Split (1)

train · 7.77k rows

html_url stringlengths 46 51	number int64 1 7.85k	title stringlengths 1 290	user dict	labels listlengths 0 4	state stringclasses 2 values	locked bool 1 class	comments listlengths 0 30	created_at timestamp[ns, tz=UTC]date 2020-04-14 10:18:02 2025-11-05 18:11:12	updated_at timestamp[ns, tz=UTC]date 2020-04-27 16:04:17 2025-11-06 09:44:34	closed_at timestamp[ns, tz=UTC]date 2020-04-14 12:01:40 2025-11-05 16:02:32 ⌀	author_association stringclasses 4 values	draft bool 2 classes	pull_request dict	body stringlengths 0 228k ⌀	closed_by dict	reactions dict	state_reason stringclasses 4 values	sub_issues_summary dict	issue_dependencies_summary dict	is_pull_request bool 2 classes
https://github.com/huggingface/datasets/issues/6299	6,299	Support for newer versions of JAX	{ "avatar_url": "https://avatars.githubusercontent.com/u/25456859?v=4", "events_url": "https://api.github.com/users/ddrous/events{/privacy}", "followers_url": "https://api.github.com/users/ddrous/followers", "following_url": "https://api.github.com/users/ddrous/following{/other_user}", "gists_url": "https://api.github.com/users/ddrous/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ddrous", "id": 25456859, "login": "ddrous", "node_id": "MDQ6VXNlcjI1NDU2ODU5", "organizations_url": "https://api.github.com/users/ddrous/orgs", "received_events_url": "https://api.github.com/users/ddrous/received_events", "repos_url": "https://api.github.com/users/ddrous/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ddrous/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ddrous/subscriptions", "type": "User", "url": "https://api.github.com/users/ddrous", "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	[]	2023-10-12T10:03:46Z	2023-10-12T16:28:59Z	2023-10-12T16:28:59Z	NONE	null	null	### Feature request Hi, I like your idea of adapting the datasets library to be usable with JAX. Thank you for that. However, in your [setup.py](https://github.com/huggingface/datasets/blob/main/setup.py), you enforce old versions of JAX <= 0.3... It is very cumbersome ! What is the rationale for such a limitation ? Can you remove it please ? Thanks, ### Motivation This library is unusable with new versions of JAX ? ### Your contribution Yes.	{ "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/6299/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/6298	6,298	Doc readme improvements	{ "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) \| 0.006761 / 0.011353 (-0.004592) \| 0.004307 / 0.011008 (-0.006701) \| 0.084682 / 0.038508 (0.046174) \| 0.083994 / 0.023109 (0.060885) \| 0.316612 / 0.275898 (0.040714) \| 0.346157 / 0.323480 (0.022678) \| 0.004490 / 0.007986 (-0.003495) \| 0.003699 / 0.004328 (-0.000629) \| 0.066144 / 0.004250 (0.061894) \| 0.057958 / 0.037052 (0.020906) \| 0.319018 / 0.258489 (0.060529) \| 0.367597 / 0.293841 (0.073756) \| 0.031146 / 0.128546 (-0.097401) \| 0.008814 / 0.075646 (-0.066832) \| 0.290971 / 0.419271 (-0.128301) \| 0.052769 / 0.043533 (0.009236) \| 0.313125 / 0.255139 (0.057986) \| 0.330473 / 0.283200 (0.047273) \| 0.025922 / 0.141683 (-0.115760) \| 1.494989 / 1.452155 (0.042834) \| 1.556140 / 1.492716 (0.063423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310580 / 0.018006 (0.292574) \| 0.563600 / 0.000490 (0.563110) \| 0.012344 / 0.000200 (0.012144) \| 0.000382 / 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) \| 0.031468 / 0.037411 (-0.005943) \| 0.084856 / 0.014526 (0.070331) \| 0.101371 / 0.176557 (-0.075186) \| 0.158735 / 0.737135 (-0.578400) \| 0.102451 / 0.296338 (-0.193888) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402288 / 0.215209 (0.187079) \| 4.001351 / 2.077655 (1.923696) \| 2.022710 / 1.504120 (0.518590) \| 1.850236 / 1.541195 (0.309041) \| 1.946779 / 1.468490 (0.478289) \| 0.485828 / 4.584777 (-4.098949) \| 3.584925 / 3.745712 (-0.160787) \| 3.400815 / 5.269862 (-1.869046) \| 2.123187 / 4.565676 (-2.442490) \| 0.057373 / 0.424275 (-0.366902) \| 0.007383 / 0.007607 (-0.000224) \| 0.479773 / 0.226044 (0.253729) \| 4.805342 / 2.268929 (2.536414) \| 2.530151 / 55.444624 (-52.914473) \| 2.190136 / 6.876477 (-4.686341) \| 2.463666 / 2.142072 (0.321593) \| 0.583512 / 4.805227 (-4.221715) \| 0.134205 / 6.500664 (-6.366459) \| 0.062021 / 0.075469 (-0.013448) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239532 / 1.841788 (-0.602255) \| 20.252941 / 8.074308 (12.178633) \| 14.265697 / 10.191392 (4.074305) \| 0.158745 / 0.680424 (-0.521679) \| 0.018605 / 0.534201 (-0.515596) \| 0.394246 / 0.579283 (-0.185037) \| 0.415260 / 0.434364 (-0.019104) \| 0.462636 / 0.540337 (-0.077701) \| 0.645318 / 1.386936 (-0.741618) \|\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) \| 0.007063 / 0.011353 (-0.004290) \| 0.004388 / 0.011008 (-0.006621) \| 0.064997 / 0.038508 (0.026489) \| 0.085135 / 0.023109 (0.062026) \| 0.424349 / 0.275898 (0.148451) \| 0.456033 / 0.323480 (0.132553) \| 0.005745 / 0.007986 (-0.002241) \| 0.003705 / 0.004328 (-0.000624) \| 0.065835 / 0.004250 (0.061585) \| 0.058366 / 0.037052 (0.021314) \| 0.421654 / 0.258489 (0.163165) \| 0.460334 / 0.293841 (0.166493) \| 0.032828 / 0.128546 (-0.095718) \| 0.008974 / 0.075646 (-0.066673) \| 0.072524 / 0.419271 (-0.346747) \| 0.048558 / 0.043533 (0.005025) \| 0.413546 / 0.255139 (0.158407) \| 0.435765 / 0.283200 (0.152565) \| 0.023754 / 0.141683 (-0.117929) \| 1.476884 / 1.452155 (0.024730) \| 1.560011 / 1.492716 (0.067294) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.318279 / 0.018006 (0.300272) \| 0.544990 / 0.000490 (0.544501) \| 0.007118 / 0.000200 (0.006918) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033352 / 0.037411 (-0.004059) \| 0.092921 / 0.014526 (0.078395) \| 0.109028 / 0.176557 (-0.067528) \| 0.161433 / 0.737135 (-0.575703) \| 0.108445 / 0.296338 (-0.187893) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438925 / 0.215209 (0.223716) \| 4.400714 / 2.077655 (2.323059) \| 2.403727 / 1.504120 (0.899607) \| 2.236472 / 1.541195 (0.695277) \| 2.319219 / 1.468490 (0.850729) \| 0.490159 / 4.584777 (-4.094618) \| 3.647474 / 3.745712 (-0.098238) \| 3.433144 / 5.269862 (-1.836718) \| 2.145367 / 4.565676 (-2.420310) \| 0.057994 / 0.424275 (-0.366281) \| 0.007452 / 0.007607 (-0.000155) \| 0.513808 / 0.226044 (0.287763) \| 5.130792 / 2.268929 (2.861863) \| 2.861691 / 55.444624 (-52.582934) \| 2.473292 / 6.876477 (-4.403185) \| 2.756445 / 2.142072 (0.614372) \| 0.586783 / 4.805227 (-4.218444) \| 0.134170 / 6.500664 (-6.366494) \| 0.061149 / 0.075469 (-0.014320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350144 / 1.841788 (-0.491644) \| 21.003528 / 8.074308 (12.929220) \| 15.174314 / 10.191392 (4.982922) \| 0.186535 / 0.680424 (-0.493888) \| 0.020821 / 0.534201 (-0.513380) \| 0.399210 / 0.579283 (-0.180073) \| 0.431942 / 0.434364 (-0.002422) \| 0.475395 / 0.540337 (-0.064942) \| 0.677457 / 1.386936 (-0.709479) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6aa5fc278324a253eab43ad1bc048e822e4ae5c7 \"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) \| 0.007062 / 0.011353 (-0.004291) \| 0.004299 / 0.011008 (-0.006710) \| 0.086019 / 0.038508 (0.047511) \| 0.085166 / 0.023109 (0.062057) \| 0.355804 / 0.275898 (0.079906) \| 0.381056 / 0.323480 (0.057577) \| 0.005500 / 0.007986 (-0.002486) \| 0.003496 / 0.004328 (-0.000833) \| 0.064866 / 0.004250 (0.060615) \| 0.057399 / 0.037052 (0.020346) \| 0.357914 / 0.258489 (0.099425) \| 0.395387 / 0.293841 (0.101546) \| 0.031763 / 0.128546 (-0.096784) \| 0.008665 / 0.075646 (-0.066981) \| 0.290097 / 0.419271 (-0.129175) \| 0.053297 / 0.043533 (0.009765) \| 0.355659 / 0.255139 (0.100520) \| 0.378232 / 0.283200 (0.095032) \| 0.026015 / 0.141683 (-0.115668) \| 1.437121 / 1.452155 (-0.015034) \| 1.538798 / 1.492716 (0.046082) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243518 / 0.018006 (0.225511) \| 0.461361 / 0.000490 (0.460871) \| 0.009529 / 0.000200 (0.009329) \| 0.000473 / 0.000054 (0.000419) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030379 / 0.037411 (-0.007032) \| 0.089851 / 0.014526 (0.075325) \| 0.098278 / 0.176557 (-0.078278) \| 0.157077 / 0.737135 (-0.580058) \| 0.098997 / 0.296338 (-0.197341) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382415 / 0.215209 (0.167206) \| 3.801964 / 2.077655 (1.724309) \| 1.887680 / 1.504120 (0.383560) \| 1.775903 / 1.541195 (0.234709) \| 1.851338 / 1.468490 (0.382848) \| 0.483616 / 4.584777 (-4.101161) \| 3.612977 / 3.745712 (-0.132736) \| 3.397700 / 5.269862 (-1.872162) \| 2.114572 / 4.565676 (-2.451105) \| 0.057250 / 0.424275 (-0.367025) \| 0.007362 / 0.007607 (-0.000245) \| 0.456873 / 0.226044 (0.230829) \| 4.567319 / 2.268929 (2.298391) \| 2.399476 / 55.444624 (-53.045148) \| 2.054542 / 6.876477 (-4.821935) \| 2.343432 / 2.142072 (0.201359) \| 0.582319 / 4.805227 (-4.222908) \| 0.134045 / 6.500664 (-6.366619) \| 0.062726 / 0.075469 (-0.012743) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283390 / 1.841788 (-0.558398) \| 20.358511 / 8.074308 (12.284202) \| 14.933989 / 10.191392 (4.742597) \| 0.164960 / 0.680424 (-0.515464) \| 0.018625 / 0.534201 (-0.515576) \| 0.394087 / 0.579283 (-0.185196) \| 0.416761 / 0.434364 (-0.017603) \| 0.466669 / 0.540337 (-0.073669) \| 0.643161 / 1.386936 (-0.743775) \|\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) \| 0.007141 / 0.011353 (-0.004212) \| 0.004185 / 0.011008 (-0.006824) \| 0.066097 / 0.038508 (0.027588) \| 0.088436 / 0.023109 (0.065327) \| 0.401189 / 0.275898 (0.125291) \| 0.440402 / 0.323480 (0.116922) \| 0.005729 / 0.007986 (-0.002257) \| 0.003527 / 0.004328 (-0.000801) \| 0.065278 / 0.004250 (0.061027) \| 0.060866 / 0.037052 (0.023813) \| 0.407035 / 0.258489 (0.148546) \| 0.443923 / 0.293841 (0.150083) \| 0.032922 / 0.128546 (-0.095625) \| 0.008739 / 0.075646 (-0.066907) \| 0.071800 / 0.419271 (-0.347472) \| 0.048994 / 0.043533 (0.005461) \| 0.403736 / 0.255139 (0.148597) \| 0.419566 / 0.283200 (0.136366) \| 0.025369 / 0.141683 (-0.116314) \| 1.474980 / 1.452155 (0.022825) \| 1.553500 / 1.492716 (0.060784) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225224 / 0.018006 (0.207218) \| 0.462891 / 0.000490 (0.462401) \| 0.006958 / 0.000200 (0.006758) \| 0.000163 / 0.000054 (0.000108) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034431 / 0.037411 (-0.002980) \| 0.100021 / 0.014526 (0.085495) \| 0.108339 / 0.176557 (-0.068217) \| 0.162762 / 0.737135 (-0.574374) \| 0.108951 / 0.296338 (-0.187388) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435966 / 0.215209 (0.220757) \| 4.351744 / 2.077655 (2.274089) \| 2.372307 / 1.504120 (0.868187) \| 2.192146 / 1.541195 (0.650951) \| 2.326839 / 1.468490 (0.858349) \| 0.488292 / 4.584777 (-4.096485) \| 3.745227 / 3.745712 (-0.000485) \| 3.456306 / 5.269862 (-1.813556) \| 2.159771 / 4.565676 (-2.405906) \| 0.057953 / 0.424275 (-0.366322) \| 0.007469 / 0.007607 (-0.000138) \| 0.515116 / 0.226044 (0.289071) \| 5.162871 / 2.268929 (2.893942) \| 2.850336 / 55.444624 (-52.594288) \| 2.514700 / 6.876477 (-4.361777) \| 2.748843 / 2.142072 (0.606770) \| 0.587687 / 4.805227 (-4.217540) \| 0.134333 / 6.500664 (-6.366331) \| 0.062097 / 0.075469 (-0.013372) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.377082 / 1.841788 (-0.464705) \| 21.103127 / 8.074308 (13.028819) \| 15.325275 / 10.191392 (5.133883) \| 0.166225 / 0.680424 (-0.514199) \| 0.020472 / 0.534201 (-0.513729) \| 0.395866 / 0.579283 (-0.183417) \| 0.444964 / 0.434364 (0.010600) \| 0.475367 / 0.540337 (-0.064970) \| 0.693325 / 1.386936 (-0.693611) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79b5bbbd52ffd90dd958c05b333d7c90a03756cc \"CML watermark\")\n" ]	2023-10-11T21:51:12Z	2023-10-12T12:47:15Z	2023-10-12T12:38:19Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6298.diff", "html_url": "https://github.com/huggingface/datasets/pull/6298", "merged_at": "2023-10-12T12:38:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/6298.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6298" }	Changes in the doc READMe: * adds two new sections (to be aligned with `transformers` and `hfh`): "Previewing the documentation" and "Writing documentation examples" * replaces the mentions of `transformers` with `datasets` * fixes some dead links	{ "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/6298/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6297	6,297	Fix ArrayXD cast	{ "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) \| 0.006920 / 0.011353 (-0.004433) \| 0.004306 / 0.011008 (-0.006703) \| 0.085961 / 0.038508 (0.047453) \| 0.087008 / 0.023109 (0.063899) \| 0.308953 / 0.275898 (0.033055) \| 0.349919 / 0.323480 (0.026440) \| 0.005705 / 0.007986 (-0.002281) \| 0.003565 / 0.004328 (-0.000763) \| 0.066272 / 0.004250 (0.062022) \| 0.056438 / 0.037052 (0.019385) \| 0.312927 / 0.258489 (0.054437) \| 0.363081 / 0.293841 (0.069240) \| 0.031947 / 0.128546 (-0.096600) \| 0.008801 / 0.075646 (-0.066845) \| 0.288657 / 0.419271 (-0.130615) \| 0.053746 / 0.043533 (0.010213) \| 0.305815 / 0.255139 (0.050676) \| 0.327174 / 0.283200 (0.043975) \| 0.024863 / 0.141683 (-0.116820) \| 1.489718 / 1.452155 (0.037563) \| 1.566726 / 1.492716 (0.074009) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289273 / 0.018006 (0.271266) \| 0.555519 / 0.000490 (0.555029) \| 0.006522 / 0.000200 (0.006322) \| 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) \| 0.031968 / 0.037411 (-0.005443) \| 0.085113 / 0.014526 (0.070587) \| 0.103931 / 0.176557 (-0.072625) \| 0.158471 / 0.737135 (-0.578665) \| 0.102633 / 0.296338 (-0.193705) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399592 / 0.215209 (0.184383) \| 4.004453 / 2.077655 (1.926798) \| 2.047224 / 1.504120 (0.543104) \| 1.896203 / 1.541195 (0.355008) \| 1.974056 / 1.468490 (0.505566) \| 0.485964 / 4.584777 (-4.098813) \| 3.650648 / 3.745712 (-0.095064) \| 3.475953 / 5.269862 (-1.793908) \| 2.168105 / 4.565676 (-2.397571) \| 0.058167 / 0.424275 (-0.366108) \| 0.007517 / 0.007607 (-0.000090) \| 0.475386 / 0.226044 (0.249342) \| 4.758300 / 2.268929 (2.489372) \| 2.527540 / 55.444624 (-52.917085) \| 2.180544 / 6.876477 (-4.695933) \| 2.460148 / 2.142072 (0.318076) \| 0.589944 / 4.805227 (-4.215284) \| 0.136474 / 6.500664 (-6.364190) \| 0.061462 / 0.075469 (-0.014007) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245816 / 1.841788 (-0.595972) \| 20.376958 / 8.074308 (12.302650) \| 14.764579 / 10.191392 (4.573187) \| 0.152436 / 0.680424 (-0.527988) \| 0.018580 / 0.534201 (-0.515621) \| 0.394680 / 0.579283 (-0.184603) \| 0.424162 / 0.434364 (-0.010202) \| 0.465604 / 0.540337 (-0.074733) \| 0.658531 / 1.386936 (-0.728405) \|\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) \| 0.007105 / 0.011353 (-0.004248) \| 0.004441 / 0.011008 (-0.006567) \| 0.068792 / 0.038508 (0.030284) \| 0.080371 / 0.023109 (0.057262) \| 0.430263 / 0.275898 (0.154365) \| 0.451743 / 0.323480 (0.128263) \| 0.005987 / 0.007986 (-0.001999) \| 0.003639 / 0.004328 (-0.000690) \| 0.065462 / 0.004250 (0.061212) \| 0.059852 / 0.037052 (0.022800) \| 0.438390 / 0.258489 (0.179901) \| 0.458679 / 0.293841 (0.164838) \| 0.033044 / 0.128546 (-0.095502) \| 0.008845 / 0.075646 (-0.066802) \| 0.071772 / 0.419271 (-0.347500) \| 0.048840 / 0.043533 (0.005307) \| 0.415707 / 0.255139 (0.160568) \| 0.431216 / 0.283200 (0.148017) \| 0.024422 / 0.141683 (-0.117260) \| 1.502249 / 1.452155 (0.050094) \| 1.566767 / 1.492716 (0.074050) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.311352 / 0.018006 (0.293346) \| 0.550395 / 0.000490 (0.549906) \| 0.005190 / 0.000200 (0.004990) \| 0.000116 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034264 / 0.037411 (-0.003147) \| 0.098712 / 0.014526 (0.084186) \| 0.110906 / 0.176557 (-0.065651) \| 0.161670 / 0.737135 (-0.575465) \| 0.111023 / 0.296338 (-0.185316) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435296 / 0.215209 (0.220087) \| 4.331231 / 2.077655 (2.253576) \| 2.305009 / 1.504120 (0.800889) \| 2.154492 / 1.541195 (0.613297) \| 2.344017 / 1.468490 (0.875527) \| 0.496924 / 4.584777 (-4.087853) \| 3.750782 / 3.745712 (0.005070) \| 3.380193 / 5.269862 (-1.889669) \| 2.161239 / 4.565676 (-2.404438) \| 0.058456 / 0.424275 (-0.365819) \| 0.007395 / 0.007607 (-0.000212) \| 0.507824 / 0.226044 (0.281780) \| 5.081564 / 2.268929 (2.812635) \| 2.824080 / 55.444624 (-52.620544) \| 2.458835 / 6.876477 (-4.417642) \| 2.747897 / 2.142072 (0.605824) \| 0.600727 / 4.805227 (-4.204500) \| 0.135085 / 6.500664 (-6.365579) \| 0.060506 / 0.075469 (-0.014963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.376873 / 1.841788 (-0.464915) \| 21.211922 / 8.074308 (13.137614) \| 15.022845 / 10.191392 (4.831453) \| 0.195388 / 0.680424 (-0.485036) \| 0.020268 / 0.534201 (-0.513933) \| 0.398971 / 0.579283 (-0.180312) \| 0.427588 / 0.434364 (-0.006776) \| 0.478044 / 0.540337 (-0.062293) \| 0.687904 / 1.386936 (-0.699033) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fb5fae8f79b3db4a94013aa2af7c63796ef2d64 \"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) \| 0.006134 / 0.011353 (-0.005219) \| 0.003655 / 0.011008 (-0.007354) \| 0.081295 / 0.038508 (0.042787) \| 0.060202 / 0.023109 (0.037093) \| 0.330005 / 0.275898 (0.054107) \| 0.361219 / 0.323480 (0.037739) \| 0.004766 / 0.007986 (-0.003220) \| 0.002942 / 0.004328 (-0.001386) \| 0.063322 / 0.004250 (0.059072) \| 0.047844 / 0.037052 (0.010791) \| 0.340375 / 0.258489 (0.081886) \| 0.406301 / 0.293841 (0.112460) \| 0.027474 / 0.128546 (-0.101072) \| 0.007991 / 0.075646 (-0.067655) \| 0.262746 / 0.419271 (-0.156526) \| 0.045575 / 0.043533 (0.002042) \| 0.324123 / 0.255139 (0.068984) \| 0.344399 / 0.283200 (0.061199) \| 0.021806 / 0.141683 (-0.119877) \| 1.425390 / 1.452155 (-0.026765) \| 1.487920 / 1.492716 (-0.004796) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217504 / 0.018006 (0.199498) \| 0.420878 / 0.000490 (0.420388) \| 0.007312 / 0.000200 (0.007112) \| 0.000218 / 0.000054 (0.000163) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023507 / 0.037411 (-0.013905) \| 0.073493 / 0.014526 (0.058967) \| 0.084857 / 0.176557 (-0.091700) \| 0.145130 / 0.737135 (-0.592005) \| 0.085204 / 0.296338 (-0.211135) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388767 / 0.215209 (0.173557) \| 3.877998 / 2.077655 (1.800344) \| 1.881447 / 1.504120 (0.377327) \| 1.714555 / 1.541195 (0.173360) \| 1.772551 / 1.468490 (0.304061) \| 0.505146 / 4.584777 (-4.079631) \| 3.045471 / 3.745712 (-0.700241) \| 2.834436 / 5.269862 (-2.435426) \| 1.859896 / 4.565676 (-2.705780) \| 0.057806 / 0.424275 (-0.366469) \| 0.006378 / 0.007607 (-0.001229) \| 0.458339 / 0.226044 (0.232294) \| 4.588125 / 2.268929 (2.319196) \| 2.302215 / 55.444624 (-53.142409) \| 1.981297 / 6.876477 (-4.895180) \| 2.152967 / 2.142072 (0.010895) \| 0.590166 / 4.805227 (-4.215061) \| 0.125753 / 6.500664 (-6.374911) \| 0.061583 / 0.075469 (-0.013887) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.232195 / 1.841788 (-0.609593) \| 17.761159 / 8.074308 (9.686851) \| 13.829498 / 10.191392 (3.638106) \| 0.131936 / 0.680424 (-0.548488) \| 0.016909 / 0.534201 (-0.517292) \| 0.332615 / 0.579283 (-0.246668) \| 0.358149 / 0.434364 (-0.076215) \| 0.384251 / 0.540337 (-0.156087) \| 0.536453 / 1.386936 (-0.850483) \|\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) \| 0.006253 / 0.011353 (-0.005100) \| 0.003639 / 0.011008 (-0.007370) \| 0.062810 / 0.038508 (0.024302) \| 0.063761 / 0.023109 (0.040652) \| 0.450538 / 0.275898 (0.174640) \| 0.483793 / 0.323480 (0.160313) \| 0.004973 / 0.007986 (-0.003013) \| 0.002918 / 0.004328 (-0.001411) \| 0.062140 / 0.004250 (0.057889) \| 0.050328 / 0.037052 (0.013275) \| 0.455860 / 0.258489 (0.197371) \| 0.492399 / 0.293841 (0.198558) \| 0.028928 / 0.128546 (-0.099618) \| 0.008166 / 0.075646 (-0.067481) \| 0.067860 / 0.419271 (-0.351411) \| 0.040990 / 0.043533 (-0.002542) \| 0.451343 / 0.255139 (0.196204) \| 0.473769 / 0.283200 (0.190569) \| 0.021585 / 0.141683 (-0.120097) \| 1.451040 / 1.452155 (-0.001115) \| 1.516065 / 1.492716 (0.023349) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230994 / 0.018006 (0.212988) \| 0.428404 / 0.000490 (0.427915) \| 0.003777 / 0.000200 (0.003577) \| 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) \| 0.027394 / 0.037411 (-0.010018) \| 0.081692 / 0.014526 (0.067166) \| 0.091568 / 0.176557 (-0.084988) \| 0.146149 / 0.737135 (-0.590987) \| 0.092200 / 0.296338 (-0.204139) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467086 / 0.215209 (0.251877) \| 4.664862 / 2.077655 (2.587207) \| 2.575703 / 1.504120 (1.071583) \| 2.396587 / 1.541195 (0.855392) \| 2.506064 / 1.468490 (1.037574) \| 0.511942 / 4.584777 (-4.072834) \| 3.196320 / 3.745712 (-0.549392) \| 2.916627 / 5.269862 (-2.353235) \| 1.919372 / 4.565676 (-2.646305) \| 0.058769 / 0.424275 (-0.365506) \| 0.006487 / 0.007607 (-0.001120) \| 0.539095 / 0.226044 (0.313051) \| 5.404675 / 2.268929 (3.135746) \| 2.988962 / 55.444624 (-52.455662) \| 2.670134 / 6.876477 (-4.206343) \| 2.837414 / 2.142072 (0.695342) \| 0.614776 / 4.805227 (-4.190451) \| 0.125806 / 6.500664 (-6.374858) \| 0.061593 / 0.075469 (-0.013876) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346171 / 1.841788 (-0.495617) \| 18.374626 / 8.074308 (10.300318) \| 14.508723 / 10.191392 (4.317331) \| 0.146771 / 0.680424 (-0.533652) \| 0.018438 / 0.534201 (-0.515763) \| 0.336944 / 0.579283 (-0.242339) \| 0.385631 / 0.434364 (-0.048733) \| 0.391922 / 0.540337 (-0.148416) \| 0.568904 / 1.386936 (-0.818032) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e8d420808718c9a1453a2e7ee3484ca12c9c70d \"CML watermark\")\n" ]	2023-10-11T21:14:59Z	2023-10-13T13:54:00Z	2023-10-13T13:45:30Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6297.diff", "html_url": "https://github.com/huggingface/datasets/pull/6297", "merged_at": "2023-10-13T13:45:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/6297.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6297" }	Fix #6291	{ "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/6297/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6296	6,296	Move `exceptions.py` to `utils/exceptions.py`	{ "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) \| 0.006695 / 0.011353 (-0.004658) \| 0.004321 / 0.011008 (-0.006687) \| 0.084558 / 0.038508 (0.046050) \| 0.076290 / 0.023109 (0.053181) \| 0.312331 / 0.275898 (0.036433) \| 0.349854 / 0.323480 (0.026374) \| 0.004267 / 0.007986 (-0.003719) \| 0.003595 / 0.004328 (-0.000733) \| 0.065077 / 0.004250 (0.060826) \| 0.057461 / 0.037052 (0.020409) \| 0.314989 / 0.258489 (0.056500) \| 0.364767 / 0.293841 (0.070926) \| 0.031726 / 0.128546 (-0.096820) \| 0.008674 / 0.075646 (-0.066972) \| 0.288282 / 0.419271 (-0.130990) \| 0.052845 / 0.043533 (0.009312) \| 0.317501 / 0.255139 (0.062362) \| 0.333241 / 0.283200 (0.050041) \| 0.026412 / 0.141683 (-0.115271) \| 1.475648 / 1.452155 (0.023493) \| 1.551656 / 1.492716 (0.058939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276512 / 0.018006 (0.258506) \| 0.576350 / 0.000490 (0.575861) \| 0.009518 / 0.000200 (0.009318) \| 0.000280 / 0.000054 (0.000226) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029332 / 0.037411 (-0.008079) \| 0.082904 / 0.014526 (0.068379) \| 0.102516 / 0.176557 (-0.074041) \| 0.159355 / 0.737135 (-0.577780) \| 0.104112 / 0.296338 (-0.192226) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.379144 / 0.215209 (0.163935) \| 3.785283 / 2.077655 (1.707629) \| 1.833753 / 1.504120 (0.329633) \| 1.667906 / 1.541195 (0.126711) \| 1.751551 / 1.468490 (0.283061) \| 0.480998 / 4.584777 (-4.103779) \| 3.533433 / 3.745712 (-0.212279) \| 3.343363 / 5.269862 (-1.926498) \| 2.094169 / 4.565676 (-2.471508) \| 0.056613 / 0.424275 (-0.367662) \| 0.007410 / 0.007607 (-0.000197) \| 0.455077 / 0.226044 (0.229033) \| 4.541380 / 2.268929 (2.272452) \| 2.269151 / 55.444624 (-53.175473) \| 1.955663 / 6.876477 (-4.920814) \| 2.227663 / 2.142072 (0.085591) \| 0.580597 / 4.805227 (-4.224630) \| 0.135034 / 6.500664 (-6.365630) \| 0.062091 / 0.075469 (-0.013378) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.276295 / 1.841788 (-0.565492) \| 20.072827 / 8.074308 (11.998519) \| 14.296462 / 10.191392 (4.105070) \| 0.164936 / 0.680424 (-0.515488) \| 0.018415 / 0.534201 (-0.515786) \| 0.390894 / 0.579283 (-0.188389) \| 0.415515 / 0.434364 (-0.018849) \| 0.462798 / 0.540337 (-0.077540) \| 0.650099 / 1.386936 (-0.736837) \|\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) \| 0.007218 / 0.011353 (-0.004135) \| 0.004246 / 0.011008 (-0.006763) \| 0.065818 / 0.038508 (0.027310) \| 0.087315 / 0.023109 (0.064206) \| 0.406449 / 0.275898 (0.130551) \| 0.442008 / 0.323480 (0.118528) \| 0.005752 / 0.007986 (-0.002233) \| 0.003624 / 0.004328 (-0.000704) \| 0.065349 / 0.004250 (0.061099) \| 0.062423 / 0.037052 (0.025371) \| 0.410099 / 0.258489 (0.151610) \| 0.448929 / 0.293841 (0.155088) \| 0.032498 / 0.128546 (-0.096048) \| 0.008877 / 0.075646 (-0.066770) \| 0.071611 / 0.419271 (-0.347661) \| 0.048038 / 0.043533 (0.004506) \| 0.407957 / 0.255139 (0.152818) \| 0.424045 / 0.283200 (0.140846) \| 0.025222 / 0.141683 (-0.116461) \| 1.496191 / 1.452155 (0.044037) \| 1.580765 / 1.492716 (0.088048) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274798 / 0.018006 (0.256792) \| 0.581410 / 0.000490 (0.580920) \| 0.007302 / 0.000200 (0.007102) \| 0.000160 / 0.000054 (0.000106) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034068 / 0.037411 (-0.003343) \| 0.096116 / 0.014526 (0.081590) \| 0.110234 / 0.176557 (-0.066323) \| 0.163246 / 0.737135 (-0.573889) \| 0.110250 / 0.296338 (-0.186089) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442381 / 0.215209 (0.227172) \| 4.427061 / 2.077655 (2.349406) \| 2.361013 / 1.504120 (0.856893) \| 2.185048 / 1.541195 (0.643853) \| 2.312544 / 1.468490 (0.844054) \| 0.498347 / 4.584777 (-4.086430) \| 3.640839 / 3.745712 (-0.104873) \| 3.353405 / 5.269862 (-1.916457) \| 2.082038 / 4.565676 (-2.483638) \| 0.058786 / 0.424275 (-0.365489) \| 0.007403 / 0.007607 (-0.000205) \| 0.517894 / 0.226044 (0.291850) \| 5.184257 / 2.268929 (2.915329) \| 2.838467 / 55.444624 (-52.606157) \| 2.511116 / 6.876477 (-4.365361) \| 2.757816 / 2.142072 (0.615743) \| 0.644050 / 4.805227 (-4.161177) \| 0.136446 / 6.500664 (-6.364218) \| 0.062219 / 0.075469 (-0.013250) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350916 / 1.841788 (-0.490872) \| 20.549280 / 8.074308 (12.474972) \| 14.697569 / 10.191392 (4.506177) \| 0.149818 / 0.680424 (-0.530606) \| 0.020187 / 0.534201 (-0.514014) \| 0.396008 / 0.579283 (-0.183275) \| 0.427535 / 0.434364 (-0.006829) \| 0.484544 / 0.540337 (-0.055794) \| 0.687076 / 1.386936 (-0.699860) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02a0d7cc9bdbc745c355c0bf8a210d8bf0b90327 \"CML watermark\")\n", "I'd rather be consistent with `huggingface_hub` and have this module in `utils/` with the exceptions exposed in `utils/__init__.py` ...", "Ok, I'll close this PR.\r\n\r\n> Maybe we could ask huggingface_hub to align with the rest of open-source libraries and expose the errors/exceptions at the root of the library...\r\n\r\ncc @Wauplin \r\n\r\nIt would be nice to have an HF style guide to ensure consistency across our libraries 🙂. ", "I can expose exceptions at root level yes.\r\n\r\nAbout having guidelines and consistency, let's try to do our best but it's not really in the essence of HF to formalize stuff in libraries :unamused: ", "Better late than never, we now have all the exceptions defined in `huggingface_hub.errors`! See https://github.com/huggingface/huggingface_hub/issues/2069.", "Thanks for taking care, @Wauplin. I am closing this PR." ]	2023-10-11T18:28:00Z	2024-09-03T16:00:04Z	2024-09-03T16:00:03Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6296.diff", "html_url": "https://github.com/huggingface/datasets/pull/6296", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6296.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6296" }	I didn't notice the path while reviewing the PR yesterday :(	{ "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/6296/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6295	6,295	Fix parquet columns argument in streaming mode	{ "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) \| 0.008112 / 0.011353 (-0.003241) \| 0.004762 / 0.011008 (-0.006247) \| 0.101349 / 0.038508 (0.062841) \| 0.092361 / 0.023109 (0.069252) \| 0.418429 / 0.275898 (0.142531) \| 0.427332 / 0.323480 (0.103852) \| 0.006112 / 0.007986 (-0.001874) \| 0.003920 / 0.004328 (-0.000408) \| 0.076813 / 0.004250 (0.072563) \| 0.064361 / 0.037052 (0.027309) \| 0.420526 / 0.258489 (0.162037) \| 0.441576 / 0.293841 (0.147735) \| 0.044760 / 0.128546 (-0.083787) \| 0.010054 / 0.075646 (-0.065592) \| 0.346063 / 0.419271 (-0.073209) \| 0.077453 / 0.043533 (0.033920) \| 0.412871 / 0.255139 (0.157732) \| 0.408307 / 0.283200 (0.125107) \| 0.033398 / 0.141683 (-0.108285) \| 1.755825 / 1.452155 (0.303671) \| 1.852347 / 1.492716 (0.359630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274201 / 0.018006 (0.256194) \| 0.536375 / 0.000490 (0.535885) \| 0.008076 / 0.000200 (0.007876) \| 0.000159 / 0.000054 (0.000105) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033567 / 0.037411 (-0.003845) \| 0.102378 / 0.014526 (0.087852) \| 0.114176 / 0.176557 (-0.062381) \| 0.180576 / 0.737135 (-0.556560) \| 0.114801 / 0.296338 (-0.181538) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450300 / 0.215209 (0.235091) \| 4.490940 / 2.077655 (2.413285) \| 2.172412 / 1.504120 (0.668292) \| 1.978746 / 1.541195 (0.437551) \| 2.065602 / 1.468490 (0.597112) \| 0.571260 / 4.584777 (-4.013517) \| 4.185485 / 3.745712 (0.439773) \| 3.885594 / 5.269862 (-1.384268) \| 2.532942 / 4.565676 (-2.032735) \| 0.067612 / 0.424275 (-0.356663) \| 0.008694 / 0.007607 (0.001087) \| 0.533375 / 0.226044 (0.307331) \| 5.321261 / 2.268929 (3.052333) \| 2.697788 / 55.444624 (-52.746836) \| 2.331328 / 6.876477 (-4.545149) \| 2.585168 / 2.142072 (0.443096) \| 0.681760 / 4.805227 (-4.123467) \| 0.157687 / 6.500664 (-6.342977) \| 0.071014 / 0.075469 (-0.004455) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.525689 / 1.841788 (-0.316098) \| 23.162280 / 8.074308 (15.087972) \| 16.644941 / 10.191392 (6.453548) \| 0.182588 / 0.680424 (-0.497836) \| 0.021653 / 0.534201 (-0.512548) \| 0.466556 / 0.579283 (-0.112727) \| 0.511902 / 0.434364 (0.077538) \| 0.553707 / 0.540337 (0.013370) \| 0.777830 / 1.386936 (-0.609106) \|\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) \| 0.007954 / 0.011353 (-0.003399) \| 0.004645 / 0.011008 (-0.006363) \| 0.079096 / 0.038508 (0.040587) \| 0.088200 / 0.023109 (0.065090) \| 0.508882 / 0.275898 (0.232984) \| 0.545986 / 0.323480 (0.222506) \| 0.006233 / 0.007986 (-0.001752) \| 0.004016 / 0.004328 (-0.000312) \| 0.078103 / 0.004250 (0.073853) \| 0.066354 / 0.037052 (0.029302) \| 0.504132 / 0.258489 (0.245643) \| 0.543714 / 0.293841 (0.249873) \| 0.038140 / 0.128546 (-0.090407) \| 0.011201 / 0.075646 (-0.064446) \| 0.085713 / 0.419271 (-0.333559) \| 0.057169 / 0.043533 (0.013637) \| 0.488161 / 0.255139 (0.233022) \| 0.516231 / 0.283200 (0.233031) \| 0.027868 / 0.141683 (-0.113814) \| 1.794084 / 1.452155 (0.341930) \| 1.884993 / 1.492716 (0.392276) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263108 / 0.018006 (0.245102) \| 0.495761 / 0.000490 (0.495272) \| 0.007056 / 0.000200 (0.006856) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039089 / 0.037411 (0.001678) \| 0.113332 / 0.014526 (0.098806) \| 0.130137 / 0.176557 (-0.046419) \| 0.189330 / 0.737135 (-0.547805) \| 0.125860 / 0.296338 (-0.170479) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.530496 / 0.215209 (0.315287) \| 5.349235 / 2.077655 (3.271581) \| 2.975886 / 1.504120 (1.471766) \| 2.786368 / 1.541195 (1.245173) \| 2.920448 / 1.468490 (1.451958) \| 0.575677 / 4.584777 (-4.009100) \| 4.215535 / 3.745712 (0.469823) \| 3.879984 / 5.269862 (-1.389878) \| 2.420193 / 4.565676 (-2.145484) \| 0.068506 / 0.424275 (-0.355769) \| 0.008785 / 0.007607 (0.001178) \| 0.611471 / 0.226044 (0.385427) \| 6.118399 / 2.268929 (3.849471) \| 3.509376 / 55.444624 (-51.935248) \| 3.149219 / 6.876477 (-3.727257) \| 3.413861 / 2.142072 (1.271788) \| 0.697586 / 4.805227 (-4.107641) \| 0.157767 / 6.500664 (-6.342897) \| 0.071539 / 0.075469 (-0.003930) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.625196 / 1.841788 (-0.216591) \| 24.347319 / 8.074308 (16.273011) \| 17.365789 / 10.191392 (7.174397) \| 0.217590 / 0.680424 (-0.462834) \| 0.023885 / 0.534201 (-0.510316) \| 0.477226 / 0.579283 (-0.102057) \| 0.529319 / 0.434364 (0.094955) \| 0.622299 / 0.540337 (0.081962) \| 0.835295 / 1.386936 (-0.551641) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3de42c8fae86c602fc71ac6d166e5c77f4149446 \"CML watermark\")\n", "CI errors are unrelated or due to flaky tests", "<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) \| 0.006288 / 0.011353 (-0.005065) \| 0.003836 / 0.011008 (-0.007172) \| 0.080958 / 0.038508 (0.042450) \| 0.065934 / 0.023109 (0.042825) \| 0.312597 / 0.275898 (0.036699) \| 0.351216 / 0.323480 (0.027736) \| 0.004864 / 0.007986 (-0.003121) \| 0.002961 / 0.004328 (-0.001368) \| 0.063142 / 0.004250 (0.058892) \| 0.049822 / 0.037052 (0.012770) \| 0.320305 / 0.258489 (0.061816) \| 0.363151 / 0.293841 (0.069310) \| 0.027561 / 0.128546 (-0.100985) \| 0.008176 / 0.075646 (-0.067470) \| 0.261290 / 0.419271 (-0.157982) \| 0.045517 / 0.043533 (0.001984) \| 0.309218 / 0.255139 (0.054079) \| 0.340140 / 0.283200 (0.056940) \| 0.021000 / 0.141683 (-0.120683) \| 1.448699 / 1.452155 (-0.003456) \| 1.523904 / 1.492716 (0.031188) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224294 / 0.018006 (0.206288) \| 0.434928 / 0.000490 (0.434439) \| 0.007541 / 0.000200 (0.007341) \| 0.000286 / 0.000054 (0.000232) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025257 / 0.037411 (-0.012154) \| 0.077364 / 0.014526 (0.062838) \| 0.085825 / 0.176557 (-0.090732) \| 0.148121 / 0.737135 (-0.589014) \| 0.086838 / 0.296338 (-0.209500) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396900 / 0.215209 (0.181691) \| 3.953381 / 2.077655 (1.875727) \| 1.933561 / 1.504120 (0.429441) \| 1.760549 / 1.541195 (0.219354) \| 1.824014 / 1.468490 (0.355523) \| 0.495385 / 4.584777 (-4.089392) \| 3.005558 / 3.745712 (-0.740154) \| 2.931022 / 5.269862 (-2.338840) \| 1.905113 / 4.565676 (-2.660563) \| 0.057232 / 0.424275 (-0.367043) \| 0.006472 / 0.007607 (-0.001135) \| 0.464261 / 0.226044 (0.238216) \| 4.629388 / 2.268929 (2.360459) \| 2.342004 / 55.444624 (-53.102620) \| 1.977295 / 6.876477 (-4.899181) \| 2.167151 / 2.142072 (0.025079) \| 0.582483 / 4.805227 (-4.222744) \| 0.129444 / 6.500664 (-6.371220) \| 0.061057 / 0.075469 (-0.014412) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259444 / 1.841788 (-0.582344) \| 18.189338 / 8.074308 (10.115030) \| 14.313174 / 10.191392 (4.121782) \| 0.146209 / 0.680424 (-0.534215) \| 0.017115 / 0.534201 (-0.517086) \| 0.336643 / 0.579283 (-0.242640) \| 0.370824 / 0.434364 (-0.063540) \| 0.387032 / 0.540337 (-0.153306) \| 0.546688 / 1.386936 (-0.840248) \|\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) \| 0.006371 / 0.011353 (-0.004982) \| 0.003693 / 0.011008 (-0.007315) \| 0.062499 / 0.038508 (0.023991) \| 0.066367 / 0.023109 (0.043257) \| 0.451481 / 0.275898 (0.175583) \| 0.482495 / 0.323480 (0.159015) \| 0.005676 / 0.007986 (-0.002310) \| 0.002940 / 0.004328 (-0.001389) \| 0.063011 / 0.004250 (0.058760) \| 0.051500 / 0.037052 (0.014447) \| 0.455482 / 0.258489 (0.196993) \| 0.488888 / 0.293841 (0.195047) \| 0.028714 / 0.128546 (-0.099832) \| 0.008178 / 0.075646 (-0.067468) \| 0.067218 / 0.419271 (-0.352053) \| 0.041323 / 0.043533 (-0.002210) \| 0.454007 / 0.255139 (0.198868) \| 0.476241 / 0.283200 (0.193041) \| 0.021530 / 0.141683 (-0.120153) \| 1.457859 / 1.452155 (0.005705) \| 1.506437 / 1.492716 (0.013721) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228280 / 0.018006 (0.210274) \| 0.427574 / 0.000490 (0.427084) \| 0.003793 / 0.000200 (0.003593) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028420 / 0.037411 (-0.008992) \| 0.087935 / 0.014526 (0.073409) \| 0.092761 / 0.176557 (-0.083796) \| 0.148084 / 0.737135 (-0.589051) \| 0.095301 / 0.296338 (-0.201037) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462457 / 0.215209 (0.247248) \| 4.618016 / 2.077655 (2.540361) \| 2.540531 / 1.504120 (1.036412) \| 2.384696 / 1.541195 (0.843501) \| 2.493108 / 1.468490 (1.024618) \| 0.511689 / 4.584777 (-4.073088) \| 3.173701 / 3.745712 (-0.572011) \| 2.917046 / 5.269862 (-2.352816) \| 1.916294 / 4.565676 (-2.649382) \| 0.058969 / 0.424275 (-0.365306) \| 0.006461 / 0.007607 (-0.001147) \| 0.540997 / 0.226044 (0.314952) \| 5.406596 / 2.268929 (3.137667) \| 3.071189 / 55.444624 (-52.373435) \| 2.701982 / 6.876477 (-4.174494) \| 2.860194 / 2.142072 (0.718121) \| 0.602684 / 4.805227 (-4.202543) \| 0.127384 / 6.500664 (-6.373280) \| 0.061718 / 0.075469 (-0.013751) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.340587 / 1.841788 (-0.501201) \| 18.543831 / 8.074308 (10.469523) \| 14.847319 / 10.191392 (4.655927) \| 0.146523 / 0.680424 (-0.533901) \| 0.018172 / 0.534201 (-0.516029) \| 0.333276 / 0.579283 (-0.246007) \| 0.375874 / 0.434364 (-0.058490) \| 0.396766 / 0.540337 (-0.143572) \| 0.572562 / 1.386936 (-0.814374) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2d9fcc0840f9d94f63635e9b40a1a7f11b34ea2 \"CML watermark\")\n" ]	2023-10-11T10:01:01Z	2023-10-11T16:30:24Z	2023-10-11T16:21:36Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6295.diff", "html_url": "https://github.com/huggingface/datasets/pull/6295", "merged_at": "2023-10-11T16:21:36Z", "patch_url": "https://github.com/huggingface/datasets/pull/6295.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6295" }	It was failing when there's a DatasetInfo with non-None info.features from the YAML (therefore containing columns that should be ignored) Fix https://github.com/huggingface/datasets/issues/6293	{ "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": 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https://github.com/huggingface/datasets/issues/6294	6,294	IndexError: Invalid key is out of bounds for size 0 despite having a populated dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/61892155?v=4", "events_url": "https://api.github.com/users/ZYM66/events{/privacy}", "followers_url": "https://api.github.com/users/ZYM66/followers", "following_url": "https://api.github.com/users/ZYM66/following{/other_user}", "gists_url": "https://api.github.com/users/ZYM66/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ZYM66", "id": 61892155, "login": "ZYM66", "node_id": "MDQ6VXNlcjYxODkyMTU1", "organizations_url": "https://api.github.com/users/ZYM66/orgs", "received_events_url": "https://api.github.com/users/ZYM66/received_events", "repos_url": "https://api.github.com/users/ZYM66/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ZYM66/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ZYM66/subscriptions", "type": "User", "url": "https://api.github.com/users/ZYM66", "user_view_type": "public" }	[]	closed	false	[ "It looks to be the same issue as the one reported in https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0.\r\n\r\nCan you check the length of `train_dataset` before the `train_sampler = self._get_train_sampler()` (and after `_remove_unused_columns`) line?" ]	2023-10-11T09:59:38Z	2023-10-17T11:24:06Z	2023-10-17T11:24:06Z	NONE	null	null	### Describe the bug I am encountering an `IndexError` when trying to access data from a DataLoader which wraps around a dataset I've loaded using the `datasets` library. The error suggests that the dataset size is `0`, but when I check the length and print the dataset, it's clear that it has `1166` entries. ### Steps to reproduce the bug 1. Load a dataset with `1166` entries. 2. Create a DataLoader using this dataset. 3. Try iterating over the DataLoader. code: ```python def get_train_dataloader(self) -> DataLoader: if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_dataset = self.train_dataset data_collator = self.data_collator print(len(train_dataset)) print(train_dataset) if is_datasets_available() and isinstance(train_dataset, datasets.Dataset): train_dataset = self._remove_unused_columns(train_dataset, description="training") else: data_collator = self._get_collator_with_removed_columns(data_collator, description="training") train_sampler = self._get_train_sampler() dl = DataLoader( train_dataset, batch_size=self._train_batch_size, sampler=train_sampler, collate_fn=data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory, worker_init_fn=seed_worker, ) print(dl) print(len(dl)) for i in dl: print(i) break return dl ``` output : ``` 1166 Dataset({ features: ['input_ids', 'special_tokens_mask'], num_rows: 1166 }) <torch.utils.data.dataloader.DataLoader object ...> 146 ``` Error: ``` Traceback (most recent call last): File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 266, in <module> train() File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 260, in train trainer.train() File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1506, in train return inner_training_loop( File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1520, in _inner_training_loop train_dataloader = self.get_train_dataloader() File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 80, in get_train_dataloader for i in dl: File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ data = self._next_data() File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 1116 is out of bounds for size 0 ``` ### Expected behavior I expect to be able to iterate over the DataLoader without encountering an IndexError since the dataset is populated. ### Environment info - `datasets` library version: [2.14.5] - Platform: [Linux] - Python version: 3.10 - Other libraries involved: HuggingFace Transformers	{ "avatar_url": "https://avatars.githubusercontent.com/u/61892155?v=4", "events_url": "https://api.github.com/users/ZYM66/events{/privacy}", "followers_url": "https://api.github.com/users/ZYM66/followers", "following_url": "https://api.github.com/users/ZYM66/following{/other_user}", "gists_url": "https://api.github.com/users/ZYM66/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ZYM66", "id": 61892155, "login": "ZYM66", "node_id": "MDQ6VXNlcjYxODkyMTU1", "organizations_url": "https://api.github.com/users/ZYM66/orgs", "received_events_url": "https://api.github.com/users/ZYM66/received_events", "repos_url": "https://api.github.com/users/ZYM66/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ZYM66/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ZYM66/subscriptions", "type": "User", "url": "https://api.github.com/users/ZYM66", "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/6294/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/6293	6,293	Choose columns to stream parquet data in streaming mode	{ "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" }	[ { "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	[]	2023-10-11T08:59:36Z	2023-10-11T16:21:38Z	2023-10-11T16:21:38Z	MEMBER	null	null	Currently passing columns= to load_dataset in streaming mode fails ``` Tried to load parquet data with columns '['link']' with mismatching features '{'caption': Value(dtype='string', id=None), 'image': {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='null', id=None)}, 'link': Value(dtype='string', id=None), 'message_id': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None)}' ``` similar to https://github.com/huggingface/datasets/issues/6039 reported at https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/3#65259a09617407d4520f4ad9	{ "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/6293/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/6292	6,292	how to load the image of dtype float32 or float64	{ "avatar_url": "https://avatars.githubusercontent.com/u/26437644?v=4", "events_url": "https://api.github.com/users/wanglaofei/events{/privacy}", "followers_url": "https://api.github.com/users/wanglaofei/followers", "following_url": "https://api.github.com/users/wanglaofei/following{/other_user}", "gists_url": "https://api.github.com/users/wanglaofei/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/wanglaofei", "id": 26437644, "login": "wanglaofei", "node_id": "MDQ6VXNlcjI2NDM3NjQ0", "organizations_url": "https://api.github.com/users/wanglaofei/orgs", "received_events_url": "https://api.github.com/users/wanglaofei/received_events", "repos_url": "https://api.github.com/users/wanglaofei/repos", "site_admin": false, "starred_url": "https://api.github.com/users/wanglaofei/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wanglaofei/subscriptions", "type": "User", "url": "https://api.github.com/users/wanglaofei", "user_view_type": "public" }	[]	open	false	[ "Hi! Can you provide a code that reproduces the issue?\r\n\r\nAlso, which version of `datasets` are you using? You can check this by running `python -c \"import datasets; print(datasets.__version__)\"` inside the env. We added support for \"float images\" in `datasets 2.9`." ]	2023-10-11T07:27:16Z	2023-10-11T13:19:11Z	null	NONE	null	null	_FEATURES = datasets.Features( { "image": datasets.Image(), "text": datasets.Value("string"), }, ) The datasets builder seems only support the unit8 data. How to load the float dtype data?	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/6292/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/6291	6,291	Casting type from Array2D int to Array2D float crashes	{ "avatar_url": "https://avatars.githubusercontent.com/u/22567306?v=4", "events_url": "https://api.github.com/users/AlanBlanchet/events{/privacy}", "followers_url": "https://api.github.com/users/AlanBlanchet/followers", "following_url": "https://api.github.com/users/AlanBlanchet/following{/other_user}", "gists_url": "https://api.github.com/users/AlanBlanchet/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/AlanBlanchet", "id": 22567306, "login": "AlanBlanchet", "node_id": "MDQ6VXNlcjIyNTY3MzA2", "organizations_url": "https://api.github.com/users/AlanBlanchet/orgs", "received_events_url": "https://api.github.com/users/AlanBlanchet/received_events", "repos_url": "https://api.github.com/users/AlanBlanchet/repos", "site_admin": false, "starred_url": "https://api.github.com/users/AlanBlanchet/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AlanBlanchet/subscriptions", "type": "User", "url": "https://api.github.com/users/AlanBlanchet", "user_view_type": "public" }	[]	closed	false	[ "Thanks for reporting! I've opened a PR with a fix" ]	2023-10-10T20:10:10Z	2023-10-13T13:45:31Z	2023-10-13T13:45:31Z	NONE	null	null	### Describe the bug I am on a school project and the initial type for feature annotations are `Array2D(shape=(None, 4))`. I am trying to cast this type to a `float64` and pyarrow gives me this error : ``` Traceback (most recent call last): File "/home/alan/dev/ClassezDesImagesAvecDesAlgorithmesDeDeeplearning/src/sdd/data/dataset.py", line 141, in <module> dataset = StanfordDogsDataset(size, 5).original(True).demo() File "<attrs generated init __main__.StanfordDogsDataset>", line 4, in __init__ File "/home/alan/dev/ClassezDesImagesAvecDesAlgorithmesDeDeeplearning/src/sdd/data/dataset.py", line 33, in __attrs_post_init__ self.dataset = self.dataset.cast_column( File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, args, kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2110, in cast_column return self.cast(features) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2055, in cast dataset = dataset.map( File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3097, in map for rank, done, content in Dataset._map_single(dataset_kwargs): File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3474, in _map_single batch = apply_function_on_filtered_inputs( File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3353, in apply_function_on_filtered_inputs processed_inputs = function(fn_args, additional_args, *fn_kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2328, in table_cast return cast_table_to_schema(table, schema) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2287, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2287, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2143, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, args, **kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast return pa_type.wrap_array(array) File "pyarrow/types.pxi", line 1369, in pyarrow.lib.BaseExtensionType.wrap_array TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: double>>, got list<item: list<item: int32>> ``` ### Steps to reproduce the bug ```python dataset = datasets.load_dataset("Alanox/stanford-dogs", split="full") dataset = dataset.cast_column("annotations", Array2D((None, 4), "float64")) ``` ### Expected behavior It should simply cast the column feature type to a `float64` without error ### Environment info datasets == 2.14.5	{ "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/6291/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/6290	6,290	Incremental dataset (e.g. `.push_to_hub(..., append=True)`)	{ "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" }	[ { "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	[ "Yea I think waiting for #6269 would be best, or branching from it. For reference, this [PR](https://github.com/LAION-AI/Discord-Scrapers/pull/2) is progressing pretty well which will do similar using the hf hub for our LAION dataset bot https://github.com/LAION-AI/Discord-Scrapers/pull/2. ", "Is there any update on this?", "Is there any update on this?", "No update so far on this feature request but for broader context, this announce will help with incremental datasets https://huggingface.co/blog/xethub-joins-hf :)", "Still no update? Whats the current recommended way to upload large datasets to the hub? I can't load it all to memory after some limit right", "> Still no update? Whats the current recommended way to upload large datasets to the hub? I can't load it all to memory after some limit right\n\n@tolgadur bro, you may set 'data_dir' as a temporary solution like that:\n\n`Dataset.push_to_hub('your_repo_aame', data_dir='data_{index}')`\n", "Related to this feature request: `pyarrow` 21 is out and added content defined chunking for parquet, which enables deduped uploads to Xet.\n\nTherefore we can have a logic in append=True that modifies an existing Parquet file to add more rows without having to reupload the full file." ]	2023-10-10T15:18:03Z	2025-08-13T12:55:28Z	null	CONTRIBUTOR	null	null	### Feature request Have the possibility to do `ds.push_to_hub(..., append=True)`. ### Motivation Requested in this [comment](https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/3#65252597c4edc168202a5eaa) and this [comment](https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/4#6524f675c9607bdffb208d8f). Discussed internally on [slack](https://huggingface.slack.com/archives/C02EMARJ65P/p1696950642610639?thread_ts=1690554266.830949&cid=C02EMARJ65P). ### Your contribution What I suggest to do for parquet datasets is to use `CommitOperationCopy` + `CommitOperationDelete` from `huggingface_hub`: 1. list files 2. copy files from parquet-0001-of-0004 to parquet-0001-of-0005 3. delete files like parquet-0001-of-0004 4. generate + add last parquet file parquet-0005-of-0005 => make a single commit with all commit operations at once I think it should be quite straightforward to implement. Happy to review a PR (maybe conflicting with the ongoing "1 commit push_to_hub" PR https://github.com/huggingface/datasets/pull/6269)	null	{ "+1": 11, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 11, "url": "https://api.github.com/repos/huggingface/datasets/issues/6290/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/6289	6,289	testing doc-builder	{ "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	[ "<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) \| 0.006424 / 0.011353 (-0.004929) \| 0.003960 / 0.011008 (-0.007048) \| 0.084022 / 0.038508 (0.045514) \| 0.070770 / 0.023109 (0.047661) \| 0.320525 / 0.275898 (0.044627) \| 0.354507 / 0.323480 (0.031027) \| 0.003939 / 0.007986 (-0.004047) \| 0.004161 / 0.004328 (-0.000168) \| 0.064754 / 0.004250 (0.060503) \| 0.053630 / 0.037052 (0.016578) \| 0.323948 / 0.258489 (0.065459) \| 0.376908 / 0.293841 (0.083067) \| 0.031063 / 0.128546 (-0.097483) \| 0.008470 / 0.075646 (-0.067177) \| 0.288110 / 0.419271 (-0.131161) \| 0.053062 / 0.043533 (0.009529) \| 0.328176 / 0.255139 (0.073037) \| 0.345203 / 0.283200 (0.062003) \| 0.024579 / 0.141683 (-0.117104) \| 1.471649 / 1.452155 (0.019495) \| 1.561458 / 1.492716 (0.068742) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223591 / 0.018006 (0.205585) \| 0.450758 / 0.000490 (0.450269) \| 0.003751 / 0.000200 (0.003552) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027859 / 0.037411 (-0.009552) \| 0.080607 / 0.014526 (0.066081) \| 0.093835 / 0.176557 (-0.082722) \| 0.150466 / 0.737135 (-0.586669) \| 0.094381 / 0.296338 (-0.201957) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.394011 / 0.215209 (0.178802) \| 3.918318 / 2.077655 (1.840664) \| 1.928684 / 1.504120 (0.424564) \| 1.765944 / 1.541195 (0.224749) \| 1.784716 / 1.468490 (0.316226) \| 0.487189 / 4.584777 (-4.097588) \| 3.537705 / 3.745712 (-0.208008) \| 3.312162 / 5.269862 (-1.957699) \| 2.024520 / 4.565676 (-2.541156) \| 0.057571 / 0.424275 (-0.366704) \| 0.007203 / 0.007607 (-0.000404) \| 0.467253 / 0.226044 (0.241208) \| 4.659934 / 2.268929 (2.391005) \| 2.377764 / 55.444624 (-53.066860) \| 2.021984 / 6.876477 (-4.854492) \| 2.197468 / 2.142072 (0.055395) \| 0.586415 / 4.805227 (-4.218812) \| 0.136636 / 6.500664 (-6.364028) \| 0.060885 / 0.075469 (-0.014584) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.241879 / 1.841788 (-0.599908) \| 18.719327 / 8.074308 (10.645019) \| 14.408689 / 10.191392 (4.217297) \| 0.155778 / 0.680424 (-0.524646) \| 0.018475 / 0.534201 (-0.515726) \| 0.392316 / 0.579283 (-0.186967) \| 0.409803 / 0.434364 (-0.024561) \| 0.458701 / 0.540337 (-0.081637) \| 0.630561 / 1.386936 (-0.756375) \|\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) \| 0.006541 / 0.011353 (-0.004812) \| 0.003915 / 0.011008 (-0.007094) \| 0.064292 / 0.038508 (0.025784) \| 0.069174 / 0.023109 (0.046065) \| 0.402048 / 0.275898 (0.126150) \| 0.423960 / 0.323480 (0.100480) \| 0.005355 / 0.007986 (-0.002631) \| 0.003295 / 0.004328 (-0.001033) \| 0.065212 / 0.004250 (0.060962) \| 0.054292 / 0.037052 (0.017240) \| 0.402930 / 0.258489 (0.144441) \| 0.441840 / 0.293841 (0.147999) \| 0.032732 / 0.128546 (-0.095814) \| 0.008565 / 0.075646 (-0.067081) \| 0.070705 / 0.419271 (-0.348567) \| 0.047908 / 0.043533 (0.004375) \| 0.401400 / 0.255139 (0.146261) \| 0.422682 / 0.283200 (0.139483) \| 0.022244 / 0.141683 (-0.119439) \| 1.532018 / 1.452155 (0.079864) \| 1.597955 / 1.492716 (0.105239) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226277 / 0.018006 (0.208271) \| 0.475578 / 0.000490 (0.475088) \| 0.005456 / 0.000200 (0.005256) \| 0.000140 / 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) \| 0.033111 / 0.037411 (-0.004300) \| 0.093138 / 0.014526 (0.078613) \| 0.104619 / 0.176557 (-0.071937) \| 0.157972 / 0.737135 (-0.579164) \| 0.105017 / 0.296338 (-0.191321) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441771 / 0.215209 (0.226562) \| 4.396981 / 2.077655 (2.319326) \| 2.410745 / 1.504120 (0.906625) \| 2.258359 / 1.541195 (0.717164) \| 2.372628 / 1.468490 (0.904138) \| 0.491411 / 4.584777 (-4.093366) \| 3.650084 / 3.745712 (-0.095628) \| 3.279557 / 5.269862 (-1.990304) \| 2.011377 / 4.565676 (-2.554300) \| 0.058283 / 0.424275 (-0.365992) \| 0.007435 / 0.007607 (-0.000172) \| 0.507212 / 0.226044 (0.281167) \| 5.080104 / 2.268929 (2.811176) \| 2.822680 / 55.444624 (-52.621945) \| 2.507608 / 6.876477 (-4.368869) \| 2.719349 / 2.142072 (0.577277) \| 0.586157 / 4.805227 (-4.219071) \| 0.132851 / 6.500664 (-6.367813) \| 0.059944 / 0.075469 (-0.015525) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.374801 / 1.841788 (-0.466987) \| 19.089359 / 8.074308 (11.015051) \| 14.525861 / 10.191392 (4.334469) \| 0.184758 / 0.680424 (-0.495666) \| 0.020206 / 0.534201 (-0.513995) \| 0.397309 / 0.579283 (-0.181975) \| 0.418120 / 0.434364 (-0.016244) \| 0.471817 / 0.540337 (-0.068520) \| 0.681691 / 1.386936 (-0.705245) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2076cb857e90cf7a6050bba230f586993c5e034a \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._" ]	2023-10-10T15:17:29Z	2023-10-13T08:57:14Z	2023-10-13T08:56:48Z	NONE	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6289.diff", "html_url": "https://github.com/huggingface/datasets/pull/6289", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6289.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6289" }	testing https://github.com/huggingface/doc-builder/pull/426	{ "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" }	{ "+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/6289/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6288	6,288	Dataset.from_pandas with a DataFrame of PIL.Images	{ "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" }	[ { "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	[ "A duplicate of https://github.com/huggingface/datasets/issues/4796.\r\n\r\nWe could get this for free by implementing the `Image` feature as an extension type, as shown in [this](https://colab.research.google.com/drive/1Uzm_tXVpGTwbzleDConWcNjacwO1yxE4?usp=sharing) Colab (example with UUIDs).\r\n", "+1 to this\r\nCalling this line with a df that contains a PIL image (as they are returned from load_dataset)\r\n`ds = Dataset.from_pandas(df)`\r\nResults in this error:\r\n`ArrowInvalid: ('Could not convert <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1024x1024 at 0x2B41F2D70> with type PngImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column image with type object')`", "I found something that can be used as solution.\r\n\r\nI have the same problem when I've try to load the images from a pamdas dataset\r\n\r\nIf you have all on a pandas dataset try \r\nDataset.from_dict( your_df.reset_index(drop=True).to_dict(orient='list'), split=set_your_split)\r\n\r\nAnd this avoid the error" ]	2023-10-10T10:29:16Z	2024-11-29T16:35:30Z	null	MEMBER	null	null	Currently type inference doesn't know what to do with a Pandas Series of PIL.Image objects, though it would be nice to get a Dataset with the Image type this way	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/6288/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/6287	6,287	map() not recognizing "text"	{ "avatar_url": "https://avatars.githubusercontent.com/u/5688359?v=4", "events_url": "https://api.github.com/users/EngineerKhan/events{/privacy}", "followers_url": "https://api.github.com/users/EngineerKhan/followers", "following_url": "https://api.github.com/users/EngineerKhan/following{/other_user}", "gists_url": "https://api.github.com/users/EngineerKhan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/EngineerKhan", "id": 5688359, "login": "EngineerKhan", "node_id": "MDQ6VXNlcjU2ODgzNTk=", "organizations_url": "https://api.github.com/users/EngineerKhan/orgs", "received_events_url": "https://api.github.com/users/EngineerKhan/received_events", "repos_url": "https://api.github.com/users/EngineerKhan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/EngineerKhan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/EngineerKhan/subscriptions", "type": "User", "url": "https://api.github.com/users/EngineerKhan", "user_view_type": "public" }	[]	closed	false	[ "There is no \"text\" column in the `amazon_reviews_multi`, hence the `KeyError`. You can get the column names by running `dataset.column_names`." ]	2023-10-09T10:27:30Z	2023-10-11T20:28:45Z	2023-10-11T20:28:45Z	NONE	null	null	### Describe the bug The [map() documentation](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Dataset.map) reads: ` ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)` I have been trying to reproduce it in my code as: `tokenizedDataset = dataset.map(lambda x: tokenizer(x['text']), batched=True)` But it doesn't work as it throws the error: > KeyError: 'text' Can you please guide me on how to fix it? ### Steps to reproduce the bug 1. `from datasets import load_dataset dataset = load_dataset("amazon_reviews_multi")` 2. Then this code: `from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")` 3. The line I quoted above (which I have been trying) ### Expected behavior As mentioned in the documentation, it should run without any error and map the tokenization on the whole dataset. ### Environment info Python 3.10.2	{ "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/6287/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/6286	6,286	Create DefunctDatasetError	{ "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) \| 0.009157 / 0.011353 (-0.002195) \| 0.004275 / 0.011008 (-0.006734) \| 0.099341 / 0.038508 (0.060833) \| 0.080634 / 0.023109 (0.057525) \| 0.373598 / 0.275898 (0.097700) \| 0.445048 / 0.323480 (0.121568) \| 0.006541 / 0.007986 (-0.001444) \| 0.003550 / 0.004328 (-0.000779) \| 0.071034 / 0.004250 (0.066784) \| 0.062637 / 0.037052 (0.025585) \| 0.379110 / 0.258489 (0.120621) \| 0.447896 / 0.293841 (0.154055) \| 0.047739 / 0.128546 (-0.080807) \| 0.012575 / 0.075646 (-0.063071) \| 0.332314 / 0.419271 (-0.086957) \| 0.065500 / 0.043533 (0.021967) \| 0.365919 / 0.255139 (0.110780) \| 0.438611 / 0.283200 (0.155412) \| 0.034243 / 0.141683 (-0.107440) \| 1.628034 / 1.452155 (0.175880) \| 1.802970 / 1.492716 (0.310253) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224528 / 0.018006 (0.206522) \| 0.482094 / 0.000490 (0.481604) \| 0.012752 / 0.000200 (0.012552) \| 0.000570 / 0.000054 (0.000515) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025456 / 0.037411 (-0.011956) \| 0.082281 / 0.014526 (0.067756) \| 0.100050 / 0.176557 (-0.076506) \| 0.156931 / 0.737135 (-0.580204) \| 0.108229 / 0.296338 (-0.188110) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.560688 / 0.215209 (0.345479) \| 5.171711 / 2.077655 (3.094056) \| 2.273178 / 1.504120 (0.769058) \| 1.948158 / 1.541195 (0.406963) \| 1.879744 / 1.468490 (0.411254) \| 0.789216 / 4.584777 (-3.795561) \| 4.529370 / 3.745712 (0.783658) \| 4.008743 / 5.269862 (-1.261118) \| 2.633555 / 4.565676 (-1.932121) \| 0.085411 / 0.424275 (-0.338864) \| 0.007256 / 0.007607 (-0.000351) \| 0.623254 / 0.226044 (0.397209) \| 6.327256 / 2.268929 (4.058327) \| 2.911787 / 55.444624 (-52.532837) \| 2.240610 / 6.876477 (-4.635867) \| 2.352811 / 2.142072 (0.210738) \| 0.930114 / 4.805227 (-3.875114) \| 0.185028 / 6.500664 (-6.315636) \| 0.062115 / 0.075469 (-0.013354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.394261 / 1.841788 (-0.447527) \| 19.689376 / 8.074308 (11.615067) \| 17.242289 / 10.191392 (7.050897) \| 0.209122 / 0.680424 (-0.471302) \| 0.027205 / 0.534201 (-0.506996) \| 0.408613 / 0.579283 (-0.170670) \| 0.503836 / 0.434364 (0.069472) \| 0.485179 / 0.540337 (-0.055158) \| 0.674333 / 1.386936 (-0.712603) \|\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) \| 0.007506 / 0.011353 (-0.003847) \| 0.004683 / 0.011008 (-0.006325) \| 0.067584 / 0.038508 (0.029076) \| 0.065635 / 0.023109 (0.042525) \| 0.458814 / 0.275898 (0.182916) \| 0.477549 / 0.323480 (0.154069) \| 0.005212 / 0.007986 (-0.002774) \| 0.003393 / 0.004328 (-0.000936) \| 0.075307 / 0.004250 (0.071057) \| 0.051989 / 0.037052 (0.014937) \| 0.484229 / 0.258489 (0.225740) \| 0.470889 / 0.293841 (0.177048) \| 0.043528 / 0.128546 (-0.085018) \| 0.014685 / 0.075646 (-0.060962) \| 0.084199 / 0.419271 (-0.335073) \| 0.053970 / 0.043533 (0.010437) \| 0.432362 / 0.255139 (0.177223) \| 0.467472 / 0.283200 (0.184272) \| 0.031109 / 0.141683 (-0.110574) \| 1.525938 / 1.452155 (0.073784) \| 1.631993 / 1.492716 (0.139276) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200196 / 0.018006 (0.182190) \| 0.479316 / 0.000490 (0.478827) \| 0.010146 / 0.000200 (0.009947) \| 0.000118 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027911 / 0.037411 (-0.009500) \| 0.089720 / 0.014526 (0.075194) \| 0.097000 / 0.176557 (-0.079557) \| 0.157549 / 0.737135 (-0.579587) \| 0.098247 / 0.296338 (-0.198092) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.581401 / 0.215209 (0.366192) \| 5.703829 / 2.077655 (3.626174) \| 2.688272 / 1.504120 (1.184152) \| 2.321691 / 1.541195 (0.780496) \| 2.355987 / 1.468490 (0.887497) \| 0.759109 / 4.584777 (-3.825668) \| 4.711288 / 3.745712 (0.965576) \| 4.093019 / 5.269862 (-1.176843) \| 2.648240 / 4.565676 (-1.917437) \| 0.087839 / 0.424275 (-0.336436) \| 0.007060 / 0.007607 (-0.000547) \| 0.702783 / 0.226044 (0.476739) \| 6.986924 / 2.268929 (4.717996) \| 3.365970 / 55.444624 (-52.078654) \| 2.670876 / 6.876477 (-4.205600) \| 2.776431 / 2.142072 (0.634358) \| 0.920005 / 4.805227 (-3.885222) \| 0.197521 / 6.500664 (-6.303143) \| 0.069974 / 0.075469 (-0.005495) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.596947 / 1.841788 (-0.244841) \| 20.606007 / 8.074308 (12.531699) \| 18.437425 / 10.191392 (8.246033) \| 0.222445 / 0.680424 (-0.457978) \| 0.028610 / 0.534201 (-0.505591) \| 0.419748 / 0.579283 (-0.159535) \| 0.513409 / 0.434364 (0.079045) \| 0.487517 / 0.540337 (-0.052820) \| 0.706637 / 1.386936 (-0.680299) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d664439eb82d62889c21c5236a5869dae75ae779 \"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) \| 0.007744 / 0.011353 (-0.003609) \| 0.004678 / 0.011008 (-0.006330) \| 0.101243 / 0.038508 (0.062735) \| 0.085653 / 0.023109 (0.062543) \| 0.383772 / 0.275898 (0.107874) \| 0.422151 / 0.323480 (0.098671) \| 0.004566 / 0.007986 (-0.003419) \| 0.003900 / 0.004328 (-0.000429) \| 0.077778 / 0.004250 (0.073528) \| 0.063761 / 0.037052 (0.026709) \| 0.385505 / 0.258489 (0.127016) \| 0.436186 / 0.293841 (0.142345) \| 0.036172 / 0.128546 (-0.092374) \| 0.009935 / 0.075646 (-0.065711) \| 0.341434 / 0.419271 (-0.077837) \| 0.061866 / 0.043533 (0.018333) \| 0.385020 / 0.255139 (0.129881) \| 0.399455 / 0.283200 (0.116256) \| 0.029324 / 0.141683 (-0.112358) \| 1.784749 / 1.452155 (0.332594) \| 1.845926 / 1.492716 (0.353209) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266322 / 0.018006 (0.248316) \| 0.508708 / 0.000490 (0.508218) \| 0.013680 / 0.000200 (0.013480) \| 0.000868 / 0.000054 (0.000814) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033887 / 0.037411 (-0.003525) \| 0.096709 / 0.014526 (0.082183) \| 0.109472 / 0.176557 (-0.067084) \| 0.174422 / 0.737135 (-0.562713) \| 0.110830 / 0.296338 (-0.185509) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457533 / 0.215209 (0.242324) \| 4.615229 / 2.077655 (2.537575) \| 2.418820 / 1.504120 (0.914700) \| 2.181079 / 1.541195 (0.639884) \| 2.229164 / 1.468490 (0.760674) \| 0.554861 / 4.584777 (-4.029916) \| 4.323787 / 3.745712 (0.578075) \| 3.769396 / 5.269862 (-1.500466) \| 2.376850 / 4.565676 (-2.188826) \| 0.065030 / 0.424275 (-0.359245) \| 0.008397 / 0.007607 (0.000790) \| 0.541109 / 0.226044 (0.315065) \| 5.477540 / 2.268929 (3.208612) \| 2.957049 / 55.444624 (-52.487576) \| 2.511732 / 6.876477 (-4.364744) \| 2.703953 / 2.142072 (0.561881) \| 0.660822 / 4.805227 (-4.144405) \| 0.147035 / 6.500664 (-6.353630) \| 0.066045 / 0.075469 (-0.009424) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.526481 / 1.841788 (-0.315307) \| 22.020256 / 8.074308 (13.945948) \| 16.854566 / 10.191392 (6.663174) \| 0.192958 / 0.680424 (-0.487466) \| 0.021505 / 0.534201 (-0.512696) \| 0.462867 / 0.579283 (-0.116416) \| 0.514813 / 0.434364 (0.080449) \| 0.546147 / 0.540337 (0.005809) \| 0.767853 / 1.386936 (-0.619083) \|\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) \| 0.007770 / 0.011353 (-0.003583) \| 0.004671 / 0.011008 (-0.006337) \| 0.080862 / 0.038508 (0.042354) \| 0.087049 / 0.023109 (0.063940) \| 0.479497 / 0.275898 (0.203599) \| 0.559787 / 0.323480 (0.236307) \| 0.007168 / 0.007986 (-0.000818) \| 0.003829 / 0.004328 (-0.000500) \| 0.079018 / 0.004250 (0.074768) \| 0.067359 / 0.037052 (0.030307) \| 0.516140 / 0.258489 (0.257651) \| 0.547000 / 0.293841 (0.253159) \| 0.037955 / 0.128546 (-0.090591) \| 0.010007 / 0.075646 (-0.065639) \| 0.087673 / 0.419271 (-0.331598) \| 0.059309 / 0.043533 (0.015777) \| 0.473920 / 0.255139 (0.218781) \| 0.529216 / 0.283200 (0.246017) \| 0.028236 / 0.141683 (-0.113447) \| 1.771127 / 1.452155 (0.318972) \| 1.918878 / 1.492716 (0.426162) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242010 / 0.018006 (0.224004) \| 0.494944 / 0.000490 (0.494454) \| 0.006319 / 0.000200 (0.006119) \| 0.000111 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039220 / 0.037411 (0.001809) \| 0.113805 / 0.014526 (0.099279) \| 0.125704 / 0.176557 (-0.050853) \| 0.189198 / 0.737135 (-0.547937) \| 0.126334 / 0.296338 (-0.170004) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.502226 / 0.215209 (0.287017) \| 5.039133 / 2.077655 (2.961478) \| 2.782352 / 1.504120 (1.278232) \| 2.587654 / 1.541195 (1.046460) \| 2.692588 / 1.468490 (1.224098) \| 0.585672 / 4.584777 (-3.999105) \| 4.553078 / 3.745712 (0.807366) \| 3.864739 / 5.269862 (-1.405123) \| 2.536109 / 4.565676 (-2.029567) \| 0.069567 / 0.424275 (-0.354708) \| 0.008749 / 0.007607 (0.001142) \| 0.620645 / 0.226044 (0.394601) \| 6.247286 / 2.268929 (3.978357) \| 3.345293 / 55.444624 (-52.099332) \| 2.873970 / 6.876477 (-4.002507) \| 3.123190 / 2.142072 (0.981118) \| 0.687391 / 4.805227 (-4.117837) \| 0.159046 / 6.500664 (-6.341618) \| 0.071019 / 0.075469 (-0.004450) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.728724 / 1.841788 (-0.113064) \| 22.828390 / 8.074308 (14.754082) \| 17.305225 / 10.191392 (7.113833) \| 0.176571 / 0.680424 (-0.503853) \| 0.023837 / 0.534201 (-0.510364) \| 0.467935 / 0.579283 (-0.111348) \| 0.503701 / 0.434364 (0.069337) \| 0.558140 / 0.540337 (0.017803) \| 0.789326 / 1.386936 (-0.597610) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d357eb4b499cd530c3f4e626f2825a50ee6c8aa \"CML watermark\")\n" ]	2023-10-09T09:23:23Z	2023-10-10T07:13:22Z	2023-10-10T07:03:04Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6286.diff", "html_url": "https://github.com/huggingface/datasets/pull/6286", "merged_at": "2023-10-10T07:03:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/6286.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6286" }	Create `DefunctDatasetError` as a specific error to be raised when a dataset is defunct and no longer accessible. See Hub discussion: https://huggingface.co/datasets/the_pile_books3/discussions/7#6523c13a94f3a1a2092d251b	{ "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/6286/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6285	6,285	TypeError: expected str, bytes or os.PathLike object, not dict	{ "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andysingal", "id": 20493493, "login": "andysingal", "node_id": "MDQ6VXNlcjIwNDkzNDkz", "organizations_url": "https://api.github.com/users/andysingal/orgs", "received_events_url": "https://api.github.com/users/andysingal/received_events", "repos_url": "https://api.github.com/users/andysingal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "type": "User", "url": "https://api.github.com/users/andysingal", "user_view_type": "public" }	[]	open	false	[ "You should be able to load the images by modifying the `load_dataset` call like this:\r\n```python\r\ndataset = load_dataset(\"imagefolder\", data_dir=\"/content/datasets/PotholeDetectionYOLOv8-1\")\r\n```\r\n\r\nThe `imagefolder` builder expects the image files to be in `path/label/image_file` (e.g. .