r\nfor i in range(torch.cuda.device_count()):\r\n model.to(f\"cuda:{i}\")\r\n```\r\n\r\n\r\nThis for me sends the model only to the second GPU\r\n```\r\nvae = AutoencoderKL.from_pretrained(\r\n pretrained_model_name_or_path, subfolder=\"vae\"\r\n)\r\nvae.to(\"cuda:0\")\r\nvae.to(\"cuda:1\")\r\n```", "Could you please provide a working example of multi-GPU mapping?\r\n\r\nNot just an example in docs, but a real working example starting from all imports loading datasets and models.", "@alex2awesome the same issue with CUDA OOM. It should not be happening, since it should 2 different GPUs be handling different loads. But in fact something wrong is happening.", "I haven't experimented much with the multi-GPU code documentation.\r\n\r\nCan you try using the code example at https://github.com/huggingface/datasets/pull/6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n\r\nThough if you have some fixes/improvements ideas feel free to open a PR !", "@lhoestq the mapping does not start at all in this case:\r\n<img width=\"855\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/7f29a3c1-c6dc-4bab-9955-5311256aa217\">\r\n\r\nHere is the updated code: https://pastebin.com/Kn9aGfZr", "@lhoestq with this code: https://pastebin.com/muDm78kp\r\ni now getting this error:\r\n\r\n```\r\nMap (num_proc=2): 1%\| \| 26288/3043663 [06:11<11:51:08, 70.72 examples/s]\r\nTraceback (most recent call last):\r\n File \"/workspace/compute.py\", line 229, in <module>\r\n map_train()\r\n File \"/workspace/compute.py\", line 224, in map_train\r\n return train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, with_rank=True, num_proc=2, keep_in_memory=True)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 593, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, *kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 558, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 3193, in map\r\n for rank, done, content in iflatmap_unordered(\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py\", line 658, in iflatmap_unordered\r\n raise RuntimeError(\r\nRuntimeError: One of the subprocesses has abruptly died during map operation.To debug the error, disable multiprocessing.\r\n```\r\n\r\nAlso when trying to download my dataset there were no issues from one machine, but from another:\r\n```\r\nSSLError: (MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/datasets/kopyl/3M_icons_monochrome_only_no_captioning/revision/753dca4be462dad7022f34cc273555ab6deb5832 (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\"), '(Request ID: 7d0881f3-1b93-4d73-bcb6-52e816d84529)')\r\n```\r\n\r\nCan't download my dataset at all...", "Hmm this is not good, do you know a way to make it work ?\r\n\r\nBasically `map` creates two subprocesses and runs the function in the subprocesses. Since each function has a parameter `rank` it should be possible to choose which GPU to use", "I can confirm that PR #6550 works. All GPUs are at full throttle. You have to manually move the model to all GPUs. \r\n\r\n> I haven't experimented much with the multi-GPU code documentation.\r\n> \r\n> Can you try using the code example at #6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n> \r\n> Though if you have some fixes/improvements ideas feel free to open a PR !\r\n\r\n", "I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example by compiling information from several PRs and issues here. Hope it can help. Let me know how it works. \r\n\r\n> Could you please provide a working example of multi-GPU mapping?\r\n> \r\n> Not just an example in docs, but a real working example starting from all imports loading datasets and models.\r\n\r\n" ]	2023-11-14T14:57:18Z	2024-01-31T00:49:15Z	2023-11-22T15:42:19Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6415.diff", "html_url": "https://github.com/huggingface/datasets/pull/6415", "merged_at": "2023-11-22T15:42:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/6415.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6415" }	- use `orch.cuda.set_device` instead of `CUDA_VISIBLE_DEVICES ` - add `if __name__ == "__main__"` fix https://github.com/huggingface/datasets/issues/6186	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6415/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6414	6,414	Set `usedforsecurity=False` in hashlib methods (FIPS compliance)	{ "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Wauplin", "id": 11801849, "login": "Wauplin", "node_id": "MDQ6VXNlcjExODAxODQ5", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "repos_url": "https://api.github.com/users/Wauplin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "type": "User", "url": "https://api.github.com/users/Wauplin", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008434 / 0.011353 (-0.002919) \| 0.006755 / 0.011008 (-0.004253) \| 0.106169 / 0.038508 (0.067661) \| 0.049329 / 0.023109 (0.026220) \| 0.433610 / 0.275898 (0.157712) \| 0.441993 / 0.323480 (0.118513) \| 0.004703 / 0.007986 (-0.003282) \| 0.006996 / 0.004328 (0.002667) \| 0.080330 / 0.004250 (0.076080) \| 0.066098 / 0.037052 (0.029045) \| 0.435444 / 0.258489 (0.176955) \| 0.490442 / 0.293841 (0.196601) \| 0.047050 / 0.128546 (-0.081496) \| 0.014520 / 0.075646 (-0.061127) \| 0.339805 / 0.419271 (-0.079467) \| 0.101161 / 0.043533 (0.057629) \| 0.423236 / 0.255139 (0.168097) \| 0.455627 / 0.283200 (0.172427) \| 0.036218 / 0.141683 (-0.105465) \| 1.766128 / 1.452155 (0.313973) \| 1.923919 / 1.492716 (0.431203) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242939 / 0.018006 (0.224933) \| 0.515582 / 0.000490 (0.515093) \| 0.020271 / 0.000200 (0.020071) \| 0.000383 / 0.000054 (0.000328) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030927 / 0.037411 (-0.006484) \| 0.093951 / 0.014526 (0.079425) \| 0.109028 / 0.176557 (-0.067529) \| 0.174947 / 0.737135 (-0.562188) \| 0.120538 / 0.296338 (-0.175800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.553884 / 0.215209 (0.338675) \| 5.424566 / 2.077655 (3.346911) \| 2.439420 / 1.504120 (0.935301) \| 2.019324 / 1.541195 (0.478129) \| 2.170781 / 1.468490 (0.702290) \| 0.924424 / 4.584777 (-3.660353) \| 5.706029 / 3.745712 (1.960317) \| 5.096911 / 5.269862 (-0.172951) \| 3.168261 / 4.565676 (-1.397416) \| 0.094336 / 0.424275 (-0.329940) \| 0.015899 / 0.007607 (0.008292) \| 0.709684 / 0.226044 (0.483639) \| 7.476865 / 2.268929 (5.207936) \| 3.350983 / 55.444624 (-52.093641) \| 2.653419 / 6.876477 (-4.223058) \| 2.802201 / 2.142072 (0.660129) \| 1.081442 / 4.805227 (-3.723785) \| 0.217025 / 6.500664 (-6.283639) \| 0.077248 / 0.075469 (0.001779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598621 / 1.841788 (-0.243167) \| 23.490338 / 8.074308 (15.416030) \| 21.853488 / 10.191392 (11.662096) \| 0.209625 / 0.680424 (-0.470799) \| 0.028166 / 0.534201 (-0.506035) \| 0.473883 / 0.579283 (-0.105400) \| 0.584226 / 0.434364 (0.149862) \| 0.538605 / 0.540337 (-0.001732) \| 0.837060 / 1.386936 (-0.549876) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009029 / 0.011353 (-0.002324) \| 0.004945 / 0.011008 (-0.006063) \| 0.084539 / 0.038508 (0.046031) \| 0.081014 / 0.023109 (0.057905) \| 0.431291 / 0.275898 (0.155393) \| 0.478913 / 0.323480 (0.155433) \| 0.006107 / 0.007986 (-0.001879) \| 0.003939 / 0.004328 (-0.000390) \| 0.079932 / 0.004250 (0.075682) \| 0.057936 / 0.037052 (0.020884) \| 0.437295 / 0.258489 (0.178806) \| 0.489790 / 0.293841 (0.195949) \| 0.049544 / 0.128546 (-0.079003) \| 0.013675 / 0.075646 (-0.061972) \| 0.093143 / 0.419271 (-0.326128) \| 0.064104 / 0.043533 (0.020571) \| 0.444699 / 0.255139 (0.189560) \| 0.443688 / 0.283200 (0.160489) \| 0.034331 / 0.141683 (-0.107352) \| 1.753014 / 1.452155 (0.300859) \| 1.877274 / 1.492716 (0.384558) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250460 / 0.018006 (0.232454) \| 0.527241 / 0.000490 (0.526752) \| 0.007679 / 0.000200 (0.007479) \| 0.000115 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033269 / 0.037411 (-0.004142) \| 0.111262 / 0.014526 (0.096736) \| 0.133503 / 0.176557 (-0.043053) \| 0.177998 / 0.737135 (-0.559137) \| 0.117899 / 0.296338 (-0.178440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633588 / 0.215209 (0.418379) \| 6.105283 / 2.077655 (4.027628) \| 2.779309 / 1.504120 (1.275189) \| 2.445788 / 1.541195 (0.904594) \| 2.396443 / 1.468490 (0.927953) \| 0.925928 / 4.584777 (-3.658849) \| 5.266142 / 3.745712 (1.520430) \| 4.868830 / 5.269862 (-0.401031) \| 2.998768 / 4.565676 (-1.566909) \| 0.103135 / 0.424275 (-0.321140) \| 0.008059 / 0.007607 (0.000452) \| 0.753159 / 0.226044 (0.527115) \| 7.532170 / 2.268929 (5.263242) \| 3.563941 / 55.444624 (-51.880683) \| 2.829208 / 6.876477 (-4.047269) \| 2.913954 / 2.142072 (0.771881) \| 1.085843 / 4.805227 (-3.719384) \| 0.214195 / 6.500664 (-6.286469) \| 0.071509 / 0.075469 (-0.003960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544819 / 1.841788 (-0.296968) \| 23.790149 / 8.074308 (15.715841) \| 23.086019 / 10.191392 (12.894627) \| 0.242695 / 0.680424 (-0.437729) \| 0.041706 / 0.534201 (-0.492495) \| 0.552402 / 0.579283 (-0.026881) \| 0.652518 / 0.434364 (0.218154) \| 0.581876 / 0.540337 (0.041539) \| 0.795425 / 1.386936 (-0.591511) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#117fdfccc8523fe150521ad74e478459fe2f297c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004573 / 0.011353 (-0.006780) \| 0.002965 / 0.011008 (-0.008043) \| 0.061913 / 0.038508 (0.023405) \| 0.029474 / 0.023109 (0.006365) \| 0.258117 / 0.275898 (-0.017781) \| 0.279854 / 0.323480 (-0.043626) \| 0.003954 / 0.007986 (-0.004031) \| 0.002479 / 0.004328 (-0.001850) \| 0.048685 / 0.004250 (0.044434) \| 0.044733 / 0.037052 (0.007681) \| 0.256659 / 0.258489 (-0.001830) \| 0.285235 / 0.293841 (-0.008606) \| 0.023566 / 0.128546 (-0.104981) \| 0.007291 / 0.075646 (-0.068355) \| 0.202701 / 0.419271 (-0.216570) \| 0.055706 / 0.043533 (0.012173) \| 0.258790 / 0.255139 (0.003651) \| 0.278675 / 0.283200 (-0.004525) \| 0.018574 / 0.141683 (-0.123109) \| 1.109359 / 1.452155 (-0.342796) \| 1.184434 / 1.492716 (-0.308282) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095048 / 0.018006 (0.077042) \| 0.305027 / 0.000490 (0.304537) \| 0.000310 / 0.000200 (0.000110) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018183 / 0.037411 (-0.019228) \| 0.066130 / 0.014526 (0.051604) \| 0.073948 / 0.176557 (-0.102608) \| 0.120458 / 0.737135 (-0.616678) \| 0.075995 / 0.296338 (-0.220343) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279419 / 0.215209 (0.064210) \| 2.728591 / 2.077655 (0.650936) \| 1.439016 / 1.504120 (-0.065104) \| 1.325798 / 1.541195 (-0.215397) \| 1.352050 / 1.468490 (-0.116440) \| 0.395041 / 4.584777 (-4.189736) \| 2.377651 / 3.745712 (-1.368061) \| 2.618473 / 5.269862 (-2.651389) \| 1.587580 / 4.565676 (-2.978096) \| 0.045910 / 0.424275 (-0.378365) \| 0.004843 / 0.007607 (-0.002764) \| 0.335491 / 0.226044 (0.109447) \| 3.378441 / 2.268929 (1.109512) \| 1.827757 / 55.444624 (-53.616868) \| 1.502360 / 6.876477 (-5.374117) \| 1.508460 / 2.142072 (-0.633612) \| 0.471309 / 4.805227 (-4.333918) \| 0.098934 / 6.500664 (-6.401730) \| 0.041705 / 0.075469 (-0.033764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945067 / 1.841788 (-0.896720) \| 11.548209 / 8.074308 (3.473900) \| 10.422628 / 10.191392 (0.231236) \| 0.141494 / 0.680424 (-0.538929) \| 0.014345 / 0.534201 (-0.519856) \| 0.267750 / 0.579283 (-0.311533) \| 0.261488 / 0.434364 (-0.172876) \| 0.307192 / 0.540337 (-0.233145) \| 0.427926 / 1.386936 (-0.959010) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004831 / 0.011353 (-0.006522) \| 0.002876 / 0.011008 (-0.008132) \| 0.048629 / 0.038508 (0.010121) \| 0.055090 / 0.023109 (0.031981) \| 0.271381 / 0.275898 (-0.004517) \| 0.292350 / 0.323480 (-0.031130) \| 0.004001 / 0.007986 (-0.003985) \| 0.002389 / 0.004328 (-0.001939) \| 0.047527 / 0.004250 (0.043277) \| 0.038065 / 0.037052 (0.001012) \| 0.277387 / 0.258489 (0.018898) \| 0.307209 / 0.293841 (0.013368) \| 0.025136 / 0.128546 (-0.103411) \| 0.007309 / 0.075646 (-0.068338) \| 0.054483 / 0.419271 (-0.364789) \| 0.032807 / 0.043533 (-0.010726) \| 0.274364 / 0.255139 (0.019225) \| 0.290280 / 0.283200 (0.007080) \| 0.017855 / 0.141683 (-0.123828) \| 1.185912 / 1.452155 (-0.266243) \| 1.228141 / 1.492716 (-0.264576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094787 / 0.018006 (0.076781) \| 0.314191 / 0.000490 (0.313701) \| 0.000217 / 0.000200 (0.000017) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020920 / 0.037411 (-0.016491) \| 0.070446 / 0.014526 (0.055920) \| 0.081371 / 0.176557 (-0.095186) \| 0.119127 / 0.737135 (-0.618009) \| 0.085658 / 0.296338 (-0.210680) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290601 / 0.215209 (0.075392) \| 2.874091 / 2.077655 (0.796436) \| 1.598934 / 1.504120 (0.094814) \| 1.464329 / 1.541195 (-0.076866) \| 1.504943 / 1.468490 (0.036453) \| 0.410457 / 4.584777 (-4.174320) \| 2.428706 / 3.745712 (-1.317006) \| 2.596510 / 5.269862 (-2.673352) \| 1.547084 / 4.565676 (-3.018592) \| 0.047546 / 0.424275 (-0.376729) \| 0.004740 / 0.007607 (-0.002867) \| 0.351168 / 0.226044 (0.125123) \| 3.424554 / 2.268929 (1.155626) \| 1.969792 / 55.444624 (-53.474832) \| 1.676731 / 6.876477 (-5.199745) \| 1.668769 / 2.142072 (-0.473304) \| 0.482486 / 4.805227 (-4.322741) \| 0.100018 / 6.500664 (-6.400646) \| 0.040956 / 0.075469 (-0.034513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966306 / 1.841788 (-0.875482) \| 12.158909 / 8.074308 (4.084601) \| 10.926447 / 10.191392 (0.735055) \| 0.130359 / 0.680424 (-0.550065) \| 0.016162 / 0.534201 (-0.518039) \| 0.269977 / 0.579283 (-0.309306) \| 0.283366 / 0.434364 (-0.150997) \| 0.304517 / 0.540337 (-0.235821) \| 0.410398 / 1.386936 (-0.976539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d5d6e57913465c22bb8074b0c0f968252cb12b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004686 / 0.011353 (-0.006667) \| 0.002764 / 0.011008 (-0.008244) \| 0.061411 / 0.038508 (0.022902) \| 0.030450 / 0.023109 (0.007341) \| 0.247648 / 0.275898 (-0.028250) \| 0.278033 / 0.323480 (-0.045447) \| 0.002903 / 0.007986 (-0.005082) \| 0.002350 / 0.004328 (-0.001979) \| 0.047514 / 0.004250 (0.043264) \| 0.044446 / 0.037052 (0.007393) \| 0.256170 / 0.258489 (-0.002319) \| 0.285977 / 0.293841 (-0.007864) \| 0.023407 / 0.128546 (-0.105139) \| 0.007223 / 0.075646 (-0.068423) \| 0.201274 / 0.419271 (-0.217997) \| 0.054022 / 0.043533 (0.010489) \| 0.253841 / 0.255139 (-0.001298) \| 0.278219 / 0.283200 (-0.004980) \| 0.017796 / 0.141683 (-0.123886) \| 1.105950 / 1.452155 (-0.346205) \| 1.182021 / 1.492716 (-0.310695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089584 / 