`.../train/dog/image_1.jpg`), so the solution for the labels in your case is to create metadata files (one for each split; as explained [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder)) that map the images to their labels.", "> You should be able to load the images by modifying the `load_dataset` call like this:\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"imagefolder\", data_dir=\"/content/datasets/PotholeDetectionYOLOv8-1\")\r\n> ```\r\n> \r\n> The `imagefolder` builder expects the image files to be in `path/label/image_file` (e.g. .`.../train/dog/image_1.jpg`), so the solution for the labels in your case is to create metadata files (one for each split; as explained [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder)) that map the images to their labels.\r\n\r\nI tried like this but only uploads images and not labels, Andyrasika/potholes-dataset", "As explained in my previous comment, you need to define metadata files to load the labels or update the paths to be in the format `train/label/image` (`train- image /n -labels` is not supported by the loader).", "I downloaded my file after annotating using roboflow . It gives train-\r\nimages, labels , test- images, labels , valid- images, labels . I hope it\r\ngives you an idea of the dataset . Please advise on this dataset\r\n\r\nOn Tue, Oct 10, 2023 at 18:12 Mario Šaško *@.> wrote:\r\n\r\n> As explained in my previous comment, you need to define metadata files to\r\n> load the labels or update the paths to be in the format train/label/image\r\n> (train- image /n -labels is not supported by the loader).\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6285#issuecomment-1755335215>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AE4LJNN56FWWTSBYTSTUWHLX6U7CVAVCNFSM6AAAAAA5YHCSTGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONJVGMZTKMRRGU>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n" ]	2023-10-09T04:56:26Z	2023-10-10T13:17:33Z	null	NONE	null	null	### Describe the bug my dataset is in form : train- image /n -labels and tried the code: ``` from datasets import load_dataset data_files = { "train": "/content/datasets/PotholeDetectionYOLOv8-1/train/", "validation": "/content/datasets/PotholeDetectionYOLOv8-1/valid/", "test": "/content/datasets/PotholeDetectionYOLOv8-1/test/" } dataset = load_dataset("imagefolder", data_dir=data_files) dataset ``` got error: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-29-2ef1926f73d9>](https://localhost:8080/#) in <cell line: 8>() 6 "test": "/content/datasets/PotholeDetectionYOLOv8-1/test/" 7 } ----> 8 dataset = load_dataset("imagefolder", data_dir=data_files) 9 dataset 6 frames [/usr/lib/python3.10/pathlib.py](https://localhost:8080/#) in _parse_args(cls, args) 576 parts += a._parts 577 else: --> 578 a = os.fspath(a) 579 if isinstance(a, str): 580 # Force-cast str subclasses to str (issue #21127) TypeError: expected str, bytes or os.PathLike object, not dict ``` ### Steps to reproduce the bug as share above ### Expected behavior load images and labels , but my dataset only uploads images - https://huggingface.co/datasets/Andyrasika/potholes-dataset ### Environment info colab pro	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/6285/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/6284	6,284	Add Belebele multiple-choice machine reading comprehension (MRC) dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/64583161?v=4", "events_url": "https://api.github.com/users/rajveer43/events{/privacy}", "followers_url": "https://api.github.com/users/rajveer43/followers", "following_url": "https://api.github.com/users/rajveer43/following{/other_user}", "gists_url": "https://api.github.com/users/rajveer43/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rajveer43", "id": 64583161, "login": "rajveer43", "node_id": "MDQ6VXNlcjY0NTgzMTYx", "organizations_url": "https://api.github.com/users/rajveer43/orgs", "received_events_url": "https://api.github.com/users/rajveer43/received_events", "repos_url": "https://api.github.com/users/rajveer43/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rajveer43/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rajveer43/subscriptions", "type": "User", "url": "https://api.github.com/users/rajveer43", "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	[ "This dataset is already available on the Hub: https://huggingface.co/datasets/facebook/belebele.\r\n" ]	2023-10-06T06:58:03Z	2023-10-06T13:26:51Z	2023-10-06T13:26:51Z	NONE	null	null	### Feature request Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems. Please refer to paper for more details, [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884). ## Composition - 900 questions per language variant - 488 distinct passages, there are 1-2 associated questions for each. - For each question, there is 4 multiple-choice answers, exactly 1 of which is correct. - 122 language/language variants (including English). - 900 x 122 = 109,800 total questions. ### Motivation official repo https://github.com/facebookresearch/belebele ### Your 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": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6284/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/6283	6,283	Fix array cast/embed with null values	{ "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) \| 0.006278 / 0.011353 (-0.005075) \| 0.003692 / 0.011008 (-0.007316) \| 0.080464 / 0.038508 (0.041956) \| 0.064751 / 0.023109 (0.041642) \| 0.318586 / 0.275898 (0.042688) \| 0.351435 / 0.323480 (0.027955) \| 0.005044 / 0.007986 (-0.002942) \| 0.003034 / 0.004328 (-0.001295) \| 0.063710 / 0.004250 (0.059460) \| 0.050607 / 0.037052 (0.013555) \| 0.318491 / 0.258489 (0.060001) \| 0.365688 / 0.293841 (0.071847) \| 0.027818 / 0.128546 (-0.100729) \| 0.008119 / 0.075646 (-0.067527) \| 0.262141 / 0.419271 (-0.157131) \| 0.044710 / 0.043533 (0.001177) \| 0.318875 / 0.255139 (0.063736) \| 0.344559 / 0.283200 (0.061360) \| 0.022861 / 0.141683 (-0.118822) \| 1.452402 / 1.452155 (0.000247) \| 1.502340 / 1.492716 (0.009624) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219355 / 0.018006 (0.201349) \| 0.433311 / 0.000490 (0.432822) \| 0.006545 / 0.000200 (0.006345) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024538 / 0.037411 (-0.012874) \| 0.073346 / 0.014526 (0.058821) \| 0.083824 / 0.176557 (-0.092733) \| 0.145176 / 0.737135 (-0.591959) \| 0.085941 / 0.296338 (-0.210397) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395153 / 0.215209 (0.179944) \| 3.944734 / 2.077655 (1.867080) \| 1.883910 / 1.504120 (0.379790) \| 1.690560 / 1.541195 (0.149365) \| 1.775180 / 1.468490 (0.306690) \| 0.506873 / 4.584777 (-4.077904) \| 3.111095 / 3.745712 (-0.634617) \| 2.915358 / 5.269862 (-2.354504) \| 1.892886 / 4.565676 (-2.672791) \| 0.058690 / 0.424275 (-0.365585) \| 0.006550 / 0.007607 (-0.001057) \| 0.463372 / 0.226044 (0.237328) \| 4.640511 / 2.268929 (2.371583) \| 2.321051 / 55.444624 (-53.123573) \| 1.986330 / 6.876477 (-4.890147) \| 2.160046 / 2.142072 (0.017973) \| 0.597833 / 4.805227 (-4.207394) \| 0.127946 / 6.500664 (-6.372718) \| 0.059709 / 0.075469 (-0.015760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278966 / 1.841788 (-0.562822) \| 17.863102 / 8.074308 (9.788794) \| 13.896057 / 10.191392 (3.704665) \| 0.147512 / 0.680424 (-0.532912) \| 0.016771 / 0.534201 (-0.517430) \| 0.335260 / 0.579283 (-0.244024) \| 0.383019 / 0.434364 (-0.051345) \| 0.384821 / 0.540337 (-0.155516) \| 0.550143 / 1.386936 (-0.836793) \|\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) \| 0.006234 / 0.011353 (-0.005118) \| 0.003695 / 0.011008 (-0.007313) \| 0.062654 / 0.038508 (0.024146) \| 0.059397 / 0.023109 (0.036287) \| 0.458375 / 0.275898 (0.182477) \| 0.488951 / 0.323480 (0.165471) \| 0.004971 / 0.007986 (-0.003014) \| 0.002914 / 0.004328 (-0.001415) \| 0.061184 / 0.004250 (0.056934) \| 0.051246 / 0.037052 (0.014194) \| 0.458035 / 0.258489 (0.199546) \| 0.490838 / 0.293841 (0.196997) \| 0.028746 / 0.128546 (-0.099800) \| 0.008167 / 0.075646 (-0.067480) \| 0.068006 / 0.419271 (-0.351265) \| 0.041809 / 0.043533 (-0.001724) \| 0.453896 / 0.255139 (0.198757) \| 0.477583 / 0.283200 (0.194383) \| 0.020906 / 0.141683 (-0.120777) \| 1.443275 / 1.452155 (-0.008879) \| 1.493431 / 1.492716 (0.000714) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219903 / 0.018006 (0.201896) \| 0.410275 / 0.000490 (0.409785) \| 0.003919 / 0.000200 (0.003719) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027850 / 0.037411 (-0.009561) \| 0.080444 / 0.014526 (0.065918) \| 0.089943 / 0.176557 (-0.086614) \| 0.145810 / 0.737135 (-0.591326) \| 0.090908 / 0.296338 (-0.205430) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.464386 / 0.215209 (0.249177) \| 4.633787 / 2.077655 (2.556133) \| 2.581658 / 1.504120 (1.077538) \| 2.408486 / 1.541195 (0.867291) \| 2.460491 / 1.468490 (0.992001) \| 0.507512 / 4.584777 (-4.077265) \| 3.190363 / 3.745712 (-0.555349) \| 2.895581 / 5.269862 (-2.374280) \| 1.871506 / 4.565676 (-2.694171) \| 0.058469 / 0.424275 (-0.365806) \| 0.006526 / 0.007607 (-0.001082) \| 0.537641 / 0.226044 (0.311596) \| 5.396660 / 2.268929 (3.127731) \| 3.027028 / 55.444624 (-52.417596) \| 2.703771 / 6.876477 (-4.172705) \| 2.865576 / 2.142072 (0.723503) \| 0.600103 / 4.805227 (-4.205124) \| 0.127109 / 6.500664 (-6.373555) \| 0.060985 / 0.075469 (-0.014484) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.365030 / 1.841788 (-0.476758) \| 17.988218 / 8.074308 (9.913909) \| 14.900796 / 10.191392 (4.709404) \| 0.158211 / 0.680424 (-0.522213) \| 0.018291 / 0.534201 (-0.515910) \| 0.337437 / 0.579283 (-0.241846) \| 0.383710 / 0.434364 (-0.050654) \| 0.392341 / 0.540337 (-0.147997) \| 0.561584 / 1.386936 (-0.825352) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7571ab4b0d9b67b767c55db400b4ffac0f752f1 \"CML watermark\")\n", "CI failures are unrelated", "I also plan to address https://github.com/huggingface/datasets/issues/6280#issuecomment-1749310065 in this PR :).", "Oh ok, ping me again whenever you want another review :)", "Have you had a chance to continue this ? I can also take a look if you want", "Yes, I'll finish it next week :).", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6283). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq Feel free to review this again. I've bumped PyArrow to 12.0.0 to simplify the implementation (no need for custom `array_concat` and less `pa.Array.from_buffers`). However, this makes `apache-beam` complain as it only supports `<12.0.0`. The next `apache-beam` release will set this boundary to `<15.0.0.`, so I think the only solution is to wait for it to be published.", "<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) \| 0.005188 / 0.011353 (-0.006165) \| 0.003997 / 0.011008 (-0.007011) \| 0.062642 / 0.038508 (0.024134) \| 0.028913 / 0.023109 (0.005804) \| 0.248289 / 0.275898 (-0.027609) \| 0.268084 / 0.323480 (-0.055396) \| 0.004093 / 0.007986 (-0.003893) \| 0.002822 / 0.004328 (-0.001506) \| 0.048263 / 0.004250 (0.044012) \| 0.041520 / 0.037052 (0.004468) \| 0.263277 / 0.258489 (0.004788) \| 0.289835 / 0.293841 (-0.004006) \| 0.027621 / 0.128546 (-0.100925) \| 0.010793 / 0.075646 (-0.064853) \| 0.207624 / 0.419271 (-0.211648) \| 0.035597 / 0.043533 (-0.007936) \| 0.245706 / 0.255139 (-0.009433) \| 0.268157 / 0.283200 (-0.015043) \| 0.017310 / 0.141683 (-0.124373) \| 1.130656 / 1.452155 (-0.321499) \| 1.162134 / 1.492716 (-0.330583) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094081 / 0.018006 (0.076075) \| 0.302298 / 0.000490 (0.301809) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.019072 / 0.037411 (-0.018339) \| 0.061162 / 0.014526 (0.046636) \| 0.072820 / 0.176557 (-0.103737) \| 0.122628 / 0.737135 (-0.614507) \| 0.074962 / 0.296338 (-0.221377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277858 / 0.215209 (0.062649) \| 2.688478 / 2.077655 (0.610823) \| 1.397366 / 1.504120 (-0.106754) \| 1.285078 / 1.541195 (-0.256117) \| 1.291559 / 1.468490 (-0.176931) \| 0.553646 / 4.584777 (-4.031131) \| 2.355737 / 3.745712 (-1.389975) \| 2.773025 / 5.269862 (-2.496836) \| 1.731195 / 4.565676 (-2.834481) \| 0.061372 / 0.424275 (-0.362903) \| 0.004928 / 0.007607 (-0.002679) \| 0.321703 / 0.226044 (0.095659) \| 3.212927 / 2.268929 (0.943999) \| 1.727104 / 55.444624 (-53.717521) \| 1.479430 / 6.876477 (-5.397047) \| 1.513436 / 2.142072 (-0.628637) \| 0.629913 / 4.805227 (-4.175315) \| 0.114607 / 6.500664 (-6.386057) \| 0.041707 / 0.075469 (-0.033762) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976060 / 1.841788 (-0.865727) \| 11.575163 / 8.074308 (3.500855) \| 9.521390 / 10.191392 (-0.670003) \| 0.138725 / 0.680424 (-0.541699) \| 0.013752 / 0.534201 (-0.520449) \| 0.286252 / 0.579283 (-0.293031) \| 0.263420 / 0.434364 (-0.170944) \| 0.325531 / 0.540337 (-0.214806) \| 0.419466 / 1.386936 (-0.967470) \|\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) \| 0.005615 / 0.011353 (-0.005738) \| 0.003884 / 0.011008 (-0.007124) \| 0.049563 / 0.038508 (0.011055) \| 0.032573 / 0.023109 (0.009464) \| 0.276917 / 0.275898 (0.001019) \| 0.298403 / 0.323480 (-0.025077) \| 0.004367 / 0.007986 (-0.003618) \| 0.002794 / 0.004328 (-0.001534) \| 0.049105 / 0.004250 (0.044855) \| 0.045597 / 0.037052 (0.008545) \| 0.289762 / 0.258489 (0.031273) \| 0.318440 / 0.293841 (0.024599) \| 0.051883 / 0.128546 (-0.076664) \| 0.010644 / 0.075646 (-0.065003) \| 0.057455 / 0.419271 (-0.361816) \| 0.033667 / 0.043533 (-0.009866) \| 0.274424 / 0.255139 (0.019285) \| 0.295890 / 0.283200 (0.012690) \| 0.017029 / 0.141683 (-0.124654) \| 1.130123 / 1.452155 (-0.322031) \| 1.214827 / 1.492716 (-0.277889) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094882 / 0.018006 (0.076876) \| 0.302505 / 0.000490 (0.302015) \| 0.000228 / 0.000200 (0.000028) \| 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) \| 0.021695 / 0.037411 (-0.015716) \| 0.075196 / 0.014526 (0.060670) \| 0.086641 / 0.176557 (-0.089915) \| 0.124893 / 0.737135 (-0.612243) \| 0.088765 / 0.296338 (-0.207574) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303388 / 0.215209 (0.088179) \| 2.934506 / 2.077655 (0.856852) \| 1.608607 / 1.504120 (0.104487) \| 1.494632 / 1.541195 (-0.046563) \| 1.512801 / 1.468490 (0.044310) \| 0.558563 / 4.584777 (-4.026214) \| 2.383212 / 3.745712 (-1.362500) \| 2.634629 / 5.269862 (-2.635233) \| 1.729319 / 4.565676 (-2.836357) \| 0.062345 / 0.424275 (-0.361930) \| 0.004981 / 0.007607 (-0.002626) \| 0.358333 / 0.226044 (0.132289) \| 3.484229 / 2.268929 (1.215301) \| 2.010043 / 55.444624 (-53.434581) \| 1.693733 / 6.876477 (-5.182744) \| 1.824150 / 2.142072 (-0.317922) \| 0.650835 / 4.805227 (-4.154392) \| 0.115933 / 6.500664 (-6.384732) \| 0.041270 / 0.075469 (-0.034199) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007949 / 1.841788 (-0.833838) \| 12.000085 / 8.074308 (3.925776) \| 10.453119 / 10.191392 (0.261727) \| 0.143583 / 0.680424 (-0.536840) \| 0.015937 / 0.534201 (-0.518264) \| 0.286653 / 0.579283 (-0.292631) \| 0.272359 / 0.434364 (-0.162005) \| 0.330520 / 0.540337 (-0.209818) \| 0.417015 / 1.386936 (-0.969921) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac05bac20fcc8e0e22a852707162e15a7e2ae357 \"CML watermark\")\n", "Still the problem is occured.\r\nHuggingface is sucks 🤮🤮🤮🤮" ]	2023-10-05T15:24:05Z	2024-07-04T07:24:20Z	2024-02-06T19:24:19Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6283.diff", "html_url": "https://github.com/huggingface/datasets/pull/6283", "merged_at": "2024-02-06T19:24:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6283.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6283" }	Fixes issues with casting/embedding PyArrow list arrays with null values. It also bumps the required PyArrow version to 12.0.0 (over 9 months old) to simplify the implementation. Fix #6280, fix #6311, fix #6360 (Also fixes https://github.com/huggingface/datasets/issues/5430 to make Beam compatible with PyArrow>=12.0.0)	{ "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/6283/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6282	6,282	Drop data_files duplicates	{ "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) \| 0.006934 / 0.011353 (-0.004419) \| 0.004097 / 0.011008 (-0.006911) \| 0.084662 / 0.038508 (0.046154) \| 0.077106 / 0.023109 (0.053996) \| 0.355035 / 0.275898 (0.079137) \| 0.381466 / 0.323480 (0.057986) \| 0.004182 / 0.007986 (-0.003803) \| 0.003411 / 0.004328 (-0.000917) \| 0.065279 / 0.004250 (0.061029) \| 0.058192 / 0.037052 (0.021140) \| 0.372363 / 0.258489 (0.113874) \| 0.401621 / 0.293841 (0.107780) \| 0.031719 / 0.128546 (-0.096827) \| 0.008753 / 0.075646 (-0.066893) \| 0.287125 / 0.419271 (-0.132146) \| 0.052943 / 0.043533 (0.009410) \| 0.349680 / 0.255139 (0.094541) \| 0.364004 / 0.283200 (0.080805) \| 0.026705 / 0.141683 (-0.114977) \| 1.472708 / 1.452155 (0.020553) \| 1.556559 / 1.492716 (0.063842) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224868 / 0.018006 (0.206862) \| 0.458793 / 0.000490 (0.458304) \| 0.009434 / 0.000200 (0.009234) \| 0.000356 / 0.000054 (0.000301) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029670 / 0.037411 (-0.007741) \| 0.086517 / 0.014526 (0.071991) \| 0.097342 / 0.176557 (-0.079215) \| 0.153722 / 0.737135 (-0.583413) \| 0.098465 / 0.296338 (-0.197874) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400739 / 0.215209 (0.185530) \| 3.998087 / 2.077655 (1.920432) \| 2.025772 / 1.504120 (0.521652) \| 1.858679 / 1.541195 (0.317485) \| 1.951573 / 1.468490 (0.483083) \| 0.483028 / 4.584777 (-4.101749) \| 3.554085 / 3.745712 (-0.191627) \| 3.306983 / 5.269862 (-1.962879) \| 2.087043 / 4.565676 (-2.478633) \| 0.057127 / 0.424275 (-0.367148) \| 0.007252 / 0.007607 (-0.000355) \| 0.480180 / 0.226044 (0.254136) \| 4.787183 / 2.268929 (2.518255) \| 2.489667 / 55.444624 (-52.954957) \| 2.150774 / 6.876477 (-4.725703) \| 2.403197 / 2.142072 (0.261124) \| 0.581843 / 4.805227 (-4.223384) \| 0.134915 / 6.500664 (-6.365749) \| 0.061283 / 0.075469 (-0.014186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285700 / 1.841788 (-0.556088) \| 19.474093 / 8.074308 (11.399785) \| 14.336349 / 10.191392 (4.144957) \| 0.170932 / 0.680424 (-0.509492) \| 0.018348 / 0.534201 (-0.515853) \| 0.391909 / 0.579283 (-0.187374) \| 0.414706 / 0.434364 (-0.019658) \| 0.458156 / 0.540337 (-0.082182) \| 0.656303 / 1.386936 (-0.730633) \|\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) \| 0.006738 / 0.011353 (-0.004615) \| 0.004029 / 0.011008 (-0.006979) \| 0.064411 / 0.038508 (0.025903) \| 0.078225 / 0.023109 (0.055116) \| 0.408468 / 0.275898 (0.132569) \| 0.445585 / 0.323480 (0.122105) \| 0.005490 / 0.007986 (-0.002495) \| 0.003419 / 0.004328 (-0.000910) \| 0.063966 / 0.004250 (0.059715) \| 0.056779 / 0.037052 (0.019727) \| 0.415258 / 0.258489 (0.156769) \| 0.461258 / 0.293841 (0.167418) \| 0.032051 / 0.128546 (-0.096495) \| 0.008471 / 0.075646 (-0.067176) \| 0.071004 / 0.419271 (-0.348267) \| 0.049068 / 0.043533 (0.005536) \| 0.409575 / 0.255139 (0.154436) \| 0.430748 / 0.283200 (0.147548) \| 0.023784 / 0.141683 (-0.117899) \| 1.507894 / 1.452155 (0.055739) \| 1.586575 / 1.492716 (0.093859) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228574 / 0.018006 (0.210568) \| 0.451389 / 0.000490 (0.450900) \| 0.006312 / 0.000200 (0.006112) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033391 / 0.037411 (-0.004020) \| 0.096816 / 0.014526 (0.082290) \| 0.107269 / 0.176557 (-0.069288) \| 0.159749 / 0.737135 (-0.577387) \| 0.108240 / 0.296338 (-0.188098) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437643 / 0.215209 (0.222434) \| 4.378173 / 2.077655 (2.300518) \| 2.367218 / 1.504120 (0.863098) \| 2.229493 / 1.541195 (0.688298) \| 2.329849 / 1.468490 (0.861359) \| 0.494985 / 4.584777 (-4.089792) \| 3.578540 / 3.745712 (-0.167172) \| 3.338220 / 5.269862 (-1.931642) \| 2.092482 / 4.565676 (-2.473194) \| 0.058495 / 0.424275 (-0.365780) \| 0.007396 / 0.007607 (-0.000211) \| 0.511001 / 0.226044 (0.284957) \| 5.113497 / 2.268929 (2.844568) \| 2.806215 / 55.444624 (-52.638409) \| 2.485428 / 6.876477 (-4.391048) \| 2.764907 / 2.142072 (0.622835) \| 0.598824 / 4.805227 (-4.206404) \| 0.134988 / 6.500664 (-6.365676) \| 0.061752 / 0.075469 (-0.013717) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.365583 / 1.841788 (-0.476205) \| 20.270297 / 8.074308 (12.195989) \| 15.331673 / 10.191392 (5.140281) \| 0.166152 / 0.680424 (-0.514272) \| 0.020678 / 0.534201 (-0.513523) \| 0.394821 / 0.579283 (-0.184462) \| 0.420493 / 0.434364 (-0.013871) \| 0.468551 / 0.540337 (-0.071787) \| 0.654903 / 1.386936 (-0.732033) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f268dd4ad4fb6dada15937d57fb367cb2810162 \"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) \| 0.007803 / 0.011353 (-0.003550) \| 0.004664 / 0.011008 (-0.006344) \| 0.099908 / 0.038508 (0.061400) \| 0.090674 / 0.023109 (0.067565) \| 0.406009 / 0.275898 (0.130111) \| 0.465098 / 0.323480 (0.141618) \| 0.004667 / 0.007986 (-0.003319) \| 0.003880 / 0.004328 (-0.000449) \| 0.076552 / 0.004250 (0.072301) \| 0.066345 / 0.037052 (0.029292) \| 0.419195 / 0.258489 (0.160706) \| 0.478581 / 0.293841 (0.184741) \| 0.036967 / 0.128546 (-0.091579) \| 0.010000 / 0.075646 (-0.065647) \| 0.347126 / 0.419271 (-0.072145) \| 0.062265 / 0.043533 (0.018733) \| 0.406653 / 0.255139 (0.151514) \| 0.439044 / 0.283200 (0.155845) \| 0.031289 / 0.141683 (-0.110394) \| 1.797674 / 1.452155 (0.345520) \| 1.835183 / 1.492716 (0.342467) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268194 / 0.018006 (0.250187) \| 0.493614 / 0.000490 (0.493124) \| 0.015636 / 0.000200 (0.015436) \| 0.000417 / 0.000054 (0.000362) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034188 / 0.037411 (-0.003223) \| 0.099127 / 0.014526 (0.084601) \| 0.113949 / 0.176557 (-0.062607) \| 0.181209 / 0.737135 (-0.555926) \| 0.114943 / 0.296338 (-0.181395) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455767 / 0.215209 (0.240558) \| 4.542947 / 2.077655 (2.465293) \| 2.214605 / 1.504120 (0.710485) \| 2.015163 / 1.541195 (0.473969) \| 2.084945 / 1.468490 (0.616455) \| 0.583827 / 4.584777 (-4.000950) \| 4.187009 / 3.745712 (0.441297) \| 3.920841 / 5.269862 (-1.349020) \| 2.447260 / 4.565676 (-2.118417) \| 0.069139 / 0.424275 (-0.355137) \| 0.008734 / 0.007607 (0.001127) \| 0.544673 / 0.226044 (0.318629) \| 5.445094 / 2.268929 (3.176165) \| 2.788284 / 55.444624 (-52.656340) \| 2.395863 / 6.876477 (-4.480614) \| 2.622632 / 2.142072 (0.480560) \| 0.703931 / 4.805227 (-4.101297) \| 0.160502 / 6.500664 (-6.340162) \| 0.073734 / 0.075469 (-0.001735) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.498992 / 1.841788 (-0.342795) \| 22.761476 / 8.074308 (14.687168) \| 17.123919 / 10.191392 (6.932527) \| 0.170272 / 0.680424 (-0.510151) \| 0.021307 / 0.534201 (-0.512894) \| 0.467548 / 0.579283 (-0.111735) \| 0.480777 / 0.434364 (0.046413) \| 0.542168 / 0.540337 (0.001830) \| 0.771092 / 1.386936 (-0.615844) \|\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) \| 0.007923 / 0.011353 (-0.003430) \| 0.004664 / 0.011008 (-0.006344) \| 0.077795 / 0.038508 (0.039286) \| 0.090293 / 0.023109 (0.067184) \| 0.494682 / 0.275898 (0.218784) \| 0.539973 / 0.323480 (0.216494) \| 0.006302 / 0.007986 (-0.001684) \| 0.003794 / 0.004328 (-0.000535) \| 0.076567 / 0.004250 (0.072317) \| 0.067141 / 0.037052 (0.030089) \| 0.501279 / 0.258489 (0.242790) \| 0.555670 / 0.293841 (0.261829) \| 0.037773 / 0.128546 (-0.090773) \| 0.009930 / 0.075646 (-0.065716) \| 0.084839 / 0.419271 (-0.334433) \| 0.056876 / 0.043533 (0.013344) \| 0.499329 / 0.255139 (0.244190) \| 0.518449 / 0.283200 (0.235249) \| 0.026041 / 0.141683 (-0.115642) \| 1.787259 / 1.452155 (0.335105) \| 1.853505 / 1.492716 (0.360788) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238413 / 0.018006 (0.220407) \| 0.488889 / 0.000490 (0.488399) \| 0.007476 / 0.000200 (0.007277) \| 0.000141 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038701 / 0.037411 (0.001290) \| 0.115391 / 0.014526 (0.100865) \| 0.125553 / 0.176557 (-0.051004) \| 0.190267 / 0.737135 (-0.546868) \| 0.126401 / 0.296338 (-0.169937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.509270 / 0.215209 (0.294061) \| 5.087631 / 2.077655 (3.009976) \| 2.745863 / 1.504120 (1.241743) \| 2.560259 / 1.541195 (1.019064) \| 2.653124 / 1.468490 (1.184634) \| 0.582118 / 4.584777 (-4.002659) \| 4.181144 / 3.745712 (0.435431) \| 3.871179 / 5.269862 (-1.398683) \| 2.459849 / 4.565676 (-2.105827) \| 0.068844 / 0.424275 (-0.355431) \| 0.008672 / 0.007607 (0.001065) \| 0.604898 / 0.226044 (0.378854) \| 6.073263 / 2.268929 (3.804334) \| 3.366638 / 55.444624 (-52.077986) \| 2.937261 / 6.876477 (-3.939215) \| 3.181173 / 2.142072 (1.039100) \| 0.700478 / 4.805227 (-4.104750) \| 0.158361 / 6.500664 (-6.342303) \| 0.072860 / 0.075469 (-0.002609) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.621363 / 1.841788 (-0.220425) \| 23.614315 / 8.074308 (15.540007) \| 17.607213 / 10.191392 (7.415821) \| 0.198031 / 0.680424 (-0.482393) \| 0.023859 / 0.534201 (-0.510342) \| 0.474674 / 0.579283 (-0.104609) \| 0.491173 / 0.434364 (0.056809) \| 0.581995 / 0.540337 (0.041658) \| 0.792168 / 1.386936 (-0.594768) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56fa9645fd24e083adee3cfd0f7d972fce391f0e \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6282). 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) \| 0.004779 / 0.011353 (-0.006574) \| 0.002916 / 0.011008 (-0.008092) \| 0.061962 / 0.038508 (0.023454) \| 0.029537 / 0.023109 (0.006428) \| 0.242574 / 0.275898 (-0.033324) \| 0.268585 / 0.323480 (-0.054894) \| 0.004006 / 0.007986 (-0.003979) \| 0.002434 / 0.004328 (-0.001895) \| 0.048289 / 0.004250 (0.044039) \| 0.045534 / 0.037052 (0.008481) \| 0.248251 / 0.258489 (-0.010239) \| 0.277037 / 0.293841 (-0.016804) \| 0.023728 / 0.128546 (-0.104818) \| 0.007295 / 0.075646 (-0.068351) \| 0.205813 / 0.419271 (-0.213459) \| 0.059093 / 0.043533 (0.015560) \| 0.244336 / 0.255139 (-0.010803) \| 0.262865 / 0.283200 (-0.020335) \| 0.017232 / 0.141683 (-0.124451) \| 1.126729 / 1.452155 (-0.325426) \| 1.198987 / 1.492716 (-0.293729) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091246 / 0.018006 (0.073240) \| 0.300747 / 0.000490 (0.300258) \| 0.000202 / 0.000200 (0.000003) \| 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) \| 0.018681 / 0.037411 (-0.018731) \| 0.063567 / 0.014526 (0.049041) \| 0.074019 / 0.176557 (-0.102538) \| 0.120856 / 0.737135 (-0.616279) \| 0.076525 / 0.296338 (-0.219814) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282230 / 0.215209 (0.067021) \| 2.731502 / 2.077655 (0.653847) \| 1.473901 / 1.504120 (-0.030219) \| 1.351165 / 1.541195 (-0.190030) \| 1.390582 / 1.468490 (-0.077908) \| 0.398443 / 4.584777 (-4.186334) \| 2.360497 / 3.745712 (-1.385215) \| 2.548158 / 5.269862 (-2.721703) \| 1.552416 / 4.565676 (-3.013260) \| 0.045659 / 0.424275 (-0.378616) \| 0.004778 / 0.007607 (-0.002829) \| 0.330191 / 0.226044 (0.104146) \| 3.262510 / 2.268929 (0.993582) \| 1.823076 / 55.444624 (-53.621549) \| 1.541206 / 6.876477 (-5.335271) \| 1.589069 / 2.142072 (-0.553004) \| 0.472265 / 4.805227 (-4.332963) \| 0.099712 / 6.500664 (-6.400952) \| 0.042803 / 0.075469 (-0.032666) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963022 / 1.841788 (-0.878766) \| 11.998807 / 8.074308 (3.924499) \| 10.526006 / 10.191392 (0.334614) \| 0.140965 / 0.680424 (-0.539459) \| 0.014197 / 0.534201 (-0.520004) \| 0.271668 / 0.579283 (-0.307615) \| 0.263993 / 0.434364 (-0.170371) \| 0.307213 / 0.540337 (-0.233124) \| 0.427411 / 1.386936 (-0.959525) \|\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) \| 0.004761 / 0.011353 (-0.006592) \| 0.002652 / 0.011008 (-0.008357) \| 0.047949 / 0.038508 (0.009441) \| 0.049714 / 0.023109 (0.026604) \| 0.274021 / 0.275898 (-0.001877) \| 0.292413 / 0.323480 (-0.031067) \| 0.003912 / 0.007986 (-0.004074) \| 0.002290 / 0.004328 (-0.002038) \| 0.047320 / 0.004250 (0.043069) \| 0.038061 / 0.037052 (0.001009) \| 0.279318 / 0.258489 (0.020829) \| 0.305167 / 0.293841 (0.011326) \| 0.024595 / 0.128546 (-0.103952) \| 0.006976 / 0.075646 (-0.068671) \| 0.052987 / 0.419271 (-0.366285) \| 0.032454 / 0.043533 (-0.011079) \| 0.273986 / 0.255139 (0.018847) \| 0.297641 / 0.283200 (0.014442) \| 0.017680 / 0.141683 (-0.124003) \| 1.141218 / 1.452155 (-0.310937) \| 1.222543 / 1.492716 (-0.270173) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092880 / 0.018006 (0.074873) \| 0.305080 / 0.000490 (0.304590) \| 0.000215 / 0.000200 (0.000016) \| 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) \| 0.021050 / 0.037411 (-0.016362) \| 0.069676 / 0.014526 (0.055150) \| 0.081082 / 0.176557 (-0.095475) \| 0.119234 / 0.737135 (-0.617902) \| 0.081242 / 0.296338 (-0.215096) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295916 / 0.215209 (0.080707) \| 2.909769 / 2.077655 (0.832115) \| 1.623118 / 1.504120 (0.118998) \| 1.502297 / 1.541195 (-0.038898) \| 1.540290 / 1.468490 (0.071800) \| 0.401176 / 4.584777 (-4.183601) \| 2.427764 / 3.745712 (-1.317948) \| 2.568610 / 5.269862 (-2.701252) \| 1.550486 / 4.565676 (-3.015190) \| 0.046895 / 0.424275 (-0.377380) \| 0.004800 / 0.007607 (-0.002807) \| 0.344524 / 0.226044 (0.118479) \| 3.429189 / 2.268929 (1.160261) \| 1.949738 / 55.444624 (-53.494887) \| 1.681440 / 6.876477 (-5.195037) \| 1.675304 / 2.142072 (-0.466769) \| 0.469663 / 4.805227 (-4.335564) \| 0.097470 / 6.500664 (-6.403194) \| 0.040121 / 0.075469 (-0.035348) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957947 / 1.841788 (-0.883841) \| 11.968455 / 8.074308 (3.894147) \| 10.809763 / 10.191392 (0.618371) \| 0.140603 / 0.680424 (-0.539820) \| 0.015562 / 0.534201 (-0.518638) \| 0.276406 / 0.579283 (-0.302877) \| 0.295267 / 0.434364 (-0.139097) \| 0.315744 / 0.540337 (-0.224593) \| 0.417985 / 1.386936 (-0.968951) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12e01642a6978cbb9d5778c8b7f1c6b20a9887d5 \"CML watermark\")\n", "I've opened #6704 with a cleaner fix for the issue :)" ]	2023-10-05T14:43:08Z	2024-09-02T14:08:35Z	2024-09-02T14:08:35Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6282.diff", "html_url": "https://github.com/huggingface/datasets/pull/6282", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6282.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6282" }	I just added drop_duplicates=True to `.from_patterns`. I used a dict to deduplicate and preserve the order close https://github.com/huggingface/datasets/issues/6259 close https://github.com/huggingface/datasets/issues/6272	{ "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/6282/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6281	6,281	Improve documentation of dataset.from_generator	{ "avatar_url": "https://avatars.githubusercontent.com/u/53510?v=4", "events_url": "https://api.github.com/users/hartmans/events{/privacy}", "followers_url": "https://api.github.com/users/hartmans/followers", "following_url": "https://api.github.com/users/hartmans/following{/other_user}", "gists_url": "https://api.github.com/users/hartmans/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/hartmans", "id": 53510, "login": "hartmans", "node_id": "MDQ6VXNlcjUzNTEw", "organizations_url": "https://api.github.com/users/hartmans/orgs", "received_events_url": "https://api.github.com/users/hartmans/received_events", "repos_url": "https://api.github.com/users/hartmans/repos", "site_admin": false, "starred_url": "https://api.github.com/users/hartmans/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hartmans/subscriptions", "type": "User", "url": "https://api.github.com/users/hartmans", "user_view_type": "public" }	[]	closed	false	[ "I have looked at the doc failures, and I do not think that my change caused the doc build failure, but I'm not 100% sure about that.\r\nI have high confidence that the integration test failures are not something I introduced:-)", "<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) \| 0.008557 / 0.011353 (-0.002796) \| 0.005224 / 0.011008 (-0.005784) \| 0.109402 / 0.038508 (0.070893) \| 0.075008 / 0.023109 (0.051899) \| 0.388910 / 0.275898 (0.113012) \| 0.425481 / 0.323480 (0.102002) \| 0.005046 / 0.007986 (-0.002939) \| 0.004166 / 0.004328 (-0.000162) \| 0.079890 / 0.004250 (0.075639) \| 0.061992 / 0.037052 (0.024940) \| 0.409933 / 0.258489 (0.151444) \| 0.444096 / 0.293841 (0.150255) \| 0.043958 / 0.128546 (-0.084588) \| 0.013655 / 0.075646 (-0.061991) \| 0.402620 / 0.419271 (-0.016651) \| 0.062784 / 0.043533 (0.019251) \| 0.399653 / 0.255139 (0.144514) \| 0.432926 / 0.283200 (0.149727) \| 0.034631 / 0.141683 (-0.107052) \| 1.801450 / 1.452155 (0.349296) \| 1.965007 / 1.492716 (0.472290) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305744 / 0.018006 (0.287738) \| 0.590825 / 0.000490 (0.590335) \| 0.014561 / 0.000200 (0.014361) \| 0.000430 / 0.000054 (0.000375) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030449 / 0.037411 (-0.006962) \| 0.091753 / 0.014526 (0.077227) \| 0.106259 / 0.176557 (-0.070298) \| 0.174599 / 0.737135 (-0.562537) \| 0.107069 / 0.296338 (-0.189269) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.607544 / 0.215209 (0.392335) \| 6.182592 / 2.077655 (4.104937) \| 2.699782 / 1.504120 (1.195663) \| 2.386915 / 1.541195 (0.845720) \| 2.441763 / 1.468490 (0.973273) \| 0.811360 / 4.584777 (-3.773417) \| 5.253799 / 3.745712 (1.508087) \| 4.762054 / 5.269862 (-0.507807) \| 3.045161 / 4.565676 (-1.520515) \| 0.095983 / 0.424275 (-0.328292) \| 0.008653 / 0.007607 (0.001046) \| 0.714218 / 0.226044 (0.488174) \| 7.279325 / 2.268929 (5.010397) \| 3.356107 / 55.444624 (-52.088517) \| 2.765867 / 6.876477 (-4.110610) \| 2.997756 / 2.142072 (0.855684) \| 1.008740 / 4.805227 (-3.796487) \| 0.201462 / 6.500664 (-6.299202) \| 0.075780 / 0.075469 (0.000311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.677034 / 1.841788 (-0.164754) \| 23.546919 / 8.074308 (15.472610) \| 21.576985 / 10.191392 (11.385593) \| 0.239253 / 0.680424 (-0.441171) \| 0.028740 / 0.534201 (-0.505460) \| 0.468519 / 0.579283 (-0.110765) \| 0.593935 / 0.434364 (0.159571) \| 0.536830 / 0.540337 (-0.003507) \| 0.779925 / 1.386936 (-0.607011) \|\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) \| 0.009582 / 0.011353 (-0.001771) \| 0.004971 / 0.011008 (-0.006037) \| 0.081304 / 0.038508 (0.042796) \| 0.077588 / 0.023109 (0.054478) \| 0.486610 / 0.275898 (0.210712) \| 0.580228 / 0.323480 (0.256748) \| 0.006707 / 0.007986 (-0.001279) \| 0.004325 / 0.004328 (-0.000004) \| 0.086170 / 0.004250 (0.081920) \| 0.060591 / 0.037052 (0.023539) \| 0.501723 / 0.258489 (0.243234) \| 0.548633 / 0.293841 (0.254793) \| 0.050306 / 0.128546 (-0.078240) \| 0.017458 / 0.075646 (-0.058188) \| 0.093295 / 0.419271 (-0.325977) \| 0.064588 / 0.043533 (0.021056) \| 0.519395 / 0.255139 (0.264256) \| 0.526021 / 0.283200 (0.242821) \| 0.035795 / 0.141683 (-0.105888) \| 1.792927 / 1.452155 (0.340772) \| 1.956499 / 1.492716 (0.463783) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296249 / 0.018006 (0.278243) \| 0.594482 / 0.000490 (0.593992) \| 0.007318 / 0.000200 (0.007118) \| 0.000182 / 0.000054 (0.000128) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036110 / 0.037411 (-0.001301) \| 0.107924 / 0.014526 (0.093399) \| 0.119975 / 0.176557 (-0.056582) \| 0.177499 / 0.737135 (-0.559636) \| 0.123299 / 0.296338 (-0.173039) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.632994 / 0.215209 (0.417785) \| 6.481663 / 2.077655 (4.404008) \| 3.231259 / 1.504120 (1.727139) \| 2.768298 / 1.541195 (1.227103) \| 2.694543 / 1.468490 (1.226053) \| 0.837384 / 4.584777 (-3.747393) \| 5.405278 / 3.745712 (1.659566) \| 4.639424 / 5.269862 (-0.630437) \| 2.944251 / 4.565676 (-1.621426) \| 0.094978 / 0.424275 (-0.329297) \| 0.008716 / 0.007607 (0.001108) \| 0.795820 / 0.226044 (0.569776) \| 8.514233 / 2.268929 (6.245304) \| 3.800463 / 55.444624 (-51.644161) \| 3.000005 / 6.876477 (-3.876472) \| 3.298853 / 2.142072 (1.156781) \| 0.994112 / 4.805227 (-3.811115) \| 0.209435 / 6.500664 (-6.291229) \| 0.075610 / 0.075469 (0.000141) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.681127 / 1.841788 (-0.160661) \| 23.874465 / 8.074308 (15.800156) \| 21.638567 / 10.191392 (11.447175) \| 0.233303 / 0.680424 (-0.447121) \| 0.032504 / 0.534201 (-0.501697) \| 0.460462 / 0.579283 (-0.118821) \| 0.560043 / 0.434364 (0.125679) \| 0.555059 / 0.540337 (0.014721) \| 0.831444 / 1.386936 (-0.555492) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#faada1742e1f25fce9cc5691ec11d3f91d4aa120 \"CML watermark\")\n" ]	2023-10-05T14:34:49Z	2023-10-05T19:09:07Z	2023-10-05T18:57:41Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6281.diff", "html_url": "https://github.com/huggingface/datasets/pull/6281", "merged_at": "2023-10-05T18:57:41Z", "patch_url": "https://github.com/huggingface/datasets/pull/6281.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6281" }	Improve documentation to clarify sharding behavior (#6270)	{ "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/6281/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6280	6,280	Couldn't cast array of type fixed_size_list to Sequence(Value(float64))	{ "avatar_url": "https://avatars.githubusercontent.com/u/1000442?v=4", "events_url": "https://api.github.com/users/jmif/events{/privacy}", "followers_url": "https://api.github.com/users/jmif/followers", "following_url": "https://api.github.com/users/jmif/following{/other_user}", "gists_url": "https://api.github.com/users/jmif/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jmif", "id": 1000442, "login": "jmif", "node_id": "MDQ6VXNlcjEwMDA0NDI=", "organizations_url": "https://api.github.com/users/jmif/orgs", "received_events_url": "https://api.github.com/users/jmif/received_events", "repos_url": "https://api.github.com/users/jmif/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jmif/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jmif/subscriptions", "type": "User", "url": "https://api.github.com/users/jmif", "user_view_type": "public" }	[]	closed	false	[ "Thanks for reporting! I've opened a PR with a fix.", "Thanks for the quick response @mariosasko! I just installed your branch via `poetry add 'git+https://github.com/huggingface/datasets#fix-array_values'` and I can confirm it works on the example provided.\r\n\r\nFollow up question for you, should `None`s be supported in these types of features as they are in others?\r\n\r\nFor example, the following script:\r\n\r\n```\r\nfrom datasets import Features, Value, Sequence, ClassLabel, Dataset\r\n\r\ndataset_features = Features({\r\n 'text': Value('string'),\r\n 'embedding': Sequence(Value('double'), length=2),\r\n 'categories': Sequence(ClassLabel(names=sorted([\r\n 'one',\r\n 'two',\r\n 'three'\r\n ]))),\r\n})\r\n\r\ndataset = Dataset.from_dict(\r\n {\r\n 'text': ['A'] * 10000,\r\n \"embedding\": [None] * 10000, # THIS LINE CHANGED\r\n 'categories': [[0]] * 10000,\r\n },\r\n features=dataset_features\r\n)\r\n\r\ndef test_mapper(r):\r\n r['text'] = list(map(lambda t: t + ' b', r['text']))\r\n return r\r\n\r\n\r\ndataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2)\r\n```\r\n\r\nfails with\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py\", line 125, in worker\r\n result = (True, func(args, kwds))\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py\", line 1354, in _write_generator_to_queue\r\n for i, result in enumerate(func(kwargs)):\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3493, in _map_single\r\n writer.write_batch(batch)\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 549, in write_batch\r\n array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 1831, in wrapper\r\n return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks])\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 1831, in <listcomp>\r\n return pa.chunked_array([func(chunk, args, **kwargs) for chunk in array.chunks])\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 2160, in cast_array_to_feature\r\n raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\nTypeError: Couldn't cast array of type\r\nfixed_size_list<item: double>[2]\r\nto\r\nSequence(feature=Value(dtype='float64', id=None), length=2, id=None)\r\n```\r\n\r\nIdeally we can have empty embedding columns as well!", "This part of PyArrow is buggy and inconsistent regarding features implemented across the types, so the only option is to operate on the Arrow buffer level to fix issues such as the above one.", "Ok - can you take the POC I did [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e)? Happy to turn this into an actual PR but would appreciate feedback on the implementation before I take another pass!" ]	2023-10-05T12:48:31Z	2024-02-06T19:24:20Z	2024-02-06T19:24:20Z	NONE	null	null	### Describe the bug I have a dataset with an embedding column, when I try to map that dataset I get the following exception: ``` Traceback (most recent call last): File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3189, in map for rank, done, content in iflatmap_unordered( File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py", line 774, in get raise self._value TypeError: Couldn't cast array of type fixed_size_list<item: float>[2] to Sequence(feature=Value(dtype='float32', id=None), length=2, id=None) ``` ### Steps to reproduce the bug Here's a simple repro script: ``` from datasets import Features, Value, Sequence, ClassLabel, Dataset dataset_features = Features({ 'text': Value('string'), 'embedding': Sequence(Value('double'), length=2), 'categories': Sequence(ClassLabel(names=sorted([ 'one', 'two', 'three' ]))), }) dataset = Dataset.from_dict( { 'text': ['A'] * 10000, 'embedding': [[0.0, 0.1]] * 10000, 'categories': [[0]] * 10000, }, features=dataset_features ) def test_mapper(r): r['text'] = list(map(lambda t: t + ' b', r['text'])) return r dataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2) ``` Removing the embedding column fixes the issue! ### Expected behavior The mapping completes successfully. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 13.0.0 - Pandas version: 2.0.3	{ "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": 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/6280/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/6279	6,279	Batched IterableDataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/7010688?v=4", "events_url": "https://api.github.com/users/lneukom/events{/privacy}", "followers_url": "https://api.github.com/users/lneukom/followers", "following_url": "https://api.github.com/users/lneukom/following{/other_user}", "gists_url": "https://api.github.com/users/lneukom/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lneukom", "id": 7010688, "login": "lneukom", "node_id": "MDQ6VXNlcjcwMTA2ODg=", "organizations_url": "https://api.github.com/users/lneukom/orgs", "received_events_url": "https://api.github.com/users/lneukom/received_events", "repos_url": "https://api.github.com/users/lneukom/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lneukom/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lneukom/subscriptions", "type": "User", "url": "https://api.github.com/users/lneukom", "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	[ "This is exactly what I was looking for. It would also be very useful for me :-)", "This issue is really smashing the selling point of HF datasets... The only workaround I've found so far is to create a customized IterableDataloader which improves the loading speed to some extent.\r\n\r\nFor example I've a HF dataset `dt_train` with `len(dt_train) == 1M`. Using plain DataLoader is extremely slow:\r\n```\r\n%%time\r\ndl_train = DataLoader(dt_train, batch_size=128, shuffle = True)\r\nfor batch in dl_train:\r\n pass\r\n``` \r\n\r\n```\r\nCPU times: user 24min 35s, sys: 704 ms, total: 24min 36s\r\nWall time: 24min 37s\r\n```\r\nAnd DataLoader works even worse with HF's iterable_dataset:\r\n```\r\n%%time\r\ndt_train_ = dt_train.with_format(None).to_iterable_dataset(num_shards=64).shuffle(buffer_size=10_000)\r\ndl_train = DataLoader(dt_train_, batch_size=128)\r\nfor batch in dl_train:\r\n pass\r\n```\r\n```\r\nCPU times: user 1h 6min 2s, sys: 4.28 s, total: 1h 6min 6s\r\nWall time: 1h 7min 53s\r\n```\r\nWorkaround by running a customized wrapper:\r\n```\r\n%%time\r\nfrom torch.utils.data import DataLoader, IterableDataset\r\n\r\nclass Dataset2Iterable(IterableDataset):\r\n \"\"\"\r\n Wrapper to use a HF dataset as pytorch IterableDataset to speed up data loading.\r\n \"\"\"\r\n def __init__(self, dataset, batch_size=1, shuffle=True):\r\n super(Dataset2Iterable).__init__()\r\n self.dataset = dataset\r\n self.batch_size = batch_size\r\n self.shuffle = shuffle\r\n\r\n def __iter__(self):\r\n if self.shuffle: self.dataset.shuffle()\r\n return self.dataset.iter(batch_size=self.batch_size)\r\n\r\ndl_train = DataLoader(Dataset2Iterable(dt_train, batch_size = 128), batch_size=1, num_workers=0)\r\nfor n in range(2):\r\n for batch in dl_train:\r\n pass\r\n```\r\nThe speed still is slower than using tensorflow's loader but improved a lot than previous code:\r\n```\r\nCPU times: user 4min 18s, sys: 0 ns, total: 4min 18s\r\nWall time: 4min 20s\r\n```\r\nNote that the way I implemented `Dataset2Iterable` will only work with `num_workers=0`.", "I can confirm that @zhh210's solution works with `num_workers=0`. However, for my use case, this was still slower than tokenizing on the fly through a collator and leveraging multiple workers in the dataloder.\r\n\r\n@lhoestq I think this is an important use case (e.g., streaming from a large dataset, online or stored on disk). What do you think might be the best solution to move forward?", "I guess it can be implemented using a batched`.map()` under the hood that returns a single item containing the input batch.\r\n\r\nIn the meantime you can use this:\r\n\r\n```python\r\ndef batch(unbatched: dict[str, list]) -> dict[str, list]:\r\n return {k: [v] for k, v in unbatched}\r\n\r\nbatched_dataset = dataset.map(batch, batched=True, batch_size=batch_size)\r\n```\r\n\r\nThough it would be great to have a `.batch()` method indeed, I'd be happy to help with anyone wants to open a PR", "If no one else is planning to work on this, I can take it on. I'll wait until next week, and if no one has started a PR by then, I'll go ahead and open one.", "It looks like the implementation of IterableDataset is still using a hardcoded batch size of 1. For example in line 2063 in [`/datsets/src/datasets/iterable_dataset.py`](https://github.com/huggingface/datasets/blob/3.0.2/src/datasets/iterable_dataset.py#L2063). Iterating over IterableDataset with large batch sizes therefore remains slow, even when using `batch()`. I guess then the data are not being read from one contiguous chunk of memory. Instead every example is retrieved one by one, leading to long dataloading times. As a minimal example: Load c4 dataset and iterate over it with a large batch size.\r\n```python\r\nimport datasets\r\nfrom timeit import default_timer as timer\r\nc4 = datasets.load_dataset(\"allenai/c4\", \"en\", streaming=True, split=\"train\")\r\nc4_batched = c4.batch(5122) # use large batch size\r\niterator = iter(c4_batched)\r\nfor i in range(5):\r\n start_time=timer()\r\n next(iterator) # get next batch\r\n end_time = timer()\r\n print(f\"time for one batch: {end_time-start_time}\")\r\n```\r\nThis results in the following output for me:\r\ntime for one batch: 12.615376660600305\r\ntime for one batch: 13.011422813870013\r\ntime for one batch: 14.157325950451195\r\ntime for one batch: 14.225894245319068\r\ntime for one batch: 13.898222777992487\r\n\r\nBecause I want to use my IterableDataset with the pytorch dataloader I rewrote the `__iter_pytorch__` and the `__iter__` functions like so and am getting much faster dataloading times. I marked the lines I changed with \"# changed here\":\r\n\r\n```python\r\nfrom datasets.iterable_dataset import _convert_to_arrow\r\nfrom datasets.formatting import TensorFormatter, get_formatter\r\nfrom datasets.features.features import cast_to_python_objects\r\nimport sys\r\nimport fsspec.asyn\r\nfrom itertools import islice\r\nfrom datasets.utils.logging import get_logger\r\nfrom datasets.iterable_dataset import _examples_to_batch, _apply_feature_types_on_batch, _apply_feature_types_on_example\r\n\r\nlogger = get_logger(__name__)\r\n\r\ndef __iter__(self):\r\n if \"torch\" in sys.modules:\r\n import torch.utils.data\r\n\r\n worker_info = torch.utils.data.get_worker_info()\r\n if isinstance(self, torch.utils.data.IterableDataset) and worker_info is not None:\r\n # We're a torch.utils.data.IterableDataset in a PyTorch worker process\r\n yield from self._iter_pytorch()\r\n return\r\n\r\n ex_iterable = self._prepare_ex_iterable_for_iteration(batch_size=self.batch_size, drop_last_batch=self.drop_last_batch) # changed here\r\n if self._formatting:\r\n formatter = get_formatter(self._formatting.format_type, features=self.features)\r\n format_dict = (\r\n formatter.recursive_tensorize if isinstance(formatter, TensorFormatter) else cast_to_python_objects\r\n )\r\n else:\r\n format_dict = None\r\n\r\n if self._formatting and (ex_iterable.iter_arrow or self._formatting.format_type == \"arrow\"):\r\n if ex_iterable.iter_arrow:\r\n iterator = ex_iterable.iter_arrow()\r\n else:\r\n iterator = _convert_to_arrow(ex_iterable, batch_size=self.batch_size) # changed here\r\n for key, pa_table in iterator:\r\n yield formatter.format_row(pa_table)\r\n return\r\n\r\n for key, example in ex_iterable:\r\n if self.features and not ex_iterable.is_typed:\r\n # `IterableDataset` automatically fills missing columns with None.\r\n # This is done with `_apply_feature_types_on_example`.\r\n example = _apply_feature_types_on_example(\r\n example, self.features, token_per_repo_id=self._token_per_repo_id\r\n )\r\n yield format_dict(example) if format_dict else example\r\n\r\n\r\n\r\ndef _iter_pytorch(self):\r\n ex_iterable = self._prepare_ex_iterable_for_iteration(batch_size=self.batch_size, drop_last_batch=self.drop_last_batch) # changed here\r\n # Fix for fsspec when using multiprocess to avoid hanging in the ML training loop. (only required for fsspec >= 0.9.0)\r\n # See https://github.com/fsspec/gcsfs/issues/379\r\n fsspec.asyn.reset_lock()\r\n # check if there aren't too many workers\r\n import torch.utils.data\r\n\r\n worker_info = torch.utils.data.get_worker_info()\r\n if self._is_main_process() and ex_iterable.n_shards < worker_info.num_workers:\r\n logger.warning(\r\n f\"Too many dataloader workers: {worker_info.num_workers} (max is dataset.n_shards={ex_iterable.n_shards}). \"\r\n f\"Stopping {worker_info.num_workers - ex_iterable.n_shards} dataloader workers.\"\r\n )\r\n logger.info(\r\n f\"To parallelize data loading, we give each process some shards (or data sources) to process. \"\r\n f\"Therefore it's unnecessary to have a number of workers greater than dataset.n_shards={ex_iterable.n_shards}. \"\r\n f\"To enable more parallelism, please split the dataset in more files than {ex_iterable.n_shards}.\"\r\n )\r\n # split workload\r\n _log_prefix = f\"node#{self._distributed.rank} \" if self._distributed else \"\"\r\n shards_indices = ex_iterable.split_shard_indices_by_worker(worker_info.id, worker_info.num_workers)\r\n if shards_indices:\r\n logger.debug(\r\n f\"{_log_prefix}dataloader worker#{worker_info.id}, ': Starting to iterate over {len(shards_indices)}/{ex_iterable.n_shards} shards.\"\r\n )\r\n ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers)\r\n self._state_dict = ex_iterable._init_state_dict()\r\n if self._starting_state_dict:\r\n ex_iterable.load_state_dict(self._starting_state_dict)\r\n\r\n if self._formatting:\r\n formatter = get_formatter(self._formatting.format_type, features=self.features)\r\n format_dict = (\r\n formatter.recursive_tensorize if isinstance(formatter, TensorFormatter) else cast_to_python_objects\r\n )\r\n else:\r\n format_dict = None\r\n\r\n if self._formatting and (ex_iterable.iter_arrow or self._formatting == \"arrow\"):\r\n if ex_iterable.iter_arrow:\r\n iterator = ex_iterable.iter_arrow()\r\n else:\r\n iterator = _convert_to_arrow(ex_iterable, batch_size=self.batch_size) # changed here\r\n if self.batch_size > 1: # changed here until end of file\r\n for key, pa_table in iterator:\r\n yield formatter.format_batch(pa_table)\r\n return\r\n else:\r\n for key, pa_table in iterator:\r\n yield formatter.format_row(pa_table)\r\n return\r\n\r\n iterator = iter(ex_iterable)\r\n if self.batch_size > 1:\r\n for key, example in iterator:\r\n # If batched, first build the batch\r\n examples = [example] + [example for key, example in islice(iterator, self.batch_size - 1)]\r\n if self.drop_last_batch and len(examples) < self.batch_size: # ignore last batch\r\n return\r\n batch = _examples_to_batch(examples)\r\n if self.features and not ex_iterable.is_typed:\r\n # `IterableDataset` automatically fills missing columns with None.\r\n # This is done with `_apply_feature_types_on_batch`.\r\n batch = _apply_feature_types_on_batch(batch, self.features, token_per_repo_id=self._token_per_repo_id)\r\n yield format_dict(batch) if format_dict else batch\r\n else:\r\n for key, example in ex_iterable:\r\n if self.features and not ex_iterable.is_typed:\r\n # `IterableDataset` automatically fills missing columns with None.\r\n # This is done with `_apply_feature_types_on_example`.\r\n example = _apply_feature_types_on_example(\r\n example, self.features, token_per_repo_id=self._token_per_repo_id\r\n )\r\n yield format_dict(example) if format_dict else example\r\n logger.debug(\r\n f\"{_log_prefix}dataloader worker#{worker_info.id}, ': Finished iterating over {len(shards_indices)}/{ex_iterable.n_shards} shards.\"\r\n )\r\n else:\r\n logger.debug(\r\n f\"{_log_prefix}dataloader worker#{worker_info.id}, ': Stopping... Number of dataset shards < num_workers ({ex_iterable.n_shards}<{worker_info.num_workers}).\"\r\n )\r\n```\r\n\r\nFor anyone wanting to try it you can patch it into datasets by overwriting the function via `setattr(datasets.IterableDataset, '_iter_pytorch', _iter_pytorch)`\r\n\r\nI don't really know what most of the rest of the code is doing so no idea if this is a valid fix or not, but it seems to work for me.\r\nExample of running the fix:\r\n```python\r\nfrom torch.utils.data.dataloader import DataLoader\r\nc4.batch_size = 5122 # set batch size here\r\ndataloader = Dataloader(c4, batch_size=None) # use custom batching from IterableDataset\r\niterator = iter(dataloader)\r\nfor i in range(5):\r\n start_time=timer()\r\n next(iterator) #get the batch\r\n end_time = timer()\r\n print(f\"time for one batch: {end_time-start_time}\")\r\n```\r\nI now get \r\ntime for one batch: 0.6047679269686341\r\ntime for one batch: 0.000248616561293602\r\ntime for one batch: 0.00017435848712921143\r\ntime for one batch: 0.00015910807996988297\r\ntime for one batch: 0.00015317369252443314\r\n\r\nI love the datasets library and it would be great if iterating with large batch sizes would be supported directly, either with a similar fix to mine or in some other way :)", "Hi @taczin , thanks for reporting !\r\n\r\nIndeed the `IterableDataset.batch()` implementation is quite naive is manipulates python objects:\r\n\r\nhttps://github.com/huggingface/datasets/blob/d37ed46ebf45981131bd3678173dbb4b7e2b2f1a/src/datasets/iterable_dataset.py#L3026-L3029\r\n\r\nHowever it can be much faster if it can be applied on the Arrow data, maybe using something like this (untested)\r\n\r\n```python\r\ndef batch_fn(unbatched): \r\n return {k: [v] for k, v in unbatched.items()} \r\n\r\ndef batch_fn_arrow(unbatched_pa_table): \r\n offsets = pa.array([0, len(unbatched_pa_table)])\r\n return pa.Table.from_arrays([\r\n pa.ListArray.from_arrays(offsets, unbatched_pa_table[k])\r\n for k in unbatched_pa_table.column_names\r\n ], unbatched_pa_table.column_names)\r\n\r\nif self._ex_iterabe.iter_arrow:\r\n return self.with_format(\"arrow\").map(\r\n batch_fn_arrow, batched=True, batch_size=batch_size, drop_last_batch=drop_last_batch\r\n ).with_format(self._formatting.format_type if self._formatting else None)\r\nelse:\r\n return self.map(batch_fn, batched=True, batch_size=batch_size, drop_last_batch=drop_last_batch)\r\n```", "Hi @lhoestq , thanks for your answer. I was wondering: is there a reason why the internal call to `ex_iterable = self._prepare_ex_iterable_for_iteration()` in the IterableDataset code does not pass the batch size even though it could? If not passed the default of 1 is used, leading to the observed slow loading.", "After calling `.batch()`, `_prepare_ex_iterable_for_iteration` should use batch_size=1 since now each row in the dataset is actually a batch of the original dataset." ]	2023-10-05T11:12:49Z	2024-11-07T10:01:22Z	null	NONE	null	null	### Feature request Hi, could you add an implementation of a batched `IterableDataset`. It already support an option to do batch iteration via `.iter(batch_size=...)` but this cannot be used in combination with a torch `DataLoader` since it just returns an iterator. ### Motivation The current implementation loads each element of a batch individually which can be very slow in cases of a big batch_size. I did some experiments [here](https://discuss.huggingface.co/t/slow-dataloader-with-big-batch-size/57224) and using a batched iteration would speed up data loading significantly. ### Your contribution N/A	null	{ "+1": 6, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 6, "url": "https://api.github.com/repos/huggingface/datasets/issues/6279/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/6278	6,278	No data files duplicates	{ "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) \| 0.009624 / 0.011353 (-0.001729) \| 0.005121 / 0.011008 (-0.005887) \| 0.105560 / 0.038508 (0.067052) \| 0.090749 / 0.023109 (0.067640) \| 0.430274 / 0.275898 (0.154376) \| 0.443399 / 0.323480 (0.119919) \| 0.006575 / 0.007986 (-0.001411) \| 0.004396 / 0.004328 (0.000068) \| 0.080900 / 0.004250 (0.076649) \| 0.064921 / 0.037052 (0.027868) \| 0.410092 / 0.258489 (0.151603) \| 0.470058 / 0.293841 (0.176217) \| 0.054160 / 0.128546 (-0.074386) \| 0.014367 / 0.075646 (-0.061279) \| 0.384844 / 0.419271 (-0.034428) \| 0.072818 / 0.043533 (0.029285) \| 0.429341 / 0.255139 (0.174202) \| 0.430968 / 0.283200 (0.147769) \| 0.038437 / 0.141683 (-0.103246) \| 1.814456 / 1.452155 (0.362301) \| 1.832122 / 1.492716 (0.339406) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.329266 / 0.018006 (0.311260) \| 0.596848 / 0.000490 (0.596358) \| 0.018291 / 0.000200 (0.018091) \| 0.000113 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030505 / 0.037411 (-0.006907) \| 0.097394 / 0.014526 (0.082869) \| 0.127144 / 0.176557 (-0.049412) \| 0.190251 / 0.737135 (-0.546884) \| 0.116543 / 0.296338 (-0.179795) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.592124 / 0.215209 (0.376915) \| 5.979801 / 2.077655 (3.902146) \| 2.837753 / 1.504120 (1.333633) \| 2.492942 / 1.541195 (0.951747) \| 2.548083 / 1.468490 (1.079593) \| 0.870446 / 4.584777 (-3.714330) \| 5.493718 / 3.745712 (1.748006) \| 4.945135 / 5.269862 (-0.324727) \| 3.133994 / 4.565676 (-1.431683) \| 0.097742 / 0.424275 (-0.326533) \| 0.008750 / 0.007607 (0.001143) \| 0.723304 / 0.226044 (0.497260) \| 7.353766 / 2.268929 (5.084838) \| 3.504808 / 55.444624 (-51.939816) \| 2.872490 / 6.876477 (-4.003987) \| 3.186628 / 2.142072 (1.044556) \| 1.035470 / 4.805227 (-3.769758) \| 0.211980 / 6.500664 (-6.288684) \| 0.080356 / 0.075469 (0.004887) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623389 / 1.841788 (-0.218399) \| 23.492350 / 8.074308 (15.418042) \| 21.053525 / 10.191392 (10.862133) \| 0.225668 / 0.680424 (-0.454756) \| 0.028311 / 0.534201 (-0.505890) \| 0.472672 / 0.579283 (-0.106611) \| 0.581536 / 0.434364 (0.147172) \| 0.525180 / 0.540337 (-0.015158) \| 0.790420 / 1.386936 (-0.596516) \|\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) \| 0.009091 / 0.011353 (-0.002262) \| 0.004978 / 0.011008 (-0.006030) \| 0.077633 / 0.038508 (0.039125) \| 0.103189 / 0.023109 (0.080080) \| 0.500194 / 0.275898 (0.224296) \| 0.524310 / 0.323480 (0.200831) \| 0.006656 / 0.007986 (-0.001329) \| 0.004586 / 0.004328 (0.000257) \| 0.075535 / 0.004250 (0.071284) \| 0.065100 / 0.037052 (0.028048) \| 0.513776 / 0.258489 (0.255287) \| 0.528483 / 0.293841 (0.234642) \| 0.049877 / 0.128546 (-0.078669) \| 0.012494 / 0.075646 (-0.063152) \| 0.090225 / 0.419271 (-0.329046) \| 0.054648 / 0.043533 (0.011116) \| 0.510369 / 0.255139 (0.255230) \| 0.540042 / 0.283200 (0.256842) \| 0.035966 / 0.141683 (-0.105717) \| 1.825965 / 1.452155 (0.373810) \| 1.965647 / 1.492716 (0.472931) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295921 / 0.018006 (0.277914) \| 0.605751 / 0.000490 (0.605262) \| 0.007243 / 0.000200 (0.007043) \| 0.000134 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032954 / 0.037411 (-0.004457) \| 0.093613 / 0.014526 (0.079087) \| 0.120010 / 0.176557 (-0.056546) \| 0.176168 / 0.737135 (-0.560967) \| 0.113978 / 0.296338 (-0.182360) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.682904 / 0.215209 (0.467695) \| 6.674640 / 2.077655 (4.596986) \| 3.360660 / 1.504120 (1.856540) \| 3.227246 / 1.541195 (1.686051) \| 3.188852 / 1.468490 (1.720362) \| 0.862293 / 4.584777 (-3.722484) \| 5.518455 / 3.745712 (1.772743) \| 4.881904 / 5.269862 (-0.387957) \| 3.066964 / 4.565676 (-1.498712) \| 0.099284 / 0.424275 (-0.324991) \| 0.008644 / 0.007607 (0.001037) \| 0.789231 / 0.226044 (0.563186) \| 7.872017 / 2.268929 (5.603089) \| 4.037105 / 55.444624 (-51.407519) \| 3.318921 / 6.876477 (-3.557555) \| 3.621953 / 2.142072 (1.479881) \| 1.012049 / 4.805227 (-3.793178) \| 0.204541 / 6.500664 (-6.296123) \| 0.074509 / 0.075469 (-0.000960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.748215 / 1.841788 (-0.093573) \| 24.274974 / 8.074308 (16.200665) \| 20.582389 / 10.191392 (10.390997) \| 0.251001 / 0.680424 (-0.429423) \| 0.032390 / 0.534201 (-0.501811) \| 0.479211 / 0.579283 (-0.100072) \| 0.607482 / 0.434364 (0.173118) \| 0.587867 / 0.540337 (0.047530) \| 0.822399 / 1.386936 (-0.564537) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b6b2fd90ba47f19e9ab125f6f7656903dd065f \"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) \| 0.009715 / 0.011353 (-0.001638) \| 0.005449 / 0.011008 (-0.005559) \| 0.108556 / 0.038508 (0.070048) \| 0.080512 / 0.023109 (0.057403) \| 0.450736 / 0.275898 (0.174838) \| 0.487771 / 0.323480 (0.164291) \| 0.005155 / 0.007986 (-0.002830) \| 0.004213 / 0.004328 (-0.000115) \| 0.087247 / 0.004250 (0.082997) \| 0.063962 / 0.037052 (0.026909) \| 0.454153 / 0.258489 (0.195664) \| 0.499917 / 0.293841 (0.206076) \| 0.052605 / 0.128546 (-0.075942) \| 0.013019 / 0.075646 (-0.062627) \| 0.379716 / 0.419271 (-0.039555) \| 0.073241 / 0.043533 (0.029708) \| 0.473488 / 0.255139 (0.218349) \| 0.482944 / 0.283200 (0.199745) \| 0.041541 / 0.141683 (-0.100142) \| 1.829415 / 1.452155 (0.377261) \| 1.953280 / 1.492716 (0.460564) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313725 / 0.018006 (0.295719) \| 0.591336 / 0.000490 (0.590847) \| 0.021224 / 0.000200 (0.021025) \| 0.000969 / 0.000054 (0.000914) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031874 / 0.037411 (-0.005537) \| 0.099786 / 0.014526 (0.085260) \| 0.116987 / 0.176557 (-0.059569) \| 0.205538 / 0.737135 (-0.531597) \| 0.118716 / 0.296338 (-0.177622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.617145 / 0.215209 (0.401936) \| 6.079144 / 2.077655 (4.001489) \| 2.567233 / 1.504120 (1.063113) \| 2.265301 / 1.541195 (0.724107) \| 2.314001 / 1.468490 (0.845511) \| 0.871561 / 4.584777 (-3.713216) \| 5.477049 / 3.745712 (1.731337) \| 4.720552 / 5.269862 (-0.549309) \| 3.107515 / 4.565676 (-1.458162) \| 0.100438 / 0.424275 (-0.323838) \| 0.008586 / 0.007607 (0.000979) \| 0.716913 / 0.226044 (0.490869) \| 7.108417 / 2.268929 (4.839489) \| 3.391336 / 55.444624 (-52.053288) \| 2.734052 / 6.876477 (-4.142425) \| 2.857226 / 2.142072 (0.715153) \| 1.024121 / 4.805227 (-3.781106) \| 0.216735 / 6.500664 (-6.283929) \| 0.081605 / 0.075469 (0.006136) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.678176 / 1.841788 (-0.163611) \| 23.606037 / 8.074308 (15.531729) \| 21.485331 / 10.191392 (11.293939) \| 0.218312 / 0.680424 (-0.462112) \| 0.027061 / 0.534201 (-0.507140) \| 0.481188 / 0.579283 (-0.098096) \| 0.620592 / 0.434364 (0.186228) \| 0.574778 / 0.540337 (0.034441) \| 0.831529 / 1.386936 (-0.555407) \|\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) \| 0.011666 / 0.011353 (0.000313) \| 0.005187 / 0.011008 (-0.005821) \| 0.080692 / 0.038508 (0.042184) \| 0.079159 / 0.023109 (0.056049) \| 0.530823 / 0.275898 (0.254925) \| 0.577807 / 0.323480 (0.254327) \| 0.006246 / 0.007986 (-0.001740) \| 0.004355 / 0.004328 (0.000026) \| 0.080702 / 0.004250 (0.076452) \| 0.062279 / 0.037052 (0.025226) \| 0.553712 / 0.258489 (0.295223) \| 0.579112 / 0.293841 (0.285271) \| 0.056374 / 0.128546 (-0.072172) \| 0.014681 / 0.075646 (-0.060966) \| 0.097110 / 0.419271 (-0.322161) \| 0.061040 / 0.043533 (0.017507) \| 0.524718 / 0.255139 (0.269579) \| 0.568586 / 0.283200 (0.285386) \| 0.035774 / 0.141683 (-0.105909) \| 1.864590 / 1.452155 (0.412435) \| 1.953715 / 1.492716 (0.460998) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271315 / 0.018006 (0.253309) \| 0.571343 / 0.000490 (0.570854) \| 0.015812 / 0.000200 (0.015612) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038582 / 0.037411 (0.001170) \| 0.117523 / 0.014526 (0.102997) \| 0.128864 / 0.176557 (-0.047693) \| 0.191164 / 0.737135 (-0.545971) \| 0.133161 / 0.296338 (-0.163178) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.679305 / 0.215209 (0.464096) \| 6.814451 / 2.077655 (4.736796) \| 3.377431 / 1.504120 (1.873311) \| 3.011008 / 1.541195 (1.469813) \| 3.093200 / 1.468490 (1.624710) \| 0.905827 / 4.584777 (-3.678950) \| 5.456094 / 3.745712 (1.710382) \| 4.848511 / 5.269862 (-0.421351) \| 3.064230 / 4.565676 (-1.501447) \| 0.107478 / 0.424275 (-0.316798) \| 0.009234 / 0.007607 (0.001627) \| 0.833944 / 0.226044 (0.607899) \| 8.286100 / 2.268929 (6.017171) \| 4.241455 / 55.444624 (-51.203169) \| 3.405460 / 6.876477 (-3.471017) \| 3.660618 / 2.142072 (1.518546) \| 1.046310 / 4.805227 (-3.758917) \| 0.210891 / 6.500664 (-6.289773) \| 0.079413 / 0.075469 (0.003944) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.825448 / 1.841788 (-0.016340) \| 24.639059 / 8.074308 (16.564750) \| 21.970417 / 10.191392 (11.779025) \| 0.247708 / 0.680424 (-0.432715) \| 0.033810 / 0.534201 (-0.500391) \| 0.495517 / 0.579283 (-0.083766) \| 0.601820 / 0.434364 (0.167456) \| 0.585618 / 0.540337 (0.045280) \| 0.858722 / 1.386936 (-0.528214) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0477e20dccb77b68f0add77fd5c9b4cb05473235 \"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) \| 0.006137 / 0.011353 (-0.005216) \| 0.003685 / 0.011008 (-0.007324) \| 0.079985 / 0.038508 (0.041476) \| 0.060937 / 0.023109 (0.037828) \| 0.390583 / 0.275898 (0.114685) \| 0.425307 / 0.323480 (0.101827) \| 0.003433 / 0.007986 (-0.004552) \| 0.002868 / 0.004328 (-0.001461) \| 0.062572 / 0.004250 (0.058322) \| 0.048642 / 0.037052 (0.011590) \| 0.401096 / 0.258489 (0.142607) \| 0.436988 / 0.293841 (0.143147) \| 0.027645 / 0.128546 (-0.100901) \| 0.007973 / 0.075646 (-0.067673) \| 0.261997 / 0.419271 (-0.157275) \| 0.045393 / 0.043533 (0.001860) \| 0.394266 / 0.255139 (0.139127) \| 0.414448 / 0.283200 (0.131248) \| 0.022551 / 0.141683 (-0.119131) \| 1.438458 / 1.452155 (-0.013697) \| 1.501568 / 1.492716 (0.008852) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224335 / 0.018006 (0.206329) \| 0.421918 / 0.000490 (0.421428) \| 0.006883 / 0.000200 (0.006683) \| 0.000210 / 0.000054 (0.000155) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023505 / 0.037411 (-0.013906) \| 0.072438 / 0.014526 (0.057912) \| 0.083576 / 0.176557 (-0.092981) \| 0.142906 / 0.737135 (-0.594229) \| 0.083910 / 0.296338 (-0.212428) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396004 / 0.215209 (0.180795) \| 3.969852 / 2.077655 (1.892197) \| 1.966000 / 1.504120 (0.461880) \| 1.786453 / 1.541195 (0.245258) \| 1.866082 / 1.468490 (0.397592) \| 0.502633 / 4.584777 (-4.082144) \| 3.114331 / 3.745712 (-0.631382) \| 2.940003 / 5.269862 (-2.329859) \| 1.901844 / 4.565676 (-2.663832) \| 0.058109 / 0.424275 (-0.366166) \| 0.006502 / 0.007607 (-0.001105) \| 0.463465 / 0.226044 (0.237420) \| 4.641531 / 2.268929 (2.372603) \| 2.315759 / 55.444624 (-53.128865) \| 2.253088 / 6.876477 (-4.623389) \| 2.151399 / 2.142072 (0.009326) \| 0.592225 / 4.805227 (-4.213002) \| 0.125072 / 6.500664 (-6.375592) \| 0.059966 / 0.075469 (-0.015503) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231392 / 1.841788 (-0.610396) \| 17.533893 / 8.074308 (9.459585) \| 13.710478 / 10.191392 (3.519086) \| 0.147389 / 0.680424 (-0.533035) \| 0.017932 / 0.534201 (-0.516269) \| 0.334144 / 0.579283 (-0.245139) \| 0.368817 / 0.434364 (-0.065547) \| 0.383790 / 0.540337 (-0.156547) \| 0.540262 / 1.386936 (-0.846674) \|\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) \| 0.006066 / 0.011353 (-0.005287) \| 0.003804 / 0.011008 (-0.007205) \| 0.062474 / 0.038508 (0.023966) \| 0.060547 / 0.023109 (0.037437) \| 0.448643 / 0.275898 (0.172745) \| 0.487005 / 0.323480 (0.163525) \| 0.004884 / 0.007986 (-0.003102) \| 0.002911 / 0.004328 (-0.001418) \| 0.062950 / 0.004250 (0.058700) \| 0.049672 / 0.037052 (0.012620) \| 0.477491 / 0.258489 (0.219002) \| 0.488234 / 0.293841 (0.194393) \| 0.028711 / 0.128546 (-0.099835) \| 0.008101 / 0.075646 (-0.067545) \| 0.068333 / 0.419271 (-0.350939) \| 0.040959 / 0.043533 (-0.002574) \| 0.450716 / 0.255139 (0.195577) \| 0.471089 / 0.283200 (0.187890) \| 0.020710 / 0.141683 (-0.120973) \| 1.474850 / 1.452155 (0.022695) \| 1.540115 / 1.492716 (0.047399) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229811 / 0.018006 (0.211805) \| 0.419526 / 0.000490 (0.419036) \| 0.003818 / 0.000200 (0.003618) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026045 / 0.037411 (-0.011366) \| 0.080325 / 0.014526 (0.065799) \| 0.091549 / 0.176557 (-0.085007) \| 0.145253 / 0.737135 (-0.591882) \| 0.091849 / 0.296338 (-0.204489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463047 / 0.215209 (0.247838) \| 4.598727 / 2.077655 (2.521072) \| 2.558996 / 1.504120 (1.054877) \| 2.405896 / 1.541195 (0.864701) \| 2.447291 / 1.468490 (0.978801) \| 0.510393 / 4.584777 (-4.074384) \| 3.173344 / 3.745712 (-0.572368) \| 2.901201 / 5.269862 (-2.368661) \| 1.896440 / 4.565676 (-2.669236) \| 0.058374 / 0.424275 (-0.365901) \| 0.006449 / 0.007607 (-0.001158) \| 0.539653 / 0.226044 (0.313608) \| 5.408217 / 2.268929 (3.139289) \| 3.042453 / 55.444624 (-52.402172) \| 2.656724 / 6.876477 (-4.219753) \| 2.838165 / 2.142072 (0.696092) \| 0.598663 / 4.805227 (-4.206565) \| 0.126211 / 6.500664 (-6.374453) \| 0.062830 / 0.075469 (-0.012639) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.392412 / 1.841788 (-0.449376) \| 18.195170 / 8.074308 (10.120862) \| 14.788251 / 10.191392 (4.596859) \| 0.132579 / 0.680424 (-0.547845) \| 0.017867 / 0.534201 (-0.516334) \| 0.340020 / 0.579283 (-0.239263) \| 0.386719 / 0.434364 (-0.047645) \| 0.398863 / 0.540337 (-0.141475) \| 0.579320 / 1.386936 (-0.807617) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2569fdfcf387f8885974a35fafa409fbc6dd059 \"CML watermark\")\n", "closing in favor of https://github.com/huggingface/datasets/pull/6282" ]	2023-10-05T10:31:58Z	2024-01-11T06:32:49Z	2023-10-05T14:43:17Z	MEMBER	true	{ "diff_url": "https://github.com/huggingface/datasets/pull/6278.diff", "html_url": "https://github.com/huggingface/datasets/pull/6278", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6278.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6278" }	I added a new DataFilesSet class to disallow duplicate data files. I also deprecated DataFilesList. EDIT: actually I might just add drop_duplicates=True to `.from_patterns` close https://github.com/huggingface/datasets/issues/6259 close https://github.com/huggingface/datasets/issues/6272 TODO: - [ ] tests - [ ] preserve data files order	{ "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/6278/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6277	6,277	FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either.	{ "avatar_url": "https://avatars.githubusercontent.com/u/66733346?v=4", "events_url": "https://api.github.com/users/diegogonzalezc/events{/privacy}", "followers_url": "https://api.github.com/users/diegogonzalezc/followers", "following_url": "https://api.github.com/users/diegogonzalezc/following{/other_user}", "gists_url": "https://api.github.com/users/diegogonzalezc/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/diegogonzalezc", "id": 66733346, "login": "diegogonzalezc", "node_id": "MDQ6VXNlcjY2NzMzMzQ2", "organizations_url": "https://api.github.com/users/diegogonzalezc/orgs", "received_events_url": "https://api.github.com/users/diegogonzalezc/received_events", "repos_url": "https://api.github.com/users/diegogonzalezc/repos", "site_admin": false, "starred_url": "https://api.github.com/users/diegogonzalezc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/diegogonzalezc/subscriptions", "type": "User", "url": "https://api.github.com/users/diegogonzalezc", "user_view_type": "public" }	[]	closed	false	[ "`evaluate.load(\"paws-x\", \"es\")` throws the error because there is no such metric in the `evaluate` lib.\r\n\r\nSo, this is unrelated to our lib." ]	2023-10-04T22:01:25Z	2023-10-08T17:05:46Z	2023-10-08T17:05:46Z	NONE	null	null	### Describe the bug I'm encountering a "FileNotFoundError" while attempting to use the "paws-x" dataset to retrain the DistilRoBERTa-base model. The error message is as follows: FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either. ### Steps to reproduce the bug https://colab.research.google.com/drive/11xUUFxloClpmqLvDy_Xxfmo3oUzjY5nx#scrollTo=kUn74FigzhHm ### Expected behavior The the trained model ### Environment info colab, "paws-x" dataset , DistilRoBERTa-base model	{ "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/6277/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/6276	6,276	I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error	{ "avatar_url": "https://avatars.githubusercontent.com/u/50768065?v=4", "events_url": "https://api.github.com/users/valaofficial/events{/privacy}", "followers_url": "https://api.github.com/users/valaofficial/followers", "following_url": "https://api.github.com/users/valaofficial/following{/other_user}", "gists_url": "https://api.github.com/users/valaofficial/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/valaofficial", "id": 50768065, "login": "valaofficial", "node_id": "MDQ6VXNlcjUwNzY4MDY1", "organizations_url": "https://api.github.com/users/valaofficial/orgs", "received_events_url": "https://api.github.com/users/valaofficial/received_events", "repos_url": "https://api.github.com/users/valaofficial/repos", "site_admin": false, "starred_url": "https://api.github.com/users/valaofficial/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/valaofficial/subscriptions", "type": "User", "url": "https://api.github.com/users/valaofficial", "user_view_type": "public" }	[]	open	false	[ "Since you are using Windows, maybe moving the `map` call inside `if __name__ == \"__main__\"` can fix the issue:\r\n```python\r\nif __name__ == \"__main__\":\r\n common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)\r\n```\r\n\r\nOtherwise, the only solution is to set `num_proc=1`.", "> Since you are using Windows, maybe moving the `map` call inside `if __name__ == \"__main__\"` can fix the issue:\r\n> \r\n> ```python\r\n> if __name__ == \"__main__\":\r\n> common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)\r\n> ```\r\n> \r\n> Otherwise, the only solution is to set `num_proc=1`.\r\n\r\nThank you very much for the response, i eventually tried setting `num_proc=1` and now the jupyter notebook kernel keers dying after running the command, what do you think the issue could be, could it be that my system is not capable of running the command \"i'm using a Lenovo Thinkpad T440 with no GPU\"" ]	2023-10-04T11:03:41Z	2023-11-27T10:39:16Z	null	NONE	null	null	### Describe the bug I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error, i'm following the steps in this blog post https://huggingface.co/blog/fine-tune-whisper I tried google collab and it works but because I'm on the free version the training doesn't complete the error comes in jupyter notebook when i run this line `common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)` here is the error message ``` Map (num_proc=4): 0% 0/2506 [00:52<?, ? examples/s] The above exception was the direct cause of the following exception: NameError Traceback (most recent call last) Cell In[19], line 1 ----> 1 common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4) File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:853, in DatasetDict.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( --> 853 { 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 ) File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:854, in <dictcomp>(.0) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( 853 { --> 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 ) File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(args, kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:557, in transmit_format.<locals>.wrapper(args, *kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:3189, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3182 logger.info(f"Spawning {num_proc} processes") 3183 with logging.tqdm( 3184 disable=not logging.is_progress_bar_enabled(), 3185 unit=" examples", 3186 total=pbar_total, 3187 desc=(desc or "Map") + f" (num_proc={num_proc})", 3188 ) as pbar: -> 3189 for rank, done, content in iflatmap_unordered( 3190 pool, Dataset._map_single, kwargs_iterable=kwargs_per_job 3191 ): 3192 if done: 3193 shards_done += 1 File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in iflatmap_unordered(pool, func, kwargs_iterable) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results] File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in <listcomp>(.0) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results] File ~\anaconda\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout) 772 return self._value 773 else: --> 774 raise self._value NameError: name 'feature_extractor' is not defined ``` ### Steps to reproduce the bug 1. follow the steps in this blog post https://huggingface.co/blog/fine-tune-whisper 2. run this line of code `common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)` 3. I'm using jupyter notebook from anaconda ### Expected behavior No error message ### Environment info datasets version: 2.8.0 Python version: 3.11 Windows 10	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/6276/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/6275	6,275	Would like to Contribute a dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/97907750?v=4", "events_url": "https://api.github.com/users/vikas70607/events{/privacy}", "followers_url": "https://api.github.com/users/vikas70607/followers", "following_url": "https://api.github.com/users/vikas70607/following{/other_user}", "gists_url": "https://api.github.com/users/vikas70607/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vikas70607", "id": 97907750, "login": "vikas70607", "node_id": "U_kgDOBdX0Jg", "organizations_url": "https://api.github.com/users/vikas70607/orgs", "received_events_url": "https://api.github.com/users/vikas70607/received_events", "repos_url": "https://api.github.com/users/vikas70607/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vikas70607/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vikas70607/subscriptions", "type": "User", "url": "https://api.github.com/users/vikas70607", "user_view_type": "public" }	[]	closed	false	[ "Hi! The process of contributing a dataset is explained here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/image_dataset for a more detailed explanation of how to share an image dataset." ]	2023-10-02T07:00:21Z	2023-10-10T16:27:54Z	2023-10-10T16:27:54Z	NONE	null	null	I have a dataset of 2500 images that can be used for color-blind machine-learning algorithms. Since , there was no dataset available online , I made this dataset myself and would like to contribute this now to community	{ "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/6275/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/6274	6,274	FileNotFoundError for dataset with multiple builder config	{ "avatar_url": "https://avatars.githubusercontent.com/u/97120485?v=4", "events_url": "https://api.github.com/users/LouisChen15/events{/privacy}", "followers_url": "https://api.github.com/users/LouisChen15/followers", "following_url": "https://api.github.com/users/LouisChen15/following{/other_user}", "gists_url": "https://api.github.com/users/LouisChen15/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LouisChen15", "id": 97120485, "login": "LouisChen15", "node_id": "U_kgDOBcnw5Q", "organizations_url": "https://api.github.com/users/LouisChen15/orgs", "received_events_url": "https://api.github.com/users/LouisChen15/received_events", "repos_url": "https://api.github.com/users/LouisChen15/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LouisChen15/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LouisChen15/subscriptions", "type": "User", "url": "https://api.github.com/users/LouisChen15", "user_view_type": "public" }	[]	closed	false	[ "Please tell me if the above info is not enough for solving the problem. I will then make my dataset public temporarily so that you can really reproduce the bug. ", "Hi! \r\nCould you share how to solve this problem? \r\nI faced this same error. " ]	2023-10-01T23:45:56Z	2024-08-14T04:42:02Z	2023-10-02T20:09:38Z	NONE	null	null	### Describe the bug When there is only one config and only the dataset name is entered when using datasets.load_dataset(), it works fine. But if I create a second builder_config for my dataset and enter the config name when using datasets.load_dataset(), the following error will happen. FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow' The "XXX.incomplete folder" in the cache folder of my dataset will disappear before "generating test split", which does not happen when config name is not entered and the config name is "default" C:\Users\chenx\.cache\huggingface\datasets\my_dataset\0_shot_multiple_choice\1.0.0 The folder that is supposed to remain under the above directory will disappear, and the data generator will not have a place to generate data into. ### Steps to reproduce the bug test = load_dataset('my_dataset', '0_shot_multiple_choice') ### Expected behavior FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow' ### Environment info datasets 2.14.5 python 3.8.18	{ "avatar_url": "https://avatars.githubusercontent.com/u/97120485?v=4", "events_url": "https://api.github.com/users/LouisChen15/events{/privacy}", "followers_url": "https://api.github.com/users/LouisChen15/followers", "following_url": "https://api.github.com/users/LouisChen15/following{/other_user}", "gists_url": "https://api.github.com/users/LouisChen15/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LouisChen15", "id": 97120485, "login": "LouisChen15", "node_id": "U_kgDOBcnw5Q", "organizations_url": "https://api.github.com/users/LouisChen15/orgs", "received_events_url": "https://api.github.com/users/LouisChen15/received_events", "repos_url": "https://api.github.com/users/LouisChen15/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LouisChen15/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LouisChen15/subscriptions", "type": "User", "url": "https://api.github.com/users/LouisChen15", "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/6274/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/6273	6,273	Broken Link to PubMed Abstracts dataset .	{ "avatar_url": "https://avatars.githubusercontent.com/u/100606327?v=4", "events_url": "https://api.github.com/users/sameemqureshi/events{/privacy}", "followers_url": "https://api.github.com/users/sameemqureshi/followers", "following_url": "https://api.github.com/users/sameemqureshi/following{/other_user}", "gists_url": "https://api.github.com/users/sameemqureshi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sameemqureshi", "id": 100606327, "login": "sameemqureshi", "node_id": "U_kgDOBf8hdw", "organizations_url": "https://api.github.com/users/sameemqureshi/orgs", "received_events_url": "https://api.github.com/users/sameemqureshi/received_events", "repos_url": "https://api.github.com/users/sameemqureshi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sameemqureshi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sameemqureshi/subscriptions", "type": "User", "url": "https://api.github.com/users/sameemqureshi", "user_view_type": "public" }	[]	open	false	[ "This has already been reported in the HF Course repo (https://github.com/huggingface/course/issues/623).", "@lhoestq @albertvillanova @lewtun I don't think we are allowed to host these data files on the Hub (due to DMCA), which means the only option is to use a different dataset in the course (and to re-record the video 🙂), no?", "Keeping the video is maybe fine, we can add a note on youtube to suggest to load a dataset with a different name. Maybe C4 ? And update the code snippets on the website ?", "Maybe you want to try it with the PUBMED dataset that I reproduced based on the The [PubMed Abstract GitHub Site](http://github.com/thoppe/The-Pile-PubMed) and uploaded on the HuggingFace:\r\n\r\n```\r\nfrom datasets import load_dataset\r\npubmed_dataset = load_dataset(\"hwang2006/PUBMED_title_abstracts_2020_baseline\")\r\npubmed_dataset\r\n\r\n#Downloading data: 100%\r\n#7.98G/7.98G [11:47<00:00, 9.68MB/s]\r\n#Generating train split: 17722096/0 [00:36<00:00, 505376.37 examples/s]\r\n\r\n#DatasetDict({\r\n# train: Dataset({\r\n# features: ['meta', 'text'],\r\n# num_rows: 17722096\r\n# })\r\n#})\r\n```", "孔令涛说感谢感谢" ]	2023-10-01T19:08:48Z	2024-04-28T02:30:42Z	null	NONE	null	null	### Describe the bug The link provided for the dataset is broken, data_files = [https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst](url) The ### Steps to reproduce the bug Steps to reproduce: 1) Head over to [https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#big-data-datasets-to-the-rescue](url) 2) In the Section "What is the Pile?", you can see a code snippet that contains the broken link. ### Expected behavior The link should Redirect to the "PubMed Abstracts dataset" as expected . ### Environment info .	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/6273/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/6272	6,272	Duplicate `data_files` when named `<split>/<split>.parquet`	{ "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" }	[ { "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	[ "Also reported in https://github.com/huggingface/datasets/issues/6259", "I think it's best to drop duplicates with a `set` (as a temporary fix) and improve the patterns when/if https://github.com/fsspec/filesystem_spec/pull/1382 gets merged. @lhoestq Do you have some other ideas?", "Alternatively we could just use this no ?\r\n\r\n```python\r\nif config.FSSPEC_VERSION < version.parse(\"2023.9.0\"):\r\n KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = [\r\n \"{keyword}[{sep}/]\",\r\n \"[{sep}]{keyword}[{sep}/]\",\r\n \"/{keyword}[{sep}/]\",\r\n ]\r\nelse:\r\n KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = [\r\n \"{keyword}[{sep}/]\",\r\n \"*/[{sep}]{keyword}[{sep}/]\",\r\n \"//{keyword}[{sep}/]\",\r\n ]\r\n```\r\n\r\nThis way no need to implement sets, which would require a bit of work since we've always considered a list of pattern to be resolved as the concatenated list of resolved files for each pattern (including duplicates)\r\n", "Arf `\"//{keyword}[{sep}/]**\"` does return `data/keyword.txt` in latest `fsspec` but not in `glob.glob`\r\n\r\nEDIT: actually forgot to set `recursive=True`", "Actually `glob.glob` does return it with `recursive=True` ! my bad", "Pff just tested and my idea sucks, pattern 1 and 3 obviously give duplicates ", "> I think it's best to drop duplicates with a set (as a temporary fix)\r\n\r\nI started https://github.com/huggingface/datasets/pull/6278 to use DataFilesSet objects instead of DataFilesList" ]	2023-10-01T15:43:56Z	2024-03-15T15:22:05Z	2024-03-15T15:22:05Z	MEMBER	null	null	e.g. with `u23429/stock_1_minute_ticker` ```ipython In [1]: from datasets import * In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker") Downloading readme: 100%\|██████████████████████████\| 627/627 [00:00<00:00, 246kB/s] In [3]: b.config.data_files Out[3]: {NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'], NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'], NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']} ``` This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko	{ "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/6272/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/6271	6,271	Overwriting Split overwrites data but not metadata, corrupting dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/13859249?v=4", "events_url": "https://api.github.com/users/govindrai/events{/privacy}", "followers_url": "https://api.github.com/users/govindrai/followers", "following_url": "https://api.github.com/users/govindrai/following{/other_user}", "gists_url": "https://api.github.com/users/govindrai/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/govindrai", "id": 13859249, "login": "govindrai", "node_id": "MDQ6VXNlcjEzODU5MjQ5", "organizations_url": "https://api.github.com/users/govindrai/orgs", "received_events_url": "https://api.github.com/users/govindrai/received_events", "repos_url": "https://api.github.com/users/govindrai/repos", "site_admin": false, "starred_url": "https://api.github.com/users/govindrai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/govindrai/subscriptions", "type": "User", "url": "https://api.github.com/users/govindrai", "user_view_type": "public" }	[]	closed	false	[]	2023-09-30T22:37:31Z	2023-10-16T13:30:50Z	2023-10-16T13:30:50Z	NONE	null	null	### Describe the bug I want to be able to overwrite/update/delete splits in my dataset. Currently the only way to do is to manually go into the dataset and delete the split. If I try to overwrite programmatically I end up in an error state and (somewhat) corrupting the dataset. Read below. Current Behavior When I push to an existing split I get this error: `ValueError: Split complexRoofLocation_01Apr2023_to_31May2023test already present` This seems to suggest that the library doesn't support overwriting splits. Potential Bug What’s strange is that datasets, despite the operation erroring out with the ValueError above, does, in fact, overwrite the split: `Pushing dataset shards to the dataset hub: 100% [.....................] 1/1 [00:00<00:00, 55.04it/s]` Even though you got an error message and your code fails, your dataset is now changed. That seems like a bug. Either don't change the dataset, or don't throw the error and allow the script to proceed. Additional Bug While it overwrites the split, it doesn’t overwrite the split’s information. Because of this when you pull down the dataset you may end up getting a `NonMatchingSplitsSizesError` if the size of the dataset during the overwrite is different. For example, my original split had 5 rows, but on my overwrite, I only had 4. Then when I try to download the dataset, I get a `NonMatchingSplitsSizesError` because the dataset's data.json states there’s 5 but only 4 exist in the split. Expected Behavior This corrupts the dataset rendering it unusable (until you take manual intervention). Either the library should let the overwrite happen (which it does but should also update the metadata) or it shouldn’t do anything. ### Steps to reproduce the bug [Colab Notebook](https://colab.research.google.com/drive/1bqVkD06Ngs9MQNdSk_ygCG6y1UqXA4pC?usp=sharing) ### Expected behavior The split should be overwritten and I should be able to use the new version of the dataset without issue. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.3 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "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/6271/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/6270	6,270	Dataset.from_generator raises with sharded gen_args	{ "avatar_url": "https://avatars.githubusercontent.com/u/53510?v=4", "events_url": "https://api.github.com/users/hartmans/events{/privacy}", "followers_url": "https://api.github.com/users/hartmans/followers", "following_url": "https://api.github.com/users/hartmans/following{/other_user}", "gists_url": "https://api.github.com/users/hartmans/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/hartmans", "id": 53510, "login": "hartmans", "node_id": "MDQ6VXNlcjUzNTEw", "organizations_url": "https://api.github.com/users/hartmans/orgs", "received_events_url": "https://api.github.com/users/hartmans/received_events", "repos_url": "https://api.github.com/users/hartmans/repos", "site_admin": false, "starred_url": "https://api.github.com/users/hartmans/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hartmans/subscriptions", "type": "User", "url": "https://api.github.com/users/hartmans", "user_view_type": "public" }	[]	closed	false	[ "`gen_kwargs` should be a `dict`, as stated in the docstring, but you are passing a `list`.\r\n\r\nSo, to fix the error, replace the list of dicts with a dict of lists (and slightly modify the generator function):\r\n```python\r\nfrom pathlib import Path\r\nimport datasets\r\n\r\ndef process_yaml(files):\r\n for f in files:\r\n # process\r\n yield dict(...)\r\n\r\n\r\nif __name__ == '__main__':\r\n import sys\r\n dir = Path(sys.argv[0]).parent\r\n ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs={'files': [f for f in dir.glob('.yml')]})\r\n ds.to_json('training.jsonl')\r\n```", "That runs, and because my dataset is small, it's what I did to get past the problem.\r\nHowever, it does not produce a sharded dataset. From the doc string I expect there ought to be a way to call from_generator such that num_shards in the resulting data set is equal to the number of items in the list.\r\nThe part of the doc string that your suggestion is not responsive to is:\r\n` You can define a sharded dataset by passing the list of shards in g\r\nen_kwargs.\r\n`\r\n\r\nWhat your suggestion does is calls the generator once, with the list argument, and produces a single shard dataset.\r\n", "The sharding mentioned here refers to using this function with `num_proc` (multiprocessing splits the `kwargs` into shards and passes them to the generator function)\r\n\r\n> That runs, and because my dataset is small, it's what I did to get past the problem.\r\n\r\n`from_generator` generates a memory-mapped dataset (can be larger than RAM), so the dataset size should not be an issue unless the generator function's implementation does not properly free the memory.\r\n", "It sounds like you are saying that num_proc affects the form of gen_kwargs.\r\nAre you saying that for non-zero num_proc gen_kwargs should be a list whose length is the same as num_proc?\r\nOr are you saying that for non-zero num_proc, gen_kwargs should be a dict whose elements are lists the length of num_proc?\r\n", "I ran some tests. So, it looks like with num_proc greater than 1, gen_kwargs is expected to be a dict of lists. It calls the generator also with a dict of lists, but the lists are split.\r\nI.E. if my original has `gen_kwargs=dict(a=[0,1,2])`, then my generator might get called with `gen_kwalrgs=dict([0])`.\r\nThat all makes sense, but I definitely think there is room for improvement in the doc string here.\r\nIn order to suggest improvements to the doc string, I need to look at how the gen_kwargs are split, and figure out if:\r\n num_proc needs to exactly equal the length of the lists\r\n* num_proc needs to evenly divide the length of the lists\r\n* Or there's no required relationship.\r\nI'll look into that and then propose an improved doc string if no one else gets to it first.", "Okay, that was fun; I took a dive through the dataset code and feel like I have a much better understanding.\r\nHere is my understanding of the behavior:\r\n* max_proc is an upper limit on the number of shards that `from_generator` produces\r\n* If `max_proc` is greater than 1, then all lists in gen_kwargs must be the same length\r\n* If the lists in gen_kwargs are shorter than num_proc elements, num_proc will be reduced and a warning produced. Put another way, `min(list_length, num_shards)` shards will be produced\r\n* The members of the lists in gen_kwargs will be partitioned among the created jobs.\r\nTo validate the above, take a look at\r\n`_number_of_shards_in_gen_kwargs` and `_distribute_shards` and `_split_gen_kwargs` in utils/sharding.py.\r\nI've also chased down starting at from_generator all the way through to GeneratorBuilder and the calls to the functions in sharding.py.\r\nTomorrow I'll take a look at the contributing guidelines and see what's involved in putting together a PR to improve the doc string." ]	2023-09-30T16:50:06Z	2023-10-11T20:29:12Z	2023-10-11T20:29:11Z	CONTRIBUTOR	null	null	### Describe the bug According to the docs of Datasets.from_generator: ``` gen_kwargs(`dict`, optional): Keyword arguments to be passed to the `generator` callable. You can define a sharded dataset by passing the list of shards in `gen_kwargs`. ``` So I'd expect that if gen_kwargs was a list, then my generator would be called once for each element in the list with the dict in the list for that element. It doesn't work that way though. ### Steps to reproduce the bug ```python #!/usr/bin/python from pathlib import Path import datasets def process_yaml(file): yield dict(example=42) if __name__ == '__main__': import sys dir = Path(sys.argv[0]).parent ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('.yml')], ) ds.to_json('training.jsonl') ``` ``` Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File "/tmp/dataset_bug.py", line 13, in <module> ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('.yml')], ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1072, in from_generator ).read() ^^^^^^ File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/io/generator.py", line 47, in read self.builder.download_and_prepare( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1656, in _prepare_split_single generator = self._generate_examples(gen_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: datasets.packaged_modules.generator.generator.Generator._generate_examples() argument after ** must be a ``` mapping, not list ### Expected behavior I would expect that process_yaml would be called once for each yaml file in the directory where the script is run. I also tried with the list being in gen_kwargs, but in that case process_yaml gets called with a list. ### Environment info - `datasets` version: 2.14.6.dev0 (git commit 0cc77d7f45c7369; also tested with 2.14.0) - Platform: Linux-6.1.0-10-amd64-x86_64-with-glibc2.36 - Python version: 3.11.2 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "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/6270/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/6269	6,269	Reduce the number of commits 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	[ "_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) \| 0.005864 / 0.011353 (-0.005489) \| 0.003535 / 0.011008 (-0.007474) \| 0.080732 / 0.038508 (0.042224) \| 0.057072 / 0.023109 (0.033963) \| 0.334342 / 0.275898 (0.058444) \| 0.361345 / 0.323480 (0.037865) \| 0.003290 / 0.007986 (-0.004696) \| 0.003794 / 0.004328 (-0.000534) \| 0.063414 / 0.004250 (0.059163) \| 0.046901 / 0.037052 (0.009848) \| 0.335973 / 0.258489 (0.077484) \| 0.377929 / 0.293841 (0.084088) \| 0.027199 / 0.128546 (-0.101348) \| 0.008049 / 0.075646 (-0.067597) \| 0.261810 / 0.419271 (-0.157462) \| 0.044669 / 0.043533 (0.001136) \| 0.333600 / 0.255139 (0.078461) \| 0.356362 / 0.283200 (0.073162) \| 0.020325 / 0.141683 (-0.121358) \| 1.458138 / 1.452155 (0.005984) \| 1.505923 / 1.492716 (0.013207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216456 / 0.018006 (0.198450) \| 0.421750 / 0.000490 (0.421261) \| 0.007359 / 0.000200 (0.007159) \| 0.000246 / 0.000054 (0.000191) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023400 / 0.037411 (-0.014012) \| 0.073363 / 0.014526 (0.058838) \| 0.083533 / 0.176557 (-0.093023) \| 0.144045 / 0.737135 (-0.593090) \| 0.084050 / 0.296338 (-0.212288) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398354 / 0.215209 (0.183145) \| 3.982875 / 2.077655 (1.905220) \| 2.047299 / 1.504120 (0.543180) \| 1.873780 / 1.541195 (0.332585) \| 1.977044 / 1.468490 (0.508554) \| 0.497038 / 4.584777 (-4.087739) \| 3.039743 / 3.745712 (-0.705969) \| 2.832885 / 5.269862 (-2.436977) \| 1.827300 / 4.565676 (-2.738377) \| 0.057503 / 0.424275 (-0.366772) \| 0.006272 / 0.007607 (-0.001335) \| 0.468681 / 0.226044 (0.242637) \| 4.696551 / 2.268929 (2.427622) \| 2.413805 / 55.444624 (-53.030819) \| 2.157199 / 6.876477 (-4.719278) \| 2.345986 / 2.142072 (0.203914) \| 0.584632 / 4.805227 (-4.220595) \| 0.124684 / 6.500664 (-6.375980) \| 0.060090 / 0.075469 (-0.015379) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293551 / 1.841788 (-0.548236) \| 17.198292 / 8.074308 (9.123984) \| 13.677910 / 10.191392 (3.486518) \| 0.146633 / 0.680424 (-0.533791) \| 0.016711 / 0.534201 (-0.517490) \| 0.331644 / 0.579283 (-0.247639) \| 0.360148 / 0.434364 (-0.074215) \| 0.381194 / 0.540337 (-0.159143) \| 0.537952 / 1.386936 (-0.848984) \|\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) \| 0.006020 / 0.011353 (-0.005333) \| 0.003557 / 0.011008 (-0.007451) \| 0.061926 / 0.038508 (0.023418) \| 0.056246 / 0.023109 (0.033137) \| 0.446679 / 0.275898 (0.170781) \| 0.479843 / 0.323480 (0.156363) \| 0.004656 / 0.007986 (-0.003330) \| 0.002823 / 0.004328 (-0.001505) \| 0.061366 / 0.004250 (0.057115) \| 0.045793 / 0.037052 (0.008740) \| 0.460807 / 0.258489 (0.202318) \| 0.485467 / 0.293841 (0.191626) \| 0.028555 / 0.128546 (-0.099991) \| 0.007973 / 0.075646 (-0.067674) \| 0.068305 / 0.419271 (-0.350966) \| 0.040844 / 0.043533 (-0.002689) \| 0.463715 / 0.255139 (0.208576) \| 0.474553 / 0.283200 (0.191354) \| 0.019959 / 0.141683 (-0.121723) \| 1.432527 / 1.452155 (-0.019628) \| 1.485410 / 1.492716 (-0.007307) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205555 / 0.018006 (0.187549) \| 0.408271 / 0.000490 (0.407781) \| 0.004325 / 0.000200 (0.004125) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026338 / 0.037411 (-0.011074) \| 0.080534 / 0.014526 (0.066008) \| 0.093935 / 0.176557 (-0.082622) \| 0.146446 / 0.737135 (-0.590689) \| 0.092890 / 0.296338 (-0.203448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463879 / 0.215209 (0.248670) \| 4.646411 / 2.077655 (2.568756) \| 2.567320 / 1.504120 (1.063200) \| 2.384376 / 1.541195 (0.843181) \| 2.412738 / 1.468490 (0.944248) \| 0.510240 / 4.584777 (-4.074537) \| 3.094988 / 3.745712 (-0.650724) \| 2.837700 / 5.269862 (-2.432161) \| 1.850163 / 4.565676 (-2.715513) \| 0.059320 / 0.424275 (-0.364955) \| 0.006330 / 0.007607 (-0.001277) \| 0.537770 / 0.226044 (0.311726) \| 5.385556 / 2.268929 (3.116627) \| 3.036088 / 55.444624 (-52.408536) \| 2.650464 / 6.876477 (-4.226013) \| 2.755676 / 2.142072 (0.613603) \| 0.607353 / 4.805227 (-4.197875) \| 0.124589 / 6.500664 (-6.376075) \| 0.060778 / 0.075469 (-0.014691) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.343243 / 1.841788 (-0.498545) \| 17.630281 / 8.074308 (9.555973) \| 14.401219 / 10.191392 (4.209827) \| 0.143252 / 0.680424 (-0.537172) \| 0.017880 / 0.534201 (-0.516321) \| 0.337391 / 0.579283 (-0.241892) \| 0.373531 / 0.434364 (-0.060833) \| 0.398408 / 0.540337 (-0.141929) \| 0.558925 / 1.386936 (-0.828011) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8f511638b486b9f83b17fd69a505fe606ad257b \"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) \| 0.006552 / 0.011353 (-0.004801) \| 0.003853 / 0.011008 (-0.007155) \| 0.077673 / 0.038508 (0.039165) \| 0.066043 / 0.023109 (0.042934) \| 0.289858 / 0.275898 (0.013960) \| 0.299009 / 0.323480 (-0.024471) \| 0.004806 / 0.007986 (-0.003179) \| 0.003517 / 0.004328 (-0.000811) \| 0.058227 / 0.004250 (0.053977) \| 0.052134 / 0.037052 (0.015082) \| 0.328800 / 0.258489 (0.070311) \| 0.317616 / 0.293841 (0.023776) \| 0.028344 / 0.128546 (-0.100202) \| 0.007853 / 0.075646 (-0.067794) \| 0.291207 / 0.419271 (-0.128065) \| 0.052977 / 0.043533 (0.009444) \| 0.287548 / 0.255139 (0.032409) \| 0.307647 / 0.283200 (0.024448) \| 0.023899 / 0.141683 (-0.117784) \| 1.382267 / 1.452155 (-0.069888) \| 1.589915 / 1.492716 (0.097199) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246244 / 0.018006 (0.228238) \| 0.478255 / 0.000490 (0.477766) \| 0.014115 / 0.000200 (0.013915) \| 0.000305 / 0.000054 (0.000250) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027033 / 0.037411 (-0.010378) \| 0.073988 / 0.014526 (0.059462) \| 0.088337 / 0.176557 (-0.088219) \| 0.144067 / 0.737135 (-0.593069) \| 0.091295 / 0.296338 (-0.205043) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.365904 / 0.215209 (0.150695) \| 3.537330 / 2.077655 (1.459675) \| 1.678341 / 1.504120 (0.174221) \| 1.530297 / 1.541195 (-0.010898) \| 1.605634 / 1.468490 (0.137144) \| 0.437461 / 4.584777 (-4.147316) \| 3.419040 / 3.745712 (-0.326672) \| 3.203549 / 5.269862 (-2.066312) \| 1.913214 / 4.565676 (-2.652463) \| 0.052675 / 0.424275 (-0.371600) \| 0.006681 / 0.007607 (-0.000926) \| 0.429269 / 0.226044 (0.203225) \| 4.214051 / 2.268929 (1.945122) \| 2.217928 / 55.444624 (-53.226696) \| 1.842679 / 6.876477 (-5.033798) \| 1.867961 / 2.142072 (-0.274111) \| 0.550566 / 4.805227 (-4.254661) \| 0.118015 / 6.500664 (-6.382649) \| 0.054749 / 0.075469 (-0.020720) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.170547 / 1.841788 (-0.671241) \| 18.410567 / 8.074308 (10.336259) \| 12.729992 / 10.191392 (2.538600) \| 0.160426 / 0.680424 (-0.519998) \| 0.021259 / 0.534201 (-0.512942) \| 0.369573 / 0.579283 (-0.209710) \| 0.440350 / 0.434364 (0.005986) \| 0.443755 / 0.540337 (-0.096582) \| 0.645614 / 1.386936 (-0.741322) \|\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) \| 0.005913 / 0.011353 (-0.005440) \| 0.003542 / 0.011008 (-0.007466) \| 0.057621 / 0.038508 (0.019113) \| 0.065822 / 0.023109 (0.042713) \| 0.390847 / 0.275898 (0.114949) \| 0.393127 / 0.323480 (0.069647) \| 0.005040 / 0.007986 (-0.002945) \| 0.002944 / 0.004328 (-0.001384) \| 0.069058 / 0.004250 (0.064808) \| 0.051594 / 0.037052 (0.014542) \| 0.383745 / 0.258489 (0.125256) \| 0.414372 / 0.293841 (0.120531) \| 0.030038 / 0.128546 (-0.098508) \| 0.008109 / 0.075646 (-0.067538) \| 0.065444 / 0.419271 (-0.353828) \| 0.045974 / 0.043533 (0.002441) \| 0.401695 / 0.255139 (0.146556) \| 0.417834 / 0.283200 (0.134635) \| 0.020137 / 0.141683 (-0.121546) \| 1.452130 / 1.452155 (-0.000025) \| 1.455259 / 1.492716 (-0.037458) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228262 / 0.018006 (0.210255) \| 0.455155 / 0.000490 (0.454665) \| 0.006667 / 0.000200 (0.006467) \| 0.000207 / 0.000054 (0.000153) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030159 / 0.037411 (-0.007252) \| 0.098478 / 0.014526 (0.083952) \| 0.101409 / 0.176557 (-0.075147) \| 0.148689 / 0.737135 (-0.588446) \| 0.103067 / 0.296338 (-0.193272) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444095 / 0.215209 (0.228886) \| 3.991588 / 2.077655 (1.913934) \| 2.147845 / 1.504120 (0.643725) \| 2.007871 / 1.541195 (0.466676) \| 2.042074 / 1.468490 (0.573584) \| 0.451592 / 4.584777 (-4.133185) \| 3.439400 / 3.745712 (-0.306312) \| 3.107756 / 5.269862 (-2.162106) \| 1.909785 / 4.565676 (-2.655891) \| 0.051718 / 0.424275 (-0.372558) \| 0.006597 / 0.007607 (-0.001010) \| 0.480822 / 0.226044 (0.254777) \| 4.913235 / 2.268929 (2.644307) \| 2.631882 / 55.444624 (-52.812742) \| 2.397209 / 6.876477 (-4.479267) \| 2.487191 / 2.142072 (0.345119) \| 0.566321 / 4.805227 (-4.238906) \| 0.121741 / 6.500664 (-6.378924) \| 0.053399 / 0.075469 (-0.022070) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256599 / 1.841788 (-0.585189) \| 18.891127 / 8.074308 (10.816819) \| 13.219662 / 10.191392 (3.028270) \| 0.154570 / 0.680424 (-0.525854) \| 0.022599 / 0.534201 (-0.511602) \| 0.361998 / 0.579283 (-0.217286) \| 0.413287 / 0.434364 (-0.021077) \| 0.464867 / 0.540337 (-0.075470) \| 0.638880 / 1.386936 (-0.748056) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#869e6bc775cf4dff1b92834426e1a286b104432b \"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) \| 0.010625 / 0.011353 (-0.000728) \| 0.005129 / 0.011008 (-0.005879) \| 0.119975 / 0.038508 (0.081467) \| 0.100128 / 0.023109 (0.077019) \| 0.448678 / 0.275898 (0.172780) \| 0.533150 / 0.323480 (0.209670) \| 0.005881 / 0.007986 (-0.002105) \| 0.007451 / 0.004328 (0.003123) \| 0.090792 / 0.004250 (0.086542) \| 0.073416 / 0.037052 (0.036363) \| 0.455395 / 0.258489 (0.196906) \| 0.497572 / 0.293841 (0.203731) \| 0.053112 / 0.128546 (-0.075434) \| 0.014619 / 0.075646 (-0.061027) \| 0.388023 / 0.419271 (-0.031248) \| 0.074004 / 0.043533 (0.030471) \| 0.435319 / 0.255139 (0.180180) \| 0.465985 / 0.283200 (0.182785) \| 0.046991 / 0.141683 (-0.094692) \| 1.895717 / 1.452155 (0.443563) \| 2.086600 / 1.492716 (0.593884) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.334412 / 0.018006 (0.316406) \| 0.645510 / 0.000490 (0.645020) \| 0.019175 / 0.000200 (0.018975) \| 0.000429 / 0.000054 (0.000374) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034385 / 0.037411 (-0.003026) \| 0.108939 / 0.014526 (0.094413) \| 0.125937 / 0.176557 (-0.050619) \| 0.205643 / 0.737135 (-0.531493) \| 0.127662 / 0.296338 (-0.168676) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.674093 / 0.215209 (0.458884) \| 6.646554 / 2.077655 (4.568900) \| 2.837698 / 1.504120 (1.333578) \| 2.397199 / 1.541195 (0.856004) \| 2.485856 / 1.468490 (1.017366) \| 0.955142 / 4.584777 (-3.629635) \| 5.667462 / 3.745712 (1.921750) \| 5.354129 / 5.269862 (0.084268) \| 3.301609 / 4.565676 (-1.264068) \| 0.106051 / 0.424275 (-0.318224) \| 0.009287 / 0.007607 (0.001680) \| 0.766678 / 0.226044 (0.540634) \| 7.786701 / 2.268929 (5.517772) \| 3.665463 / 55.444624 (-51.779161) \| 2.982912 / 6.876477 (-3.893564) \| 3.053363 / 2.142072 (0.911290) \| 1.141090 / 4.805227 (-3.664137) \| 0.223975 / 6.500664 (-6.276689) \| 0.093024 / 0.075469 (0.017555) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.728175 / 1.841788 (-0.113613) \| 25.640134 / 8.074308 (17.565826) \| 22.124769 / 10.191392 (11.933377) \| 0.237489 / 0.680424 (-0.442935) \| 0.030353 / 0.534201 (-0.503848) \| 0.509371 / 0.579283 (-0.069913) \| 0.642320 / 0.434364 (0.207956) \| 0.576889 / 0.540337 (0.036552) \| 0.899377 / 1.386936 (-0.487559) \|\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) \| 0.010846 / 0.011353 (-0.000507) \| 0.005876 / 0.011008 (-0.005132) \| 0.090810 / 0.038508 (0.052302) \| 0.106651 / 0.023109 (0.083542) \| 0.551064 / 0.275898 (0.275166) \| 0.608328 / 0.323480 (0.284848) \| 0.007563 / 0.007986 (-0.000423) \| 0.004595 / 0.004328 (0.000267) \| 0.089125 / 0.004250 (0.084874) \| 0.076577 / 0.037052 (0.039525) \| 0.579970 / 0.258489 (0.321481) \| 0.620214 / 0.293841 (0.326373) \| 0.052577 / 0.128546 (-0.075970) \| 0.013734 / 0.075646 (-0.061912) \| 0.099825 / 0.419271 (-0.319447) \| 0.068391 / 0.043533 (0.024858) \| 0.564733 / 0.255139 (0.309594) \| 0.593925 / 0.283200 (0.310726) \| 0.037201 / 0.141683 (-0.104482) \| 1.880969 / 1.452155 (0.428815) \| 2.065094 / 1.492716 (0.572377) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426148 / 0.018006 (0.408141) \| 0.673935 / 0.000490 (0.673445) \| 0.124190 / 0.000200 (0.123990) \| 0.001219 / 0.000054 (0.001164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040280 / 0.037411 (0.002868) \| 0.122042 / 0.014526 (0.107516) \| 0.131333 / 0.176557 (-0.045223) \| 0.203039 / 0.737135 (-0.534096) \| 0.134851 / 0.296338 (-0.161487) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.684599 / 0.215209 (0.469390) \| 6.727529 / 2.077655 (4.649874) \| 3.255228 / 1.504120 (1.751108) \| 2.925865 / 1.541195 (1.384670) \| 2.978762 / 1.468490 (1.510272) \| 0.931769 / 4.584777 (-3.653008) \| 5.988956 / 3.745712 (2.243244) \| 5.228049 / 5.269862 (-0.041812) \| 3.341470 / 4.565676 (-1.224206) \| 0.106737 / 0.424275 (-0.317539) \| 0.009847 / 0.007607 (0.002240) \| 0.813954 / 0.226044 (0.587909) \| 8.137071 / 2.268929 (5.868143) \| 4.140725 / 55.444624 (-51.303899) \| 3.500579 / 6.876477 (-3.375898) \| 3.623120 / 2.142072 (1.481047) \| 1.096634 / 4.805227 (-3.708593) \| 0.236938 / 6.500664 (-6.263726) \| 0.083099 / 0.075469 (0.007630) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.856112 / 1.841788 (0.014324) \| 26.531325 / 8.074308 (18.457017) \| 24.435866 / 10.191392 (14.244474) \| 0.264093 / 0.680424 (-0.416331) \| 0.034872 / 0.534201 (-0.499329) \| 0.520682 / 0.579283 (-0.058601) \| 0.635010 / 0.434364 (0.200646) \| 0.645451 / 0.540337 (0.105113) \| 0.914616 / 1.386936 (-0.472320) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d8c29b9416371283e8aaabee235a91b2f45a05ee \"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) \| 0.005928 / 0.011353 (-0.005425) \| 0.003633 / 0.011008 (-0.007375) \| 0.079554 / 0.038508 (0.041046) \| 0.057093 / 0.023109 (0.033984) \| 0.311374 / 0.275898 (0.035476) \| 0.343778 / 0.323480 (0.020298) \| 0.004634 / 0.007986 (-0.003352) \| 0.002886 / 0.004328 (-0.001443) \| 0.061888 / 0.004250 (0.057637) \| 0.045895 / 0.037052 (0.008843) \| 0.316447 / 0.258489 (0.057958) \| 0.358141 / 0.293841 (0.064300) \| 0.027247 / 0.128546 (-0.101300) \| 0.007947 / 0.075646 (-0.067699) \| 0.259070 / 0.419271 (-0.160201) \| 0.043802 / 0.043533 (0.000269) \| 0.315453 / 0.255139 (0.060314) \| 0.335282 / 0.283200 (0.052082) \| 0.021096 / 0.141683 (-0.120587) \| 1.443219 / 1.452155 (-0.008936) \| 1.523140 / 1.492716 (0.030423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222957 / 0.018006 (0.204951) \| 0.414611 / 0.000490 (0.414122) \| 0.008354 / 0.000200 (0.008154) \| 0.000249 / 0.000054 (0.000195) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023880 / 0.037411 (-0.013532) \| 0.074523 / 0.014526 (0.059997) \| 0.084803 / 0.176557 (-0.091754) \| 0.146701 / 0.737135 (-0.590435) \| 0.084990 / 0.296338 (-0.211348) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397736 / 0.215209 (0.182527) \| 3.961740 / 2.077655 (1.884086) \| 1.909014 / 1.504120 (0.404894) \| 1.823026 / 1.541195 (0.281831) \| 1.966235 / 1.468490 (0.497745) \| 0.498056 / 4.584777 (-4.086721) \| 3.041408 / 3.745712 (-0.704304) \| 2.998010 / 5.269862 (-2.271852) \| 1.887293 / 4.565676 (-2.678384) \| 0.057096 / 0.424275 (-0.367179) \| 0.006338 / 0.007607 (-0.001269) \| 0.465166 / 0.226044 (0.239122) \| 4.667710 / 2.268929 (2.398781) \| 2.480798 / 55.444624 (-52.963826) \| 2.270701 / 6.876477 (-4.605776) \| 2.376470 / 2.142072 (0.234397) \| 0.579873 / 4.805227 (-4.225355) \| 0.125032 / 6.500664 (-6.375632) \| 0.061057 / 0.075469 (-0.014412) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229916 / 1.841788 (-0.611872) \| 17.829628 / 8.074308 (9.755320) \| 13.860184 / 10.191392 (3.668792) \| 0.143507 / 0.680424 (-0.536917) \| 0.016943 / 0.534201 (-0.517258) \| 0.350106 / 0.579283 (-0.229178) \| 0.364547 / 0.434364 (-0.069817) \| 0.398889 / 0.540337 (-0.141448) \| 0.557948 / 1.386936 (-0.828988) \|\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) \| 0.006052 / 0.011353 (-0.005301) \| 0.003636 / 0.011008 (-0.007372) \| 0.062705 / 0.038508 (0.024197) \| 0.057753 / 0.023109 (0.034644) \| 0.453219 / 0.275898 (0.177321) \| 0.485179 / 0.323480 (0.161699) \| 0.004886 / 0.007986 (-0.003100) \| 0.002838 / 0.004328 (-0.001490) \| 0.062593 / 0.004250 (0.058343) \| 0.047476 / 0.037052 (0.010423) \| 0.454266 / 0.258489 (0.195777) \| 0.487939 / 0.293841 (0.194098) \| 0.028124 / 0.128546 (-0.100422) \| 0.008000 / 0.075646 (-0.067647) \| 0.068335 / 0.419271 (-0.350937) \| 0.040491 / 0.043533 (-0.003042) \| 0.457868 / 0.255139 (0.202729) \| 0.476355 / 0.283200 (0.193155) \| 0.019557 / 0.141683 (-0.122126) \| 1.507111 / 1.452155 (0.054956) \| 1.569720 / 1.492716 (0.077003) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209205 / 0.018006 (0.191199) \| 0.411782 / 0.000490 (0.411292) \| 0.003544 / 0.000200 (0.003344) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026569 / 0.037411 (-0.010842) \| 0.081213 / 0.014526 (0.066687) \| 0.090971 / 0.176557 (-0.085585) \| 0.145287 / 0.737135 (-0.591849) \| 0.091792 / 0.296338 (-0.204546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458329 / 0.215209 (0.243120) \| 4.574463 / 2.077655 (2.496808) \| 2.516693 / 1.504120 (1.012573) \| 2.329463 / 1.541195 (0.788269) \| 2.386704 / 1.468490 (0.918214) \| 0.503526 / 4.584777 (-4.081251) \| 3.113382 / 3.745712 (-0.632331) \| 2.872538 / 5.269862 (-2.397323) \| 1.865483 / 4.565676 (-2.700194) \| 0.058292 / 0.424275 (-0.365983) \| 0.006434 / 0.007607 (-0.001173) \| 0.530804 / 0.226044 (0.304760) \| 5.312666 / 2.268929 (3.043738) \| 2.992569 / 55.444624 (-52.452055) \| 2.611524 / 6.876477 (-4.264953) \| 2.779569 / 2.142072 (0.637497) \| 0.595200 / 4.805227 (-4.210028) \| 0.123957 / 6.500664 (-6.376707) \| 0.060601 / 0.075469 (-0.014868) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345536 / 1.841788 (-0.496252) \| 18.183827 / 8.074308 (10.109519) \| 14.814084 / 10.191392 (4.622692) \| 0.145305 / 0.680424 (-0.535119) \| 0.018812 / 0.534201 (-0.515389) \| 0.334793 / 0.579283 (-0.244490) \| 0.375331 / 0.434364 (-0.059033) \| 0.392499 / 0.540337 (-0.147839) \| 0.563286 / 1.386936 (-0.823650) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e186f0b7fe851f1f474020f0d6b1dc35114f994 \"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) \| 0.008922 / 0.011353 (-0.002431) \| 0.005169 / 0.011008 (-0.005840) \| 0.106275 / 0.038508 (0.067767) \| 0.076446 / 0.023109 (0.053337) \| 0.400207 / 0.275898 (0.124309) \| 0.476262 / 0.323480 (0.152782) \| 0.006032 / 0.007986 (-0.001954) \| 0.004266 / 0.004328 (-0.000063) \| 0.083518 / 0.004250 (0.079267) \| 0.059644 / 0.037052 (0.022592) \| 0.409094 / 0.258489 (0.150605) \| 0.470400 / 0.293841 (0.176559) \| 0.050161 / 0.128546 (-0.078385) \| 0.013580 / 0.075646 (-0.062066) \| 0.375047 / 0.419271 (-0.044224) \| 0.068319 / 0.043533 (0.024786) \| 0.433765 / 0.255139 (0.178626) \| 0.449221 / 0.283200 (0.166021) \| 0.037636 / 0.141683 (-0.104047) \| 1.825855 / 1.452155 (0.373700) \| 1.889665 / 1.492716 (0.396948) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319622 / 0.018006 (0.301616) \| 0.588878 / 0.000490 (0.588388) \| 0.017790 / 0.000200 (0.017590) \| 0.000532 / 0.000054 (0.000477) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031152 / 0.037411 (-0.006259) \| 0.093808 / 0.014526 (0.079282) \| 0.119296 / 0.176557 (-0.057261) \| 0.181845 / 0.737135 (-0.555291) \| 0.108527 / 0.296338 (-0.187811) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575106 / 0.215209 (0.359896) \| 5.776322 / 2.077655 (3.698668) \| 2.592913 / 1.504120 (1.088793) \| 2.389481 / 1.541195 (0.848286) \| 2.390117 / 1.468490 (0.921627) \| 0.852420 / 4.584777 (-3.732357) \| 5.474171 / 3.745712 (1.728459) \| 4.967188 / 5.269862 (-0.302674) \| 3.053712 / 4.565676 (-1.511965) \| 0.098128 / 0.424275 (-0.326147) \| 0.008722 / 0.007607 (0.001115) \| 0.699838 / 0.226044 (0.473794) \| 7.103622 / 2.268929 (4.834693) \| 3.359326 / 55.444624 (-52.085299) \| 2.733943 / 6.876477 (-4.142534) \| 2.770001 / 2.142072 (0.627929) \| 1.058217 / 4.805227 (-3.747011) \| 0.215845 / 6.500664 (-6.284820) \| 0.078532 / 0.075469 (0.003063) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.633173 / 1.841788 (-0.208614) \| 23.795045 / 8.074308 (15.720737) \| 21.094433 / 10.191392 (10.903041) \| 0.234522 / 0.680424 (-0.445902) \| 0.033632 / 0.534201 (-0.500569) \| 0.496701 / 0.579283 (-0.082582) \| 0.626861 / 0.434364 (0.192497) \| 0.558267 / 0.540337 (0.017930) \| 0.807461 / 1.386936 (-0.579475) \|\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) \| 0.009136 / 0.011353 (-0.002217) \| 0.005425 / 0.011008 (-0.005584) \| 0.081478 / 0.038508 (0.042970) \| 0.077240 / 0.023109 (0.054130) \| 0.512156 / 0.275898 (0.236258) \| 0.561593 / 0.323480 (0.238113) \| 0.006499 / 0.007986 (-0.001486) \| 0.004080 / 0.004328 (-0.000248) \| 0.082121 / 0.004250 (0.077870) \| 0.063774 / 0.037052 (0.026722) \| 0.509801 / 0.258489 (0.251312) \| 0.572826 / 0.293841 (0.278985) \| 0.050969 / 0.128546 (-0.077578) \| 0.014876 / 0.075646 (-0.060771) \| 0.094815 / 0.419271 (-0.324456) \| 0.063904 / 0.043533 (0.020371) \| 0.530572 / 0.255139 (0.275433) \| 0.545940 / 0.283200 (0.262741) \| 0.036729 / 0.141683 (-0.104954) \| 1.799493 / 1.452155 (0.347339) \| 1.931955 / 1.492716 (0.439239) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291405 / 0.018006 (0.273398) \| 0.590257 / 0.000490 (0.589767) \| 0.008394 / 0.000200 (0.008194) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037613 / 0.037411 (0.000201) \| 0.103136 / 0.014526 (0.088610) \| 0.121744 / 0.176557 (-0.054813) \| 0.198503 / 0.737135 (-0.538632) \| 0.120183 / 0.296338 (-0.176156) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.659872 / 0.215209 (0.444663) \| 6.616775 / 2.077655 (4.539120) \| 3.031679 / 1.504120 (1.527559) \| 2.743489 / 1.541195 (1.202294) \| 2.786786 / 1.468490 (1.318296) \| 0.866625 / 4.584777 (-3.718152) \| 5.637705 / 3.745712 (1.891993) \| 4.702563 / 5.269862 (-0.567298) \| 3.017797 / 4.565676 (-1.547879) \| 0.100107 / 0.424275 (-0.324169) \| 0.008443 / 0.007607 (0.000836) \| 0.791385 / 0.226044 (0.565341) \| 7.869504 / 2.268929 (5.600576) \| 3.856634 / 55.444624 (-51.587991) \| 3.140089 / 6.876477 (-3.736388) \| 3.489339 / 2.142072 (1.347267) \| 1.132170 / 4.805227 (-3.673058) \| 0.219630 / 6.500664 (-6.281034) \| 0.082289 / 0.075469 (0.006820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.781902 / 1.841788 (-0.059885) \| 24.912604 / 8.074308 (16.838296) \| 21.626512 / 10.191392 (11.435120) \| 0.228194 / 0.680424 (-0.452230) \| 0.032799 / 0.534201 (-0.501402) \| 0.483683 / 0.579283 (-0.095600) \| 0.604966 / 0.434364 (0.170602) \| 0.617278 / 0.540337 (0.076940) \| 0.887337 / 1.386936 (-0.499599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#579c31fda7182ca6fc33ab1e95db9e3a21fb5518 \"CML watermark\")\n", "I used [this](https://colab.research.google.com/drive/1q2FYnkJFDMM3OZbhnYeZkfzmBa6ksofQ?usp=sharing) Colab to test the new `push_to_hub` on a large dataset (55 GB). It works great. \r\n\r\nOne thing that could be improved is the performance of `dataset.data.nbytes` - it takes ≈ 3 minutes to compute for the dataset in question (50k array chunks per column). It probably makes sense to store larger chunks locally. But this can be addressed in a subsequent PR.", "<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) \| 0.007190 / 0.011353 (-0.004163) \| 0.004394 / 0.011008 (-0.006614) \| 0.085506 / 0.038508 (0.046998) \| 0.092177 / 0.023109 (0.069068) \| 0.351636 / 0.275898 (0.075738) \| 0.389716 / 0.323480 (0.066236) \| 0.004443 / 0.007986 (-0.003543) \| 0.003641 / 0.004328 (-0.000687) \| 0.066578 / 0.004250 (0.062328) \| 0.061399 / 0.037052 (0.024346) \| 0.356008 / 0.258489 (0.097519) \| 0.398677 / 0.293841 (0.104836) \| 0.031958 / 0.128546 (-0.096588) \| 0.008857 / 0.075646 (-0.066789) \| 0.289613 / 0.419271 (-0.129659) \| 0.053555 / 0.043533 (0.010022) \| 0.349268 / 0.255139 (0.094129) \| 0.368666 / 0.283200 (0.085466) \| 0.028267 / 0.141683 (-0.113416) \| 1.502857 / 1.452155 (0.050702) \| 1.598422 / 1.492716 (0.105705) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319938 / 0.018006 (0.301931) \| 0.566925 / 0.000490 (0.566435) \| 0.014625 / 0.000200 (0.014425) \| 0.000372 / 0.000054 (0.000318) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030156 / 0.037411 (-0.007255) \| 0.083128 / 0.014526 (0.068602) \| 0.101435 / 0.176557 (-0.075122) \| 0.158971 / 0.737135 (-0.578165) \| 0.101488 / 0.296338 (-0.194851) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383904 / 0.215209 (0.168695) \| 3.829201 / 2.077655 (1.751546) \| 1.815224 / 1.504120 (0.311104) \| 1.647865 / 1.541195 (0.106670) \| 1.738411 / 1.468490 (0.269921) \| 0.484963 / 4.584777 (-4.099814) \| 3.494811 / 3.745712 (-0.250901) \| 3.505811 / 5.269862 (-1.764051) \| 2.115467 / 4.565676 (-2.450210) \| 0.057271 / 0.424275 (-0.367004) \| 0.007285 / 0.007607 (-0.000322) \| 0.467162 / 0.226044 (0.241118) \| 4.661572 / 2.268929 (2.392643) \| 2.330443 / 55.444624 (-53.114182) \| 1.986116 / 6.876477 (-4.890361) \| 2.055350 / 2.142072 (-0.086723) \| 0.580369 / 4.805227 (-4.224858) \| 0.132700 / 6.500664 (-6.367964) \| 0.061219 / 0.075469 (-0.014251) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270843 / 1.841788 (-0.570945) \| 19.870723 / 8.074308 (11.796415) \| 14.368932 / 10.191392 (4.177540) \| 0.167345 / 0.680424 (-0.513079) \| 0.018358 / 0.534201 (-0.515843) \| 0.390833 / 0.579283 (-0.188450) \| 0.419884 / 0.434364 (-0.014480) \| 0.465683 / 0.540337 (-0.074655) \| 0.646101 / 1.386936 (-0.740835) \|\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) \| 0.007027 / 0.011353 (-0.004326) \| 0.004578 / 0.011008 (-0.006430) \| 0.066468 / 0.038508 (0.027960) \| 0.081576 / 0.023109 (0.058466) \| 0.414928 / 0.275898 (0.139030) \| 0.452130 / 0.323480 (0.128651) \| 0.005861 / 0.007986 (-0.002124) \| 0.003740 / 0.004328 (-0.000588) \| 0.066943 / 0.004250 (0.062692) \| 0.060100 / 0.037052 (0.023048) \| 0.418697 / 0.258489 (0.160208) \| 0.466604 / 0.293841 (0.172764) \| 0.031887 / 0.128546 (-0.096660) \| 0.009119 / 0.075646 (-0.066527) \| 0.072285 / 0.419271 (-0.346986) \| 0.047599 / 0.043533 (0.004066) \| 0.410791 / 0.255139 (0.155652) \| 0.434182 / 0.283200 (0.150982) \| 0.024799 / 0.141683 (-0.116884) \| 1.500310 / 1.452155 (0.048155) \| 1.567151 / 1.492716 (0.074434) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.322482 / 0.018006 (0.304476) \| 0.550234 / 0.000490 (0.549744) \| 0.007796 / 0.000200 (0.007596) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036013 / 0.037411 (-0.001398) \| 0.098482 / 0.014526 (0.083956) \| 0.111641 / 0.176557 (-0.064916) \| 0.166251 / 0.737135 (-0.570884) \| 0.112426 / 0.296338 (-0.183912) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429181 / 0.215209 (0.213972) \| 4.273126 / 2.077655 (2.195472) \| 2.277440 / 1.504120 (0.773321) \| 2.112567 / 1.541195 (0.571372) \| 2.224118 / 1.468490 (0.755628) \| 0.488876 / 4.584777 (-4.095901) \| 3.711638 / 3.745712 (-0.034074) \| 3.480995 / 5.269862 (-1.788867) \| 2.122114 / 4.565676 (-2.443563) \| 0.057538 / 0.424275 (-0.366737) \| 0.007416 / 0.007607 (-0.000191) \| 0.506881 / 0.226044 (0.280836) \| 5.067601 / 2.268929 (2.798672) \| 2.769216 / 55.444624 (-52.675408) \| 2.420448 / 6.876477 (-4.456029) \| 2.694225 / 2.142072 (0.552153) \| 0.588911 / 4.805227 (-4.216316) \| 0.133542 / 6.500664 (-6.367122) \| 0.061135 / 0.075469 (-0.014334) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.378029 / 1.841788 (-0.463758) \| 20.660942 / 8.074308 (12.586634) \| 15.725969 / 10.191392 (5.534577) \| 0.169078 / 0.680424 (-0.511346) \| 0.020540 / 0.534201 (-0.513661) \| 0.399409 / 0.579283 (-0.179874) \| 0.432572 / 0.434364 (-0.001792) \| 0.477106 / 0.540337 (-0.063231) \| 0.675593 / 1.386936 (-0.711343) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9764c49d8bfdad5439e16faa6c52e510feabf6fa \"CML watermark\")\n", "@lhoestq \r\n\r\n> single commit can fail (time out) if there are too many operations so we might have to do multi commits anyway in that case\r\n\r\nMultiple commits complicate the logic significantly. Maybe, let's keep things simple and emit a warning if there are more than 100 additions (we can suggest increasing `max_shard_size` in that case). Additionally, we can set the default `max_shard_size` to a higher value, e.g., 5GB. I think handling up to 500GB of data in the default case seems reasonable. In rare cases where this is a problem, one could increase the default `max_shard_size` even further (if RAM is not a limiting factor) or use `to_parquet` + `huggingface_hub` (we could have a docstring or a doc note that explains this).\r\n\r\nNote that we split the dataset based on the Arrow data size, which means Parquet shards will be considerably smaller unless there are binary fields such as image JPEGs in the dataset, which are hard to compress efficiently.\r\n\r\n> how to let users resume a push_to_hub that failed mid-way because of a connection error for example\r\n\r\nThey can resume by rerunning the failed `push_to_hub`.\r\n\r\n`preupload_lfs_files` will be instant in that scenario, as explained in https://github.com/huggingface/huggingface_hub/pull/1699#discussion_r1342446406", "> Multiple commits complicate the logic significantly. Maybe, let's keep things simple and emit a warning if there are more than 100 additions (we can suggest increasing max_shard_size in that case)\r\n\r\nI don't think we can do that, many people are uploading files with 100+ files and it would break their workflow", "Indeed, we should not break this, considering the number of datasets with more than 100 shards on the Hub (over 1k)", "<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) \| 0.006834 / 0.011353 (-0.004519) \| 0.004424 / 0.011008 (-0.006584) \| 0.085199 / 0.038508 (0.046691) \| 0.080237 / 0.023109 (0.057128) \| 0.308800 / 0.275898 (0.032902) \| 0.346314 / 0.323480 (0.022835) \| 0.004399 / 0.007986 (-0.003586) \| 0.003773 / 0.004328 (-0.000556) \| 0.065886 / 0.004250 (0.061636) \| 0.057830 / 0.037052 (0.020777) \| 0.312035 / 0.258489 (0.053546) \| 0.362646 / 0.293841 (0.068805) \| 0.031223 / 0.128546 (-0.097323) \| 0.008851 / 0.075646 (-0.066795) \| 0.288264 / 0.419271 (-0.131007) \| 0.052600 / 0.043533 (0.009067) \| 0.316127 / 0.255139 (0.060988) \| 0.328539 / 0.283200 (0.045340) \| 0.026068 / 0.141683 (-0.115615) \| 1.458928 / 1.452155 (0.006773) \| 1.547619 / 1.492716 (0.054902) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274382 / 0.018006 (0.256375) \| 0.591192 / 0.000490 (0.590703) \| 0.009290 / 0.000200 (0.009090) \| 0.000327 / 0.000054 (0.000273) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031428 / 0.037411 (-0.005983) \| 0.087523 / 0.014526 (0.072997) \| 0.101427 / 0.176557 (-0.075130) \| 0.159228 / 0.737135 (-0.577907) \| 0.101430 / 0.296338 (-0.194909) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393914 / 0.215209 (0.178705) \| 3.917323 / 2.077655 (1.839668) \| 1.940577 / 1.504120 (0.436457) \| 1.760996 / 1.541195 (0.219801) \| 1.865858 / 1.468490 (0.397368) \| 0.488920 / 4.584777 (-4.095857) \| 3.513465 / 3.745712 (-0.232248) \| 3.506600 / 5.269862 (-1.763261) \| 2.072583 / 4.565676 (-2.493093) \| 0.058256 / 0.424275 (-0.366019) \| 0.007420 / 0.007607 (-0.000187) \| 0.467241 / 0.226044 (0.241197) \| 4.671470 / 2.268929 (2.402542) \| 2.422717 / 55.444624 (-53.021908) \| 2.069501 / 6.876477 (-4.806975) \| 2.159257 / 2.142072 (0.017184) \| 0.583808 / 4.805227 (-4.221419) \| 0.134160 / 6.500664 (-6.366504) \| 0.068855 / 0.075469 (-0.006614) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305299 / 1.841788 (-0.536488) \| 19.913902 / 8.074308 (11.839593) \| 14.708057 / 10.191392 (4.516665) \| 0.160113 / 0.680424 (-0.520311) \| 0.018431 / 0.534201 (-0.515770) \| 0.396147 / 0.579283 (-0.183136) \| 0.411738 / 0.434364 (-0.022626) \| 0.459297 / 0.540337 (-0.081041) \| 0.636599 / 1.386936 (-0.750337) \|\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) \| 0.006936 / 0.011353 (-0.004417) \| 0.004290 / 0.011008 (-0.006718) \| 0.065754 / 0.038508 (0.027246) \| 0.080655 / 0.023109 (0.057546) \| 0.399701 / 0.275898 (0.123803) \| 0.435999 / 0.323480 (0.112519) \| 0.005690 / 0.007986 (-0.002295) \| 0.003580 / 0.004328 (-0.000748) \| 0.065685 / 0.004250 (0.061434) \| 0.059299 / 0.037052 (0.022246) \| 0.404295 / 0.258489 (0.145806) \| 0.438745 / 0.293841 (0.144904) \| 0.032241 / 0.128546 (-0.096305) \| 0.008699 / 0.075646 (-0.066947) \| 0.072053 / 0.419271 (-0.347218) \| 0.047489 / 0.043533 (0.003956) \| 0.395638 / 0.255139 (0.140499) \| 0.417224 / 0.283200 (0.134025) \| 0.022734 / 0.141683 (-0.118949) \| 1.507519 / 1.452155 (0.055364) \| 1.570459 / 1.492716 (0.077743) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260442 / 0.018006 (0.242435) \| 0.551933 / 0.000490 (0.551444) \| 0.005240 / 0.000200 (0.005040) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033718 / 0.037411 (-0.003694) \| 0.095710 / 0.014526 (0.081184) \| 0.109970 / 0.176557 (-0.066586) \| 0.167930 / 0.737135 (-0.569205) \| 0.109977 / 0.296338 (-0.186362) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430067 / 0.215209 (0.214857) \| 4.292564 / 2.077655 (2.214910) \| 2.313511 / 1.504120 (0.809391) \| 2.158153 / 1.541195 (0.616959) \| 2.262486 / 1.468490 (0.793996) \| 0.492376 / 4.584777 (-4.092401) \| 3.622287 / 3.745712 (-0.123425) \| 3.380162 / 5.269862 (-1.889699) \| 2.111874 / 4.565676 (-2.453803) \| 0.057882 / 0.424275 (-0.366393) \| 0.007317 / 0.007607 (-0.000290) \| 0.504722 / 0.226044 (0.278678) \| 5.039009 / 2.268929 (2.770080) \| 2.772162 / 55.444624 (-52.672463) \| 2.430928 / 6.876477 (-4.445549) \| 2.666556 / 2.142072 (0.524484) \| 0.586722 / 4.805227 (-4.218505) \| 0.133780 / 6.500664 (-6.366884) \| 0.060269 / 0.075469 (-0.015200) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.339064 / 1.841788 (-0.502724) \| 20.743931 / 8.074308 (12.669623) \| 15.491066 / 10.191392 (5.299674) \| 0.159236 / 0.680424 (-0.521188) \| 0.020722 / 0.534201 (-0.513479) \| 0.399440 / 0.579283 (-0.179843) \| 0.424501 / 0.434364 (-0.009863) \| 0.474026 / 0.540337 (-0.066311) \| 0.685239 / 1.386936 (-0.701697) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58406f61c52e7ff064ac6c19ebdb3e5247c70862 \"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) \| 0.005930 / 0.011353 (-0.005422) \| 0.003496 / 0.011008 (-0.007512) \| 0.079631 / 0.038508 (0.041123) \| 0.058250 / 0.023109 (0.035141) \| 0.310108 / 0.275898 (0.034210) \| 0.352747 / 0.323480 (0.029267) \| 0.005367 / 0.007986 (-0.002619) \| 0.002943 / 0.004328 (-0.001386) \| 0.062449 / 0.004250 (0.058199) \| 0.046433 / 0.037052 (0.009381) \| 0.311020 / 0.258489 (0.052531) \| 0.361033 / 0.293841 (0.067192) \| 0.027419 / 0.128546 (-0.101128) \| 0.008073 / 0.075646 (-0.067574) \| 0.261403 / 0.419271 (-0.157869) \| 0.045059 / 0.043533 (0.001527) \| 0.310622 / 0.255139 (0.055483) \| 0.344361 / 0.283200 (0.061161) \| 0.020561 / 0.141683 (-0.121122) \| 1.427409 / 1.452155 (-0.024746) \| 1.506612 / 1.492716 (0.013896) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234095 / 0.018006 (0.216089) \| 0.432603 / 0.000490 (0.432113) \| 0.010283 / 0.000200 (0.010083) \| 0.000289 / 0.000054 (0.000235) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024263 / 0.037411 (-0.013148) \| 0.073672 / 0.014526 (0.059146) \| 0.084080 / 0.176557 (-0.092476) \| 0.146679 / 0.737135 (-0.590457) \| 0.084337 / 0.296338 (-0.212001) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434297 / 0.215209 (0.219088) \| 4.358287 / 2.077655 (2.280633) \| 2.268461 / 1.504120 (0.764341) \| 2.107924 / 1.541195 (0.566729) \| 2.165136 / 1.468490 (0.696646) \| 0.498421 / 4.584777 (-4.086356) \| 3.094414 / 3.745712 (-0.651298) \| 2.991511 / 5.269862 (-2.278351) \| 1.998052 / 4.565676 (-2.567624) \| 0.057363 / 0.424275 (-0.366912) \| 0.006405 / 0.007607 (-0.001203) \| 0.508396 / 0.226044 (0.282351) \| 5.104756 / 2.268929 (2.835828) \| 2.720462 / 55.444624 (-52.724163) \| 2.391840 / 6.876477 (-4.484637) \| 2.443063 / 2.142072 (0.300991) \| 0.590015 / 4.805227 (-4.215212) \| 0.125414 / 6.500664 (-6.375250) \| 0.061122 / 0.075469 (-0.014347) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221883 / 1.841788 (-0.619904) \| 17.788248 / 8.074308 (9.713940) \| 13.753315 / 10.191392 (3.561923) \| 0.146388 / 0.680424 (-0.534036) \| 0.017038 / 0.534201 (-0.517163) \| 0.339162 / 0.579283 (-0.240121) \| 0.372054 / 0.434364 (-0.062309) \| 0.381507 / 0.540337 (-0.158830) \| 0.538603 / 1.386936 (-0.848333) \|\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) \| 0.006044 / 0.011353 (-0.005309) \| 0.003654 / 0.011008 (-0.007354) \| 0.062956 / 0.038508 (0.024448) \| 0.061325 / 0.023109 (0.038216) \| 0.450006 / 0.275898 (0.174108) \| 0.474560 / 0.323480 (0.151080) \| 0.004846 / 0.007986 (-0.003140) \| 0.002904 / 0.004328 (-0.001425) \| 0.064206 / 0.004250 (0.059956) \| 0.047850 / 0.037052 (0.010798) \| 0.448431 / 0.258489 (0.189942) \| 0.481363 / 0.293841 (0.187523) \| 0.028622 / 0.128546 (-0.099925) \| 0.008255 / 0.075646 (-0.067391) \| 0.068461 / 0.419271 (-0.350810) \| 0.040234 / 0.043533 (-0.003299) \| 0.447396 / 0.255139 (0.192257) \| 0.465383 / 0.283200 (0.182184) \| 0.021864 / 0.141683 (-0.119819) \| 1.402197 / 1.452155 (-0.049957) \| 1.475337 / 1.492716 (-0.017379) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227093 / 0.018006 (0.209087) \| 0.407908 / 0.000490 (0.407419) \| 0.006709 / 0.000200 (0.006509) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026560 / 0.037411 (-0.010851) \| 0.080926 / 0.014526 (0.066400) \| 0.091531 / 0.176557 (-0.085026) \| 0.145742 / 0.737135 (-0.591393) \| 0.092203 / 0.296338 (-0.204135) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473029 / 0.215209 (0.257820) \| 4.703613 / 2.077655 (2.625958) \| 2.642622 / 1.504120 (1.138502) \| 2.465376 / 1.541195 (0.924181) \| 2.510125 / 1.468490 (1.041635) \| 0.512606 / 4.584777 (-4.072171) \| 3.132127 / 3.745712 (-0.613585) \| 2.890098 / 5.269862 (-2.379763) \| 1.908140 / 4.565676 (-2.657537) \| 0.058938 / 0.424275 (-0.365337) \| 0.006486 / 0.007607 (-0.001121) \| 0.542279 / 0.226044 (0.316235) \| 5.435621 / 2.268929 (3.166693) \| 3.083943 / 55.444624 (-52.360681) \| 2.761575 / 6.876477 (-4.114901) \| 2.919672 / 2.142072 (0.777599) \| 0.608022 / 4.805227 (-4.197205) \| 0.126821 / 6.500664 (-6.373843) \| 0.061374 / 0.075469 (-0.014095) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.348848 / 1.841788 (-0.492940) \| 18.323507 / 8.074308 (10.249199) \| 14.713411 / 10.191392 (4.522019) \| 0.155277 / 0.680424 (-0.525146) \| 0.017739 / 0.534201 (-0.516462) \| 0.337357 / 0.579283 (-0.241926) \| 0.376519 / 0.434364 (-0.057844) \| 0.398011 / 0.540337 (-0.142327) \| 0.589797 / 1.386936 (-0.797139) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#26d8bfca337e01bd78d5590d5e49c6d8d022a3ff \"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) \| 0.007823 / 0.011353 (-0.003530) \| 0.004136 / 0.011008 (-0.006872) \| 0.087282 / 0.038508 (0.048774) \| 0.086352 / 0.023109 (0.063243) \| 0.328107 / 0.275898 (0.052209) \| 0.368717 / 0.323480 (0.045237) \| 0.005452 / 0.007986 (-0.002533) \| 0.003460 / 0.004328 (-0.000868) \| 0.064360 / 0.004250 (0.060110) \| 0.062215 / 0.037052 (0.025162) \| 0.334666 / 0.258489 (0.076177) \| 0.388688 / 0.293841 (0.094847) \| 0.031093 / 0.128546 (-0.097454) \| 0.008510 / 0.075646 (-0.067137) \| 0.295965 / 0.419271 (-0.123306) \| 0.052858 / 0.043533 (0.009325) \| 0.320104 / 0.255139 (0.064965) \| 0.346761 / 0.283200 (0.063562) \| 0.024864 / 0.141683 (-0.116819) \| 1.483164 / 1.452155 (0.031010) \| 1.580363 / 1.492716 (0.087647) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243523 / 0.018006 (0.225516) \| 0.459741 / 0.000490 (0.459251) \| 0.010508 / 0.000200 (0.010308) \| 0.000384 / 0.000054 (0.000330) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029896 / 0.037411 (-0.007515) \| 0.089150 / 0.014526 (0.074624) \| 0.098855 / 0.176557 (-0.077702) \| 0.154469 / 0.737135 (-0.582667) \| 0.099546 / 0.296338 (-0.196792) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403547 / 0.215209 (0.188338) \| 4.036711 / 2.077655 (1.959056) \| 2.030882 / 1.504120 (0.526762) \| 1.850432 / 1.541195 (0.309238) \| 1.924248 / 1.468490 (0.455758) \| 0.493153 / 4.584777 (-4.091624) \| 3.634074 / 3.745712 (-0.111638) \| 3.546145 / 5.269862 (-1.723717) \| 2.120819 / 4.565676 (-2.444858) \| 0.057137 / 0.424275 (-0.367138) \| 0.007454 / 0.007607 (-0.000153) \| 0.481687 / 0.226044 (0.255642) \| 4.813203 / 2.268929 (2.544275) \| 2.481260 / 55.444624 (-52.963364) \| 2.194185 / 6.876477 (-4.682292) \| 2.255381 / 2.142072 (0.113308) \| 0.575160 / 4.805227 (-4.230068) \| 0.132310 / 6.500664 (-6.368355) \| 0.061917 / 0.075469 (-0.013553) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.265722 / 1.841788 (-0.576066) \| 19.949624 / 8.074308 (11.875315) \| 14.804356 / 10.191392 (4.612964) \| 0.170485 / 0.680424 (-0.509939) \| 0.018831 / 0.534201 (-0.515370) \| 0.407051 / 0.579283 (-0.172233) \| 0.420560 / 0.434364 (-0.013804) \| 0.470721 / 0.540337 (-0.069616) \| 0.651665 / 1.386936 (-0.735271) \|\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) \| 0.007113 / 0.011353 (-0.004240) \| 0.004186 / 0.011008 (-0.006822) \| 0.065082 / 0.038508 (0.026574) \| 0.080275 / 0.023109 (0.057166) \| 0.393460 / 0.275898 (0.117562) \| 0.426702 / 0.323480 (0.103223) \| 0.005639 / 0.007986 (-0.002347) \| 0.003492 / 0.004328 (-0.000836) \| 0.065774 / 0.004250 (0.061523) \| 0.059708 / 0.037052 (0.022656) \| 0.395598 / 0.258489 (0.137109) \| 0.437088 / 0.293841 (0.143247) \| 0.033165 / 0.128546 (-0.095381) \| 0.008559 / 0.075646 (-0.067087) \| 0.071782 / 0.419271 (-0.347490) \| 0.048672 / 0.043533 (0.005139) \| 0.393883 / 0.255139 (0.138744) \| 0.412817 / 0.283200 (0.129617) \| 0.024115 / 0.141683 (-0.117568) \| 1.522752 / 1.452155 (0.070597) \| 1.577311 / 1.492716 (0.084595) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225569 / 0.018006 (0.207563) \| 0.460310 / 0.000490 (0.459820) \| 0.004733 / 0.000200 (0.004533) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035241 / 0.037411 (-0.002170) \| 0.098092 / 0.014526 (0.083566) \| 0.108025 / 0.176557 (-0.068531) \| 0.162910 / 0.737135 (-0.574225) \| 0.108649 / 0.296338 (-0.187689) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441723 / 0.215209 (0.226514) \| 4.400656 / 2.077655 (2.323001) \| 2.413588 / 1.504120 (0.909468) \| 2.261890 / 1.541195 (0.720696) \| 2.420878 / 1.468490 (0.952388) \| 0.496456 / 4.584777 (-4.088321) \| 3.679930 / 3.745712 (-0.065782) \| 3.390539 / 5.269862 (-1.879322) \| 2.109599 / 4.565676 (-2.456078) \| 0.058896 / 0.424275 (-0.365379) \| 0.007483 / 0.007607 (-0.000125) \| 0.521108 / 0.226044 (0.295064) \| 5.209468 / 2.268929 (2.940540) \| 2.948595 / 55.444624 (-52.496029) \| 2.658864 / 6.876477 (-4.217613) \| 2.913653 / 2.142072 (0.771580) \| 0.602776 / 4.805227 (-4.202451) \| 0.136166 / 6.500664 (-6.364498) \| 0.063812 / 0.075469 (-0.011657) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350306 / 1.841788 (-0.491482) \| 20.453980 / 8.074308 (12.379672) \| 15.758719 / 10.191392 (5.567327) \| 0.165847 / 0.680424 (-0.514577) \| 0.020254 / 0.534201 (-0.513947) \| 0.400006 / 0.579283 (-0.179277) \| 0.440336 / 0.434364 (0.005972) \| 0.480122 / 0.540337 (-0.060215) \| 0.688994 / 1.386936 (-0.697942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#997082a2a3c599ea1b23a70759d3af98a78f7f33 \"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) \| 0.008633 / 0.011353 (-0.002720) \| 0.004851 / 0.011008 (-0.006157) \| 0.100647 / 0.038508 (0.062139) \| 0.084701 / 0.023109 (0.061592) \| 0.410489 / 0.275898 (0.134590) \| 0.440231 / 0.323480 (0.116751) \| 0.004679 / 0.007986 (-0.003307) \| 0.004172 / 0.004328 (-0.000157) \| 0.079911 / 0.004250 (0.075661) \| 0.069537 / 0.037052 (0.032485) \| 0.423506 / 0.258489 (0.165017) \| 0.466098 / 0.293841 (0.172257) \| 0.048773 / 0.128546 (-0.079773) \| 0.014446 / 0.075646 (-0.061200) \| 0.342776 / 0.419271 (-0.076495) \| 0.065672 / 0.043533 (0.022139) \| 0.411845 / 0.255139 (0.156706) \| 0.466662 / 0.283200 (0.183462) \| 0.035752 / 0.141683 (-0.105931) \| 1.684956 / 1.452155 (0.232801) \| 1.832173 / 1.492716 (0.339456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250744 / 0.018006 (0.232738) \| 0.528860 / 0.000490 (0.528371) \| 0.013301 / 0.000200 (0.013101) \| 0.000413 / 0.000054 (0.000359) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032376 / 0.037411 (-0.005035) \| 0.094630 / 0.014526 (0.080104) \| 0.107163 / 0.176557 (-0.069394) \| 0.172503 / 0.737135 (-0.564633) \| 0.108407 / 0.296338 (-0.187932) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.671251 / 0.215209 (0.456042) \| 6.235361 / 2.077655 (4.157706) \| 2.650328 / 1.504120 (1.146208) \| 2.341199 / 1.541195 (0.800004) \| 2.368803 / 1.468490 (0.900313) \| 0.841347 / 4.584777 (-3.743430) \| 5.042508 / 3.745712 (1.296796) \| 4.807565 / 5.269862 (-0.462296) \| 3.007420 / 4.565676 (-1.558257) \| 0.099953 / 0.424275 (-0.324322) \| 0.008412 / 0.007607 (0.000805) \| 0.747803 / 0.226044 (0.521759) \| 7.481245 / 2.268929 (5.212316) \| 3.416157 / 55.444624 (-52.028467) \| 2.724608 / 6.876477 (-4.151869) \| 2.832982 / 2.142072 (0.690910) \| 1.072423 / 4.805227 (-3.732804) \| 0.211314 / 6.500664 (-6.289351) \| 0.074098 / 0.075469 (-0.001371) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.566010 / 1.841788 (-0.275778) \| 23.137708 / 8.074308 (15.063400) \| 21.440132 / 10.191392 (11.248740) \| 0.230713 / 0.680424 (-0.449711) \| 0.028271 / 0.534201 (-0.505930) \| 0.450821 / 0.579283 (-0.128463) \| 0.548399 / 0.434364 (0.114035) \| 0.543588 / 0.540337 (0.003250) \| 0.805522 / 1.386936 (-0.581414) \|\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) \| 0.008969 / 0.011353 (-0.002384) \| 0.004793 / 0.011008 (-0.006216) \| 0.075804 / 0.038508 (0.037296) \| 0.079893 / 0.023109 (0.056783) \| 0.464358 / 0.275898 (0.188460) \| 0.507243 / 0.323480 (0.183763) \| 0.005945 / 0.007986 (-0.002040) \| 0.005341 / 0.004328 (0.001012) \| 0.077952 / 0.004250 (0.073701) \| 0.059965 / 0.037052 (0.022913) \| 0.478947 / 0.258489 (0.220458) \| 0.528444 / 0.293841 (0.234603) \| 0.052878 / 0.128546 (-0.075668) \| 0.013939 / 0.075646 (-0.061707) \| 0.087351 / 0.419271 (-0.331920) \| 0.058448 / 0.043533 (0.014916) \| 0.478664 / 0.255139 (0.223525) \| 0.491239 / 0.283200 (0.208039) \| 0.032674 / 0.141683 (-0.109008) \| 1.753911 / 1.452155 (0.301756) \| 1.858923 / 1.492716 (0.366206) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239278 / 0.018006 (0.221271) \| 0.507372 / 0.000490 (0.506882) \| 0.005489 / 0.000200 (0.005289) \| 0.000142 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032919 / 0.037411 (-0.004493) \| 0.097726 / 0.014526 (0.083200) \| 0.119159 / 0.176557 (-0.057398) \| 0.174545 / 0.737135 (-0.562590) \| 0.115319 / 0.296338 (-0.181020) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627107 / 0.215209 (0.411898) \| 6.211925 / 2.077655 (4.134270) \| 2.731484 / 1.504120 (1.227365) \| 2.488847 / 1.541195 (0.947652) \| 2.372445 / 1.468490 (0.903955) \| 0.822663 / 4.584777 (-3.762114) \| 4.924001 / 3.745712 (1.178289) \| 4.371161 / 5.269862 (-0.898700) \| 2.850314 / 4.565676 (-1.715363) \| 0.099156 / 0.424275 (-0.325119) \| 0.007941 / 0.007607 (0.000334) \| 0.721539 / 0.226044 (0.495495) \| 7.260874 / 2.268929 (4.991946) \| 3.351072 / 55.444624 (-52.093552) \| 2.757115 / 6.876477 (-4.119362) \| 2.858899 / 2.142072 (0.716827) \| 0.994054 / 4.805227 (-3.811173) \| 0.209186 / 6.500664 (-6.291478) \| 0.072070 / 0.075469 (-0.003399) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.748073 / 1.841788 (-0.093714) \| 23.514638 / 8.074308 (15.440330) \| 20.372037 / 10.191392 (10.180645) \| 0.220020 / 0.680424 (-0.460404) \| 0.057130 / 0.534201 (-0.477071) \| 0.458204 / 0.579283 (-0.121079) \| 0.600509 / 0.434364 (0.166145) \| 0.557100 / 0.540337 (0.016762) \| 0.814360 / 1.386936 (-0.572576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#072f0ceafde60c16516fe1297e4aba981fc56052 \"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) \| 0.007341 / 0.011353 (-0.004012) \| 0.004606 / 0.011008 (-0.006402) \| 0.087903 / 0.038508 (0.049395) \| 0.094090 / 0.023109 (0.070981) \| 0.322278 / 0.275898 (0.046380) \| 0.356770 / 0.323480 (0.033290) \| 0.005988 / 0.007986 (-0.001997) \| 0.003667 / 0.004328 (-0.000662) \| 0.066105 / 0.004250 (0.061854) \| 0.061220 / 0.037052 (0.024167) \| 0.331190 / 0.258489 (0.072701) \| 0.381402 / 0.293841 (0.087561) \| 0.032261 / 0.128546 (-0.096285) \| 0.009281 / 0.075646 (-0.066366) \| 0.293694 / 0.419271 (-0.125577) \| 0.055041 / 0.043533 (0.011508) \| 0.318080 / 0.255139 (0.062941) \| 0.348763 / 0.283200 (0.065563) \| 0.027379 / 0.141683 (-0.114304) \| 1.496294 / 1.452155 (0.044139) \| 1.581942 / 1.492716 (0.089226) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307592 / 0.018006 (0.289586) \| 0.591805 / 0.000490 (0.591316) \| 0.017082 / 0.000200 (0.016882) \| 0.000721 / 0.000054 (0.000666) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032157 / 0.037411 (-0.005254) \| 0.096249 / 0.014526 (0.081724) \| 0.106656 / 0.176557 (-0.069901) \| 0.162966 / 0.737135 (-0.574169) \| 0.107068 / 0.296338 (-0.189271) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409083 / 0.215209 (0.193874) \| 4.044307 / 2.077655 (1.966652) \| 2.062887 / 1.504120 (0.558767) \| 1.900568 / 1.541195 (0.359373) \| 2.011862 / 1.468490 (0.543372) \| 0.489250 / 4.584777 (-4.095527) \| 3.519531 / 3.745712 (-0.226182) \| 3.631713 / 5.269862 (-1.638149) \| 2.163967 / 4.565676 (-2.401709) \| 0.057723 / 0.424275 (-0.366552) \| 0.007474 / 0.007607 (-0.000133) \| 0.479562 / 0.226044 (0.253517) \| 4.799825 / 2.268929 (2.530897) \| 2.530036 / 55.444624 (-52.914588) \| 2.195344 / 6.876477 (-4.681133) \| 2.341046 / 2.142072 (0.198974) \| 0.625105 / 4.805227 (-4.180122) \| 0.132823 / 6.500664 (-6.367841) \| 0.061721 / 0.075469 (-0.013748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301313 / 1.841788 (-0.540475) \| 21.218468 / 8.074308 (13.144159) \| 15.466347 / 10.191392 (5.274955) \| 0.166115 / 0.680424 (-0.514309) \| 0.018866 / 0.534201 (-0.515335) \| 0.399307 / 0.579283 (-0.179976) \| 0.430537 / 0.434364 (-0.003827) \| 0.467110 / 0.540337 (-0.073228) \| 0.645686 / 1.386936 (-0.741250) \|\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) \| 0.007288 / 0.011353 (-0.004065) \| 0.004298 / 0.011008 (-0.006710) \| 0.065515 / 0.038508 (0.027007) \| 0.089948 / 0.023109 (0.066839) \| 0.410121 / 0.275898 (0.134223) \| 0.449312 / 0.323480 (0.125832) \| 0.006749 / 0.007986 (-0.001237) \| 0.003927 / 0.004328 (-0.000401) \| 0.065321 / 0.004250 (0.061071) \| 0.062480 / 0.037052 (0.025428) \| 0.410796 / 0.258489 (0.152307) \| 0.457356 / 0.293841 (0.163515) \| 0.032632 / 0.128546 (-0.095914) \| 0.008798 / 0.075646 (-0.066849) \| 0.075936 / 0.419271 (-0.343335) \| 0.048402 / 0.043533 (0.004869) \| 0.403385 / 0.255139 (0.148246) \| 0.426094 / 0.283200 (0.142895) \| 0.025326 / 0.141683 (-0.116357) \| 1.551550 / 1.452155 (0.099395) \| 1.628622 / 1.492716 (0.135905) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279689 / 0.018006 (0.261682) \| 0.583754 / 0.000490 (0.583265) \| 0.006579 / 0.000200 (0.006379) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034906 / 0.037411 (-0.002505) \| 0.099232 / 0.014526 (0.084706) \| 0.113093 / 0.176557 (-0.063464) \| 0.165499 / 0.737135 (-0.571636) \| 0.113398 / 0.296338 (-0.182941) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439154 / 0.215209 (0.223945) \| 4.377041 / 2.077655 (2.299387) \| 2.395058 / 1.504120 (0.890938) \| 2.233359 / 1.541195 (0.692164) \| 2.357281 / 1.468490 (0.888791) \| 0.486036 / 4.584777 (-4.098741) \| 3.568794 / 3.745712 (-0.176918) \| 3.485421 / 5.269862 (-1.784440) \| 2.174325 / 4.565676 (-2.391351) \| 0.057855 / 0.424275 (-0.366420) \| 0.007545 / 0.007607 (-0.000062) \| 0.516853 / 0.226044 (0.290808) \| 5.173340 / 2.268929 (2.904412) \| 2.931475 / 55.444624 (-52.513149) \| 2.566814 / 6.876477 (-4.309663) \| 2.873304 / 2.142072 (0.731232) \| 0.597072 / 4.805227 (-4.208155) \| 0.133589 / 6.500664 (-6.367075) \| 0.061882 / 0.075469 (-0.013587) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.382845 / 1.841788 (-0.458943) \| 21.608316 / 8.074308 (13.534008) \| 15.702152 / 10.191392 (5.510759) \| 0.190629 / 0.680424 (-0.489795) \| 0.020572 / 0.534201 (-0.513629) \| 0.396207 / 0.579283 (-0.183076) \| 0.421184 / 0.434364 (-0.013180) \| 0.477700 / 0.540337 (-0.062638) \| 0.690828 / 1.386936 (-0.696108) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5e7374b453911cda5e0f866ad45b51c3fbe267c9 \"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) \| 0.008450 / 0.011353 (-0.002903) \| 0.004958 / 0.011008 (-0.006051) \| 0.105397 / 0.038508 (0.066889) \| 0.079508 / 0.023109 (0.056399) \| 0.403050 / 0.275898 (0.127152) \| 0.443679 / 0.323480 (0.120199) \| 0.004654 / 0.007986 (-0.003332) \| 0.005629 / 0.004328 (0.001301) \| 0.078755 / 0.004250 (0.074505) \| 0.055694 / 0.037052 (0.018642) \| 0.409952 / 0.258489 (0.151463) \| 0.454931 / 0.293841 (0.161090) \| 0.045124 / 0.128546 (-0.083422) \| 0.014031 / 0.075646 (-0.061616) \| 0.347340 / 0.419271 (-0.071931) \| 0.064359 / 0.043533 (0.020826) \| 0.414158 / 0.255139 (0.159019) \| 0.428442 / 0.283200 (0.145243) \| 0.033726 / 0.141683 (-0.107957) \| 1.770483 / 1.452155 (0.318328) \| 1.795267 / 1.492716 (0.302551) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251020 / 0.018006 (0.233014) \| 0.507066 / 0.000490 (0.506576) \| 0.015751 / 0.000200 (0.015551) \| 0.000531 / 0.000054 (0.000477) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028897 / 0.037411 (-0.008515) \| 0.087393 / 0.014526 (0.072867) \| 0.097365 / 0.176557 (-0.079192) \| 0.164833 / 0.737135 (-0.572303) \| 0.101281 / 0.296338 (-0.195058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.610806 / 0.215209 (0.395597) \| 6.011697 / 2.077655 (3.934042) \| 2.544268 / 1.504120 (1.040148) \| 2.127103 / 1.541195 (0.585908) \| 2.133330 / 1.468490 (0.664839) \| 0.860964 / 4.584777 (-3.723813) \| 4.982374 / 3.745712 (1.236662) \| 5.073026 / 5.269862 (-0.196836) \| 3.033056 / 4.565676 (-1.532621) \| 0.118835 / 0.424275 (-0.305440) \| 0.010122 / 0.007607 (0.002515) \| 0.805807 / 0.226044 (0.579763) \| 7.839166 / 2.268929 (5.570238) \| 3.512405 / 55.444624 (-51.932219) \| 2.767578 / 6.876477 (-4.108898) \| 2.936885 / 2.142072 (0.794813) \| 1.058533 / 4.805227 (-3.746695) \| 0.222260 / 6.500664 (-6.278404) \| 0.073890 / 0.075469 (-0.001580) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.628307 / 1.841788 (-0.213480) \| 22.827116 / 8.074308 (14.752808) \| 21.809759 / 10.191392 (11.618367) \| 0.220637 / 0.680424 (-0.459786) \| 0.028030 / 0.534201 (-0.506171) \| 0.448620 / 0.579283 (-0.130663) \| 0.540442 / 0.434364 (0.106078) \| 0.548601 / 0.540337 (0.008264) \| 0.770387 / 1.386936 (-0.616549) \|\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) \| 0.009198 / 0.011353 (-0.002155) \| 0.004935 / 0.011008 (-0.006073) \| 0.079095 / 0.038508 (0.040587) \| 0.090490 / 0.023109 (0.067381) \| 0.453374 / 0.275898 (0.177476) \| 0.519483 / 0.323480 (0.196003) \| 0.006539 / 0.007986 (-0.001447) \| 0.004160 / 0.004328 (-0.000169) \| 0.078433 / 0.004250 (0.074182) \| 0.068022 / 0.037052 (0.030969) \| 0.467686 / 0.258489 (0.209197) \| 0.523863 / 0.293841 (0.230022) \| 0.050926 / 0.128546 (-0.077620) \| 0.013664 / 0.075646 (-0.061982) \| 0.088787 / 0.419271 (-0.330485) \| 0.060503 / 0.043533 (0.016971) \| 0.474692 / 0.255139 (0.219553) \| 0.516461 / 0.283200 (0.233261) \| 0.034482 / 0.141683 (-0.107200) \| 1.747939 / 1.452155 (0.295784) \| 1.915212 / 1.492716 (0.422496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247400 / 0.018006 (0.229394) \| 0.516829 / 0.000490 (0.516339) \| 0.005770 / 0.000200 (0.005570) \| 0.000121 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034334 / 0.037411 (-0.003077) \| 0.102397 / 0.014526 (0.087871) \| 0.114187 / 0.176557 (-0.062370) \| 0.171093 / 0.737135 (-0.566043) \| 0.117281 / 0.296338 (-0.179058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.635710 / 0.215209 (0.420501) \| 6.400656 / 2.077655 (4.323002) \| 2.896896 / 1.504120 (1.392776) \| 2.682890 / 1.541195 (1.141696) \| 2.656445 / 1.468490 (1.187955) \| 1.044244 / 4.584777 (-3.540533) \| 5.393212 / 3.745712 (1.647500) \| 4.592928 / 5.269862 (-0.676934) \| 2.798525 / 4.565676 (-1.767151) \| 0.103720 / 0.424275 (-0.320555) \| 0.010196 / 0.007607 (0.002589) \| 0.762756 / 0.226044 (0.536711) \| 7.232939 / 2.268929 (4.964011) \| 3.714015 / 55.444624 (-51.730609) \| 3.050766 / 6.876477 (-3.825711) \| 3.149715 / 2.142072 (1.007643) \| 1.058827 / 4.805227 (-3.746400) \| 0.214079 / 6.500664 (-6.286585) \| 0.076712 / 0.075469 (0.001243) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.701032 / 1.841788 (-0.140755) \| 23.742023 / 8.074308 (15.667715) \| 22.486043 / 10.191392 (12.294651) \| 0.249757 / 0.680424 (-0.430667) \| 0.031714 / 0.534201 (-0.502486) \| 0.479914 / 0.579283 (-0.099369) \| 0.593315 / 0.434364 (0.158951) \| 0.562897 / 0.540337 (0.022560) \| 0.826636 / 1.386936 (-0.560300) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#429f9c69d1813ec643c316313b69ff23aaf208f6 \"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) \| 0.007816 / 0.011353 (-0.003537) \| 0.004541 / 0.011008 (-0.006467) \| 0.097256 / 0.038508 (0.058748) \| 0.081376 / 0.023109 (0.058267) \| 0.356635 / 0.275898 (0.080737) \| 0.394969 / 0.323480 (0.071489) \| 0.004670 / 0.007986 (-0.003316) \| 0.003537 / 0.004328 (-0.000791) \| 0.075564 / 0.004250 (0.071314) \| 0.063459 / 0.037052 (0.026407) \| 0.363846 / 0.258489 (0.105357) \| 0.416337 / 0.293841 (0.122496) \| 0.036690 / 0.128546 (-0.091857) \| 0.009653 / 0.075646 (-0.065993) \| 0.337265 / 0.419271 (-0.082007) \| 0.061446 / 0.043533 (0.017913) \| 0.359190 / 0.255139 (0.104051) \| 0.385866 / 0.283200 (0.102666) \| 0.030474 / 0.141683 (-0.111209) \| 1.796903 / 1.452155 (0.344748) \| 1.852332 / 1.492716 (0.359616) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.264008 / 0.018006 (0.246002) \| 0.507387 / 0.000490 (0.506897) \| 0.012309 / 0.000200 (0.012109) \| 0.000377 / 0.000054 (0.000323) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033224 / 0.037411 (-0.004188) \| 0.097136 / 0.014526 (0.082610) \| 0.113035 / 0.176557 (-0.063522) \| 0.181778 / 0.737135 (-0.555357) \| 0.130511 / 0.296338 (-0.165827) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444512 / 0.215209 (0.229303) \| 4.453285 / 2.077655 (2.375631) \| 2.154123 / 1.504120 (0.650003) \| 1.955451 / 1.541195 (0.414256) \| 2.015089 / 1.468490 (0.546599) \| 0.567824 / 4.584777 (-4.016953) \| 4.083084 / 3.745712 (0.337371) \| 3.912417 / 5.269862 (-1.357445) \| 2.366197 / 4.565676 (-2.199480) \| 0.066468 / 0.424275 (-0.357807) \| 0.008478 / 0.007607 (0.000870) \| 0.531196 / 0.226044 (0.305152) \| 5.311285 / 2.268929 (3.042356) \| 2.743252 / 55.444624 (-52.701372) \| 2.322353 / 6.876477 (-4.554124) \| 2.368168 / 2.142072 (0.226095) \| 0.679223 / 4.805227 (-4.126004) \| 0.152401 / 6.500664 (-6.348263) \| 0.071954 / 0.075469 (-0.003515) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.489114 / 1.841788 (-0.352674) \| 22.114956 / 8.074308 (14.040648) \| 16.072564 / 10.191392 (5.881172) \| 0.164303 / 0.680424 (-0.516121) \| 0.021317 / 0.534201 (-0.512884) \| 0.460250 / 0.579283 (-0.119033) \| 0.467554 / 0.434364 (0.033190) \| 0.539773 / 0.540337 (-0.000564) \| 0.751904 / 1.386936 (-0.635032) \|\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) \| 0.007520 / 0.011353 (-0.003833) \| 0.004487 / 0.011008 (-0.006521) \| 0.075074 / 0.038508 (0.036566) \| 0.083135 / 0.023109 (0.060026) \| 0.474052 / 0.275898 (0.198154) \| 0.524051 / 0.323480 (0.200571) \| 0.006192 / 0.007986 (-0.001793) \| 0.003835 / 0.004328 (-0.000494) \| 0.074643 / 0.004250 (0.070392) \| 0.065334 / 0.037052 (0.028282) \| 0.507033 / 0.258489 (0.248544) \| 0.519846 / 0.293841 (0.226005) \| 0.036985 / 0.128546 (-0.091561) \| 0.009828 / 0.075646 (-0.065818) \| 0.082992 / 0.419271 (-0.336279) \| 0.055942 / 0.043533 (0.012409) \| 0.480652 / 0.255139 (0.225513) \| 0.503683 / 0.283200 (0.220483) \| 0.025560 / 0.141683 (-0.116123) \| 1.801390 / 1.452155 (0.349235) \| 1.892929 / 1.492716 (0.400213) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246771 / 0.018006 (0.228765) \| 0.498901 / 0.000490 (0.498411) \| 0.008186 / 0.000200 (0.007986) \| 0.000166 / 0.000054 (0.000112) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038666 / 0.037411 (0.001254) \| 0.110317 / 0.014526 (0.095791) \| 0.122995 / 0.176557 (-0.053562) \| 0.185355 / 0.737135 (-0.551781) \| 0.123720 / 0.296338 (-0.172619) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.508421 / 0.215209 (0.293212) \| 5.046464 / 2.077655 (2.968809) \| 2.660004 / 1.504120 (1.155884) \| 2.482841 / 1.541195 (0.941646) \| 2.573941 / 1.468490 (1.105451) \| 0.565702 / 4.584777 (-4.019075) \| 4.197895 / 3.745712 (0.452183) \| 3.755480 / 5.269862 (-1.514381) \| 2.308066 / 4.565676 (-2.257610) \| 0.066559 / 0.424275 (-0.357716) \| 0.008436 / 0.007607 (0.000829) \| 0.589858 / 0.226044 (0.363814) \| 5.873488 / 2.268929 (3.604559) \| 3.241810 / 55.444624 (-52.202814) \| 2.789831 / 6.876477 (-4.086645) \| 3.008989 / 2.142072 (0.866917) \| 0.679624 / 4.805227 (-4.125603) \| 0.150868 / 6.500664 (-6.349796) \| 0.068581 / 0.075469 (-0.006889) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.582955 / 1.841788 (-0.258833) \| 22.684969 / 8.074308 (14.610661) \| 16.829855 / 10.191392 (6.638463) \| 0.201599 / 0.680424 (-0.478825) \| 0.023261 / 0.534201 (-0.510940) \| 0.465009 / 0.579283 (-0.114274) \| 0.497701 / 0.434364 (0.063337) \| 0.557822 / 0.540337 (0.017485) \| 0.803234 / 1.386936 (-0.583702) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9241c1070b5c9021705c17b12548b6fea75782d8 \"CML watermark\")\n", "Well done! :clap: :fire: ", "<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) \| 0.008866 / 0.011353 (-0.002487) \| 0.005910 / 0.011008 (-0.005098) \| 0.099916 / 0.038508 (0.061408) \| 0.085787 / 0.023109 (0.062678) \| 0.391028 / 0.275898 (0.115130) \| 0.412689 / 0.323480 (0.089209) \| 0.006527 / 0.007986 (-0.001459) \| 0.004629 / 0.004328 (0.000301) \| 0.084627 / 0.004250 (0.080377) \| 0.063404 / 0.037052 (0.026352) \| 0.408923 / 0.258489 (0.150434) \| 0.437130 / 0.293841 (0.143289) \| 0.050256 / 0.128546 (-0.078290) \| 0.013914 / 0.075646 (-0.061732) \| 0.350893 / 0.419271 (-0.068379) \| 0.067931 / 0.043533 (0.024398) \| 0.383807 / 0.255139 (0.128668) \| 0.424150 / 0.283200 (0.140950) \| 0.039978 / 0.141683 (-0.101705) \| 1.697631 / 1.452155 (0.245476) \| 1.925568 / 1.492716 (0.432851) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.315417 / 0.018006 (0.297410) \| 0.607050 / 0.000490 (0.606560) \| 0.017314 / 0.000200 (0.017114) \| 0.000514 / 0.000054 (0.000459) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032994 / 0.037411 (-0.004417) \| 0.103993 / 0.014526 (0.089467) \| 0.125369 / 0.176557 (-0.051187) \| 0.185984 / 0.737135 (-0.551151) \| 0.139192 / 0.296338 (-0.157146) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.639769 / 0.215209 (0.424560) \| 6.236187 / 2.077655 (4.158532) \| 2.775777 / 1.504120 (1.271657) \| 2.599683 / 1.541195 (1.058488) \| 2.780064 / 1.468490 (1.311574) \| 1.107247 / 4.584777 (-3.477530) \| 5.724223 / 3.745712 (1.978511) \| 5.284786 / 5.269862 (0.014925) \| 3.342465 / 4.565676 (-1.223211) \| 0.107685 / 0.424275 (-0.316590) \| 0.009237 / 0.007607 (0.001630) \| 0.760282 / 0.226044 (0.534238) \| 7.570859 / 2.268929 (5.301930) \| 3.572498 / 55.444624 (-51.872126) \| 2.997482 / 6.876477 (-3.878995) \| 2.910001 / 2.142072 (0.767929) \| 1.249272 / 4.805227 (-3.555955) \| 0.229425 / 6.500664 (-6.271239) \| 0.091974 / 0.075469 (0.016505) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.663859 / 1.841788 (-0.177929) \| 25.283961 / 8.074308 (17.209653) \| 20.793389 / 10.191392 (10.601997) \| 0.239263 / 0.680424 (-0.441161) \| 0.028808 / 0.534201 (-0.505393) \| 0.521045 / 0.579283 (-0.058238) \| 0.602451 / 0.434364 (0.168087) \| 0.544536 / 0.540337 (0.004198) \| 0.819732 / 1.386936 (-0.567204) \|\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) \| 0.008970 / 0.011353 (-0.002383) \| 0.009663 / 0.011008 (-0.001345) \| 0.083471 / 0.038508 (0.044963) \| 0.090695 / 0.023109 (0.067585) \| 0.562539 / 0.275898 (0.286641) \| 0.572092 / 0.323480 (0.248612) \| 0.007269 / 0.007986 (-0.000717) \| 0.004664 / 0.004328 (0.000335) \| 0.084212 / 0.004250 (0.079961) \| 0.072716 / 0.037052 (0.035664) \| 0.559810 / 0.258489 (0.301320) \| 0.574296 / 0.293841 (0.280455) \| 0.048555 / 0.128546 (-0.079991) \| 0.015901 / 0.075646 (-0.059746) \| 0.107815 / 0.419271 (-0.311456) \| 0.065404 / 0.043533 (0.021871) \| 0.544787 / 0.255139 (0.289648) \| 0.586993 / 0.283200 (0.303794) \| 0.042613 / 0.141683 (-0.099069) \| 1.919266 / 1.452155 (0.467111) \| 2.095189 / 1.492716 (0.602473) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298512 / 0.018006 (0.280506) \| 0.597745 / 0.000490 (0.597256) \| 0.008806 / 0.000200 (0.008606) \| 0.000119 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039420 / 0.037411 (0.002009) \| 0.111378 / 0.014526 (0.096852) \| 0.136421 / 0.176557 (-0.040135) \| 0.192006 / 0.737135 (-0.545129) \| 0.130037 / 0.296338 (-0.166301) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.679169 / 0.215209 (0.463960) \| 6.750881 / 2.077655 (4.673226) \| 3.220411 / 1.504120 (1.716291) \| 2.851988 / 1.541195 (1.310794) \| 2.974247 / 1.468490 (1.505757) \| 0.892593 / 4.584777 (-3.692184) \| 5.659975 / 3.745712 (1.914263) \| 5.172641 / 5.269862 (-0.097220) \| 3.308429 / 4.565676 (-1.257248) \| 0.100580 / 0.424275 (-0.323695) \| 0.009320 / 0.007607 (0.001713) \| 0.833290 / 0.226044 (0.607245) \| 8.091847 / 2.268929 (5.822918) \| 4.023734 / 55.444624 (-51.420890) \| 3.441583 / 6.876477 (-3.434894) \| 3.763562 / 2.142072 (1.621489) \| 1.055105 / 4.805227 (-3.750122) \| 0.239218 / 6.500664 (-6.261446) \| 0.081922 / 0.075469 (0.006453) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.796495 / 1.841788 (-0.045293) \| 25.942492 / 8.074308 (17.868184) \| 23.211617 / 10.191392 (13.020225) \| 0.256054 / 0.680424 (-0.424370) \| 0.030491 / 0.534201 (-0.503710) \| 0.520474 / 0.579283 (-0.058809) \| 0.626331 / 0.434364 (0.191967) \| 0.619897 / 0.540337 (0.079560) \| 0.900833 / 1.386936 (-0.486103) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e74f80255700c4b8cde383a426c4b2def6db1253 \"CML watermark\")\n", "Congrats on merging this! 👏 " ]	2023-09-29T16:22:31Z	2023-10-16T16:03:18Z	2023-10-16T13:30:46Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6269.diff", "html_url": "https://github.com/huggingface/datasets/pull/6269", "merged_at": "2023-10-16T13:30:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/6269.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6269" }	Reduces the number of commits in `push_to_hub` by using the `preupload` API from https://github.com/huggingface/huggingface_hub/pull/1699. Each commit contains a maximum of 50 uploaded files. A shard's fingerprint no longer needs to be added as a suffix to support resuming an upload, meaning the shards' naming scheme is the same as the initial one. Also, it adds support for the following params: `create_pr`, `commit_message` and `revision` (`branch` deprecated; unlike the previous implementation, this one creates a branch if the branch does not exist to be consistent with `transformers`). (Nit) This implementation keeps the markdown section of the generated README.md empty to enable importing the card template (when the card is accessed on the Hub). Fixes https://github.com/huggingface/datasets/issues/5492, fixes https://github.com/huggingface/datasets/issues/6257, fixes https://github.com/huggingface/datasets/issues/5045, fixes https://github.com/huggingface/datasets/issues/6271 TODO: - [x] set the minimal version to the next `hfh` release (once it's published)	{ "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": 3, "laugh": 0, "rocket": 1, "total_count": 4, "url": "https://api.github.com/repos/huggingface/datasets/issues/6269/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6268	6,268	Add repo_id to DatasetInfo	{ "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" }	[]	open	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6268). All of your documentation changes will be reflected on that endpoint.", "In https://github.com/huggingface/datasets/issues/4129 we want to track the origin of a dataset, e.g. if it comes from multiple datasets.\r\n\r\nI think it's out of scope of DatasetInfo alone, which has info for one dataset only.\r\nTherefore it makes sense to add repo_id, which is for one dataset only.\r\n\r\nIMO if we want to track multiple origins we will need a new DatasetInfo that would have fields relevant to a mix of datasets (out of scope of this PR)\r\n\r\ncc @mariosasko I'd like your opinion on 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) \| 0.009009 / 0.011353 (-0.002344) \| 0.004169 / 0.011008 (-0.006840) \| 0.098634 / 0.038508 (0.060126) \| 0.069526 / 0.023109 (0.046417) \| 0.337963 / 0.275898 (0.062065) \| 0.379737 / 0.323480 (0.056257) \| 0.004318 / 0.007986 (-0.003668) \| 0.005347 / 0.004328 (0.001019) \| 0.069875 / 0.004250 (0.065624) \| 0.055964 / 0.037052 (0.018912) \| 0.340305 / 0.258489 (0.081816) \| 0.429718 / 0.293841 (0.135877) \| 0.045101 / 0.128546 (-0.083445) \| 0.012610 / 0.075646 (-0.063036) \| 0.312366 / 0.419271 (-0.106905) \| 0.064711 / 0.043533 (0.021178) \| 0.345216 / 0.255139 (0.090077) \| 0.367245 / 0.283200 (0.084046) \| 0.034638 / 0.141683 (-0.107045) \| 1.541947 / 1.452155 (0.089793) \| 1.645268 / 1.492716 (0.152551) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233501 / 0.018006 (0.215495) \| 0.514207 / 0.000490 (0.513717) \| 0.014271 / 0.000200 (0.014072) \| 0.000366 / 0.000054 (0.000311) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026288 / 0.037411 (-0.011124) \| 0.083206 / 0.014526 (0.068680) \| 0.098172 / 0.176557 (-0.078385) \| 0.158529 / 0.737135 (-0.578606) \| 0.095183 / 0.296338 (-0.201155) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.538300 / 0.215209 (0.323091) \| 5.486939 / 2.077655 (3.409285) \| 2.321812 / 1.504120 (0.817692) \| 2.002124 / 1.541195 (0.460929) \| 2.045043 / 1.468490 (0.576553) \| 0.852772 / 4.584777 (-3.732005) \| 5.014897 / 3.745712 (1.269185) \| 4.428115 / 5.269862 (-0.841746) \| 2.750126 / 4.565676 (-1.815550) \| 0.099028 / 0.424275 (-0.325247) \| 0.007678 / 0.007607 (0.000070) \| 0.664463 / 0.226044 (0.438418) \| 6.617811 / 2.268929 (4.348883) \| 2.888382 / 55.444624 (-52.556242) \| 2.190753 / 6.876477 (-4.685724) \| 2.414586 / 2.142072 (0.272513) \| 1.010302 / 4.805227 (-3.794925) \| 0.194925 / 6.500664 (-6.305739) \| 0.063490 / 0.075469 (-0.011979) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.543464 / 1.841788 (-0.298323) \| 20.566666 / 8.074308 (12.492358) \| 19.410745 / 10.191392 (9.219353) \| 0.207077 / 0.680424 (-0.473347) \| 0.028895 / 0.534201 (-0.505306) \| 0.427525 / 0.579283 (-0.151758) \| 0.535450 / 0.434364 (0.101086) \| 0.494632 / 0.540337 (-0.045705) \| 0.723705 / 1.386936 (-0.663231) \|\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) \| 0.008209 / 0.011353 (-0.003144) \| 0.004184 / 0.011008 (-0.006824) \| 0.072420 / 0.038508 (0.033912) \| 0.066851 / 0.023109 (0.043742) \| 0.424137 / 0.275898 (0.148239) \| 0.473156 / 0.323480 (0.149676) \| 0.005394 / 0.007986 (-0.002591) \| 0.003898 / 0.004328 (-0.000430) \| 0.069996 / 0.004250 (0.065746) \| 0.053113 / 0.037052 (0.016061) \| 0.453214 / 0.258489 (0.194725) \| 0.495921 / 0.293841 (0.202080) \| 0.043028 / 0.128546 (-0.085519) \| 0.012320 / 0.075646 (-0.063326) \| 0.080270 / 0.419271 (-0.339002) \| 0.053337 / 0.043533 (0.009804) \| 0.436604 / 0.255139 (0.181465) \| 0.463422 / 0.283200 (0.180223) \| 0.030277 / 0.141683 (-0.111406) \| 1.560261 / 1.452155 (0.108106) \| 1.647209 / 1.492716 (0.154493) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232556 / 0.018006 (0.214550) \| 0.502387 / 0.000490 (0.501897) \| 0.006688 / 0.000200 (0.006488) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030204 / 0.037411 (-0.007207) \| 0.089438 / 0.014526 (0.074912) \| 0.118939 / 0.176557 (-0.057617) \| 0.160537 / 0.737135 (-0.576598) \| 0.113432 / 0.296338 (-0.182906) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.586469 / 0.215209 (0.371260) \| 5.916156 / 2.077655 (3.838502) \| 2.904960 / 1.504120 (1.400840) \| 2.346838 / 1.541195 (0.805644) \| 2.373688 / 1.468490 (0.905198) \| 0.829917 / 4.584777 (-3.754860) \| 4.851283 / 3.745712 (1.105571) \| 4.220103 / 5.269862 (-1.049758) \| 2.706139 / 4.565676 (-1.859538) \| 0.094095 / 0.424275 (-0.330180) \| 0.008201 / 0.007607 (0.000594) \| 0.699099 / 0.226044 (0.473054) \| 7.046940 / 2.268929 (4.778011) \| 3.374837 / 55.444624 (-52.069788) \| 2.690839 / 6.876477 (-4.185638) \| 2.845717 / 2.142072 (0.703645) \| 0.989698 / 4.805227 (-3.815529) \| 0.190413 / 6.500664 (-6.310251) \| 0.066233 / 0.075469 (-0.009236) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.513607 / 1.841788 (-0.328180) \| 21.544200 / 8.074308 (13.469892) \| 20.297337 / 10.191392 (10.105945) \| 0.216390 / 0.680424 (-0.464034) \| 0.029962 / 0.534201 (-0.504239) \| 0.451531 / 0.579283 (-0.127752) \| 0.530147 / 0.434364 (0.095783) \| 0.520739 / 0.540337 (-0.019598) \| 0.716431 / 1.386936 (-0.670505) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fcaa9f218ad1505bb5474060889b4b9578e24423 \"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) \| 0.006509 / 0.011353 (-0.004844) \| 0.003987 / 0.011008 (-0.007022) \| 0.085233 / 0.038508 (0.046725) \| 0.077765 / 0.023109 (0.054656) \| 0.310467 / 0.275898 (0.034569) \| 0.343363 / 0.323480 (0.019883) \| 0.005557 / 0.007986 (-0.002429) \| 0.003430 / 0.004328 (-0.000898) \| 0.064948 / 0.004250 (0.060697) \| 0.056864 / 0.037052 (0.019812) \| 0.314005 / 0.258489 (0.055516) \| 0.360638 / 0.293841 (0.066798) \| 0.031134 / 0.128546 (-0.097412) \| 0.008869 / 0.075646 (-0.066777) \| 0.286409 / 0.419271 (-0.132862) \| 0.051338 / 0.043533 (0.007805) \| 0.311329 / 0.255139 (0.056190) \| 0.334373 / 0.283200 (0.051174) \| 0.024816 / 0.141683 (-0.116867) \| 1.502872 / 1.452155 (0.050718) \| 1.569941 / 1.492716 (0.077224) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269639 / 0.018006 (0.251633) \| 0.558510 / 0.000490 (0.558020) \| 0.011748 / 0.000200 (0.011548) \| 0.000234 / 0.000054 (0.000180) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029139 / 0.037411 (-0.008272) \| 0.083586 / 0.014526 (0.069060) \| 0.102426 / 0.176557 (-0.074131) \| 0.162398 / 0.737135 (-0.574737) \| 0.101364 / 0.296338 (-0.194975) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382281 / 0.215209 (0.167072) \| 3.826412 / 2.077655 (1.748758) \| 1.815911 / 1.504120 (0.311791) \| 1.644539 / 1.541195 (0.103344) \| 1.688487 / 1.468490 (0.219996) \| 0.482115 / 4.584777 (-4.102662) \| 3.574773 / 3.745712 (-0.170939) \| 3.262733 / 5.269862 (-2.007129) \| 2.058115 / 4.565676 (-2.507562) \| 0.056367 / 0.424275 (-0.367908) \| 0.007233 / 0.007607 (-0.000374) \| 0.456859 / 0.226044 (0.230815) \| 4.565935 / 2.268929 (2.297006) \| 2.311802 / 55.444624 (-53.132823) \| 1.943936 / 6.876477 (-4.932541) \| 2.129811 / 2.142072 (-0.012261) \| 0.575098 / 4.805227 (-4.230129) \| 0.130495 / 6.500664 (-6.370169) \| 0.059757 / 0.075469 (-0.015712) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238495 / 1.841788 (-0.603293) \| 18.940000 / 8.074308 (10.865692) \| 14.034240 / 10.191392 (3.842848) \| 0.166418 / 0.680424 (-0.514006) \| 0.018420 / 0.534201 (-0.515781) \| 0.395330 / 0.579283 (-0.183953) \| 0.413518 / 0.434364 (-0.020846) \| 0.461499 / 0.540337 (-0.078838) \| 0.661371 / 1.386936 (-0.725565) \|\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) \| 0.006673 / 0.011353 (-0.004680) \| 0.004335 / 0.011008 (-0.006673) \| 0.064568 / 0.038508 (0.026060) \| 0.072763 / 0.023109 (0.049653) \| 0.429488 / 0.275898 (0.153590) \| 0.456900 / 0.323480 (0.133420) \| 0.005481 / 0.007986 (-0.002505) \| 0.003649 / 0.004328 (-0.000680) \| 0.064975 / 0.004250 (0.060724) \| 0.056839 / 0.037052 (0.019786) \| 0.439451 / 0.258489 (0.180962) \| 0.461691 / 0.293841 (0.167850) \| 0.031455 / 0.128546 (-0.097092) \| 0.008848 / 0.075646 (-0.066798) \| 0.071719 / 0.419271 (-0.347553) \| 0.047116 / 0.043533 (0.003583) \| 0.429055 / 0.255139 (0.173916) \| 0.434204 / 0.283200 (0.151004) \| 0.022594 / 0.141683 (-0.119089) \| 1.539231 / 1.452155 (0.087077) \| 1.568111 / 1.492716 (0.075394) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267374 / 0.018006 (0.249368) \| 0.553202 / 0.000490 (0.552712) \| 0.005410 / 0.000200 (0.005210) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031478 / 0.037411 (-0.005933) \| 0.092438 / 0.014526 (0.077912) \| 0.103874 / 0.176557 (-0.072682) \| 0.158428 / 0.737135 (-0.578708) \| 0.111617 / 0.296338 (-0.184721) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434783 / 0.215209 (0.219574) \| 4.332536 / 2.077655 (2.254881) \| 2.354522 / 1.504120 (0.850402) \| 2.220271 / 1.541195 (0.679076) \| 2.338524 / 1.468490 (0.870034) \| 0.494508 / 4.584777 (-4.090269) \| 3.619592 / 3.745712 (-0.126120) \| 3.320897 / 5.269862 (-1.948964) \| 2.107475 / 4.565676 (-2.458202) \| 0.058479 / 0.424275 (-0.365796) \| 0.007427 / 0.007607 (-0.000180) \| 0.509298 / 0.226044 (0.283254) \| 5.067940 / 2.268929 (2.799012) \| 2.815336 / 55.444624 (-52.629288) \| 2.470958 / 6.876477 (-4.405519) \| 2.672801 / 2.142072 (0.530728) \| 0.588199 / 4.805227 (-4.217028) \| 0.134062 / 6.500664 (-6.366602) \| 0.060951 / 0.075469 (-0.014518) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353955 / 1.841788 (-0.487832) \| 20.386012 / 8.074308 (12.311704) \| 15.032463 / 10.191392 (4.841071) \| 0.167243 / 0.680424 (-0.513181) \| 0.020426 / 0.534201 (-0.513775) \| 0.396815 / 0.579283 (-0.182469) \| 0.421806 / 0.434364 (-0.012558) \| 0.471866 / 0.540337 (-0.068471) \| 0.667206 / 1.386936 (-0.719730) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aade5a0c79398c84632a3ff253111e694c7b598b \"CML watermark\")\n", "Really happy to see this! It could also be helpful to track some other metadata about how the dataset was built in the future. i.e. for the Stack loaded like this:\r\n\r\n```\r\nds = load_dataset(\"bigcode/the-stack\", data_dir=\"data/dockerfile\", split=\"train\")\r\n```\r\nIt could be helpful to have easy access to the `data_dir` argument used during loading since that changes the training data quite a bit vs. loading the full dataset. You can also recover this from `download_checksums`, which seems a bit hacky. That is not necessary for this PR, though.\r\n", "Perhaps it is also interesting to track the revision? I suppose the version also kind of covers that.\r\n\r\nThat said, this is looking great already! I'm quite excited about this. Losing the `repo_id` after merging (different) datasets also makes sense to me, well done.", "One other thought. Is it worth tracking if a `token` was passed during loading? \r\n\r\nThe Hub ID for private datasets could in some cases contain information someone wouldn't want to make public i.e. `davanstrien/super_secret_dataset_using_GPT_created_data`. \r\n\r\nAdding a bool like `is_private` could then be used by another library to determine if the dataset ID should be shared or not (or default to not sharing the ID for private datasets). i.e. in SpanMarker @tomaarsen might do a check like \r\n\r\n```python\r\nif ds.is_private and not push_hub_id_for_private_ds:\r\n\tds_name = None\r\n```\r\nPotentially this is overkill but could be useful for downstream libraries who might use this information for creating automatic model cards. \r\n\r\n\r\n", "We should probably find a way to remove `DatasetInfo`, as (most of) its attributes are outdated (homepage, description, etc.), not introduce new ones :). But I guess storing `repo_id` there is the simplest solution for now, so I'm OK with it.", "<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) \| 0.007757 / 0.011353 (-0.003595) \| 0.004543 / 0.011008 (-0.006465) \| 0.100193 / 0.038508 (0.061685) \| 0.082333 / 0.023109 (0.059224) \| 0.374586 / 0.275898 (0.098688) \| 0.412617 / 0.323480 (0.089137) \| 0.006148 / 0.007986 (-0.001838) \| 0.003826 / 0.004328 (-0.000503) \| 0.077077 / 0.004250 (0.072827) \| 0.064057 / 0.037052 (0.027005) \| 0.391435 / 0.258489 (0.132946) \| 0.436439 / 0.293841 (0.142599) \| 0.036534 / 0.128546 (-0.092012) \| 0.009986 / 0.075646 (-0.065660) \| 0.344243 / 0.419271 (-0.075028) \| 0.062013 / 0.043533 (0.018480) \| 0.378113 / 0.255139 (0.122974) \| 0.398476 / 0.283200 (0.115276) \| 0.026552 / 0.141683 (-0.115131) \| 1.740505 / 1.452155 (0.288350) \| 1.835684 / 1.492716 (0.342968) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267917 / 0.018006 (0.249911) \| 0.510676 / 0.000490 (0.510186) \| 0.010810 / 0.000200 (0.010610) \| 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) \| 0.032113 / 0.037411 (-0.005299) \| 0.097679 / 0.014526 (0.083153) \| 0.113213 / 0.176557 (-0.063344) \| 0.177897 / 0.737135 (-0.559238) \| 0.111761 / 0.296338 (-0.184577) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450544 / 0.215209 (0.235335) \| 4.476746 / 2.077655 (2.399091) \| 2.205391 / 1.504120 (0.701271) \| 2.006457 / 1.541195 (0.465262) \| 2.058859 / 1.468490 (0.590369) \| 0.571549 / 4.584777 (-4.013228) \| 4.175039 / 3.745712 (0.429327) \| 3.815445 / 5.269862 (-1.454416) \| 2.376673 / 4.565676 (-2.189004) \| 0.067048 / 0.424275 (-0.357227) \| 0.008544 / 0.007607 (0.000937) \| 0.536384 / 0.226044 (0.310340) \| 5.386232 / 2.268929 (3.117304) \| 2.825620 / 55.444624 (-52.619004) \| 2.339821 / 6.876477 (-4.536656) \| 2.535736 / 2.142072 (0.393663) \| 0.679572 / 4.805227 (-4.125655) \| 0.156799 / 6.500664 (-6.343865) \| 0.071667 / 0.075469 (-0.003802) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.512198 / 1.841788 (-0.329590) \| 21.786760 / 8.074308 (13.712452) \| 16.386274 / 10.191392 (6.194882) \| 0.169108 / 0.680424 (-0.511316) \| 0.021312 / 0.534201 (-0.512889) \| 0.466153 / 0.579283 (-0.113130) \| 0.496192 / 0.434364 (0.061829) \| 0.549420 / 0.540337 (0.009082) \| 0.780974 / 1.386936 (-0.605962) \|\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) \| 0.007644 / 0.011353 (-0.003709) \| 0.004654 / 0.011008 (-0.006354) \| 0.075280 / 0.038508 (0.036772) \| 0.083044 / 0.023109 (0.059935) \| 0.481704 / 0.275898 (0.205805) \| 0.514828 / 0.323480 (0.191348) \| 0.006245 / 0.007986 (-0.001740) \| 0.003715 / 0.004328 (-0.000614) \| 0.074498 / 0.004250 (0.070248) \| 0.064406 / 0.037052 (0.027353) \| 0.481874 / 0.258489 (0.223385) \| 0.518527 / 0.293841 (0.224686) \| 0.037549 / 0.128546 (-0.090997) \| 0.010106 / 0.075646 (-0.065541) \| 0.084266 / 0.419271 (-0.335006) \| 0.056659 / 0.043533 (0.013126) \| 0.497707 / 0.255139 (0.242568) \| 0.503201 / 0.283200 (0.220001) \| 0.027086 / 0.141683 (-0.114597) \| 1.834715 / 1.452155 (0.382560) \| 1.919927 / 1.492716 (0.427210) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249288 / 0.018006 (0.231282) \| 0.500950 / 0.000490 (0.500460) \| 0.005856 / 0.000200 (0.005656) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037674 / 0.037411 (0.000263) \| 0.111141 / 0.014526 (0.096615) \| 0.123408 / 0.176557 (-0.053149) \| 0.186604 / 0.737135 (-0.550531) \| 0.125360 / 0.296338 (-0.170979) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.520480 / 0.215209 (0.305271) \| 5.171108 / 2.077655 (3.093453) \| 2.812746 / 1.504120 (1.308626) \| 2.602941 / 1.541195 (1.061746) \| 2.666196 / 1.468490 (1.197706) \| 0.578684 / 4.584777 (-4.006092) \| 4.238722 / 3.745712 (0.493010) \| 3.844361 / 5.269862 (-1.425501) \| 2.369214 / 4.565676 (-2.196462) \| 0.068543 / 0.424275 (-0.355732) \| 0.008695 / 0.007607 (0.001088) \| 0.621869 / 0.226044 (0.395825) \| 6.200566 / 2.268929 (3.931637) \| 3.340846 / 55.444624 (-52.103779) \| 2.920691 / 6.876477 (-3.955786) \| 3.132438 / 2.142072 (0.990366) \| 0.697394 / 4.805227 (-4.107834) \| 0.158385 / 6.500664 (-6.342280) \| 0.072566 / 0.075469 (-0.002903) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.599070 / 1.841788 (-0.242717) \| 22.767139 / 8.074308 (14.692831) \| 17.053988 / 10.191392 (6.862596) \| 0.188414 / 0.680424 (-0.492009) \| 0.023409 / 0.534201 (-0.510792) \| 0.472092 / 0.579283 (-0.107191) \| 0.486107 / 0.434364 (0.051743) \| 0.562190 / 0.540337 (0.021852) \| 0.791606 / 1.386936 (-0.595330) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aacbaf45c93f88e8c95924f6224153fb37c3064a \"CML watermark\")\n" ]	2023-09-29T10:24:55Z	2023-10-01T15:29:45Z	null	MEMBER	true	{ "diff_url": "https://github.com/huggingface/datasets/pull/6268.diff", "html_url": "https://github.com/huggingface/datasets/pull/6268", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6268.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6268" }	```python from datasets import load_dataset ds = load_dataset("lhoestq/demo1", split="train") ds = ds.map(lambda x: {}, num_proc=2).filter(lambda x: True).remove_columns(["id"]) print(ds.repo_id) # lhoestq/demo1 ``` - repo_id is None when the dataset doesn't come from the Hub, e.g. from Dataset.from_dict - repo_id is set to None when concatenating datasets with different repo ids related to https://github.com/huggingface/datasets/issues/4129 TODO: - [ ] discuss if it's ok for now - [ ] tests	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 2, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6268/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6267	6,267	Multi label class encoding	{ "avatar_url": "https://avatars.githubusercontent.com/u/1000442?v=4", "events_url": "https://api.github.com/users/jmif/events{/privacy}", "followers_url": "https://api.github.com/users/jmif/followers", "following_url": "https://api.github.com/users/jmif/following{/other_user}", "gists_url": "https://api.github.com/users/jmif/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jmif", "id": 1000442, "login": "jmif", "node_id": "MDQ6VXNlcjEwMDA0NDI=", "organizations_url": "https://api.github.com/users/jmif/orgs", "received_events_url": "https://api.github.com/users/jmif/received_events", "repos_url": "https://api.github.com/users/jmif/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jmif/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jmif/subscriptions", "type": "User", "url": "https://api.github.com/users/jmif", "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	[ "You can use a `Sequence(ClassLabel(...))` feature type to represent a list of labels, and `cast_column`/`cast` to perform the \"string to label\" conversion (`class_encode_column` does support nested fields), e.g., in your case:\r\n```python\r\nfrom datasets import Dataset, Sequence, ClassLabel\r\ndata = {\r\n 'text': ['one', 'two', 'three', 'four'],\r\n 'labels': [['a', 'b'], ['b'], ['b', 'c'], ['a', 'd']]\r\n}\r\n\r\ndataset = Dataset.from_dict(data)\r\ndataset = dataset.cast_column('labels', Sequence(ClassLabel(names=[\"a\", \"b\", \"c\", \"d\"])))\r\n```", "Great! Can you elaborate on \"class_encode_column does support nested fields\"? Do you mean that there is a way to `class_encode_column` on a Sequence?", "Yes, exactly! This would be a nice contribution, though.", "Sorry, I'm still not following. Are you saying that there currently exists a way to call `class_encode_column` on a `Sequence(ClassLabel)` type? Or that the underlying data structures support it and a contribution of a method to do that would be welcome?", "`class_encode_column ` currently does not support `Sequence(ClassLabel)`. Implementing support for this would be a nice contribution.\r\n\r\nIn the meantime, this limitation can be circumvented by fetching (unique) labels and calling `.cast_column(col, Sequence(ClassLabel(names=labels)))`.", "Ok makes sense, can you take a look at the POC implementation I did [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e)? Happy to take another pass / submit as a PR but would be helpful if I got a thumbs up that this was directionally correct with respect to implementation / architecture. ", "There is no need to introduce a new type (`MultiLabel`) for this feature. Also, I think we can keep the logic inside a single method instead of separating the two cases.\r\n\r\nMaybe https://github.com/huggingface/datasets/pull/4277 can help with the implementation. We extended `align_labels_with_mapping` to support `Sequence(ClassLabel(...))` in that PR (initially, it only worked with `ClassLabel(...)`)" ]	2023-09-27T22:48:08Z	2023-10-26T18:46:08Z	null	NONE	null	null	### Feature request I have a multi label dataset and I'd like to be able to class encode the column and store the mapping directly in the features just as I can with a single label column. `class_encode_column` currently does not support multi labels. Here's an example of what I'd like to encode: ``` data = { 'text': ['one', 'two', 'three', 'four'], 'labels': [['a', 'b'], ['b'], ['b', 'c'], ['a', 'd']] } dataset = Dataset.from_dict(data) dataset = dataset.class_encode_column('labels') ``` I did some digging into the code base to evaluate the feasibility of this (note I'm very new to this code base) and from what I noticed the `ClassLabel` feature is still stored as an underlying raw data type of int so I thought a `MultiLabel` feature could similarly be stored as a Sequence of ints, thus not requiring significant serialization / conversion work to / from arrow. I did a POC of this [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e) and included a simple test case (please excuse all the commented out tests, going for speed of POC here and didn't want to fight IDE to debug a single test). In the test I just assert that `num_classes` is the same to show that things are properly serializing, but if you break after loading from disk you'll see the dataset correct and the dataset feature is as expected. After digging more I did notice a few issues - After loading from disk I noticed type of the `labels` class is `Sequence` not `MultiLabel` (though the added `feature` attribute came through). This doesn't happen for `ClassLabel` but I couldn't find the encode / decode code paths that handle this. - I subclass `Sequence` in `MultiLabel` to leverage existing serialization, but this does miss the custom encode logic that `ClassLabel` has. I'm not sure of the best way to approach this as I haven't fully understood the encode / decode flow for datasets. I suspect my simple implementation will need some improvement as it'll require a significant amount of repeated logic to mimic `ClassLabel` behavior. ### Motivation See above - would like to support multi label class encodings. ### Your contribution This would be a big help for us and we're open to contributing but I'll likely need some guidance on how to implement to fit the encode / decode flow. Some suggestions on tests / would be great too, I'm guessing in addition to the class encode tests (that I'll need to expand) we'll need encode / decode tests.	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/6267/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/6266	6,266	Use LibYAML with PyYAML if available	{ "avatar_url": "https://avatars.githubusercontent.com/u/3905501?v=4", "events_url": "https://api.github.com/users/bryant1410/events{/privacy}", "followers_url": "https://api.github.com/users/bryant1410/followers", "following_url": "https://api.github.com/users/bryant1410/following{/other_user}", "gists_url": "https://api.github.com/users/bryant1410/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bryant1410", "id": 3905501, "login": "bryant1410", "node_id": "MDQ6VXNlcjM5MDU1MDE=", "organizations_url": "https://api.github.com/users/bryant1410/orgs", "received_events_url": "https://api.github.com/users/bryant1410/received_events", "repos_url": "https://api.github.com/users/bryant1410/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bryant1410/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bryant1410/subscriptions", "type": "User", "url": "https://api.github.com/users/bryant1410", "user_view_type": "public" }	[]	open	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6266). All of your documentation changes will be reflected on that endpoint.", "On Ubuntu, if `libyaml-dev` is installed, you can install PyYAML 6.0.1 with LibYAML with the following command (as it's automatically detected):\r\n\r\n```bash\r\npip install git+https://github.com/yaml/pyyaml.git@6.0.1\r\n```", "Are the failing tests flaky?", "We use `huggingface_hub`'s RepoCard API instead of these modules to parse the YAML block (notice the deprecations), so the `huggingface_hub` repo is the right place to suggest these changes.\r\n\r\nPersonally, I'm not a fan of these changes, as a single non-standard usage of the `ClassLabel` type is not a sufficient reason to merge them. Also, the dataset in question stores data in a single Parquet file, with the features info embedded in its (schema) metadata, which means the YAML parsing can be skipped while preserving the features by directly loading the Parquet file:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/HuggingFaceM4/SugarCrepe_swap_obj/resolve/main/data/test-00000-of-00001-ca2ae6017a2336d7.parquet\")\r\n```\r\n\r\nPS: Yes, these tests are flaky. We are working on fixing them.", "Oh, I didn't realize they were deprecated. Thanks for the tip on how to work around this issue!\r\n\r\nFor future reference, the places to change the code in `huggingface_hub` would be:\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/repocard.py#L506\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/utils/_fixes.py#L34" ]	2023-09-27T21:13:36Z	2023-09-28T14:29:24Z	null	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6266.diff", "html_url": "https://github.com/huggingface/datasets/pull/6266", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6266.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6266" }	PyYAML, the YAML framework used in this library, allows the use of LibYAML to accelerate the methods `load` and `dump`. To use it, a user would need to first install a PyYAML version that uses LibYAML (not available in PyPI; needs to be manually installed). Then, to actually use them, PyYAML suggests importing the LibYAML version of the `Loader` and `Dumper` and falling back to the default ones. This PR implements this change. See [PyYAML docs](https://pyyaml.org/wiki/PyYAMLDocumentation) for more info. This change was motivated after trying to use any of [the SugarCREPE datasets in the Hub](https://huggingface.co/datasets?search=sugarcrepe) provided by [the org HuggingFaceM4](https://huggingface.co/datasets/HuggingFaceM4). Such datasets save a lot of information (~1MB) in the YAML metadata from the `README.md` file and I noticed this slowed down the data loading process. BTW, I also noticed cache files for it is also slow because it tries to hash an instance of `DatasetInfo`, which in turn has all this metadata. Also, I changed two list comprehensions into generator expressions to avoid allocating extra memory unnecessarily. And BTW, there's [an issue in PyYAML suggesting to make this automatic](https://github.com/yaml/pyyaml/issues/437).	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/6266/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6265	6,265	Remove `apache_beam` import in `BeamBasedBuilder._save_info`	{ "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) \| 0.005896 / 0.011353 (-0.005457) \| 0.003642 / 0.011008 (-0.007366) \| 0.081917 / 0.038508 (0.043409) \| 0.059513 / 0.023109 (0.036404) \| 0.341422 / 0.275898 (0.065524) \| 0.359278 / 0.323480 (0.035798) \| 0.004707 / 0.007986 (-0.003279) \| 0.002938 / 0.004328 (-0.001390) \| 0.063095 / 0.004250 (0.058845) \| 0.051777 / 0.037052 (0.014725) \| 0.321114 / 0.258489 (0.062625) \| 0.363823 / 0.293841 (0.069982) \| 0.027590 / 0.128546 (-0.100957) \| 0.007846 / 0.075646 (-0.067800) \| 0.261197 / 0.419271 (-0.158074) \| 0.045812 / 0.043533 (0.002279) \| 0.319787 / 0.255139 (0.064648) \| 0.341839 / 0.283200 (0.058640) \| 0.021913 / 0.141683 (-0.119770) \| 1.397525 / 1.452155 (-0.054630) \| 1.495902 / 1.492716 (0.003186) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224815 / 0.018006 (0.206809) \| 0.425780 / 0.000490 (0.425290) \| 0.006934 / 0.000200 (0.006734) \| 0.000225 / 0.000054 (0.000171) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024342 / 0.037411 (-0.013070) \| 0.073923 / 0.014526 (0.059398) \| 0.082108 / 0.176557 (-0.094448) \| 0.143017 / 0.737135 (-0.594119) \| 0.083163 / 0.296338 (-0.213175) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398244 / 0.215209 (0.183035) \| 3.957688 / 2.077655 (1.880033) \| 1.904615 / 1.504120 (0.400495) \| 1.710353 / 1.541195 (0.169158) \| 1.798980 / 1.468490 (0.330490) \| 0.499307 / 4.584777 (-4.085470) \| 3.026734 / 3.745712 (-0.718978) \| 2.923940 / 5.269862 (-2.345922) \| 1.831870 / 4.565676 (-2.733807) \| 0.058551 / 0.424275 (-0.365724) \| 0.006403 / 0.007607 (-0.001204) \| 0.464164 / 0.226044 (0.238119) \| 4.644556 / 2.268929 (2.375628) \| 2.341455 / 55.444624 (-53.103169) \| 2.004385 / 6.876477 (-4.872092) \| 2.051819 / 2.142072 (-0.090253) \| 0.585610 / 4.805227 (-4.219617) \| 0.124735 / 6.500664 (-6.375929) \| 0.061150 / 0.075469 (-0.014319) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224665 / 1.841788 (-0.617122) \| 17.476227 / 8.074308 (9.401919) \| 13.867617 / 10.191392 (3.676225) \| 0.144177 / 0.680424 (-0.536247) \| 0.017045 / 0.534201 (-0.517156) \| 0.337468 / 0.579283 (-0.241815) \| 0.374476 / 0.434364 (-0.059888) \| 0.393428 / 0.540337 (-0.146910) \| 0.535335 / 1.386936 (-0.851601) \|\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) \| 0.006208 / 0.011353 (-0.005145) \| 0.003650 / 0.011008 (-0.007359) \| 0.062843 / 0.038508 (0.024335) \| 0.062272 / 0.023109 (0.039162) \| 0.446336 / 0.275898 (0.170438) \| 0.477476 / 0.323480 (0.153996) \| 0.004862 / 0.007986 (-0.003124) \| 0.002822 / 0.004328 (-0.001506) \| 0.063427 / 0.004250 (0.059177) \| 0.049023 / 0.037052 (0.011971) \| 0.453633 / 0.258489 (0.195144) \| 0.486494 / 0.293841 (0.192653) \| 0.028634 / 0.128546 (-0.099912) \| 0.008187 / 0.075646 (-0.067460) \| 0.068846 / 0.419271 (-0.350425) \| 0.041104 / 0.043533 (-0.002429) \| 0.446646 / 0.255139 (0.191507) \| 0.468860 / 0.283200 (0.185660) \| 0.020980 / 0.141683 (-0.120703) \| 1.455565 / 1.452155 (0.003410) \| 1.511142 / 1.492716 (0.018426) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224242 / 0.018006 (0.206236) \| 0.408483 / 0.000490 (0.407993) \| 0.003495 / 0.000200 (0.003296) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027286 / 0.037411 (-0.010125) \| 0.081151 / 0.014526 (0.066625) \| 0.096598 / 0.176557 (-0.079959) \| 0.146193 / 0.737135 (-0.590942) \| 0.092213 / 0.296338 (-0.204125) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463837 / 0.215209 (0.248628) \| 4.636820 / 2.077655 (2.559165) \| 2.576100 / 1.504120 (1.071980) \| 2.396974 / 1.541195 (0.855779) \| 2.461526 / 1.468490 (0.993036) \| 0.502360 / 4.584777 (-4.082417) \| 3.099973 / 3.745712 (-0.645739) \| 2.937260 / 5.269862 (-2.332602) \| 1.871274 / 4.565676 (-2.694402) \| 0.057913 / 0.424275 (-0.366362) \| 0.006511 / 0.007607 (-0.001096) \| 0.536917 / 0.226044 (0.310873) \| 5.396966 / 2.268929 (3.128038) \| 3.015646 / 55.444624 (-52.428978) \| 2.673793 / 6.876477 (-4.202684) \| 2.712376 / 2.142072 (0.570304) \| 0.591632 / 4.805227 (-4.213595) \| 0.124872 / 6.500664 (-6.375792) \| 0.061820 / 0.075469 (-0.013649) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356828 / 1.841788 (-0.484960) \| 18.076995 / 8.074308 (10.002687) \| 15.116482 / 10.191392 (4.925090) \| 0.151375 / 0.680424 (-0.529049) \| 0.017867 / 0.534201 (-0.516334) \| 0.335012 / 0.579283 (-0.244271) \| 0.384137 / 0.434364 (-0.050226) \| 0.397792 / 0.540337 (-0.142546) \| 0.551521 / 1.386936 (-0.835415) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46a0506765d0f92916ed5c37eb19e5fa1a77736a \"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) \| 0.009418 / 0.011353 (-0.001935) \| 0.005186 / 0.011008 (-0.005822) \| 0.112270 / 0.038508 (0.073761) \| 0.114856 / 0.023109 (0.091747) \| 0.402267 / 0.275898 (0.126369) \| 0.445213 / 0.323480 (0.121733) \| 0.005588 / 0.007986 (-0.002398) \| 0.004315 / 0.004328 (-0.000013) \| 0.083561 / 0.004250 (0.079311) \| 0.087319 / 0.037052 (0.050267) \| 0.400989 / 0.258489 (0.142500) \| 0.455636 / 0.293841 (0.161795) \| 0.045168 / 0.128546 (-0.083378) \| 0.010939 / 0.075646 (-0.064707) \| 0.400120 / 0.419271 (-0.019151) \| 0.071599 / 0.043533 (0.028066) \| 0.418112 / 0.255139 (0.162973) \| 0.443889 / 0.283200 (0.160690) \| 0.032433 / 0.141683 (-0.109250) \| 1.886313 / 1.452155 (0.434159) \| 2.012909 / 1.492716 (0.520193) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306991 / 0.018006 (0.288985) \| 0.590426 / 0.000490 (0.589937) \| 0.011811 / 0.000200 (0.011611) \| 0.000596 / 0.000054 (0.000542) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.042520 / 0.037411 (0.005108) \| 0.129808 / 0.014526 (0.115283) \| 0.125481 / 0.176557 (-0.051075) \| 0.199181 / 0.737135 (-0.537954) \| 0.130426 / 0.296338 (-0.165913) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.526455 / 0.215209 (0.311246) \| 5.213304 / 2.077655 (3.135649) \| 2.643406 / 1.504120 (1.139286) \| 2.611214 / 1.541195 (1.070019) \| 2.586730 / 1.468490 (1.118240) \| 0.639103 / 4.584777 (-3.945674) \| 5.197421 / 3.745712 (1.451709) \| 4.634642 / 5.269862 (-0.635220) \| 2.741079 / 4.565676 (-1.824598) \| 0.073064 / 0.424275 (-0.351211) \| 0.009441 / 0.007607 (0.001834) \| 0.635984 / 0.226044 (0.409940) \| 6.283268 / 2.268929 (4.014339) \| 3.337205 / 55.444624 (-52.107419) \| 3.192362 / 6.876477 (-3.684114) \| 2.910367 / 2.142072 (0.768294) \| 0.767937 / 4.805227 (-4.037290) \| 0.177467 / 6.500664 (-6.323198) \| 0.081162 / 0.075469 (0.005693) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.803717 / 1.841788 (-0.038071) \| 26.823235 / 8.074308 (18.748927) \| 19.714471 / 10.191392 (9.523079) \| 0.204048 / 0.680424 (-0.476376) \| 0.025992 / 0.534201 (-0.508209) \| 0.521438 / 0.579283 (-0.057845) \| 0.596524 / 0.434364 (0.162160) \| 0.600763 / 0.540337 (0.060425) \| 0.945971 / 1.386936 (-0.440965) \|\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) \| 0.009126 / 0.011353 (-0.002226) \| 0.005109 / 0.011008 (-0.005899) \| 0.083046 / 0.038508 (0.044538) \| 0.115930 / 0.023109 (0.092821) \| 0.534311 / 0.275898 (0.258413) \| 0.552846 / 0.323480 (0.229366) \| 0.007240 / 0.007986 (-0.000746) \| 0.004617 / 0.004328 (0.000289) \| 0.083927 / 0.004250 (0.079676) \| 0.075926 / 0.037052 (0.038873) \| 0.534750 / 0.258489 (0.276261) \| 0.575122 / 0.293841 (0.281281) \| 0.041001 / 0.128546 (-0.087545) \| 0.010851 / 0.075646 (-0.064795) \| 0.096574 / 0.419271 (-0.322697) \| 0.063533 / 0.043533 (0.020001) \| 0.546850 / 0.255139 (0.291711) \| 0.547122 / 0.283200 (0.263922) \| 0.032437 / 0.141683 (-0.109245) \| 1.926191 / 1.452155 (0.474036) \| 2.029841 / 1.492716 (0.537125) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275582 / 0.018006 (0.257576) \| 0.574212 / 0.000490 (0.573722) \| 0.006863 / 0.000200 (0.006663) \| 0.000236 / 0.000054 (0.000181) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.045340 / 0.037411 (0.007928) \| 0.129196 / 0.014526 (0.114670) \| 0.136637 / 0.176557 (-0.039920) \| 0.200040 / 0.737135 (-0.537096) \| 0.136328 / 0.296338 (-0.160011) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.612379 / 0.215209 (0.397170) \| 5.874664 / 2.077655 (3.797010) \| 3.070626 / 1.504120 (1.566506) \| 2.999319 / 1.541195 (1.458124) \| 3.000571 / 1.468490 (1.532081) \| 0.732119 / 4.584777 (-3.852658) \| 5.193226 / 3.745712 (1.447514) \| 4.714571 / 5.269862 (-0.555291) \| 2.870438 / 4.565676 (-1.695239) \| 0.075793 / 0.424275 (-0.348482) \| 0.009238 / 0.007607 (0.001631) \| 0.695192 / 0.226044 (0.469148) \| 6.897996 / 2.268929 (4.629067) \| 3.923474 / 55.444624 (-51.521150) \| 3.458326 / 6.876477 (-3.418151) \| 3.331652 / 2.142072 (1.189579) \| 0.821132 / 4.805227 (-3.984095) \| 0.182252 / 6.500664 (-6.318412) \| 0.084730 / 0.075469 (0.009260) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.919861 / 1.841788 (0.078073) \| 27.437228 / 8.074308 (19.362920) \| 21.109899 / 10.191392 (10.918507) \| 0.245998 / 0.680424 (-0.434426) \| 0.025817 / 0.534201 (-0.508384) \| 0.517757 / 0.579283 (-0.061526) \| 0.576375 / 0.434364 (0.142011) \| 0.625283 / 0.540337 (0.084945) \| 0.956877 / 1.386936 (-0.430059) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8ddee15a8650a0ea52073477036d8c973da50f11 \"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) \| 0.008099 / 0.011353 (-0.003254) \| 0.004815 / 0.011008 (-0.006194) \| 0.099657 / 0.038508 (0.061149) \| 0.064737 / 0.023109 (0.041628) \| 0.461773 / 0.275898 (0.185875) \| 0.444810 / 0.323480 (0.121330) \| 0.004247 / 0.007986 (-0.003739) \| 0.004956 / 0.004328 (0.000628) \| 0.068664 / 0.004250 (0.064414) \| 0.052039 / 0.037052 (0.014986) \| 0.406750 / 0.258489 (0.148261) \| 0.452832 / 0.293841 (0.158991) \| 0.044518 / 0.128546 (-0.084028) \| 0.013220 / 0.075646 (-0.062426) \| 0.317713 / 0.419271 (-0.101558) \| 0.061897 / 0.043533 (0.018364) \| 0.398664 / 0.255139 (0.143525) \| 0.531494 / 0.283200 (0.248294) \| 0.064033 / 0.141683 (-0.077650) \| 1.590385 / 1.452155 (0.138231) \| 1.769918 / 1.492716 (0.277202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230795 / 0.018006 (0.212789) \| 0.568797 / 0.000490 (0.568308) \| 0.013498 / 0.000200 (0.013298) \| 0.000448 / 0.000054 (0.000393) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028394 / 0.037411 (-0.009017) \| 0.081973 / 0.014526 (0.067447) \| 0.097623 / 0.176557 (-0.078934) \| 0.158691 / 0.737135 (-0.578445) \| 0.101548 / 0.296338 (-0.194791) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.574459 / 0.215209 (0.359249) \| 5.709871 / 2.077655 (3.632217) \| 2.521460 / 1.504120 (1.017340) \| 2.239463 / 1.541195 (0.698268) \| 2.195067 / 1.468490 (0.726577) \| 0.792390 / 4.584777 (-3.792387) \| 4.841665 / 3.745712 (1.095952) \| 4.201620 / 5.269862 (-1.068241) \| 2.664081 / 4.565676 (-1.901595) \| 0.097661 / 0.424275 (-0.326614) \| 0.008428 / 0.007607 (0.000821) \| 0.698729 / 0.226044 (0.472684) \| 6.908867 / 2.268929 (4.639939) \| 3.247480 / 55.444624 (-52.197145) \| 2.563921 / 6.876477 (-4.312556) \| 2.738249 / 2.142072 (0.596177) \| 0.972066 / 4.805227 (-3.833161) \| 0.191196 / 6.500664 (-6.309468) \| 0.064732 / 0.075469 (-0.010737) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.421910 / 1.841788 (-0.419877) \| 20.633538 / 8.074308 (12.559230) \| 18.054562 / 10.191392 (7.863170) \| 0.194125 / 0.680424 (-0.486299) \| 0.028097 / 0.534201 (-0.506104) \| 0.417857 / 0.579283 (-0.161426) \| 0.518758 / 0.434364 (0.084394) \| 0.500199 / 0.540337 (-0.040138) \| 0.754662 / 1.386936 (-0.632274) \|\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) \| 0.008452 / 0.011353 (-0.002901) \| 0.004646 / 0.011008 (-0.006362) \| 0.077286 / 0.038508 (0.038778) \| 0.072507 / 0.023109 (0.049398) \| 0.439580 / 0.275898 (0.163682) \| 0.506166 / 0.323480 (0.182686) \| 0.006035 / 0.007986 (-0.001950) \| 0.003886 / 0.004328 (-0.000442) \| 0.075091 / 0.004250 (0.070841) \| 0.063163 / 0.037052 (0.026110) \| 0.468550 / 0.258489 (0.210061) \| 0.523273 / 0.293841 (0.229432) \| 0.048728 / 0.128546 (-0.079818) \| 0.012991 / 0.075646 (-0.062655) \| 0.087964 / 0.419271 (-0.331308) \| 0.058920 / 0.043533 (0.015387) \| 0.451247 / 0.255139 (0.196108) \| 0.489827 / 0.283200 (0.206628) \| 0.031164 / 0.141683 (-0.110519) \| 1.675504 / 1.452155 (0.223349) \| 1.806098 / 1.492716 (0.313382) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253567 / 0.018006 (0.235561) \| 0.508971 / 0.000490 (0.508481) \| 0.010882 / 0.000200 (0.010682) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029490 / 0.037411 (-0.007921) \| 0.090255 / 0.014526 (0.075729) \| 0.110075 / 0.176557 (-0.066482) \| 0.159375 / 0.737135 (-0.577760) \| 0.109313 / 0.296338 (-0.187025) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.580252 / 0.215209 (0.365043) \| 5.911741 / 2.077655 (3.834086) \| 2.659405 / 1.504120 (1.155285) \| 2.344943 / 1.541195 (0.803749) \| 2.390748 / 1.468490 (0.922258) \| 0.827823 / 4.584777 (-3.756954) \| 4.973544 / 3.745712 (1.227832) \| 4.300220 / 5.269862 (-0.969642) \| 2.826181 / 4.565676 (-1.739495) \| 0.101013 / 0.424275 (-0.323263) \| 0.008025 / 0.007607 (0.000418) \| 0.728414 / 0.226044 (0.502369) \| 7.508045 / 2.268929 (5.239117) \| 3.687627 / 55.444624 (-51.756997) \| 2.902953 / 6.876477 (-3.973524) \| 3.094624 / 2.142072 (0.952551) \| 1.054696 / 4.805227 (-3.750531) \| 0.212297 / 6.500664 (-6.288367) \| 0.070211 / 0.075469 (-0.005258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.567117 / 1.841788 (-0.274670) \| 21.420746 / 8.074308 (13.346438) \| 19.857467 / 10.191392 (9.666075) \| 0.228554 / 0.680424 (-0.451870) \| 0.032278 / 0.534201 (-0.501923) \| 0.459966 / 0.579283 (-0.119317) \| 0.541219 / 0.434364 (0.106855) \| 0.549599 / 0.540337 (0.009261) \| 0.731476 / 1.386936 (-0.655460) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0cc77d7f45c73698c31eab4f8cfff901044d0020 \"CML watermark\")\n" ]	2023-09-27T13:56:34Z	2023-09-28T18:34:02Z	2023-09-28T18:23:35Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6265.diff", "html_url": "https://github.com/huggingface/datasets/pull/6265", "merged_at": "2023-09-28T18:23:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/6265.