0.018006 (0.071578) \| 0.299338 / 0.000490 (0.298849) \| 0.000202 / 0.000200 (0.000003) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018974 / 0.037411 (-0.018437) \| 0.062352 / 0.014526 (0.047826) \| 0.073667 / 0.176557 (-0.102889) \| 0.119225 / 0.737135 (-0.617911) \| 0.075393 / 0.296338 (-0.220945) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282749 / 0.215209 (0.067540) \| 2.795822 / 2.077655 (0.718167) \| 1.492946 / 1.504120 (-0.011174) \| 1.382340 / 1.541195 (-0.158855) \| 1.377281 / 1.468490 (-0.091209) \| 0.397361 / 4.584777 (-4.187415) \| 2.379416 / 3.745712 (-1.366296) \| 2.552967 / 5.269862 (-2.716895) \| 1.546347 / 4.565676 (-3.019330) \| 0.045851 / 0.424275 (-0.378424) \| 0.004830 / 0.007607 (-0.002777) \| 0.351194 / 0.226044 (0.125150) \| 3.407406 / 2.268929 (1.138478) \| 1.852983 / 55.444624 (-53.591641) \| 1.536381 / 6.876477 (-5.340095) \| 1.542786 / 2.142072 (-0.599287) \| 0.471960 / 4.805227 (-4.333267) \| 0.098336 / 6.500664 (-6.402328) \| 0.041569 / 0.075469 (-0.033900) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.912718 / 1.841788 (-0.929070) \| 11.339404 / 8.074308 (3.265095) \| 10.480593 / 10.191392 (0.289201) \| 0.139508 / 0.680424 (-0.540916) \| 0.014210 / 0.534201 (-0.519991) \| 0.268152 / 0.579283 (-0.311131) \| 0.260503 / 0.434364 (-0.173860) \| 0.304735 / 0.540337 (-0.235602) \| 0.422155 / 1.386936 (-0.964781) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004714 / 0.011353 (-0.006638) \| 0.002638 / 0.011008 (-0.008370) \| 0.047967 / 0.038508 (0.009459) \| 0.050758 / 0.023109 (0.027649) \| 0.265619 / 0.275898 (-0.010279) \| 0.286920 / 0.323480 (-0.036560) \| 0.003936 / 0.007986 (-0.004050) \| 0.002351 / 0.004328 (-0.001977) \| 0.047642 / 0.004250 (0.043392) \| 0.038412 / 0.037052 (0.001360) \| 0.269561 / 0.258489 (0.011072) \| 0.302057 / 0.293841 (0.008216) \| 0.023893 / 0.128546 (-0.104653) \| 0.006793 / 0.075646 (-0.068854) \| 0.053091 / 0.419271 (-0.366180) \| 0.032228 / 0.043533 (-0.011305) \| 0.267110 / 0.255139 (0.011971) \| 0.287211 / 0.283200 (0.004011) \| 0.017945 / 0.141683 (-0.123738) \| 1.191770 / 1.452155 (-0.260384) \| 1.269644 / 1.492716 (-0.223072) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088067 / 0.018006 (0.070061) \| 0.298383 / 0.000490 (0.297893) \| 0.000202 / 0.000200 (0.000002) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020685 / 0.037411 (-0.016726) \| 0.069883 / 0.014526 (0.055357) \| 0.080107 / 0.176557 (-0.096450) \| 0.119311 / 0.737135 (-0.617825) \| 0.080791 / 0.296338 (-0.215548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295781 / 0.215209 (0.080572) \| 2.905536 / 2.077655 (0.827881) \| 1.579184 / 1.504120 (0.075064) \| 1.475937 / 1.541195 (-0.065258) \| 1.533708 / 1.468490 (0.065218) \| 0.409851 / 4.584777 (-4.174926) \| 2.443217 / 3.745712 (-1.302496) \| 2.543980 / 5.269862 (-2.725882) \| 1.512187 / 4.565676 (-3.053489) \| 0.046390 / 0.424275 (-0.377885) \| 0.004762 / 0.007607 (-0.002845) \| 0.345066 / 0.226044 (0.119021) \| 3.485133 / 2.268929 (1.216204) \| 1.954690 / 55.444624 (-53.489934) \| 1.671104 / 6.876477 (-5.205372) \| 1.655330 / 2.142072 (-0.486743) \| 0.487910 / 4.805227 (-4.317317) \| 0.097707 / 6.500664 (-6.402957) \| 0.040379 / 0.075469 (-0.035090) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981620 / 1.841788 (-0.860168) \| 11.806530 / 8.074308 (3.732222) \| 10.868275 / 10.191392 (0.676883) \| 0.141230 / 0.680424 (-0.539194) \| 0.015785 / 0.534201 (-0.518416) \| 0.271416 / 0.579283 (-0.307867) \| 0.276048 / 0.434364 (-0.158316) \| 0.310988 / 0.540337 (-0.229349) \| 0.410078 / 1.386936 (-0.976858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ec565740dee10c466ade16f81dee2783e442ba55 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.002961 / 0.011008 (-0.008047) \| 0.061431 / 0.038508 (0.022923) \| 0.030189 / 0.023109 (0.007080) \| 0.255755 / 0.275898 (-0.020143) \| 0.277841 / 0.323480 (-0.045639) \| 0.003083 / 0.007986 (-0.004902) \| 0.002432 / 0.004328 (-0.001896) \| 0.047674 / 0.004250 (0.043424) \| 0.045066 / 0.037052 (0.008014) \| 0.268701 / 0.258489 (0.010211) \| 0.286673 / 0.293841 (-0.007168) \| 0.023663 / 0.128546 (-0.104883) \| 0.007148 / 0.075646 (-0.068499) \| 0.201962 / 0.419271 (-0.217310) \| 0.054953 / 0.043533 (0.011420) \| 0.257155 / 0.255139 (0.002016) \| 0.277769 / 0.283200 (-0.005431) \| 0.017803 / 0.141683 (-0.123880) \| 1.100270 / 1.452155 (-0.351884) \| 1.146975 / 1.492716 (-0.345741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092776 / 0.018006 (0.074770) \| 0.303786 / 0.000490 (0.303296) \| 0.000237 / 0.000200 (0.000037) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019647 / 0.037411 (-0.017765) \| 0.063211 / 0.014526 (0.048686) \| 0.076684 / 0.176557 (-0.099873) \| 0.121952 / 0.737135 (-0.615184) \| 0.077202 / 0.296338 (-0.219137) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282087 / 0.215209 (0.066878) \| 2.789204 / 2.077655 (0.711550) \| 1.510376 / 1.504120 (0.006256) \| 1.384241 / 1.541195 (-0.156954) \| 1.414949 / 1.468490 (-0.053541) \| 0.402206 / 4.584777 (-4.182570) \| 2.377601 / 3.745712 (-1.368111) \| 2.585354 / 5.269862 (-2.684508) \| 1.592937 / 4.565676 (-2.972740) \| 0.045217 / 0.424275 (-0.379058) \| 0.004772 / 0.007607 (-0.002835) \| 0.339584 / 0.226044 (0.113539) \| 3.373184 / 2.268929 (1.104256) \| 1.855196 / 55.444624 (-53.589428) \| 1.599559 / 6.876477 (-5.276918) \| 1.604421 / 2.142072 (-0.537651) \| 0.467754 / 4.805227 (-4.337474) \| 0.098244 / 6.500664 (-6.402420) \| 0.042631 / 0.075469 (-0.032838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947680 / 1.841788 (-0.894108) \| 11.539875 / 8.074308 (3.465567) \| 10.340830 / 10.191392 (0.149438) \| 0.145591 / 0.680424 (-0.534833) \| 0.014367 / 0.534201 (-0.519834) \| 0.270506 / 0.579283 (-0.308777) \| 0.268825 / 0.434364 (-0.165539) \| 0.308372 / 0.540337 (-0.231966) \| 0.425039 / 1.386936 (-0.961897) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004813 / 0.011353 (-0.006540) \| 0.002931 / 0.011008 (-0.008078) \| 0.047997 / 0.038508 (0.009489) \| 0.050753 / 0.023109 (0.027644) \| 0.272704 / 0.275898 (-0.003194) \| 0.294045 / 0.323480 (-0.029435) \| 0.004059 / 0.007986 (-0.003927) \| 0.002491 / 0.004328 (-0.001838) \| 0.047621 / 0.004250 (0.043371) \| 0.038824 / 0.037052 (0.001772) \| 0.275322 / 0.258489 (0.016833) \| 0.306447 / 0.293841 (0.012606) \| 0.024402 / 0.128546 (-0.104145) \| 0.007252 / 0.075646 (-0.068394) \| 0.053346 / 0.419271 (-0.365925) \| 0.032224 / 0.043533 (-0.011309) \| 0.271468 / 0.255139 (0.016329) \| 0.289429 / 0.283200 (0.006229) \| 0.018285 / 0.141683 (-0.123398) \| 1.116743 / 1.452155 (-0.335412) \| 1.182724 / 1.492716 (-0.309993) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091899 / 0.018006 (0.073893) \| 0.299161 / 0.000490 (0.298671) \| 0.000224 / 0.000200 (0.000024) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021823 / 0.037411 (-0.015588) \| 0.071227 / 0.014526 (0.056701) \| 0.080503 / 0.176557 (-0.096053) \| 0.120243 / 0.737135 (-0.616892) \| 0.082328 / 0.296338 (-0.214010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.324951 / 0.215209 (0.109742) \| 2.842358 / 2.077655 (0.764703) \| 1.602317 / 1.504120 (0.098197) \| 1.481103 / 1.541195 (-0.060091) \| 1.497557 / 1.468490 (0.029067) \| 0.406523 / 4.584777 (-4.178254) \| 2.402743 / 3.745712 (-1.342970) \| 2.545435 / 5.269862 (-2.724427) \| 1.534071 / 4.565676 (-3.031605) \| 0.046914 / 0.424275 (-0.377361) \| 0.004728 / 0.007607 (-0.002879) \| 0.341544 / 0.226044 (0.115499) \| 3.412017 / 2.268929 (1.143089) \| 1.937442 / 55.444624 (-53.507182) \| 1.668774 / 6.876477 (-5.207703) \| 1.668908 / 2.142072 (-0.473165) \| 0.477398 / 4.805227 (-4.327829) \| 0.098531 / 6.500664 (-6.402133) \| 0.041077 / 0.075469 (-0.034392) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983888 / 1.841788 (-0.857900) \| 12.072703 / 8.074308 (3.998395) \| 11.028622 / 10.191392 (0.837230) \| 0.148097 / 0.680424 (-0.532327) \| 0.015869 / 0.534201 (-0.518332) \| 0.267609 / 0.579283 (-0.311674) \| 0.272345 / 0.434364 (-0.162019) \| 0.303840 / 0.540337 (-0.236497) \| 0.409199 / 1.386936 (-0.977737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1487df064580bd23458234fab2e85876d9364e03 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005016 / 0.011353 (-0.006337) \| 0.002931 / 0.011008 (-0.008077) \| 0.062142 / 0.038508 (0.023634) \| 0.030758 / 0.023109 (0.007648) \| 0.251689 / 0.275898 (-0.024209) \| 0.272114 / 0.323480 (-0.051366) \| 0.004102 / 0.007986 (-0.003884) \| 0.002500 / 0.004328 (-0.001828) \| 0.049187 / 0.004250 (0.044937) \| 0.047150 / 0.037052 (0.010098) \| 0.256497 / 0.258489 (-0.001992) \| 0.288069 / 0.293841 (-0.005772) \| 0.023915 / 0.128546 (-0.104632) \| 0.007204 / 0.075646 (-0.068442) \| 0.204257 / 0.419271 (-0.215015) \| 0.063879 / 0.043533 (0.020346) \| 0.253008 / 0.255139 (-0.002131) \| 0.266554 / 0.283200 (-0.016645) \| 0.018929 / 0.141683 (-0.122754) \| 1.140547 / 1.452155 (-0.311608) \| 1.197049 / 1.492716 (-0.295668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094111 / 0.018006 (0.076105) \| 0.301618 / 0.000490 (0.301128) \| 0.000219 / 0.000200 (0.000019) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018614 / 0.037411 (-0.018797) \| 0.062426 / 0.014526 (0.047900) \| 0.073079 / 0.176557 (-0.103477) \| 0.120313 / 0.737135 (-0.616823) \| 0.076445 / 0.296338 (-0.219894) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285151 / 0.215209 (0.069942) \| 2.754272 / 2.077655 (0.676617) \| 1.485254 / 1.504120 (-0.018866) \| 1.368412 / 1.541195 (-0.172783) \| 1.402819 / 1.468490 (-0.065671) \| 0.396561 / 4.584777 (-4.188216) \| 2.375708 / 3.745712 (-1.370004) \| 2.656088 / 5.269862 (-2.613773) \| 1.588676 / 4.565676 (-2.977001) \| 0.048662 / 0.424275 (-0.375613) \| 0.004963 / 0.007607 (-0.002644) \| 0.339747 / 0.226044 (0.113702) \| 3.315841 / 2.268929 (1.046912) \| 1.841439 / 55.444624 (-53.603186) \| 1.547803 / 6.876477 (-5.328674) \| 1.601872 / 2.142072 (-0.540200) \| 0.468637 / 4.805227 (-4.336591) \| 0.099423 / 6.500664 (-6.401241) \| 0.041926 / 0.075469 (-0.033543) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933058 / 1.841788 (-0.908730) \| 11.680870 / 8.074308 (3.606561) \| 10.239009 / 10.191392 (0.047617) \| 0.129974 / 0.680424 (-0.550450) \| 0.014081 / 0.534201 (-0.520120) \| 0.273076 / 0.579283 (-0.306207) \| 0.261914 / 0.434364 (-0.172450) \| 0.305982 / 0.540337 (-0.234356) \| 0.430623 / 1.386936 (-0.956313) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004969 / 0.011353 (-0.006384) \| 0.003084 / 0.011008 (-0.007924) \| 0.048686 / 0.038508 (0.010178) \| 0.057234 / 0.023109 (0.034125) \| 0.295408 / 0.275898 (0.019510) \| 0.323774 / 0.323480 (0.000294) \| 0.004014 / 0.007986 (-0.003972) \| 0.002423 / 0.004328 (-0.001905) \| 0.048000 / 0.004250 (0.043749) \| 0.039872 / 0.037052 (0.002820) \| 0.294717 / 0.258489 (0.036228) \| 0.331149 / 0.293841 (0.037309) \| 0.027884 / 0.128546 (-0.100662) \| 0.007155 / 0.075646 (-0.068491) \| 0.053812 / 0.419271 (-0.365460) \| 0.032483 / 0.043533 (-0.011050) \| 0.293402 / 0.255139 (0.038263) \| 0.312553 / 0.283200 (0.029354) \| 0.017848 / 0.141683 (-0.123835) \| 1.125600 / 1.452155 (-0.326554) \| 1.189469 / 1.492716 (-0.303248) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096198 / 0.018006 (0.078191) \| 0.305096 / 0.000490 (0.304607) \| 0.000229 / 0.000200 (0.000029) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021992 / 0.037411 (-0.015419) \| 0.072082 / 0.014526 (0.057556) \| 0.082704 / 0.176557 (-0.093853) \| 0.124512 / 0.737135 (-0.612624) \| 0.084541 / 0.296338 (-0.211797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296440 / 0.215209 (0.081231) \| 2.923392 / 2.077655 (0.845738) \| 1.599057 / 1.504120 (0.094937) \| 1.480473 / 1.541195 (-0.060722) \| 1.551837 / 1.468490 (0.083347) \| 0.418618 / 4.584777 (-4.166159) \| 2.472727 / 3.745712 (-1.272985) \| 2.796141 / 5.269862 (-2.473721) \| 1.629139 / 4.565676 (-2.936538) \| 0.047703 / 0.424275 (-0.376572) \| 0.004971 / 0.007607 (-0.002636) \| 0.354453 / 0.226044 (0.128408) \| 3.514861 / 2.268929 (1.245932) \| 1.993597 / 55.444624 (-53.451028) \| 1.694386 / 6.876477 (-5.182090) \| 1.748562 / 2.142072 (-0.393510) \| 0.487158 / 4.805227 (-4.318070) \| 0.102021 / 6.500664 (-6.398643) \| 0.042648 / 0.075469 (-0.032821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974950 / 1.841788 (-0.866837) \| 13.391204 / 8.074308 (5.316896) \| 11.474696 / 10.191392 (1.283304) \| 0.142618 / 0.680424 (-0.537806) \| 0.016163 / 0.534201 (-0.518038) \| 0.271453 / 0.579283 (-0.307830) \| 0.287049 / 0.434364 (-0.147315) \| 0.309069 / 0.540337 (-0.231268) \| 0.417117 / 1.386936 (-0.969819) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35a3422cfcebfef5b09ae70c22843ffadaf44c46 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004974 / 0.011353 (-0.006379) \| 0.002950 / 0.011008 (-0.008058) \| 0.061856 / 0.038508 (0.023348) \| 0.030539 / 0.023109 (0.007429) \| 0.250105 / 0.275898 (-0.025793) \| 0.276687 / 0.323480 (-0.046793) \| 0.003077 / 0.007986 (-0.004908) \| 0.002412 / 0.004328 (-0.001916) \| 0.048336 / 0.004250 (0.044086) \| 0.045849 / 0.037052 (0.008797) \| 0.251757 / 0.258489 (-0.006732) \| 0.284914 / 0.293841 (-0.008927) \| 0.024033 / 0.128546 (-0.104513) \| 0.007343 / 0.075646 (-0.068303) \| 0.202867 / 0.419271 (-0.216405) \| 0.061294 / 0.043533 (0.017762) \| 0.263590 / 0.255139 (0.008451) \| 0.272744 / 0.283200 (-0.010455) \| 0.019613 / 0.141683 (-0.122070) \| 1.104263 / 1.452155 (-0.347892) \| 1.164128 / 1.492716 (-0.328588) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094261 / 0.018006 (0.076255) \| 0.303340 / 0.000490 (0.302850) \| 0.000215 / 0.000200 (0.000015) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018381 / 0.037411 (-0.019030) \| 0.062727 / 0.014526 (0.048201) \| 0.074955 / 0.176557 (-0.101602) \| 0.124810 / 0.737135 (-0.612326) \| 0.074335 / 0.296338 (-0.222004) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279368 / 0.215209 (0.064159) \| 2.721641 / 2.077655 (0.643986) \| 1.510773 / 1.504120 (0.006653) \| 1.364349 / 1.541195 (-0.176845) \| 1.386044 / 1.468490 (-0.082446) \| 0.403051 / 4.584777 (-4.181726) \| 2.416525 / 3.745712 (-1.329187) \| 2.623198 / 5.269862 (-2.646663) \| 1.560869 / 4.565676 (-3.004808) \| 0.046613 / 0.424275 (-0.377662) \| 0.004861 / 0.007607 (-0.002746) \| 0.337875 / 0.226044 (0.111830) \| 3.289956 / 2.268929 (1.021028) \| 1.851707 / 55.444624 (-53.592917) \| 1.571092 / 6.876477 (-5.305385) \| 1.600328 / 2.142072 (-0.541745) \| 0.480766 / 4.805227 (-4.324461) \| 0.099138 / 6.500664 (-6.401526) \| 0.041691 / 0.075469 (-0.033779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941162 / 1.841788 (-0.900626) \| 11.745335 / 8.074308 (3.671027) \| 10.645509 / 10.191392 (0.454117) \| 0.132506 / 0.680424 (-0.547918) \| 0.015192 / 0.534201 (-0.519009) \| 0.272483 / 0.579283 (-0.306800) \| 0.270269 / 0.434364 (-0.164094) \| 0.309580 / 0.540337 (-0.230758) \| 0.431513 / 1.386936 (-0.955423) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006285) \| 0.003069 / 0.011008 (-0.007939) \| 0.048605 / 0.038508 (0.010097) \| 0.059557 / 0.023109 (0.036448) \| 0.275092 / 0.275898 (-0.000806) \| 0.298910 / 0.323480 (-0.024570) \| 0.004198 / 0.007986 (-0.003788) \| 0.002499 / 0.004328 (-0.001830) \| 0.048248 / 0.004250 (0.043997) \| 0.040302 / 0.037052 (0.003249) \| 0.279539 / 0.258489 (0.021050) \| 0.312500 / 0.293841 (0.018659) \| 0.025407 / 0.128546 (-0.103140) \| 0.007364 / 0.075646 (-0.068282) \| 0.053086 / 0.419271 (-0.366186) \| 0.033291 / 0.043533 (-0.010242) \| 0.276521 / 0.255139 (0.021382) \| 0.292943 / 0.283200 (0.009743) \| 0.019416 / 0.141683 (-0.122267) \| 1.151734 / 1.452155 (-0.300421) \| 1.205021 / 1.492716 (-0.287695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094112 / 0.018006 (0.076106) \| 0.309534 / 0.000490 (0.309044) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021539 / 0.037411 (-0.015872) \| 0.070325 / 0.014526 (0.055799) \| 0.080468 / 0.176557 (-0.096089) \| 0.121095 / 0.737135 (-0.616040) \| 0.082008 / 0.296338 (-0.214331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302591 / 0.215209 (0.087382) \| 2.943475 / 2.077655 (0.865820) \| 1.597970 / 1.504120 (0.093850) \| 1.468774 / 1.541195 (-0.072421) \| 1.504812 / 1.468490 (0.036322) \| 0.413715 / 4.584777 (-4.171062) \| 2.418319 / 3.745712 (-1.327393) \| 2.616656 / 5.269862 (-2.653206) \| 1.558165 / 4.565676 (-3.007512) \| 0.047169 / 0.424275 (-0.377106) \| 0.004761 / 0.007607 (-0.002846) \| 0.347225 / 0.226044 (0.121180) \| 3.479624 / 2.268929 (1.210696) \| 1.961253 / 55.444624 (-53.483371) \| 1.673532 / 6.876477 (-5.202944) \| 1.698900 / 2.142072 (-0.443172) \| 0.488373 / 4.805227 (-4.316855) \| 0.098322 / 6.500664 (-6.402342) \| 0.040832 / 0.075469 (-0.034637) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009133 / 1.841788 (-0.832655) \| 13.373258 / 8.074308 (5.298949) \| 11.327360 / 10.191392 (1.135968) \| 0.135778 / 0.680424 (-0.544646) \| 0.015813 / 0.534201 (-0.518388) \| 0.275404 / 0.579283 (-0.303879) \| 0.282564 / 0.434364 (-0.151799) \| 0.311830 / 0.540337 (-0.228507) \| 0.419008 / 1.386936 (-0.967928) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4592709e5399f91b5b392f4fd73687985365c909 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004899 / 0.011353 (-0.006454) \| 0.002780 / 0.011008 (-0.008229) \| 0.061997 / 0.038508 (0.023489) \| 0.029909 / 0.023109 (0.006800) \| 0.233445 / 0.275898 (-0.042453) \| 0.254128 / 0.323480 (-0.069351) \| 0.002927 / 0.007986 (-0.005058) \| 0.002396 / 0.004328 (-0.001932) \| 0.048118 / 0.004250 (0.043868) \| 0.044520 / 0.037052 (0.007468) \| 0.237594 / 0.258489 (-0.020895) \| 0.268407 / 0.293841 (-0.025434) \| 0.023517 / 0.128546 (-0.105029) \| 0.007035 / 0.075646 (-0.068612) \| 0.202803 / 0.419271 (-0.216469) \| 0.057692 / 0.043533 (0.014159) \| 0.237058 / 0.255139 (-0.018081) \| 0.252966 / 0.283200 (-0.030233) \| 0.017934 / 0.141683 (-0.123748) \| 1.096406 / 1.452155 (-0.355749) \| 1.153509 / 1.492716 (-0.339207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091812 / 0.018006 (0.073806) \| 0.298410 / 0.000490 (0.297920) \| 0.000228 / 0.000200 (0.000028) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018333 / 0.037411 (-0.019078) \| 0.062685 / 0.014526 (0.048159) \| 0.073295 / 0.176557 (-0.103261) \| 0.119234 / 0.737135 (-0.617901) \| 0.074603 / 0.296338 (-0.221736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279078 / 0.215209 (0.063869) \| 2.768535 / 2.077655 (0.690880) \| 1.457049 / 1.504120 (-0.047071) \| 1.326870 / 1.541195 (-0.214325) \| 1.349657 / 1.468490 (-0.118833) \| 0.405003 / 4.584777 (-4.179774) \| 2.428726 / 3.745712 (-1.316986) \| 2.595776 / 5.269862 (-2.674086) \| 1.557879 / 4.565676 (-3.007797) \| 0.045985 / 0.424275 (-0.378291) \| 0.004854 / 0.007607 (-0.002753) \| 0.336437 / 0.226044 (0.110392) \| 3.317330 / 2.268929 (1.048401) \| 1.784525 / 55.444624 (-53.660100) \| 1.500295 / 6.876477 (-5.376182) \| 1.529869 / 2.142072 (-0.612203) \| 0.473426 / 4.805227 (-4.331801) \| 0.099609 / 6.500664 (-6.401055) \| 0.042054 / 0.075469 (-0.033415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937154 / 1.841788 (-0.904633) \| 11.482383 / 8.074308 (3.408075) \| 10.468769 / 10.191392 (0.277377) \| 0.132724 / 0.680424 (-0.547700) \| 0.015242 / 0.534201 (-0.518959) \| 0.281124 / 0.579283 (-0.298159) \| 0.268603 / 0.434364 (-0.165761) \| 0.311410 / 0.540337 (-0.228928) \| 0.431817 / 1.386936 (-0.955119) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004695 / 0.011353 (-0.006658) \| 0.002873 / 0.011008 (-0.008135) \| 0.048133 / 0.038508 (0.009625) \| 0.052505 / 0.023109 (0.029396) \| 0.271679 / 0.275898 (-0.004219) \| 0.292530 / 0.323480 (-0.030950) \| 0.003844 / 0.007986 (-0.004142) \| 0.002417 / 0.004328 (-0.001912) \| 0.048619 / 0.004250 (0.044369) \| 0.039152 / 0.037052 (0.002100) \| 0.276575 / 0.258489 (0.018086) \| 0.307836 / 0.293841 (0.013995) \| 0.023877 / 0.128546 (-0.104669) \| 0.006897 / 0.075646 (-0.068749) \| 0.053241 / 0.419271 (-0.366031) \| 0.032487 / 0.043533 (-0.011046) \| 0.274205 / 0.255139 (0.019066) \| 0.289701 / 0.283200 (0.006502) \| 0.018250 / 0.141683 (-0.123432) \| 1.137902 / 1.452155 (-0.314253) \| 1.202043 / 1.492716 (-0.290673) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091453 / 0.018006 (0.073446) \| 0.297032 / 0.000490 (0.296543) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021062 / 0.037411 (-0.016349) \| 0.069848 / 0.014526 (0.055322) \| 0.084337 / 0.176557 (-0.092219) \| 0.119951 / 0.737135 (-0.617184) \| 0.082805 / 0.296338 (-0.213533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297056 / 0.215209 (0.081846) \| 2.890110 / 2.077655 (0.812456) \| 1.609918 / 1.504120 (0.105798) \| 1.491184 / 1.541195 (-0.050011) \| 1.529433 / 1.468490 (0.060943) \| 0.396081 / 4.584777 (-4.188696) \| 2.408310 / 3.745712 (-1.337402) \| 2.567905 / 5.269862 (-2.701957) \| 1.514465 / 4.565676 (-3.051212) \| 0.045329 / 0.424275 (-0.378946) \| 0.004738 / 0.007607 (-0.002869) \| 0.344373 / 0.226044 (0.118328) \| 3.428333 / 2.268929 (1.159404) \| 1.981401 / 55.444624 (-53.463223) \| 1.688007 / 6.876477 (-5.188470) \| 1.685542 / 2.142072 (-0.456531) \| 0.478045 / 4.805227 (-4.327182) \| 0.096664 / 6.500664 (-6.404001) \| 0.040335 / 0.075469 (-0.035135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972912 / 1.841788 (-0.868876) \| 12.055045 / 8.074308 (3.980737) \| 10.821073 / 10.191392 (0.629681) \| 0.139177 / 0.680424 (-0.541247) \| 0.015046 / 0.534201 (-0.519155) \| 0.275670 / 0.579283 (-0.303613) \| 0.280366 / 0.434364 (-0.153998) \| 0.315781 / 0.540337 (-0.224556) \| 0.424536 / 1.386936 (-0.962400) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0684b471d6ca8a235162f5575f624b6eda7956c5 \"CML watermark\")\n", "I'm finally merging as `transformers`/`tokenizers` dependency pins have been removed + `huggingface_hub 0.19.4` has fixed the deps incompatibility issue. All good now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004435 / 0.011353 (-0.006918) \| 0.002924 / 0.011008 (-0.008084) \| 0.062159 / 0.038508 (0.023651) \| 0.029639 / 0.023109 (0.006529) \| 0.237470 / 0.275898 (-0.038428) \| 0.269641 / 0.323480 (-0.053839) \| 0.004124 / 0.007986 (-0.003862) \| 0.002528 / 0.004328 (-0.001800) \| 0.048114 / 0.004250 (0.043864) \| 0.046055 / 0.037052 (0.009002) \| 0.245844 / 0.258489 (-0.012645) \| 0.278085 / 0.293841 (-0.015756) \| 0.023152 / 0.128546 (-0.105394) \| 0.007194 / 0.075646 (-0.068452) \| 0.206493 / 0.419271 (-0.212778) \| 0.055687 / 0.043533 (0.012155) \| 0.243301 / 0.255139 (-0.011838) \| 0.267645 / 0.283200 (-0.015555) \| 0.017413 / 0.141683 (-0.124270) \| 1.113071 / 1.452155 (-0.339083) \| 1.201436 / 1.492716 (-0.291280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092576 / 0.018006 (0.074570) \| 0.303516 / 0.000490 (0.303027) \| 0.000213 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019108 / 0.037411 (-0.018303) \| 0.062326 / 0.014526 (0.047800) \| 0.073711 / 0.176557 (-0.102846) \| 0.120414 / 0.737135 (-0.616721) \| 0.075837 / 0.296338 (-0.220501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278267 / 0.215209 (0.063058) \| 2.766231 / 2.077655 (0.688576) \| 1.455613 / 1.504120 (-0.048507) \| 1.337128 / 1.541195 (-0.204066) \| 1.357659 / 1.468490 (-0.110831) \| 0.404549 / 4.584777 (-4.180228) \| 2.409084 / 3.745712 (-1.336628) \| 2.645000 / 5.269862 (-2.624861) \| 1.600475 / 4.565676 (-2.965201) \| 0.046680 / 0.424275 (-0.377595) \| 0.004887 / 0.007607 (-0.002720) \| 0.340338 / 0.226044 (0.114294) \| 3.332647 / 2.268929 (1.063719) \| 1.852529 / 55.444624 (-53.592096) \| 1.532442 / 6.876477 (-5.344035) \| 1.550383 / 2.142072 (-0.591689) \| 0.482702 / 4.805227 (-4.322525) \| 0.101067 / 6.500664 (-6.399597) \| 0.042132 / 0.075469 (-0.033337) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945481 / 1.841788 (-0.896307) \| 11.886240 / 8.074308 (3.811932) \| 10.484620 / 10.191392 (0.293228) \| 0.130906 / 0.680424 (-0.549518) \| 0.014880 / 0.534201 (-0.519321) \| 0.268836 / 0.579283 (-0.310447) \| 0.268112 / 0.434364 (-0.166251) \| 0.304300 / 0.540337 (-0.236038) \| 0.440262 / 1.386936 (-0.946674) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005028 / 0.011353 (-0.006325) \| 0.002937 / 0.011008 (-0.008071) \| 0.049038 / 0.038508 (0.010530) \| 0.057763 / 0.023109 (0.034653) \| 0.273196 / 0.275898 (-0.002702) \| 0.295519 / 0.323480 (-0.027961) \| 0.004102 / 0.007986 (-0.003883) \| 0.002487 / 0.004328 (-0.001841) \| 0.049148 / 0.004250 (0.044898) \| 0.040303 / 0.037052 (0.003251) \| 0.279187 / 0.258489 (0.020698) \| 0.311086 / 0.293841 (0.017245) \| 0.024961 / 0.128546 (-0.103585) \| 0.007264 / 0.075646 (-0.068382) \| 0.055711 / 0.419271 (-0.363561) \| 0.032355 / 0.043533 (-0.011178) \| 0.274304 / 0.255139 (0.019165) \| 0.290953 / 0.283200 (0.007753) \| 0.018358 / 0.141683 (-0.123325) \| 1.115984 / 1.452155 (-0.336170) \| 1.190409 / 1.492716 (-0.302308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095765 / 0.018006 (0.077759) \| 0.287947 / 0.000490 (0.287457) \| 0.000242 / 0.000200 (0.000042) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022165 / 0.037411 (-0.015246) \| 0.070465 / 0.014526 (0.055940) \| 0.082078 / 0.176557 (-0.094479) \| 0.120209 / 0.737135 (-0.616926) \| 0.084573 / 0.296338 (-0.211765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298492 / 0.215209 (0.083283) \| 2.924981 / 2.077655 (0.847327) \| 1.597326 / 1.504120 (0.093206) \| 1.459132 / 1.541195 (-0.082062) \| 1.511471 / 1.468490 (0.042981) \| 0.406671 / 4.584777 (-4.178106) \| 2.443154 / 3.745712 (-1.302558) \| 2.591131 / 5.269862 (-2.678731) \| 1.549931 / 4.565676 (-3.015745) \| 0.047042 / 0.424275 (-0.377234) \| 0.004891 / 0.007607 (-0.002716) \| 0.346274 / 0.226044 (0.120230) \| 3.456050 / 2.268929 (1.187121) \| 1.959328 / 55.444624 (-53.485296) \| 1.647631 / 6.876477 (-5.228845) \| 1.692024 / 2.142072 (-0.450049) \| 0.478307 / 4.805227 (-4.326920) \| 0.098738 / 6.500664 (-6.401926) \| 0.041743 / 0.075469 (-0.033726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984619 / 1.841788 (-0.857168) \| 12.403984 / 8.074308 (4.329676) \| 10.974347 / 10.191392 (0.782955) \| 0.132893 / 0.680424 (-0.547530) \| 0.015504 / 0.534201 (-0.518697) \| 0.275354 / 0.579283 (-0.303929) \| 0.283312 / 0.434364 (-0.151052) \| 0.313661 / 0.540337 (-0.226677) \| 0.419065 / 1.386936 (-0.967871) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65315e4a8308f04fcb025039afe2a2e43b5684e \"CML watermark\")\n" ]	2023-11-14T10:47:09Z	2023-11-17T14:23:20Z	2023-11-17T14:17:00Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6414.diff", "html_url": "https://github.com/huggingface/datasets/pull/6414", "merged_at": "2023-11-17T14:17:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6414.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6414" }	Related to https://github.com/huggingface/transformers/issues/27034 and https://github.com/huggingface/huggingface_hub/pull/1782. TL;DR: `hashlib` is not a secure library for cryptography-related stuff. We are only using `hashlib` for non-security-related purposes in `datasets` so it's fine. From Python 3.9 we set can `usedforsecurity=False` in any `hashlib` method which is mandatory for companies that forbid the use of `hashlib` for security purposes. This PR fixes that. Note: before merging this we need to release a new tokenizers version that would allow the newest `huggingface_hub` version (see https://github.com/huggingface/tokenizers/pull/1385). Otherwise it might create friction to users that want to install `datasets` + `tokenizers` at the same time.	{ "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Wauplin", "id": 11801849, "login": "Wauplin", "node_id": "MDQ6VXNlcjExODAxODQ5", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "repos_url": "https://api.github.com/users/Wauplin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "type": "User", "url": "https://api.github.com/users/Wauplin", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6414/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6412	6,412	User token is printed out!	{ "avatar_url": "https://avatars.githubusercontent.com/u/25702692?v=4", "events_url": "https://api.github.com/users/mohsen-goodarzi/events{/privacy}", "followers_url": "https://api.github.com/users/mohsen-goodarzi/followers", "following_url": "https://api.github.com/users/mohsen-goodarzi/following{/other_user}", "gists_url": "https://api.github.com/users/mohsen-goodarzi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mohsen-goodarzi", "id": 25702692, "login": "mohsen-goodarzi", "node_id": "MDQ6VXNlcjI1NzAyNjky", "organizations_url": "https://api.github.com/users/mohsen-goodarzi/orgs", "received_events_url": "https://api.github.com/users/mohsen-goodarzi/received_events", "repos_url": "https://api.github.com/users/mohsen-goodarzi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mohsen-goodarzi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mohsen-goodarzi/subscriptions", "type": "User", "url": "https://api.github.com/users/mohsen-goodarzi", "user_view_type": "public" }	[]	closed	false	[ "Indeed, this is not a good practice. I've opened a PR that removes the token value from the (deprecation) warning." ]	2023-11-14T10:01:34Z	2023-11-14T22:19:46Z	2023-11-14T22:19:46Z	NONE	null	null	This line prints user token on command line! Is it safe? https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6412/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6411	6,411	Fix dependency conflict within CI build documentation	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2023-11-14T09:52:51Z	2023-11-14T10:05:59Z	2023-11-14T10:05:35Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6411.diff", "html_url": "https://github.com/huggingface/datasets/pull/6411", "merged_at": "2023-11-14T10:05:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/6411.