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6265" }	... to avoid an `ImportError` raised in `BeamBasedBuilder._save_info` when `apache_beam` is not installed (e.g., when downloading the processed version of a dataset from the HF GCS) Fix https://github.com/huggingface/datasets/issues/6260	{ "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/6265/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6264	6,264	Temporarily pin tensorflow < 2.14.0	{ "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) \| 0.008356 / 0.011353 (-0.002997) \| 0.004553 / 0.011008 (-0.006455) \| 0.101025 / 0.038508 (0.062517) \| 0.090194 / 0.023109 (0.067085) \| 0.427127 / 0.275898 (0.151229) \| 0.469116 / 0.323480 (0.145636) \| 0.007593 / 0.007986 (-0.000393) \| 0.003751 / 0.004328 (-0.000578) \| 0.077432 / 0.004250 (0.073182) \| 0.082744 / 0.037052 (0.045692) \| 0.433638 / 0.258489 (0.175149) \| 0.482387 / 0.293841 (0.188546) \| 0.040658 / 0.128546 (-0.087888) \| 0.009799 / 0.075646 (-0.065848) \| 0.345274 / 0.419271 (-0.073998) \| 0.076642 / 0.043533 (0.033109) \| 0.424417 / 0.255139 (0.169278) \| 0.457045 / 0.283200 (0.173846) \| 0.033642 / 0.141683 (-0.108041) \| 1.765446 / 1.452155 (0.313291) \| 1.859279 / 1.492716 (0.366562) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273629 / 0.018006 (0.255623) \| 0.505743 / 0.000490 (0.505253) \| 0.009300 / 0.000200 (0.009100) \| 0.000359 / 0.000054 (0.000305) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032510 / 0.037411 (-0.004901) \| 0.099628 / 0.014526 (0.085103) \| 0.112904 / 0.176557 (-0.063652) \| 0.179118 / 0.737135 (-0.558018) \| 0.115946 / 0.296338 (-0.180393) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456431 / 0.215209 (0.241222) \| 4.556559 / 2.077655 (2.478904) \| 2.207893 / 1.504120 (0.703773) \| 2.024706 / 1.541195 (0.483512) \| 2.165424 / 1.468490 (0.696934) \| 0.571745 / 4.584777 (-4.013031) \| 4.341017 / 3.745712 (0.595305) \| 3.980520 / 5.269862 (-1.289342) \| 2.333077 / 4.565676 (-2.232599) \| 0.067200 / 0.424275 (-0.357075) \| 0.008563 / 0.007607 (0.000956) \| 0.545294 / 0.226044 (0.319250) \| 5.445152 / 2.268929 (3.176224) \| 2.740657 / 55.444624 (-52.703968) \| 2.370635 / 6.876477 (-4.505842) \| 2.451642 / 2.142072 (0.309570) \| 0.679385 / 4.805227 (-4.125842) \| 0.155967 / 6.500664 (-6.344697) \| 0.072812 / 0.075469 (-0.002657) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.494483 / 1.841788 (-0.347305) \| 23.673700 / 8.074308 (15.599392) \| 16.608529 / 10.191392 (6.417137) \| 0.170220 / 0.680424 (-0.510204) \| 0.021630 / 0.534201 (-0.512571) \| 0.470771 / 0.579283 (-0.108512) \| 0.535874 / 0.434364 (0.101510) \| 0.550376 / 0.540337 (0.010039) \| 0.776633 / 1.386936 (-0.610303) \|\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) \| 0.007899 / 0.011353 (-0.003454) \| 0.004581 / 0.011008 (-0.006427) \| 0.076520 / 0.038508 (0.038012) \| 0.090374 / 0.023109 (0.067265) \| 0.495016 / 0.275898 (0.219118) \| 0.532384 / 0.323480 (0.208904) \| 0.006160 / 0.007986 (-0.001825) \| 0.003780 / 0.004328 (-0.000548) \| 0.077164 / 0.004250 (0.072914) \| 0.064444 / 0.037052 (0.027391) \| 0.501642 / 0.258489 (0.243153) \| 0.549170 / 0.293841 (0.255329) \| 0.038051 / 0.128546 (-0.090495) \| 0.010081 / 0.075646 (-0.065565) \| 0.083752 / 0.419271 (-0.335520) \| 0.061334 / 0.043533 (0.017801) \| 0.493502 / 0.255139 (0.238363) \| 0.518018 / 0.283200 (0.234818) \| 0.029534 / 0.141683 (-0.112149) \| 1.929432 / 1.452155 (0.477277) \| 1.889985 / 1.492716 (0.397268) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254802 / 0.018006 (0.236795) \| 0.494463 / 0.000490 (0.493974) \| 0.005040 / 0.000200 (0.004840) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038372 / 0.037411 (0.000960) \| 0.112247 / 0.014526 (0.097721) \| 0.124365 / 0.176557 (-0.052191) \| 0.187142 / 0.737135 (-0.549993) \| 0.126070 / 0.296338 (-0.170269) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513418 / 0.215209 (0.298209) \| 5.132267 / 2.077655 (3.054613) \| 2.773676 / 1.504120 (1.269556) \| 2.576840 / 1.541195 (1.035645) \| 2.681729 / 1.468490 (1.213238) \| 0.581809 / 4.584777 (-4.002968) \| 4.327075 / 3.745712 (0.581363) \| 4.040264 / 5.269862 (-1.229598) \| 2.436192 / 4.565676 (-2.129484) \| 0.067819 / 0.424275 (-0.356456) \| 0.008760 / 0.007607 (0.001153) \| 0.610765 / 0.226044 (0.384720) \| 6.105679 / 2.268929 (3.836750) \| 3.341341 / 55.444624 (-52.103284) \| 2.926695 / 6.876477 (-3.949781) \| 3.017269 / 2.142072 (0.875196) \| 0.707289 / 4.805227 (-4.097938) \| 0.157379 / 6.500664 (-6.343285) \| 0.072549 / 0.075469 (-0.002920) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.666738 / 1.841788 (-0.175050) \| 23.698567 / 8.074308 (15.624259) \| 17.806437 / 10.191392 (7.615045) \| 0.172103 / 0.680424 (-0.508321) \| 0.023508 / 0.534201 (-0.510693) \| 0.473171 / 0.579283 (-0.106112) \| 0.524834 / 0.434364 (0.090470) \| 0.562562 / 0.540337 (0.022224) \| 0.788667 / 1.386936 (-0.598269) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e7338259b26b32a095d251d5cdbc779c3573307 \"CML watermark\")\n", "CI 404 errors are unrelated. See:\r\n- #6262 ", "<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) \| 0.006657 / 0.011353 (-0.004696) \| 0.003975 / 0.011008 (-0.007033) \| 0.084614 / 0.038508 (0.046106) \| 0.074557 / 0.023109 (0.051448) \| 0.309213 / 0.275898 (0.033315) \| 0.338245 / 0.323480 (0.014765) \| 0.005375 / 0.007986 (-0.002610) \| 0.003355 / 0.004328 (-0.000973) \| 0.064406 / 0.004250 (0.060156) \| 0.061763 / 0.037052 (0.024711) \| 0.313405 / 0.258489 (0.054916) \| 0.352149 / 0.293841 (0.058308) \| 0.031597 / 0.128546 (-0.096949) \| 0.008499 / 0.075646 (-0.067147) \| 0.289098 / 0.419271 (-0.130174) \| 0.054415 / 0.043533 (0.010882) \| 0.313210 / 0.255139 (0.058071) \| 0.326728 / 0.283200 (0.043528) \| 0.024597 / 0.141683 (-0.117086) \| 1.449916 / 1.452155 (-0.002239) \| 1.526314 / 1.492716 (0.033598) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231435 / 0.018006 (0.213429) \| 0.537224 / 0.000490 (0.536734) \| 0.007287 / 0.000200 (0.007088) \| 0.000227 / 0.000054 (0.000172) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028340 / 0.037411 (-0.009071) \| 0.084085 / 0.014526 (0.069560) \| 0.428211 / 0.176557 (0.251655) \| 0.157360 / 0.737135 (-0.579775) \| 0.139470 / 0.296338 (-0.156868) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.389311 / 0.215209 (0.174102) \| 3.871329 / 2.077655 (1.793674) \| 1.861533 / 1.504120 (0.357413) \| 1.688082 / 1.541195 (0.146887) \| 1.804036 / 1.468490 (0.335546) \| 0.489154 / 4.584777 (-4.095623) \| 3.603843 / 3.745712 (-0.141869) \| 3.424868 / 5.269862 (-1.844994) \| 2.013525 / 4.565676 (-2.552152) \| 0.057387 / 0.424275 (-0.366888) \| 0.007274 / 0.007607 (-0.000333) \| 0.462340 / 0.226044 (0.236295) \| 4.620095 / 2.268929 (2.351167) \| 2.326641 / 55.444624 (-53.117984) \| 1.990082 / 6.876477 (-4.886395) \| 2.037841 / 2.142072 (-0.104232) \| 0.581973 / 4.805227 (-4.223254) \| 0.135932 / 6.500664 (-6.364732) \| 0.061092 / 0.075469 (-0.014377) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249586 / 1.841788 (-0.592202) \| 19.036233 / 8.074308 (10.961925) \| 14.083365 / 10.191392 (3.891973) \| 0.169802 / 0.680424 (-0.510622) \| 0.018547 / 0.534201 (-0.515654) \| 0.392926 / 0.579283 (-0.186357) \| 0.409993 / 0.434364 (-0.024371) \| 0.460081 / 0.540337 (-0.080257) \| 0.643836 / 1.386936 (-0.743100) \|\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) \| 0.006889 / 0.011353 (-0.004464) \| 0.004060 / 0.011008 (-0.006948) \| 0.064332 / 0.038508 (0.025824) \| 0.077067 / 0.023109 (0.053958) \| 0.401235 / 0.275898 (0.125337) \| 0.437139 / 0.323480 (0.113659) \| 0.005510 / 0.007986 (-0.002476) \| 0.003338 / 0.004328 (-0.000991) \| 0.064446 / 0.004250 (0.060195) \| 0.055537 / 0.037052 (0.018485) \| 0.432689 / 0.258489 (0.174200) \| 0.460005 / 0.293841 (0.166164) \| 0.033122 / 0.128546 (-0.095424) \| 0.008637 / 0.075646 (-0.067010) \| 0.071088 / 0.419271 (-0.348183) \| 0.049024 / 0.043533 (0.005491) \| 0.400258 / 0.255139 (0.145119) \| 0.419324 / 0.283200 (0.136124) \| 0.022050 / 0.141683 (-0.119632) \| 1.475744 / 1.452155 (0.023589) \| 1.546565 / 1.492716 (0.053848) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226241 / 0.018006 (0.208235) \| 0.448574 / 0.000490 (0.448085) \| 0.004732 / 0.000200 (0.004533) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033260 / 0.037411 (-0.004151) \| 0.092622 / 0.014526 (0.078096) \| 0.105501 / 0.176557 (-0.071056) \| 0.157981 / 0.737135 (-0.579155) \| 0.105993 / 0.296338 (-0.190345) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445716 / 0.215209 (0.230507) \| 4.451848 / 2.077655 (2.374194) \| 2.404769 / 1.504120 (0.900649) \| 2.232594 / 1.541195 (0.691399) \| 2.312735 / 1.468490 (0.844245) \| 0.491208 / 4.584777 (-4.093569) \| 3.561629 / 3.745712 (-0.184083) \| 3.444269 / 5.269862 (-1.825592) \| 2.060365 / 4.565676 (-2.505311) \| 0.057723 / 0.424275 (-0.366552) \| 0.007392 / 0.007607 (-0.000215) \| 0.526447 / 0.226044 (0.300403) \| 5.264307 / 2.268929 (2.995379) \| 2.951481 / 55.444624 (-52.493143) \| 2.593178 / 6.876477 (-4.283299) \| 2.689780 / 2.142072 (0.547707) \| 0.588649 / 4.805227 (-4.216579) \| 0.133566 / 6.500664 (-6.367098) \| 0.060462 / 0.075469 (-0.015008) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.381008 / 1.841788 (-0.460780) \| 19.452394 / 8.074308 (11.378086) \| 15.255912 / 10.191392 (5.064520) \| 0.171043 / 0.680424 (-0.509381) \| 0.020395 / 0.534201 (-0.513806) \| 0.396429 / 0.579283 (-0.182854) \| 0.422820 / 0.434364 (-0.011544) \| 0.477305 / 0.540337 (-0.063032) \| 0.658274 / 1.386936 (-0.728663) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#faedc670ca896584d0f8edcb1fd9c13d1d6cc903 \"CML watermark\")\n" ]	2023-09-27T08:16:06Z	2023-09-27T08:45:24Z	2023-09-27T08:36:39Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6264.diff", "html_url": "https://github.com/huggingface/datasets/pull/6264", "merged_at": "2023-09-27T08:36:39Z", "patch_url": "https://github.com/huggingface/datasets/pull/6264.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6264" }	Temporarily pin tensorflow < 2.14.0 until permanent solution is found. Hot fix #6263.	{ "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/6264/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6263	6,263	CI is broken: ImportError: cannot import name 'context' from 'tensorflow.python'	{ "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	[]	2023-09-27T08:12:05Z	2023-09-27T08:36:40Z	2023-09-27T08:36:40Z	MEMBER	null	null	Python 3.10 CI is broken for `test_py310`. See: https://github.com/huggingface/datasets/actions/runs/6322990957/job/17169678812?pr=6262 ``` FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py) ``` ``` _________________________ TempSeedTest.test_tensorflow _________________________ [gw1] linux -- Python 3.10.13 /opt/hostedtoolcache/Python/3.10.13/x64/bin/python self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow> @require_tf def test_tensorflow(self): import tensorflow as tf from tensorflow.keras import layers model = layers.Dense(2) def gen_random_output(): x = tf.random.uniform((1, 3)) return model(x).numpy() > with temp_seed(42, set_tensorflow=True): tests/test_py_utils.py:155: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/contextlib.py:135: in __enter__ return next(self.gen) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ seed = 42, set_pytorch = False, set_tensorflow = True @contextmanager def temp_seed(seed: int, set_pytorch=False, set_tensorflow=False): """Temporarily set the random seed. This works for python numpy, pytorch and tensorflow.""" np_state = np.random.get_state() np.random.seed(seed) if set_pytorch and config.TORCH_AVAILABLE: import torch torch_state = torch.random.get_rng_state() torch.random.manual_seed(seed) if torch.cuda.is_available(): torch_cuda_states = torch.cuda.get_rng_state_all() torch.cuda.manual_seed_all(seed) if set_tensorflow and config.TF_AVAILABLE: import tensorflow as tf > from tensorflow.python import context as tfpycontext E ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py) /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/datasets/utils/py_utils.py:257: ImportError ```	{ "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/6263/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/6262	6,262	Fix CI 404 errors	{ "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) \| 0.008220 / 0.011353 (-0.003133) \| 0.005560 / 0.011008 (-0.005448) \| 0.100147 / 0.038508 (0.061639) \| 0.070106 / 0.023109 (0.046996) \| 0.411906 / 0.275898 (0.136008) \| 0.432825 / 0.323480 (0.109345) \| 0.004795 / 0.007986 (-0.003190) \| 0.004094 / 0.004328 (-0.000235) \| 0.075719 / 0.004250 (0.071468) \| 0.067426 / 0.037052 (0.030374) \| 0.428531 / 0.258489 (0.170042) \| 0.437114 / 0.293841 (0.143273) \| 0.045603 / 0.128546 (-0.082943) \| 0.013333 / 0.075646 (-0.062313) \| 0.353137 / 0.419271 (-0.066134) \| 0.067902 / 0.043533 (0.024369) \| 0.396633 / 0.255139 (0.141494) \| 0.399185 / 0.283200 (0.115985) \| 0.036377 / 0.141683 (-0.105306) \| 1.624249 / 1.452155 (0.172094) \| 1.792575 / 1.492716 (0.299859) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.315847 / 0.018006 (0.297840) \| 0.595009 / 0.000490 (0.594519) \| 0.018876 / 0.000200 (0.018676) \| 0.000613 / 0.000054 (0.000558) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029886 / 0.037411 (-0.007526) \| 0.085765 / 0.014526 (0.071239) \| 0.108680 / 0.176557 (-0.067877) \| 0.174588 / 0.737135 (-0.562548) \| 0.104494 / 0.296338 (-0.191844) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.594429 / 0.215209 (0.379220) \| 5.912352 / 2.077655 (3.834698) \| 2.408501 / 1.504120 (0.904381) \| 2.050914 / 1.541195 (0.509720) \| 2.199349 / 1.468490 (0.730859) \| 0.813797 / 4.584777 (-3.770980) \| 5.169577 / 3.745712 (1.423864) \| 4.653951 / 5.269862 (-0.615911) \| 2.805423 / 4.565676 (-1.760253) \| 0.092278 / 0.424275 (-0.331997) \| 0.007394 / 0.007607 (-0.000213) \| 0.684029 / 0.226044 (0.457985) \| 6.964260 / 2.268929 (4.695331) \| 3.108408 / 55.444624 (-52.336217) \| 2.470907 / 6.876477 (-4.405569) \| 2.460153 / 2.142072 (0.318081) \| 0.986445 / 4.805227 (-3.818782) \| 0.213069 / 6.500664 (-6.287596) \| 0.074061 / 0.075469 (-0.001408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.590732 / 1.841788 (-0.251056) \| 23.736918 / 8.074308 (15.662609) \| 21.223910 / 10.191392 (11.032518) \| 0.236173 / 0.680424 (-0.444251) \| 0.030056 / 0.534201 (-0.504145) \| 0.489461 / 0.579283 (-0.089822) \| 0.607582 / 0.434364 (0.173218) \| 0.539889 / 0.540337 (-0.000449) \| 0.817942 / 1.386936 (-0.568994) \|\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) \| 0.008042 / 0.011353 (-0.003311) \| 0.004836 / 0.011008 (-0.006173) \| 0.075434 / 0.038508 (0.036926) \| 0.080818 / 0.023109 (0.057709) \| 0.474797 / 0.275898 (0.198899) \| 0.526168 / 0.323480 (0.202689) \| 0.006463 / 0.007986 (-0.001522) \| 0.004031 / 0.004328 (-0.000297) \| 0.074141 / 0.004250 (0.069891) \| 0.068265 / 0.037052 (0.031212) \| 0.562550 / 0.258489 (0.304061) \| 0.544820 / 0.293841 (0.250979) \| 0.047263 / 0.128546 (-0.081283) \| 0.014113 / 0.075646 (-0.061534) \| 0.086061 / 0.419271 (-0.333210) \| 0.062475 / 0.043533 (0.018942) \| 0.479912 / 0.255139 (0.224773) \| 0.494784 / 0.283200 (0.211584) \| 0.035847 / 0.141683 (-0.105836) \| 1.726452 / 1.452155 (0.274297) \| 1.770113 / 1.492716 (0.277396) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286713 / 0.018006 (0.268707) \| 0.609704 / 0.000490 (0.609214) \| 0.009342 / 0.000200 (0.009143) \| 0.000134 / 0.000054 (0.000080) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035137 / 0.037411 (-0.002275) \| 0.099331 / 0.014526 (0.084805) \| 0.108971 / 0.176557 (-0.067586) \| 0.170952 / 0.737135 (-0.566183) \| 0.111736 / 0.296338 (-0.184603) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.617434 / 0.215209 (0.402225) \| 6.204351 / 2.077655 (4.126697) \| 2.854347 / 1.504120 (1.350227) \| 2.557424 / 1.541195 (1.016229) \| 2.638173 / 1.468490 (1.169683) \| 0.854234 / 4.584777 (-3.730543) \| 5.383288 / 3.745712 (1.637576) \| 4.698098 / 5.269862 (-0.571763) \| 2.903860 / 4.565676 (-1.661817) \| 0.094689 / 0.424275 (-0.329586) \| 0.007892 / 0.007607 (0.000285) \| 0.729420 / 0.226044 (0.503376) \| 7.356691 / 2.268929 (5.087763) \| 3.708039 / 55.444624 (-51.736585) \| 2.979734 / 6.876477 (-3.896743) \| 2.978983 / 2.142072 (0.836911) \| 1.040554 / 4.805227 (-3.764673) \| 0.211246 / 6.500664 (-6.289418) \| 0.079880 / 0.075469 (0.004411) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.676057 / 1.841788 (-0.165731) \| 23.428443 / 8.074308 (15.354135) \| 21.016293 / 10.191392 (10.824901) \| 0.260927 / 0.680424 (-0.419497) \| 0.030689 / 0.534201 (-0.503512) \| 0.495652 / 0.579283 (-0.083632) \| 0.622976 / 0.434364 (0.188612) \| 0.561175 / 0.540337 (0.020837) \| 0.786733 / 1.386936 (-0.600203) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb621b9630a69643255d25f192fdb011935122b1 \"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) \| 0.005942 / 0.011353 (-0.005410) \| 0.003706 / 0.011008 (-0.007302) \| 0.081002 / 0.038508 (0.042493) \| 0.056854 / 0.023109 (0.033745) \| 0.358668 / 0.275898 (0.082770) \| 0.369718 / 0.323480 (0.046238) \| 0.005202 / 0.007986 (-0.002784) \| 0.002841 / 0.004328 (-0.001487) \| 0.062976 / 0.004250 (0.058726) \| 0.051308 / 0.037052 (0.014255) \| 0.373636 / 0.258489 (0.115147) \| 0.390480 / 0.293841 (0.096639) \| 0.027480 / 0.128546 (-0.101067) \| 0.007960 / 0.075646 (-0.067686) \| 0.262719 / 0.419271 (-0.156552) \| 0.046488 / 0.043533 (0.002955) \| 0.347299 / 0.255139 (0.092160) \| 0.393448 / 0.283200 (0.110249) \| 0.019445 / 0.141683 (-0.122238) \| 1.431314 / 1.452155 (-0.020841) \| 1.495578 / 1.492716 (0.002862) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223724 / 0.018006 (0.205718) \| 0.416929 / 0.000490 (0.416440) \| 0.005253 / 0.000200 (0.005053) \| 0.000217 / 0.000054 (0.000163) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023571 / 0.037411 (-0.013841) \| 0.073503 / 0.014526 (0.058978) \| 0.081366 / 0.176557 (-0.095190) \| 0.142716 / 0.737135 (-0.594420) \| 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) \| 0.407319 / 0.215209 (0.192109) \| 4.141404 / 2.077655 (2.063749) \| 1.910842 / 1.504120 (0.406722) \| 1.731694 / 1.541195 (0.190499) \| 1.805228 / 1.468490 (0.336738) \| 0.497109 / 4.584777 (-4.087668) \| 3.107624 / 3.745712 (-0.638088) \| 2.890687 / 5.269862 (-2.379174) \| 1.795913 / 4.565676 (-2.769763) \| 0.057099 / 0.424275 (-0.367176) \| 0.006414 / 0.007607 (-0.001194) \| 0.482127 / 0.226044 (0.256083) \| 4.835158 / 2.268929 (2.566229) \| 2.368909 / 55.444624 (-53.075715) \| 2.001608 / 6.876477 (-4.874868) \| 2.004492 / 2.142072 (-0.137580) \| 0.579910 / 4.805227 (-4.225317) \| 0.123541 / 6.500664 (-6.377123) \| 0.059651 / 0.075469 (-0.015818) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.242364 / 1.841788 (-0.599424) \| 16.982676 / 8.074308 (8.908368) \| 13.718885 / 10.191392 (3.527493) \| 0.132759 / 0.680424 (-0.547665) \| 0.017012 / 0.534201 (-0.517189) \| 0.333447 / 0.579283 (-0.245836) \| 0.360149 / 0.434364 (-0.074215) \| 0.385526 / 0.540337 (-0.154811) \| 0.536915 / 1.386936 (-0.850021) \|\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) \| 0.005946 / 0.011353 (-0.005407) \| 0.003442 / 0.011008 (-0.007566) \| 0.062595 / 0.038508 (0.024087) \| 0.058699 / 0.023109 (0.035590) \| 0.442626 / 0.275898 (0.166728) \| 0.473773 / 0.323480 (0.150293) \| 0.004622 / 0.007986 (-0.003364) \| 0.002812 / 0.004328 (-0.001516) \| 0.064099 / 0.004250 (0.059849) \| 0.046784 / 0.037052 (0.009731) \| 0.466049 / 0.258489 (0.207560) \| 0.487912 / 0.293841 (0.194071) \| 0.028372 / 0.128546 (-0.100174) \| 0.007992 / 0.075646 (-0.067654) \| 0.068151 / 0.419271 (-0.351120) \| 0.041010 / 0.043533 (-0.002523) \| 0.442331 / 0.255139 (0.187192) \| 0.469686 / 0.283200 (0.186487) \| 0.019694 / 0.141683 (-0.121989) \| 1.467928 / 1.452155 (0.015774) \| 1.525635 / 1.492716 (0.032918) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204459 / 0.018006 (0.186453) \| 0.407766 / 0.000490 (0.407276) \| 0.003898 / 0.000200 (0.003698) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025909 / 0.037411 (-0.011503) \| 0.080341 / 0.014526 (0.065816) \| 0.088231 / 0.176557 (-0.088325) \| 0.144056 / 0.737135 (-0.593079) \| 0.089769 / 0.296338 (-0.206569) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462876 / 0.215209 (0.247667) \| 4.625983 / 2.077655 (2.548329) \| 2.580079 / 1.504120 (1.075959) \| 2.402792 / 1.541195 (0.861597) \| 2.424982 / 1.468490 (0.956491) \| 0.503654 / 4.584777 (-4.081123) \| 3.178995 / 3.745712 (-0.566717) \| 2.956126 / 5.269862 (-2.313735) \| 1.847837 / 4.565676 (-2.717840) \| 0.057964 / 0.424275 (-0.366311) \| 0.006405 / 0.007607 (-0.001202) \| 0.536036 / 0.226044 (0.309992) \| 5.374416 / 2.268929 (3.105487) \| 3.036440 / 55.444624 (-52.408184) \| 2.682054 / 6.876477 (-4.194422) \| 2.683462 / 2.142072 (0.541390) \| 0.592751 / 4.805227 (-4.212477) \| 0.124313 / 6.500664 (-6.376351) \| 0.061127 / 0.075469 (-0.014342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.383539 / 1.841788 (-0.458249) \| 17.766221 / 8.074308 (9.691913) \| 15.306600 / 10.191392 (5.115208) \| 0.145035 / 0.680424 (-0.535389) \| 0.018078 / 0.534201 (-0.516123) \| 0.330102 / 0.579283 (-0.249181) \| 0.375380 / 0.434364 (-0.058984) \| 0.388531 / 0.540337 (-0.151807) \| 0.548720 / 1.386936 (-0.838216) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0082342ac792a05f4a615e4985d1c791e155115a \"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) \| 0.006757 / 0.011353 (-0.004596) \| 0.004110 / 0.011008 (-0.006898) \| 0.084727 / 0.038508 (0.046219) \| 0.074328 / 0.023109 (0.051219) \| 0.310467 / 0.275898 (0.034569) \| 0.343209 / 0.323480 (0.019729) \| 0.004228 / 0.007986 (-0.003757) \| 0.003400 / 0.004328 (-0.000929) \| 0.065546 / 0.004250 (0.061296) \| 0.063057 / 0.037052 (0.026005) \| 0.315023 / 0.258489 (0.056534) \| 0.356395 / 0.293841 (0.062554) \| 0.031959 / 0.128546 (-0.096588) \| 0.008577 / 0.075646 (-0.067069) \| 0.289075 / 0.419271 (-0.130196) \| 0.055011 / 0.043533 (0.011478) \| 0.308861 / 0.255139 (0.053722) \| 0.328691 / 0.283200 (0.045491) \| 0.027037 / 0.141683 (-0.114646) \| 1.464314 / 1.452155 (0.012159) \| 1.549644 / 1.492716 (0.056927) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238330 / 0.018006 (0.220324) \| 0.451570 / 0.000490 (0.451080) \| 0.010873 / 0.000200 (0.010673) \| 0.000341 / 0.000054 (0.000286) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029909 / 0.037411 (-0.007503) \| 0.085222 / 0.014526 (0.070696) \| 0.100180 / 0.176557 (-0.076377) \| 0.154842 / 0.737135 (-0.582293) \| 0.099253 / 0.296338 (-0.197086) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401603 / 0.215209 (0.186394) \| 4.009781 / 2.077655 (1.932126) \| 2.021807 / 1.504120 (0.517687) \| 1.861017 / 1.541195 (0.319822) \| 2.009072 / 1.468490 (0.540582) \| 0.483798 / 4.584777 (-4.100979) \| 3.580394 / 3.745712 (-0.165318) \| 3.464587 / 5.269862 (-1.805275) \| 2.018400 / 4.565676 (-2.547276) \| 0.057134 / 0.424275 (-0.367141) \| 0.007303 / 0.007607 (-0.000304) \| 0.473627 / 0.226044 (0.247582) \| 4.722634 / 2.268929 (2.453706) \| 2.490884 / 55.444624 (-52.953741) \| 2.121568 / 6.876477 (-4.754909) \| 2.200699 / 2.142072 (0.058626) \| 0.576728 / 4.805227 (-4.228499) \| 0.135633 / 6.500664 (-6.365032) \| 0.061625 / 0.075469 (-0.013844) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.250545 / 1.841788 (-0.591243) \| 19.167642 / 8.074308 (11.093334) \| 14.189891 / 10.191392 (3.998499) \| 0.164552 / 0.680424 (-0.515872) \| 0.018215 / 0.534201 (-0.515986) \| 0.389962 / 0.579283 (-0.189321) \| 0.413972 / 0.434364 (-0.020392) \| 0.460253 / 0.540337 (-0.080085) \| 0.647897 / 1.386936 (-0.739039) \|\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) \| 0.006714 / 0.011353 (-0.004639) \| 0.004081 / 0.011008 (-0.006927) \| 0.065627 / 0.038508 (0.027119) \| 0.077644 / 0.023109 (0.054535) \| 0.409950 / 0.275898 (0.134052) \| 0.442940 / 0.323480 (0.119460) \| 0.005523 / 0.007986 (-0.002463) \| 0.003366 / 0.004328 (-0.000962) \| 0.065425 / 0.004250 (0.061174) \| 0.056222 / 0.037052 (0.019169) \| 0.429928 / 0.258489 (0.171439) \| 0.457136 / 0.293841 (0.163296) \| 0.032356 / 0.128546 (-0.096190) \| 0.008676 / 0.075646 (-0.066970) \| 0.071785 / 0.419271 (-0.347486) \| 0.048458 / 0.043533 (0.004925) \| 0.408003 / 0.255139 (0.152864) \| 0.433529 / 0.283200 (0.150330) \| 0.023232 / 0.141683 (-0.118450) \| 1.483640 / 1.452155 (0.031485) \| 1.552425 / 1.492716 (0.059709) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282347 / 0.018006 (0.264341) \| 0.448742 / 0.000490 (0.448253) \| 0.039590 / 0.000200 (0.039390) \| 0.000407 / 0.000054 (0.000353) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032516 / 0.037411 (-0.004896) \| 0.095269 / 0.014526 (0.080744) \| 0.106363 / 0.176557 (-0.070193) \| 0.157945 / 0.737135 (-0.579191) \| 0.106783 / 0.296338 (-0.189556) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436334 / 0.215209 (0.221125) \| 4.348147 / 2.077655 (2.270492) \| 2.326830 / 1.504120 (0.822710) \| 2.162586 / 1.541195 (0.621391) \| 2.257769 / 1.468490 (0.789279) \| 0.491677 / 4.584777 (-4.093099) \| 3.707385 / 3.745712 (-0.038328) \| 3.567147 / 5.269862 (-1.702715) \| 2.099451 / 4.565676 (-2.466226) \| 0.058486 / 0.424275 (-0.365789) \| 0.007324 / 0.007607 (-0.000283) \| 0.510962 / 0.226044 (0.284917) \| 5.106550 / 2.268929 (2.837622) \| 2.785723 / 55.444624 (-52.658901) \| 2.452928 / 6.876477 (-4.423548) \| 2.545034 / 2.142072 (0.402961) \| 0.611124 / 4.805227 (-4.194103) \| 0.133503 / 6.500664 (-6.367161) \| 0.061118 / 0.075469 (-0.014351) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.386640 / 1.841788 (-0.455148) \| 20.485670 / 8.074308 (12.411362) \| 15.332223 / 10.191392 (5.140831) \| 0.164070 / 0.680424 (-0.516354) \| 0.019962 / 0.534201 (-0.514239) \| 0.394217 / 0.579283 (-0.185066) \| 0.428442 / 0.434364 (-0.005922) \| 0.473784 / 0.540337 (-0.066553) \| 0.665141 / 1.386936 (-0.721795) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c722eb75a6cc56eac530c44a17ff679ca805aa89 \"CML watermark\")\n", "The CI errors seem unrelated to this PR but I think they need further investigation in another PR.\r\n```\r\nFAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files - KeyError: 'url'\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) \| 0.008766 / 0.011353 (-0.002587) \| 0.005289 / 0.011008 (-0.005720) \| 0.097220 / 0.038508 (0.058712) \| 0.072246 / 0.023109 (0.049137) \| 0.369359 / 0.275898 (0.093461) \| 0.422571 / 0.323480 (0.099091) \| 0.004941 / 0.007986 (-0.003044) \| 0.006103 / 0.004328 (0.001774) \| 0.075828 / 0.004250 (0.071578) \| 0.065795 / 0.037052 (0.028743) \| 0.412835 / 0.258489 (0.154346) \| 0.430062 / 0.293841 (0.136221) \| 0.045806 / 0.128546 (-0.082741) \| 0.013760 / 0.075646 (-0.061887) \| 0.351542 / 0.419271 (-0.067729) \| 0.064836 / 0.043533 (0.021304) \| 0.370162 / 0.255139 (0.115023) \| 0.434949 / 0.283200 (0.151749) \| 0.039198 / 0.141683 (-0.102485) \| 1.670940 / 1.452155 (0.218785) \| 1.809677 / 1.492716 (0.316961) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295104 / 0.018006 (0.277097) \| 0.594584 / 0.000490 (0.594095) \| 0.010923 / 0.000200 (0.010723) \| 0.000479 / 0.000054 (0.000425) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029174 / 0.037411 (-0.008237) \| 0.094637 / 0.014526 (0.080111) \| 0.102948 / 0.176557 (-0.073608) \| 0.171048 / 0.737135 (-0.566087) \| 0.111465 / 0.296338 (-0.184873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.582017 / 0.215209 (0.366808) \| 5.727008 / 2.077655 (3.649354) \| 2.563211 / 1.504120 (1.059091) \| 2.308912 / 1.541195 (0.767717) \| 2.301258 / 1.468490 (0.832768) \| 0.819594 / 4.584777 (-3.765183) \| 5.177536 / 3.745712 (1.431824) \| 4.473602 / 5.269862 (-0.796260) \| 2.743819 / 4.565676 (-1.821857) \| 0.090052 / 0.424275 (-0.334223) \| 0.007903 / 0.007607 (0.000295) \| 0.679142 / 0.226044 (0.453097) \| 6.887891 / 2.268929 (4.618962) \| 3.337926 / 55.444624 (-52.106699) \| 2.659228 / 6.876477 (-4.217249) \| 2.641289 / 2.142072 (0.499216) \| 0.974829 / 4.805227 (-3.830398) \| 0.205775 / 6.500664 (-6.294890) \| 0.075268 / 0.075469 (-0.000201) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.500562 / 1.841788 (-0.341226) \| 22.688483 / 8.074308 (14.614175) \| 19.634878 / 10.191392 (9.443486) \| 0.227409 / 0.680424 (-0.453015) \| 0.029794 / 0.534201 (-0.504407) \| 0.475204 / 0.579283 (-0.104079) \| 0.579379 / 0.434364 (0.145016) \| 0.541244 / 0.540337 (0.000907) \| 0.739187 / 1.386936 (-0.647749) \|\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) \| 0.008641 / 0.011353 (-0.002712) \| 0.006139 / 0.011008 (-0.004870) \| 0.075048 / 0.038508 (0.036540) \| 0.074070 / 0.023109 (0.050961) \| 0.508288 / 0.275898 (0.232390) \| 0.539770 / 0.323480 (0.216290) \| 0.006092 / 0.007986 (-0.001894) \| 0.003748 / 0.004328 (-0.000581) \| 0.077945 / 0.004250 (0.073695) \| 0.056989 / 0.037052 (0.019936) \| 0.526889 / 0.258489 (0.268400) \| 0.560862 / 0.293841 (0.267021) \| 0.046507 / 0.128546 (-0.082040) \| 0.013249 / 0.075646 (-0.062397) \| 0.088363 / 0.419271 (-0.330908) \| 0.058776 / 0.043533 (0.015243) \| 0.495869 / 0.255139 (0.240730) \| 0.538615 / 0.283200 (0.255415) \| 0.034055 / 0.141683 (-0.107628) \| 1.658713 / 1.452155 (0.206558) \| 1.736599 / 1.492716 (0.243883) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288355 / 0.018006 (0.270349) \| 0.571481 / 0.000490 (0.570991) \| 0.006765 / 0.000200 (0.006565) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031836 / 0.037411 (-0.005575) \| 0.101312 / 0.014526 (0.086786) \| 0.111433 / 0.176557 (-0.065124) \| 0.169599 / 0.737135 (-0.567536) \| 0.114595 / 0.296338 (-0.181743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.645258 / 0.215209 (0.430049) \| 6.446653 / 2.077655 (4.368998) \| 2.983498 / 1.504120 (1.479379) \| 2.573820 / 1.541195 (1.032625) \| 2.624286 / 1.468490 (1.155796) \| 0.815997 / 4.584777 (-3.768780) \| 5.140248 / 3.745712 (1.394536) \| 4.636915 / 5.269862 (-0.632947) \| 2.866313 / 4.565676 (-1.699364) \| 0.096643 / 0.424275 (-0.327633) \| 0.008452 / 0.007607 (0.000845) \| 0.765837 / 0.226044 (0.539793) \| 7.622897 / 2.268929 (5.353968) \| 3.796247 / 55.444624 (-51.648378) \| 3.019349 / 6.876477 (-3.857128) \| 3.034187 / 2.142072 (0.892115) \| 1.001682 / 4.805227 (-3.803546) \| 0.211841 / 6.500664 (-6.288823) \| 0.073351 / 0.075469 (-0.002119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.740254 / 1.841788 (-0.101534) \| 23.465619 / 8.074308 (15.391311) \| 21.651670 / 10.191392 (11.460278) \| 0.226129 / 0.680424 (-0.454294) \| 0.029611 / 0.534201 (-0.504590) \| 0.441140 / 0.579283 (-0.138143) \| 0.605591 / 0.434364 (0.171227) \| 0.552427 / 0.540337 (0.012090) \| 0.771975 / 1.386936 (-0.614961) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef5751522c424c758df0647ff9a449b8b0404b6a \"CML watermark\")\n", "> The CI errors seem unrelated to this PR but I think they need further investigation in another PR.\r\n> \r\n> ```\r\n> FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files - KeyError: 'url'\r\n> ```\r\n\r\nWe need to wait for `huggingface_hub`'s next release to fix this (see https://github.com/huggingface/huggingface_hub/pull/1675; 409 error is currently ignored, hence the `KeyError`)\r\n\r\nAlso, we should be able to fix `test_push_dataset_dict_to_hub_overwrite_files` by inserting `gc.collect()` (to drop the \"reference\" to an Arrow file) between the `load_dataset` calls to avoid the `PermissionError` (also reported in https://github.com/huggingface/datasets/issues/3139)\r\n\r\n(Indeed, this can be addressed in subsequent PRs.)\r\n\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) \| 0.008988 / 0.011353 (-0.002365) \| 0.005270 / 0.011008 (-0.005738) \| 0.114577 / 0.038508 (0.076068) \| 0.091630 / 0.023109 (0.068521) \| 0.409217 / 0.275898 (0.133319) \| 0.440903 / 0.323480 (0.117424) \| 0.005226 / 0.007986 (-0.002760) \| 0.004289 / 0.004328 (-0.000040) \| 0.082246 / 0.004250 (0.077995) \| 0.084926 / 0.037052 (0.047873) \| 0.407822 / 0.258489 (0.149333) \| 0.440891 / 0.293841 (0.147051) \| 0.052225 / 0.128546 (-0.076321) \| 0.014218 / 0.075646 (-0.061429) \| 0.436994 / 0.419271 (0.017722) \| 0.066433 / 0.043533 (0.022901) \| 0.413909 / 0.255139 (0.158770) \| 0.425729 / 0.283200 (0.142530) \| 0.039576 / 0.141683 (-0.102107) \| 1.905604 / 1.452155 (0.453449) \| 1.907032 / 1.492716 (0.414315) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313662 / 0.018006 (0.295655) \| 0.614541 / 0.000490 (0.614051) \| 0.015631 / 0.000200 (0.015431) \| 0.000507 / 0.000054 (0.000453) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029049 / 0.037411 (-0.008362) \| 0.094626 / 0.014526 (0.080100) \| 0.104718 / 0.176557 (-0.071838) \| 0.187346 / 0.737135 (-0.549790) \| 0.108001 / 0.296338 (-0.188337) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.578997 / 0.215209 (0.363788) \| 5.815546 / 2.077655 (3.737892) \| 2.411301 / 1.504120 (0.907181) \| 2.110088 / 1.541195 (0.568893) \| 2.147839 / 1.468490 (0.679349) \| 0.861285 / 4.584777 (-3.723492) \| 5.264245 / 3.745712 (1.518533) \| 4.695786 / 5.269862 (-0.574076) \| 2.867522 / 4.565676 (-1.698154) \| 0.096523 / 0.424275 (-0.327752) \| 0.008777 / 0.007607 (0.001170) \| 0.716316 / 0.226044 (0.490272) \| 7.257574 / 2.268929 (4.988645) \| 3.141502 / 55.444624 (-52.303123) \| 2.480604 / 6.876477 (-4.395872) \| 2.530031 / 2.142072 (0.387958) \| 1.054274 / 4.805227 (-3.750953) \| 0.210781 / 6.500664 (-6.289883) \| 0.073837 / 0.075469 (-0.001632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.607689 / 1.841788 (-0.234099) \| 23.856780 / 8.074308 (15.782472) \| 19.507196 / 10.191392 (9.315804) \| 0.232712 / 0.680424 (-0.447712) \| 0.027037 / 0.534201 (-0.507164) \| 0.466613 / 0.579283 (-0.112670) \| 0.571139 / 0.434364 (0.136775) \| 0.543109 / 0.540337 (0.002771) \| 0.785558 / 1.386936 (-0.601378) \|\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) \| 0.008104 / 0.011353 (-0.003249) \| 0.004923 / 0.011008 (-0.006086) \| 0.075093 / 0.038508 (0.036585) \| 0.075218 / 0.023109 (0.052109) \| 0.476615 / 0.275898 (0.200717) \| 0.506984 / 0.323480 (0.183504) \| 0.006371 / 0.007986 (-0.001614) \| 0.004818 / 0.004328 (0.000489) \| 0.075634 / 0.004250 (0.071383) \| 0.059513 / 0.037052 (0.022461) \| 0.523763 / 0.258489 (0.265274) \| 0.531858 / 0.293841 (0.238017) \| 0.048168 / 0.128546 (-0.080379) \| 0.014110 / 0.075646 (-0.061537) \| 0.086052 / 0.419271 (-0.333219) \| 0.058369 / 0.043533 (0.014836) \| 0.475537 / 0.255139 (0.220398) \| 0.509429 / 0.283200 (0.226229) \| 0.033924 / 0.141683 (-0.107758) \| 1.657490 / 1.452155 (0.205336) \| 1.762544 / 1.492716 (0.269828) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263863 / 0.018006 (0.245857) \| 0.584468 / 0.000490 (0.583978) \| 0.007063 / 0.000200 (0.006863) \| 0.000181 / 0.000054 (0.000126) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032229 / 0.037411 (-0.005183) \| 0.096750 / 0.014526 (0.082224) \| 0.117798 / 0.176557 (-0.058758) \| 0.173376 / 0.737135 (-0.563760) \| 0.117241 / 0.296338 (-0.179098) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.701935 / 0.215209 (0.486726) \| 6.544655 / 2.077655 (4.467001) \| 3.055531 / 1.504120 (1.551411) \| 2.896339 / 1.541195 (1.355144) \| 3.013157 / 1.468490 (1.544667) \| 0.852989 / 4.584777 (-3.731788) \| 5.399355 / 3.745712 (1.653643) \| 5.119811 / 5.269862 (-0.150051) \| 3.167269 / 4.565676 (-1.398407) \| 0.096962 / 0.424275 (-0.327313) \| 0.008843 / 0.007607 (0.001236) \| 0.776170 / 0.226044 (0.550125) \| 7.735093 / 2.268929 (5.466164) \| 3.792629 / 55.444624 (-51.651996) \| 3.249911 / 6.876477 (-3.626565) \| 3.235590 / 2.142072 (1.093517) \| 1.046426 / 4.805227 (-3.758801) \| 0.239854 / 6.500664 (-6.260810) \| 0.100648 / 0.075469 (0.025179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.774488 / 1.841788 (-0.067300) \| 25.646958 / 8.074308 (17.572650) \| 23.181577 / 10.191392 (12.990185) \| 0.231948 / 0.680424 (-0.448476) \| 0.030147 / 0.534201 (-0.504054) \| 0.464161 / 0.579283 (-0.115122) \| 0.598980 / 0.434364 (0.164616) \| 0.571156 / 0.540337 (0.030819) \| 0.833221 / 1.386936 (-0.553715) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ad876e8908188dcd56759a35c4da182bf121535a \"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) \| 0.006010 / 0.011353 (-0.005343) \| 0.003662 / 0.011008 (-0.007346) \| 0.079971 / 0.038508 (0.041463) \| 0.066790 / 0.023109 (0.043681) \| 0.311387 / 0.275898 (0.035489) \| 0.346781 / 0.323480 (0.023301) \| 0.003500 / 0.007986 (-0.004485) \| 0.002831 / 0.004328 (-0.001498) \| 0.063238 / 0.004250 (0.058988) \| 0.056163 / 0.037052 (0.019110) \| 0.317456 / 0.258489 (0.058967) \| 0.356106 / 0.293841 (0.062265) \| 0.027358 / 0.128546 (-0.101188) \| 0.007906 / 0.075646 (-0.067741) \| 0.261779 / 0.419271 (-0.157492) \| 0.046385 / 0.043533 (0.002852) \| 0.312587 / 0.255139 (0.057448) \| 0.339513 / 0.283200 (0.056314) \| 0.021474 / 0.141683 (-0.120209) \| 1.418637 / 1.452155 (-0.033518) \| 1.510257 / 1.492716 (0.017540) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211761 / 0.018006 (0.193755) \| 0.424387 / 0.000490 (0.423898) \| 0.002579 / 0.000200 (0.002379) \| 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) \| 0.024038 / 0.037411 (-0.013374) \| 0.072524 / 0.014526 (0.057998) \| 0.083443 / 0.176557 (-0.093113) \| 0.144835 / 0.737135 (-0.592300) \| 0.084754 / 0.296338 (-0.211585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392423 / 0.215209 (0.177214) \| 3.927220 / 2.077655 (1.849565) \| 1.877853 / 1.504120 (0.373733) \| 1.699275 / 1.541195 (0.158081) \| 1.793144 / 1.468490 (0.324654) \| 0.503809 / 4.584777 (-4.080968) \| 3.052569 / 3.745712 (-0.693143) \| 2.907432 / 5.269862 (-2.362429) \| 1.811220 / 4.565676 (-2.754457) \| 0.057249 / 0.424275 (-0.367026) \| 0.006433 / 0.007607 (-0.001174) \| 0.463257 / 0.226044 (0.237213) \| 4.631038 / 2.268929 (2.362109) \| 2.315870 / 55.444624 (-53.128754) \| 2.000476 / 6.876477 (-4.876001) \| 2.043581 / 2.142072 (-0.098492) \| 0.588911 / 4.805227 (-4.216317) \| 0.125370 / 6.500664 (-6.375295) \| 0.061721 / 0.075469 (-0.013748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244486 / 1.841788 (-0.597301) \| 17.862422 / 8.074308 (9.788114) \| 13.890205 / 10.191392 (3.698813) \| 0.145467 / 0.680424 (-0.534957) \| 0.016856 / 0.534201 (-0.517345) \| 0.329357 / 0.579283 (-0.249926) \| 0.367550 / 0.434364 (-0.066814) \| 0.377541 / 0.540337 (-0.162796) \| 0.534087 / 1.386936 (-0.852849) \|\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) \| 0.006030 / 0.011353 (-0.005323) \| 0.003650 / 0.011008 (-0.007359) \| 0.063300 / 0.038508 (0.024792) \| 0.058877 / 0.023109 (0.035767) \| 0.454662 / 0.275898 (0.178764) \| 0.489362 / 0.323480 (0.165882) \| 0.004856 / 0.007986 (-0.003130) \| 0.002909 / 0.004328 (-0.001420) \| 0.063356 / 0.004250 (0.059105) \| 0.047867 / 0.037052 (0.010814) \| 0.465461 / 0.258489 (0.206972) \| 0.506684 / 0.293841 (0.212843) \| 0.028599 / 0.128546 (-0.099947) \| 0.008076 / 0.075646 (-0.067570) \| 0.068695 / 0.419271 (-0.350576) \| 0.041487 / 0.043533 (-0.002045) \| 0.448676 / 0.255139 (0.193537) \| 0.471206 / 0.283200 (0.188007) \| 0.020401 / 0.141683 (-0.121282) \| 1.461181 / 1.452155 (0.009026) \| 1.517079 / 1.492716 (0.024363) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222827 / 0.018006 (0.204821) \| 0.425074 / 0.000490 (0.424585) \| 0.004153 / 0.000200 (0.003953) \| 0.000081 / 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) \| 0.026980 / 0.037411 (-0.010431) \| 0.080786 / 0.014526 (0.066260) \| 0.092040 / 0.176557 (-0.084517) \| 0.146082 / 0.737135 (-0.591053) \| 0.092739 / 0.296338 (-0.203600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461663 / 0.215209 (0.246454) \| 4.604828 / 2.077655 (2.527173) \| 2.566926 / 1.504120 (1.062806) \| 2.394419 / 1.541195 (0.853224) \| 2.458375 / 1.468490 (0.989885) \| 0.505140 / 4.584777 (-4.079637) \| 3.155916 / 3.745712 (-0.589796) \| 3.014474 / 5.269862 (-2.255388) \| 1.900296 / 4.565676 (-2.665380) \| 0.058063 / 0.424275 (-0.366212) \| 0.006409 / 0.007607 (-0.001198) \| 0.541165 / 0.226044 (0.315120) \| 5.410700 / 2.268929 (3.141772) \| 3.010239 / 55.444624 (-52.434386) \| 2.668103 / 6.876477 (-4.208373) \| 2.730418 / 2.142072 (0.588346) \| 0.603471 / 4.805227 (-4.201756) \| 0.129852 / 6.500664 (-6.370812) \| 0.061507 / 0.075469 (-0.013962) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.355272 / 1.841788 (-0.486516) \| 18.170088 / 8.074308 (10.095780) \| 15.583855 / 10.191392 (5.392463) \| 0.146246 / 0.680424 (-0.534178) \| 0.018093 / 0.534201 (-0.516108) \| 0.331695 / 0.579283 (-0.247588) \| 0.380845 / 0.434364 (-0.053519) \| 0.388564 / 0.540337 (-0.151774) \| 0.551465 / 1.386936 (-0.835471) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#afc3c2b034481a3502f5476186a110cf8613a248 \"CML watermark\")\n" ]	2023-09-27T07:40:18Z	2023-09-28T15:39:16Z	2023-09-28T15:30:40Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6262.diff", "html_url": "https://github.com/huggingface/datasets/pull/6262", "merged_at": "2023-09-28T15:30:40Z", "patch_url": "https://github.com/huggingface/datasets/pull/6262.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6262" }	Currently our CI usually raises 404 errors when trying to delete temporary repositories. See, e.g.: https://github.com/huggingface/datasets/actions/runs/6314980985/job/17146507884 ``` FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files_with_max_shard_size - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fb99-4a52c561752ece3d77eb6d57;2b61cae4-613d-4a73-bbb1-2faf9e32b02d) Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_audio - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fbb2-0333dd666d42f0e173c2bb68;dfdc4271-b49b-4008-8c49-f05cf7c1d53d) Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_custom_splits - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fbca-167690694f39770a5b3a444e;baeaa905-0a57-4585-ac97-9aaae12dd47d) Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. ``` I think this can be caused by collisions in temporary repository IDs because we create them in multiprocessing: ```python with temporary_repo(f"{CI_HUB_USER}/test-{int(time.time() * 10e3)}") as ds_name: ``` This can also be caused when there is another issue that does not allow the creation of the repository, thus making it impossible to delete it. This PR tries to fix this issue by increasing the precision of the number on the repository ID: `10e6` instead of `10e3`. Additionally, this PR catches RepositoryNotFoundError.	{ "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/6262/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6261	6,261	Can't load a dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/37955817?v=4", "events_url": "https://api.github.com/users/joaopedrosdmm/events{/privacy}", "followers_url": "https://api.github.com/users/joaopedrosdmm/followers", "following_url": "https://api.github.com/users/joaopedrosdmm/following{/other_user}", "gists_url": "https://api.github.com/users/joaopedrosdmm/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/joaopedrosdmm", "id": 37955817, "login": "joaopedrosdmm", "node_id": "MDQ6VXNlcjM3OTU1ODE3", "organizations_url": "https://api.github.com/users/joaopedrosdmm/orgs", "received_events_url": "https://api.github.com/users/joaopedrosdmm/received_events", "repos_url": "https://api.github.com/users/joaopedrosdmm/repos", "site_admin": false, "starred_url": "https://api.github.com/users/joaopedrosdmm/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/joaopedrosdmm/subscriptions", "type": "User", "url": "https://api.github.com/users/joaopedrosdmm", "user_view_type": "public" }	[]	closed	false	[ "I believe is due to the fact that doesn't work with .tgz files.", "`JourneyDB/JourneyDB` is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.\r\n\r\n> I believe is due to the fact that doesn't work with .tgz files.\r\n\r\nIndeed, the dataset's data files structure is not supported natively by `datasets`. To load it, one option is to clone the repo (or download it with `huggingface_hub.snapshot_download`) and use `Dataset.from_generator` to process the files.", "> JourneyDB/JourneyDB is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.´\r\n\r\nI did authentication with:\r\n\r\n```\r\nfrom huggingface_hub import notebook_login\r\nnotebook_login()\r\n```\r\n\r\nIsn't that the correct way to do it?\r\n\r\n> Indeed, the dataset's data files structure is not supported natively by datasets. To load it, one option is to clone the repo (or download it with huggingface_hub.snapshot_download) and use Dataset.from_generator to process the files.\r\n\r\nGreat suggestion I will give it a try.", "Have you accepted the terms in the dialog [here](https://huggingface.co/datasets/JourneyDB/JourneyDB)?\r\n\r\nIIRC Kaggle preinstalls an outdated `datasets` version, so it's also a good idea to update it before importing `datasets` (and do the same for `huggingface_hub`)", "Sorry for the late reply. Yes, I did. Thanks for the tip!" ]	2023-09-26T15:46:25Z	2023-10-05T10:23:23Z	2023-10-05T10:23:22Z	NONE	null	null	### Describe the bug Can't seem to load the JourneyDB dataset. It throws the following error: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[15], line 2 1 # If the dataset is gated/private, make sure you have run huggingface-cli login ----> 2 dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, config_kwargs) 1661 ignore_verifications = ignore_verifications or save_infos 1663 # Create a dataset builder -> 1664 builder_instance = load_dataset_builder( 1665 path=path, 1666 name=name, 1667 data_dir=data_dir, 1668 data_files=data_files, 1669 cache_dir=cache_dir, 1670 features=features, 1671 download_config=download_config, 1672 download_mode=download_mode, 1673 revision=revision, 1674 use_auth_token=use_auth_token, 1675 config_kwargs, 1676 ) 1678 # Return iterable dataset in case of streaming 1679 if streaming: File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, config_kwargs) 1488 download_config = download_config.copy() if download_config else DownloadConfig() 1489 download_config.use_auth_token = use_auth_token -> 1490 dataset_module = dataset_module_factory( 1491 path, 1492 revision=revision, 1493 download_config=download_config, 1494 download_mode=download_mode, 1495 data_dir=data_dir, 1496 data_files=data_files, 1497 ) 1499 # Get dataset builder class from the processing script 1500 builder_cls = import_main_class(dataset_module.module_path) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1238, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, download_kwargs) 1236 raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1237 if isinstance(e1, FileNotFoundError): -> 1238 raise FileNotFoundError( 1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1241 ) from None 1242 raise e1 from None 1243 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/JourneyDB/JourneyDB/JourneyDB.py or any data file in the same directory. Couldn't find 'JourneyDB/JourneyDB' on the Hugging Face Hub either: FileNotFoundError: Unable to find data in dataset repository JourneyDB/JourneyDB with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', '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', 'zip'] ``` ### Steps to reproduce the bug 1) ``` from huggingface_hub import notebook_login notebook_login() ``` 2) ``` !pip install -q datasets from datasets import load_dataset ``` 3) `dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)` ### Expected behavior Load the dataset ### Environment info Notebook	{ "avatar_url": "https://avatars.githubusercontent.com/u/37955817?v=4", "events_url": "https://api.github.com/users/joaopedrosdmm/events{/privacy}", "followers_url": "https://api.github.com/users/joaopedrosdmm/followers", "following_url": "https://api.github.com/users/joaopedrosdmm/following{/other_user}", "gists_url": "https://api.github.com/users/joaopedrosdmm/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/joaopedrosdmm", "id": 37955817, "login": "joaopedrosdmm", "node_id": "MDQ6VXNlcjM3OTU1ODE3", "organizations_url": "https://api.github.com/users/joaopedrosdmm/orgs", "received_events_url": "https://api.github.com/users/joaopedrosdmm/received_events", "repos_url": "https://api.github.com/users/joaopedrosdmm/repos", "site_admin": false, "starred_url": "https://api.github.com/users/joaopedrosdmm/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/joaopedrosdmm/subscriptions", "type": "User", "url": "https://api.github.com/users/joaopedrosdmm", "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/6261/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/6260	6,260	REUSE_DATASET_IF_EXISTS don't work	{ "avatar_url": "https://avatars.githubusercontent.com/u/88258534?v=4", "events_url": "https://api.github.com/users/rangehow/events{/privacy}", "followers_url": "https://api.github.com/users/rangehow/followers", "following_url": "https://api.github.com/users/rangehow/following{/other_user}", "gists_url": "https://api.github.com/users/rangehow/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rangehow", "id": 88258534, "login": "rangehow", "node_id": "MDQ6VXNlcjg4MjU4NTM0", "organizations_url": "https://api.github.com/users/rangehow/orgs", "received_events_url": "https://api.github.com/users/rangehow/received_events", "repos_url": "https://api.github.com/users/rangehow/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rangehow/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rangehow/subscriptions", "type": "User", "url": "https://api.github.com/users/rangehow", "user_view_type": "public" }	[]	closed	false	[ "Hi! Unfortunately, the current behavior is to delete the downloaded data when this error happens. So, I've opened a PR that removes the problematic import to avoid losing data due to `apache_beam` not being installed (we host the preprocessed version of `natual_questions` on the HF GCS, so requiring `apache_beam` in that case doesn't make sense)", "Thanks for your reply. I met another question that I set `export HF_DATASETS_CACHE=/data/lxy/.cache` , but each time I run load_datasets, the datasets module still looking for NQ in the wrong default cache dir '/home/lxy/.cache' 。How to avoid this incorrect behavior. I am sure HF_DATASETS_CACHE was set correctly since I use echo & to check it.\r\n![image](https://github.com/huggingface/datasets/assets/88258534/e7029f27-b9f9-496c-8948-6234ef695646)\r\nby the way I delete the file in '/home/lxy/.cache' since I found there has some kb size file seems useless.", "You need to set this variable before the `datasets` import. Then, you can use `import datasets; datasets.config.HF_DATASETS_CACHE` to verify the cache location." ]	2023-09-26T03:02:16Z	2023-09-28T18:23:36Z	2023-09-28T18:23:36Z	NONE	null	null	### Describe the bug I use the following code to download natural_question dataset. Even though I have completely download it, the next time I run this code, the new download procedure will start and cover the original /data/lxy/NQ config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ') data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) --- Since I don't have apache_beam installed, it throw a exception. After I pip install apache_beam ,the download restart.. ![image](https://github.com/huggingface/datasets/assets/88258534/f28ce7fe-29ea-4348-b87f-e69182a8bd41) ### Steps to reproduce the bug run this two line code config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ') data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) ### Expected behavior Download behavior can be correctly follow DownloadMode ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.88.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.3	{ "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/6260/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/6259	6,259	Duplicated Rows When Loading Parquet Files from Root Directory with Subdirectories	{ "avatar_url": "https://avatars.githubusercontent.com/u/141304309?v=4", "events_url": "https://api.github.com/users/MF-FOOM/events{/privacy}", "followers_url": "https://api.github.com/users/MF-FOOM/followers", "following_url": "https://api.github.com/users/MF-FOOM/following{/other_user}", "gists_url": "https://api.github.com/users/MF-FOOM/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MF-FOOM", "id": 141304309, "login": "MF-FOOM", "node_id": "U_kgDOCGwh9Q", "organizations_url": "https://api.github.com/users/MF-FOOM/orgs", "received_events_url": "https://api.github.com/users/MF-FOOM/received_events", "repos_url": "https://api.github.com/users/MF-FOOM/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MF-FOOM/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MF-FOOM/subscriptions", "type": "User", "url": "https://api.github.com/users/MF-FOOM", "user_view_type": "public" }	[]	closed	false	[ "Thanks for reporting this issue! We should be able to avoid this by making our `glob` patterns more precise. In the meantime, you can load the dataset by directly assigning splits to the data files: \r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files={\"train\": \"testing123/train/output_train.parquet\", \"validation\": \"testing123/val/output_val.parquet\"})\r\n```" ]	2023-09-25T17:20:54Z	2024-03-15T15:22:04Z	2024-03-15T15:22:04Z	NONE	null	null	### Describe the bug When parquet files are saved in "train" and "val" subdirectories under a root directory, and datasets are then loaded using `load_dataset("parquet", data_dir="root_directory")`, the resulting dataset has duplicated rows for both the training and validation sets. ### Steps to reproduce the bug 1. Create a root directory, e.g., "testing123". 2. Under "testing123", create two subdirectories: "train" and "val". 3. Create and save a parquet file with 3 unique rows in the "train" subdirectory. 4. Create and save a parquet file with 4 unique rows in the "val" subdirectory. 5. Load the datasets from the root directory using `load_dataset("parquet", data_dir="testing123")` 6. Iterate through the datasets and print the rows Here's a collab reproducing these steps: https://colab.research.google.com/drive/11NEdImnQ3OqJlwKSHRMhr7jCBesNdLY4?usp=sharing ### Expected behavior - Training set should contain 3 unique rows. - Validation set should contain 4 unique rows. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.2 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "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/6259/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/6258	6,258	[DOCS] Fix typo: Elasticsearch	{ "avatar_url": "https://avatars.githubusercontent.com/u/32779855?v=4", "events_url": "https://api.github.com/users/leemthompo/events{/privacy}", "followers_url": "https://api.github.com/users/leemthompo/followers", "following_url": "https://api.github.com/users/leemthompo/following{/other_user}", "gists_url": "https://api.github.com/users/leemthompo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/leemthompo", "id": 32779855, "login": "leemthompo", "node_id": "MDQ6VXNlcjMyNzc5ODU1", "organizations_url": "https://api.github.com/users/leemthompo/orgs", "received_events_url": "https://api.github.com/users/leemthompo/received_events", "repos_url": "https://api.github.com/users/leemthompo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/leemthompo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/leemthompo/subscriptions", "type": "User", "url": "https://api.github.com/users/leemthompo", "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) \| 0.006131 / 0.011353 (-0.005222) \| 0.003682 / 0.011008 (-0.007327) \| 0.081108 / 0.038508 (0.042600) \| 0.061580 / 0.023109 (0.038471) \| 0.395880 / 0.275898 (0.119982) \| 0.427429 / 0.323480 (0.103949) \| 0.003570 / 0.007986 (-0.004416) \| 0.003874 / 0.004328 (-0.000455) \| 0.063322 / 0.004250 (0.059072) \| 0.049742 / 0.037052 (0.012690) \| 0.396547 / 0.258489 (0.138058) \| 0.434759 / 0.293841 (0.140918) \| 0.028137 / 0.128546 (-0.100409) \| 0.008103 / 0.075646 (-0.067544) \| 0.262504 / 0.419271 (-0.156767) \| 0.045944 / 0.043533 (0.002411) \| 0.397659 / 0.255139 (0.142520) \| 0.416479 / 0.283200 (0.133280) \| 0.022870 / 0.141683 (-0.118813) \| 1.478280 / 1.452155 (0.026126) \| 1.543748 / 1.492716 (0.051031) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228851 / 0.018006 (0.210845) \| 0.432845 / 0.000490 (0.432355) \| 0.005922 / 0.000200 (0.005722) \| 0.000227 / 0.000054 (0.000172) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025545 / 0.037411 (-0.011867) \| 0.073506 / 0.014526 (0.058980) \| 0.087622 / 0.176557 (-0.088935) \| 0.145455 / 0.737135 (-0.591680) \| 0.085236 / 0.296338 (-0.211102) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433083 / 0.215209 (0.217874) \| 4.323121 / 2.077655 (2.245466) \| 2.297947 / 1.504120 (0.793827) \| 2.126405 / 1.541195 (0.585211) \| 2.201635 / 1.468490 (0.733145) \| 0.509902 / 4.584777 (-4.074875) \| 3.116877 / 3.745712 (-0.628835) \| 2.892949 / 5.269862 (-2.376912) \| 1.866833 / 4.565676 (-2.698844) \| 0.058087 / 0.424275 (-0.366189) \| 0.006464 / 0.007607 (-0.001143) \| 0.503594 / 0.226044 (0.277550) \| 5.027634 / 2.268929 (2.758705) \| 2.718030 / 55.444624 (-52.726595) \| 2.373876 / 6.876477 (-4.502600) \| 2.515496 / 2.142072 (0.373423) \| 0.602648 / 4.805227 (-4.202579) \| 0.126119 / 6.500664 (-6.374545) \| 0.060623 / 0.075469 (-0.014846) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236429 / 1.841788 (-0.605359) \| 17.760532 / 8.074308 (9.686224) \| 13.970093 / 10.191392 (3.778701) \| 0.145455 / 0.680424 (-0.534969) \| 0.017110 / 0.534201 (-0.517091) \| 0.329649 / 0.579283 (-0.249634) \| 0.366942 / 0.434364 (-0.067421) \| 0.384418 / 0.540337 (-0.155920) \| 0.552330 / 1.386936 (-0.834606) \|\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) \| 0.006302 / 0.011353 (-0.005051) \| 0.003677 / 0.011008 (-0.007331) \| 0.062836 / 0.038508 (0.024328) \| 0.063317 / 0.023109 (0.040207) \| 0.449970 / 0.275898 (0.174072) \| 0.480903 / 0.323480 (0.157423) \| 0.005013 / 0.007986 (-0.002972) \| 0.002934 / 0.004328 (-0.001394) \| 0.062975 / 0.004250 (0.058724) \| 0.051285 / 0.037052 (0.014233) \| 0.448417 / 0.258489 (0.189928) \| 0.486022 / 0.293841 (0.192181) \| 0.029215 / 0.128546 (-0.099332) \| 0.008189 / 0.075646 (-0.067457) \| 0.068203 / 0.419271 (-0.351068) \| 0.041942 / 0.043533 (-0.001591) \| 0.445749 / 0.255139 (0.190610) \| 0.465442 / 0.283200 (0.182243) \| 0.020681 / 0.141683 (-0.121002) \| 1.500704 / 1.452155 (0.048549) \| 1.550511 / 1.492716 (0.057795) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224922 / 0.018006 (0.206915) \| 0.419714 / 0.000490 (0.419224) \| 0.003804 / 0.000200 (0.003604) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026924 / 0.037411 (-0.010487) \| 0.082400 / 0.014526 (0.067874) \| 0.092193 / 0.176557 (-0.084363) \| 0.147045 / 0.737135 (-0.590090) \| 0.093173 / 0.296338 (-0.203166) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462510 / 0.215209 (0.247300) \| 4.635249 / 2.077655 (2.557594) \| 2.627127 / 1.504120 (1.123007) \| 2.442879 / 1.541195 (0.901684) \| 2.502456 / 1.468490 (1.033966) \| 0.506607 / 4.584777 (-4.078170) \| 3.127348 / 3.745712 (-0.618364) \| 2.901818 / 5.269862 (-2.368044) \| 1.906876 / 4.565676 (-2.658801) \| 0.058025 / 0.424275 (-0.366250) \| 0.006442 / 0.007607 (-0.001165) \| 0.534438 / 0.226044 (0.308394) \| 5.352481 / 2.268929 (3.083553) \| 3.058068 / 55.444624 (-52.386556) \| 2.697310 / 6.876477 (-4.179167) \| 2.873141 / 2.142072 (0.731069) \| 0.594517 / 4.805227 (-4.210710) \| 0.125369 / 6.500664 (-6.375295) \| 0.061411 / 0.075469 (-0.014058) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.369549 / 1.841788 (-0.472238) \| 17.933507 / 8.074308 (9.859199) \| 14.890107 / 10.191392 (4.698715) \| 0.154398 / 0.680424 (-0.526026) \| 0.018021 / 0.534201 (-0.516180) \| 0.335163 / 0.579283 (-0.244120) \| 0.350396 / 0.434364 (-0.083968) \| 0.397694 / 0.540337 (-0.142643) \| 0.554853 / 1.386936 (-0.832083) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f56fd9d6c877ffa6fb44fb832c13b61227c9cc5b \"CML watermark\")\n" ]	2023-09-25T12:50:59Z	2023-09-26T14:55:35Z	2023-09-26T13:36:40Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6258.diff", "html_url": "https://github.com/huggingface/datasets/pull/6258", "merged_at": "2023-09-26T13:36:40Z", "patch_url": "https://github.com/huggingface/datasets/pull/6258.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6258" }	Not ElasticSearch :)	{ "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": 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https://github.com/huggingface/datasets/issues/6257	6,257	HfHubHTTPError - exceeded our hourly quotas for action: commit	{ "avatar_url": "https://avatars.githubusercontent.com/u/57996478?v=4", "events_url": "https://api.github.com/users/yuvalkirstain/events{/privacy}", "followers_url": "https://api.github.com/users/yuvalkirstain/followers", "following_url": "https://api.github.com/users/yuvalkirstain/following{/other_user}", "gists_url": "https://api.github.com/users/yuvalkirstain/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yuvalkirstain", "id": 57996478, "login": "yuvalkirstain", "node_id": "MDQ6VXNlcjU3OTk2NDc4", "organizations_url": "https://api.github.com/users/yuvalkirstain/orgs", "received_events_url": "https://api.github.com/users/yuvalkirstain/received_events", "repos_url": "https://api.github.com/users/yuvalkirstain/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yuvalkirstain/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yuvalkirstain/subscriptions", "type": "User", "url": "https://api.github.com/users/yuvalkirstain", "user_view_type": "public" }	[]	closed	false	[ "how is your dataset structured? (file types, how many commits and files are you trying to push, etc)", "I succeeded in uploading it after several attempts with an hour gap between each attempt (inconvenient but worked). The final dataset is [here](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2), code and context to the dataset can be found [here](https://github.com/yuvalkirstain/PickScore/).\r\nI can close the issue if this behavior is intended, as most users probably do not need to upload large-scale datasets.", "We could fix this by creating a single commit for all the (Parquet) shards in `push_to_hub` instead of one commit per shard, as we currently do. \r\n\r\n@Wauplin Any updates on the 2-step commit process suggested by you that we need to implement this?", "> Any updates on the 2-step commit process suggested by you that we need to implement this?\r\n\r\nRe-prioritizing this, sorry. Will let you know but probably can be done this week." ]	2023-09-25T06:11:43Z	2023-10-16T13:30:49Z	2023-10-16T13:30:48Z	NONE	null	null	### Describe the bug I try to upload a very large dataset of images, and get the following error: ``` File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2712, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future) 2710 try: 2711 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params) -> 2712 hf_raise_for_status(commit_resp, endpoint_name="commit") 2713 except RepositoryNotFoundError as e: 2714 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE) File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name) 297 raise BadRequestError(message, response=response) from e 299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information 300 # as well (request id and/or server error message) --> 301 raise HfHubHTTPError(str(e), response=response) from e HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/yuvalkirstain/pickapic_v2/commit/main (Request ID: Root=1-65112399-12d63f7d7f28bfa40a36a0fd) You have exceeded our hourly quotas for action: commit. We invite you to retry later. ``` this makes it much less convenient to host large datasets on HF hub. ### Steps to reproduce the bug Upload a very large dataset of images ### Expected behavior the upload to work well ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "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/6257/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/6256	6,256	load_dataset() function's cache_dir does not seems to work	{ "avatar_url": "https://avatars.githubusercontent.com/u/171831?v=4", "events_url": "https://api.github.com/users/andyzhu/events{/privacy}", "followers_url": "https://api.github.com/users/andyzhu/followers", "following_url": "https://api.github.com/users/andyzhu/following{/other_user}", "gists_url": "https://api.github.com/users/andyzhu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andyzhu", "id": 171831, "login": "andyzhu", "node_id": "MDQ6VXNlcjE3MTgzMQ==", "organizations_url": "https://api.github.com/users/andyzhu/orgs", "received_events_url": "https://api.github.com/users/andyzhu/received_events", "repos_url": "https://api.github.com/users/andyzhu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andyzhu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andyzhu/subscriptions", "type": "User", "url": "https://api.github.com/users/andyzhu", "user_view_type": "public" }	[]	closed	false	[ "Can you share the error message?\r\n\r\nAlso, it would help if you could check whether `huggingface_hub`'s download behaves the same:\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nIn the next major release, we aim to switch to `huggingface_hub` for file download/caching, but we could align the `cache_dir`'s `umask` behavior earlier than this if their solution works for your use case.", "@mariosasko, Hey , is there any update on this? ", "Yes, I am still facing the same issue.\r\n\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nBut this is working for me.\r\n\r\nThanks.", "Could you clarify a bit? What if I wanted only 100 samples from train split, how would i change the above? Thanks", "I have exactly same issue", "@lhoestq Can this be open again? Reading #7200 this doesn't even address not being able to specify `cache_dir`, it just updates the documentation? Their own comment states this:\n\n> In my testing, I could not specify the cache directory even by load_dataset(\"dataset_name\" cache_dir=\"...\"). It might be another issue. I also welcome any advice to solve this issue.\n\nNothing else in the PR seems to indicate that this is actually resolved.", "I've noticed the same issue. Looks like #7499 was fixing it, but from what I see in the comments this setting is for different caches?", "Hi ! you can find the information about the `datasets` and `huggingface_hub` cache directories here: https://huggingface.co/docs/datasets/v3.6.0/en/cache#cache-directory" ]	2023-09-24T15:34:06Z	2025-05-14T10:08:53Z	2024-10-08T15:45:18Z	NONE	null	null	### Describe the bug datasets version: 2.14.5 when trying to run the following command trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir') I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine. It seems the cache_dir parameter cannot change the dataset saving directory from the default what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work ### Steps to reproduce the bug datasets version: 2.14.5 when trying to run the following command trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir') I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine. It seems the cache_dir parameter cannot change the dataset saving directory from the default what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work ### Expected behavior the dataset should be saved to the cache_dir points to ### Environment info datasets version: 2.14.5 macos X: Ventura 13.4.1 (c)	{ "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": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6256/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/6255	6,255	Parallelize builder configs creation	{ "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) \| 0.005905 / 0.011353 (-0.005448) \| 0.003623 / 0.011008 (-0.007385) \| 0.079616 / 0.038508 (0.041108) \| 0.059840 / 0.023109 (0.036730) \| 0.392281 / 0.275898 (0.116383) \| 0.434539 / 0.323480 (0.111059) \| 0.004746 / 0.007986 (-0.003239) \| 0.002935 / 0.004328 (-0.001394) \| 0.062907 / 0.004250 (0.058657) \| 0.048233 / 0.037052 (0.011181) \| 0.394170 / 0.258489 (0.135681) \| 0.427430 / 0.293841 (0.133589) \| 0.027382 / 0.128546 (-0.101164) \| 0.007890 / 0.075646 (-0.067756) \| 0.259681 / 0.419271 (-0.159591) \| 0.044085 / 0.043533 (0.000552) \| 0.388640 / 0.255139 (0.133501) \| 0.412665 / 0.283200 (0.129465) \| 0.021256 / 0.141683 (-0.120427) \| 1.485672 / 1.452155 (0.033518) \| 1.531410 / 1.492716 (0.038694) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220346 / 0.018006 (0.202340) \| 0.425329 / 0.000490 (0.424840) \| 0.006224 / 0.000200 (0.006024) \| 0.000208 / 0.000054 (0.000153) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024864 / 0.037411 (-0.012547) \| 0.072925 / 0.014526 (0.058399) \| 0.083711 / 0.176557 (-0.092845) \| 0.144213 / 0.737135 (-0.592923) \| 0.084201 / 0.296338 (-0.212137) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399467 / 0.215209 (0.184258) \| 3.978979 / 2.077655 (1.901325) \| 1.916994 / 1.504120 (0.412874) \| 1.753098 / 1.541195 (0.211903) \| 1.809866 / 1.468490 (0.341376) \| 0.506806 / 4.584777 (-4.077971) \| 3.051044 / 3.745712 (-0.694668) \| 2.857624 / 5.269862 (-2.412237) \| 1.872033 / 4.565676 (-2.693644) \| 0.058543 / 0.424275 (-0.365732) \| 0.006569 / 0.007607 (-0.001038) \| 0.472630 / 0.226044 (0.246586) \| 4.724862 / 2.268929 (2.455934) \| 2.413068 / 55.444624 (-53.031556) \| 2.046910 / 6.876477 (-4.829567) \| 2.190455 / 2.142072 (0.048383) \| 0.595228 / 4.805227 (-4.210000) \| 0.125942 / 6.500664 (-6.374722) \| 0.059474 / 0.075469 (-0.015995) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235927 / 1.841788 (-0.605861) \| 17.367803 / 8.074308 (9.293495) \| 13.550362 / 10.191392 (3.358970) \| 0.131664 / 0.680424 (-0.548760) \| 0.016331 / 0.534201 (-0.517870) \| 0.331295 / 0.579283 (-0.247988) \| 0.367641 / 0.434364 (-0.066723) \| 0.382595 / 0.540337 (-0.157742) \| 0.540361 / 1.386936 (-0.846575) \|\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) \| 0.006120 / 0.011353 (-0.005233) \| 0.003691 / 0.011008 (-0.007318) \| 0.062768 / 0.038508 (0.024259) \| 0.058045 / 0.023109 (0.034936) \| 0.443616 / 0.275898 (0.167718) \| 0.473854 / 0.323480 (0.150374) \| 0.004710 / 0.007986 (-0.003275) \| 0.002915 / 0.004328 (-0.001414) \| 0.062922 / 0.004250 (0.058672) \| 0.048557 / 0.037052 (0.011505) \| 0.446136 / 0.258489 (0.187647) \| 0.479235 / 0.293841 (0.185394) \| 0.028704 / 0.128546 (-0.099842) \| 0.008170 / 0.075646 (-0.067477) \| 0.068853 / 0.419271 (-0.350419) \| 0.041393 / 0.043533 (-0.002140) \| 0.444683 / 0.255139 (0.189544) \| 0.466607 / 0.283200 (0.183407) \| 0.020890 / 0.141683 (-0.120793) \| 1.473745 / 1.452155 (0.021590) \| 1.498772 / 1.492716 (0.006055) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216875 / 0.018006 (0.198868) \| 0.411700 / 0.000490 (0.411211) \| 0.003337 / 0.000200 (0.003137) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027054 / 0.037411 (-0.010357) \| 0.080617 / 0.014526 (0.066092) \| 0.091052 / 0.176557 (-0.085505) \| 0.144126 / 0.737135 (-0.593009) \| 0.090123 / 0.296338 (-0.206216) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461132 / 0.215209 (0.245922) \| 4.598662 / 2.077655 (2.521008) \| 2.539213 / 1.504120 (1.035093) \| 2.362782 / 1.541195 (0.821587) \| 2.428648 / 1.468490 (0.960157) \| 0.506305 / 4.584777 (-4.078472) \| 3.091132 / 3.745712 (-0.654581) \| 2.884870 / 5.269862 (-2.384992) \| 1.880806 / 4.565676 (-2.684870) \| 0.058727 / 0.424275 (-0.365548) \| 0.006452 / 0.007607 (-0.001155) \| 0.533519 / 0.226044 (0.307474) \| 5.346406 / 2.268929 (3.077478) \| 2.987920 / 55.444624 (-52.456704) \| 2.667591 / 6.876477 (-4.208885) \| 2.848696 / 2.142072 (0.706623) \| 0.601018 / 4.805227 (-4.204209) \| 0.124929 / 6.500664 (-6.375735) \| 0.061583 / 0.075469 (-0.013886) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356825 / 1.841788 (-0.484962) \| 17.964503 / 8.074308 (9.890195) \| 14.691471 / 10.191392 (4.500079) \| 0.132525 / 0.680424 (-0.547899) \| 0.018061 / 0.534201 (-0.516140) \| 0.335459 / 0.579283 (-0.243824) \| 0.378260 / 0.434364 (-0.056104) \| 0.390681 / 0.540337 (-0.149657) \| 0.547030 / 1.386936 (-0.839906) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c55213a6c5fcff9b3dacce491caa68eacebe10d \"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) \| 0.006624 / 0.011353 (-0.004729) \| 0.004039 / 0.011008 (-0.006970) \| 0.085862 / 0.038508 (0.047354) \| 0.077183 / 0.023109 (0.054074) \| 0.319132 / 0.275898 (0.043234) \| 0.350818 / 0.323480 (0.027338) \| 0.004122 / 0.007986 (-0.003864) \| 0.003395 / 0.004328 (-0.000934) \| 0.065237 / 0.004250 (0.060987) \| 0.056675 / 0.037052 (0.019623) \| 0.321040 / 0.258489 (0.062551) \| 0.362011 / 0.293841 (0.068170) \| 0.030988 / 0.128546 (-0.097559) \| 0.008623 / 0.075646 (-0.067023) \| 0.289433 / 0.419271 (-0.129839) \| 0.052755 / 0.043533 (0.009222) \| 0.323291 / 0.255139 (0.068152) \| 0.340110 / 0.283200 (0.056911) \| 0.026299 / 0.141683 (-0.115383) \| 1.509405 / 1.452155 (0.057250) \| 1.559993 / 1.492716 (0.067277) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233285 / 0.018006 (0.215279) \| 0.451633 / 0.000490 (0.451143) \| 0.009954 / 0.000200 (0.009754) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029623 / 0.037411 (-0.007788) \| 0.083942 / 0.014526 (0.069416) \| 0.097378 / 0.176557 (-0.079178) \| 0.152630 / 0.737135 (-0.584506) \| 0.098379 / 0.296338 (-0.197959) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386237 / 0.215209 (0.171028) \| 3.850805 / 2.077655 (1.773150) \| 1.896032 / 1.504120 (0.391912) \| 1.729746 / 1.541195 (0.188551) \| 1.867831 / 1.468490 (0.399341) \| 0.481496 / 4.584777 (-4.103281) \| 3.564432 / 3.745712 (-0.181280) \| 3.336084 / 5.269862 (-1.933777) \| 2.040944 / 4.565676 (-2.524732) \| 0.057247 / 0.424275 (-0.367028) \| 0.007275 / 0.007607 (-0.000332) \| 0.464600 / 0.226044 (0.238556) \| 4.648562 / 2.268929 (2.379634) \| 2.394430 / 55.444624 (-53.050195) \| 2.029748 / 6.876477 (-4.846728) \| 2.280975 / 2.142072 (0.138902) \| 0.619073 / 4.805227 (-4.186154) \| 0.150504 / 6.500664 (-6.350160) \| 0.061206 / 0.075469 (-0.014263) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267309 / 1.841788 (-0.574479) \| 19.062725 / 8.074308 (10.988417) \| 14.192565 / 10.191392 (4.001173) \| 0.162908 / 0.680424 (-0.517515) \| 0.018445 / 0.534201 (-0.515756) \| 0.392110 / 0.579283 (-0.187173) \| 0.415340 / 0.434364 (-0.019024) \| 0.456783 / 0.540337 (-0.083554) \| 0.653019 / 1.386936 (-0.733917) \|\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) \| 0.006995 / 0.011353 (-0.004358) \| 0.004027 / 0.011008 (-0.006981) \| 0.064124 / 0.038508 (0.025616) \| 0.076004 / 0.023109 (0.052895) \| 0.401760 / 0.275898 (0.125862) \| 0.432339 / 0.323480 (0.108859) \| 0.005471 / 0.007986 (-0.002515) \| 0.003335 / 0.004328 (-0.000993) \| 0.064164 / 0.004250 (0.059913) \| 0.058101 / 0.037052 (0.021048) \| 0.401698 / 0.258489 (0.143209) \| 0.436033 / 0.293841 (0.142192) \| 0.032789 / 0.128546 (-0.095757) \| 0.008482 / 0.075646 (-0.067165) \| 0.070707 / 0.419271 (-0.348565) \| 0.048287 / 0.043533 (0.004755) \| 0.395501 / 0.255139 (0.140362) \| 0.419385 / 0.283200 (0.136186) \| 0.024043 / 0.141683 (-0.117640) \| 1.503310 / 1.452155 (0.051156) \| 1.562160 / 1.492716 (0.069444) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227629 / 0.018006 (0.209623) \| 0.457306 / 0.000490 (0.456816) \| 0.005835 / 0.000200 (0.005635) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032991 / 0.037411 (-0.004420) \| 0.093265 / 0.014526 (0.078739) \| 0.106595 / 0.176557 (-0.069961) \| 0.158557 / 0.737135 (-0.578578) \| 0.106805 / 0.296338 (-0.189533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436573 / 0.215209 (0.221364) \| 4.355777 / 2.077655 (2.278122) \| 2.323151 / 1.504120 (0.819031) \| 2.164101 / 1.541195 (0.622906) \| 2.252808 / 1.468490 (0.784318) \| 0.494902 / 4.584777 (-4.089875) \| 3.615073 / 3.745712 (-0.130639) \| 3.329738 / 5.269862 (-1.940124) \| 2.059137 / 4.565676 (-2.506539) \| 0.058384 / 0.424275 (-0.365891) \| 0.007330 / 0.007607 (-0.000277) \| 0.512326 / 0.226044 (0.286281) \| 5.125652 / 2.268929 (2.856724) \| 2.861981 / 55.444624 (-52.582644) \| 2.500172 / 6.876477 (-4.376305) \| 2.715862 / 2.142072 (0.573789) \| 0.597299 / 4.805227 (-4.207928) \| 0.134346 / 6.500664 (-6.366318) \| 0.060396 / 0.075469 (-0.015074) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353771 / 1.841788 (-0.488017) \| 19.334801 / 8.074308 (11.260493) \| 14.669875 / 10.191392 (4.478483) \| 0.167607 / 0.680424 (-0.512817) \| 0.019839 / 0.534201 (-0.514362) \| 0.395473 / 0.579283 (-0.183810) \| 0.419822 / 0.434364 (-0.014542) \| 0.471400 / 0.540337 (-0.068938) \| 0.648297 / 1.386936 (-0.738639) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d5a112e7f1ce1635725773d911c825adca7bcfe0 \"CML watermark\")\n", "@mariosasko let me know what you think or if you have better ideas to make it faster", "Yea lazy data files resolution seems a better approach actually" ]	2023-09-23T11:56:20Z	2024-01-11T06:32:34Z	2023-09-26T15:44:19Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6255.diff", "html_url": "https://github.com/huggingface/datasets/pull/6255", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6255.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6255" }	For datasets with lots of configs defined in YAML E.g. `load_dataset("uonlp/CulturaX", "fr", revision="refs/pr/6")` from >1min to 15sec	{ "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": 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https://github.com/huggingface/datasets/issues/6254	6,254	Dataset.from_generator() cost much more time in vscode debugging mode then running mode	{ "avatar_url": "https://avatars.githubusercontent.com/u/56437469?v=4", "events_url": "https://api.github.com/users/dontnet-wuenze/events{/privacy}", "followers_url": "https://api.github.com/users/dontnet-wuenze/followers", "following_url": "https://api.github.com/users/dontnet-wuenze/following{/other_user}", "gists_url": "https://api.github.com/users/dontnet-wuenze/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/dontnet-wuenze", "id": 56437469, "login": "dontnet-wuenze", "node_id": "MDQ6VXNlcjU2NDM3NDY5", "organizations_url": "https://api.github.com/users/dontnet-wuenze/orgs", "received_events_url": "https://api.github.com/users/dontnet-wuenze/received_events", "repos_url": "https://api.github.com/users/dontnet-wuenze/repos", "site_admin": false, "starred_url": "https://api.github.com/users/dontnet-wuenze/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dontnet-wuenze/subscriptions", "type": "User", "url": "https://api.github.com/users/dontnet-wuenze", "user_view_type": "public" }	[]	closed	false	[ "Answered on the forum: https://discuss.huggingface.co/t/dataset-from-generator-cost-much-more-time-in-vscode-debugging-mode-then-running-mode/56005/2" ]	2023-09-23T02:07:26Z	2023-10-03T14:42:53Z	2023-10-03T14:42:53Z	NONE	null	null	### Describe the bug Hey there, I’m using Dataset.from_generator() to convert a torch_dataset to the Huggingface Dataset. However, when I debug my code on vscode, I find that it runs really slow on Dataset.from_generator() which may even 20 times longer then run the script on terminal. ### Steps to reproduce the bug I write a simple test code : ```python import os from functools import partial from typing import Callable import torch import time from torch.utils.data import Dataset as TorchDataset from datasets import load_from_disk, Dataset as HFDataset import torch from torch.utils.data import Dataset class SimpleDataset(Dataset): def __init__(self, data): self.data = data self.keys = list(data[0].keys()) def __len__(self): return len(self.data) def __getitem__(self, index): sample = self.data[index] return {key: sample[key] for key in self.keys} def TorchDataset2HuggingfaceDataset(torch_dataset: TorchDataset, cache_dir: str = None ) -> HFDataset: """ convert torch dataset to huggingface dataset """ generator : Callable[[], TorchDataset] = lambda: (sample for sample in torch_dataset) return HFDataset.from_generator(generator, cache_dir=cache_dir) if __name__ == '__main__': data = [ {'id': 1, 'name': 'Alice'}, {'id': 2, 'name': 'Bob'}, {'id': 3, 'name': 'Charlie'} ] torch_dataset = SimpleDataset(data) start_time = time.time() huggingface_dataset = TorchDataset2HuggingfaceDataset(torch_dataset) end_time = time.time() print("time: ", end_time - start_time) print(huggingface_dataset) ``` ### Expected behavior this test on my machine report that the running time on terminal is 0.086, however the running time in debugging mode on vscode is 0.25, which I think is much longer than expected. I’d like to know is the anything wrong in the code or just because of debugging? I have traced the code and I find is this func which I get stuck. ```python def create_config_id( self, config_kwargs: dict, custom_features: Optional[Features] = None, ) -> str: ... # stuck in this line suffix = Hasher.hash(config_kwargs_to_add_to_suffix) ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31 - Python version: 3.11.3 - Huggingface_hub version: 0.17.2 - PyArrow version: 11.0.0 - Pandas version: 2.0.1	{ "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/6254/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/6253	6,253	Check builder cls default config name in inspect	{ "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) \| 0.006591 / 0.011353 (-0.004762) \| 0.003991 / 0.011008 (-0.007017) \| 0.085197 / 0.038508 (0.046689) \| 0.080312 / 0.023109 (0.057202) \| 0.342026 / 0.275898 (0.066128) \| 0.370749 / 0.323480 (0.047269) \| 0.004124 / 0.007986 (-0.003861) \| 0.003413 / 0.004328 (-0.000916) \| 0.064363 / 0.004250 (0.060113) \| 0.055920 / 0.037052 (0.018868) \| 0.340667 / 0.258489 (0.082178) \| 0.380138 / 0.293841 (0.086297) \| 0.031115 / 0.128546 (-0.097431) \| 0.008511 / 0.075646 (-0.067135) \| 0.289065 / 0.419271 (-0.130207) \| 0.052266 / 0.043533 (0.008734) \| 0.343808 / 0.255139 (0.088669) \| 0.353578 / 0.283200 (0.070378) \| 0.024006 / 0.141683 (-0.117676) \| 1.490322 / 1.452155 (0.038168) \| 1.591133 / 1.492716 (0.098417) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234718 / 0.018006 (0.216712) \| 0.447023 / 0.000490 (0.446533) \| 0.009343 / 0.000200 (0.009143) \| 0.000259 / 0.000054 (0.000204) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030466 / 0.037411 (-0.006945) \| 0.083367 / 0.014526 (0.068841) \| 0.100532 / 0.176557 (-0.076024) \| 0.158018 / 0.737135 (-0.579117) \| 0.098280 / 0.296338 (-0.198059) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408501 / 0.215209 (0.193292) \| 4.066937 / 2.077655 (1.989282) \| 2.034029 / 1.504120 (0.529909) \| 1.842982 / 1.541195 (0.301788) \| 1.987319 / 1.468490 (0.518829) \| 0.492126 / 4.584777 (-4.092651) \| 3.554027 / 3.745712 (-0.191685) \| 3.289023 / 5.269862 (-1.980839) \| 2.069796 / 4.565676 (-2.495880) \| 0.057930 / 0.424275 (-0.366346) \| 0.007308 / 0.007607 (-0.000299) \| 0.482596 / 0.226044 (0.256552) \| 4.830714 / 2.268929 (2.561785) \| 2.506787 / 55.444624 (-52.937838) \| 2.163498 / 6.876477 (-4.712979) \| 2.389135 / 2.142072 (0.247062) \| 0.597538 / 4.805227 (-4.207689) \| 0.134268 / 6.500664 (-6.366396) \| 0.061189 / 0.075469 (-0.014280) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245328 / 1.841788 (-0.596460) \| 19.145151 / 8.074308 (11.070843) \| 14.742121 / 10.191392 (4.550729) \| 0.144749 / 0.680424 (-0.535675) \| 0.018433 / 0.534201 (-0.515768) \| 0.391867 / 0.579283 (-0.187416) \| 0.416555 / 0.434364 (-0.017809) \| 0.454341 / 0.540337 (-0.085997) \| 0.646833 / 1.386936 (-0.740103) \|\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) \| 0.006669 / 0.011353 (-0.004684) \| 0.004031 / 0.011008 (-0.006978) \| 0.064347 / 0.038508 (0.025839) \| 0.076857 / 0.023109 (0.053748) \| 0.415864 / 0.275898 (0.139966) \| 0.468615 / 0.323480 (0.145135) \| 0.005383 / 0.007986 (-0.002603) \| 0.003314 / 0.004328 (-0.001015) \| 0.064829 / 0.004250 (0.060578) \| 0.057182 / 0.037052 (0.020129) \| 0.417055 / 0.258489 (0.158566) \| 0.472725 / 0.293841 (0.178884) \| 0.031938 / 0.128546 (-0.096608) \| 0.008564 / 0.075646 (-0.067082) \| 0.070649 / 0.419271 (-0.348623) \| 0.047439 / 0.043533 (0.003906) \| 0.409589 / 0.255139 (0.154450) \| 0.433700 / 0.283200 (0.150500) \| 0.024132 / 0.141683 (-0.117551) \| 1.500825 / 1.452155 (0.048670) \| 1.592059 / 1.492716 (0.099343) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225652 / 0.018006 (0.207646) \| 0.444188 / 0.000490 (0.443698) \| 0.004581 / 0.000200 (0.004381) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033272 / 0.037411 (-0.004139) \| 0.096833 / 0.014526 (0.082307) \| 0.107134 / 0.176557 (-0.069422) \| 0.159299 / 0.737135 (-0.577836) \| 0.107533 / 0.296338 (-0.188806) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429100 / 0.215209 (0.213890) \| 4.281051 / 2.077655 (2.203396) \| 2.318713 / 1.504120 (0.814593) \| 2.165645 / 1.541195 (0.624451) \| 2.250224 / 1.468490 (0.781734) \| 0.495791 / 4.584777 (-4.088986) \| 3.591953 / 3.745712 (-0.153760) \| 3.303426 / 5.269862 (-1.966436) \| 2.076861 / 4.565676 (-2.488816) \| 0.058369 / 0.424275 (-0.365906) \| 0.007387 / 0.007607 (-0.000220) \| 0.501270 / 0.226044 (0.275225) \| 5.014987 / 2.268929 (2.746059) \| 2.800951 / 55.444624 (-52.643673) \| 2.464316 / 6.876477 (-4.412161) \| 2.685259 / 2.142072 (0.543187) \| 0.584797 / 4.805227 (-4.220430) \| 0.131889 / 6.500664 (-6.368775) \| 0.061021 / 0.075469 (-0.014448) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.366982 / 1.841788 (-0.474806) \| 19.820376 / 8.074308 (11.746068) \| 14.968664 / 10.191392 (4.777272) \| 0.165344 / 0.680424 (-0.515080) \| 0.019956 / 0.534201 (-0.514245) \| 0.395843 / 0.579283 (-0.183441) \| 0.420854 / 0.434364 (-0.013510) \| 0.465065 / 0.540337 (-0.075272) \| 0.651531 / 1.386936 (-0.735405) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ca0e13300f5392cd87189d5afd5942927afc7 \"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) \| 0.005974 / 0.011353 (-0.005379) \| 0.003714 / 0.011008 (-0.007294) \| 0.080049 / 0.038508 (0.041541) \| 0.061233 / 0.023109 (0.038124) \| 0.317187 / 0.275898 (0.041289) \| 0.352725 / 0.323480 (0.029245) \| 0.004867 / 0.007986 (-0.003119) \| 0.002953 / 0.004328 (-0.001376) \| 0.063156 / 0.004250 (0.058905) \| 0.046752 / 0.037052 (0.009700) \| 0.320171 / 0.258489 (0.061682) \| 0.367572 / 0.293841 (0.073731) \| 0.027253 / 0.128546 (-0.101293) \| 0.008100 / 0.075646 (-0.067546) \| 0.261206 / 0.419271 (-0.158066) \| 0.044581 / 0.043533 (0.001048) \| 0.331169 / 0.255139 (0.076030) \| 0.348719 / 0.283200 (0.065519) \| 0.021397 / 0.141683 (-0.120286) \| 1.528315 / 1.452155 (0.076160) \| 1.533789 / 1.492716 (0.041073) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233336 / 0.018006 (0.215329) \| 0.416866 / 0.000490 (0.416376) \| 0.008805 / 0.000200 (0.008605) \| 0.000240 / 0.000054 (0.000186) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024754 / 0.037411 (-0.012657) \| 0.073311 / 0.014526 (0.058785) \| 0.085419 / 0.176557 (-0.091138) \| 0.146380 / 0.737135 (-0.590756) \| 0.085545 / 0.296338 (-0.210793) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431426 / 0.215209 (0.216217) \| 4.315899 / 2.077655 (2.238244) \| 2.232492 / 1.504120 (0.728372) \| 2.064174 / 1.541195 (0.522979) \| 2.158982 / 1.468490 (0.690492) \| 0.499375 / 4.584777 (-4.085402) \| 3.093259 / 3.745712 (-0.652454) \| 2.848260 / 5.269862 (-2.421601) \| 1.853097 / 4.565676 (-2.712579) \| 0.057143 / 0.424275 (-0.367132) \| 0.006349 / 0.007607 (-0.001258) \| 0.507747 / 0.226044 (0.281702) \| 5.078872 / 2.268929 (2.809944) \| 2.717697 / 55.444624 (-52.726927) \| 2.363564 / 6.876477 (-4.512913) \| 2.485756 / 2.142072 (0.343684) \| 0.595888 / 4.805227 (-4.209340) \| 0.127285 / 6.500664 (-6.373379) \| 0.060639 / 0.075469 (-0.014830) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219287 / 1.841788 (-0.622501) \| 17.300038 / 8.074308 (9.225730) \| 13.747230 / 10.191392 (3.555838) \| 0.144841 / 0.680424 (-0.535583) \| 0.016587 / 0.534201 (-0.517614) \| 0.336891 / 0.579283 (-0.242392) \| 0.376128 / 0.434364 (-0.058236) \| 0.385749 / 0.540337 (-0.154588) \| 0.552218 / 1.386936 (-0.834718) \|\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) \| 0.006477 / 0.011353 (-0.004876) \| 0.003709 / 0.011008 (-0.007299) \| 0.064708 / 0.038508 (0.026200) \| 0.062627 / 0.023109 (0.039518) \| 0.444721 / 0.275898 (0.168823) \| 0.477825 / 0.323480 (0.154345) \| 0.004890 / 0.007986 (-0.003096) \| 0.002896 / 0.004328 (-0.001432) \| 0.063781 / 0.004250 (0.059530) \| 0.050488 / 0.037052 (0.013436) \| 0.453466 / 0.258489 (0.194977) \| 0.483303 / 0.293841 (0.189462) \| 0.028814 / 0.128546 (-0.099732) \| 0.008207 / 0.075646 (-0.067440) \| 0.070140 / 0.419271 (-0.349131) \| 0.041487 / 0.043533 (-0.002045) \| 0.454599 / 0.255139 (0.199460) \| 0.468374 / 0.283200 (0.185174) \| 0.019758 / 0.141683 (-0.121925) \| 1.437542 / 1.452155 (-0.014613) \| 1.507965 / 1.492716 (0.015249) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223358 / 0.018006 (0.205352) \| 0.413824 / 0.000490 (0.413334) \| 0.004593 / 0.000200 (0.004393) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026278 / 0.037411 (-0.011134) \| 0.081992 / 0.014526 (0.067466) \| 0.089969 / 0.176557 (-0.086587) \| 0.143668 / 0.737135 (-0.593467) \| 0.091273 / 0.296338 (-0.205066) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461198 / 0.215209 (0.245989) \| 4.615398 / 2.077655 (2.537743) \| 2.552291 / 1.504120 (1.048171) \| 2.373789 / 1.541195 (0.832595) \| 2.431591 / 1.468490 (0.963101) \| 0.507683 / 4.584777 (-4.077094) \| 3.148771 / 3.745712 (-0.596941) \| 2.849118 / 5.269862 (-2.420744) \| 1.883001 / 4.565676 (-2.682675) \| 0.059423 / 0.424275 (-0.364852) \| 0.006463 / 0.007607 (-0.001144) \| 0.535129 / 0.226044 (0.309085) \| 5.362870 / 2.268929 (3.093941) \| 3.016548 / 55.444624 (-52.428076) \| 2.666205 / 6.876477 (-4.210271) \| 2.821396 / 2.142072 (0.679324) \| 0.606596 / 4.805227 (-4.198631) \| 0.125991 / 6.500664 (-6.374673) \| 0.063566 / 0.075469 (-0.011903) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.364771 / 1.841788 (-0.477017) \| 18.000713 / 8.074308 (9.926404) \| 14.840330 / 10.191392 (4.648937) \| 0.144770 / 0.680424 (-0.535653) \| 0.018060 / 0.534201 (-0.516141) \| 0.334470 / 0.579283 (-0.244813) \| 0.387386 / 0.434364 (-0.046978) \| 0.398743 / 0.540337 (-0.141595) \| 0.555437 / 1.386936 (-0.831499) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b974c9af6b45b6ebdbbf4b3418f25506c1c0618 \"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) \| 0.006491 / 0.011353 (-0.004862) \| 0.004058 / 0.011008 (-0.006950) \| 0.084462 / 0.038508 (0.045954) \| 0.072310 / 0.023109 (0.049201) \| 0.352458 / 0.275898 (0.076560) \| 0.385829 / 0.323480 (0.062350) \| 0.003978 / 0.007986 (-0.004007) \| 0.003455 / 0.004328 (-0.000873) \| 0.064070 / 0.004250 (0.059819) \| 0.055577 / 0.037052 (0.018525) \| 0.361288 / 0.258489 (0.102799) \| 0.400147 / 0.293841 (0.106306) \| 0.030785 / 0.128546 (-0.097761) \| 0.008676 / 0.075646 (-0.066971) \| 0.287481 / 0.419271 (-0.131791) \| 0.052643 / 0.043533 (0.009110) \| 0.354670 / 0.255139 (0.099531) \| 0.382322 / 0.283200 (0.099122) \| 0.025657 / 0.141683 (-0.116026) \| 1.486798 / 1.452155 (0.034643) \| 1.588439 / 1.492716 (0.095723) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240881 / 0.018006 (0.222875) \| 0.463997 / 0.000490 (0.463507) \| 0.009688 / 0.000200 (0.009488) \| 0.000601 / 0.000054 (0.000546) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029071 / 0.037411 (-0.008340) \| 0.083077 / 0.014526 (0.068551) \| 0.119857 / 0.176557 (-0.056699) \| 0.153387 / 0.737135 (-0.583749) \| 0.132162 / 0.296338 (-0.164177) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383822 / 0.215209 (0.168613) \| 3.828572 / 2.077655 (1.750918) \| 1.877629 / 1.504120 (0.373509) \| 1.708757 / 1.541195 (0.167562) \| 1.771658 / 1.468490 (0.303168) \| 0.482439 / 4.584777 (-4.102338) \| 3.496247 / 3.745712 (-0.249466) \| 3.282055 / 5.269862 (-1.987807) \| 2.053069 / 4.565676 (-2.512607) \| 0.056626 / 0.424275 (-0.367649) \| 0.007338 / 0.007607 (-0.000269) \| 0.461257 / 0.226044 (0.235213) \| 4.605326 / 2.268929 (2.336397) \| 2.408365 / 55.444624 (-53.036260) \| 1.986550 / 6.876477 (-4.889926) \| 2.225220 / 2.142072 (0.083148) \| 0.601301 / 4.805227 (-4.203927) \| 0.132217 / 6.500664 (-6.368447) \| 0.061217 / 0.075469 (-0.014252) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.268706 / 1.841788 (-0.573081) \| 18.892026 / 8.074308 (10.817717) \| 14.093892 / 10.191392 (3.902500) \| 0.162483 / 0.680424 (-0.517941) \| 0.018372 / 0.534201 (-0.515829) \| 0.391901 / 0.579283 (-0.187382) \| 0.401578 / 0.434364 (-0.032786) \| 0.456741 / 0.540337 (-0.083596) \| 0.646760 / 1.386936 (-0.740176) \|\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) \| 0.006657 / 0.011353 (-0.004696) \| 0.003981 / 0.011008 (-0.007027) \| 0.066126 / 0.038508 (0.027617) \| 0.072673 / 0.023109 (0.049564) \| 0.409970 / 0.275898 (0.134072) \| 0.430797 / 0.323480 (0.107317) \| 0.005477 / 0.007986 (-0.002508) \| 0.003362 / 0.004328 (-0.000966) \| 0.065532 / 0.004250 (0.061282) \| 0.056018 / 0.037052 (0.018966) \| 0.406676 / 0.258489 (0.148187) \| 0.438516 / 0.293841 (0.144675) \| 0.032795 / 0.128546 (-0.095751) \| 0.008580 / 0.075646 (-0.067066) \| 0.072692 / 0.419271 (-0.346579) \| 0.048110 / 0.043533 (0.004577) \| 0.396826 / 0.255139 (0.141687) \| 0.418442 / 0.283200 (0.135242) \| 0.023269 / 0.141683 (-0.118414) \| 1.499438 / 1.452155 (0.047283) \| 1.568842 / 1.492716 (0.076126) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218729 / 0.018006 (0.200723) \| 0.450771 / 0.000490 (0.450281) \| 0.004996 / 0.000200 (0.004796) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031484 / 0.037411 (-0.005928) \| 0.092927 / 0.014526 (0.078401) \| 0.107849 / 0.176557 (-0.068707) \| 0.156658 / 0.737135 (-0.580478) \| 0.106373 / 0.296338 (-0.189965) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434658 / 0.215209 (0.219449) \| 4.336386 / 2.077655 (2.258731) \| 2.322577 / 1.504120 (0.818457) \| 2.149505 / 1.541195 (0.608310) \| 2.201967 / 1.468490 (0.733476) \| 0.496994 / 4.584777 (-4.087783) \| 3.533065 / 3.745712 (-0.212647) \| 3.235750 / 5.269862 (-2.034112) \| 2.034854 / 4.565676 (-2.530823) \| 0.058258 / 0.424275 (-0.366017) \| 0.007260 / 0.007607 (-0.000347) \| 0.509115 / 0.226044 (0.283071) \| 5.088427 / 2.268929 (2.819499) \| 2.793551 / 55.444624 (-52.651073) \| 2.430588 / 6.876477 (-4.445889) \| 2.625998 / 2.142072 (0.483926) \| 0.611676 / 4.805227 (-4.193552) \| 0.133343 / 6.500664 (-6.367321) \| 0.059888 / 0.075469 (-0.015581) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.377292 / 1.841788 (-0.464496) \| 19.214299 / 8.074308 (11.139991) \| 14.629146 / 10.191392 (4.437754) \| 0.171283 / 0.680424 (-0.509141) \| 0.020348 / 0.534201 (-0.513853) \| 0.397823 / 0.579283 (-0.181461) \| 0.411590 / 0.434364 (-0.022774) \| 0.470850 / 0.540337 (-0.069487) \| 0.658667 / 1.386936 (-0.728269) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1e1867e932f14233244fb25713f3c94c46ff50a \"CML watermark\")\n" ]	2023-09-21T10:15:32Z	2023-09-21T14:16:44Z	2023-09-21T14:08:00Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6253.diff", "html_url": "https://github.com/huggingface/datasets/pull/6253", "merged_at": "2023-09-21T14:08:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6253.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6253" }	Fix https://github.com/huggingface/datasets-server/issues/1812 this was causing this issue: ```ipython In [1]: from datasets import * In [2]: inspect.get_dataset_config_names("aakanksha/udpos") Out[2]: ['default'] In [3]: load_dataset_builder("aakanksha/udpos").config.name Out[3]: 'en' ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": 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https://github.com/huggingface/datasets/issues/6252	6,252	exif_transpose not done to Image (PIL problem)	{ "avatar_url": "https://avatars.githubusercontent.com/u/108274349?v=4", "events_url": "https://api.github.com/users/rhajou/events{/privacy}", "followers_url": "https://api.github.com/users/rhajou/followers", "following_url": "https://api.github.com/users/rhajou/following{/other_user}", "gists_url": "https://api.github.com/users/rhajou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rhajou", "id": 108274349, "login": "rhajou", "node_id": "U_kgDOBnQirQ", "organizations_url": "https://api.github.com/users/rhajou/orgs", "received_events_url": "https://api.github.com/users/rhajou/received_events", "repos_url": "https://api.github.com/users/rhajou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rhajou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rhajou/subscriptions", "type": "User", "url": "https://api.github.com/users/rhajou", "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	[ "Indeed, it makes sense to do this by default. \r\n\r\nIn the meantime, you can use `.with_transform` to transpose the images when accessing them:\r\n\r\n```python\r\nimport PIL.ImageOps\r\n\r\ndef exif_transpose_transform(batch):\r\n batch[\"image\"] = [PIL.ImageOps.exif_transpose(image) for image in batch[\"image\"]]\r\n return batch\r\n\r\ndataset = dataset.with_transform(exif_transpose_transform)\r\n```", "This operation sets some `Image` attributes to `None` (`.format`, `.filename`, etc.), causing our tests to fail, so I think we should wait for Datasets 3.0 to make this change. In version 3.0, storing image paths will be replaced by embedding image bytes, so there will be fewer instances where we use the `.filename` attribute." ]	2023-09-21T08:11:46Z	2024-03-19T15:29:43Z	2024-03-19T15:29:43Z	NONE	null	null	### Feature request I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading. Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images). For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference): ``` from PIL import Image, ImageOps pil = ImageOps.exif_transpose(pil) ``` reference: https://stackoverflow.com/a/63950647/5720150 Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose? Thank you ### Motivation Prevent having inverted data related to exif metadata that may affect object detection tasks ### Your contribution Changing in datasets.featrues.Image I can help with that.	{ "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/6252/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/6251	6,251	Support streaming datasets with pyarrow.parquet.read_table	{ "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._", "This function reads an entire Arrow table in one go, which is not ideal memory-wise, so I don't think we should encourage using this function, considering we want to keep RAM usage as low as possible in the streaming mode. \r\n\r\n(Note that Parquet files are compressed, meaning the loaded table can be significantly larger than the size in Parquet.)\r\n\r\nInstead, we should suggest the authors to use:\r\n```python\r\nwith open(doc_path, \"rb\") as f:\r\n parquet_file = pq.ParquetFile(f)\r\n for batch in parquet_file.iter_batches():\r\n pa_table = pa.Table.from_batches([batch])\r\n yield idx, pa_table\r\n idx += 1\r\n```", "@mariosasko I think the potential problem you evoke is independent of whether or not we support streaming mode:\r\n- if the user's script with `read_table` works in non-streaming mode, it will also work in streaming mode after this PR\r\n\r\nIn fact, what we should suggest instead is to follow the scriptless approach, so that our `parquet` packaged module is used, with all the optimizations implemented. But this approach is not possible in all cases, and some use cases need to implement a script. And if they have small Parquet files and use `read_table`, I think we should support streaming.\r\n\r\nIn summary, let me clarify the goal and the scope of this PR:\r\n- a user needs using a loading script\r\n- their files are small enough so that they use `read_table`\r\n- their loading script works in non-streaming mode\r\n- therefore, this PR allows loading their dataset in streaming mode as well", "Yes, the no-script approach with metadata configs makes the most sense.\r\n\r\n> their files are small enough so that they use read_table\r\n\r\nSome of the Parquet files in that repo are larger than 1 GB ...\r\n\r\nAlso, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.", "@mariosasko, yes, this solution is not specifically for the \"uonlp/CulturaX\" dataset, but for other use cases as I explained above: indeed, they finally removed the use of `read_table` because their data files are too large.\r\n\r\n> Also, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.\r\n\r\nDo you know how many datasets are currently using `read_table`?", "> Do you know how many datasets are currently using read_table?\r\n\r\nZero (based on the script that checks the script contents of the public Hub datasets). ", "I see... Thanks! :hugs: ", "@mariosasko thanks for pointing the script! :hugs: \r\n\r\nHowever, I have found some Hub datasets that are using `read_table`, e.g.:\r\n- https://huggingface.co/datasets/jglaser/protein_ligand_contacts\r\n- https://huggingface.co/datasets/AresEkb/prof_standards_sbert_large_mt_nlu_ru\r\n- https://huggingface.co/datasets/victorcosta/pt_legislation\r\n- https://huggingface.co/datasets/jglaser/binding_affinity\r\n- https://huggingface.co/datasets/jglaser/pdbbind_complexes\r\n- https://huggingface.co/datasets/victorcosta/ria_pt__proems_format", "I'm merging this PR as discussed in private.", "<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) \| 0.008267 / 0.011353 (-0.003086) \| 0.005813 / 0.011008 (-0.005195) \| 0.108802 / 0.038508 (0.070294) \| 0.093996 / 0.023109 (0.070886) \| 0.403115 / 0.275898 (0.127217) \| 0.457299 / 0.323480 (0.133819) \| 0.006277 / 0.007986 (-0.001709) \| 0.004701 / 0.004328 (0.000373) \| 0.080700 / 0.004250 (0.076449) \| 0.077906 / 0.037052 (0.040854) \| 0.409972 / 0.258489 (0.151483) \| 0.477707 / 0.293841 (0.183867) \| 0.041816 / 0.128546 (-0.086731) \| 0.011250 / 0.075646 (-0.064397) \| 0.390634 / 0.419271 (-0.028637) \| 0.065361 / 0.043533 (0.021828) \| 0.404501 / 0.255139 (0.149362) \| 0.448162 / 0.283200 (0.164962) \| 0.032823 / 0.141683 (-0.108860) \| 1.899892 / 1.452155 (0.447737) \| 2.044561 / 1.492716 (0.551844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241093 / 0.018006 (0.223086) \| 0.482111 / 0.000490 (0.481622) \| 0.005505 / 0.000200 (0.005305) \| 0.000094 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034861 / 0.037411 (-0.002551) \| 0.109296 / 0.014526 (0.094770) \| 0.127594 / 0.176557 (-0.048962) \| 0.191815 / 0.737135 (-0.545320) \| 0.122630 / 0.296338 (-0.173709) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452194 / 0.215209 (0.236985) \| 4.486200 / 2.077655 (2.408545) \| 2.155635 / 1.504120 (0.651515) \| 2.004569 / 1.541195 (0.463374) \| 2.142570 / 1.468490 (0.674080) \| 0.561488 / 4.584777 (-4.023289) \| 4.381102 / 3.745712 (0.635390) \| 3.914920 / 5.269862 (-1.354942) \| 2.474271 / 4.565676 (-2.091406) \| 0.067528 / 0.424275 (-0.356747) \| 0.008723 / 0.007607 (0.001116) \| 0.536077 / 0.226044 (0.310033) \| 5.342050 / 2.268929 (3.073122) \| 2.735747 / 55.444624 (-52.708877) \| 2.353938 / 6.876477 (-4.522539) \| 2.442878 / 2.142072 (0.300805) \| 0.685404 / 4.805227 (-4.119823) \| 0.156657 / 6.500664 (-6.344007) \| 0.071714 / 0.075469 (-0.003755) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.562852 / 1.841788 (-0.278935) \| 24.538203 / 8.074308 (16.463895) \| 16.857777 / 10.191392 (6.666385) \| 0.184221 / 0.680424 (-0.496203) \| 0.021688 / 0.534201 (-0.512513) \| 0.470700 / 0.579283 (-0.108583) \| 0.470593 / 0.434364 (0.036229) \| 0.645066 / 0.540337 (0.104729) \| 0.756075 / 1.386936 (-0.630861) \|\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) \| 0.009486 / 0.011353 (-0.001867) \| 0.004694 / 0.011008 (-0.006314) \| 0.080216 / 0.038508 (0.041708) \| 0.093479 / 0.023109 (0.070369) \| 0.537353 / 0.275898 (0.261455) \| 0.551631 / 0.323480 (0.228151) \| 0.007373 / 0.007986 (-0.000613) \| 0.004044 / 0.004328 (-0.000285) \| 0.075301 / 0.004250 (0.071051) \| 0.069408 / 0.037052 (0.032355) \| 0.527962 / 0.258489 (0.269473) \| 0.559423 / 0.293841 (0.265582) \| 0.039351 / 0.128546 (-0.089195) \| 0.010801 / 0.075646 (-0.064845) \| 0.092803 / 0.419271 (-0.326468) \| 0.058876 / 0.043533 (0.015343) \| 0.513742 / 0.255139 (0.258603) \| 0.574666 / 0.283200 (0.291466) \| 0.030277 / 0.141683 (-0.111406) \| 1.884936 / 1.452155 (0.432782) \| 2.008260 / 1.492716 (0.515543) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242162 / 0.018006 (0.224156) \| 0.467400 / 0.000490 (0.466910) \| 0.005348 / 0.000200 (0.005148) \| 0.000103 / 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) \| 0.038022 / 0.037411 (0.000611) \| 0.108239 / 0.014526 (0.093713) \| 0.121514 / 0.176557 (-0.055042) \| 0.184951 / 0.737135 (-0.552184) \| 0.123138 / 0.296338 (-0.173200) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.558587 / 0.215209 (0.343377) \| 5.740312 / 2.077655 (3.662657) \| 3.172164 / 1.504120 (1.668044) \| 2.852908 / 1.541195 (1.311713) \| 2.894435 / 1.468490 (1.425945) \| 0.586399 / 4.584777 (-3.998378) \| 4.498342 / 3.745712 (0.752630) \| 4.000569 / 5.269862 (-1.269292) \| 2.610988 / 4.565676 (-1.954688) \| 0.068415 / 0.424275 (-0.355860) \| 0.008602 / 0.007607 (0.000994) \| 0.614731 / 0.226044 (0.388686) \| 6.068158 / 2.268929 (3.799229) \| 3.301070 / 55.444624 (-52.143554) \| 2.868034 / 6.876477 (-4.008443) \| 2.959072 / 2.142072 (0.816999) \| 0.684174 / 4.805227 (-4.121053) \| 0.154099 / 6.500664 (-6.346565) \| 0.070641 / 0.075469 (-0.004828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.835667 / 1.841788 (-0.006120) \| 24.981645 / 8.074308 (16.907337) \| 17.218517 / 10.191392 (7.027125) \| 0.197055 / 0.680424 (-0.483368) \| 0.025465 / 0.534201 (-0.508736) \| 0.523498 / 0.579283 (-0.055785) \| 0.528268 / 0.434364 (0.093904) \| 0.599518 / 0.540337 (0.059180) \| 0.887206 / 1.386936 (-0.499730) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd786d3b8dc94f1ab717327e88f65879b525091d \"CML watermark\")\n" ]	2023-09-20T08:07:02Z	2023-09-27T06:37:03Z	2023-09-27T06:26:24Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6251.diff", "html_url": "https://github.com/huggingface/datasets/pull/6251", "merged_at": "2023-09-27T06:26:24Z", "patch_url": "https://github.com/huggingface/datasets/pull/6251.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6251" }	Support streaming datasets with `pyarrow.parquet.read_table`. See: https://huggingface.co/datasets/uonlp/CulturaX/discussions/2 CC: @AndreaFrancis	{ "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/6251/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6247	6,247	Update create_dataset.mdx	{ "avatar_url": "https://avatars.githubusercontent.com/u/76403422?v=4", "events_url": "https://api.github.com/users/EswarDivi/events{/privacy}", "followers_url": "https://api.github.com/users/EswarDivi/followers", "following_url": "https://api.github.com/users/EswarDivi/following{/other_user}", "gists_url": "https://api.github.com/users/EswarDivi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/EswarDivi", "id": 76403422, "login": "EswarDivi", "node_id": "MDQ6VXNlcjc2NDAzNDIy", "organizations_url": "https://api.github.com/users/EswarDivi/orgs", "received_events_url": "https://api.github.com/users/EswarDivi/received_events", "repos_url": "https://api.github.com/users/EswarDivi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/EswarDivi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/EswarDivi/subscriptions", "type": "User", "url": "https://api.github.com/users/EswarDivi", "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) \| 0.008892 / 0.011353 (-0.002461) \| 0.005140 / 0.011008 (-0.005868) \| 0.110951 / 0.038508 (0.072442) \| 0.086159 / 0.023109 (0.063050) \| 0.391117 / 0.275898 (0.115218) \| 0.440884 / 0.323480 (0.117404) \| 0.006562 / 0.007986 (-0.001423) \| 0.003711 / 0.004328 (-0.000618) \| 0.081848 / 0.004250 (0.077598) \| 0.063187 / 0.037052 (0.026135) \| 0.369771 / 0.258489 (0.111282) \| 0.447685 / 0.293841 (0.153844) \| 0.046623 / 0.128546 (-0.081923) \| 0.014024 / 0.075646 (-0.061622) \| 0.418556 / 0.419271 (-0.000715) \| 0.064660 / 0.043533 (0.021127) \| 0.379416 / 0.255139 (0.124277) \| 0.415800 / 0.283200 (0.132600) \| 0.036899 / 0.141683 (-0.104784) \| 1.710280 / 1.452155 (0.258125) \| 1.932326 / 1.492716 (0.439610) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.311351 / 0.018006 (0.293345) \| 0.621121 / 0.000490 (0.620631) \| 0.013677 / 0.000200 (0.013477) \| 0.000543 / 0.000054 (0.000488) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031310 / 0.037411 (-0.006102) \| 0.099546 / 0.014526 (0.085020) \| 0.122100 / 0.176557 (-0.054457) \| 0.186477 / 0.737135 (-0.550659) \| 0.116634 / 0.296338 (-0.179704) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.574639 / 0.215209 (0.359430) \| 5.976678 / 2.077655 (3.899023) \| 2.535482 / 1.504120 (1.031362) \| 2.248873 / 1.541195 (0.707678) \| 2.361696 / 1.468490 (0.893205) \| 0.866700 / 4.584777 (-3.718077) \| 5.298018 / 3.745712 (1.552306) \| 4.753240 / 5.269862 (-0.516622) \| 3.124698 / 4.565676 (-1.440979) \| 0.101852 / 0.424275 (-0.322423) \| 0.009117 / 0.007607 (0.001510) \| 0.723730 / 0.226044 (0.497685) \| 7.172649 / 2.268929 (4.903720) \| 3.400410 / 55.444624 (-52.044214) \| 2.626619 / 6.876477 (-4.249857) \| 2.948692 / 2.142072 (0.806620) \| 0.991589 / 4.805227 (-3.813638) \| 0.208902 / 6.500664 (-6.291762) \| 0.076172 / 0.075469 (0.000703) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.621880 / 1.841788 (-0.219907) \| 22.735673 / 8.074308 (14.661365) \| 20.376990 / 10.191392 (10.185598) \| 0.232219 / 0.680424 (-0.448204) \| 0.028616 / 0.534201 (-0.505585) \| 0.455725 / 0.579283 (-0.123558) \| 0.562796 / 0.434364 (0.128432) \| 0.545344 / 0.540337 (0.005007) \| 0.759440 / 1.386936 (-0.627496) \|\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) \| 0.009845 / 0.011353 (-0.001508) \| 0.005289 / 0.011008 (-0.005719) \| 0.083117 / 0.038508 (0.044609) \| 0.098467 / 0.023109 (0.075357) \| 0.532345 / 0.275898 (0.256447) \| 0.571000 / 0.323480 (0.247520) \| 0.007223 / 0.007986 (-0.000763) \| 0.004442 / 0.004328 (0.000114) \| 0.081710 / 0.004250 (0.077459) \| 0.071132 / 0.037052 (0.034080) \| 0.540093 / 0.258489 (0.281604) \| 0.582244 / 0.293841 (0.288403) \| 0.048509 / 0.128546 (-0.080038) \| 0.013897 / 0.075646 (-0.061749) \| 0.092579 / 0.419271 (-0.326692) \| 0.073409 / 0.043533 (0.029876) \| 0.537369 / 0.255139 (0.282230) \| 0.551403 / 0.283200 (0.268203) \| 0.038847 / 0.141683 (-0.102835) \| 1.940848 / 1.452155 (0.488693) \| 2.045597 / 1.492716 (0.552881) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303883 / 0.018006 (0.285877) \| 0.600237 / 0.000490 (0.599748) \| 0.006030 / 0.000200 (0.005830) \| 0.000124 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036633 / 0.037411 (-0.000778) \| 0.105853 / 0.014526 (0.091327) \| 0.126289 / 0.176557 (-0.050267) \| 0.190022 / 0.737135 (-0.547113) \| 0.123251 / 0.296338 (-0.173087) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.711893 / 0.215209 (0.496684) \| 6.979781 / 2.077655 (4.902126) \| 3.491514 / 1.504120 (1.987394) \| 3.268077 / 1.541195 (1.726882) \| 3.241777 / 1.468490 (1.773287) \| 0.875913 / 4.584777 (-3.708864) \| 5.458421 / 3.745712 (1.712709) \| 4.818355 / 5.269862 (-0.451507) \| 3.256046 / 4.565676 (-1.309631) \| 0.095000 / 0.424275 (-0.329275) \| 0.009072 / 0.007607 (0.001465) \| 0.818468 / 0.226044 (0.592424) \| 8.027702 / 2.268929 (5.758773) \| 4.363234 / 55.444624 (-51.081390) \| 3.695269 / 6.876477 (-3.181207) \| 3.902601 / 2.142072 (1.760528) \| 1.039007 / 4.805227 (-3.766220) \| 0.212050 / 6.500664 (-6.288614) \| 0.081438 / 0.075469 (0.005969) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.746945 / 1.841788 (-0.094842) \| 25.274283 / 8.074308 (17.199975) \| 23.514717 / 10.191392 (13.323325) \| 0.232580 / 0.680424 (-0.447843) \| 0.032083 / 0.534201 (-0.502118) \| 0.482873 / 0.579283 (-0.096410) \| 0.585730 / 0.434364 (0.151366) \| 0.602066 / 0.540337 (0.061729) \| 0.796391 / 1.386936 (-0.590546) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d7cb68fe37dbfd81e5f82e19d8f9847c337788d \"CML watermark\")\n" ]	2023-09-18T17:06:29Z	2023-09-19T18:51:49Z	2023-09-19T18:40:10Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6247.diff", "html_url": "https://github.com/huggingface/datasets/pull/6247", "merged_at": "2023-09-19T18:40:10Z", "patch_url": "https://github.com/huggingface/datasets/pull/6247.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6247" }	modified , as AudioFolder and ImageFolder not in Dataset Library. ``` from datasets import AudioFolder ``` and ```from datasets import ImageFolder``` to ```from datasets import load_dataset``` ``` cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py) ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": 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"rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6247/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6246	6,246	Add new column to dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andysingal", "id": 20493493, "login": "andysingal", "node_id": "MDQ6VXNlcjIwNDkzNDkz", "organizations_url": "https://api.github.com/users/andysingal/orgs", "received_events_url": "https://api.github.com/users/andysingal/received_events", "repos_url": "https://api.github.com/users/andysingal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "type": "User", "url": "https://api.github.com/users/andysingal", "user_view_type": "public" }	[]	closed	false	[ "I think it's an issue with the code.\r\n\r\nSpecifically:\r\n```python\r\ndataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nNow `dataset` is the train set with a new column. \r\nTo fix this, you can do:\r\n\r\n```python\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```", "> I think it's an issue with the code.\r\n> \r\n> Specifically:\r\n> \r\n> ```python\r\n> dataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> Now `dataset` is the train set with a new column. To fix this, you can do:\r\n> \r\n> ```python\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n\r\nThanks for your response, but i can not access mask images, please let me know why the problem still persists. Here is the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ", "I think there is a slight misunderstanding.\r\n```python\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nadds a column with the string `mask` to your dataset.\r\nIf you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n\r\n```\r\nfrom datasets import Image\r\n\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n```\r\n\r\nWhat this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n\r\nThis [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n\r\nHope this helps!", "> I think there is a slight misunderstanding.\r\n> \r\n> ```python\r\n> new_column = [\"mask\"] * len(dataset[\"train\"])\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> adds a column with the string `mask` to your dataset. If you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n> \r\n> ```\r\n> from datasets import Image\r\n> \r\n> dataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n> ```\r\n> \r\n> What this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n> \r\n> This [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n> \r\n> Hope this helps!\r\n\r\nThank you very much, this is really helpful...\r\ni made some changes for it to work:\r\n```\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/content/data/mask_{idx}.png\"}, with_indices=True).cast_column(\"mask\", Image())\r\n```\r\nThanks Again @Dref360 " ]	2023-09-17T16:59:48Z	2023-09-18T16:20:09Z	2023-09-18T16:20:09Z	NONE	null	null	### Describe the bug ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-9-bd197b36b6a0>](https://localhost:8080/#) in <cell line: 1>() ----> 1 dataset['train']['/workspace/data'] 3 frames [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_column_key(key, columns) 518 def _check_valid_column_key(key: str, columns: List[str]) -> None: 519 if key not in columns: --> 520 raise KeyError(f"Column {key} not in the dataset. Current columns in the dataset: {columns}") 521 522 KeyError: "Column train not in the dataset. Current columns in the dataset: ['image', '/workspace/data']" ``` ### Steps to reproduce the bug please find the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ### Expected behavior add column to the dataset ### Environment info colab pro	{ "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andysingal", "id": 20493493, "login": "andysingal", "node_id": "MDQ6VXNlcjIwNDkzNDkz", "organizations_url": "https://api.github.com/users/andysingal/orgs", "received_events_url": "https://api.github.com/users/andysingal/received_events", "repos_url": "https://api.github.com/users/andysingal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "type": "User", "url": "https://api.github.com/users/andysingal", "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/6246/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/6244	6,244	Add support for `fsspec>=2023.9.0`	{ "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" }	[]	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) \| 0.006410 / 0.011353 (-0.004943) \| 0.003995 / 0.011008 (-0.007013) \| 0.083585 / 0.038508 (0.045076) \| 0.074285 / 0.023109 (0.051176) \| 0.307163 / 0.275898 (0.031265) \| 0.344691 / 0.323480 (0.021212) \| 0.004277 / 0.007986 (-0.003708) \| 0.004192 / 0.004328 (-0.000136) \| 0.065156 / 0.004250 (0.060905) \| 0.056774 / 0.037052 (0.019721) \| 0.315483 / 0.258489 (0.056994) \| 0.361911 / 0.293841 (0.068070) \| 0.030454 / 0.128546 (-0.098092) \| 0.008600 / 0.075646 (-0.067047) \| 0.286692 / 0.419271 (-0.132579) \| 0.052354 / 0.043533 (0.008821) \| 0.308997 / 0.255139 (0.053858) \| 0.337847 / 0.283200 (0.054647) \| 0.022459 / 0.141683 (-0.119224) \| 1.482758 / 1.452155 (0.030604) \| 1.572853 / 1.492716 (0.080137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288603 / 0.018006 (0.270597) \| 0.632903 / 0.000490 (0.632413) \| 0.013702 / 0.000200 (0.013502) \| 0.000284 / 0.000054 (0.000230) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028448 / 0.037411 (-0.008964) \| 0.082441 / 0.014526 (0.067916) \| 0.099048 / 0.176557 (-0.077508) \| 0.154370 / 0.737135 (-0.582765) \| 0.146143 / 0.296338 (-0.150195) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399250 / 0.215209 (0.184040) \| 3.986683 / 2.077655 (1.909028) \| 1.962606 / 1.504120 (0.458486) \| 1.782653 / 1.541195 (0.241459) \| 1.830251 / 1.468490 (0.361761) \| 0.492498 / 4.584777 (-4.092278) \| 3.549581 / 3.745712 (-0.196131) \| 3.200056 / 5.269862 (-2.069806) \| 2.028109 / 4.565676 (-2.537568) \| 0.058222 / 0.424275 (-0.366053) \| 0.007629 / 0.007607 (0.000022) \| 0.482083 / 0.226044 (0.256039) \| 4.824728 / 2.268929 (2.555800) \| 2.448772 / 55.444624 (-52.995852) \| 2.079629 / 6.876477 (-4.796848) \| 2.267739 / 2.142072 (0.125667) \| 0.586712 / 4.805227 (-4.218515) \| 0.134073 / 6.500664 (-6.366591) \| 0.060565 / 0.075469 (-0.014904) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.263244 / 1.841788 (-0.578544) \| 18.964498 / 8.074308 (10.890190) \| 14.125062 / 10.191392 (3.933670) \| 0.167635 / 0.680424 (-0.512789) \| 0.018469 / 0.534201 (-0.515732) \| 0.390395 / 0.579283 (-0.188888) \| 0.406055 / 0.434364 (-0.028309) \| 0.460717 / 0.540337 (-0.079620) \| 0.642746 / 1.386936 (-0.744190) \|\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) \| 0.006637 / 0.011353 (-0.004716) \| 0.003972 / 0.011008 (-0.007036) \| 0.064569 / 0.038508 (0.026061) \| 0.075450 / 0.023109 (0.052341) \| 0.405250 / 0.275898 (0.129352) \| 0.433530 / 0.323480 (0.110050) \| 0.005625 / 0.007986 (-0.002361) \| 0.004118 / 0.004328 (-0.000211) \| 0.065092 / 0.004250 (0.060842) \| 0.057979 / 0.037052 (0.020927) \| 0.413732 / 0.258489 (0.155243) \| 0.451983 / 0.293841 (0.158142) \| 0.032170 / 0.128546 (-0.096377) \| 0.008690 / 0.075646 (-0.066957) \| 0.071792 / 0.419271 (-0.347479) \| 0.048560 / 0.043533 (0.005027) \| 0.410312 / 0.255139 (0.155173) \| 0.427294 / 0.283200 (0.144095) \| 0.023006 / 0.141683 (-0.118677) \| 1.496319 / 1.452155 (0.044164) \| 1.566744 / 1.492716 (0.074027) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266812 / 0.018006 (0.248805) \| 0.540277 / 0.000490 (0.539788) \| 0.008998 / 0.000200 (0.008799) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032496 / 0.037411 (-0.004915) \| 0.091387 / 0.014526 (0.076861) \| 0.107516 / 0.176557 (-0.069041) \| 0.160019 / 0.737135 (-0.577116) \| 0.107686 / 0.296338 (-0.188652) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433321 / 0.215209 (0.218111) \| 4.330221 / 2.077655 (2.252566) \| 2.367215 / 1.504120 (0.863095) \| 2.192464 / 1.541195 (0.651269) \| 2.200204 / 1.468490 (0.731714) \| 0.488057 / 4.584777 (-4.096720) \| 3.625429 / 3.745712 (-0.120283) \| 3.282859 / 5.269862 (-1.987003) \| 2.038716 / 4.565676 (-2.526960) \| 0.057968 / 0.424275 (-0.366307) \| 0.007753 / 0.007607 (0.000146) \| 0.509133 / 0.226044 (0.283089) \| 5.086445 / 2.268929 (2.817516) \| 2.846017 / 55.444624 (-52.598607) \| 2.469546 / 6.876477 (-4.406931) \| 2.673218 / 2.142072 (0.531145) \| 0.591228 / 4.805227 (-4.213999) \| 0.131920 / 6.500664 (-6.368744) \| 0.059967 / 0.075469 (-0.015502) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.375634 / 1.841788 (-0.466153) \| 19.506752 / 8.074308 (11.432444) \| 14.677876 / 10.191392 (4.486484) \| 0.165071 / 0.680424 (-0.515353) \| 0.020614 / 0.534201 (-0.513587) \| 0.395967 / 0.579283 (-0.183316) \| 0.424358 / 0.434364 (-0.010006) \| 0.469954 / 0.540337 (-0.070384) \| 0.643169 / 1.386936 (-0.743767) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887a854f03c4ac6d2e99b9ef4d89e6fe8c46d6f1 \"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) \| 0.006072 / 0.011353 (-0.005281) \| 0.003691 / 0.011008 (-0.007318) \| 0.081683 / 0.038508 (0.043175) \| 0.059114 / 0.023109 (0.036005) \| 0.317053 / 0.275898 (0.041155) \| 0.357672 / 0.323480 (0.034192) \| 0.003577 / 0.007986 (-0.004408) \| 0.003890 / 0.004328 (-0.000438) \| 0.063667 / 0.004250 (0.059417) \| 0.048233 / 0.037052 (0.011181) \| 0.322854 / 0.258489 (0.064365) \| 0.368014 / 0.293841 (0.074173) \| 0.027750 / 0.128546 (-0.100796) \| 0.008137 / 0.075646 (-0.067509) \| 0.263906 / 0.419271 (-0.155366) \| 0.045402 / 0.043533 (0.001870) \| 0.315414 / 0.255139 (0.060275) \| 0.340906 / 0.283200 (0.057707) \| 0.023475 / 0.141683 (-0.118208) \| 1.443922 / 1.452155 (-0.008233) \| 1.550332 / 1.492716 (0.057616) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211914 / 0.018006 (0.193908) \| 0.423577 / 0.000490 (0.423088) \| 0.003436 / 0.000200 (0.003236) \| 0.000077 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024675 / 0.037411 (-0.012737) \| 0.072550 / 0.014526 (0.058024) \| 0.084533 / 0.176557 (-0.092024) \| 0.146106 / 0.737135 (-0.591029) \| 0.085523 / 0.296338 (-0.210816) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403498 / 0.215209 (0.188289) \| 4.019000 / 2.077655 (1.941345) \| 1.984821 / 1.504120 (0.480701) \| 1.805071 / 1.541195 (0.263876) \| 1.860906 / 1.468490 (0.392416) \| 0.499570 / 4.584777 (-4.085207) \| 3.088424 / 3.745712 (-0.657288) \| 2.833693 / 5.269862 (-2.436169) \| 1.869731 / 4.565676 (-2.695945) \| 0.057606 / 0.424275 (-0.366669) \| 0.006960 / 0.007607 (-0.000647) \| 0.476085 / 0.226044 (0.250040) \| 4.774063 / 2.268929 (2.505134) \| 2.458079 / 55.444624 (-52.986545) \| 2.106075 / 6.876477 (-4.770402) \| 2.248373 / 2.142072 (0.106301) \| 0.589767 / 4.805227 (-4.215460) \| 0.124382 / 6.500664 (-6.376282) \| 0.060705 / 0.075469 (-0.014764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.287031 / 1.841788 (-0.554756) \| 17.662455 / 8.074308 (9.588147) \| 14.288812 / 10.191392 (4.097420) \| 0.156168 / 0.680424 (-0.524256) \| 0.016795 / 0.534201 (-0.517406) \| 0.333726 / 0.579283 (-0.245557) \| 0.362327 / 0.434364 (-0.072037) \| 0.387773 / 0.540337 (-0.152564) \| 0.547232 / 1.386936 (-0.839704) \|\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) \| 0.006494 / 0.011353 (-0.004859) \| 0.003762 / 0.011008 (-0.007247) \| 0.062373 / 0.038508 (0.023864) \| 0.066357 / 0.023109 (0.043247) \| 0.448687 / 0.275898 (0.172789) \| 0.482445 / 0.323480 (0.158965) \| 0.004990 / 0.007986 (-0.002996) \| 0.002945 / 0.004328 (-0.001384) \| 0.062444 / 0.004250 (0.058194) \| 0.051381 / 0.037052 (0.014329) \| 0.449310 / 0.258489 (0.190821) \| 0.483188 / 0.293841 (0.189347) \| 0.029078 / 0.128546 (-0.099468) \| 0.008146 / 0.075646 (-0.067501) \| 0.067369 / 0.419271 (-0.351903) \| 0.041732 / 0.043533 (-0.001801) \| 0.451675 / 0.255139 (0.196536) \| 0.470445 / 0.283200 (0.187246) \| 0.021053 / 0.141683 (-0.120630) \| 1.483627 / 1.452155 (0.031472) \| 1.541594 / 1.492716 (0.048878) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210247 / 0.018006 (0.192240) \| 0.424663 / 0.000490 (0.424173) \| 0.005394 / 0.000200 (0.005194) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026894 / 0.037411 (-0.010517) \| 0.081324 / 0.014526 (0.066798) \| 0.091362 / 0.176557 (-0.085195) \| 0.145602 / 0.737135 (-0.591533) \| 0.091896 / 0.296338 (-0.204443) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469662 / 0.215209 (0.254453) \| 4.689495 / 2.077655 (2.611840) \| 2.596462 / 1.504120 (1.092342) \| 2.422584 / 1.541195 (0.881389) \| 2.476710 / 1.468490 (1.008220) \| 0.507049 / 4.584777 (-4.077728) \| 3.185519 / 3.745712 (-0.560193) \| 2.879842 / 5.269862 (-2.390019) \| 1.882643 / 4.565676 (-2.683034) \| 0.058046 / 0.424275 (-0.366229) \| 0.006797 / 0.007607 (-0.000811) \| 0.545245 / 0.226044 (0.319201) \| 5.449248 / 2.268929 (3.180319) \| 3.057341 / 55.444624 (-52.387283) \| 2.728385 / 6.876477 (-4.148092) \| 2.898945 / 2.142072 (0.756873) \| 0.600035 / 4.805227 (-4.205192) \| 0.126337 / 6.500664 (-6.374327) \| 0.061333 / 0.075469 (-0.014136) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.332966 / 1.841788 (-0.508821) \| 17.960805 / 8.074308 (9.886497) \| 14.978838 / 10.191392 (4.787446) \| 0.148852 / 0.680424 (-0.531572) \| 0.018307 / 0.534201 (-0.515894) \| 0.335234 / 0.579283 (-0.244050) \| 0.389659 / 0.434364 (-0.044704) \| 0.393259 / 0.540337 (-0.147078) \| 0.549237 / 1.386936 (-0.837699) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#278a5673172c30b915a9ebf64cc7aff9667b58fd \"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) \| 0.008808 / 0.011353 (-0.002545) \| 0.005001 / 0.011008 (-0.006008) \| 0.110022 / 0.038508 (0.071514) \| 0.078015 / 0.023109 (0.054906) \| 0.384724 / 0.275898 (0.108826) \| 0.441354 / 0.323480 (0.117874) \| 0.005116 / 0.007986 (-0.002870) \| 0.004308 / 0.004328 (-0.000020) \| 0.081679 / 0.004250 (0.077429) \| 0.061386 / 0.037052 (0.024333) \| 0.398149 / 0.258489 (0.139660) \| 0.464859 / 0.293841 (0.171018) \| 0.047443 / 0.128546 (-0.081104) \| 0.014693 / 0.075646 (-0.060954) \| 0.365438 / 0.419271 (-0.053833) \| 0.081689 / 0.043533 (0.038156) \| 0.400458 / 0.255139 (0.145319) \| 0.449958 / 0.283200 (0.166758) \| 0.038266 / 0.141683 (-0.103417) \| 1.795043 / 1.452155 (0.342888) \| 1.908819 / 1.492716 (0.416102) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297911 / 0.018006 (0.279905) \| 0.601640 / 0.000490 (0.601150) \| 0.015406 / 0.000200 (0.015206) \| 0.000163 / 0.000054 (0.000108) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034520 / 0.037411 (-0.002891) \| 0.092657 / 0.014526 (0.078131) \| 0.113992 / 0.176557 (-0.062564) \| 0.189075 / 0.737135 (-0.548061) \| 0.106602 / 0.296338 (-0.189736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.585838 / 0.215209 (0.370629) \| 5.719281 / 2.077655 (3.641627) \| 2.525914 / 1.504120 (1.021794) \| 2.231908 / 1.541195 (0.690713) \| 2.215272 / 1.468490 (0.746782) \| 0.814425 / 4.584777 (-3.770352) \| 5.243406 / 3.745712 (1.497694) \| 4.476642 / 5.269862 (-0.793220) \| 2.929438 / 4.565676 (-1.636239) \| 0.092070 / 0.424275 (-0.332205) \| 0.009358 / 0.007607 (0.001751) \| 0.713975 / 0.226044 (0.487931) \| 6.948846 / 2.268929 (4.679918) \| 3.361125 / 55.444624 (-52.083500) \| 2.575224 / 6.876477 (-4.301253) \| 2.783082 / 2.142072 (0.641009) \| 1.016205 / 4.805227 (-3.789022) \| 0.202578 / 6.500664 (-6.298086) \| 0.076696 / 0.075469 (0.001227) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.650889 / 1.841788 (-0.190898) \| 23.358273 / 8.074308 (15.283965) \| 19.882450 / 10.191392 (9.691058) \| 0.228971 / 0.680424 (-0.451453) \| 0.027736 / 0.534201 (-0.506465) \| 0.472405 / 0.579283 (-0.106878) \| 0.581799 / 0.434364 (0.147435) \| 0.533000 / 0.540337 (-0.007338) \| 0.815588 / 1.386936 (-0.571348) \|\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) \| 0.009151 / 0.011353 (-0.002202) \| 0.005074 / 0.011008 (-0.005934) \| 0.078709 / 0.038508 (0.040201) \| 0.077696 / 0.023109 (0.054586) \| 0.522356 / 0.275898 (0.246458) \| 0.562345 / 0.323480 (0.238865) \| 0.006411 / 0.007986 (-0.001575) \| 0.004379 / 0.004328 (0.000051) \| 0.082402 / 0.004250 (0.078151) \| 0.064223 / 0.037052 (0.027170) \| 0.518184 / 0.258489 (0.259695) \| 0.566221 / 0.293841 (0.272380) \| 0.046796 / 0.128546 (-0.081750) \| 0.013987 / 0.075646 (-0.061659) \| 0.094925 / 0.419271 (-0.324346) \| 0.058810 / 0.043533 (0.015277) \| 0.520252 / 0.255139 (0.265113) \| 0.566403 / 0.283200 (0.283203) \| 0.034720 / 0.141683 (-0.106963) \| 1.796809 / 1.452155 (0.344654) \| 1.913787 / 1.492716 (0.421070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.317449 / 0.018006 (0.299443) \| 0.620154 / 0.000490 (0.619664) \| 0.007066 / 0.000200 (0.006866) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035252 / 0.037411 (-0.002160) \| 0.111648 / 0.014526 (0.097122) \| 0.120692 / 0.176557 (-0.055864) \| 0.193202 / 0.737135 (-0.543933) \| 0.127905 / 0.296338 (-0.168434) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.661012 / 0.215209 (0.445803) \| 6.626680 / 2.077655 (4.549026) \| 3.243065 / 1.504120 (1.738945) \| 2.904053 / 1.541195 (1.362858) \| 2.880516 / 1.468490 (1.412026) \| 0.875650 / 4.584777 (-3.709127) \| 5.381993 / 3.745712 (1.636281) \| 4.743997 / 5.269862 (-0.525864) \| 3.020736 / 4.565676 (-1.544940) \| 0.106573 / 0.424275 (-0.317702) \| 0.011151 / 0.007607 (0.003544) \| 0.821990 / 0.226044 (0.595946) \| 8.225383 / 2.268929 (5.956454) \| 3.963232 / 55.444624 (-51.481392) \| 3.288916 / 6.876477 (-3.587561) \| 3.579435 / 2.142072 (1.437363) \| 1.043379 / 4.805227 (-3.761848) \| 0.207508 / 6.500664 (-6.293156) \| 0.085109 / 0.075469 (0.009640) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.723798 / 1.841788 (-0.117990) \| 24.709848 / 8.074308 (16.635540) \| 22.484674 / 10.191392 (12.293282) \| 0.260357 / 0.680424 (-0.420067) \| 0.033539 / 0.534201 (-0.500662) \| 0.487814 / 0.579283 (-0.091469) \| 0.610171 / 0.434364 (0.175807) \| 0.585012 / 0.540337 (0.044674) \| 0.803764 / 1.386936 (-0.583172) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f611e5815ce1bdcb4fa8556f55d85a6739cba0ea \"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) \| 0.006661 / 0.011353 (-0.004692) \| 0.004022 / 0.011008 (-0.006987) \| 0.084269 / 0.038508 (0.045760) \| 0.070707 / 0.023109 (0.047598) \| 0.315035 / 0.275898 (0.039137) \| 0.339830 / 0.323480 (0.016350) \| 0.003994 / 0.007986 (-0.003991) \| 0.004129 / 0.004328 (-0.000199) \| 0.065383 / 0.004250 (0.061133) \| 0.055493 / 0.037052 (0.018441) \| 0.320521 / 0.258489 (0.062032) \| 0.354301 / 0.293841 (0.060460) \| 0.031177 / 0.128546 (-0.097370) \| 0.008724 / 0.075646 (-0.066922) \| 0.288298 / 0.419271 (-0.130974) \| 0.052418 / 0.043533 (0.008885) \| 0.319122 / 0.255139 (0.063983) \| 0.335859 / 0.283200 (0.052659) \| 0.026260 / 0.141683 (-0.115423) \| 1.450039 / 1.452155 (-0.002115) \| 1.545172 / 1.492716 (0.052455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234232 / 0.018006 (0.216226) \| 0.454983 / 0.000490 (0.454493) \| 0.007590 / 0.000200 (0.007390) \| 0.000550 / 0.000054 (0.000495) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028714 / 0.037411 (-0.008698) \| 0.083686 / 0.014526 (0.069160) \| 0.162986 / 0.176557 (-0.013570) \| 0.167574 / 0.737135 (-0.569561) \| 0.273158 / 0.296338 (-0.023180) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388275 / 0.215209 (0.173066) \| 3.862034 / 2.077655 (1.784379) \| 1.843561 / 1.504120 (0.339441) \| 1.675224 / 1.541195 (0.134029) \| 1.730394 / 1.468490 (0.261904) \| 0.495259 / 4.584777 (-4.089518) \| 3.627155 / 3.745712 (-0.118557) \| 3.290590 / 5.269862 (-1.979272) \| 2.032432 / 4.565676 (-2.533245) \| 0.058212 / 0.424275 (-0.366063) \| 0.007815 / 0.007607 (0.000208) \| 0.460625 / 0.226044 (0.234580) \| 4.616845 / 2.268929 (2.347916) \| 2.339280 / 55.444624 (-53.105344) \| 1.957216 / 6.876477 (-4.919261) \| 2.129511 / 2.142072 (-0.012562) \| 0.591782 / 4.805227 (-4.213446) \| 0.136391 / 6.500664 (-6.364273) \| 0.059627 / 0.075469 (-0.015842) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278998 / 1.841788 (-0.562789) \| 18.485496 / 8.074308 (10.411188) \| 14.161273 / 10.191392 (3.969881) \| 0.164346 / 0.680424 (-0.516078) \| 0.018144 / 0.534201 (-0.516057) \| 0.391601 / 0.579283 (-0.187682) \| 0.424391 / 0.434364 (-0.009973) \| 0.458209 / 0.540337 (-0.082129) \| 0.645124 / 1.386936 (-0.741812) \|\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) \| 0.006799 / 0.011353 (-0.004554) \| 0.004023 / 0.011008 (-0.006985) \| 0.065206 / 0.038508 (0.026698) \| 0.074386 / 0.023109 (0.051277) \| 0.437399 / 0.275898 (0.161501) \| 0.467382 / 0.323480 (0.143903) \| 0.005467 / 0.007986 (-0.002519) \| 0.003324 / 0.004328 (-0.001005) \| 0.064289 / 0.004250 (0.060039) \| 0.057257 / 0.037052 (0.020205) \| 0.440035 / 0.258489 (0.181546) \| 0.477138 / 0.293841 (0.183298) \| 0.032171 / 0.128546 (-0.096375) \| 0.008400 / 0.075646 (-0.067247) \| 0.070877 / 0.419271 (-0.348395) \| 0.048180 / 0.043533 (0.004648) \| 0.441274 / 0.255139 (0.186135) \| 0.461386 / 0.283200 (0.178187) \| 0.022576 / 0.141683 (-0.119106) \| 1.520914 / 1.452155 (0.068759) \| 1.575593 / 1.492716 (0.082877) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221551 / 0.018006 (0.203545) \| 0.447213 / 0.000490 (0.446723) \| 0.004435 / 0.000200 (0.004235) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032123 / 0.037411 (-0.005288) \| 0.091809 / 0.014526 (0.077283) \| 0.103938 / 0.176557 (-0.072618) \| 0.156878 / 0.737135 (-0.580258) \| 0.105071 / 0.296338 (-0.191268) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430389 / 0.215209 (0.215180) \| 4.293496 / 2.077655 (2.215841) \| 2.292801 / 1.504120 (0.788681) \| 2.135320 / 1.541195 (0.594126) \| 2.195720 / 1.468490 (0.727229) \| 0.493277 / 4.584777 (-4.091500) \| 3.685617 / 3.745712 (-0.060096) \| 3.278897 / 5.269862 (-1.990965) \| 2.036939 / 4.565676 (-2.528737) \| 0.058766 / 0.424275 (-0.365509) \| 0.007783 / 0.007607 (0.000176) \| 0.511165 / 0.226044 (0.285120) \| 5.126757 / 2.268929 (2.857829) \| 2.756690 / 55.444624 (-52.687935) \| 2.421745 / 6.876477 (-4.454732) \| 2.597249 / 2.142072 (0.455177) \| 0.647206 / 4.805227 (-4.158021) \| 0.143392 / 6.500664 (-6.357273) \| 0.060110 / 0.075469 (-0.015359) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.340289 / 1.841788 (-0.501499) \| 19.057620 / 8.074308 (10.983312) \| 14.832892 / 10.191392 (4.641500) \| 0.167730 / 0.680424 (-0.512694) \| 0.020178 / 0.534201 (-0.514023) \| 0.394060 / 0.579283 (-0.185223) \| 0.433976 / 0.434364 (-0.000388) \| 0.474417 / 0.540337 (-0.065921) \| 0.640653 / 1.386936 (-0.746283) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0519c6a1988a3344ecae37f7348c208bcbc99d6 \"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) \| 0.007661 / 0.011353 (-0.003692) \| 0.004541 / 0.011008 (-0.006467) \| 0.100547 / 0.038508 (0.062039) \| 0.084257 / 0.023109 (0.061148) \| 0.377627 / 0.275898 (0.101729) \| 0.433764 / 0.323480 (0.110284) \| 0.005995 / 0.007986 (-0.001990) \| 0.003810 / 0.004328 (-0.000518) \| 0.076409 / 0.004250 (0.072158) \| 0.063411 / 0.037052 (0.026359) \| 0.382504 / 0.258489 (0.124015) \| 0.449721 / 0.293841 (0.155880) \| 0.036499 / 0.128546 (-0.092047) \| 0.009942 / 0.075646 (-0.065705) \| 0.343839 / 0.419271 (-0.075433) \| 0.062147 / 0.043533 (0.018614) \| 0.383244 / 0.255139 (0.128105) \| 0.415606 / 0.283200 (0.132406) \| 0.027475 / 0.141683 (-0.114207) \| 1.740413 / 1.452155 (0.288258) \| 1.862210 / 1.492716 (0.369493) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260064 / 0.018006 (0.242058) \| 0.499001 / 0.000490 (0.498511) \| 0.015811 / 0.000200 (0.015611) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033599 / 0.037411 (-0.003812) \| 0.099354 / 0.014526 (0.084828) \| 0.114693 / 0.176557 (-0.061864) \| 0.180231 / 0.737135 (-0.556904) \| 0.114715 / 0.296338 (-0.181623) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459884 / 0.215209 (0.244675) \| 4.580806 / 2.077655 (2.503151) \| 2.270770 / 1.504120 (0.766650) \| 2.077127 / 1.541195 (0.535932) \| 2.167175 / 1.468490 (0.698685) \| 0.570593 / 4.584777 (-4.014184) \| 4.120926 / 3.745712 (0.375214) \| 3.817595 / 5.269862 (-1.452267) \| 2.404782 / 4.565676 (-2.160894) \| 0.067972 / 0.424275 (-0.356304) \| 0.009378 / 0.007607 (0.001771) \| 0.549642 / 0.226044 (0.323597) \| 5.490369 / 2.268929 (3.221440) \| 2.905264 / 55.444624 (-52.539361) \| 2.452935 / 6.876477 (-4.423542) \| 2.700760 / 2.142072 (0.558688) \| 0.700407 / 4.805227 (-4.104820) \| 0.159349 / 6.500664 (-6.341315) \| 0.074605 / 0.075469 (-0.000864) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.517803 / 1.841788 (-0.323985) \| 22.343700 / 8.074308 (14.269392) \| 16.411639 / 10.191392 (6.220247) \| 0.169816 / 0.680424 (-0.510608) \| 0.021532 / 0.534201 (-0.512668) \| 0.470161 / 0.579283 (-0.109122) \| 0.473412 / 0.434364 (0.039048) \| 0.539690 / 0.540337 (-0.000647) \| 0.774011 / 1.386936 (-0.612925) \|\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) \| 0.007629 / 0.011353 (-0.003724) \| 0.004651 / 0.011008 (-0.006357) \| 0.075162 / 0.038508 (0.036654) \| 0.085365 / 0.023109 (0.062256) \| 0.493272 / 0.275898 (0.217374) \| 0.535776 / 0.323480 (0.212296) \| 0.006323 / 0.007986 (-0.001663) \| 0.003785 / 0.004328 (-0.000544) \| 0.076161 / 0.004250 (0.071911) \| 0.065982 / 0.037052 (0.028929) \| 0.513355 / 0.258489 (0.254866) \| 0.549219 / 0.293841 (0.255378) \| 0.038052 / 0.128546 (-0.090494) \| 0.010055 / 0.075646 (-0.065592) \| 0.083744 / 0.419271 (-0.335527) \| 0.056708 / 0.043533 (0.013175) \| 0.496273 / 0.255139 (0.241135) \| 0.523709 / 0.283200 (0.240509) \| 0.026502 / 0.141683 (-0.115181) \| 1.793032 / 1.452155 (0.340877) \| 1.870534 / 1.492716 (0.377817) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252288 / 0.018006 (0.234281) \| 0.490380 / 0.000490 (0.489890) \| 0.005884 / 0.000200 (0.005684) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038238 / 0.037411 (0.000827) \| 0.110010 / 0.014526 (0.095485) \| 0.125497 / 0.176557 (-0.051059) \| 0.188154 / 0.737135 (-0.548981) \| 0.126112 / 0.296338 (-0.170227) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515837 / 0.215209 (0.300628) \| 5.135153 / 2.077655 (3.057498) \| 2.761740 / 1.504120 (1.257620) \| 2.552718 / 1.541195 (1.011523) \| 2.636425 / 1.468490 (1.167935) \| 0.588442 / 4.584777 (-3.996335) \| 4.220833 / 3.745712 (0.475120) \| 3.874637 / 5.269862 (-1.395225) \| 2.424668 / 4.565676 (-2.141009) \| 0.069979 / 0.424275 (-0.354296) \| 0.009349 / 0.007607 (0.001742) \| 0.608936 / 0.226044 (0.382891) \| 6.081209 / 2.268929 (3.812280) \| 3.348067 / 55.444624 (-52.096557) \| 2.919130 / 6.876477 (-3.957347) \| 3.159093 / 2.142072 (1.017020) \| 0.704059 / 4.805227 (-4.101169) \| 0.158417 / 6.500664 (-6.342247) \| 0.071321 / 0.075469 (-0.004148) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.595287 / 1.841788 (-0.246501) \| 23.096619 / 8.074308 (15.022311) \| 17.258041 / 10.191392 (7.066649) \| 0.186197 / 0.680424 (-0.494227) \| 0.023633 / 0.534201 (-0.510567) \| 0.472181 / 0.579283 (-0.107102) \| 0.493817 / 0.434364 (0.059453) \| 0.567657 / 0.540337 (0.027320) \| 0.793789 / 1.386936 (-0.593147) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0bd8444689c5d82344a62ddf79e5dc103fc67b8 \"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) \| 0.007084 / 0.011353 (-0.004268) \| 0.004093 / 0.011008 (-0.006915) \| 0.086395 / 0.038508 (0.047887) \| 0.087734 / 0.023109 (0.064625) \| 0.356936 / 0.275898 (0.081038) \| 0.386413 / 0.323480 (0.062933) \| 0.005531 / 0.007986 (-0.002454) \| 0.003462 / 0.004328 (-0.000866) \| 0.065503 / 0.004250 (0.061252) \| 0.058973 / 0.037052 (0.021920) \| 0.354151 / 0.258489 (0.095662) \| 0.398812 / 0.293841 (0.104971) \| 0.031508 / 0.128546 (-0.097038) \| 0.008537 / 0.075646 (-0.067109) \| 0.290942 / 0.419271 (-0.128329) \| 0.053537 / 0.043533 (0.010004) \| 0.352067 / 0.255139 (0.096928) \| 0.375142 / 0.283200 (0.091943) \| 0.025658 / 0.141683 (-0.116025) \| 1.468496 / 1.452155 (0.016341) \| 1.556926 / 1.492716 (0.064210) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238858 / 0.018006 (0.220852) \| 0.460018 / 0.000490 (0.459528) \| 0.009613 / 0.000200 (0.009414) \| 0.000326 / 0.000054 (0.000272) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030333 / 0.037411 (-0.007078) \| 0.088431 / 0.014526 (0.073905) \| 0.098130 / 0.176557 (-0.078427) \| 0.155160 / 0.737135 (-0.581975) \| 0.099963 / 0.296338 (-0.196375) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.385769 / 0.215209 (0.170560) \| 3.836723 / 2.077655 (1.759069) \| 1.861065 / 1.504120 (0.356945) \| 1.685159 / 1.541195 (0.143965) \| 1.780679 / 1.468490 (0.312189) \| 0.491865 / 4.584777 (-4.092912) \| 3.581139 / 3.745712 (-0.164573) \| 3.366278 / 5.269862 (-1.903584) \| 2.093094 / 4.565676 (-2.472583) \| 0.058063 / 0.424275 (-0.366212) \| 0.007903 / 0.007607 (0.000296) \| 0.464866 / 0.226044 (0.238821) \| 4.647754 / 2.268929 (2.378825) \| 2.316466 / 55.444624 (-53.128158) \| 1.984079 / 6.876477 (-4.892398) \| 2.235020 / 2.142072 (0.092948) \| 0.592591 / 4.805227 (-4.212636) \| 0.135586 / 6.500664 (-6.365078) \| 0.061434 / 0.075469 (-0.014035) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.282940 / 1.841788 (-0.558848) \| 19.635975 / 8.074308 (11.561667) \| 14.426135 / 10.191392 (4.234743) \| 0.166732 / 0.680424 (-0.513692) \| 0.018438 / 0.534201 (-0.515763) \| 0.393173 / 0.579283 (-0.186110) \| 0.417291 / 0.434364 (-0.017073) \| 0.459188 / 0.540337 (-0.081149) \| 0.632568 / 1.386936 (-0.754368) \|\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) \| 0.007166 / 0.011353 (-0.004187) \| 0.004254 / 0.011008 (-0.006754) \| 0.064667 / 0.038508 (0.026159) \| 0.085142 / 0.023109 (0.062033) \| 0.410081 / 0.275898 (0.134183) \| 0.445803 / 0.323480 (0.122323) \| 0.005600 / 0.007986 (-0.002385) \| 0.003520 / 0.004328 (-0.000809) \| 0.064148 / 0.004250 (0.059897) \| 0.059869 / 0.037052 (0.022817) \| 0.407439 / 0.258489 (0.148950) \| 0.451169 / 0.293841 (0.157329) \| 0.032619 / 0.128546 (-0.095927) \| 0.008706 / 0.075646 (-0.066940) \| 0.071230 / 0.419271 (-0.348041) \| 0.048499 / 0.043533 (0.004966) \| 0.416401 / 0.255139 (0.161262) \| 0.430737 / 0.283200 (0.147537) \| 0.022511 / 0.141683 (-0.119172) \| 1.517296 / 1.452155 (0.065141) \| 1.581704 / 1.492716 (0.088988) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220738 / 0.018006 (0.202732) \| 0.454026 / 0.000490 (0.453536) \| 0.004695 / 0.000200 (0.004495) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033202 / 0.037411 (-0.004209) \| 0.097506 / 0.014526 (0.082980) \| 0.106661 / 0.176557 (-0.069896) \| 0.160554 / 0.737135 (-0.576581) \| 0.109203 / 0.296338 (-0.187135) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432013 / 0.215209 (0.216804) \| 4.310399 / 2.077655 (2.232744) \| 2.296529 / 1.504120 (0.792409) \| 2.139929 / 1.541195 (0.598734) \| 2.227432 / 1.468490 (0.758942) \| 0.493697 / 4.584777 (-4.091080) \| 3.639877 / 3.745712 (-0.105835) \| 3.323165 / 5.269862 (-1.946697) \| 2.084527 / 4.565676 (-2.481150) \| 0.058812 / 0.424275 (-0.365463) \| 0.007813 / 0.007607 (0.000206) \| 0.512366 / 0.226044 (0.286321) \| 5.119660 / 2.268929 (2.850732) \| 2.783819 / 55.444624 (-52.660806) \| 2.490669 / 6.876477 (-4.385808) \| 2.696653 / 2.142072 (0.554581) \| 0.627161 / 4.805227 (-4.178066) \| 0.137032 / 6.500664 (-6.363632) \| 0.064040 / 0.075469 (-0.011429) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.369578 / 1.841788 (-0.472210) \| 20.421182 / 8.074308 (12.346873) \| 15.719347 / 10.191392 (5.527955) \| 0.166150 / 0.680424 (-0.514274) \| 0.020262 / 0.534201 (-0.513939) \| 0.395645 / 0.579283 (-0.183638) \| 0.430363 / 0.434364 (-0.004001) \| 0.477843 / 0.540337 (-0.062494) \| 0.638501 / 1.386936 (-0.748435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c89e60cc50563dfc41ea039c6d3a1f6e43033e8e \"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) \| 0.006141 / 0.011353 (-0.005211) \| 0.003683 / 0.011008 (-0.007325) \| 0.081127 / 0.038508 (0.042618) \| 0.064285 / 0.023109 (0.041176) \| 0.323038 / 0.275898 (0.047140) \| 0.347280 / 0.323480 (0.023800) \| 0.003518 / 0.007986 (-0.004467) \| 0.002958 / 0.004328 (-0.001370) \| 0.063093 / 0.004250 (0.058843) \| 0.050682 / 0.037052 (0.013629) \| 0.321222 / 0.258489 (0.062733) \| 0.359266 / 0.293841 (0.065425) \| 0.027515 / 0.128546 (-0.101032) \| 0.007964 / 0.075646 (-0.067682) \| 0.261305 / 0.419271 (-0.157966) \| 0.044897 / 0.043533 (0.001365) \| 0.320684 / 0.255139 (0.065545) \| 0.335722 / 0.283200 (0.052522) \| 0.023378 / 0.141683 (-0.118305) \| 1.418211 / 1.452155 (-0.033943) \| 1.523728 / 1.492716 (0.031011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222316 / 0.018006 (0.204310) \| 0.426943 / 0.000490 (0.426454) \| 0.008785 / 0.000200 (0.008585) \| 0.000081 / 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) \| 0.024716 / 0.037411 (-0.012695) \| 0.075341 / 0.014526 (0.060816) \| 0.089532 / 0.176557 (-0.087024) \| 0.147638 / 0.737135 (-0.589498) \| 0.085697 / 0.296338 (-0.210641) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396395 / 0.215209 (0.181186) \| 3.947280 / 2.077655 (1.869625) \| 1.894762 / 1.504120 (0.390642) \| 1.712094 / 1.541195 (0.170899) \| 1.779049 / 1.468490 (0.310559) \| 0.509206 / 4.584777 (-4.075571) \| 3.073951 / 3.745712 (-0.671761) \| 2.886826 / 5.269862 (-2.383035) \| 1.894444 / 4.565676 (-2.671232) \| 0.059519 / 0.424275 (-0.364756) \| 0.006951 / 0.007607 (-0.000656) \| 0.468213 / 0.226044 (0.242169) \| 4.667134 / 2.268929 (2.398206) \| 2.342516 / 55.444624 (-53.102108) \| 1.992047 / 6.876477 (-4.884430) \| 2.142059 / 2.142072 (-0.000014) \| 0.600507 / 4.805227 (-4.204720) \| 0.128982 / 6.500664 (-6.371682) \| 0.062100 / 0.075469 (-0.013369) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234500 / 1.841788 (-0.607288) \| 17.951646 / 8.074308 (9.877338) \| 13.862763 / 10.191392 (3.671371) \| 0.143133 / 0.680424 (-0.537291) \| 0.016643 / 0.534201 (-0.517558) \| 0.333174 / 0.579283 (-0.246109) \| 0.366956 / 0.434364 (-0.067408) \| 0.384569 / 0.540337 (-0.155769) \| 0.546830 / 1.386936 (-0.840106) \|\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) \| 0.006146 / 0.011353 (-0.005207) \| 0.003725 / 0.011008 (-0.007283) \| 0.062099 / 0.038508 (0.023591) \| 0.064117 / 0.023109 (0.041008) \| 0.456100 / 0.275898 (0.180202) \| 0.490794 / 0.323480 (0.167314) \| 0.005652 / 0.007986 (-0.002334) \| 0.002897 / 0.004328 (-0.001432) \| 0.061909 / 0.004250 (0.057659) \| 0.050634 / 0.037052 (0.013582) \| 0.454422 / 0.258489 (0.195933) \| 0.493208 / 0.293841 (0.199367) \| 0.028822 / 0.128546 (-0.099724) \| 0.008115 / 0.075646 (-0.067531) \| 0.067214 / 0.419271 (-0.352058) \| 0.041529 / 0.043533 (-0.002004) \| 0.458016 / 0.255139 (0.202877) \| 0.476059 / 0.283200 (0.192859) \| 0.019926 / 0.141683 (-0.121757) \| 1.465345 / 1.452155 (0.013190) \| 1.533518 / 1.492716 (0.040802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218830 / 0.018006 (0.200823) \| 0.418869 / 0.000490 (0.418380) \| 0.005154 / 0.000200 (0.004954) \| 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) \| 0.027648 / 0.037411 (-0.009763) \| 0.083842 / 0.014526 (0.069316) \| 0.092300 / 0.176557 (-0.084257) \| 0.146098 / 0.737135 (-0.591037) \| 0.093441 / 0.296338 (-0.202898) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.464426 / 0.215209 (0.249217) \| 4.632705 / 2.077655 (2.555051) \| 2.642091 / 1.504120 (1.137971) \| 2.461768 / 1.541195 (0.920573) \| 2.535554 / 1.468490 (1.067064) \| 0.507506 / 4.584777 (-4.077271) \| 3.095485 / 3.745712 (-0.650227) \| 2.884261 / 5.269862 (-2.385601) \| 1.908943 / 4.565676 (-2.656734) \| 0.058622 / 0.424275 (-0.365653) \| 0.006892 / 0.007607 (-0.000715) \| 0.536045 / 0.226044 (0.310001) \| 5.377448 / 2.268929 (3.108519) \| 3.076023 / 55.444624 (-52.368602) \| 2.745586 / 6.876477 (-4.130890) \| 2.939582 / 2.142072 (0.797510) \| 0.595639 / 4.805227 (-4.209589) \| 0.125086 / 6.500664 (-6.375578) \| 0.061075 / 0.075469 (-0.014394) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.342820 / 1.841788 (-0.498968) \| 18.326240 / 8.074308 (10.251932) \| 15.007094 / 10.191392 (4.815702) \| 0.133037 / 0.680424 (-0.547387) \| 0.018702 / 0.534201 (-0.515499) \| 0.330245 / 0.579283 (-0.249038) \| 0.381494 / 0.434364 (-0.052870) \| 0.393705 / 0.540337 (-0.146633) \| 0.533676 / 1.386936 (-0.853260) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45291275d84448c235829fb62aa951070aa4061d \"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) \| 0.007644 / 0.011353 (-0.003709) \| 0.004759 / 0.011008 (-0.006249) \| 0.100569 / 0.038508 (0.062061) \| 0.089645 / 0.023109 (0.066536) \| 0.376679 / 0.275898 (0.100781) \| 0.413214 / 0.323480 (0.089735) \| 0.006087 / 0.007986 (-0.001899) \| 0.003832 / 0.004328 (-0.000496) \| 0.075892 / 0.004250 (0.071641) \| 0.064635 / 0.037052 (0.027582) \| 0.376874 / 0.258489 (0.118385) \| 0.436756 / 0.293841 (0.142915) \| 0.036372 / 0.128546 (-0.092174) \| 0.010047 / 0.075646 (-0.065599) \| 0.345073 / 0.419271 (-0.074198) \| 0.062092 / 0.043533 (0.018559) \| 0.380503 / 0.255139 (0.125364) \| 0.414800 / 0.283200 (0.131600) \| 0.028274 / 0.141683 (-0.113409) \| 1.732463 / 1.452155 (0.280308) \| 1.859049 / 1.492716 (0.366333) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267129 / 0.018006 (0.249123) \| 0.509109 / 0.000490 (0.508619) \| 0.012329 / 0.000200 (0.012130) \| 0.000432 / 0.000054 (0.000377) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033773 / 0.037411 (-0.003638) \| 0.102800 / 0.014526 (0.088274) \| 0.114256 / 0.176557 (-0.062300) \| 0.182048 / 0.737135 (-0.555087) \| 0.118225 / 0.296338 (-0.178113) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457553 / 0.215209 (0.242344) \| 4.588212 / 2.077655 (2.510557) \| 2.184138 / 1.504120 (0.680018) \| 2.003570 / 1.541195 (0.462375) \| 2.093217 / 1.468490 (0.624727) \| 0.585679 / 4.584777 (-3.999098) \| 4.175319 / 3.745712 (0.429607) \| 3.914168 / 5.269862 (-1.355693) \| 2.452992 / 4.565676 (-2.112684) \| 0.068363 / 0.424275 (-0.355912) \| 0.009314 / 0.007607 (0.001707) \| 0.543640 / 0.226044 (0.317595) \| 5.440853 / 2.268929 (3.171925) \| 2.782415 / 55.444624 (-52.662210) \| 2.332359 / 6.876477 (-4.544118) \| 2.628520 / 2.142072 (0.486448) \| 0.696838 / 4.805227 (-4.108389) \| 0.160653 / 6.500664 (-6.340012) \| 0.075599 / 0.075469 (0.000130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.545305 / 1.841788 (-0.296483) \| 23.073174 / 8.074308 (14.998866) \| 16.974977 / 10.191392 (6.783585) \| 0.183719 / 0.680424 (-0.496705) \| 0.021633 / 0.534201 (-0.512568) \| 0.471202 / 0.579283 (-0.108081) \| 0.479385 / 0.434364 (0.045021) \| 0.550872 / 0.540337 (0.010535) \| 0.766825 / 1.386936 (-0.620111) \|\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) \| 0.007918 / 0.011353 (-0.003435) \| 0.004793 / 0.011008 (-0.006215) \| 0.077273 / 0.038508 (0.038765) \| 0.092079 / 0.023109 (0.068969) \| 0.483269 / 0.275898 (0.207371) \| 0.524919 / 0.323480 (0.201439) \| 0.006273 / 0.007986 (-0.001713) \| 0.004018 / 0.004328 (-0.000310) \| 0.077188 / 0.004250 (0.072937) \| 0.067891 / 0.037052 (0.030839) \| 0.478531 / 0.258489 (0.220042) \| 0.526956 / 0.293841 (0.233115) \| 0.038309 / 0.128546 (-0.090237) \| 0.010133 / 0.075646 (-0.065513) \| 0.083892 / 0.419271 (-0.335379) \| 0.057369 / 0.043533 (0.013836) \| 0.509427 / 0.255139 (0.254288) \| 0.506574 / 0.283200 (0.223374) \| 0.027987 / 0.141683 (-0.113696) \| 1.897469 / 1.452155 (0.445314) \| 1.893102 / 1.492716 (0.400385) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243003 / 0.018006 (0.224997) \| 0.500267 / 0.000490 (0.499777) \| 0.007442 / 0.000200 (0.007242) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039266 / 0.037411 (0.001855) \| 0.114438 / 0.014526 (0.099912) \| 0.124528 / 0.176557 (-0.052029) \| 0.189399 / 0.737135 (-0.547736) \| 0.126703 / 0.296338 (-0.169635) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.518139 / 0.215209 (0.302930) \| 5.162058 / 2.077655 (3.084403) \| 2.835111 / 1.504120 (1.330991) \| 2.640919 / 1.541195 (1.099724) \| 2.736800 / 1.468490 (1.268310) \| 0.582813 / 4.584777 (-4.001964) \| 4.246269 / 3.745712 (0.500557) \| 3.891161 / 5.269862 (-1.378701) \| 2.445392 / 4.565676 (-2.120285) \| 0.068943 / 0.424275 (-0.355332) \| 0.009248 / 0.007607 (0.001641) \| 0.604859 / 0.226044 (0.378815) \| 6.030660 / 2.268929 (3.761731) \| 3.409778 / 55.444624 (-52.034846) \| 2.990488 / 6.876477 (-3.885988) \| 3.281317 / 2.142072 (1.139245) \| 0.697705 / 4.805227 (-4.107523) \| 0.159502 / 6.500664 (-6.341162) \| 0.072471 / 0.075469 (-0.002999) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.625428 / 1.841788 (-0.216360) \| 23.602509 / 8.074308 (15.528201) \| 18.091474 / 10.191392 (7.900082) \| 0.172816 / 0.680424 (-0.507608) \| 0.023708 / 0.534201 (-0.510493) \| 0.473768 / 0.579283 (-0.105515) \| 0.493713 / 0.434364 (0.059349) \| 0.566326 / 0.540337 (0.025989) \| 0.788670 / 1.386936 (-0.598266) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ee2359c17ccb35b57e195f2bfe8478f49630039 \"CML watermark\")\n", "> Thanks. Any comment on my comment below?