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6411" }	Manually fix dependency conflict on `typing-extensions` version originated by `apache-beam` + `pydantic` (now a dependency of `huggingface-hub`). This is a temporary hot fix of our CI build documentation until we stop using `apache-beam`. Fix #6406.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6411/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6410	6,410	Datasets does not load HuggingFace Repository properly	{ "avatar_url": "https://avatars.githubusercontent.com/u/40600201?v=4", "events_url": "https://api.github.com/users/MikeDoes/events{/privacy}", "followers_url": "https://api.github.com/users/MikeDoes/followers", "following_url": "https://api.github.com/users/MikeDoes/following{/other_user}", "gists_url": "https://api.github.com/users/MikeDoes/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MikeDoes", "id": 40600201, "login": "MikeDoes", "node_id": "MDQ6VXNlcjQwNjAwMjAx", "organizations_url": "https://api.github.com/users/MikeDoes/orgs", "received_events_url": "https://api.github.com/users/MikeDoes/received_events", "repos_url": "https://api.github.com/users/MikeDoes/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MikeDoes/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MikeDoes/subscriptions", "type": "User", "url": "https://api.github.com/users/MikeDoes", "user_view_type": "public" }	[]	open	false	[ "Hi! You can avoid the error by requesting only the `jsonl` files. `dataset = load_dataset(\"ai4privacy/pii-masking-200k\", data_files=[\"*.jsonl\"])`.\r\n\r\nOur data file inference does not filter out (incompatible) `json` files because `json` and `jsonl` use the same builder. Still, I think the inference should differentiate these extensions because it's safe to assume that loading them together will lead to an error. WDYT @lhoestq? ", "Raising an error if there is a mix of json and jsonl in the builder makes sense yea" ]	2023-11-14T06:50:49Z	2023-11-16T06:54:36Z	null	NONE	null	null	### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ``` ``` Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	null	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6410/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6409	6,409	using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception	{ "avatar_url": "https://avatars.githubusercontent.com/u/16574677?v=4", "events_url": "https://api.github.com/users/neiblegy/events{/privacy}", "followers_url": "https://api.github.com/users/neiblegy/followers", "following_url": "https://api.github.com/users/neiblegy/following{/other_user}", "gists_url": "https://api.github.com/users/neiblegy/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/neiblegy", "id": 16574677, "login": "neiblegy", "node_id": "MDQ6VXNlcjE2NTc0Njc3", "organizations_url": "https://api.github.com/users/neiblegy/orgs", "received_events_url": "https://api.github.com/users/neiblegy/received_events", "repos_url": "https://api.github.com/users/neiblegy/repos", "site_admin": false, "starred_url": "https://api.github.com/users/neiblegy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/neiblegy/subscriptions", "type": "User", "url": "https://api.github.com/users/neiblegy", "user_view_type": "public" }	[]	closed	false	[]	2023-11-14T04:21:01Z	2023-11-22T16:42:09Z	2023-11-22T16:42:09Z	NONE	null	null	### Describe the bug i'm using datasets.download.download_manager.DownloadManager to download files like "file:///a/b/c.txt", and i disable_progress_bar() to disable bar. there will be an exception as follows: `AttributeError: 'function' object has no attribute 'close' Exception ignored in: <function TqdmCallback.__del__ at 0x7fa8683d84c0> Traceback (most recent call last): File "/home/protoss.gao/.local/lib/python3.9/site-packages/fsspec/callbacks.py", line 233, in __del__ self.tqdm.close()` i check your source code in datasets/utils/file_utils.py:348 you define TqdmCallback derive from fsspec.callbacks.TqdmCallback but in the newest fsspec code [https://github.com/fsspec/filesystem_spec/blob/master/fsspec/callbacks.py](url) , line 146, in this case, _DEFAULT_CALLBACK will take effect, but in line 234, it calls "close()" function which _DEFAULT_CALLBACK don't have such thing. so i think the class "TqdmCallback" in datasets/utils/file_utils.py may override "__del__" function or report this bug to fsspec. ### Steps to reproduce the bug as i said ### Expected behavior no exception ### Environment info datasets: 2.14.4 python: 3.9 platform: x86_64	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6409/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6408	6,408	`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`	{ "avatar_url": "https://avatars.githubusercontent.com/u/24571857?v=4", "events_url": "https://api.github.com/users/shmily326/events{/privacy}", "followers_url": "https://api.github.com/users/shmily326/followers", "following_url": "https://api.github.com/users/shmily326/following{/other_user}", "gists_url": "https://api.github.com/users/shmily326/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/shmily326", "id": 24571857, "login": "shmily326", "node_id": "MDQ6VXNlcjI0NTcxODU3", "organizations_url": "https://api.github.com/users/shmily326/orgs", "received_events_url": "https://api.github.com/users/shmily326/received_events", "repos_url": "https://api.github.com/users/shmily326/repos", "site_admin": false, "starred_url": "https://api.github.com/users/shmily326/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shmily326/subscriptions", "type": "User", "url": "https://api.github.com/users/shmily326", "user_view_type": "public" }	[]	open	false	[]	2023-11-14T03:12:08Z	2023-11-16T06:24:10Z	null	NONE	null	null	### Describe the bug IterableDataset lost but not keep columns when map function adding columns with names in remove_columns, Dataset not. May be related to the code below: https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756 ### Steps to reproduce the bug ```python dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx} ``` ```python # when load_dataset with streaming=False, the column_names are kept: dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx} ``` ### Expected behavior IterableDataset keep columns when map function adding columns with names in remove_columns ### Environment info datasets==2.14.6	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6408/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6407	6,407	Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"	{ "avatar_url": "https://avatars.githubusercontent.com/u/1741779?v=4", "events_url": "https://api.github.com/users/eawer/events{/privacy}", "followers_url": "https://api.github.com/users/eawer/followers", "following_url": "https://api.github.com/users/eawer/following{/other_user}", "gists_url": "https://api.github.com/users/eawer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/eawer", "id": 1741779, "login": "eawer", "node_id": "MDQ6VXNlcjE3NDE3Nzk=", "organizations_url": "https://api.github.com/users/eawer/orgs", "received_events_url": "https://api.github.com/users/eawer/received_events", "repos_url": "https://api.github.com/users/eawer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/eawer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/eawer/subscriptions", "type": "User", "url": "https://api.github.com/users/eawer", "user_view_type": "public" }	[]	open	false	[ "I have encountered the same problem with `datasets-2.20.0`. \r\n\r\nI found the following workaround for this issue (including the fix from #6598):\r\n1. specify the AWS profile name in the `storage_options` instead of passing an existing session object\r\n2. use a custom `DownloadConfig` object to fix #6598\r\n3. pass the `storage_options` to the `DownloadConfig`\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n# Fix for DownloadConfig from https://github.com/huggingface/datasets/issues/6598#issuecomment-1986699619\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\nstorage_options={\"profile\": \"my-aws-profile-name\"}\r\n\r\nds = load_dataset(\r\n \"parquet\", \r\n data_files={\"train\": DATA_PATH}, \r\n storage_options=storage_options,\r\n download_config=ReviseDownloadConfig(storage_options=storage_options)\r\n)\r\n```" ]	2023-11-13T21:27:43Z	2024-07-30T12:35:09Z	null	NONE	null	null	### Describe the bug I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs. ### Steps to reproduce the bug ```python import s3fs from datasets import load_dataset from aiobotocore.session import get_session DATA_PATH = "s3://bucket_name/path/validation.parquet" fs = s3fs.S3FileSystem(session=get_session()) ``` `fs.stat` returns the data, so we can say that fs is working and we have all permissions ```python fs.stat(DATA_PATH) # Returns: # {'ETag': '"123123a-19"', # 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()), # 'size': 312237170, # 'name': 'bucket_name/path/validation.parquet', # 'type': 'file', # 'StorageClass': 'STANDARD', # 'VersionId': 'Abc.HtmsC9h.as', # 'ContentType': 'binary/octet-stream'} ``` ```python fs.storage_options # Returns: # {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>} ``` ```python ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) ``` <details> <summary>Returns such error (expandable)</summary> ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[88], line 1 ----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 prepare_split_kwargs, 958 download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1025 split_dict = SplitDict(dataset_name=self.dataset_name) 1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -> 1027 split_generators = self._split_generators(dl_manager, split_generators_kwargs) 1029 # Checksums verification 1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager) 32 if not self.config.data_files: 33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 34 data_files = dl_manager.download_and_extract(self.config.data_files) 35 if isinstance(data_files, (str, list, tuple)): 36 files = data_files File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls) 549 def download_and_extract(self, url_or_urls): 550 """Download and extract given `url_or_urls`. 551 552 Is roughly equivalent to: (...) 563 extracted_path(s): `str`, extracted paths of given URL(s). 564 """ --> 565 return self.extract(self.download(url_or_urls)) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls) 401 def download(self, url_or_urls): 402 """Download given URL(s). 403 404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior. (...) 418 ``` 419 """ --> 420 download_config = self.download_config.copy() 421 download_config.extract_compressed_file = False 422 if download_config.download_desc is None: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy) 204 append = y.append 205 for a in x: --> 206 append(deepcopy(a, memo)) 207 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo) 237 def _deepcopy_method(x, memo): # Copy instance methods --> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo)) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 263 if deep and args: 264 args = (deepcopy(arg, memo) for arg in args) --> 265 y = func(args) 266 if deep: 267 memo[id(x)] = y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0) 262 deep = memo is not None 263 if deep and args: --> 264 args = (deepcopy(arg, memo) for arg in args) 265 y = func(args) 266 if deep: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil) 159 reductor = getattr(x, "__reduce_ex__", None) 160 if reductor is not None: --> 161 rv = reductor(4) 162 else: 163 reductor = getattr(x, "__reduce__", None) TypeError: cannot pickle '_contextvars.Context' object ``` </details> ### Expected behavior If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well ### Environment info - `datasets` version: 2.14.6 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.13 - Huggingface_hub version: 0.19.1 - PyArrow version: 14.0.1 - Pandas version: 1.5.3 - s3fs version: 2023.10.0 - fsspec version: 2023.10.0 - aiobotocore version: 2.7.0	null	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6407/reactions" }	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6406	6,406	CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[]	2023-11-13T11:36:10Z	2023-11-14T10:05:36Z	2023-11-14T10:05:36Z	MEMBER	null	null	Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390 ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6406/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/issues/6405	6,405	ConfigNamesError on a simple CSV file	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	[ "The viewer is working now. \r\n\r\nBased on the repo commit history, the bug was due to the incorrect format of the `features` field in the README YAML (`Value` requires `dtype`, e.g., `Value(\"string\")`, but it was not specified)", "Feel free to close the issue", "Oh, OK! Thanks. So, there was no reason to open an issue" ]	2023-11-13T10:28:29Z	2023-11-13T20:01:24Z	2023-11-13T20:01:24Z	COLLABORATOR	null	null	See https://huggingface.co/datasets/Nguyendo1999/mmath/discussions/1 ``` Error code: ConfigNamesError Exception: TypeError Message: __init__() missing 1 required positional argument: 'dtype' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1039, in get_module dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1838, in _from_yaml_list return cls.from_dict(from_yaml_inner(yaml_data)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1690, in from_dict obj = generate_from_dict(dic) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1353, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) TypeError: __init__() missing 1 required positional argument: 'dtype' ``` This is the CSV file: https://huggingface.co/datasets/Nguyendo1999/mmath/blob/dbcdd7c2c6fc447f852ec136a7532292802bb46f/math_train.csv	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6405/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6404	6,404	Support pyarrow 14.0.1 and fix vulnerability CVE-2023-47248	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005974 / 0.011353 (-0.005378) \| 0.003707 / 0.011008 (-0.007301) \| 0.079908 / 0.038508 (0.041399) \| 0.036891 / 0.023109 (0.013781) \| 0.390355 / 0.275898 (0.114457) \| 0.424439 / 0.323480 (0.100960) \| 0.004936 / 0.007986 (-0.003050) \| 0.002886 / 0.004328 (-0.001442) \| 0.062793 / 0.004250 (0.058542) \| 0.054192 / 0.037052 (0.017139) \| 0.394697 / 0.258489 (0.136208) \| 0.437775 / 0.293841 (0.143934) \| 0.027596 / 0.128546 (-0.100950) \| 0.008006 / 0.075646 (-0.067640) \| 0.262515 / 0.419271 (-0.156757) \| 0.071014 / 0.043533 (0.027481) \| 0.392964 / 0.255139 (0.137825) \| 0.417449 / 0.283200 (0.134249) \| 0.021819 / 0.141683 (-0.119864) \| 1.458083 / 1.452155 (0.005929) \| 1.489042 / 1.492716 (-0.003674) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230303 / 0.018006 (0.212297) \| 0.439361 / 0.000490 (0.438871) \| 0.010615 / 0.000200 (0.010415) \| 0.000303 / 0.000054 (0.000249) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026600 / 0.037411 (-0.010811) \| 0.078605 / 0.014526 (0.064079) \| 0.088552 / 0.176557 (-0.088005) \| 0.149429 / 0.737135 (-0.587706) \| 0.087921 / 0.296338 (-0.208417) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422063 / 0.215209 (0.206854) \| 4.201333 / 2.077655 (2.123678) \| 1.982284 / 1.504120 (0.478164) \| 1.779625 / 1.541195 (0.238431) \| 1.872454 / 1.468490 (0.403964) \| 0.502713 / 4.584777 (-4.082063) \| 3.103372 / 3.745712 (-0.642340) \| 3.030516 / 5.269862 (-2.239346) \| 1.909123 / 4.565676 (-2.656554) \| 0.057134 / 0.424275 (-0.367141) \| 0.006405 / 0.007607 (-0.001202) \| 0.494452 / 0.226044 (0.268408) \| 4.839345 / 2.268929 (2.570417) \| 2.424721 / 55.444624 (-53.019904) \| 2.028618 / 6.876477 (-4.847859) \| 2.082528 / 2.142072 (-0.059545) \| 0.587396 / 4.805227 (-4.217831) \| 0.125013 / 6.500664 (-6.375651) \| 0.061369 / 0.075469 (-0.014100) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235799 / 1.841788 (-0.605989) \| 17.919977 / 8.074308 (9.845669) \| 13.868524 / 10.191392 (3.677132) \| 0.146058 / 0.680424 (-0.534366) \| 0.016826 / 0.534201 (-0.517375) \| 0.337512 / 0.579283 (-0.241771) \| 0.390263 / 0.434364 (-0.044101) \| 0.385336 / 0.540337 (-0.155001) \| 0.566004 / 1.386936 (-0.820932) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006537 / 0.011353 (-0.004816) \| 0.003787 / 0.011008 (-0.007221) \| 0.062568 / 0.038508 (0.024060) \| 0.066672 / 0.023109 (0.043563) \| 0.420447 / 0.275898 (0.144549) \| 0.457260 / 0.323480 (0.133780) \| 0.005005 / 0.007986 (-0.002981) \| 0.003037 / 0.004328 (-0.001291) \| 0.062095 / 0.004250 (0.057844) \| 0.049619 / 0.037052 (0.012567) \| 0.429935 / 0.258489 (0.171446) \| 0.471566 / 0.293841 (0.177725) \| 0.029688 / 0.128546 (-0.098859) \| 0.008028 / 0.075646 (-0.067619) \| 0.067915 / 0.419271 (-0.351356) \| 0.042066 / 0.043533 (-0.001467) \| 0.419275 / 0.255139 (0.164136) \| 0.444819 / 0.283200 (0.161619) \| 0.020100 / 0.141683 (-0.121583) \| 1.439057 / 1.452155 (-0.013098) \| 1.495657 / 1.492716 (0.002940) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211148 / 0.018006 (0.193142) \| 0.423777 / 0.000490 (0.423288) \| 0.005892 / 0.000200 (0.005693) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026469 / 0.037411 (-0.010942) \| 0.081438 / 0.014526 (0.066912) \| 0.092007 / 0.176557 (-0.084550) \| 0.143433 / 0.737135 (-0.593703) \| 0.093039 / 0.296338 (-0.203300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410468 / 0.215209 (0.195259) \| 4.083783 / 2.077655 (2.006128) \| 2.234501 / 1.504120 (0.730381) \| 2.122323 / 1.541195 (0.581128) \| 2.255036 / 1.468490 (0.786546) \| 0.497712 / 4.584777 (-4.087065) \| 3.231187 / 3.745712 (-0.514525) \| 3.005399 / 5.269862 (-2.264463) \| 1.909516 / 4.565676 (-2.656161) \| 0.057529 / 0.424275 (-0.366746) \| 0.006475 / 0.007607 (-0.001132) \| 0.477282 / 0.226044 (0.251238) \| 4.799566 / 2.268929 (2.530637) \| 2.497070 / 55.444624 (-52.947554) \| 2.206359 / 6.876477 (-4.670118) \| 2.281614 / 2.142072 (0.139541) \| 0.581710 / 4.805227 (-4.223518) \| 0.121572 / 6.500664 (-6.379092) \| 0.058774 / 0.075469 (-0.016695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301880 / 1.841788 (-0.539908) \| 18.287330 / 8.074308 (10.213021) \| 14.939642 / 10.191392 (4.748250) \| 0.153941 / 0.680424 (-0.526483) \| 0.018345 / 0.534201 (-0.515856) \| 0.335986 / 0.579283 (-0.243297) \| 0.384264 / 0.434364 (-0.050099) \| 0.393115 / 0.540337 (-0.147223) \| 0.573343 / 1.386936 (-0.813594) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d54b6459f4ed0b2519ddec605dd71956d2d1d3e4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004805 / 0.011353 (-0.006548) \| 0.003261 / 0.011008 (-0.007747) \| 0.061585 / 0.038508 (0.023077) \| 0.030236 / 0.023109 (0.007127) \| 0.234767 / 0.275898 (-0.041131) \| 0.260478 / 0.323480 (-0.063002) \| 0.004121 / 0.007986 (-0.003865) \| 0.002525 / 0.004328 (-0.001803) \| 0.048213 / 0.004250 (0.043962) \| 0.045229 / 0.037052 (0.008176) \| 0.245143 / 0.258489 (-0.013346) \| 0.271818 / 0.293841 (-0.022023) \| 0.023594 / 0.128546 (-0.104952) \| 0.007335 / 0.075646 (-0.068311) \| 0.206246 / 0.419271 (-0.213026) \| 0.060783 / 0.043533 (0.017250) \| 0.238588 / 0.255139 (-0.016551) \| 0.274985 / 0.283200 (-0.008214) \| 0.018342 / 0.141683 (-0.123341) \| 1.135445 / 1.452155 (-0.316710) \| 1.184836 / 1.492716 (-0.307881) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095603 / 0.018006 (0.077597) \| 0.290340 / 0.000490 (0.289850) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018804 / 0.037411 (-0.018607) \| 0.062525 / 0.014526 (0.047999) \| 0.074797 / 0.176557 (-0.101760) \| 0.120360 / 0.737135 (-0.616775) \| 0.076182 / 0.296338 (-0.220156) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274981 / 0.215209 (0.059772) \| 2.684931 / 2.077655 (0.607276) \| 1.453845 / 1.504120 (-0.050275) \| 1.348361 / 1.541195 (-0.192834) \| 1.402820 / 1.468490 (-0.065670) \| 0.396311 / 4.584777 (-4.188466) \| 2.396314 / 3.745712 (-1.349398) \| 2.744379 / 5.269862 (-2.525482) \| 1.615268 / 4.565676 (-2.950409) \| 0.045920 / 0.424275 (-0.378355) \| 0.004844 / 0.007607 (-0.002763) \| 0.331132 / 0.226044 (0.105087) \| 3.325484 / 2.268929 (1.056556) \| 1.845734 / 55.444624 (-53.598890) \| 1.537268 / 6.876477 (-5.339209) \| 1.565155 / 2.142072 (-0.576918) \| 0.480032 / 4.805227 (-4.325195) \| 0.099917 / 6.500664 (-6.400747) \| 0.042276 / 0.075469 (-0.033193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973128 / 1.841788 (-0.868660) \| 12.643790 / 8.074308 (4.569482) \| 10.319586 / 10.191392 (0.128194) \| 0.131733 / 0.680424 (-0.548691) \| 0.014849 / 0.534201 (-0.519352) \| 0.270960 / 0.579283 (-0.308323) \| 0.265409 / 0.434364 (-0.168955) \| 0.309073 / 0.540337 (-0.231264) \| 0.466204 / 1.386936 (-0.920732) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005067 / 0.011353 (-0.006286) \| 0.003344 / 0.011008 (-0.007665) \| 0.047917 / 0.038508 (0.009409) \| 0.059556 / 0.023109 (0.036447) \| 0.275777 / 0.275898 (-0.000121) \| 0.299703 / 0.323480 (-0.023777) \| 0.004185 / 0.007986 (-0.003801) \| 0.002602 / 0.004328 (-0.001726) \| 0.048723 / 0.004250 (0.044472) \| 0.040686 / 0.037052 (0.003634) \| 0.281078 / 0.258489 (0.022589) \| 0.314725 / 0.293841 (0.020885) \| 0.024645 / 0.128546 (-0.103901) \| 0.007465 / 0.075646 (-0.068182) \| 0.053827 / 0.419271 (-0.365445) \| 0.033395 / 0.043533 (-0.010138) \| 0.273675 / 0.255139 (0.018536) \| 0.291261 / 0.283200 (0.008062) \| 0.019733 / 0.141683 (-0.121950) \| 1.134084 / 1.452155 (-0.318071) \| 1.189186 / 1.492716 (-0.303531) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.114960 / 0.018006 (0.096954) \| 0.308800 / 0.000490 (0.308311) \| 0.000237 / 0.000200 (0.000037) \| 0.000061 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021633 / 0.037411 (-0.015778) \| 0.073192 / 0.014526 (0.058666) \| 0.081598 / 0.176557 (-0.094959) \| 0.123085 / 0.737135 (-0.614050) \| 0.088677 / 0.296338 (-0.207661) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300865 / 0.215209 (0.085656) \| 2.956847 / 2.077655 (0.879192) \| 1.613890 / 1.504120 (0.109770) \| 1.494074 / 1.541195 (-0.047121) \| 1.550345 / 1.468490 (0.081855) \| 0.408880 / 4.584777 (-4.175897) \| 2.422848 / 3.745712 (-1.322865) \| 2.690623 / 5.269862 (-2.579239) \| 1.546922 / 4.565676 (-3.018755) \| 0.047192 / 0.424275 (-0.377083) \| 0.004882 / 0.007607 (-0.002725) \| 0.360625 / 0.226044 (0.134580) \| 3.512678 / 2.268929 (1.243749) \| 1.978633 / 55.444624 (-53.465992) \| 1.686927 / 6.876477 (-5.189549) \| 1.748387 / 2.142072 (-0.393685) \| 0.480780 / 4.805227 (-4.324447) \| 0.099163 / 6.500664 (-6.401501) \| 0.041194 / 0.075469 (-0.034275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989087 / 1.841788 (-0.852700) \| 12.341951 / 8.074308 (4.267643) \| 11.109329 / 10.191392 (0.917936) \| 0.143329 / 0.680424 (-0.537095) \| 0.015565 / 0.534201 (-0.518636) \| 0.269532 / 0.579283 (-0.309751) \| 0.274899 / 0.434364 (-0.159465) \| 0.309308 / 0.540337 (-0.231030) \| 0.439651 / 1.386936 (-0.947285) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a3f006a1a88c894ea10610d66dfddd73ad1490 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007880 / 0.011353 (-0.003473) \| 0.004386 / 0.011008 (-0.006622) \| 0.099067 / 0.038508 (0.060559) \| 0.048036 / 0.023109 (0.024927) \| 0.368349 / 0.275898 (0.092451) \| 0.400052 / 0.323480 (0.076572) \| 0.004493 / 0.007986 (-0.003493) \| 0.003732 / 0.004328 (-0.000597) \| 0.076153 / 0.004250 (0.071902) \| 0.071024 / 0.037052 (0.033972) \| 0.379771 / 0.258489 (0.121282) \| 0.425005 / 0.293841 (0.131164) \| 0.036092 / 0.128546 (-0.092454) \| 0.009825 / 0.075646 (-0.065822) \| 0.340217 / 0.419271 (-0.079055) \| 0.089571 / 0.043533 (0.046038) \| 0.371426 / 0.255139 (0.116287) \| 0.397864 / 0.283200 (0.114664) \| 0.029440 / 0.141683 (-0.112243) \| 1.778100 / 1.452155 (0.325945) \| 1.857202 / 1.492716 (0.364486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254022 / 0.018006 (0.236015) \| 0.549844 / 0.000490 (0.549354) \| 0.012824 / 0.000200 (0.012624) \| 0.000378 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032334 / 0.037411 (-0.005077) \| 0.096101 / 0.014526 (0.081576) \| 0.117825 / 0.176557 (-0.058731) \| 0.179277 / 0.737135 (-0.557858) \| 0.112614 / 0.296338 (-0.183724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455051 / 0.215209 (0.239842) \| 4.537086 / 2.077655 (2.459431) \| 2.198662 / 1.504120 (0.694542) \| 1.982772 / 1.541195 (0.441578) \| 2.058673 / 1.468490 (0.590182) \| 0.569268 / 4.584777 (-4.015509) \| 4.095000 / 3.745712 (0.349288) \| 3.891680 / 5.269862 (-1.378182) \| 2.345129 / 4.565676 (-2.220548) \| 0.066974 / 0.424275 (-0.357301) \| 0.008557 / 0.007607 (0.000950) \| 0.545290 / 0.226044 (0.319245) \| 5.453377 / 2.268929 (3.184448) \| 2.858688 / 55.444624 (-52.585936) \| 2.502367 / 6.876477 (-4.374109) \| 2.515658 / 2.142072 (0.373586) \| 0.681423 / 4.805227 (-4.123804) \| 0.155975 / 6.500664 (-6.344689) \| 0.070872 / 0.075469 (-0.004597) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.474674 / 1.841788 (-0.367114) \| 21.653619 / 8.074308 (13.579311) \| 16.277111 / 10.191392 (6.085719) \| 0.166445 / 0.680424 (-0.513979) \| 0.021676 / 0.534201 (-0.512525) \| 0.466949 / 0.579283 (-0.112334) \| 0.500953 / 0.434364 (0.066589) \| 0.540413 / 0.540337 (0.000076) \| 0.792989 / 1.386936 (-0.593947) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007633 / 0.011353 (-0.003720) \| 0.004468 / 0.011008 (-0.006540) \| 0.075573 / 0.038508 (0.037065) \| 0.081174 / 0.023109 (0.058064) \| 0.440741 / 0.275898 (0.164843) \| 0.489493 / 0.323480 (0.166013) \| 0.006180 / 0.007986 (-0.001805) \| 0.003693 / 0.004328 (-0.000636) \| 0.074692 / 0.004250 (0.070441) \| 0.061732 / 0.037052 (0.024680) \| 0.460391 / 0.258489 (0.201902) \| 0.505575 / 0.293841 (0.211734) \| 0.037692 / 0.128546 (-0.090854) \| 0.009870 / 0.075646 (-0.065776) \| 0.083830 / 0.419271 (-0.335442) \| 0.056255 / 0.043533 (0.012723) \| 0.439330 / 0.255139 (0.184191) \| 0.475598 / 0.283200 (0.192399) \| 0.026626 / 0.141683 (-0.115056) \| 1.794410 / 1.452155 (0.342255) \| 1.882510 / 1.492716 (0.389794) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236194 / 0.018006 (0.218187) \| 0.486109 / 0.000490 (0.485619) \| 0.006652 / 0.000200 (0.006453) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037277 / 0.037411 (-0.000134) \| 0.108904 / 0.014526 (0.094378) \| 0.122699 / 0.176557 (-0.053857) \| 0.182388 / 0.737135 (-0.554747) \| 0.122826 / 0.296338 (-0.173512) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485989 / 0.215209 (0.270780) \| 4.913263 / 2.077655 (2.835609) \| 2.571618 / 1.504120 (1.067498) \| 2.401248 / 1.541195 (0.860054) \| 2.501117 / 1.468490 (1.032627) \| 0.570989 / 4.584777 (-4.013788) \| 4.107420 / 3.745712 (0.361708) \| 3.814977 / 5.269862 (-1.454885) \| 2.282539 / 4.565676 (-2.283138) \| 0.067765 / 0.424275 (-0.356511) \| 0.008561 / 0.007607 (0.000954) \| 0.584515 / 0.226044 (0.358471) \| 5.817821 / 2.268929 (3.548893) \| 3.211202 / 55.444624 (-52.233422) \| 2.764480 / 6.876477 (-4.111996) \| 2.807301 / 2.142072 (0.665229) \| 0.676882 / 4.805227 (-4.128346) \| 0.150124 / 6.500664 (-6.350540) \| 0.067205 / 0.075469 (-0.008265) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.594945 / 1.841788 (-0.246843) \| 22.533511 / 8.074308 (14.459203) \| 17.099693 / 10.191392 (6.908301) \| 0.195954 / 0.680424 (-0.484470) \| 0.023968 / 0.534201 (-0.510233) \| 0.471337 / 0.579283 (-0.107946) \| 0.491017 / 0.434364 (0.056653) \| 0.561342 / 0.540337 (0.021004) \| 0.797116 / 1.386936 (-0.589820) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98871b9ba46e89e75e9d0dddc49f4241373c575d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006235 / 0.011353 (-0.005118) \| 0.003688 / 0.011008 (-0.007321) \| 0.080801 / 0.038508 (0.042293) \| 0.036243 / 0.023109 (0.013134) \| 0.312173 / 0.275898 (0.036275) \| 0.346239 / 0.323480 (0.022759) \| 0.003429 / 0.007986 (-0.004556) \| 0.003806 / 0.004328 (-0.000523) \| 0.063236 / 0.004250 (0.058986) \| 0.053229 / 0.037052 (0.016177) \| 0.315184 / 0.258489 (0.056695) \| 0.360124 / 0.293841 (0.066283) \| 0.027447 / 0.128546 (-0.101099) \| 0.008029 / 0.075646 (-0.067618) \| 0.262766 / 0.419271 (-0.156505) \| 0.068421 / 0.043533 (0.024888) \| 0.309028 / 0.255139 (0.053889) \| 0.345859 / 0.283200 (0.062659) \| 0.021388 / 0.141683 (-0.120295) \| 1.452807 / 1.452155 (0.000652) \| 1.502803 / 1.492716 (0.010087) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211297 / 0.018006 (0.193291) \| 0.423364 / 0.000490 (0.422874) \| 0.004574 / 0.000200 (0.004374) \| 0.000272 / 0.000054 (0.000218) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023805 / 0.037411 (-0.013606) \| 0.072309 / 0.014526 (0.057783) \| 0.083274 / 0.176557 (-0.093283) \| 0.143594 / 0.737135 (-0.593541) \| 0.083777 / 0.296338 (-0.212561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415691 / 0.215209 (0.200482) \| 4.128621 / 2.077655 (2.050967) \| 1.931128 / 1.504120 (0.427008) \| 1.737486 / 1.541195 (0.196292) \| 1.806314 / 1.468490 (0.337823) \| 0.501405 / 4.584777 (-4.083372) \| 3.082042 / 3.745712 (-0.663670) \| 2.980224 / 5.269862 (-2.289637) \| 1.879780 / 4.565676 (-2.685897) \| 0.057546 / 0.424275 (-0.366729) \| 0.006422 / 0.007607 (-0.001186) \| 0.479813 / 0.226044 (0.253768) \| 4.854497 / 2.268929 (2.585568) \| 2.529674 / 55.444624 (-52.914950) \| 2.283041 / 6.876477 (-4.593436) \| 2.377173 / 2.142072 (0.235101) \| 0.589654 / 4.805227 (-4.215573) \| 0.126190 / 6.500664 (-6.374474) \| 0.062391 / 0.075469 (-0.013079) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.232023 / 1.841788 (-0.609764) \| 17.576621 / 8.074308 (9.502313) \| 13.437075 / 10.191392 (3.245683) \| 0.143367 / 0.680424 (-0.537057) \| 0.016638 / 0.534201 (-0.517563) \| 0.332806 / 0.579283 (-0.246477) \| 0.356029 / 0.434364 (-0.078335) \| 0.385610 / 0.540337 (-0.154727) \| 0.563268 / 1.386936 (-0.823668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006293 / 0.011353 (-0.005060) \| 0.003692 / 0.011008 (-0.007317) \| 0.062075 / 0.038508 (0.023567) \| 0.062104 / 0.023109 (0.038995) \| 0.407478 / 0.275898 (0.131580) \| 0.434982 / 0.323480 (0.111502) \| 0.004889 / 0.007986 (-0.003097) \| 0.002915 / 0.004328 (-0.001413) \| 0.061426 / 0.004250 (0.057176) \| 0.048027 / 0.037052 (0.010974) \| 0.410504 / 0.258489 (0.152015) \| 0.435383 / 0.293841 (0.141542) \| 0.029419 / 0.128546 (-0.099127) \| 0.008275 / 0.075646 (-0.067371) \| 0.067796 / 0.419271 (-0.351476) \| 0.041696 / 0.043533 (-0.001837) \| 0.398882 / 0.255139 (0.143743) \| 0.419480 / 0.283200 (0.136281) \| 0.021519 / 0.141683 (-0.120164) \| 1.436961 / 1.452155 (-0.015194) \| 1.507961 / 1.492716 (0.015245) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223190 / 0.018006 (0.205184) \| 0.416281 / 0.000490 (0.415791) \| 0.003370 / 0.000200 (0.003170) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025923 / 0.037411 (-0.011488) \| 0.079989 / 0.014526 (0.065463) \| 0.091289 / 0.176557 (-0.085268) \| 0.141212 / 0.737135 (-0.595923) \| 0.091717 / 0.296338 (-0.204622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434640 / 0.215209 (0.219431) \| 4.326154 / 2.077655 (2.248500) \| 2.364845 / 1.504120 (0.860725) \| 2.194040 / 1.541195 (0.652846) \| 2.276665 / 1.468490 (0.808175) \| 0.501879 / 4.584777 (-4.082898) \| 3.073307 / 3.745712 (-0.672405) \| 2.893823 / 5.269862 (-2.376039) \| 1.820594 / 4.565676 (-2.745083) \| 0.057595 / 0.424275 (-0.366680) \| 0.006516 / 0.007607 (-0.001091) \| 0.513633 / 0.226044 (0.287589) \| 5.104799 / 2.268929 (2.835870) \| 2.845025 / 55.444624 (-52.599599) \| 2.513852 / 6.876477 (-4.362624) \| 2.561044 / 2.142072 (0.418972) \| 0.582711 / 4.805227 (-4.222516) \| 0.120631 / 6.500664 (-6.380034) \| 0.056738 / 0.075469 (-0.018731) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303370 / 1.841788 (-0.538418) \| 18.023568 / 8.074308 (9.949259) \| 14.637973 / 10.191392 (4.446581) \| 0.145182 / 0.680424 (-0.535241) \| 0.018061 / 0.534201 (-0.516140) \| 0.333219 / 0.579283 (-0.246065) \| 0.373184 / 0.434364 (-0.061180) \| 0.388176 / 0.540337 (-0.152161) \| 0.564752 / 1.386936 (-0.822184) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aecdc94580d105d4b70c94e8e238ce097f97af90 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007230 / 0.011353 (-0.004122) \| 0.003727 / 0.011008 (-0.007281) \| 0.078893 / 0.038508 (0.040385) \| 0.042600 / 0.023109 (0.019491) \| 0.301905 / 0.275898 (0.026007) \| 0.328478 / 0.323480 (0.004998) \| 0.003960 / 0.007986 (-0.004026) \| 0.004530 / 0.004328 (0.000201) \| 0.059446 / 0.004250 (0.055196) \| 0.061241 / 0.037052 (0.024189) \| 0.301878 / 0.258489 (0.043389) \| 0.340935 / 0.293841 (0.047095) \| 0.030559 / 0.128546 (-0.097988) \| 0.008016 / 0.075646 (-0.067630) \| 0.305174 / 0.419271 (-0.114097) \| 0.080374 / 0.043533 (0.036842) \| 0.307162 / 0.255139 (0.052023) \| 0.342459 / 0.283200 (0.059259) \| 0.025881 / 0.141683 (-0.115801) \| 1.443311 / 1.452155 (-0.008844) \| 1.631060 / 1.492716 (0.138344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242676 / 0.018006 (0.224670) \| 0.463941 / 0.000490 (0.463451) \| 0.007762 / 0.000200 (0.007562) \| 0.000582 / 0.000054 (0.000527) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027334 / 0.037411 (-0.010077) \| 0.078910 / 0.014526 (0.064384) \| 0.091399 / 0.176557 (-0.085157) \| 0.143318 / 0.737135 (-0.593818) \| 0.089761 / 0.296338 (-0.206577) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463002 / 0.215209 (0.247793) \| 4.627235 / 2.077655 (2.549580) \| 2.256699 / 1.504120 (0.752579) \| 2.057615 / 1.541195 (0.516421) \| 2.126424 / 1.468490 (0.657934) \| 0.571969 / 4.584777 (-4.012808) \| 4.130260 / 3.745712 (0.384548) \| 3.833521 / 5.269862 (-1.436341) \| 2.320141 / 4.565676 (-2.245535) \| 0.067587 / 0.424275 (-0.356688) \| 0.008452 / 0.007607 (0.000845) \| 0.546478 / 0.226044 (0.320433) \| 5.070678 / 2.268929 (2.801750) \| 2.325387 / 55.444624 (-53.119237) \| 2.044041 / 6.876477 (-4.832435) \| 2.019714 / 2.142072 (-0.122358) \| 0.563589 / 4.805227 (-4.241639) \| 0.135269 / 6.500664 (-6.365395) \| 0.058208 / 0.075469 (-0.017261) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283156 / 1.841788 (-0.558631) \| 18.617776 / 8.074308 (10.543468) \| 13.360700 / 10.191392 (3.169308) \| 0.160001 / 0.680424 (-0.520423) \| 0.021538 / 0.534201 (-0.512663) \| 0.384169 / 0.579283 (-0.195114) \| 0.407517 / 0.434364 (-0.026847) \| 0.427295 / 0.540337 (-0.113042) \| 0.655288 / 1.386936 (-0.731648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006854 / 0.011353 (-0.004499) \| 0.003442 / 0.011008 (-0.007566) \| 0.060622 / 0.038508 (0.022114) \| 0.074649 / 0.023109 (0.051540) \| 0.341733 / 0.275898 (0.065835) \| 0.360096 / 0.323480 (0.036616) \| 0.006235 / 0.007986 (-0.001751) \| 0.003447 / 0.004328 (-0.000882) \| 0.057301 / 0.004250 (0.053051) \| 0.059022 / 0.037052 (0.021970) \| 0.369523 / 0.258489 (0.111034) \| 0.386280 / 0.293841 (0.092439) \| 0.034319 / 0.128546 (-0.094228) \| 0.008291 / 0.075646 (-0.067355) \| 0.070403 / 0.419271 (-0.348868) \| 0.050433 / 0.043533 (0.006901) \| 0.347262 / 0.255139 (0.092123) \| 0.380543 / 0.283200 (0.097343) \| 0.024492 / 0.141683 (-0.117191) \| 1.446721 / 1.452155 (-0.005433) \| 1.541614 / 1.492716 (0.048898) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226148 / 0.018006 (0.208142) \| 0.442150 / 0.000490 (0.441660) \| 0.004997 / 0.000200 (0.004797) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032866 / 0.037411 (-0.004546) \| 0.088097 / 0.014526 (0.073571) \| 0.102178 / 0.176557 (-0.074379) \| 0.151129 / 0.737135 (-0.586006) \| 0.103953 / 0.296338 (-0.192386) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376701 / 0.215209 (0.161492) \| 3.886997 / 2.077655 (1.809342) \| 2.027143 / 1.504120 (0.523023) \| 1.808647 / 1.541195 (0.267453) \| 1.867664 / 1.468490 (0.399173) \| 0.459487 / 4.584777 (-4.125290) \| 3.640801 / 3.745712 (-0.104911) \| 3.242512 / 5.269862 (-2.027350) \| 1.889174 / 4.565676 (-2.676503) \| 0.052415 / 0.424275 (-0.371860) \| 0.007479 / 0.007607 (-0.000128) \| 0.457706 / 0.226044 (0.231662) \| 4.815041 / 2.268929 (2.546112) \| 2.542470 / 55.444624 (-52.902154) \| 2.137084 / 6.876477 (-4.739392) \| 2.122867 / 2.142072 (-0.019205) \| 0.553756 / 4.805227 (-4.251471) \| 0.118902 / 6.500664 (-6.381763) \| 0.058149 / 0.075469 (-0.017320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272615 / 1.841788 (-0.569173) \| 19.455709 / 8.074308 (11.381401) \| 14.111693 / 10.191392 (3.920301) \| 0.165741 / 0.680424 (-0.514683) \| 0.023680 / 0.534201 (-0.510521) \| 0.431458 / 0.579283 (-0.147825) \| 0.433612 / 0.434364 (-0.000752) \| 0.465615 / 0.540337 (-0.074722) \| 0.678177 / 1.386936 (-0.708759) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#998623fa51991320740b945d0853ee20807304d7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004870 / 0.011353 (-0.006483) \| 0.002834 / 0.011008 (-0.008175) \| 0.061359 / 0.038508 (0.022851) \| 0.031286 / 0.023109 (0.008177) \| 0.236701 / 0.275898 (-0.039197) \| 0.258139 / 0.323480 (-0.065341) \| 0.002943 / 0.007986 (-0.005043) \| 0.002989 / 0.004328 (-0.001339) \| 0.048046 / 0.004250 (0.043796) \| 0.044927 / 0.037052 (0.007874) \| 0.241339 / 0.258489 (-0.017151) \| 0.273912 / 0.293841 (-0.019929) \| 0.023427 / 0.128546 (-0.105119) \| 0.007251 / 0.075646 (-0.068395) \| 0.202730 / 0.419271 (-0.216542) \| 0.056223 / 0.043533 (0.012691) \| 0.239908 / 0.255139 (-0.015231) \| 0.254723 / 0.283200 (-0.028476) \| 0.018223 / 0.141683 (-0.123460) \| 1.119691 / 1.452155 (-0.332464) \| 1.163802 / 1.492716 (-0.328915) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091303 / 0.018006 (0.073297) \| 0.302097 / 0.000490 (0.301607) \| 0.000214 / 0.000200 (0.000014) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018201 / 0.037411 (-0.019210) \| 0.062092 / 0.014526 (0.047566) \| 0.074806 / 0.176557 (-0.101751) \| 0.119625 / 0.737135 (-0.617510) \| 0.074680 / 0.296338 (-0.221659) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281140 / 0.215209 (0.065931) \| 2.752094 / 2.077655 (0.674439) \| 1.436813 / 1.504120 (-0.067307) \| 1.312947 / 1.541195 (-0.228247) \| 1.331022 / 1.468490 (-0.137468) \| 0.396579 / 4.584777 (-4.188198) \| 2.406181 / 3.745712 (-1.339531) \| 2.597180 / 5.269862 (-2.672682) \| 1.565879 / 4.565676 (-2.999798) \| 0.046330 / 0.424275 (-0.377945) \| 0.004776 / 0.007607 (-0.002831) \| 0.339681 / 0.226044 (0.113637) \| 3.279533 / 2.268929 (1.010605) \| 1.793352 / 55.444624 (-53.651272) \| 1.493910 / 6.876477 (-5.382567) \| 1.514494 / 2.142072 (-0.627579) \| 0.467955 / 4.805227 (-4.337272) \| 0.097764 / 6.500664 (-6.402900) \| 0.041659 / 0.075469 (-0.033810) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943204 / 1.841788 (-0.898583) \| 11.350848 / 8.074308 (3.276540) \| 10.169944 / 10.191392 (-0.021448) \| 0.130882 / 0.680424 (-0.549542) \| 0.013804 / 0.534201 (-0.520397) \| 0.269107 / 0.579283 (-0.310177) \| 0.261685 / 0.434364 (-0.172679) \| 0.305610 / 0.540337 (-0.234727) \| 0.430586 / 1.386936 (-0.956350) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004835 / 0.011353 (-0.006518) \| 0.002530 / 0.011008 (-0.008479) \| 0.047383 / 0.038508 (0.008875) \| 0.052559 / 0.023109 (0.029450) \| 0.265015 / 0.275898 (-0.010883) \| 0.286955 / 0.323480 (-0.036525) \| 0.003931 / 0.007986 (-0.004054) \| 0.002038 / 0.004328 (-0.002290) \| 0.047458 / 0.004250 (0.043207) \| 0.038257 / 0.037052 (0.001205) \| 0.270569 / 0.258489 (0.012080) \| 0.298968 / 0.293841 (0.005127) \| 0.024615 / 0.128546 (-0.103932) \| 0.006969 / 0.075646 (-0.068677) \| 0.052361 / 0.419271 (-0.366911) \| 0.032701 / 0.043533 (-0.010832) \| 0.269126 / 0.255139 (0.013987) \| 0.285934 / 0.283200 (0.002735) \| 0.018121 / 0.141683 (-0.123562) \| 1.129796 / 1.452155 (-0.322359) \| 1.272831 / 1.492716 (-0.219885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092058 / 0.018006 (0.074051) \| 0.303544 / 0.000490 (0.303054) \| 0.000232 / 0.000200 (0.000032) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020983 / 0.037411 (-0.016428) \| 0.069798 / 0.014526 (0.055272) \| 0.081410 / 0.176557 (-0.095146) \| 0.120403 / 0.737135 (-0.616732) \| 0.082813 / 0.296338 (-0.213525) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295943 / 0.215209 (0.080734) \| 2.895761 / 2.077655 (0.818106) \| 1.583534 / 1.504120 (0.079414) \| 1.458397 / 1.541195 (-0.082798) \| 1.492113 / 1.468490 (0.023623) \| 0.402364 / 4.584777 (-4.182413) \| 2.469777 / 3.745712 (-1.275935) \| 2.565262 / 5.269862 (-2.704599) \| 1.525914 / 4.565676 (-3.039763) \| 0.047168 / 0.424275 (-0.377107) \| 0.004800 / 0.007607 (-0.002808) \| 0.348356 / 0.226044 (0.122311) \| 3.463184 / 2.268929 (1.194255) \| 1.930240 / 55.444624 (-53.514385) \| 1.644312 / 6.876477 (-5.232165) \| 1.625477 / 2.142072 (-0.516596) \| 0.480781 / 4.805227 (-4.324446) \| 0.098431 / 6.500664 (-6.402233) \| 0.041071 / 0.075469 (-0.034398) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973633 / 1.841788 (-0.868154) \| 11.952261 / 8.074308 (3.877953) \| 11.038222 / 10.191392 (0.846830) \| 0.142755 / 0.680424 (-0.537669) \| 0.015389 / 0.534201 (-0.518812) \| 0.274144 / 0.579283 (-0.305139) \| 0.282319 / 0.434364 (-0.152045) \| 0.314330 / 0.540337 (-0.226007) \| 0.435315 / 1.386936 (-0.951621) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05200c0a4f8f02c3890ab79a10b44ab0bcf11629 \"CML watermark\")\n", "The red CI job is unrelated to this PR. It appeared 5 days ago. See:\r\n- https://github.com/huggingface/datasets/pull/6390#pullrequestreview-1721070927\r\n- https://github.com/huggingface/datasets/issues/6406", "Let's do a new release once this is merged ? cc @mariosasko as well let us know if the fix sounds good to you", "@lhoestq Yes, this sounds good to me!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004932 / 0.011353 (-0.006421) \| 0.002956 / 0.011008 (-0.008052) \| 0.061999 / 0.038508 (0.023491) \| 0.030174 / 0.023109 (0.007065) \| 0.241483 / 0.275898 (-0.034415) \| 0.261578 / 0.323480 (-0.061902) \| 0.002881 / 0.007986 (-0.005105) \| 0.002451 / 0.004328 (-0.001878) \| 0.048176 / 0.004250 (0.043925) \| 0.045028 / 0.037052 (0.007976) \| 0.244304 / 0.258489 (-0.014185) \| 0.275834 / 0.293841 (-0.018007) \| 0.023312 / 0.128546 (-0.105234) \| 0.007361 / 0.075646 (-0.068286) \| 0.204433 / 0.419271 (-0.214838) \| 0.054561 / 0.043533 (0.011028) \| 0.236902 / 0.255139 (-0.018237) \| 0.269358 / 0.283200 (-0.013842) \| 0.017736 / 0.141683 (-0.123947) \| 1.112444 / 1.452155 (-0.339711) \| 1.170260 / 1.492716 (-0.322456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093081 / 0.018006 (0.075074) \| 0.311470 / 0.000490 (0.310981) \| 0.000212 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018654 / 0.037411 (-0.018757) \| 0.063239 / 0.014526 (0.048714) \| 0.073759 / 0.176557 (-0.102798) \| 0.120279 / 0.737135 (-0.616857) \| 0.076214 / 0.296338 (-0.220124) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287219 / 0.215209 (0.072010) \| 2.765378 / 2.077655 (0.687723) \| 1.459733 / 1.504120 (-0.044387) \| 1.325999 / 1.541195 (-0.215196) \| 1.349957 / 1.468490 (-0.118533) \| 0.413093 / 4.584777 (-4.171684) \| 2.394758 / 3.745712 (-1.350954) \| 2.633916 / 5.269862 (-2.635945) \| 1.621629 / 4.565676 (-2.944047) \| 0.046839 / 0.424275 (-0.377436) \| 0.004786 / 0.007607 (-0.002822) \| 0.336261 / 0.226044 (0.110217) \| 3.348196 / 2.268929 (1.079267) \| 1.853050 / 55.444624 (-53.591574) \| 1.543926 / 6.876477 (-5.332551) \| 1.573675 / 2.142072 (-0.568398) \| 0.484088 / 4.805227 (-4.321139) \| 0.100820 / 6.500664 (-6.399845) \| 0.042194 / 0.075469 (-0.033275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945186 / 1.841788 (-0.896601) \| 11.859855 / 8.074308 (3.785547) \| 10.459883 / 10.191392 (0.268491) \| 0.142024 / 0.680424 (-0.538400) \| 0.013882 / 0.534201 (-0.520319) \| 0.269584 / 0.579283 (-0.309699) \| 0.264353 / 0.434364 (-0.170011) \| 0.307988 / 0.540337 (-0.232349) \| 0.423655 / 1.386936 (-0.963281) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004891 / 0.011353 (-0.006461) \| 0.003087 / 0.011008 (-0.007921) \| 0.048206 / 0.038508 (0.009697) \| 0.058570 / 0.023109 (0.035461) \| 0.268552 / 0.275898 (-0.007346) \| 0.287839 / 0.323480 (-0.035641) \| 0.004044 / 0.007986 (-0.003942) \| 0.002388 / 0.004328 (-0.001940) \| 0.048186 / 0.004250 (0.043935) \| 0.038719 / 0.037052 (0.001667) \| 0.271940 / 0.258489 (0.013451) \| 0.299716 / 0.293841 (0.005875) \| 0.027166 / 0.128546 (-0.101380) \| 0.007388 / 0.075646 (-0.068258) \| 0.053885 / 0.419271 (-0.365387) \| 0.032804 / 0.043533 (-0.010729) \| 0.271664 / 0.255139 (0.016525) \| 0.284613 / 0.283200 (0.001414) \| 0.018488 / 0.141683 (-0.123195) \| 1.125854 / 1.452155 (-0.326301) \| 1.195896 / 1.492716 (-0.296820) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092438 / 0.018006 (0.074431) \| 0.315265 / 0.000490 (0.314775) \| 0.000228 / 0.000200 (0.000028) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021373 / 0.037411 (-0.016038) \| 0.070611 / 0.014526 (0.056085) \| 0.080391 / 0.176557 (-0.096165) \| 0.118749 / 0.737135 (-0.618386) \| 0.082340 / 0.296338 (-0.213999) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295583 / 0.215209 (0.080374) \| 2.882152 / 2.077655 (0.804497) \| 1.565088 / 1.504120 (0.060968) \| 1.451954 / 1.541195 (-0.089241) \| 1.505783 / 1.468490 (0.037293) \| 0.404699 / 4.584777 (-4.180078) \| 2.451703 / 3.745712 (-1.294009) \| 2.596301 / 5.269862 (-2.673560) \| 1.547014 / 4.565676 (-3.018662) \| 0.047750 / 0.424275 (-0.376525) \| 0.004850 / 0.007607 (-0.002757) \| 0.346893 / 0.226044 (0.120849) \| 3.383355 / 2.268929 (1.114426) \| 1.943933 / 55.444624 (-53.500692) \| 1.657513 / 6.876477 (-5.218964) \| 1.687166 / 2.142072 (-0.454906) \| 0.478543 / 4.805227 (-4.326685) \| 0.097804 / 6.500664 (-6.402860) \| 0.041392 / 0.075469 (-0.034078) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983894 / 1.841788 (-0.857893) \| 12.446443 / 8.074308 (4.372135) \| 10.973461 / 10.191392 (0.782069) \| 0.131630 / 0.680424 (-0.548794) \| 0.017196 / 0.534201 (-0.517005) \| 0.270873 / 0.579283 (-0.308411) \| 0.284379 / 0.434364 (-0.149985) \| 0.306103 / 0.540337 (-0.234234) \| 0.413762 / 1.386936 (-0.973174) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ad4c6e6e33f0129db8745e84de8c298741aa2 \"CML watermark\")\n", "Note I had to add `pa.ExtensionType.__reduce__` because this is used by `copy.deepcopy` when using `.with_format`. See error below.\r\n\r\nThis method was added in pyarrow-13.0.0: https://github.com/apache/arrow/pull/36170\r\n- We need to re-implement it as long we support lower pyarrow versions\r\n\r\nErrors: https://github.com/huggingface/datasets/actions/runs/6861278161/job/18656665772\r\n```\r\n ____________________________ test_dataset_map[True] ____________________________\r\n[gw1] linux -- Python 3.8.18 /opt/hostedtoolcache/Python/3.8.18/x64/bin/python\r\n\r\n> ???\r\nE KeyError: 'extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>'\r\n\r\npyarrow/types.pxi:3155: KeyError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nwith_none = True\r\n\r\n @pytest.mark.parametrize(\"with_none\", [False, True])\r\n def test_dataset_map(with_none):\r\n ds = datasets.Dataset.from_dict({\"path\": [\"path1\", \"path2\"]})\r\n \r\n def process_data(batch):\r\n batch = {\r\n \"image\": [\r\n np.array(\r\n [\r\n [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\r\n [[10, 20, 30], [40, 50, 60], [70, 80, 90]],\r\n [[100, 200, 300], [400, 500, 600], [700, 800, 900]],\r\n ]\r\n )\r\n for _ in batch[\"path\"]\r\n ]\r\n }\r\n if with_none:\r\n batch[\"image\"][0] = None\r\n return batch\r\n \r\n features = datasets.Features({\"image\": Array3D(dtype=\"int32\", shape=(3, 3, 3))})\r\n processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)\r\n assert processed_ds.shape == (2, 1)\r\n> with processed_ds.with_format(\"numpy\") as pds:\r\n\r\ntests/features/test_array_xd.py:459: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:2669: in with_format\r\n dataset = copy.deepcopy(self)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:270: in _reconstruct\r\n state = deepcopy(state, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:230: in _deepcopy_dict\r\n y[deepcopy(key, memo)] = deepcopy(value, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:153: in deepcopy\r\n y = copier(memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:188: in __deepcopy__\r\n return _deepcopy(self, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:86: in _deepcopy\r\n setattr(result, k, copy.deepcopy(v, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:205: in _deepcopy_list\r\n append(deepcopy(a, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\n> ???\r\nE ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n\r\npyarrow/types.pxi:3157: ValueError\r\n```\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_class_encode_column_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_dummy_dataset_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_csv_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_sql_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[True] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[False] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[mix] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::ArrayXDDynamicTest::test_map_dataset - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[False] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[True] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n===== 15 failed,\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007338 / 0.011353 (-0.004015) \| 0.004308 / 0.011008 (-0.006700) \| 0.088788 / 0.038508 (0.050280) \| 0.039369 / 0.023109 (0.016260) \| 0.334527 / 0.275898 (0.058629) \| 0.373748 / 0.323480 (0.050268) \| 0.005550 / 0.007986 (-0.002435) \| 0.003606 / 0.004328 (-0.000723) \| 0.072238 / 0.004250 (0.067988) \| 0.061271 / 0.037052 (0.024218) \| 0.336333 / 0.258489 (0.077844) \| 0.398256 / 0.293841 (0.104415) \| 0.041941 / 0.128546 (-0.086605) \| 0.013372 / 0.075646 (-0.062274) \| 0.336221 / 0.419271 (-0.083050) \| 0.083013 / 0.043533 (0.039480) \| 0.334743 / 0.255139 (0.079604) \| 0.362572 / 0.283200 (0.079373) \| 0.031161 / 0.141683 (-0.110521) \| 1.563441 / 1.452155 (0.111287) \| 1.704059 / 1.492716 (0.211343) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252978 / 0.018006 (0.234972) \| 0.506348 / 0.000490 (0.505859) \| 0.011679 / 0.000200 (0.011479) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026257 / 0.037411 (-0.011154) \| 0.085936 / 0.014526 (0.071410) \| 0.098542 / 0.176557 (-0.078015) \| 0.154507 / 0.737135 (-0.582628) \| 0.111493 / 0.296338 (-0.184845) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575941 / 0.215209 (0.360732) \| 5.590230 / 2.077655 (3.512576) \| 2.463330 / 1.504120 (0.959211) \| 2.125760 / 1.541195 (0.584565) \| 2.095933 / 1.468490 (0.627443) \| 0.844768 / 4.584777 (-3.740009) \| 4.768995 / 3.745712 (1.023282) \| 4.670484 / 5.269862 (-0.599377) \| 2.630386 / 4.565676 (-1.935290) \| 0.085996 / 0.424275 (-0.338279) \| 0.007900 / 0.007607 (0.000293) \| 0.685463 / 0.226044 (0.459419) \| 6.699310 / 2.268929 (4.430381) \| 3.132542 / 55.444624 (-52.312083) \| 2.527963 / 6.876477 (-4.348513) \| 2.381835 / 2.142072 (0.239763) \| 0.909668 / 4.805227 (-3.895559) \| 0.209979 / 6.500664 (-6.290685) \| 0.079222 / 0.075469 (0.003753) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.444895 / 1.841788 (-0.396892) \| 20.388140 / 8.074308 (12.313832) \| 19.354148 / 10.191392 (9.162756) \| 0.222433 / 0.680424 (-0.457991) \| 0.029710 / 0.534201 (-0.504491) \| 0.427153 / 0.579283 (-0.152130) \| 0.537500 / 0.434364 (0.103136) \| 0.506917 / 0.540337 (-0.033421) \| 0.726088 / 1.386936 (-0.660848) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007652 / 0.011353 (-0.003701) \| 0.004320 / 0.011008 (-0.006688) \| 0.072721 / 0.038508 (0.034212) \| 0.068204 / 0.023109 (0.045095) \| 0.392087 / 0.275898 (0.116189) \| 0.431638 / 0.323480 (0.108158) \| 0.005419 / 0.007986 (-0.002566) \| 0.004305 / 0.004328 (-0.000023) \| 0.069042 / 0.004250 (0.064791) \| 0.051555 / 0.037052 (0.014503) \| 0.412141 / 0.258489 (0.153651) \| 0.438802 / 0.293841 (0.144961) \| 0.043631 / 0.128546 (-0.084915) \| 0.014169 / 0.075646 (-0.061478) \| 0.079571 / 0.419271 (-0.339701) \| 0.056707 / 0.043533 (0.013174) \| 0.413698 / 0.255139 (0.158559) \| 0.414127 / 0.283200 (0.130928) \| 0.031380 / 0.141683 (-0.110303) \| 1.677157 / 1.452155 (0.225003) \| 1.755155 / 1.492716 (0.262439) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257236 / 0.018006 (0.239230) \| 0.521347 / 0.000490 (0.520858) \| 0.006282 / 0.000200 (0.006082) \| 0.000139 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028433 / 0.037411 (-0.008978) \| 0.087698 / 0.014526 (0.073172) \| 0.108840 / 0.176557 (-0.067716) \| 0.157432 / 0.737135 (-0.579704) \| 0.103144 / 0.296338 (-0.193195) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598745 / 0.215209 (0.383536) \| 5.981460 / 2.077655 (3.903805) \| 2.556931 / 1.504120 (1.052811) \| 2.179915 / 1.541195 (0.638720) \| 2.240841 / 1.468490 (0.772351) \| 0.811501 / 4.584777 (-3.773276) \| 4.718282 / 3.745712 (0.972570) \| 4.365738 / 5.269862 (-0.904124) \| 2.669798 / 4.565676 (-1.895878) \| 0.099135 / 0.424275 (-0.325140) \| 0.007369 / 0.007607 (-0.000238) \| 0.669491 / 0.226044 (0.443447) \| 6.700389 / 2.268929 (4.431461) \| 3.155328 / 55.444624 (-52.289296) \| 2.563375 / 6.876477 (-4.313102) \| 2.545191 / 2.142072 (0.403119) \| 0.961359 / 4.805227 (-3.843868) \| 0.189391 / 6.500664 (-6.311273) \| 0.061597 / 0.075469 (-0.013873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.564008 / 1.841788 (-0.277780) \| 21.401307 / 8.074308 (13.326999) \| 20.693441 / 10.191392 (10.502049) \| 0.229340 / 0.680424 (-0.451084) \| 0.033637 / 0.534201 (-0.500564) \| 0.429394 / 0.579283 (-0.149889) \| 0.557202 / 0.434364 (0.122838) \| 0.510284 / 0.540337 (-0.030054) \| 0.725661 / 1.386936 (-0.661276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45abe297c178b829afcee853f9958b0c5a6469aa \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004820 / 0.011353 (-0.006533) \| 0.003152 / 0.011008 (-0.007856) \| 0.061842 / 0.038508 (0.023334) \| 0.030127 / 0.023109 (0.007018) \| 0.257409 / 0.275898 (-0.018489) \| 0.269382 / 0.323480 (-0.054097) \| 0.004288 / 0.007986 (-0.003698) \| 0.002500 / 0.004328 (-0.001829) \| 0.048520 / 0.004250 (0.044270) \| 0.046815 / 0.037052 (0.009763) \| 0.245858 / 0.258489 (-0.012631) \| 0.289636 / 0.293841 (-0.004205) \| 0.023983 / 0.128546 (-0.104563) \| 0.007336 / 0.075646 (-0.068310) \| 0.202347 / 0.419271 (-0.216924) \| 0.057737 / 0.043533 (0.014204) \| 0.245922 / 0.255139 (-0.009217) \| 0.268788 / 0.283200 (-0.014412) \| 0.017819 / 0.141683 (-0.123864) \| 1.149889 / 1.452155 (-0.302265) \| 1.227192 / 1.492716 (-0.265524) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092234 / 0.018006 (0.074228) \| 0.310259 / 0.000490 (0.309769) \| 0.000223 / 0.000200 (0.000023) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019059 / 0.037411 (-0.018352) \| 0.064904 / 0.014526 (0.050378) \| 0.073531 / 0.176557 (-0.103026) \| 0.120879 / 0.737135 (-0.616257) \| 0.075410 / 0.296338 (-0.220929) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275364 / 0.215209 (0.060155) \| 2.724379 / 2.077655 (0.646725) \| 1.447617 / 1.504120 (-0.056503) \| 1.366794 / 1.541195 (-0.174401) \| 1.345849 / 1.468490 (-0.122641) \| 0.411205 / 4.584777 (-4.173572) \| 2.412712 / 3.745712 (-1.333000) \| 2.612469 / 5.269862 (-2.657393) \| 1.552113 / 4.565676 (-3.013564) \| 0.045783 / 0.424275 (-0.378492) \| 0.004782 / 0.007607 (-0.002825) \| 0.339218 / 0.226044 (0.113174) \| 3.359540 / 2.268929 (1.090612) \| 1.821369 / 55.444624 (-53.623256) \| 1.540742 / 6.876477 (-5.335734) \| 1.531845 / 2.142072 (-0.610227) \| 0.462009 / 4.805227 (-4.343218) \| 0.097794 / 6.500664 (-6.402870) \| 0.041222 / 0.075469 (-0.034247) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938319 / 1.841788 (-0.903469) \| 11.712003 / 8.074308 (3.637695) \| 10.325317 / 10.191392 (0.133925) \| 0.126812 / 0.680424 (-0.553612) \| 0.013734 / 0.534201 (-0.520467) \| 0.279509 / 0.579283 (-0.299774) \| 0.269265 / 0.434364 (-0.165099) \| 0.322033 / 0.540337 (-0.218304) \| 0.441610 / 1.386936 (-0.945326) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004882 / 0.011353 (-0.006471) \| 0.002984 / 0.011008 (-0.008024) \| 0.048318 / 0.038508 (0.009810) \| 0.054642 / 0.023109 (0.031533) \| 0.268599 / 0.275898 (-0.007299) \| 0.292916 / 0.323480 (-0.030564) \| 0.004108 / 0.007986 (-0.003878) \| 0.002500 / 0.004328 (-0.001829) \| 0.048452 / 0.004250 (0.044202) \| 0.038835 / 0.037052 (0.001782) \| 0.275410 / 0.258489 (0.016921) \| 0.307284 / 0.293841 (0.013443) \| 0.024720 / 0.128546 (-0.103826) \| 0.007274 / 0.075646 (-0.068372) \| 0.054419 / 0.419271 (-0.364853) \| 0.032815 / 0.043533 (-0.010718) \| 0.273660 / 0.255139 (0.018521) \| 0.289183 / 0.283200 (0.005984) \| 0.017746 / 0.141683 (-0.123937) \| 1.153876 / 1.452155 (-0.298278) \| 1.212778 / 1.492716 (-0.279938) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095286 / 0.018006 (0.077280) \| 0.305185 / 0.000490 (0.304696) \| 0.000230 / 0.000200 (0.000030) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021556 / 0.037411 (-0.015855) \| 0.071029 / 0.014526 (0.056503) \| 0.081914 / 0.176557 (-0.094643) \| 0.120553 / 0.737135 (-0.616582) \| 0.086696 / 0.296338 (-0.209642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289750 / 0.215209 (0.074541) \| 2.794247 / 2.077655 (0.716592) \| 1.577105 / 1.504120 (0.072985) \| 1.457706 / 1.541195 (-0.083489) \| 1.500481 / 1.468490 (0.031991) \| 0.403834 / 4.584777 (-4.180943) \| 2.466810 / 3.745712 (-1.278902) \| 2.701008 / 5.269862 (-2.568854) \| 1.634821 / 4.565676 (-2.930856) \| 0.046954 / 0.424275 (-0.377322) \| 0.004811 / 0.007607 (-0.002796) \| 0.347622 / 0.226044 (0.121578) \| 3.407125 / 2.268929 (1.138197) \| 1.987121 / 55.444624 (-53.457504) \| 1.689978 / 6.876477 (-5.186499) \| 1.731801 / 2.142072 (-0.410271) \| 0.478926 / 4.805227 (-4.326301) \| 0.100730 / 6.500664 (-6.399934) \| 0.043078 / 0.075469 (-0.032391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963575 / 1.841788 (-0.878212) \| 12.675331 / 8.074308 (4.601023) \| 11.167584 / 10.191392 (0.976192) \| 0.131199 / 0.680424 (-0.549225) \| 0.016030 / 0.534201 (-0.518171) \| 0.277783 / 0.579283 (-0.301500) \| 0.278693 / 0.434364 (-0.155671) \| 0.315141 / 0.540337 (-0.225196) \| 0.429104 / 1.386936 (-0.957832) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#825c1d25835b64fc3533a63d60bd237f4465f15e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004807 / 0.011353 (-0.006546) \| 0.002925 / 0.011008 (-0.008083) \| 0.062560 / 0.038508 (0.024052) \| 0.029926 / 0.023109 (0.006817) \| 0.264708 / 0.275898 (-0.011190) \| 0.273464 / 0.323480 (-0.050016) \| 0.003197 / 0.007986 (-0.004788) \| 0.002544 / 0.004328 (-0.001784) \| 0.048230 / 0.004250 (0.043980) \| 0.046552 / 0.037052 (0.009500) \| 0.249553 / 0.258489 (-0.008936) \| 0.282078 / 0.293841 (-0.011762) \| 0.023201 / 0.128546 (-0.105346) \| 0.007306 / 0.075646 (-0.068340) \| 0.241361 / 0.419271 (-0.177910) \| 0.058286 / 0.043533 (0.014753) \| 0.245854 / 0.255139 (-0.009285) \| 0.266053 / 0.283200 (-0.017146) \| 0.020294 / 0.141683 (-0.121388) \| 1.102215 / 1.452155 (-0.349939) \| 1.170733 / 1.492716 (-0.321984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094647 / 0.018006 (0.076641) \| 0.303819 / 0.000490 (0.303329) \| 0.000250 / 0.000200 (0.000050) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019036 / 0.037411 (-0.018375) \| 0.064729 / 0.014526 (0.050203) \| 0.074143 / 0.176557 (-0.102414) \| 0.120082 / 0.737135 (-0.617054) \| 0.076835 / 0.296338 (-0.219503) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283786 / 0.215209 (0.068577) \| 2.751446 / 2.077655 (0.673791) \| 1.473789 / 1.504120 (-0.030331) \| 1.336968 / 1.541195 (-0.204226) \| 1.384148 / 1.468490 (-0.084342) \| 0.397452 / 4.584777 (-4.187325) \| 2.388042 / 3.745712 (-1.357670) \| 2.661291 / 5.269862 (-2.608571) \| 1.595454 / 4.565676 (-2.970223) \| 0.045919 / 0.424275 (-0.378356) \| 0.004879 / 0.007607 (-0.002728) \| 0.337862 / 0.226044 (0.111818) \| 3.355665 / 2.268929 (1.086737) \| 1.875261 / 55.444624 (-53.569363) \| 1.540874 / 6.876477 (-5.335603) \| 1.653632 / 2.142072 (-0.488440) \| 0.473090 / 4.805227 (-4.332138) \| 0.100151 / 6.500664 (-6.400513) \| 0.042357 / 0.075469 (-0.033112) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959550 / 1.841788 (-0.882238) \| 12.307145 / 8.074308 (4.232837) \| 10.719321 / 10.191392 (0.527929) \| 0.128376 / 0.680424 (-0.552048) \| 0.014406 / 0.534201 (-0.519795) \| 0.295208 / 0.579283 (-0.284075) \| 0.268891 / 0.434364 (-0.165473) \| 0.305446 / 0.540337 (-0.234892) \| 0.429591 / 1.386936 (-0.957345) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005189 / 0.011353 (-0.006164) \| 0.003082 / 0.011008 (-0.007926) \| 0.048956 / 0.038508 (0.010448) \| 0.063403 / 0.023109 (0.040294) \| 0.272858 / 0.275898 (-0.003040) \| 0.295207 / 0.323480 (-0.028273) \| 0.004253 / 0.007986 (-0.003733) \| 0.002552 / 0.004328 (-0.001776) \| 0.048042 / 0.004250 (0.043792) \| 0.040429 / 0.037052 (0.003377) \| 0.269614 / 0.258489 (0.011125) \| 0.307205 / 0.293841 (0.013364) \| 0.027912 / 0.128546 (-0.100634) \| 0.007621 / 0.075646 (-0.068026) \| 0.054020 / 0.419271 (-0.365251) \| 0.036958 / 0.043533 (-0.006574) \| 0.272457 / 0.255139 (0.017318) \| 0.287966 / 0.283200 (0.004766) \| 0.019542 / 0.141683 (-0.122141) \| 1.116742 / 1.452155 (-0.335413) \| 1.194739 / 1.492716 (-0.297977) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093532 / 0.018006 (0.075526) \| 0.303262 / 0.000490 (0.302773) \| 0.000217 / 0.000200 (0.000017) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021984 / 0.037411 (-0.015428) \| 0.075024 / 0.014526 (0.060498) \| 0.080959 / 0.176557 (-0.095598) \| 0.121780 / 0.737135 (-0.615356) \| 0.082817 / 0.296338 (-0.213522) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292766 / 0.215209 (0.077557) \| 2.857457 / 2.077655 (0.779802) \| 1.621860 / 1.504120 (0.117740) \| 1.473783 / 1.541195 (-0.067412) \| 1.535211 / 1.468490 (0.066721) \| 0.402212 / 4.584777 (-4.182565) \| 2.467143 / 3.745712 (-1.278569) \| 2.618162 / 5.269862 (-2.651700) \| 1.568682 / 4.565676 (-2.996994) \| 0.047123 / 0.424275 (-0.377152) \| 0.004780 / 0.007607 (-0.002827) \| 0.346959 / 0.226044 (0.120914) \| 3.395196 / 2.268929 (1.126268) \| 1.957835 / 55.444624 (-53.486789) \| 1.674287 / 6.876477 (-5.202190) \| 1.715879 / 2.142072 (-0.426193) \| 0.479481 / 4.805227 (-4.325746) \| 0.100043 / 6.500664 (-6.400621) \| 0.041289 / 0.075469 (-0.034180) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965418 / 1.841788 (-0.876370) \| 12.703830 / 8.074308 (4.629522) \| 11.301401 / 10.191392 (1.110009) \| 0.131429 / 0.680424 (-0.548995) \| 0.016597 / 0.534201 (-0.517604) \| 0.273290 / 0.579283 (-0.305993) \| 0.285400 / 0.434364 (-0.148964) \| 0.307327 / 0.540337 (-0.233011) \| 0.434186 / 1.386936 (-0.952750) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c096bd288d07ed86f340ae090e5d4d9c5351f76f \"CML watermark\")\n" ]	2023-11-13T09:15:39Z	2023-11-14T10:29:48Z	2023-11-14T10:23:29Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6404.diff", "html_url": "https://github.com/huggingface/datasets/pull/6404", "merged_at": "2023-11-14T10:23:29Z", "patch_url": "https://github.com/huggingface/datasets/pull/6404.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6404" }	Support `pyarrow` 14.0.1 and fix vulnerability [CVE-2023-47248](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). Fix #6396.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6404/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6403	6,403	Cannot import datasets on google colab (python 3.10.12)	{ "avatar_url": "https://avatars.githubusercontent.com/u/15389235?v=4", "events_url": "https://api.github.com/users/nabilaannisa/events{/privacy}", "followers_url": "https://api.github.com/users/nabilaannisa/followers", "following_url": "https://api.github.com/users/nabilaannisa/following{/other_user}", "gists_url": "https://api.github.com/users/nabilaannisa/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/nabilaannisa", "id": 15389235, "login": "nabilaannisa", "node_id": "MDQ6VXNlcjE1Mzg5MjM1", "organizations_url": "https://api.github.com/users/nabilaannisa/orgs", "received_events_url": "https://api.github.com/users/nabilaannisa/received_events", "repos_url": "https://api.github.com/users/nabilaannisa/repos", "site_admin": false, "starred_url": "https://api.github.com/users/nabilaannisa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/nabilaannisa/subscriptions", "type": "User", "url": "https://api.github.com/users/nabilaannisa", "user_view_type": "public" }	[]	closed	false	[ "You are most likely using an outdated version of `datasets` in the notebook, which can be verified with the `!datasets-cli env` command. You can run `!pip install -U datasets` to update the installation.", "okay, it works! thank you so much! 😄 " ]	2023-11-13T08:14:43Z	2023-11-16T05:04:22Z	2023-11-16T05:04:21Z	NONE	null	null	### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12) ![image](https://github.com/huggingface/datasets/assets/15389235/6f7758a2-681d-4436-87d0-5e557838e368) I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free	{ "avatar_url": "https://avatars.githubusercontent.com/u/15389235?v=4", "events_url": "https://api.github.com/users/nabilaannisa/events{/privacy}", "followers_url": "https://api.github.com/users/nabilaannisa/followers", "following_url": "https://api.github.com/users/nabilaannisa/following{/other_user}", "gists_url": "https://api.github.com/users/nabilaannisa/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/nabilaannisa", "id": 15389235, "login": "nabilaannisa", "node_id": "MDQ6VXNlcjE1Mzg5MjM1", "organizations_url": "https://api.github.com/users/nabilaannisa/orgs", "received_events_url": "https://api.github.com/users/nabilaannisa/received_events", "repos_url": "https://api.github.com/users/nabilaannisa/repos", "site_admin": false, "starred_url": "https://api.github.com/users/nabilaannisa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/nabilaannisa/subscriptions", "type": "User", "url": "https://api.github.com/users/nabilaannisa", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6403/reactions" }	completed	{ "completed": 0, "percent_completed": 0, "total": 0 }	{ "blocked_by": 0, "blocking": 0, "total_blocked_by": 0, "total_blocking": 0 }	false
https://github.com/huggingface/datasets/pull/6402	6,402	Update torch_formatter.py	{ "avatar_url": "https://avatars.githubusercontent.com/u/32204417?v=4", "events_url": "https://api.github.com/users/varunneal/events{/privacy}", "followers_url": "https://api.github.com/users/varunneal/followers", "following_url": "https://api.github.com/users/varunneal/following{/other_user}", "gists_url": "https://api.github.com/users/varunneal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/varunneal", "id": 32204417, "login": "varunneal", "node_id": "MDQ6VXNlcjMyMjA0NDE3", "organizations_url": "https://api.github.com/users/varunneal/orgs", "received_events_url": "https://api.github.com/users/varunneal/received_events", "repos_url": "https://api.github.com/users/varunneal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/varunneal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/varunneal/subscriptions", "type": "User", "url": "https://api.github.com/users/varunneal", "user_view_type": "public" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6402). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005391 / 0.011353 (-0.005962) \| 0.003908 / 0.011008 (-0.007100) \| 0.064342 / 0.038508 (0.025834) \| 0.031385 / 0.023109 (0.008275) \| 0.251869 / 0.275898 (-0.024030) \| 0.294875 / 0.323480 (-0.028605) \| 0.003053 / 0.007986 (-0.004933) \| 0.002881 / 0.004328 (-0.001448) \| 0.050072 / 0.004250 (0.045822) \| 0.044463 / 0.037052 (0.007411) \| 0.264646 / 0.258489 (0.006157) \| 0.296024 / 0.293841 (0.002183) \| 0.027832 / 0.128546 (-0.100714) \| 0.010937 / 0.075646 (-0.064710) \| 0.207799 / 0.419271 (-0.211472) \| 0.036423 / 0.043533 (-0.007110) \| 0.251022 / 0.255139 (-0.004117) \| 0.271366 / 0.283200 (-0.011833) \| 0.019780 / 0.141683 (-0.121903) \| 1.149634 / 1.452155 (-0.302521) \| 1.196476 / 1.492716 (-0.296240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094857 / 0.018006 (0.076850) \| 0.312159 / 0.000490 (0.311669) \| 0.000215 / 0.000200 (0.000015) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018858 / 0.037411 (-0.018553) \| 0.062022 / 0.014526 (0.047496) \| 0.078302 / 0.176557 (-0.098255) \| 0.122199 / 0.737135 (-0.614936) \| 0.076044 / 0.296338 (-0.220294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286414 / 0.215209 (0.071205) \| 2.785446 / 2.077655 (0.707791) \| 1.438248 / 1.504120 (-0.065872) \| 1.307558 / 1.541195 (-0.233636) \| 1.337172 / 1.468490 (-0.131318) \| 0.582228 / 4.584777 (-4.002549) \| 2.457207 / 3.745712 (-1.288505) \| 2.906692 / 5.269862 (-2.363169) \| 1.833020 / 4.565676 (-2.732656) \| 0.063549 / 0.424275 (-0.360726) \| 0.005080 / 0.007607 (-0.002527) \| 0.333178 / 0.226044 (0.107133) \| 3.332463 / 2.268929 (1.063534) \| 1.797209 / 55.444624 (-53.647415) \| 1.514446 / 6.876477 (-5.362031) \| 1.601252 / 2.142072 (-0.540820) \| 0.664933 / 4.805227 (-4.140294) \| 0.120140 / 6.500664 (-6.380524) \| 0.042769 / 0.075469 (-0.032700) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983738 / 1.841788 (-0.858050) \| 11.996685 / 8.074308 (3.922376) \| 9.594757 / 10.191392 (-0.596635) \| 0.146680 / 0.680424 (-0.533744) \| 0.014455 / 0.534201 (-0.519746) \| 0.292546 / 0.579283 (-0.286737) \| 0.270381 / 0.434364 (-0.163983) \| 0.326759 / 0.540337 (-0.213579) \| 0.423387 / 1.386936 (-0.963549) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005659 / 0.011353 (-0.005694) \| 0.004841 / 0.011008 (-0.006167) \| 0.049598 / 0.038508 (0.011090) \| 0.031796 / 0.023109 (0.008687) \| 0.265967 / 0.275898 (-0.009931) \| 0.287648 / 0.323480 (-0.035832) \| 0.004264 / 0.007986 (-0.003721) \| 0.002759 / 0.004328 (-0.001569) \| 0.049249 / 0.004250 (0.044999) \| 0.049526 / 0.037052 (0.012474) \| 0.357765 / 0.258489 (0.099276) \| 0.307563 / 0.293841 (0.013722) \| 0.030329 / 0.128546 (-0.098217) \| 0.010536 / 0.075646 (-0.065111) \| 0.057547 / 0.419271 (-0.361724) \| 0.059874 / 0.043533 (0.016341) \| 0.264946 / 0.255139 (0.009807) \| 0.283666 / 0.283200 (0.000466) \| 0.021005 / 0.141683 (-0.120677) \| 1.170959 / 1.452155 (-0.281195) \| 1.206232 / 1.492716 (-0.286484) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097567 / 0.018006 (0.079561) \| 0.308585 / 0.000490 (0.308095) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023267 / 0.037411 (-0.014144) \| 0.075896 / 0.014526 (0.061370) \| 0.087341 / 0.176557 (-0.089216) \| 0.130270 / 0.737135 (-0.606866) \| 0.091086 / 0.296338 (-0.205252) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298728 / 0.215209 (0.083519) \| 2.876498 / 2.077655 (0.798843) \| 1.591584 / 1.504120 (0.087464) \| 1.461251 / 1.541195 (-0.079944) \| 1.483276 / 1.468490 (0.014786) \| 0.572344 / 4.584777 (-4.012433) \| 2.474050 / 3.745712 (-1.271662) \| 2.707051 / 5.269862 (-2.562811) \| 1.759210 / 4.565676 (-2.806466) \| 0.063420 / 0.424275 (-0.360855) \| 0.005014 / 0.007607 (-0.002593) \| 0.344490 / 0.226044 (0.118446) \| 3.411065 / 2.268929 (1.142137) \| 1.937232 / 55.444624 (-53.507392) \| 1.665826 / 6.876477 (-5.210650) \| 1.824377 / 2.142072 (-0.317696) \| 0.631630 / 4.805227 (-4.173597) \| 0.115791 / 6.500664 (-6.384873) \| 0.040846 / 0.075469 (-0.034623) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.015248 / 1.841788 (-0.826540) \| 12.738696 / 8.074308 (4.664388) \| 10.324303 / 10.191392 (0.132911) \| 0.153597 / 0.680424 (-0.526827) \| 0.015396 / 0.534201 (-0.518805) \| 0.287160 / 0.579283 (-0.292123) \| 0.279886 / 0.434364 (-0.154478) \| 0.324128 / 0.540337 (-0.216210) \| 0.456089 / 1.386936 (-0.930847) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a02997de6e990eb78dccd905f05e12404a919156 \"CML watermark\")\n" ]	2023-11-11T19:40:41Z	2024-03-15T11:31:53Z	2024-03-15T11:25:37Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6402.diff", "html_url": "https://github.com/huggingface/datasets/pull/6402", "merged_at": "2024-03-15T11:25:36Z", "patch_url": "https://github.com/huggingface/datasets/pull/6402.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6402" }	Ensure PyTorch images are converted to (C, H, W) instead of (H, W, C). See #6394 for motivation.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6402/reactions" }	null	null	null	true

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