\r\n> \r\n> >Maybe we should update the docstring of get_data_patterns accordingly? Currently it only gives examples of outputs with not in a single path segment (i.e. not with a / as prefix or suffix).\r\n\r\nYea right we need to update it indeed, the outputs are the ones from older versions of fsspec, and from older patterns that we don't use anymore.\r\n\r\nIn general in docstrings I also think we should encourage users to use `/` instead of `` (which has a behavior that is unique to fsspec)", "Also just noticed that `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` seems to include `KEYWORDS_IN_FILENAME_BASE_PATTERNS`. I guess we can try to remove the filename one in another PR to remove this redundancy \r\n\r\n(noticed this by checking that the data pattern is the same for both the dir name and filename examples in the get_data_patterns docstring)", "<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) \| 0.006922 / 0.011353 (-0.004431) \| 0.004459 / 0.011008 (-0.006549) \| 0.084742 / 0.038508 (0.046234) \| 0.089002 / 0.023109 (0.065893) \| 0.310886 / 0.275898 (0.034988) \| 0.340518 / 0.323480 (0.017038) \| 0.007011 / 0.007986 (-0.000975) \| 0.004566 / 0.004328 (0.000237) \| 0.067260 / 0.004250 (0.063009) \| 0.066349 / 0.037052 (0.029297) \| 0.324029 / 0.258489 (0.065540) \| 0.373785 / 0.293841 (0.079944) \| 0.031780 / 0.128546 (-0.096766) \| 0.009208 / 0.075646 (-0.066438) \| 0.288871 / 0.419271 (-0.130401) \| 0.054548 / 0.043533 (0.011015) \| 0.313344 / 0.255139 (0.058205) \| 0.336430 / 0.283200 (0.053231) \| 0.029037 / 0.141683 (-0.112646) \| 1.483797 / 1.452155 (0.031642) \| 1.581884 / 1.492716 (0.089167) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.370520 / 0.018006 (0.352514) \| 0.796720 / 0.000490 (0.796230) \| 0.009329 / 0.000200 (0.009129) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033002 / 0.037411 (-0.004410) \| 0.083442 / 0.014526 (0.068916) \| 0.106468 / 0.176557 (-0.070088) \| 0.165315 / 0.737135 (-0.571820) \| 0.103048 / 0.296338 (-0.193291) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386800 / 0.215209 (0.171591) \| 3.843312 / 2.077655 (1.765658) \| 1.848953 / 1.504120 (0.344834) \| 1.679508 / 1.541195 (0.138313) \| 1.733578 / 1.468490 (0.265088) \| 0.488455 / 4.584777 (-4.096322) \| 3.613594 / 3.745712 (-0.132118) \| 3.533334 / 5.269862 (-1.736528) \| 2.176216 / 4.565676 (-2.389460) \| 0.056915 / 0.424275 (-0.367360) \| 0.007349 / 0.007607 (-0.000258) \| 0.465132 / 0.226044 (0.239088) \| 4.638479 / 2.268929 (2.369550) \| 2.354741 / 55.444624 (-53.089883) \| 1.991777 / 6.876477 (-4.884700) \| 2.249823 / 2.142072 (0.107751) \| 0.582748 / 4.805227 (-4.222480) \| 0.133829 / 6.500664 (-6.366835) \| 0.060949 / 0.075469 (-0.014520) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.252027 / 1.841788 (-0.589760) \| 20.660234 / 8.074308 (12.585926) \| 14.328496 / 10.191392 (4.137104) \| 0.164872 / 0.680424 (-0.515552) \| 0.018867 / 0.534201 (-0.515334) \| 0.392850 / 0.579283 (-0.186433) \| 0.425684 / 0.434364 (-0.008679) \| 0.461776 / 0.540337 (-0.078562) \| 0.663688 / 1.386936 (-0.723248) \|\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) \| 0.007010 / 0.011353 (-0.004343) \| 0.004791 / 0.011008 (-0.006217) \| 0.064738 / 0.038508 (0.026230) \| 0.088648 / 0.023109 (0.065539) \| 0.418106 / 0.275898 (0.142208) \| 0.446767 / 0.323480 (0.123287) \| 0.006761 / 0.007986 (-0.001224) \| 0.004649 / 0.004328 (0.000320) \| 0.066345 / 0.004250 (0.062094) \| 0.068326 / 0.037052 (0.031274) \| 0.423426 / 0.258489 (0.164937) \| 0.463160 / 0.293841 (0.169319) \| 0.032689 / 0.128546 (-0.095858) \| 0.009299 / 0.075646 (-0.066347) \| 0.071321 / 0.419271 (-0.347951) \| 0.048752 / 0.043533 (0.005219) \| 0.418932 / 0.255139 (0.163793) \| 0.440673 / 0.283200 (0.157473) \| 0.027898 / 0.141683 (-0.113785) \| 1.531860 / 1.452155 (0.079705) \| 1.620456 / 1.492716 (0.127739) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.354917 / 0.018006 (0.336911) \| 0.792432 / 0.000490 (0.791943) \| 0.006626 / 0.000200 (0.006426) \| 0.000124 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036190 / 0.037411 (-0.001222) \| 0.093052 / 0.014526 (0.078526) \| 0.111927 / 0.176557 (-0.064629) \| 0.165571 / 0.737135 (-0.571564) \| 0.112159 / 0.296338 (-0.184180) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437798 / 0.215209 (0.222589) \| 4.367166 / 2.077655 (2.289511) \| 2.343292 / 1.504120 (0.839172) \| 2.169298 / 1.541195 (0.628103) \| 2.224471 / 1.468490 (0.755981) \| 0.487317 / 4.584777 (-4.097460) \| 3.627825 / 3.745712 (-0.117887) \| 3.500914 / 5.269862 (-1.768947) \| 2.175862 / 4.565676 (-2.389815) \| 0.057975 / 0.424275 (-0.366300) \| 0.007509 / 0.007607 (-0.000098) \| 0.517389 / 0.226044 (0.291345) \| 5.169694 / 2.268929 (2.900766) \| 2.850993 / 55.444624 (-52.593631) \| 2.473111 / 6.876477 (-4.403366) \| 2.746731 / 2.142072 (0.604659) \| 0.586597 / 4.805227 (-4.218630) \| 0.134082 / 6.500664 (-6.366582) \| 0.061035 / 0.075469 (-0.014434) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.375186 / 1.841788 (-0.466602) \| 20.960817 / 8.074308 (12.886509) \| 15.035071 / 10.191392 (4.843679) \| 0.169494 / 0.680424 (-0.510930) \| 0.020654 / 0.534201 (-0.513547) \| 0.398047 / 0.579283 (-0.181236) \| 0.438117 / 0.434364 (0.003753) \| 0.483896 / 0.540337 (-0.056441) \| 0.690728 / 1.386936 (-0.696208) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e7fc64af912e5fcdcf949ed09d954332f0ae94a \"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) \| 0.006892 / 0.011353 (-0.004461) \| 0.004087 / 0.011008 (-0.006921) \| 0.084695 / 0.038508 (0.046187) \| 0.078084 / 0.023109 (0.054975) \| 0.322976 / 0.275898 (0.047078) \| 0.355332 / 0.323480 (0.031852) \| 0.004235 / 0.007986 (-0.003750) \| 0.003450 / 0.004328 (-0.000879) \| 0.065355 / 0.004250 (0.061104) \| 0.058593 / 0.037052 (0.021541) \| 0.335761 / 0.258489 (0.077272) \| 0.370392 / 0.293841 (0.076551) \| 0.031720 / 0.128546 (-0.096827) \| 0.008611 / 0.075646 (-0.067036) \| 0.288213 / 0.419271 (-0.131059) \| 0.053374 / 0.043533 (0.009842) \| 0.321863 / 0.255139 (0.066724) \| 0.341587 / 0.283200 (0.058387) \| 0.025694 / 0.141683 (-0.115989) \| 1.470502 / 1.452155 (0.018348) \| 1.565068 / 1.492716 (0.072352) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231063 / 0.018006 (0.213057) \| 0.464996 / 0.000490 (0.464506) \| 0.007316 / 0.000200 (0.007116) \| 0.000288 / 0.000054 (0.000233) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029244 / 0.037411 (-0.008167) \| 0.086303 / 0.014526 (0.071777) \| 0.097281 / 0.176557 (-0.079276) \| 0.153552 / 0.737135 (-0.583583) \| 0.098488 / 0.296338 (-0.197850) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382753 / 0.215209 (0.167544) \| 3.826503 / 2.077655 (1.748848) \| 1.848439 / 1.504120 (0.344319) \| 1.688519 / 1.541195 (0.147324) \| 1.787867 / 1.468490 (0.319377) \| 0.489708 / 4.584777 (-4.095069) \| 3.576780 / 3.745712 (-0.168932) \| 3.341536 / 5.269862 (-1.928325) \| 2.108787 / 4.565676 (-2.456889) \| 0.057409 / 0.424275 (-0.366866) \| 0.007325 / 0.007607 (-0.000282) \| 0.459536 / 0.226044 (0.233492) \| 4.590609 / 2.268929 (2.321681) \| 2.313005 / 55.444624 (-53.131620) \| 1.972389 / 6.876477 (-4.904087) \| 2.218511 / 2.142072 (0.076439) \| 0.613817 / 4.805227 (-4.191410) \| 0.133846 / 6.500664 (-6.366818) \| 0.062190 / 0.075469 (-0.013279) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279860 / 1.841788 (-0.561928) \| 19.549777 / 8.074308 (11.475469) \| 14.225844 / 10.191392 (4.034452) \| 0.164682 / 0.680424 (-0.515741) \| 0.018321 / 0.534201 (-0.515880) \| 0.389874 / 0.579283 (-0.189409) \| 0.408597 / 0.434364 (-0.025767) \| 0.454327 / 0.540337 (-0.086011) \| 0.645571 / 1.386936 (-0.741365) \|\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) \| 0.007021 / 0.011353 (-0.004332) \| 0.004119 / 0.011008 (-0.006889) \| 0.065393 / 0.038508 (0.026885) \| 0.085005 / 0.023109 (0.061896) \| 0.412221 / 0.275898 (0.136323) \| 0.438266 / 0.323480 (0.114786) \| 0.005594 / 0.007986 (-0.002392) \| 0.003499 / 0.004328 (-0.000829) \| 0.065053 / 0.004250 (0.060802) \| 0.060608 / 0.037052 (0.023555) \| 0.413938 / 0.258489 (0.155449) \| 0.446192 / 0.293841 (0.152351) \| 0.032232 / 0.128546 (-0.096314) \| 0.008617 / 0.075646 (-0.067029) \| 0.071296 / 0.419271 (-0.347976) \| 0.048756 / 0.043533 (0.005223) \| 0.404977 / 0.255139 (0.149838) \| 0.426801 / 0.283200 (0.143602) \| 0.023650 / 0.141683 (-0.118033) \| 1.526928 / 1.452155 (0.074773) \| 1.627504 / 1.492716 (0.134787) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224318 / 0.018006 (0.206312) \| 0.469717 / 0.000490 (0.469227) \| 0.005539 / 0.000200 (0.005339) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034240 / 0.037411 (-0.003171) \| 0.096449 / 0.014526 (0.081923) \| 0.107309 / 0.176557 (-0.069247) \| 0.160246 / 0.737135 (-0.576889) \| 0.107595 / 0.296338 (-0.188743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434266 / 0.215209 (0.219057) \| 4.325571 / 2.077655 (2.247916) \| 2.324066 / 1.504120 (0.819946) \| 2.140238 / 1.541195 (0.599044) \| 2.244593 / 1.468490 (0.776103) \| 0.486259 / 4.584777 (-4.098518) \| 3.644120 / 3.745712 (-0.101592) \| 3.372330 / 5.269862 (-1.897531) \| 2.074779 / 4.565676 (-2.490897) \| 0.057154 / 0.424275 (-0.367121) \| 0.007304 / 0.007607 (-0.000303) \| 0.516944 / 0.226044 (0.290899) \| 5.174300 / 2.268929 (2.905372) \| 2.816269 / 55.444624 (-52.628356) \| 2.462943 / 6.876477 (-4.413534) \| 2.735851 / 2.142072 (0.593779) \| 0.589028 / 4.805227 (-4.216200) \| 0.131804 / 6.500664 (-6.368860) \| 0.060173 / 0.075469 (-0.015296) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354540 / 1.841788 (-0.487248) \| 20.436511 / 8.074308 (12.362203) \| 15.541981 / 10.191392 (5.350589) \| 0.168399 / 0.680424 (-0.512025) \| 0.020716 / 0.534201 (-0.513485) \| 0.396275 / 0.579283 (-0.183008) \| 0.427232 / 0.434364 (-0.007132) \| 0.475121 / 0.540337 (-0.065216) \| 0.648579 / 1.386936 (-0.738357) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4fa138fc0d9aa1536194fd46566840e698ccde03 \"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) \| 0.009071 / 0.011353 (-0.002282) \| 0.005820 / 0.011008 (-0.005188) \| 0.119974 / 0.038508 (0.081466) \| 0.092145 / 0.023109 (0.069036) \| 0.445349 / 0.275898 (0.169451) \| 0.442488 / 0.323480 (0.119008) \| 0.005352 / 0.007986 (-0.002634) \| 0.004332 / 0.004328 (0.000003) \| 0.084397 / 0.004250 (0.080147) \| 0.064624 / 0.037052 (0.027572) \| 0.430938 / 0.258489 (0.172448) \| 0.503574 / 0.293841 (0.209733) \| 0.047900 / 0.128546 (-0.080647) \| 0.014237 / 0.075646 (-0.061409) \| 0.366145 / 0.419271 (-0.053127) \| 0.066344 / 0.043533 (0.022811) \| 0.424582 / 0.255139 (0.169443) \| 0.451845 / 0.283200 (0.168646) \| 0.041409 / 0.141683 (-0.100274) \| 1.886998 / 1.452155 (0.434843) \| 2.011676 / 1.492716 (0.518960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301008 / 0.018006 (0.283001) \| 0.608670 / 0.000490 (0.608180) \| 0.011963 / 0.000200 (0.011763) \| 0.000117 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031996 / 0.037411 (-0.005415) \| 0.102274 / 0.014526 (0.087748) \| 0.121437 / 0.176557 (-0.055120) \| 0.181647 / 0.737135 (-0.555489) \| 0.121634 / 0.296338 (-0.174704) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.597070 / 0.215209 (0.381861) \| 5.973808 / 2.077655 (3.896154) \| 2.486345 / 1.504120 (0.982225) \| 2.125395 / 1.541195 (0.584201) \| 2.270864 / 1.468490 (0.802374) \| 0.880031 / 4.584777 (-3.704746) \| 5.396522 / 3.745712 (1.650809) \| 4.702005 / 5.269862 (-0.567857) \| 3.023087 / 4.565676 (-1.542589) \| 0.097093 / 0.424275 (-0.327182) \| 0.008457 / 0.007607 (0.000850) \| 0.712164 / 0.226044 (0.486120) \| 7.112867 / 2.268929 (4.843938) \| 3.364509 / 55.444624 (-52.080115) \| 2.646953 / 6.876477 (-4.229524) \| 2.795967 / 2.142072 (0.653894) \| 1.067182 / 4.805227 (-3.738046) \| 0.218297 / 6.500664 (-6.282368) \| 0.071720 / 0.075469 (-0.003750) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.640477 / 1.841788 (-0.201311) \| 24.875163 / 8.074308 (16.800855) \| 22.125706 / 10.191392 (11.934314) \| 0.247267 / 0.680424 (-0.433157) \| 0.033717 / 0.534201 (-0.500484) \| 0.492422 / 0.579283 (-0.086862) \| 0.578323 / 0.434364 (0.143959) \| 0.579503 / 0.540337 (0.039165) \| 0.816721 / 1.386936 (-0.570215) \|\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) \| 0.009372 / 0.011353 (-0.001981) \| 0.005449 / 0.011008 (-0.005559) \| 0.095371 / 0.038508 (0.056863) \| 0.086320 / 0.023109 (0.063211) \| 0.539573 / 0.275898 (0.263675) \| 0.580338 / 0.323480 (0.256858) \| 0.007028 / 0.007986 (-0.000958) \| 0.004196 / 0.004328 (-0.000133) \| 0.082710 / 0.004250 (0.078460) \| 0.064336 / 0.037052 (0.027284) \| 0.521490 / 0.258489 (0.263001) \| 0.567942 / 0.293841 (0.274101) \| 0.049659 / 0.128546 (-0.078887) \| 0.017297 / 0.075646 (-0.058350) \| 0.093874 / 0.419271 (-0.325398) \| 0.061664 / 0.043533 (0.018131) \| 0.524476 / 0.255139 (0.269337) \| 0.563255 / 0.283200 (0.280055) \| 0.039990 / 0.141683 (-0.101693) \| 1.854438 / 1.452155 (0.402283) \| 1.819321 / 1.492716 (0.326605) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298817 / 0.018006 (0.280811) \| 0.629381 / 0.000490 (0.628891) \| 0.006259 / 0.000200 (0.006059) \| 0.000690 / 0.000054 (0.000635) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.041009 / 0.037411 (0.003598) \| 0.123845 / 0.014526 (0.109319) \| 0.138606 / 0.176557 (-0.037951) \| 0.215042 / 0.737135 (-0.522093) \| 0.129572 / 0.296338 (-0.166767) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668823 / 0.215209 (0.453614) \| 6.596762 / 2.077655 (4.519108) \| 3.275429 / 1.504120 (1.771309) \| 2.921747 / 1.541195 (1.380553) \| 2.963748 / 1.468490 (1.495258) \| 0.897588 / 4.584777 (-3.687188) \| 5.683618 / 3.745712 (1.937906) \| 5.051102 / 5.269862 (-0.218760) \| 3.178855 / 4.565676 (-1.386822) \| 0.107446 / 0.424275 (-0.316829) \| 0.008967 / 0.007607 (0.001360) \| 0.785577 / 0.226044 (0.559532) \| 8.236556 / 2.268929 (5.967628) \| 3.914725 / 55.444624 (-51.529899) \| 3.129068 / 6.876477 (-3.747409) \| 3.368383 / 2.142072 (1.226310) \| 1.004307 / 4.805227 (-3.800920) \| 0.204788 / 6.500664 (-6.295876) \| 0.078250 / 0.075469 (0.002780) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.778574 / 1.841788 (-0.063213) \| 25.583659 / 8.074308 (17.509351) \| 23.505866 / 10.191392 (13.314474) \| 0.228759 / 0.680424 (-0.451665) \| 0.038348 / 0.534201 (-0.495853) \| 0.468980 / 0.579283 (-0.110303) \| 0.630194 / 0.434364 (0.195830) \| 0.587535 / 0.540337 (0.047198) \| 0.831761 / 1.386936 (-0.555175) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68f4f847f3248f02fc99458310d9d786906d7a6f \"CML watermark\")\n", "I've addressed the comments. Let me know if it looks all good now :)", "Actually just found out that the current `/[-._ 0-9/]train[-._ 0-9/]**` doesn't match `data/train.csv` in bash (but does match in fsspec right now).\r\n\r\nSo there might be a risk that this pattern breaks in the future no ?", "@lhoestq `fsspec` has tests to check their specific (non-posix) behavior, so I think merging in the current state is fine. And if they make a breaking change in the future, we can align the patterns once again :) ", "Yea after more thoughts I also think it's fine. Feel free to merge !", "<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) \| 0.006920 / 0.011353 (-0.004433) \| 0.004182 / 0.011008 (-0.006826) \| 0.084629 / 0.038508 (0.046121) \| 0.086052 / 0.023109 (0.062943) \| 0.326062 / 0.275898 (0.050164) \| 0.344190 / 0.323480 (0.020710) \| 0.005393 / 0.007986 (-0.002593) \| 0.003410 / 0.004328 (-0.000918) \| 0.064327 / 0.004250 (0.060076) \| 0.056556 / 0.037052 (0.019504) \| 0.319255 / 0.258489 (0.060766) \| 0.357943 / 0.293841 (0.064102) \| 0.032097 / 0.128546 (-0.096450) \| 0.008778 / 0.075646 (-0.066868) \| 0.291057 / 0.419271 (-0.128215) \| 0.053225 / 0.043533 (0.009692) \| 0.307713 / 0.255139 (0.052574) \| 0.350058 / 0.283200 (0.066858) \| 0.024380 / 0.141683 (-0.117303) \| 1.459482 / 1.452155 (0.007328) \| 1.555711 / 1.492716 (0.062994) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239487 / 0.018006 (0.221480) \| 0.467604 / 0.000490 (0.467114) \| 0.010742 / 0.000200 (0.010542) \| 0.000285 / 0.000054 (0.000230) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029394 / 0.037411 (-0.008018) \| 0.087404 / 0.014526 (0.072879) \| 0.098701 / 0.176557 (-0.077855) \| 0.154145 / 0.737135 (-0.582990) \| 0.099726 / 0.296338 (-0.196612) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.389008 / 0.215209 (0.173799) \| 3.873165 / 2.077655 (1.795510) \| 1.860676 / 1.504120 (0.356556) \| 1.679668 / 1.541195 (0.138474) \| 1.782347 / 1.468490 (0.313857) \| 0.489469 / 4.584777 (-4.095308) \| 3.678706 / 3.745712 (-0.067006) \| 3.404076 / 5.269862 (-1.865785) \| 2.110972 / 4.565676 (-2.454704) \| 0.057478 / 0.424275 (-0.366797) \| 0.007443 / 0.007607 (-0.000164) \| 0.464780 / 0.226044 (0.238736) \| 4.643606 / 2.268929 (2.374678) \| 2.355744 / 55.444624 (-53.088881) \| 1.993992 / 6.876477 (-4.882485) \| 2.245520 / 2.142072 (0.103447) \| 0.592773 / 4.805227 (-4.212454) \| 0.135369 / 6.500664 (-6.365295) \| 0.062478 / 0.075469 (-0.012991) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257537 / 1.841788 (-0.584251) \| 19.828010 / 8.074308 (11.753702) \| 14.709260 / 10.191392 (4.517868) \| 0.168359 / 0.680424 (-0.512065) \| 0.018907 / 0.534201 (-0.515294) \| 0.397223 / 0.579283 (-0.182060) \| 0.421760 / 0.434364 (-0.012604) \| 0.464597 / 0.540337 (-0.075740) \| 0.665905 / 1.386936 (-0.721031) \|\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) \| 0.007247 / 0.011353 (-0.004106) \| 0.004104 / 0.011008 (-0.006904) \| 0.065008 / 0.038508 (0.026500) \| 0.083485 / 0.023109 (0.060376) \| 0.399808 / 0.275898 (0.123910) \| 0.433374 / 0.323480 (0.109894) \| 0.005453 / 0.007986 (-0.002532) \| 0.003479 / 0.004328 (-0.000850) \| 0.065126 / 0.004250 (0.060876) \| 0.059945 / 0.037052 (0.022893) \| 0.402018 / 0.258489 (0.143529) \| 0.437927 / 0.293841 (0.144086) \| 0.032654 / 0.128546 (-0.095892) \| 0.008717 / 0.075646 (-0.066929) \| 0.071737 / 0.419271 (-0.347534) \| 0.048903 / 0.043533 (0.005370) \| 0.402107 / 0.255139 (0.146968) \| 0.417602 / 0.283200 (0.134402) \| 0.024821 / 0.141683 (-0.116862) \| 1.474471 / 1.452155 (0.022316) \| 1.559571 / 1.492716 (0.066855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232010 / 0.018006 (0.214003) \| 0.460768 / 0.000490 (0.460278) \| 0.005250 / 0.000200 (0.005050) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033839 / 0.037411 (-0.003573) \| 0.101617 / 0.014526 (0.087091) \| 0.107984 / 0.176557 (-0.068573) \| 0.160923 / 0.737135 (-0.576212) \| 0.110367 / 0.296338 (-0.185971) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433087 / 0.215209 (0.217878) \| 4.324100 / 2.077655 (2.246445) \| 2.312937 / 1.504120 (0.808817) \| 2.159903 / 1.541195 (0.618708) \| 2.240235 / 1.468490 (0.771745) \| 0.500659 / 4.584777 (-4.084118) \| 3.743801 / 3.745712 (-0.001911) \| 3.441350 / 5.269862 (-1.828512) \| 2.141370 / 4.565676 (-2.424306) \| 0.059078 / 0.424275 (-0.365197) \| 0.007468 / 0.007607 (-0.000139) \| 0.508108 / 0.226044 (0.282064) \| 5.076738 / 2.268929 (2.807809) \| 2.825939 / 55.444624 (-52.618685) \| 2.467762 / 6.876477 (-4.408715) \| 2.705079 / 2.142072 (0.563006) \| 0.603363 / 4.805227 (-4.201864) \| 0.136267 / 6.500664 (-6.364397) \| 0.062887 / 0.075469 (-0.012582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.359344 / 1.841788 (-0.482443) \| 20.581510 / 8.074308 (12.507202) \| 15.534489 / 10.191392 (5.343097) \| 0.192068 / 0.680424 (-0.488356) \| 0.020831 / 0.534201 (-0.513370) \| 0.403330 / 0.579283 (-0.175953) \| 0.429536 / 0.434364 (-0.004828) \| 0.479906 / 0.540337 (-0.060431) \| 0.674170 / 1.386936 (-0.712766) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ac74c2df928dece49ca2cf25e14172896b442e \"CML watermark\")\n" ]	2023-09-15T17:58:25Z	2023-09-26T15:41:38Z	2023-09-26T15:32:51Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6244.diff", "html_url": "https://github.com/huggingface/datasets/pull/6244", "merged_at": "2023-09-26T15:32:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/6244.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6244" }	Fix #6214	{ "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/6244/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6243	6,243	Fix cast from fixed size list to variable size list	{ "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) \| 0.006784 / 0.011353 (-0.004569) \| 0.004051 / 0.011008 (-0.006957) \| 0.083790 / 0.038508 (0.045282) \| 0.081219 / 0.023109 (0.058110) \| 0.313195 / 0.275898 (0.037297) \| 0.336954 / 0.323480 (0.013475) \| 0.004324 / 0.007986 (-0.003662) \| 0.004516 / 0.004328 (0.000188) \| 0.065051 / 0.004250 (0.060801) \| 0.057647 / 0.037052 (0.020595) \| 0.316675 / 0.258489 (0.058186) \| 0.357936 / 0.293841 (0.064095) \| 0.030980 / 0.128546 (-0.097566) \| 0.008844 / 0.075646 (-0.066802) \| 0.287027 / 0.419271 (-0.132245) \| 0.052130 / 0.043533 (0.008597) \| 0.308125 / 0.255139 (0.052986) \| 0.337345 / 0.283200 (0.054145) \| 0.025781 / 0.141683 (-0.115902) \| 1.466161 / 1.452155 (0.014006) \| 1.565824 / 1.492716 (0.073108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299112 / 0.018006 (0.281106) \| 0.640520 / 0.000490 (0.640030) \| 0.008846 / 0.000200 (0.008647) \| 0.000273 / 0.000054 (0.000219) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029853 / 0.037411 (-0.007559) \| 0.081697 / 0.014526 (0.067172) \| 0.099110 / 0.176557 (-0.077447) \| 0.155864 / 0.737135 (-0.581271) \| 0.098749 / 0.296338 (-0.197590) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.385722 / 0.215209 (0.170512) \| 3.851490 / 2.077655 (1.773835) \| 1.851995 / 1.504120 (0.347875) \| 1.660398 / 1.541195 (0.119204) \| 1.769370 / 1.468490 (0.300879) \| 0.481523 / 4.584777 (-4.103254) \| 3.550449 / 3.745712 (-0.195263) \| 3.424782 / 5.269862 (-1.845079) \| 2.106470 / 4.565676 (-2.459206) \| 0.056500 / 0.424275 (-0.367775) \| 0.007891 / 0.007607 (0.000284) \| 0.465564 / 0.226044 (0.239520) \| 4.662892 / 2.268929 (2.393964) \| 2.305424 / 55.444624 (-53.139201) \| 1.980524 / 6.876477 (-4.895953) \| 2.218423 / 2.142072 (0.076350) \| 0.584662 / 4.805227 (-4.220565) \| 0.132325 / 6.500664 (-6.368340) \| 0.060773 / 0.075469 (-0.014696) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254261 / 1.841788 (-0.587527) \| 19.479805 / 8.074308 (11.405497) \| 14.222687 / 10.191392 (4.031295) \| 0.149829 / 0.680424 (-0.530595) \| 0.018630 / 0.534201 (-0.515571) \| 0.395284 / 0.579283 (-0.183999) \| 0.413385 / 0.434364 (-0.020978) \| 0.462931 / 0.540337 (-0.077406) \| 0.645359 / 1.386936 (-0.741577) \|\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) \| 0.006991 / 0.011353 (-0.004362) \| 0.004306 / 0.011008 (-0.006702) \| 0.065213 / 0.038508 (0.026705) \| 0.082442 / 0.023109 (0.059332) \| 0.411294 / 0.275898 (0.135396) \| 0.452176 / 0.323480 (0.128696) \| 0.005802 / 0.007986 (-0.002183) \| 0.003556 / 0.004328 (-0.000772) \| 0.066163 / 0.004250 (0.061913) \| 0.060680 / 0.037052 (0.023628) \| 0.416975 / 0.258489 (0.158486) \| 0.456353 / 0.293841 (0.162512) \| 0.033584 / 0.128546 (-0.094963) \| 0.008687 / 0.075646 (-0.066959) \| 0.071300 / 0.419271 (-0.347972) \| 0.049382 / 0.043533 (0.005849) \| 0.409329 / 0.255139 (0.154190) \| 0.434829 / 0.283200 (0.151629) \| 0.022966 / 0.141683 (-0.118716) \| 1.493847 / 1.452155 (0.041692) \| 1.582372 / 1.492716 (0.089656) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280578 / 0.018006 (0.262572) \| 0.538122 / 0.000490 (0.537632) \| 0.004515 / 0.000200 (0.004315) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033383 / 0.037411 (-0.004028) \| 0.093426 / 0.014526 (0.078901) \| 0.109314 / 0.176557 (-0.067242) \| 0.162349 / 0.737135 (-0.574786) \| 0.109849 / 0.296338 (-0.186490) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431073 / 0.215209 (0.215864) \| 4.311942 / 2.077655 (2.234287) \| 2.291170 / 1.504120 (0.787051) \| 2.132266 / 1.541195 (0.591072) \| 2.236526 / 1.468490 (0.768036) \| 0.492001 / 4.584777 (-4.092776) \| 3.523013 / 3.745712 (-0.222699) \| 3.413481 / 5.269862 (-1.856381) \| 2.112979 / 4.565676 (-2.452698) \| 0.058654 / 0.424275 (-0.365621) \| 0.007729 / 0.007607 (0.000121) \| 0.512027 / 0.226044 (0.285982) \| 5.125264 / 2.268929 (2.856336) \| 2.836281 / 55.444624 (-52.608344) \| 2.447253 / 6.876477 (-4.429224) \| 2.711908 / 2.142072 (0.569835) \| 0.592598 / 4.805227 (-4.212629) \| 0.134837 / 6.500664 (-6.365827) \| 0.059813 / 0.075469 (-0.015656) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.373464 / 1.841788 (-0.468323) \| 20.548983 / 8.074308 (12.474675) \| 14.799833 / 10.191392 (4.608441) \| 0.168601 / 0.680424 (-0.511823) \| 0.020358 / 0.534201 (-0.513843) \| 0.398790 / 0.579283 (-0.180494) \| 0.416921 / 0.434364 (-0.017443) \| 0.480542 / 0.540337 (-0.059795) \| 0.645062 / 1.386936 (-0.741874) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#afd6fc193a91cb0461c8bf3b64db6943c23de846 \"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) \| 0.008616 / 0.011353 (-0.002737) \| 0.004957 / 0.011008 (-0.006051) \| 0.102629 / 0.038508 (0.064121) \| 0.080492 / 0.023109 (0.057383) \| 0.461817 / 0.275898 (0.185919) \| 0.487964 / 0.323480 (0.164484) \| 0.006336 / 0.007986 (-0.001649) \| 0.004607 / 0.004328 (0.000278) \| 0.074311 / 0.004250 (0.070061) \| 0.060368 / 0.037052 (0.023315) \| 0.458076 / 0.258489 (0.199587) \| 0.493028 / 0.293841 (0.199187) \| 0.044153 / 0.128546 (-0.084394) \| 0.014066 / 0.075646 (-0.061581) \| 0.369848 / 0.419271 (-0.049424) \| 0.061690 / 0.043533 (0.018157) \| 0.439728 / 0.255139 (0.184590) \| 0.484706 / 0.283200 (0.201506) \| 0.034657 / 0.141683 (-0.107026) \| 1.710591 / 1.452155 (0.258437) \| 1.900225 / 1.492716 (0.407509) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.308837 / 0.018006 (0.290831) \| 0.579561 / 0.000490 (0.579072) \| 0.010163 / 0.000200 (0.009963) \| 0.000613 / 0.000054 (0.000558) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028108 / 0.037411 (-0.009303) \| 0.085072 / 0.014526 (0.070546) \| 0.103375 / 0.176557 (-0.073182) \| 0.173765 / 0.737135 (-0.563371) \| 0.102460 / 0.296338 (-0.193879) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.602642 / 0.215209 (0.387433) \| 5.582537 / 2.077655 (3.504882) \| 2.405553 / 1.504120 (0.901434) \| 2.057298 / 1.541195 (0.516103) \| 2.223787 / 1.468490 (0.755297) \| 0.846138 / 4.584777 (-3.738638) \| 5.290306 / 3.745712 (1.544594) \| 4.836066 / 5.269862 (-0.433795) \| 2.951901 / 4.565676 (-1.613775) \| 0.099432 / 0.424275 (-0.324843) \| 0.009198 / 0.007607 (0.001591) \| 0.731370 / 0.226044 (0.505325) \| 6.663026 / 2.268929 (4.394098) \| 3.200932 / 55.444624 (-52.243692) \| 2.486654 / 6.876477 (-4.389823) \| 2.833195 / 2.142072 (0.691123) \| 0.989481 / 4.805227 (-3.815746) \| 0.205176 / 6.500664 (-6.295488) \| 0.073760 / 0.075469 (-0.001709) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.745494 / 1.841788 (-0.096294) \| 24.649294 / 8.074308 (16.574986) \| 22.312182 / 10.191392 (12.120790) \| 0.245207 / 0.680424 (-0.435217) \| 0.031971 / 0.534201 (-0.502230) \| 0.495179 / 0.579283 (-0.084104) \| 0.603233 / 0.434364 (0.168869) \| 0.560906 / 0.540337 (0.020569) \| 0.788292 / 1.386936 (-0.598644) \|\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) \| 0.008922 / 0.011353 (-0.002431) \| 0.005203 / 0.011008 (-0.005805) \| 0.074414 / 0.038508 (0.035906) \| 0.077552 / 0.023109 (0.054443) \| 0.547217 / 0.275898 (0.271319) \| 0.625298 / 0.323480 (0.301818) \| 0.006135 / 0.007986 (-0.001851) \| 0.004163 / 0.004328 (-0.000165) \| 0.078014 / 0.004250 (0.073764) \| 0.064484 / 0.037052 (0.027431) \| 0.562356 / 0.258489 (0.303867) \| 0.643613 / 0.293841 (0.349772) \| 0.050155 / 0.128546 (-0.078391) \| 0.013665 / 0.075646 (-0.061981) \| 0.090224 / 0.419271 (-0.329048) \| 0.063852 / 0.043533 (0.020319) \| 0.560914 / 0.255139 (0.305775) \| 0.591531 / 0.283200 (0.308331) \| 0.036491 / 0.141683 (-0.105192) \| 1.670898 / 1.452155 (0.218743) \| 1.783924 / 1.492716 (0.291208) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.312764 / 0.018006 (0.294758) \| 0.611116 / 0.000490 (0.610626) \| 0.006367 / 0.000200 (0.006167) \| 0.000130 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033967 / 0.037411 (-0.003445) \| 0.101550 / 0.014526 (0.087025) \| 0.116953 / 0.176557 (-0.059604) \| 0.180061 / 0.737135 (-0.557075) \| 0.115220 / 0.296338 (-0.181118) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.642110 / 0.215209 (0.426901) \| 6.361381 / 2.077655 (4.283727) \| 2.948175 / 1.504120 (1.444055) \| 2.633935 / 1.541195 (1.092740) \| 2.822150 / 1.468490 (1.353660) \| 0.931412 / 4.584777 (-3.653365) \| 5.428540 / 3.745712 (1.682828) \| 4.672920 / 5.269862 (-0.596941) \| 3.102046 / 4.565676 (-1.463630) \| 0.100825 / 0.424275 (-0.323450) \| 0.009464 / 0.007607 (0.001857) \| 0.774102 / 0.226044 (0.548058) \| 7.715003 / 2.268929 (5.446074) \| 3.987807 / 55.444624 (-51.456817) \| 3.089129 / 6.876477 (-3.787347) \| 3.333247 / 2.142072 (1.191174) \| 1.012427 / 4.805227 (-3.792800) \| 0.200662 / 6.500664 (-6.300002) \| 0.072422 / 0.075469 (-0.003047) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.680364 / 1.841788 (-0.161424) \| 24.484576 / 8.074308 (16.410268) \| 21.920990 / 10.191392 (11.729598) \| 0.218604 / 0.680424 (-0.461820) \| 0.035818 / 0.534201 (-0.498383) \| 0.470648 / 0.579283 (-0.108635) \| 0.585108 / 0.434364 (0.150744) \| 0.539152 / 0.540337 (-0.001185) \| 0.763999 / 1.386936 (-0.622937) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfed1d09ed6c680085624d96eb99bfb2b0b27599 \"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) \| 0.006304 / 0.011353 (-0.005049) \| 0.003884 / 0.011008 (-0.007125) \| 0.084847 / 0.038508 (0.046339) \| 0.069372 / 0.023109 (0.046263) \| 0.318876 / 0.275898 (0.042978) \| 0.344733 / 0.323480 (0.021253) \| 0.005139 / 0.007986 (-0.002847) \| 0.003203 / 0.004328 (-0.001125) \| 0.065758 / 0.004250 (0.061507) \| 0.054189 / 0.037052 (0.017137) \| 0.317475 / 0.258489 (0.058986) \| 0.359310 / 0.293841 (0.065469) \| 0.030639 / 0.128546 (-0.097908) \| 0.008657 / 0.075646 (-0.066989) \| 0.289127 / 0.419271 (-0.130144) \| 0.052344 / 0.043533 (0.008811) \| 0.316122 / 0.255139 (0.060983) \| 0.338339 / 0.283200 (0.055140) \| 0.022677 / 0.141683 (-0.119006) \| 1.551629 / 1.452155 (0.099474) \| 1.617917 / 1.492716 (0.125201) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231067 / 0.018006 (0.213061) \| 0.450559 / 0.000490 (0.450070) \| 0.008484 / 0.000200 (0.008284) \| 0.000234 / 0.000054 (0.000179) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027054 / 0.037411 (-0.010357) \| 0.081560 / 0.014526 (0.067034) \| 0.094162 / 0.176557 (-0.082395) \| 0.148583 / 0.737135 (-0.588552) \| 0.093596 / 0.296338 (-0.202742) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388616 / 0.215209 (0.173407) \| 3.874905 / 2.077655 (1.797251) \| 1.915845 / 1.504120 (0.411725) \| 1.746410 / 1.541195 (0.205215) \| 1.828789 / 1.468490 (0.360299) \| 0.483270 / 4.584777 (-4.101506) \| 3.489157 / 3.745712 (-0.256555) \| 3.190086 / 5.269862 (-2.079776) \| 1.978023 / 4.565676 (-2.587653) \| 0.056290 / 0.424275 (-0.367985) \| 0.007585 / 0.007607 (-0.000022) \| 0.467051 / 0.226044 (0.241007) \| 4.665971 / 2.268929 (2.397043) \| 2.418550 / 55.444624 (-53.026075) \| 2.048338 / 6.876477 (-4.828139) \| 2.225275 / 2.142072 (0.083203) \| 0.576601 / 4.805227 (-4.228626) \| 0.131960 / 6.500664 (-6.368704) \| 0.060177 / 0.075469 (-0.015292) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249797 / 1.841788 (-0.591991) \| 18.552939 / 8.074308 (10.478631) \| 14.016616 / 10.191392 (3.825224) \| 0.162869 / 0.680424 (-0.517555) \| 0.018105 / 0.534201 (-0.516096) \| 0.394838 / 0.579283 (-0.184445) \| 0.403378 / 0.434364 (-0.030986) \| 0.460931 / 0.540337 (-0.079407) \| 0.637365 / 1.386936 (-0.749571) \|\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) \| 0.006497 / 0.011353 (-0.004856) \| 0.003928 / 0.011008 (-0.007080) \| 0.063958 / 0.038508 (0.025450) \| 0.069609 / 0.023109 (0.046500) \| 0.401599 / 0.275898 (0.125701) \| 0.428128 / 0.323480 (0.104648) \| 0.005296 / 0.007986 (-0.002689) \| 0.003332 / 0.004328 (-0.000996) \| 0.063903 / 0.004250 (0.059652) \| 0.056303 / 0.037052 (0.019250) \| 0.400704 / 0.258489 (0.142214) \| 0.435982 / 0.293841 (0.142141) \| 0.032434 / 0.128546 (-0.096112) \| 0.008570 / 0.075646 (-0.067077) \| 0.070788 / 0.419271 (-0.348483) \| 0.048252 / 0.043533 (0.004719) \| 0.403269 / 0.255139 (0.148130) \| 0.419796 / 0.283200 (0.136596) \| 0.022598 / 0.141683 (-0.119085) \| 1.481627 / 1.452155 (0.029472) \| 1.578388 / 1.492716 (0.085672) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224552 / 0.018006 (0.206546) \| 0.444059 / 0.000490 (0.443570) \| 0.003757 / 0.000200 (0.003557) \| 0.000225 / 0.000054 (0.000171) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032173 / 0.037411 (-0.005239) \| 0.092562 / 0.014526 (0.078036) \| 0.104972 / 0.176557 (-0.071584) \| 0.156467 / 0.737135 (-0.580669) \| 0.104274 / 0.296338 (-0.192065) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441693 / 0.215209 (0.226484) \| 4.400217 / 2.077655 (2.322562) \| 2.393862 / 1.504120 (0.889742) \| 2.281178 / 1.541195 (0.739983) \| 2.339895 / 1.468490 (0.871405) \| 0.488734 / 4.584777 (-4.096043) \| 3.523352 / 3.745712 (-0.222360) \| 3.216761 / 5.269862 (-2.053101) \| 2.007553 / 4.565676 (-2.558123) \| 0.058050 / 0.424275 (-0.366225) \| 0.007566 / 0.007607 (-0.000041) \| 0.515439 / 0.226044 (0.289394) \| 5.155086 / 2.268929 (2.886157) \| 2.864958 / 55.444624 (-52.579666) \| 2.592460 / 6.876477 (-4.284016) \| 2.800449 / 2.142072 (0.658376) \| 0.588441 / 4.805227 (-4.216786) \| 0.131589 / 6.500664 (-6.369075) \| 0.059075 / 0.075469 (-0.016394) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353889 / 1.841788 (-0.487898) \| 18.938285 / 8.074308 (10.863977) \| 14.937141 / 10.191392 (4.745749) \| 0.168811 / 0.680424 (-0.511613) \| 0.020118 / 0.534201 (-0.514083) \| 0.394791 / 0.579283 (-0.184492) \| 0.414434 / 0.434364 (-0.019930) \| 0.466821 / 0.540337 (-0.073517) \| 0.629894 / 1.386936 (-0.757042) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#23921b08390db7dbb3186a8de40dc49a4066da76 \"CML watermark\")\n", "CI failures are unrelated", "<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) \| 0.005959 / 0.011353 (-0.005394) \| 0.004164 / 0.011008 (-0.006844) \| 0.082336 / 0.038508 (0.043828) \| 0.070344 / 0.023109 (0.047234) \| 0.348032 / 0.275898 (0.072134) \| 0.366328 / 0.323480 (0.042848) \| 0.003882 / 0.007986 (-0.004104) \| 0.003619 / 0.004328 (-0.000709) \| 0.063343 / 0.004250 (0.059093) \| 0.056617 / 0.037052 (0.019564) \| 0.351625 / 0.258489 (0.093136) \| 0.395839 / 0.293841 (0.101998) \| 0.030842 / 0.128546 (-0.097704) \| 0.008363 / 0.075646 (-0.067284) \| 0.300535 / 0.419271 (-0.118737) \| 0.053303 / 0.043533 (0.009770) \| 0.354782 / 0.255139 (0.099643) \| 0.364918 / 0.283200 (0.081719) \| 0.025365 / 0.141683 (-0.116318) \| 1.555009 / 1.452155 (0.102854) \| 1.597443 / 1.492716 (0.104727) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239808 / 0.018006 (0.221801) \| 0.488164 / 0.000490 (0.487675) \| 0.013183 / 0.000200 (0.012983) \| 0.000483 / 0.000054 (0.000429) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027938 / 0.037411 (-0.009473) \| 0.078521 / 0.014526 (0.063995) \| 0.095498 / 0.176557 (-0.081059) \| 0.150884 / 0.737135 (-0.586251) \| 0.097577 / 0.296338 (-0.198762) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384546 / 0.215209 (0.169337) \| 4.037707 / 2.077655 (1.960053) \| 1.940321 / 1.504120 (0.436201) \| 1.716741 / 1.541195 (0.175546) \| 1.837200 / 1.468490 (0.368710) \| 0.502112 / 4.584777 (-4.082665) \| 3.770452 / 3.745712 (0.024740) \| 3.325691 / 5.269862 (-1.944171) \| 2.015622 / 4.565676 (-2.550055) \| 0.056246 / 0.424275 (-0.368029) \| 0.007320 / 0.007607 (-0.000287) \| 0.445553 / 0.226044 (0.219509) \| 4.567233 / 2.268929 (2.298304) \| 2.319531 / 55.444624 (-53.125093) \| 1.968664 / 6.876477 (-4.907813) \| 2.122349 / 2.142072 (-0.019724) \| 0.573688 / 4.805227 (-4.231540) \| 0.131410 / 6.500664 (-6.369254) \| 0.062767 / 0.075469 (-0.012702) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255244 / 1.841788 (-0.586543) \| 19.042480 / 8.074308 (10.968172) \| 13.935342 / 10.191392 (3.743950) \| 0.161259 / 0.680424 (-0.519165) \| 0.020582 / 0.534201 (-0.513619) \| 0.391365 / 0.579283 (-0.187918) \| 0.417462 / 0.434364 (-0.016902) \| 0.473121 / 0.540337 (-0.067216) \| 0.674768 / 1.386936 (-0.712168) \|\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) \| 0.006299 / 0.011353 (-0.005054) \| 0.003969 / 0.011008 (-0.007040) \| 0.063558 / 0.038508 (0.025050) \| 0.073847 / 0.023109 (0.050738) \| 0.407064 / 0.275898 (0.131166) \| 0.440695 / 0.323480 (0.117215) \| 0.005783 / 0.007986 (-0.002203) \| 0.003517 / 0.004328 (-0.000812) \| 0.065721 / 0.004250 (0.061470) \| 0.056390 / 0.037052 (0.019338) \| 0.419019 / 0.258489 (0.160530) \| 0.450721 / 0.293841 (0.156880) \| 0.034094 / 0.128546 (-0.094452) \| 0.008594 / 0.075646 (-0.067052) \| 0.069254 / 0.419271 (-0.350017) \| 0.049218 / 0.043533 (0.005685) \| 0.413312 / 0.255139 (0.158173) \| 0.439454 / 0.283200 (0.156255) \| 0.021481 / 0.141683 (-0.120202) \| 1.517536 / 1.452155 (0.065382) \| 1.530532 / 1.492716 (0.037815) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235392 / 0.018006 (0.217386) \| 0.477371 / 0.000490 (0.476881) \| 0.007070 / 0.000200 (0.006870) \| 0.000132 / 0.000054 (0.000077) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031909 / 0.037411 (-0.005502) \| 0.092459 / 0.014526 (0.077933) \| 0.105795 / 0.176557 (-0.070761) \| 0.157745 / 0.737135 (-0.579390) \| 0.104187 / 0.296338 (-0.192152) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424385 / 0.215209 (0.209176) \| 4.445371 / 2.077655 (2.367716) \| 2.423639 / 1.504120 (0.919519) \| 2.188167 / 1.541195 (0.646972) \| 2.171023 / 1.468490 (0.702532) \| 0.483566 / 4.584777 (-4.101211) \| 3.825702 / 3.745712 (0.079990) \| 3.276350 / 5.269862 (-1.993512) \| 2.063075 / 4.565676 (-2.502602) \| 0.061628 / 0.424275 (-0.362647) \| 0.008176 / 0.007607 (0.000569) \| 0.506697 / 0.226044 (0.280653) \| 5.067924 / 2.268929 (2.798995) \| 2.785567 / 55.444624 (-52.659057) \| 2.457340 / 6.876477 (-4.419137) \| 2.599646 / 2.142072 (0.457574) \| 0.581550 / 4.805227 (-4.223677) \| 0.131712 / 6.500664 (-6.368952) \| 0.058776 / 0.075469 (-0.016693) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356639 / 1.841788 (-0.485148) \| 20.103463 / 8.074308 (12.029155) \| 14.481010 / 10.191392 (4.289618) \| 0.162870 / 0.680424 (-0.517554) \| 0.023197 / 0.534201 (-0.511004) \| 0.413042 / 0.579283 (-0.166241) \| 0.427494 / 0.434364 (-0.006870) \| 0.508457 / 0.540337 (-0.031880) \| 0.662412 / 1.386936 (-0.724524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05fe5c06d42f84408b933c2809acb9b7449cbbb3 \"CML watermark\")\n" ]	2023-09-15T14:23:33Z	2023-09-19T18:02:21Z	2023-09-19T17:53:17Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6243.diff", "html_url": "https://github.com/huggingface/datasets/pull/6243", "merged_at": "2023-09-19T17:53:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/6243.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6243" }	Fix #6242	{ "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/6243/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6242	6,242	Data alteration when loading dataset with unspecified inner sequence length	{ "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	[ "While this issue may seem specific, it led to a silent problem in my workflow that took days to diagnose. If this feature is not intended to be supported, an error should be raised when encountering this configuration to prevent such issues.", "Thanks for reporting! This is a MRE:\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.table import cast_array_to_feature\r\nfrom datasets import Sequence, Value\r\ndata = [\r\n [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],\r\n [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],\r\n]\r\narr = pa.array(data, pa.list_(pa.list_(pa.float32(), 3)))\r\ncast_array_to_feature(arr, Sequence(Sequence(Value(\"float32\"))))\r\n```\r\n\r\nI've opened a PR with a fix." ]	2023-09-14T16:12:45Z	2023-09-19T17:53:18Z	2023-09-19T17:53:18Z	MEMBER	null	null	### Describe the bug When a dataset saved with a specified inner sequence length is loaded without specifying that length, the original data is altered and becomes inconsistent. ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Value, Sequence, load_dataset # Repository ID repo_id = "my_repo_id" # Define features with a specific length of 3 for each inner sequence specified_features = Features({"key": Sequence(Sequence(Value("float32"), length=3))}) # Create a dataset with the specified features data = [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]], ] dataset = Dataset.from_dict({"key": data}, features=specified_features) # Push the dataset to the hub dataset.push_to_hub(repo_id) # Define features without specifying the length unspecified_features = Features({"key": Sequence(Sequence(Value("float32")))}) # Load the dataset from the hub with this new feature definition dataset = load_dataset(f"qgallouedec/{repo_id}", split="train", features=unspecified_features) # The obtained data is altered print(dataset.to_dict()) # {'key': [[[1.0], [2.0]], [[3.0], [4.0]]]} ``` ### Expected behavior ```python print(dataset.to_dict()) # {'key': [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]]} ``` ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "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/6242/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/6241	6,241	Remove unused global variables in `audio.py`	{ "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) \| 0.006753 / 0.011353 (-0.004600) \| 0.004027 / 0.011008 (-0.006982) \| 0.084200 / 0.038508 (0.045692) \| 0.072233 / 0.023109 (0.049124) \| 0.361535 / 0.275898 (0.085637) \| 0.386196 / 0.323480 (0.062716) \| 0.004047 / 0.007986 (-0.003939) \| 0.003416 / 0.004328 (-0.000912) \| 0.064724 / 0.004250 (0.060474) \| 0.055740 / 0.037052 (0.018688) \| 0.360422 / 0.258489 (0.101933) \| 0.399230 / 0.293841 (0.105389) \| 0.031537 / 0.128546 (-0.097009) \| 0.008630 / 0.075646 (-0.067016) \| 0.289652 / 0.419271 (-0.129620) \| 0.052881 / 0.043533 (0.009348) \| 0.359538 / 0.255139 (0.104399) \| 0.379410 / 0.283200 (0.096211) \| 0.024539 / 0.141683 (-0.117144) \| 1.470891 / 1.452155 (0.018736) \| 1.578879 / 1.492716 (0.086163) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239200 / 0.018006 (0.221194) \| 0.462100 / 0.000490 (0.461610) \| 0.009055 / 0.000200 (0.008856) \| 0.000406 / 0.000054 (0.000352) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028736 / 0.037411 (-0.008675) \| 0.088051 / 0.014526 (0.073525) \| 0.098101 / 0.176557 (-0.078456) \| 0.152399 / 0.737135 (-0.584737) \| 0.098776 / 0.296338 (-0.197563) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401761 / 0.215209 (0.186552) \| 4.014143 / 2.077655 (1.936488) \| 2.033255 / 1.504120 (0.529135) \| 1.855347 / 1.541195 (0.314152) \| 1.996144 / 1.468490 (0.527654) \| 0.488545 / 4.584777 (-4.096232) \| 3.712030 / 3.745712 (-0.033682) \| 3.439725 / 5.269862 (-1.830137) \| 2.119289 / 4.565676 (-2.446388) \| 0.057523 / 0.424275 (-0.366752) \| 0.007780 / 0.007607 (0.000173) \| 0.479522 / 0.226044 (0.253477) \| 4.798218 / 2.268929 (2.529290) \| 2.543816 / 55.444624 (-52.900809) \| 2.180392 / 6.876477 (-4.696085) \| 2.427195 / 2.142072 (0.285122) \| 0.602071 / 4.805227 (-4.203156) \| 0.133450 / 6.500664 (-6.367214) \| 0.061975 / 0.075469 (-0.013494) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.250040 / 1.841788 (-0.591748) \| 19.532327 / 8.074308 (11.458019) \| 14.200298 / 10.191392 (4.008906) \| 0.165165 / 0.680424 (-0.515259) \| 0.018326 / 0.534201 (-0.515875) \| 0.389788 / 0.579283 (-0.189495) \| 0.419301 / 0.434364 (-0.015063) \| 0.452645 / 0.540337 (-0.087693) \| 0.643409 / 1.386936 (-0.743527) \|\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) \| 0.007040 / 0.011353 (-0.004313) \| 0.004157 / 0.011008 (-0.006851) \| 0.065439 / 0.038508 (0.026931) \| 0.083210 / 0.023109 (0.060101) \| 0.406707 / 0.275898 (0.130809) \| 0.442759 / 0.323480 (0.119279) \| 0.006321 / 0.007986 (-0.001665) \| 0.003684 / 0.004328 (-0.000645) \| 0.064517 / 0.004250 (0.060266) \| 0.060676 / 0.037052 (0.023624) \| 0.413395 / 0.258489 (0.154906) \| 0.446776 / 0.293841 (0.152935) \| 0.032542 / 0.128546 (-0.096004) \| 0.008614 / 0.075646 (-0.067033) \| 0.071760 / 0.419271 (-0.347511) \| 0.049646 / 0.043533 (0.006113) \| 0.402409 / 0.255139 (0.147270) \| 0.422775 / 0.283200 (0.139575) \| 0.024846 / 0.141683 (-0.116836) \| 1.522915 / 1.452155 (0.070761) \| 1.566518 / 1.492716 (0.073802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234478 / 0.018006 (0.216472) \| 0.461318 / 0.000490 (0.460828) \| 0.006304 / 0.000200 (0.006105) \| 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) \| 0.036904 / 0.037411 (-0.000508) \| 0.102144 / 0.014526 (0.087619) \| 0.108985 / 0.176557 (-0.067572) \| 0.162609 / 0.737135 (-0.574526) \| 0.110295 / 0.296338 (-0.186044) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438735 / 0.215209 (0.223526) \| 4.377602 / 2.077655 (2.299948) \| 2.375305 / 1.504120 (0.871185) \| 2.215877 / 1.541195 (0.674682) \| 2.317468 / 1.468490 (0.848978) \| 0.495137 / 4.584777 (-4.089640) \| 3.726323 / 3.745712 (-0.019389) \| 3.493785 / 5.269862 (-1.776077) \| 2.177891 / 4.565676 (-2.387785) \| 0.058975 / 0.424275 (-0.365300) \| 0.007897 / 0.007607 (0.000290) \| 0.514063 / 0.226044 (0.288019) \| 5.132714 / 2.268929 (2.863786) \| 2.914125 / 55.444624 (-52.530499) \| 2.532912 / 6.876477 (-4.343564) \| 2.776438 / 2.142072 (0.634365) \| 0.624831 / 4.805227 (-4.180396) \| 0.135023 / 6.500664 (-6.365641) \| 0.062040 / 0.075469 (-0.013429) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.359970 / 1.841788 (-0.481818) \| 20.816464 / 8.074308 (12.742156) \| 16.103544 / 10.191392 (5.912152) \| 0.149120 / 0.680424 (-0.531304) \| 0.020279 / 0.534201 (-0.513922) \| 0.408727 / 0.579283 (-0.170556) \| 0.436191 / 0.434364 (0.001827) \| 0.485056 / 0.540337 (-0.055281) \| 0.737727 / 1.386936 (-0.649209) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d15280f435b7e27c9350a0cc37a07dbc5e2ea9ca \"CML watermark\")\n", "CI failures are unrelated", "<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) \| 0.008102 / 0.011353 (-0.003251) \| 0.004886 / 0.011008 (-0.006123) \| 0.090482 / 0.038508 (0.051974) \| 0.071594 / 0.023109 (0.048485) \| 0.428678 / 0.275898 (0.152780) \| 0.442179 / 0.323480 (0.118699) \| 0.004329 / 0.007986 (-0.003657) \| 0.003756 / 0.004328 (-0.000573) \| 0.087125 / 0.004250 (0.082874) \| 0.055159 / 0.037052 (0.018107) \| 0.437646 / 0.258489 (0.179157) \| 0.446665 / 0.293841 (0.152824) \| 0.046402 / 0.128546 (-0.082145) \| 0.014248 / 0.075646 (-0.061398) \| 0.331401 / 0.419271 (-0.087871) \| 0.062010 / 0.043533 (0.018478) \| 0.434774 / 0.255139 (0.179635) \| 0.441063 / 0.283200 (0.157863) \| 0.037424 / 0.141683 (-0.104258) \| 1.720276 / 1.452155 (0.268121) \| 1.731491 / 1.492716 (0.238775) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302935 / 0.018006 (0.284929) \| 0.590556 / 0.000490 (0.590067) \| 0.014473 / 0.000200 (0.014274) \| 0.000712 / 0.000054 (0.000658) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031289 / 0.037411 (-0.006122) \| 0.091175 / 0.014526 (0.076649) \| 0.112895 / 0.176557 (-0.063661) \| 0.199558 / 0.737135 (-0.537577) \| 0.113397 / 0.296338 (-0.182942) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.571586 / 0.215209 (0.356377) \| 5.706894 / 2.077655 (3.629240) \| 2.512701 / 1.504120 (1.008581) \| 2.151705 / 1.541195 (0.610510) \| 2.252738 / 1.468490 (0.784248) \| 0.857524 / 4.584777 (-3.727253) \| 5.189027 / 3.745712 (1.443315) \| 4.464979 / 5.269862 (-0.804882) \| 2.787486 / 4.565676 (-1.778190) \| 0.090161 / 0.424275 (-0.334115) \| 0.008649 / 0.007607 (0.001042) \| 0.703367 / 0.226044 (0.477322) \| 7.128971 / 2.268929 (4.860043) \| 3.437475 / 55.444624 (-52.007149) \| 2.562291 / 6.876477 (-4.314186) \| 2.753419 / 2.142072 (0.611346) \| 0.981964 / 4.805227 (-3.823263) \| 0.194533 / 6.500664 (-6.306131) \| 0.069659 / 0.075469 (-0.005810) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.510356 / 1.841788 (-0.331431) \| 22.414117 / 8.074308 (14.339809) \| 20.325418 / 10.191392 (10.134025) \| 0.226823 / 0.680424 (-0.453601) \| 0.029123 / 0.534201 (-0.505078) \| 0.454656 / 0.579283 (-0.124627) \| 0.559588 / 0.434364 (0.125224) \| 0.547386 / 0.540337 (0.007048) \| 0.770169 / 1.386936 (-0.616767) \|\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) \| 0.010167 / 0.011353 (-0.001186) \| 0.005164 / 0.011008 (-0.005844) \| 0.094897 / 0.038508 (0.056388) \| 0.078027 / 0.023109 (0.054918) \| 0.474442 / 0.275898 (0.198544) \| 0.503362 / 0.323480 (0.179882) \| 0.006988 / 0.007986 (-0.000998) \| 0.005369 / 0.004328 (0.001041) \| 0.079547 / 0.004250 (0.075297) \| 0.059382 / 0.037052 (0.022329) \| 0.468759 / 0.258489 (0.210270) \| 0.566780 / 0.293841 (0.272939) \| 0.050791 / 0.128546 (-0.077755) \| 0.013191 / 0.075646 (-0.062455) \| 0.086086 / 0.419271 (-0.333186) \| 0.060399 / 0.043533 (0.016866) \| 0.492985 / 0.255139 (0.237846) \| 0.509139 / 0.283200 (0.225940) \| 0.034537 / 0.141683 (-0.107146) \| 1.699166 / 1.452155 (0.247011) \| 1.789781 / 1.492716 (0.297065) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278776 / 0.018006 (0.260769) \| 0.615877 / 0.000490 (0.615387) \| 0.009062 / 0.000200 (0.008862) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032931 / 0.037411 (-0.004481) \| 0.094796 / 0.014526 (0.080270) \| 0.126697 / 0.176557 (-0.049859) \| 0.168172 / 0.737135 (-0.568963) \| 0.113906 / 0.296338 (-0.182433) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.602378 / 0.215209 (0.387169) \| 5.987708 / 2.077655 (3.910054) \| 2.800339 / 1.504120 (1.296219) \| 2.474127 / 1.541195 (0.932932) \| 2.502387 / 1.468490 (1.033897) \| 0.808147 / 4.584777 (-3.776630) \| 5.212691 / 3.745712 (1.466979) \| 4.479452 / 5.269862 (-0.790409) \| 2.831960 / 4.565676 (-1.733717) \| 0.086777 / 0.424275 (-0.337498) \| 0.009492 / 0.007607 (0.001885) \| 0.716848 / 0.226044 (0.490803) \| 7.099904 / 2.268929 (4.830975) \| 3.794708 / 55.444624 (-51.649916) \| 2.859826 / 6.876477 (-4.016650) \| 3.109673 / 2.142072 (0.967600) \| 0.936776 / 4.805227 (-3.868451) \| 0.195152 / 6.500664 (-6.305512) \| 0.074184 / 0.075469 (-0.001285) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.585419 / 1.841788 (-0.256369) \| 22.420377 / 8.074308 (14.346068) \| 20.761533 / 10.191392 (10.570141) \| 0.228480 / 0.680424 (-0.451943) \| 0.030944 / 0.534201 (-0.503257) \| 0.444717 / 0.579283 (-0.134566) \| 0.579632 / 0.434364 (0.145268) \| 0.521669 / 0.540337 (-0.018669) \| 0.748274 / 1.386936 (-0.638662) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#94e07965a400e6901f12e6f0f25c7090656c828c \"CML watermark\")\n" ]	2023-09-14T12:06:32Z	2023-09-15T15:57:10Z	2023-09-15T15:46:07Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6241.diff", "html_url": "https://github.com/huggingface/datasets/pull/6241", "merged_at": "2023-09-15T15:46:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/6241.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6241" }	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/6241/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6240	6,240	Dataloader stuck on multiple GPUs	{ "avatar_url": "https://avatars.githubusercontent.com/u/40049003?v=4", "events_url": "https://api.github.com/users/kuri54/events{/privacy}", "followers_url": "https://api.github.com/users/kuri54/followers", "following_url": "https://api.github.com/users/kuri54/following{/other_user}", "gists_url": "https://api.github.com/users/kuri54/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kuri54", "id": 40049003, "login": "kuri54", "node_id": "MDQ6VXNlcjQwMDQ5MDAz", "organizations_url": "https://api.github.com/users/kuri54/orgs", "received_events_url": "https://api.github.com/users/kuri54/received_events", "repos_url": "https://api.github.com/users/kuri54/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kuri54/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kuri54/subscriptions", "type": "User", "url": "https://api.github.com/users/kuri54", "user_view_type": "public" }	[]	closed	false	[ "What type of dataset are you using in this script? `torch.utils.data.Dataset` or `datasets.Dataset`? Please share the `datasets` package version if it's the latter. Otherwise, it's better to move this issue to the `accelerate` repo.", "Very sorry, I thought I had a repo in `accelerate!`\r\nI will close this issue and repo the issue in the appropriate place." ]	2023-09-14T05:30:30Z	2023-09-14T23:54:42Z	2023-09-14T23:54:42Z	NONE	null	null	### Describe the bug I am trying to get CLIP to fine-tuning with my code. When I tried to run it on multiple GPUs using accelerate, I encountered the following phenomenon. - Validation dataloader stuck in 2nd epoch only on multi-GPU Specifically, when the "for inputs in valid_loader:" process is finished, it does not proceed to the next step. train_loader process is completed. Also, both train and valid are working correctly in the first epoch. The accelerate command at that time is as follows. `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` - This will not happen when single GPU is used. `CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` - Setting num_workers=0 in dataloader did not change the result. ### Steps to reproduce the bug 1. The codes for fine-tuning the regular CLIP were updated for accelerate. 2. Run the code with the accelerate command as `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` and the above problem will occur. 3. CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` , it works fine. ### Expected behavior It Should end normally as if it was run on a single GPU. ### Environment info Since `datasets-cli env` did not work, the environment is described below. - OS: Ubuntu 22.04 with Docker - Docker: 24.0.5, build ced0996 - Python: 3.10.12 - torch==2.0.1 - accelerate==0.21.0 - transformers==4.33.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/40049003?v=4", "events_url": "https://api.github.com/users/kuri54/events{/privacy}", "followers_url": "https://api.github.com/users/kuri54/followers", "following_url": "https://api.github.com/users/kuri54/following{/other_user}", "gists_url": "https://api.github.com/users/kuri54/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kuri54", "id": 40049003, "login": "kuri54", "node_id": "MDQ6VXNlcjQwMDQ5MDAz", "organizations_url": "https://api.github.com/users/kuri54/orgs", "received_events_url": "https://api.github.com/users/kuri54/received_events", "repos_url": "https://api.github.com/users/kuri54/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kuri54/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kuri54/subscriptions", "type": "User", "url": "https://api.github.com/users/kuri54", "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/6240/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/6239	6,239	Load local audio data doesn't work	{ "avatar_url": "https://avatars.githubusercontent.com/u/554032?v=4", "events_url": "https://api.github.com/users/abodacs/events{/privacy}", "followers_url": "https://api.github.com/users/abodacs/followers", "following_url": "https://api.github.com/users/abodacs/following{/other_user}", "gists_url": "https://api.github.com/users/abodacs/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/abodacs", "id": 554032, "login": "abodacs", "node_id": "MDQ6VXNlcjU1NDAzMg==", "organizations_url": "https://api.github.com/users/abodacs/orgs", "received_events_url": "https://api.github.com/users/abodacs/received_events", "repos_url": "https://api.github.com/users/abodacs/repos", "site_admin": false, "starred_url": "https://api.github.com/users/abodacs/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/abodacs/subscriptions", "type": "User", "url": "https://api.github.com/users/abodacs", "user_view_type": "public" }	[]	closed	false	[ "I think this is the same issue as https://github.com/huggingface/datasets/issues/4776. Maybe installing `ffmpeg` can fix it:\r\n```python\r\nadd-apt-repository -y ppa:savoury1/ffmpeg4\r\napt-get -qq install -y ffmpeg\r\n```\r\n\r\nHowever, the best solution is to use a newer version of `datasets`. In the recent releases, we've replaced `torchaudio` with `soundfile`, which is easier to install and faster.", "@mariosasko \r\nThanks for your help" ]	2023-09-13T22:30:01Z	2023-09-15T14:32:10Z	2023-09-15T14:32:10Z	NONE	null	null	### Describe the bug I get a RuntimeError from the following code: ```python audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio()) audio_dataset[0] ``` ### Traceback <details> ```python RuntimeError Traceback (most recent call last) Cell In[33], line 1 ----> 1 train_dataset[0] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row) 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example) 1376 def decode_example(self, example: dict): 1377 """Decode example with custom feature decoding. 1378 1379 Args: (...) 1383 :obj:`dict[str, Any]` 1384 """ -> 1386 return { 1387 column_name: decode_nested_example(feature, value) 1388 if self._column_requires_decoding[column_name] 1389 else value 1390 for column_name, (feature, value) in zip_dict( 1391 {key: value for key, value in self.items() if key in example}, example 1392 ) 1393 } File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0) 1376 def decode_example(self, example: dict): 1377 """Decode example with custom feature decoding. 1378 1379 Args: (...) 1383 :obj:`dict[str, Any]` 1384 """ 1386 return { -> 1387 column_name: decode_nested_example(feature, value) 1388 if self._column_requires_decoding[column_name] 1389 else value 1390 for column_name, (feature, value) in zip_dict( 1391 {key: value for key, value in self.items() if key in example}, example 1392 ) 1393 } File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj) 1085 # Object with special decoding: 1086 elif isinstance(schema, (Audio, Image)): -> 1087 return schema.decode_example(obj) if obj is not None else None 1088 return obj File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value) 101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.") 102 elif path is not None and path.endswith("mp3"): --> 103 array, sampling_rate = self._decode_mp3(file if file else path) 104 elif path is not None and path.endswith("opus"): 105 if file: File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file) 238 except RuntimeError as err: 239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err --> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3") 242 if self.sampling_rate and self.sampling_rate != sampling_rate: 243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate: File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 254 if ret is not None: 255 return ret --> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format) File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format) 22 def _fail_load( 23 filepath: str, 24 frame_offset: int = 0, (...) 28 format: Optional[str] = None, 29 ) -> Tuple[torch.Tensor, int]: ---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath)) RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3 ``` </details> ### Steps to reproduce the bug 1. - Create a custom dataset using Local files of type mp3. 3. - Try to read the first audio item. ### Expected behavior Expected output ```python audio_dataset[0]["audio"] {'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414, 0. , 0. ], dtype=float32), 'path': 'path/to/audio_1', 'sampling_rate': 16000} ``` ### Environment info N/A	{ "avatar_url": "https://avatars.githubusercontent.com/u/554032?v=4", "events_url": "https://api.github.com/users/abodacs/events{/privacy}", "followers_url": "https://api.github.com/users/abodacs/followers", "following_url": "https://api.github.com/users/abodacs/following{/other_user}", "gists_url": "https://api.github.com/users/abodacs/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/abodacs", "id": 554032, "login": "abodacs", "node_id": "MDQ6VXNlcjU1NDAzMg==", "organizations_url": "https://api.github.com/users/abodacs/orgs", "received_events_url": "https://api.github.com/users/abodacs/received_events", "repos_url": "https://api.github.com/users/abodacs/repos", "site_admin": false, "starred_url": "https://api.github.com/users/abodacs/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/abodacs/subscriptions", "type": "User", "url": "https://api.github.com/users/abodacs", "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/6239/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/6238	6,238	`dataset.filter` ALWAYS removes the first item from the dataset when using batched=True	{ "avatar_url": "https://avatars.githubusercontent.com/u/1330693?v=4", "events_url": "https://api.github.com/users/Taytay/events{/privacy}", "followers_url": "https://api.github.com/users/Taytay/followers", "following_url": "https://api.github.com/users/Taytay/following{/other_user}", "gists_url": "https://api.github.com/users/Taytay/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Taytay", "id": 1330693, "login": "Taytay", "node_id": "MDQ6VXNlcjEzMzA2OTM=", "organizations_url": "https://api.github.com/users/Taytay/orgs", "received_events_url": "https://api.github.com/users/Taytay/received_events", "repos_url": "https://api.github.com/users/Taytay/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Taytay/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Taytay/subscriptions", "type": "User", "url": "https://api.github.com/users/Taytay", "user_view_type": "public" }	[]	closed	false	[ "`filter` treats the function's output as a (selection) mask - `True` keeps the sample, and `False` drops it. In your case, `bool(0)` evaluates to `False`, so dropping the first sample is the correct behavior.", "Oh gosh! 🤦 I totally misunderstood the API! My apologies!" ]	2023-09-13T20:20:37Z	2023-09-17T07:05:07Z	2023-09-17T07:05:07Z	NONE	null	null	### Describe the bug If you call batched=True when calling `filter`, the first item is _always_ filtered out, regardless of the filter condition. ### Steps to reproduce the bug Here's a minimal example: ```python def filter_batch_always_true(batch, indices): print("First index being passed into this filter function: ", indices[0]) return indices # Keep all indices data = {"value": list(range(10))} dataset = Dataset.from_dict(data) filtered_dataset = dataset.filter(filter_batch_always_true, with_indices=True, batched=True) print("Length of original dataset: ", len(dataset)) print("Length of filtered_dataset: ", len(filtered_dataset)) print("Is equal to original? ", len(filtered_dataset) == len(dataset)) print("First item of filtered dataset: ", filtered_dataset[0]) print("Last item of filtered dataset: ", filtered_dataset[-1]) ``` prints: ``` First index being passed into this filter function: 0 Length of original dataset: 10 Length of filtered_dataset: 9 Is equal to original? False First item of filtered dataset: {'value': 1} Last item of filtered dataset: {'value': 9} ``` ### Expected behavior Filter should respect the filter condition. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.5-arm64-arm-64bit - Python version: 3.9.18 - Huggingface_hub version: 0.17.1 - PyArrow version: 10.0.1 - Pandas version: 2.0.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/1330693?v=4", "events_url": "https://api.github.com/users/Taytay/events{/privacy}", "followers_url": "https://api.github.com/users/Taytay/followers", "following_url": "https://api.github.com/users/Taytay/following{/other_user}", "gists_url": "https://api.github.com/users/Taytay/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Taytay", "id": 1330693, "login": "Taytay", "node_id": "MDQ6VXNlcjEzMzA2OTM=", "organizations_url": "https://api.github.com/users/Taytay/orgs", "received_events_url": "https://api.github.com/users/Taytay/received_events", "repos_url": "https://api.github.com/users/Taytay/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Taytay/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Taytay/subscriptions", "type": "User", "url": "https://api.github.com/users/Taytay", "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/6238/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/6237	6,237	Tokenization with multiple workers is too slow	{ "avatar_url": "https://avatars.githubusercontent.com/u/25720695?v=4", "events_url": "https://api.github.com/users/macabdul9/events{/privacy}", "followers_url": "https://api.github.com/users/macabdul9/followers", "following_url": "https://api.github.com/users/macabdul9/following{/other_user}", "gists_url": "https://api.github.com/users/macabdul9/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/macabdul9", "id": 25720695, "login": "macabdul9", "node_id": "MDQ6VXNlcjI1NzIwNjk1", "organizations_url": "https://api.github.com/users/macabdul9/orgs", "received_events_url": "https://api.github.com/users/macabdul9/received_events", "repos_url": "https://api.github.com/users/macabdul9/repos", "site_admin": false, "starred_url": "https://api.github.com/users/macabdul9/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/macabdul9/subscriptions", "type": "User", "url": "https://api.github.com/users/macabdul9", "user_view_type": "public" }	[]	closed	false	[ "[This](https://huggingface.co/docs/datasets/nlp_process#map) is the most performant way to tokenize a dataset (`batched=True, num_proc=None, return_tensors=\"np\"`) \r\n\r\nIf`tokenizer.is_fast` returns `True`, `num_proc` must be `None/1` to benefit from the fast tokenizers' parallelism (the fast tokenizers are implemented in Rust, and Rust multi-threading doesn't work well with Python multi-processing)" ]	2023-09-13T06:18:34Z	2023-09-19T21:54:58Z	2023-09-19T21:54:58Z	NONE	null	null	I am trying to tokenize a few million documents with multiple workers but the tokenization process is taking forever. Code snippet: ``` raw_datasets.map( encode_function, batched=False, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, remove_columns=[name for name in raw_datasets["train"].column_names if name not in ["input_ids", "labels", "attention_mask"]], desc="Tokenizing data", ) ``` Details: ``` transformers==4.28.0.dev0 datasets==4.28.0.dev0 preprocessing_num_workers==48 ``` tokenizer == decapoda-research/llama-7b-hf	{ "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/6237/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/6236	6,236	Support buffer shuffle for to_tf_dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/7635551?v=4", "events_url": "https://api.github.com/users/EthanRock/events{/privacy}", "followers_url": "https://api.github.com/users/EthanRock/followers", "following_url": "https://api.github.com/users/EthanRock/following{/other_user}", "gists_url": "https://api.github.com/users/EthanRock/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/EthanRock", "id": 7635551, "login": "EthanRock", "node_id": "MDQ6VXNlcjc2MzU1NTE=", "organizations_url": "https://api.github.com/users/EthanRock/orgs", "received_events_url": "https://api.github.com/users/EthanRock/received_events", "repos_url": "https://api.github.com/users/EthanRock/repos", "site_admin": false, "starred_url": "https://api.github.com/users/EthanRock/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/EthanRock/subscriptions", "type": "User", "url": "https://api.github.com/users/EthanRock", "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	[ "cc @Rocketknight1 ", "Hey! You can implement this yourself, just:\r\n\r\n1) Create the dataset with `to_tf_dataset()` with `shuffle=False`\r\n2) Add an `unbatch()` at the end (or use batch_size=1)\r\n3) Add a `shuffle()` to the resulting dataset with your desired buffer size\r\n4) Add a `batch()` at the end again to re-batch your dataset.\r\n\r\nNote that the way we construct datasets in `to_tf_dataset()`, we don't actually shuffle the entire dataset in-memory, using `tf.data.Dataset.shuffle()`! Instead, we shuffle an index array and then load from the dataset with that. This means that shuffling with `tf.data.Dataset.shuffle()` will probably be slower and use more memory than our approach - I don't think adding the option for smaller shuffle buffers will actually save you memory on this!", "Thanks for your reply! @Rocketknight1 \r\n\"We don't actually shuffle the entire dataset in-memory, using tf.data.Dataset.shuffle()! Instead, we shuffle an index array and then load from the dataset with that.\"\r\nIn such case, there will be random access to dataset data during shuffling. When the dataset is large, the performance can be X10 times slow. I have tried many ways with to_tf_dataset() trying to achieve comparable performance with tf.data.Dataset().shuffle(buffer_size).batch(). But the performance with to_tf_dataset() is still slow. \r\n" ]	2023-09-13T03:19:44Z	2023-09-18T01:11:21Z	null	NONE	null	null	### Feature request I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model. Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset. tf.data.Dataset support buffer shuffle by default. shuffle( buffer_size, seed=None, reshuffle_each_iteration=None, name=None ) ### Motivation I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset. ### Your contribution NA	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/6236/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/6235	6,235	Support multiprocessing for download/extract nestedly	{ "avatar_url": "https://avatars.githubusercontent.com/u/22725729?v=4", "events_url": "https://api.github.com/users/hgt312/events{/privacy}", "followers_url": "https://api.github.com/users/hgt312/followers", "following_url": "https://api.github.com/users/hgt312/following{/other_user}", "gists_url": "https://api.github.com/users/hgt312/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/hgt312", "id": 22725729, "login": "hgt312", "node_id": "MDQ6VXNlcjIyNzI1NzI5", "organizations_url": "https://api.github.com/users/hgt312/orgs", "received_events_url": "https://api.github.com/users/hgt312/received_events", "repos_url": "https://api.github.com/users/hgt312/repos", "site_admin": false, "starred_url": "https://api.github.com/users/hgt312/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hgt312/subscriptions", "type": "User", "url": "https://api.github.com/users/hgt312", "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	[]	2023-09-12T21:51:08Z	2023-09-12T21:51:08Z	null	NONE	null	null	### Feature request Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders ``` Downloading data files #0: 0%\| \| 0/1 [00:00<?, ?obj/s] Downloading data files #1: 0%\| \| 0/1 [00:00<?, ?obj/s] Downloading data files #2: 0%\| \| 0/1 [00:00<?, ?obj/s] Extracting data files #0: 0%\| \| 0/1 [00:00<?, ?obj/s] Extracting data files #1: 0%\| \| 0/1 [00:00<?, ?obj/s][A Extracting data files #2: 0%\| \| 0/1 [00:00<?, ?obj/s][A[A ``` ### Motivation speedup dataset loading ### Your contribution I can help test the feature	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/6235/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/6233	6,233	Update README.md	{ "avatar_url": "https://avatars.githubusercontent.com/u/95188570?v=4", "events_url": "https://api.github.com/users/NinoRisteski/events{/privacy}", "followers_url": "https://api.github.com/users/NinoRisteski/followers", "following_url": "https://api.github.com/users/NinoRisteski/following{/other_user}", "gists_url": "https://api.github.com/users/NinoRisteski/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/NinoRisteski", "id": 95188570, "login": "NinoRisteski", "node_id": "U_kgDOBax2Wg", "organizations_url": "https://api.github.com/users/NinoRisteski/orgs", "received_events_url": "https://api.github.com/users/NinoRisteski/received_events", "repos_url": "https://api.github.com/users/NinoRisteski/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NinoRisteski/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NinoRisteski/subscriptions", "type": "User", "url": "https://api.github.com/users/NinoRisteski", "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) \| 0.008370 / 0.011353 (-0.002983) \| 0.004674 / 0.011008 (-0.006334) \| 0.103912 / 0.038508 (0.065404) \| 0.101668 / 0.023109 (0.078559) \| 0.417945 / 0.275898 (0.142047) \| 0.454805 / 0.323480 (0.131325) \| 0.004763 / 0.007986 (-0.003223) \| 0.003934 / 0.004328 (-0.000394) \| 0.078446 / 0.004250 (0.074196) \| 0.068383 / 0.037052 (0.031331) \| 0.415100 / 0.258489 (0.156611) \| 0.475272 / 0.293841 (0.181431) \| 0.036884 / 0.128546 (-0.091662) \| 0.010097 / 0.075646 (-0.065549) \| 0.354962 / 0.419271 (-0.064309) \| 0.062688 / 0.043533 (0.019155) \| 0.420643 / 0.255139 (0.165504) \| 0.446504 / 0.283200 (0.163304) \| 0.029075 / 0.141683 (-0.112608) \| 1.791517 / 1.452155 (0.339363) \| 1.859820 / 1.492716 (0.367104) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246929 / 0.018006 (0.228923) \| 0.519593 / 0.000490 (0.519103) \| 0.006848 / 0.000200 (0.006648) \| 0.000168 / 0.000054 (0.000114) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035179 / 0.037411 (-0.002232) \| 0.115582 / 0.014526 (0.101057) \| 0.128235 / 0.176557 (-0.048321) \| 0.187123 / 0.737135 (-0.550012) \| 0.120862 / 0.296338 (-0.175477) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463406 / 0.215209 (0.248197) \| 4.615517 / 2.077655 (2.537863) \| 2.250513 / 1.504120 (0.746393) \| 2.061226 / 1.541195 (0.520032) \| 2.189938 / 1.468490 (0.721448) \| 0.582984 / 4.584777 (-4.001793) \| 4.299464 / 3.745712 (0.553751) \| 4.037274 / 5.269862 (-1.232588) \| 2.608967 / 4.565676 (-1.956710) \| 0.068944 / 0.424275 (-0.355331) \| 0.009501 / 0.007607 (0.001894) \| 0.567436 / 0.226044 (0.341392) \| 5.662738 / 2.268929 (3.393809) \| 2.849094 / 55.444624 (-52.595530) \| 2.461013 / 6.876477 (-4.415464) \| 2.663245 / 2.142072 (0.521172) \| 0.704528 / 4.805227 (-4.100699) \| 0.163583 / 6.500664 (-6.337081) \| 0.075719 / 0.075469 (0.000250) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.604743 / 1.841788 (-0.237044) \| 24.512054 / 8.074308 (16.437746) \| 17.870939 / 10.191392 (7.679547) \| 0.199188 / 0.680424 (-0.481236) \| 0.023820 / 0.534201 (-0.510381) \| 0.487520 / 0.579283 (-0.091763) \| 0.512543 / 0.434364 (0.078179) \| 0.575138 / 0.540337 (0.034801) \| 0.759863 / 1.386936 (-0.627073) \|\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) \| 0.010516 / 0.011353 (-0.000837) \| 0.004779 / 0.011008 (-0.006229) \| 0.078482 / 0.038508 (0.039974) \| 0.108533 / 0.023109 (0.085424) \| 0.498692 / 0.275898 (0.222794) \| 0.534698 / 0.323480 (0.211218) \| 0.007624 / 0.007986 (-0.000362) \| 0.003938 / 0.004328 (-0.000391) \| 0.077317 / 0.004250 (0.073067) \| 0.078056 / 0.037052 (0.041004) \| 0.493648 / 0.258489 (0.235159) \| 0.540891 / 0.293841 (0.247050) \| 0.040377 / 0.128546 (-0.088169) \| 0.010155 / 0.075646 (-0.065491) \| 0.084384 / 0.419271 (-0.334888) \| 0.061419 / 0.043533 (0.017886) \| 0.494474 / 0.255139 (0.239335) \| 0.524656 / 0.283200 (0.241456) \| 0.029052 / 0.141683 (-0.112631) \| 1.794584 / 1.452155 (0.342429) \| 1.939987 / 1.492716 (0.447270) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.377404 / 0.018006 (0.359398) \| 0.516562 / 0.000490 (0.516072) \| 0.109555 / 0.000200 (0.109356) \| 0.001126 / 0.000054 (0.001071) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039793 / 0.037411 (0.002382) \| 0.123001 / 0.014526 (0.108475) \| 0.127536 / 0.176557 (-0.049021) \| 0.191681 / 0.737135 (-0.545455) \| 0.128590 / 0.296338 (-0.167748) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513689 / 0.215209 (0.298480) \| 5.135114 / 2.077655 (3.057459) \| 2.797885 / 1.504120 (1.293765) \| 2.715332 / 1.541195 (1.174137) \| 2.746437 / 1.468490 (1.277947) \| 0.596480 / 4.584777 (-3.988297) \| 4.382013 / 3.745712 (0.636301) \| 3.965956 / 5.269862 (-1.303906) \| 2.545206 / 4.565676 (-2.020471) \| 0.069620 / 0.424275 (-0.354655) \| 0.009321 / 0.007607 (0.001714) \| 0.612424 / 0.226044 (0.386379) \| 6.107037 / 2.268929 (3.838109) \| 3.447246 / 55.444624 (-51.997379) \| 3.073262 / 6.876477 (-3.803215) \| 3.280185 / 2.142072 (1.138113) \| 0.704776 / 4.805227 (-4.100451) \| 0.160488 / 6.500664 (-6.340176) \| 0.075730 / 0.075469 (0.000261) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.697035 / 1.841788 (-0.144753) \| 24.766118 / 8.074308 (16.691809) \| 18.476699 / 10.191392 (8.285307) \| 0.176594 / 0.680424 (-0.503830) \| 0.024249 / 0.534201 (-0.509952) \| 0.478743 / 0.579283 (-0.100541) \| 0.518774 / 0.434364 (0.084410) \| 0.581498 / 0.540337 (0.041161) \| 0.797784 / 1.386936 (-0.589152) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#722cea0f4929ff4ffcdbb7ca6b72cba229b9701a \"CML watermark\")\n" ]	2023-09-12T06:53:06Z	2023-09-13T18:20:50Z	2023-09-13T18:10:04Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6233.diff", "html_url": "https://github.com/huggingface/datasets/pull/6233", "merged_at": "2023-09-13T18:10:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/6233.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6233" }	fixed a typo	{ "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/6233/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6232	6,232	Improve error message for missing function parameters	{ "avatar_url": "https://avatars.githubusercontent.com/u/4016832?v=4", "events_url": "https://api.github.com/users/suavemint/events{/privacy}", "followers_url": "https://api.github.com/users/suavemint/followers", "following_url": "https://api.github.com/users/suavemint/following{/other_user}", "gists_url": "https://api.github.com/users/suavemint/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/suavemint", "id": 4016832, "login": "suavemint", "node_id": "MDQ6VXNlcjQwMTY4MzI=", "organizations_url": "https://api.github.com/users/suavemint/orgs", "received_events_url": "https://api.github.com/users/suavemint/received_events", "repos_url": "https://api.github.com/users/suavemint/repos", "site_admin": false, "starred_url": "https://api.github.com/users/suavemint/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/suavemint/subscriptions", "type": "User", "url": "https://api.github.com/users/suavemint", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "CI errors are unrelated", "<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) \| 0.006681 / 0.011353 (-0.004672) \| 0.004132 / 0.011008 (-0.006876) \| 0.085045 / 0.038508 (0.046536) \| 0.077680 / 0.023109 (0.054571) \| 0.382042 / 0.275898 (0.106144) \| 0.412932 / 0.323480 (0.089452) \| 0.005339 / 0.007986 (-0.002646) \| 0.003408 / 0.004328 (-0.000921) \| 0.065280 / 0.004250 (0.061030) \| 0.055732 / 0.037052 (0.018680) \| 0.400231 / 0.258489 (0.141742) \| 0.432497 / 0.293841 (0.138656) \| 0.031532 / 0.128546 (-0.097014) \| 0.008721 / 0.075646 (-0.066925) \| 0.289612 / 0.419271 (-0.129660) \| 0.053089 / 0.043533 (0.009556) \| 0.383300 / 0.255139 (0.128161) \| 0.401204 / 0.283200 (0.118004) \| 0.023582 / 0.141683 (-0.118100) \| 1.493854 / 1.452155 (0.041699) \| 1.583497 / 1.492716 (0.090781) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239163 / 0.018006 (0.221157) \| 0.469555 / 0.000490 (0.469065) \| 0.008325 / 0.000200 (0.008125) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028975 / 0.037411 (-0.008436) \| 0.084195 / 0.014526 (0.069669) \| 0.189394 / 0.176557 (0.012837) \| 0.158010 / 0.737135 (-0.579125) \| 0.097502 / 0.296338 (-0.198837) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383085 / 0.215209 (0.167876) \| 3.827030 / 2.077655 (1.749375) \| 1.872279 / 1.504120 (0.368159) \| 1.705808 / 1.541195 (0.164613) \| 1.833706 / 1.468490 (0.365216) \| 0.484744 / 4.584777 (-4.100033) \| 3.658221 / 3.745712 (-0.087491) \| 3.398462 / 5.269862 (-1.871399) \| 2.064974 / 4.565676 (-2.500703) \| 0.057740 / 0.424275 (-0.366535) \| 0.007926 / 0.007607 (0.000319) \| 0.465358 / 0.226044 (0.239314) \| 4.652951 / 2.268929 (2.384022) \| 2.328390 / 55.444624 (-53.116235) \| 2.000606 / 6.876477 (-4.875870) \| 2.268391 / 2.142072 (0.126318) \| 0.586537 / 4.805227 (-4.218690) \| 0.134749 / 6.500664 (-6.365915) \| 0.061276 / 0.075469 (-0.014193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.337913 / 1.841788 (-0.503875) \| 20.232122 / 8.074308 (12.157814) \| 14.478579 / 10.191392 (4.287187) \| 0.167545 / 0.680424 (-0.512878) \| 0.018745 / 0.534201 (-0.515456) \| 0.401209 / 0.579283 (-0.178074) \| 0.425748 / 0.434364 (-0.008616) \| 0.462539 / 0.540337 (-0.077798) \| 0.652446 / 1.386936 (-0.734490) \|\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) \| 0.007159 / 0.011353 (-0.004194) \| 0.004091 / 0.011008 (-0.006917) \| 0.066202 / 0.038508 (0.027694) \| 0.083096 / 0.023109 (0.059987) \| 0.402160 / 0.275898 (0.126261) \| 0.440565 / 0.323480 (0.117085) \| 0.005757 / 0.007986 (-0.002228) \| 0.003445 / 0.004328 (-0.000884) \| 0.065498 / 0.004250 (0.061248) \| 0.059787 / 0.037052 (0.022735) \| 0.407017 / 0.258489 (0.148528) \| 0.448270 / 0.293841 (0.154429) \| 0.033606 / 0.128546 (-0.094941) \| 0.008744 / 0.075646 (-0.066902) \| 0.072902 / 0.419271 (-0.346369) \| 0.050144 / 0.043533 (0.006611) \| 0.401069 / 0.255139 (0.145930) \| 0.426389 / 0.283200 (0.143189) \| 0.023297 / 0.141683 (-0.118386) \| 1.506152 / 1.452155 (0.053998) \| 1.570211 / 1.492716 (0.077495) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235759 / 0.018006 (0.217753) \| 0.488410 / 0.000490 (0.487921) \| 0.004587 / 0.000200 (0.004387) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034123 / 0.037411 (-0.003289) \| 0.102163 / 0.014526 (0.087638) \| 0.110892 / 0.176557 (-0.065664) \| 0.166000 / 0.737135 (-0.571135) \| 0.110845 / 0.296338 (-0.185494) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431397 / 0.215209 (0.216188) \| 4.291540 / 2.077655 (2.213885) \| 2.298248 / 1.504120 (0.794128) \| 2.134752 / 1.541195 (0.593557) \| 2.207913 / 1.468490 (0.739423) \| 0.490607 / 4.584777 (-4.094170) \| 3.683078 / 3.745712 (-0.062635) \| 3.314266 / 5.269862 (-1.955596) \| 2.059488 / 4.565676 (-2.506188) \| 0.057876 / 0.424275 (-0.366399) \| 0.007696 / 0.007607 (0.000089) \| 0.512186 / 0.226044 (0.286142) \| 5.124071 / 2.268929 (2.855142) \| 2.803913 / 55.444624 (-52.640711) \| 2.428558 / 6.876477 (-4.447919) \| 2.655207 / 2.142072 (0.513135) \| 0.584589 / 4.805227 (-4.220638) \| 0.133518 / 6.500664 (-6.367146) \| 0.060729 / 0.075469 (-0.014740) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.352916 / 1.841788 (-0.488872) \| 20.249632 / 8.074308 (12.175323) \| 15.283079 / 10.191392 (5.091686) \| 0.157601 / 0.680424 (-0.522823) \| 0.019650 / 0.534201 (-0.514551) \| 0.396398 / 0.579283 (-0.182885) \| 0.430111 / 0.434364 (-0.004252) \| 0.480627 / 0.540337 (-0.059710) \| 0.642165 / 1.386936 (-0.744771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9b21e181b642bd55b3ef68c1948bfbcd388136d6 \"CML watermark\")\n" ]	2023-09-11T19:11:58Z	2023-09-15T18:07:56Z	2023-09-15T17:59:02Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6232.diff", "html_url": "https://github.com/huggingface/datasets/pull/6232", "merged_at": "2023-09-15T17:59:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/6232.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6232" }	The error message in the fingerprint module was missing the f-string 'f' symbol, so the error message returned by fingerprint.py, line 469 was literally "function {func} is missing parameters {fingerprint_names} in signature." This has been fixed.	{ "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/6232/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6231	6,231	Overwrite legacy default config name in `dataset_infos.json` in packaged datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[]	open	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6231). All of your documentation changes will be reflected on that endpoint.", "realized that this pr is still not merged, @lhoestq maybe you can take a look at it? ", "I think https://github.com/huggingface/datasets/pull/6218 fixed the issue (a bit differently though)", "ah actually nope, let me check", "@lhoestq yeah the pr you're referencing doesn't fix the problem when two semantically analogous configs occur in datasets_info.json, i suggest to rewrite the legacy one if it exists during .push_to_hub", "Only the old versions of `datasets` use the JSON file over the README and they can only load one config so the name doesn't really matter.\r\n\r\nThat's why I chose to load the info from the JSON no matter the name (no check to see if it's \"username--dataset_name\") in my previous PR.\r\n\r\nI think you can remove the old info without even checking the name. In this case maybe no need to update load.py ", "(also minor: not checking the name makes it more robust to dataset renaming)", "@lhoestq okay makes sense... so you think it's not a problem that in some cases we might end up with `dataset_infos.json` having two keys in it?", "> @lhoestq okay makes sense... so you think it's not a problem that in some cases we might end up with dataset_infos.json having two keys in it?\r\n\r\nIdeally they should have only one config no ? Since old versions of `datasets` simply load the first config in the JSON.\r\nWe can overwrite it with the new default one (and no matter the name of the outdated config in the JSON)\r\n\r\n" ]	2023-09-11T16:27:09Z	2023-09-26T11:19:36Z	null	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6231.diff", "html_url": "https://github.com/huggingface/datasets/pull/6231", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6231.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6231" }	Currently if we push data as default config with `.push_to_hub` to a repo that has a legacy `dataset_infos.json` file containing a legacy default config name like `{username}--{dataset_name}`, new key `"default"` is added to `dataset_infos.json` along with the legacy one. I think the legacy one should be dropped in this case. Also, in `load.py` I suggest to check if a legacy config name is indeed a legacy config name because after this fix it might not be the case (this check was first introduced in https://github.com/huggingface/datasets/pull/6218)	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/6231/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6230	6,230	Don't skip hidden files in `dl_manager.iter_files` when they are given as input	{ "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) \| 0.005894 / 0.011353 (-0.005459) \| 0.003621 / 0.011008 (-0.007387) \| 0.080446 / 0.038508 (0.041938) \| 0.056800 / 0.023109 (0.033691) \| 0.326485 / 0.275898 (0.050587) \| 0.376207 / 0.323480 (0.052727) \| 0.004640 / 0.007986 (-0.003346) \| 0.002795 / 0.004328 (-0.001533) \| 0.062815 / 0.004250 (0.058565) \| 0.045761 / 0.037052 (0.008709) \| 0.341417 / 0.258489 (0.082928) \| 0.373129 / 0.293841 (0.079288) \| 0.027226 / 0.128546 (-0.101321) \| 0.007873 / 0.075646 (-0.067774) \| 0.261737 / 0.419271 (-0.157535) \| 0.044648 / 0.043533 (0.001115) \| 0.320195 / 0.255139 (0.065056) \| 0.381892 / 0.283200 (0.098692) \| 0.020431 / 0.141683 (-0.121252) \| 1.405332 / 1.452155 (-0.046823) \| 1.455592 / 1.492716 (-0.037125) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191539 / 0.018006 (0.173533) \| 0.423655 / 0.000490 (0.423165) \| 0.002741 / 0.000200 (0.002541) \| 0.000069 / 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) \| 0.023952 / 0.037411 (-0.013459) \| 0.073387 / 0.014526 (0.058861) \| 0.083746 / 0.176557 (-0.092810) \| 0.144977 / 0.737135 (-0.592159) \| 0.083808 / 0.296338 (-0.212530) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436228 / 0.215209 (0.221019) \| 4.370510 / 2.077655 (2.292855) \| 2.340426 / 1.504120 (0.836306) \| 2.202215 / 1.541195 (0.661021) \| 2.258528 / 1.468490 (0.790037) \| 0.503455 / 4.584777 (-4.081322) \| 3.043695 / 3.745712 (-0.702017) \| 2.784033 / 5.269862 (-2.485829) \| 1.847956 / 4.565676 (-2.717721) \| 0.057702 / 0.424275 (-0.366573) \| 0.006703 / 0.007607 (-0.000904) \| 0.510628 / 0.226044 (0.284583) \| 5.101890 / 2.268929 (2.832961) \| 2.816469 / 55.444624 (-52.628155) \| 2.474220 / 6.876477 (-4.402257) \| 2.617851 / 2.142072 (0.475779) \| 0.593585 / 4.805227 (-4.211642) \| 0.125895 / 6.500664 (-6.374769) \| 0.062170 / 0.075469 (-0.013299) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238792 / 1.841788 (-0.602996) \| 18.096417 / 8.074308 (10.022108) \| 13.548778 / 10.191392 (3.357386) \| 0.144878 / 0.680424 (-0.535546) \| 0.016644 / 0.534201 (-0.517557) \| 0.334556 / 0.579283 (-0.244728) \| 0.343680 / 0.434364 (-0.090684) \| 0.383093 / 0.540337 (-0.157244) \| 0.525075 / 1.386936 (-0.861861) \|\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) \| 0.006125 / 0.011353 (-0.005228) \| 0.003668 / 0.011008 (-0.007340) \| 0.062650 / 0.038508 (0.024142) \| 0.058882 / 0.023109 (0.035772) \| 0.454643 / 0.275898 (0.178745) \| 0.486659 / 0.323480 (0.163179) \| 0.005558 / 0.007986 (-0.002427) \| 0.002858 / 0.004328 (-0.001471) \| 0.062603 / 0.004250 (0.058353) \| 0.049701 / 0.037052 (0.012649) \| 0.455903 / 0.258489 (0.197413) \| 0.491544 / 0.293841 (0.197703) \| 0.028581 / 0.128546 (-0.099965) \| 0.008040 / 0.075646 (-0.067607) \| 0.068314 / 0.419271 (-0.350957) \| 0.040637 / 0.043533 (-0.002896) \| 0.450288 / 0.255139 (0.195149) \| 0.476330 / 0.283200 (0.193131) \| 0.018989 / 0.141683 (-0.122693) \| 1.455122 / 1.452155 (0.002967) \| 1.496941 / 1.492716 (0.004225) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227382 / 0.018006 (0.209376) \| 0.432637 / 0.000490 (0.432147) \| 0.002727 / 0.000200 (0.002527) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026125 / 0.037411 (-0.011286) \| 0.081342 / 0.014526 (0.066817) \| 0.091227 / 0.176557 (-0.085329) \| 0.145175 / 0.737135 (-0.591960) \| 0.091988 / 0.296338 (-0.204351) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.454293 / 0.215209 (0.239083) \| 4.537912 / 2.077655 (2.460257) \| 2.489146 / 1.504120 (0.985026) \| 2.307166 / 1.541195 (0.765971) \| 2.380866 / 1.468490 (0.912376) \| 0.509015 / 4.584777 (-4.075762) \| 3.111069 / 3.745712 (-0.634644) \| 2.839181 / 5.269862 (-2.430681) \| 1.874630 / 4.565676 (-2.691047) \| 0.058540 / 0.424275 (-0.365735) \| 0.006693 / 0.007607 (-0.000914) \| 0.528408 / 0.226044 (0.302363) \| 5.285802 / 2.268929 (3.016874) \| 2.952090 / 55.444624 (-52.492534) \| 2.591496 / 6.876477 (-4.284980) \| 2.741080 / 2.142072 (0.599007) \| 0.595610 / 4.805227 (-4.209617) \| 0.124387 / 6.500664 (-6.376277) \| 0.061032 / 0.075469 (-0.014437) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.365816 / 1.841788 (-0.475972) \| 18.684534 / 8.074308 (10.610226) \| 14.540438 / 10.191392 (4.349046) \| 0.146793 / 0.680424 (-0.533631) \| 0.018165 / 0.534201 (-0.516036) \| 0.333794 / 0.579283 (-0.245489) \| 0.345533 / 0.434364 (-0.088830) \| 0.384453 / 0.540337 (-0.155885) \| 0.529104 / 1.386936 (-0.857832) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6c884967dd5f4e8aa3d1f3c2e3a414ae53afe261 \"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) \| 0.006121 / 0.011353 (-0.005232) \| 0.003683 / 0.011008 (-0.007325) \| 0.083329 / 0.038508 (0.044821) \| 0.063350 / 0.023109 (0.040241) \| 0.329959 / 0.275898 (0.054061) \| 0.396111 / 0.323480 (0.072631) \| 0.003554 / 0.007986 (-0.004432) \| 0.002907 / 0.004328 (-0.001421) \| 0.064152 / 0.004250 (0.059902) \| 0.049182 / 0.037052 (0.012130) \| 0.343862 / 0.258489 (0.085373) \| 0.414568 / 0.293841 (0.120727) \| 0.027157 / 0.128546 (-0.101389) \| 0.007957 / 0.075646 (-0.067689) \| 0.261868 / 0.419271 (-0.157404) \| 0.044938 / 0.043533 (0.001405) \| 0.318470 / 0.255139 (0.063331) \| 0.393319 / 0.283200 (0.110119) \| 0.022848 / 0.141683 (-0.118835) \| 1.419916 / 1.452155 (-0.032238) \| 1.508783 / 1.492716 (0.016067) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200530 / 0.018006 (0.182523) \| 0.433586 / 0.000490 (0.433097) \| 0.002063 / 0.000200 (0.001863) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024803 / 0.037411 (-0.012609) \| 0.075894 / 0.014526 (0.061368) \| 0.086488 / 0.176557 (-0.090069) \| 0.149058 / 0.737135 (-0.588077) \| 0.087046 / 0.296338 (-0.209292) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390771 / 0.215209 (0.175562) \| 3.886178 / 2.077655 (1.808523) \| 1.868626 / 1.504120 (0.364506) \| 1.708532 / 1.541195 (0.167338) \| 1.788491 / 1.468490 (0.320001) \| 0.505706 / 4.584777 (-4.079071) \| 3.062094 / 3.745712 (-0.683618) \| 2.898559 / 5.269862 (-2.371302) \| 1.901225 / 4.565676 (-2.664452) \| 0.058366 / 0.424275 (-0.365909) \| 0.006851 / 0.007607 (-0.000756) \| 0.465382 / 0.226044 (0.239337) \| 4.650187 / 2.268929 (2.381258) \| 2.316152 / 55.444624 (-53.128472) \| 1.989597 / 6.876477 (-4.886879) \| 2.169266 / 2.142072 (0.027194) \| 0.593257 / 4.805227 (-4.211970) \| 0.126440 / 6.500664 (-6.374224) \| 0.062227 / 0.075469 (-0.013242) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283591 / 1.841788 (-0.558197) \| 18.384667 / 8.074308 (10.310358) \| 14.079611 / 10.191392 (3.888219) \| 0.150453 / 0.680424 (-0.529971) \| 0.017100 / 0.534201 (-0.517101) \| 0.330503 / 0.579283 (-0.248780) \| 0.348134 / 0.434364 (-0.086230) \| 0.385726 / 0.540337 (-0.154612) \| 0.529147 / 1.386936 (-0.857789) \|\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) \| 0.006168 / 0.011353 (-0.005185) \| 0.003801 / 0.011008 (-0.007208) \| 0.063168 / 0.038508 (0.024660) \| 0.062331 / 0.023109 (0.039221) \| 0.448321 / 0.275898 (0.172423) \| 0.484416 / 0.323480 (0.160937) \| 0.004827 / 0.007986 (-0.003159) \| 0.002848 / 0.004328 (-0.001480) \| 0.062736 / 0.004250 (0.058486) \| 0.049128 / 0.037052 (0.012075) \| 0.449276 / 0.258489 (0.190787) \| 0.499035 / 0.293841 (0.205194) \| 0.028577 / 0.128546 (-0.099969) \| 0.008114 / 0.075646 (-0.067532) \| 0.068297 / 0.419271 (-0.350974) \| 0.040835 / 0.043533 (-0.002698) \| 0.453556 / 0.255139 (0.198417) \| 0.475420 / 0.283200 (0.192220) \| 0.020292 / 0.141683 (-0.121390) \| 1.472226 / 1.452155 (0.020071) \| 1.523809 / 1.492716 (0.031093) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230662 / 0.018006 (0.212655) \| 0.439697 / 0.000490 (0.439207) \| 0.009899 / 0.000200 (0.009699) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026418 / 0.037411 (-0.010993) \| 0.082188 / 0.014526 (0.067662) \| 0.091039 / 0.176557 (-0.085518) \| 0.146646 / 0.737135 (-0.590489) \| 0.091693 / 0.296338 (-0.204645) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462086 / 0.215209 (0.246877) \| 4.620925 / 2.077655 (2.543271) \| 2.539234 / 1.504120 (1.035114) \| 2.371178 / 1.541195 (0.829983) \| 2.440538 / 1.468490 (0.972048) \| 0.511047 / 4.584777 (-4.073730) \| 3.082088 / 3.745712 (-0.663624) \| 2.918162 / 5.269862 (-2.351700) \| 1.899651 / 4.565676 (-2.666025) \| 0.059003 / 0.424275 (-0.365272) \| 0.006746 / 0.007607 (-0.000861) \| 0.537863 / 0.226044 (0.311819) \| 5.382355 / 2.268929 (3.113426) \| 3.060091 / 55.444624 (-52.384534) \| 2.754969 / 6.876477 (-4.121507) \| 2.863156 / 2.142072 (0.721084) \| 0.606888 / 4.805227 (-4.198339) \| 0.127448 / 6.500664 (-6.373216) \| 0.062975 / 0.075469 (-0.012494) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336065 / 1.841788 (-0.505722) \| 19.019902 / 8.074308 (10.945594) \| 15.057979 / 10.191392 (4.866587) \| 0.160646 / 0.680424 (-0.519778) \| 0.018340 / 0.534201 (-0.515861) \| 0.341664 / 0.579283 (-0.237619) \| 0.356536 / 0.434364 (-0.077828) \| 0.393974 / 0.540337 (-0.146363) \| 0.546036 / 1.386936 (-0.840900) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd04e445bd36d7eb4af4d5a6b8519ab8e306ecf5 \"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) \| 0.007220 / 0.011353 (-0.004132) \| 0.004537 / 0.011008 (-0.006471) \| 0.087333 / 0.038508 (0.048825) \| 0.095637 / 0.023109 (0.072528) \| 0.323819 / 0.275898 (0.047921) \| 0.358838 / 0.323480 (0.035358) \| 0.005910 / 0.007986 (-0.002076) \| 0.003781 / 0.004328 (-0.000548) \| 0.064565 / 0.004250 (0.060315) \| 0.062818 / 0.037052 (0.025766) \| 0.322595 / 0.258489 (0.064106) \| 0.371865 / 0.293841 (0.078024) \| 0.031667 / 0.128546 (-0.096880) \| 0.009068 / 0.075646 (-0.066579) \| 0.290574 / 0.419271 (-0.128697) \| 0.054618 / 0.043533 (0.011085) \| 0.314708 / 0.255139 (0.059569) \| 0.336647 / 0.283200 (0.053447) \| 0.027070 / 0.141683 (-0.114613) \| 1.500640 / 1.452155 (0.048485) \| 1.586775 / 1.492716 (0.094059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294461 / 0.018006 (0.276455) \| 0.580125 / 0.000490 (0.579635) \| 0.008165 / 0.000200 (0.007965) \| 0.000320 / 0.000054 (0.000266) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032352 / 0.037411 (-0.005059) \| 0.092187 / 0.014526 (0.077661) \| 0.104993 / 0.176557 (-0.071564) \| 0.162738 / 0.737135 (-0.574397) \| 0.103242 / 0.296338 (-0.193096) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396732 / 0.215209 (0.181523) \| 3.955049 / 2.077655 (1.877394) \| 1.876762 / 1.504120 (0.372642) \| 1.698477 / 1.541195 (0.157282) \| 1.847086 / 1.468490 (0.378596) \| 0.488306 / 4.584777 (-4.096471) \| 3.658922 / 3.745712 (-0.086790) \| 3.559050 / 5.269862 (-1.710812) \| 2.187363 / 4.565676 (-2.378313) \| 0.059795 / 0.424275 (-0.364480) \| 0.008966 / 0.007607 (0.001359) \| 0.474212 / 0.226044 (0.248168) \| 4.732540 / 2.268929 (2.463611) \| 2.466370 / 55.444624 (-52.978254) \| 2.112105 / 6.876477 (-4.764372) \| 2.414624 / 2.142072 (0.272552) \| 0.595447 / 4.805227 (-4.209780) \| 0.136705 / 6.500664 (-6.363959) \| 0.062267 / 0.075469 (-0.013202) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266518 / 1.841788 (-0.575270) \| 21.009975 / 8.074308 (12.935666) \| 14.823960 / 10.191392 (4.632568) \| 0.165630 / 0.680424 (-0.514793) \| 0.018499 / 0.534201 (-0.515702) \| 0.396720 / 0.579283 (-0.182563) \| 0.424807 / 0.434364 (-0.009557) \| 0.463326 / 0.540337 (-0.077011) \| 0.653132 / 1.386936 (-0.733804) \|\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) \| 0.007789 / 0.011353 (-0.003564) \| 0.004720 / 0.011008 (-0.006288) \| 0.066656 / 0.038508 (0.028148) \| 0.094219 / 0.023109 (0.071109) \| 0.414965 / 0.275898 (0.139067) \| 0.454808 / 0.323480 (0.131328) \| 0.006088 / 0.007986 (-0.001898) \| 0.003980 / 0.004328 (-0.000349) \| 0.066048 / 0.004250 (0.061797) \| 0.065875 / 0.037052 (0.028823) \| 0.419994 / 0.258489 (0.161505) \| 0.462001 / 0.293841 (0.168160) \| 0.033534 / 0.128546 (-0.095013) \| 0.009010 / 0.075646 (-0.066636) \| 0.072778 / 0.419271 (-0.346493) \| 0.049834 / 0.043533 (0.006301) \| 0.411003 / 0.255139 (0.155864) \| 0.430918 / 0.283200 (0.147718) \| 0.025664 / 0.141683 (-0.116019) \| 1.526771 / 1.452155 (0.074616) \| 1.634767 / 1.492716 (0.142051) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271180 / 0.018006 (0.253174) \| 0.576704 / 0.000490 (0.576214) \| 0.004362 / 0.000200 (0.004162) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035648 / 0.037411 (-0.001763) \| 0.102407 / 0.014526 (0.087881) \| 0.111613 / 0.176557 (-0.064944) \| 0.166173 / 0.737135 (-0.570962) \| 0.113371 / 0.296338 (-0.182967) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436031 / 0.215209 (0.220822) \| 4.347071 / 2.077655 (2.269416) \| 2.366937 / 1.504120 (0.862817) \| 2.216356 / 1.541195 (0.675161) \| 2.335933 / 1.468490 (0.867443) \| 0.490484 / 4.584777 (-4.094293) \| 3.730656 / 3.745712 (-0.015056) \| 3.497248 / 5.269862 (-1.772613) \| 2.215729 / 4.565676 (-2.349947) \| 0.057905 / 0.424275 (-0.366370) \| 0.007983 / 0.007607 (0.000376) \| 0.510413 / 0.226044 (0.284369) \| 5.114502 / 2.268929 (2.845574) \| 2.871599 / 55.444624 (-52.573026) \| 2.537514 / 6.876477 (-4.338962) \| 2.819135 / 2.142072 (0.677063) \| 0.588397 / 4.805227 (-4.216830) \| 0.134665 / 6.500664 (-6.365999) \| 0.063349 / 0.075469 (-0.012120) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.352962 / 1.841788 (-0.488826) \| 21.628664 / 8.074308 (13.554356) \| 15.962105 / 10.191392 (5.770713) \| 0.167781 / 0.680424 (-0.512643) \| 0.020965 / 0.534201 (-0.513236) \| 0.402809 / 0.579283 (-0.176474) \| 0.435153 / 0.434364 (0.000789) \| 0.481394 / 0.540337 (-0.058944) \| 0.658068 / 1.386936 (-0.728868) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12adf38b90fde8e2a4e46fcbb023ee23b5c4e98c \"CML watermark\")\n" ]	2023-09-11T13:29:19Z	2023-09-13T18:21:28Z	2023-09-13T18:12:09Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6230.diff", "html_url": "https://github.com/huggingface/datasets/pull/6230", "merged_at": "2023-09-13T18:12:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/6230.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6230" }	Required for `load_dataset(<format>, data_files=["path/to/.hidden_file"])` to work as expected	{ "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/6230/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6229	6,229	Apply inference on all images in the dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andysingal", "id": 20493493, "login": "andysingal", "node_id": "MDQ6VXNlcjIwNDkzNDkz", "organizations_url": "https://api.github.com/users/andysingal/orgs", "received_events_url": "https://api.github.com/users/andysingal/received_events", "repos_url": "https://api.github.com/users/andysingal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "type": "User", "url": "https://api.github.com/users/andysingal", "user_view_type": "public" }	[]	closed	false	[ "From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object). ", "> From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object).\r\n\r\nThanks @mariosasko for your reply...\r\ni tried :\r\n```\r\n# Define a function to apply the code to each image in the dataset\r\ndef process_image(image_path):\r\n print(\"Processing image:\", image_path)\r\n result = inferencer(image_path)['predictions']\r\n mask = np.where(result == 12, 255, 0).astype('uint8')\r\n return Image.fromarray(mask)\r\n\r\n# Process and save masks for each image in the dataset\r\nfor idx, example in enumerate(dataset['train']):\r\n image_path = np.array(example['image'])\r\n mask_image = process_image(image_path)\r\n mask_image.save(f\"mask_{idx}.png\")\r\n```\r\nand got\r\n```\r\nProcessing image: [[[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [243 205 122]\r\n [244 202 120]\r\n [242 200 118]]\r\n\r\n [[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [244 206 123]\r\n [245 203 121]\r\n [243 201 119]]\r\n\r\n [[203 164 87]\r\n [204 165 88]\r\n [207 168 91]\r\n ...\r\n [245 207 126]\r\n [246 204 122]\r\n [245 203 121]]\r\n\r\n ...\r\n\r\n [[154 123 56]\r\n [155 124 57]\r\n [158 125 56]\r\n ...\r\n [ 3 3 1]\r\n [ 3 3 1]\r\n [ 3 3 1]]\r\n\r\n [[154 123 56]\r\n [154 123 56]\r\n [155 124 57]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]\r\n\r\n [[152 121 54]\r\n [152 121 54]\r\n [153 122 55]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 39 44 40]\r\n [ 39 44 40]\r\n [ 39 43 44]\r\n ...\r\n [187 185 164]\r\n [208 204 175]\r\n [203 198 166]]\r\n\r\n [[ 42 47 43]\r\n [ 40 45 41]\r\n [ 40 44 45]\r\n ...\r\n [188 186 165]\r\n [202 198 169]\r\n [201 196 164]]\r\n\r\n [[ 41 46 42]\r\n [ 39 44 40]\r\n [ 40 44 45]\r\n ...\r\n [187 184 165]\r\n [197 193 166]\r\n [201 196 166]]\r\n\r\n ...\r\n\r\n [[ 29 27 30]\r\n [ 28 26 29]\r\n [ 25 23 26]\r\n ...\r\n [ 48 33 28]\r\n [ 44 31 25]\r\n [ 39 26 20]]\r\n\r\n [[ 34 29 33]\r\n [ 32 27 31]\r\n [ 29 24 28]\r\n ...\r\n [ 30 17 11]\r\n [ 36 23 15]\r\n [ 41 28 20]]\r\n\r\n [[ 35 30 34]\r\n [ 33 28 32]\r\n [ 28 23 27]\r\n ...\r\n [ 28 15 9]\r\n [ 41 28 20]\r\n [ 46 33 25]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 65 53 55]\r\n [ 65 53 55]\r\n [ 51 39 41]\r\n ...\r\n [133 127 111]\r\n [150 141 124]\r\n [133 124 107]]\r\n\r\n [[ 58 45 52]\r\n [ 61 48 55]\r\n [ 51 38 45]\r\n ...\r\n [148 141 123]\r\n [178 169 152]\r\n [144 135 118]]\r\n\r\n [[ 79 66 83]\r\n [ 73 60 77]\r\n [ 65 51 66]\r\n ...\r\n [140 131 114]\r\n [142 133 116]\r\n [147 136 118]]\r\n\r\n ...\r\n\r\n [[132 122 133]\r\n [ 95 85 94]\r\n [ 61 51 60]\r\n ...\r\n [ 39 28 42]\r\n [ 46 36 45]\r\n [ 25 16 21]]\r\n\r\n [[150 143 151]\r\n [114 107 115]\r\n [ 64 54 63]\r\n ...\r\n [ 47 35 47]\r\n [ 38 27 35]\r\n [140 129 133]]\r\n\r\n [[145 138 146]\r\n [115 108 116]\r\n [ 69 59 67]\r\n ...\r\n [ 31 19 31]\r\n [128 117 123]\r\n [196 185 189]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[159 151 140]\r\n [171 163 152]\r\n [161 148 142]\r\n ...\r\n [198 184 171]\r\n [189 175 162]\r\n [183 169 156]]\r\n\r\n [[128 118 106]\r\n [138 128 116]\r\n [138 125 116]\r\n ...\r\n [200 186 173]\r\n [190 176 163]\r\n [187 173 160]]\r\n\r\n [[165 153 137]\r\n [170 158 142]\r\n [174 162 148]\r\n ...\r\n [200 187 171]\r\n [188 175 159]\r\n [182 169 153]]\r\n```\r\nHowever , when trying to add to:\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('Andyrasika/cat_kingdom')\r\ndataset\r\n```\r\ni did \r\n```\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\nnew_column\r\ndataset = dataset.add_column(\"/workspace/data\", new_column)\r\n\r\nprint(dataset)\r\n```\r\ngot error:\r\n```\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[11], line 3\r\n 1 new_column = [\"mask\"] * len(dataset[\"train\"])\r\n 2 new_column\r\n----> 3 dataset = dataset.add_column(\"/workspace/data\", new_column)\r\n 5 print(dataset)\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'add_column'\r\n```", "https://github.com/huggingface/datasets/issues/6246 resolved the `add_column` error, so I'm closing this issue :) " ]	2023-09-10T08:36:12Z	2023-09-20T16:11:53Z	2023-09-20T16:11:52Z	NONE	null	null	### Describe the bug ``` --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[14], line 11 9 for idx, example in enumerate(dataset['train']): 10 image_path = example['image'] ---> 11 mask_image = process_image(image_path) 12 mask_image.save(f"mask_{idx}.png") Cell In[14], line 4, in process_image(image_path) 2 def process_image(image_path): 3 print("Processing image:", image_path) ----> 4 result = inferencer(image_path)['predictions'] 5 mask = np.where(result == 12, 255, 0).astype('uint8') 6 return Image.fromarray(mask) File /usr/local/lib/python3.10/dist-packages/mmseg/apis/mmseg_inferencer.py:183, in MMSegInferencer.__call__(self, inputs, return_datasamples, batch_size, show, wait_time, out_dir, img_out_dir, pred_out_dir, kwargs) 180 pred_out_dir = '' 181 img_out_dir = '' --> 183 return super().__call__( 184 inputs=inputs, 185 return_datasamples=return_datasamples, 186 batch_size=batch_size, 187 show=show, 188 wait_time=wait_time, 189 img_out_dir=img_out_dir, 190 pred_out_dir=pred_out_dir, 191 kwargs) File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:221, in BaseInferencer.__call__(self, inputs, return_datasamples, batch_size, kwargs) 218 inputs = self.preprocess( 219 ori_inputs, batch_size=batch_size, preprocess_kwargs) 220 preds = [] --> 221 for data in (track(inputs, description='Inference') 222 if self.show_progress else inputs): 223 preds.extend(self.forward(data, forward_kwargs)) 224 visualization = self.visualize( 225 ori_inputs, preds, 226 visualize_kwargs) # type: ignore # noqa: E501 File /usr/local/lib/python3.10/dist-packages/rich/progress.py:168, in track(sequence, description, total, auto_refresh, console, transient, get_time, refresh_per_second, style, complete_style, finished_style, pulse_style, update_period, disable, show_speed) 157 progress = Progress( 158 columns, 159 auto_refresh=auto_refresh, (...) 164 disable=disable, 165 ) 167 with progress: --> 168 yield from progress.track( 169 sequence, total=total, description=description, update_period=update_period 170 ) File /usr/local/lib/python3.10/dist-packages/rich/progress.py:1210, in Progress.track(self, sequence, total, task_id, description, update_period) 1208 if self.live.auto_refresh: 1209 with _TrackThread(self, task_id, update_period) as track_thread: -> 1210 for value in sequence: 1211 yield value 1212 track_thread.completed += 1 File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:291, in BaseInferencer.preprocess(self, inputs, batch_size, *kwargs) 266 """Process the inputs into a model-feedable format. 267 268 Customize your preprocess by overriding this method. Preprocess should (...) 287 Any: Data processed by the ``pipeline`` and ``collate_fn``. 288 """ 289 chunked_data = self._get_chunk_data( 290 map(self.pipeline, inputs), batch_size) --> 291 yield from map(self.collate_fn, chunked_data) File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:588, in BaseInferencer._get_chunk_data(self, inputs, chunk_size) 586 chunk_data = [] 587 for _ in range(chunk_size): --> 588 processed_data = next(inputs_iter) 589 chunk_data.append(processed_data) 590 yield chunk_data File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results) 9 def __call__(self, 10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]: ---> 12 return self.transform(results) File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/wrappers.py:88, in Compose.transform(self, results) 79 """Call function to apply transforms sequentially. 80 81 Args: (...) 85 dict or None: Transformed results. 86 """ 87 for t in self.transforms: ---> 88 results = t(results) # type: ignore 89 if results is None: 90 return None File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results) 9 def __call__(self, 10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]: ---> 12 return self.transform(results) File /usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/loading.py:496, in InferencerLoader.transform(self, single_input) 494 inputs = single_input 495 else: --> 496 raise NotImplementedError 498 if 'img' in inputs: 499 return self.from_ndarray(inputs) NotImplementedError: ```` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('Andyrasika/cat_kingdom') dataset from mmseg.apis import MMSegInferencer checkpoint_name = 'segformer_mit-b5_8xb2-160k_ade20k-640x640' inferencer = MMSegInferencer(model=checkpoint_name) # Define a function to apply the code to each image in the dataset def process_image(image_path): print("Processing image:", image_path) result = inferencer(image_path)['predictions'] mask = np.where(result == 12, 255, 0).astype('uint8') return Image.fromarray(mask) # Process and save masks for each image in the dataset for idx, example in enumerate(dataset['train']): image_path = example['image'] mask_image = process_image(image_path) mask_image.save(f"mask_{idx}.png") ``` ### Expected behavior create a separate column with masks in the dataset and further shows as a separate column in hub ### Environment info jupyter notebook RTX 3090	{ "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/6229/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/6228	6,228	Remove RGB -> BGR image conversion in Object Detection tutorial	{ "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) \| 0.009443 / 0.011353 (-0.001910) \| 0.005274 / 0.011008 (-0.005734) \| 0.105950 / 0.038508 (0.067441) \| 0.079947 / 0.023109 (0.056837) \| 0.414248 / 0.275898 (0.138350) \| 0.440611 / 0.323480 (0.117131) \| 0.006779 / 0.007986 (-0.001206) \| 0.004301 / 0.004328 (-0.000028) \| 0.080616 / 0.004250 (0.076366) \| 0.061425 / 0.037052 (0.024372) \| 0.418460 / 0.258489 (0.159971) \| 0.468108 / 0.293841 (0.174267) \| 0.051090 / 0.128546 (-0.077456) \| 0.014133 / 0.075646 (-0.061513) \| 0.376121 / 0.419271 (-0.043151) \| 0.070715 / 0.043533 (0.027182) \| 0.415435 / 0.255139 (0.160296) \| 0.457925 / 0.283200 (0.174725) \| 0.053653 / 0.141683 (-0.088030) \| 1.872681 / 1.452155 (0.420527) \| 1.961187 / 1.492716 (0.468470) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255829 / 0.018006 (0.237823) \| 0.574224 / 0.000490 (0.573735) \| 0.007597 / 0.000200 (0.007397) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032562 / 0.037411 (-0.004849) \| 0.097528 / 0.014526 (0.083003) \| 0.113487 / 0.176557 (-0.063070) \| 0.185670 / 0.737135 (-0.551465) \| 0.118909 / 0.296338 (-0.177430) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.611441 / 0.215209 (0.396232) \| 5.908576 / 2.077655 (3.830921) \| 2.586758 / 1.504120 (1.082638) \| 2.310199 / 1.541195 (0.769004) \| 2.333396 / 1.468490 (0.864906) \| 0.900884 / 4.584777 (-3.683893) \| 5.438304 / 3.745712 (1.692591) \| 4.806611 / 5.269862 (-0.463250) \| 2.970631 / 4.565676 (-1.595046) \| 0.097861 / 0.424275 (-0.326414) \| 0.009873 / 0.007607 (0.002266) \| 0.739553 / 0.226044 (0.513509) \| 7.104953 / 2.268929 (4.836024) \| 3.150128 / 55.444624 (-52.294497) \| 2.469552 / 6.876477 (-4.406924) \| 2.709206 / 2.142072 (0.567133) \| 0.983081 / 4.805227 (-3.822147) \| 0.205150 / 6.500664 (-6.295514) \| 0.075947 / 0.075469 (0.000478) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.631255 / 1.841788 (-0.210532) \| 24.213679 / 8.074308 (16.139370) \| 21.514481 / 10.191392 (11.323089) \| 0.220360 / 0.680424 (-0.460063) \| 0.031663 / 0.534201 (-0.502538) \| 0.516029 / 0.579283 (-0.063254) \| 0.591461 / 0.434364 (0.157097) \| 0.612398 / 0.540337 (0.072061) \| 0.807609 / 1.386936 (-0.579328) \|\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) \| 0.009443 / 0.011353 (-0.001910) \| 0.005510 / 0.011008 (-0.005498) \| 0.085722 / 0.038508 (0.047214) \| 0.076256 / 0.023109 (0.053146) \| 0.604248 / 0.275898 (0.328349) \| 0.596222 / 0.323480 (0.272742) \| 0.006786 / 0.007986 (-0.001200) \| 0.004135 / 0.004328 (-0.000193) \| 0.085934 / 0.004250 (0.081683) \| 0.065890 / 0.037052 (0.028838) \| 0.592080 / 0.258489 (0.333591) \| 0.624560 / 0.293841 (0.330719) \| 0.048200 / 0.128546 (-0.080346) \| 0.015477 / 0.075646 (-0.060169) \| 0.097042 / 0.419271 (-0.322230) \| 0.060513 / 0.043533 (0.016981) \| 0.557171 / 0.255139 (0.302032) \| 0.582057 / 0.283200 (0.298858) \| 0.035678 / 0.141683 (-0.106005) \| 1.894947 / 1.452155 (0.442792) \| 1.956652 / 1.492716 (0.463936) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268927 / 0.018006 (0.250921) \| 0.566086 / 0.000490 (0.565597) \| 0.007190 / 0.000200 (0.006990) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.042090 / 0.037411 (0.004679) \| 0.109618 / 0.014526 (0.095092) \| 0.126588 / 0.176557 (-0.049968) \| 0.200426 / 0.737135 (-0.536709) \| 0.127032 / 0.296338 (-0.169306) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.669773 / 0.215209 (0.454564) \| 6.453417 / 2.077655 (4.375763) \| 3.119147 / 1.504120 (1.615027) \| 2.818632 / 1.541195 (1.277437) \| 2.930880 / 1.468490 (1.462390) \| 0.922164 / 4.584777 (-3.662612) \| 5.769564 / 3.745712 (2.023852) \| 4.885108 / 5.269862 (-0.384754) \| 3.041640 / 4.565676 (-1.524037) \| 0.100186 / 0.424275 (-0.324090) \| 0.009417 / 0.007607 (0.001810) \| 0.783138 / 0.226044 (0.557094) \| 8.113361 / 2.268929 (5.844432) \| 4.018630 / 55.444624 (-51.425995) \| 3.246772 / 6.876477 (-3.629704) \| 3.520690 / 2.142072 (1.378618) \| 1.063686 / 4.805227 (-3.741541) \| 0.218667 / 6.500664 (-6.281997) \| 0.084169 / 0.075469 (0.008700) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.791949 / 1.841788 (-0.049839) \| 23.148341 / 8.074308 (15.074033) \| 23.321125 / 10.191392 (13.129733) \| 0.245391 / 0.680424 (-0.435032) \| 0.031911 / 0.534201 (-0.502290) \| 0.470707 / 0.579283 (-0.108576) \| 0.608195 / 0.434364 (0.173832) \| 0.559590 / 0.540337 (0.019253) \| 0.786007 / 1.386936 (-0.600929) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8e071f565cc0801f73f7f34fba92dc30a43946a9 \"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) \| 0.008428 / 0.011353 (-0.002925) \| 0.004064 / 0.011008 (-0.006944) \| 0.088421 / 0.038508 (0.049913) \| 0.078042 / 0.023109 (0.054933) \| 0.306356 / 0.275898 (0.030458) \| 0.349766 / 0.323480 (0.026286) \| 0.004086 / 0.007986 (-0.003900) \| 0.003900 / 0.004328 (-0.000428) \| 0.068379 / 0.004250 (0.064129) \| 0.056214 / 0.037052 (0.019161) \| 0.310211 / 0.258489 (0.051722) \| 0.363692 / 0.293841 (0.069851) \| 0.050421 / 0.128546 (-0.078125) \| 0.011661 / 0.075646 (-0.063985) \| 0.298400 / 0.419271 (-0.120871) \| 0.063503 / 0.043533 (0.019970) \| 0.339799 / 0.255139 (0.084660) \| 0.359479 / 0.283200 (0.076279) \| 0.039265 / 0.141683 (-0.102418) \| 1.390578 / 1.452155 (-0.061576) \| 1.573333 / 1.492716 (0.080617) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260442 / 0.018006 (0.242436) \| 0.560390 / 0.000490 (0.559900) \| 0.003926 / 0.000200 (0.003726) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025809 / 0.037411 (-0.011602) \| 0.081902 / 0.014526 (0.067376) \| 0.093655 / 0.176557 (-0.082901) \| 0.149432 / 0.737135 (-0.587703) \| 0.099059 / 0.296338 (-0.197279) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.505644 / 0.215209 (0.290435) \| 5.108292 / 2.077655 (3.030638) \| 2.121689 / 1.504120 (0.617569) \| 1.846576 / 1.541195 (0.305381) \| 1.836587 / 1.468490 (0.368097) \| 0.708088 / 4.584777 (-3.876689) \| 4.562630 / 3.745712 (0.816918) \| 3.934747 / 5.269862 (-1.335115) \| 2.453409 / 4.565676 (-2.112267) \| 0.081908 / 0.424275 (-0.342367) \| 0.012996 / 0.007607 (0.005389) \| 0.636588 / 0.226044 (0.410544) \| 6.361086 / 2.268929 (4.092157) \| 2.911681 / 55.444624 (-52.532943) \| 2.271809 / 6.876477 (-4.604667) \| 2.670327 / 2.142072 (0.528254) \| 0.943688 / 4.805227 (-3.861539) \| 0.191677 / 6.500664 (-6.308988) \| 0.066008 / 0.075469 (-0.009461) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.400139 / 1.841788 (-0.441648) \| 21.896198 / 8.074308 (13.821890) \| 17.853604 / 10.191392 (7.662212) \| 0.226603 / 0.680424 (-0.453821) \| 0.026682 / 0.534201 (-0.507518) \| 0.460131 / 0.579283 (-0.119152) \| 0.536790 / 0.434364 (0.102427) \| 0.492913 / 0.540337 (-0.047424) \| 0.724290 / 1.386936 (-0.662646) \|\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) \| 0.007795 / 0.011353 (-0.003557) \| 0.009045 / 0.011008 (-0.001963) \| 0.085480 / 0.038508 (0.046972) \| 0.071881 / 0.023109 (0.048772) \| 0.514520 / 0.275898 (0.238622) \| 0.569762 / 0.323480 (0.246282) \| 0.006126 / 0.007986 (-0.001859) \| 0.004153 / 0.004328 (-0.000175) \| 0.072150 / 0.004250 (0.067900) \| 0.056511 / 0.037052 (0.019458) \| 0.484097 / 0.258489 (0.225607) \| 0.532673 / 0.293841 (0.238832) \| 0.040974 / 0.128546 (-0.087572) \| 0.012071 / 0.075646 (-0.063575) \| 0.102608 / 0.419271 (-0.316663) \| 0.052893 / 0.043533 (0.009360) \| 0.485832 / 0.255139 (0.230693) \| 0.530479 / 0.283200 (0.247280) \| 0.031556 / 0.141683 (-0.110127) \| 1.737508 / 1.452155 (0.285354) \| 1.834637 / 1.492716 (0.341921) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423314 / 0.018006 (0.405308) \| 0.614163 / 0.000490 (0.613673) \| 0.052784 / 0.000200 (0.052584) \| 0.000206 / 0.000054 (0.000151) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031728 / 0.037411 (-0.005684) \| 0.088048 / 0.014526 (0.073522) \| 0.105759 / 0.176557 (-0.070798) \| 0.181433 / 0.737135 (-0.555703) \| 0.103133 / 0.296338 (-0.193205) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.659710 / 0.215209 (0.444501) \| 5.876378 / 2.077655 (3.798723) \| 2.899444 / 1.504120 (1.395324) \| 2.871592 / 1.541195 (1.330397) \| 2.861205 / 1.468490 (1.392715) \| 0.879452 / 4.584777 (-3.705325) \| 5.395988 / 3.745712 (1.650275) \| 4.548359 / 5.269862 (-0.721502) \| 2.946601 / 4.565676 (-1.619076) \| 0.099832 / 0.424275 (-0.324443) \| 0.008958 / 0.007607 (0.001351) \| 0.778480 / 0.226044 (0.552435) \| 7.672282 / 2.268929 (5.403354) \| 3.963701 / 55.444624 (-51.480923) \| 3.154950 / 6.876477 (-3.721527) \| 3.351070 / 2.142072 (1.208997) \| 1.059459 / 4.805227 (-3.745768) \| 0.212035 / 6.500664 (-6.288629) \| 0.076941 / 0.075469 (0.001472) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.639813 / 1.841788 (-0.201975) \| 24.807517 / 8.074308 (16.733208) \| 20.662500 / 10.191392 (10.471108) \| 0.244486 / 0.680424 (-0.435937) \| 0.032335 / 0.534201 (-0.501866) \| 0.470896 / 0.579283 (-0.108387) \| 0.581561 / 0.434364 (0.147197) \| 0.495158 / 0.540337 (-0.045179) \| 0.788350 / 1.386936 (-0.598586) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#99641ced2e08a28cb876f483babcdd43f7dd76d2 \"CML watermark\")\n" ]	2023-09-08T16:09:13Z	2023-09-08T18:02:49Z	2023-09-08T17:52:16Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6228.diff", "html_url": "https://github.com/huggingface/datasets/pull/6228", "merged_at": "2023-09-08T17:52:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/6228.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6228" }	Fix #6225	{ "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/6228/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6226	6,226	Add push_to_hub with multiple configs docs	{ "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) \| 0.005920 / 0.011353 (-0.005433) \| 0.003623 / 0.011008 (-0.007385) \| 0.079283 / 0.038508 (0.040775) \| 0.058325 / 0.023109 (0.035216) \| 0.313733 / 0.275898 (0.037835) \| 0.360790 / 0.323480 (0.037310) \| 0.004653 / 0.007986 (-0.003332) \| 0.002876 / 0.004328 (-0.001452) \| 0.062137 / 0.004250 (0.057886) \| 0.045084 / 0.037052 (0.008031) \| 0.328569 / 0.258489 (0.070079) \| 0.368965 / 0.293841 (0.075124) \| 0.027085 / 0.128546 (-0.101461) \| 0.008051 / 0.075646 (-0.067595) \| 0.260222 / 0.419271 (-0.159050) \| 0.045477 / 0.043533 (0.001944) \| 0.315344 / 0.255139 (0.060205) \| 0.348215 / 0.283200 (0.065015) \| 0.021352 / 0.141683 (-0.120331) \| 1.432200 / 1.452155 (-0.019955) \| 1.509217 / 1.492716 (0.016501) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.199843 / 0.018006 (0.181837) \| 0.427925 / 0.000490 (0.427435) \| 0.002903 / 0.000200 (0.002703) \| 0.000067 / 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) \| 0.023121 / 0.037411 (-0.014291) \| 0.072451 / 0.014526 (0.057925) \| 0.083260 / 0.176557 (-0.093296) \| 0.142879 / 0.737135 (-0.594257) \| 0.084053 / 0.296338 (-0.212286) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.394922 / 0.215209 (0.179713) \| 3.956111 / 2.077655 (1.878456) \| 1.926411 / 1.504120 (0.422291) \| 1.743840 / 1.541195 (0.202646) \| 1.776957 / 1.468490 (0.308467) \| 0.502134 / 4.584777 (-4.082643) \| 3.001721 / 3.745712 (-0.743991) \| 2.852496 / 5.269862 (-2.417365) \| 1.862794 / 4.565676 (-2.702883) \| 0.057544 / 0.424275 (-0.366731) \| 0.006751 / 0.007607 (-0.000856) \| 0.470619 / 0.226044 (0.244575) \| 4.696674 / 2.268929 (2.427746) \| 2.326545 / 55.444624 (-53.118080) \| 1.980888 / 6.876477 (-4.895589) \| 2.139172 / 2.142072 (-0.002901) \| 0.590256 / 4.805227 (-4.214971) \| 0.125815 / 6.500664 (-6.374849) \| 0.061000 / 0.075469 (-0.014469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.261948 / 1.841788 (-0.579839) \| 18.317473 / 8.074308 (10.243165) \| 13.810883 / 10.191392 (3.619491) \| 0.146180 / 0.680424 (-0.534244) \| 0.016701 / 0.534201 (-0.517500) \| 0.330731 / 0.579283 (-0.248552) \| 0.345103 / 0.434364 (-0.089261) \| 0.374449 / 0.540337 (-0.165889) \| 0.522463 / 1.386936 (-0.864473) \|\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) \| 0.006217 / 0.011353 (-0.005136) \| 0.003678 / 0.011008 (-0.007331) \| 0.062321 / 0.038508 (0.023813) \| 0.059256 / 0.023109 (0.036147) \| 0.444501 / 0.275898 (0.168603) \| 0.475881 / 0.323480 (0.152401) \| 0.004863 / 0.007986 (-0.003123) \| 0.002916 / 0.004328 (-0.001412) \| 0.062197 / 0.004250 (0.057946) \| 0.048449 / 0.037052 (0.011396) \| 0.443680 / 0.258489 (0.185191) \| 0.484570 / 0.293841 (0.190729) \| 0.028694 / 0.128546 (-0.099852) \| 0.008096 / 0.075646 (-0.067550) \| 0.068347 / 0.419271 (-0.350924) \| 0.041031 / 0.043533 (-0.002502) \| 0.443907 / 0.255139 (0.188768) \| 0.469888 / 0.283200 (0.186689) \| 0.020237 / 0.141683 (-0.121445) \| 1.438484 / 1.452155 (-0.013671) \| 1.512652 / 1.492716 (0.019936) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243118 / 0.018006 (0.225111) \| 0.416797 / 0.000490 (0.416308) \| 0.010421 / 0.000200 (0.010221) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026191 / 0.037411 (-0.011220) \| 0.080881 / 0.014526 (0.066355) \| 0.093207 / 0.176557 (-0.083349) \| 0.146708 / 0.737135 (-0.590428) \| 0.091676 / 0.296338 (-0.204663) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461475 / 0.215209 (0.246266) \| 4.617351 / 2.077655 (2.539696) \| 2.564369 / 1.504120 (1.060249) \| 2.393263 / 1.541195 (0.852068) \| 2.447343 / 1.468490 (0.978853) \| 0.508764 / 4.584777 (-4.076013) \| 3.075460 / 3.745712 (-0.670252) \| 2.884683 / 5.269862 (-2.385179) \| 1.866432 / 4.565676 (-2.699244) \| 0.058759 / 0.424275 (-0.365516) \| 0.006591 / 0.007607 (-0.001016) \| 0.537718 / 0.226044 (0.311674) \| 5.378709 / 2.268929 (3.109781) \| 3.006751 / 55.444624 (-52.437873) \| 2.666653 / 6.876477 (-4.209824) \| 2.847559 / 2.142072 (0.705486) \| 0.596878 / 4.805227 (-4.208350) \| 0.125073 / 6.500664 (-6.375591) \| 0.061345 / 0.075469 (-0.014124) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.349066 / 1.841788 (-0.492721) \| 18.684735 / 8.074308 (10.610427) \| 15.128142 / 10.191392 (4.936750) \| 0.149254 / 0.680424 (-0.531170) \| 0.017911 / 0.534201 (-0.516290) \| 0.344057 / 0.579283 (-0.235226) \| 0.363474 / 0.434364 (-0.070890) \| 0.399425 / 0.540337 (-0.140912) \| 0.549329 / 1.386936 (-0.837607) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e675a2396efb5204a4553721001f3b46aa4cc334 \"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) \| 0.005843 / 0.011353 (-0.005510) \| 0.003549 / 0.011008 (-0.007460) \| 0.082318 / 0.038508 (0.043810) \| 0.056835 / 0.023109 (0.033726) \| 0.312968 / 0.275898 (0.037070) \| 0.345918 / 0.323480 (0.022438) \| 0.003239 / 0.007986 (-0.004747) \| 0.002762 / 0.004328 (-0.001567) \| 0.062362 / 0.004250 (0.058111) \| 0.045934 / 0.037052 (0.008882) \| 0.317035 / 0.258489 (0.058546) \| 0.358473 / 0.293841 (0.064632) \| 0.027311 / 0.128546 (-0.101235) \| 0.007994 / 0.075646 (-0.067652) \| 0.261565 / 0.419271 (-0.157706) \| 0.044942 / 0.043533 (0.001410) \| 0.313092 / 0.255139 (0.057953) \| 0.339021 / 0.283200 (0.055821) \| 0.021555 / 0.141683 (-0.120127) \| 1.421232 / 1.452155 (-0.030923) \| 1.487597 / 1.492716 (-0.005119) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206432 / 0.018006 (0.188425) \| 0.421932 / 0.000490 (0.421442) \| 0.002825 / 0.000200 (0.002625) \| 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) \| 0.022795 / 0.037411 (-0.014616) \| 0.072666 / 0.014526 (0.058140) \| 0.082779 / 0.176557 (-0.093778) \| 0.142320 / 0.737135 (-0.594815) \| 0.083343 / 0.296338 (-0.212995) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.394227 / 0.215209 (0.179018) \| 3.931858 / 2.077655 (1.854203) \| 1.909953 / 1.504120 (0.405833) \| 1.711298 / 1.541195 (0.170104) \| 1.745816 / 1.468490 (0.277326) \| 0.503670 / 4.584777 (-4.081107) \| 3.053677 / 3.745712 (-0.692035) \| 2.802597 / 5.269862 (-2.467264) \| 1.825315 / 4.565676 (-2.740362) \| 0.057741 / 0.424275 (-0.366534) \| 0.006581 / 0.007607 (-0.001027) \| 0.463597 / 0.226044 (0.237552) \| 4.638821 / 2.268929 (2.369893) \| 2.301266 / 55.444624 (-53.143358) \| 1.967111 / 6.876477 (-4.909365) \| 2.097756 / 2.142072 (-0.044317) \| 0.589840 / 4.805227 (-4.215387) \| 0.125538 / 6.500664 (-6.375126) \| 0.061203 / 0.075469 (-0.014266) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291815 / 1.841788 (-0.549973) \| 17.997040 / 8.074308 (9.922732) \| 13.616252 / 10.191392 (3.424860) \| 0.137349 / 0.680424 (-0.543075) \| 0.016626 / 0.534201 (-0.517575) \| 0.329611 / 0.579283 (-0.249672) \| 0.346592 / 0.434364 (-0.087772) \| 0.379521 / 0.540337 (-0.160817) \| 0.528058 / 1.386936 (-0.858878) \|\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) \| 0.006073 / 0.011353 (-0.005280) \| 0.003594 / 0.011008 (-0.007414) \| 0.062537 / 0.038508 (0.024029) \| 0.057503 / 0.023109 (0.034394) \| 0.449427 / 0.275898 (0.173529) \| 0.482729 / 0.323480 (0.159249) \| 0.004690 / 0.007986 (-0.003295) \| 0.002901 / 0.004328 (-0.001428) \| 0.062421 / 0.004250 (0.058171) \| 0.046405 / 0.037052 (0.009353) \| 0.456578 / 0.258489 (0.198089) \| 0.492268 / 0.293841 (0.198427) \| 0.028283 / 0.128546 (-0.100263) \| 0.008028 / 0.075646 (-0.067618) \| 0.067885 / 0.419271 (-0.351387) \| 0.041273 / 0.043533 (-0.002260) \| 0.449870 / 0.255139 (0.194731) \| 0.472305 / 0.283200 (0.189106) \| 0.018556 / 0.141683 (-0.123127) \| 1.449016 / 1.452155 (-0.003138) \| 1.490839 / 1.492716 (-0.001877) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226569 / 0.018006 (0.208563) \| 0.417106 / 0.000490 (0.416616) \| 0.002784 / 0.000200 (0.002584) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025803 / 0.037411 (-0.011608) \| 0.081084 / 0.014526 (0.066559) \| 0.091851 / 0.176557 (-0.084706) \| 0.143982 / 0.737135 (-0.593153) \| 0.090511 / 0.296338 (-0.205827) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463664 / 0.215209 (0.248454) \| 4.634528 / 2.077655 (2.556874) \| 2.574739 / 1.504120 (1.070619) \| 2.412857 / 1.541195 (0.871662) \| 2.442858 / 1.468490 (0.974368) \| 0.511990 / 4.584777 (-4.072787) \| 3.070345 / 3.745712 (-0.675367) \| 2.842290 / 5.269862 (-2.427571) \| 1.846727 / 4.565676 (-2.718950) \| 0.058852 / 0.424275 (-0.365424) \| 0.006624 / 0.007607 (-0.000983) \| 0.539616 / 0.226044 (0.313571) \| 5.410784 / 2.268929 (3.141856) \| 3.065593 / 55.444624 (-52.379031) \| 2.677930 / 6.876477 (-4.198547) \| 2.817548 / 2.142072 (0.675476) \| 0.602672 / 4.805227 (-4.202555) \| 0.125689 / 6.500664 (-6.374975) \| 0.062007 / 0.075469 (-0.013462) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.335336 / 1.841788 (-0.506452) \| 18.310099 / 8.074308 (10.235791) \| 14.818452 / 10.191392 (4.627060) \| 0.154001 / 0.680424 (-0.526423) \| 0.017892 / 0.534201 (-0.516309) \| 0.345989 / 0.579283 (-0.233294) \| 0.352108 / 0.434364 (-0.082256) \| 0.394333 / 0.540337 (-0.146004) \| 0.547680 / 1.386936 (-0.839256) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d058d6e9b849acb5bc61d7df597a94253b487eb6 \"CML watermark\")\n" ]	2023-09-08T11:08:55Z	2023-09-08T12:29:21Z	2023-09-08T12:20:51Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6226.diff", "html_url": "https://github.com/huggingface/datasets/pull/6226", "merged_at": "2023-09-08T12:20:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/6226.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6226" }	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": 2, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6226/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6225	6,225	Conversion from RGB to BGR in Object Detection tutorial	{ "avatar_url": "https://avatars.githubusercontent.com/u/33297401?v=4", "events_url": "https://api.github.com/users/samokhinv/events{/privacy}", "followers_url": "https://api.github.com/users/samokhinv/followers", "following_url": "https://api.github.com/users/samokhinv/following{/other_user}", "gists_url": "https://api.github.com/users/samokhinv/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/samokhinv", "id": 33297401, "login": "samokhinv", "node_id": "MDQ6VXNlcjMzMjk3NDAx", "organizations_url": "https://api.github.com/users/samokhinv/orgs", "received_events_url": "https://api.github.com/users/samokhinv/received_events", "repos_url": "https://api.github.com/users/samokhinv/repos", "site_admin": false, "starred_url": "https://api.github.com/users/samokhinv/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/samokhinv/subscriptions", "type": "User", "url": "https://api.github.com/users/samokhinv", "user_view_type": "public" }	[]	closed	false	[ "Good catch!" ]	2023-09-08T06:49:19Z	2023-09-08T17:52:18Z	2023-09-08T17:52:17Z	NONE	null	null	The [tutorial](https://huggingface.co/docs/datasets/main/en/object_detection) mentions the necessity of conversion the input image from BGR to RGB > albumentations expects the image to be in BGR format, not RGB, so you’ll have to convert the image before applying the transform. [Link to tutorial](https://github.com/huggingface/datasets/blob/0a068dbf3b446417ffd89d32857608394ec699e6/docs/source/object_detection.mdx#L77) However, relevant albumentations' tutorials [on channels conversion](https://albumentations.ai/docs/examples/example/#read-the-image-from-the-disk-and-convert-it-from-the-bgr-color-space-to-the-rgb-color-space) and [on boxes](https://albumentations.ai/docs/examples/example_bboxes/) imply that it's not really true no more. I suggest removing this outdated conversion from the tutorial.	{ "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/6225/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/6224	6,224	Ignore `dataset_info.json` in data files resolution	{ "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) \| 0.009450 / 0.011353 (-0.001903) \| 0.007339 / 0.011008 (-0.003669) \| 0.110150 / 0.038508 (0.071641) \| 0.087794 / 0.023109 (0.064685) \| 0.472099 / 0.275898 (0.196201) \| 0.476622 / 0.323480 (0.153142) \| 0.005057 / 0.007986 (-0.002929) \| 0.005262 / 0.004328 (0.000933) \| 0.103059 / 0.004250 (0.098808) \| 0.069815 / 0.037052 (0.032763) \| 0.489377 / 0.258489 (0.230888) \| 0.547087 / 0.293841 (0.253247) \| 0.048883 / 0.128546 (-0.079663) \| 0.019192 / 0.075646 (-0.056454) \| 0.410865 / 0.419271 (-0.008407) \| 0.076215 / 0.043533 (0.032682) \| 0.484825 / 0.255139 (0.229686) \| 0.519035 / 0.283200 (0.235835) \| 0.042030 / 0.141683 (-0.099653) \| 1.909630 / 1.452155 (0.457475) \| 2.120869 / 1.492716 (0.628153) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267600 / 0.018006 (0.249594) \| 0.619135 / 0.000490 (0.618645) \| 0.005897 / 0.000200 (0.005697) \| 0.000142 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033265 / 0.037411 (-0.004146) \| 0.104476 / 0.014526 (0.089950) \| 0.129199 / 0.176557 (-0.047358) \| 0.196898 / 0.737135 (-0.540238) \| 0.118852 / 0.296338 (-0.177487) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598908 / 0.215209 (0.383699) \| 6.263096 / 2.077655 (4.185441) \| 2.672134 / 1.504120 (1.168014) \| 2.428706 / 1.541195 (0.887511) \| 2.431651 / 1.468490 (0.963161) \| 0.918465 / 4.584777 (-3.666312) \| 5.667857 / 3.745712 (1.922145) \| 5.113696 / 5.269862 (-0.156166) \| 3.276805 / 4.565676 (-1.288872) \| 0.101829 / 0.424275 (-0.322446) \| 0.010224 / 0.007607 (0.002617) \| 0.741547 / 0.226044 (0.515502) \| 7.517002 / 2.268929 (5.248073) \| 3.546353 / 55.444624 (-51.898272) \| 2.845956 / 6.876477 (-4.030521) \| 3.172777 / 2.142072 (1.030705) \| 1.153485 / 4.805227 (-3.651742) \| 0.225758 / 6.500664 (-6.274906) \| 0.084333 / 0.075469 (0.008864) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.704645 / 1.841788 (-0.137143) \| 27.044110 / 8.074308 (18.969801) \| 24.653837 / 10.191392 (14.462445) \| 0.235452 / 0.680424 (-0.444971) \| 0.029285 / 0.534201 (-0.504916) \| 0.576122 / 0.579283 (-0.003161) \| 0.626263 / 0.434364 (0.191899) \| 0.600201 / 0.540337 (0.059864) \| 0.838406 / 1.386936 (-0.548530) \|\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) \| 0.013754 / 0.011353 (0.002401) \| 0.005954 / 0.011008 (-0.005054) \| 0.089766 / 0.038508 (0.051258) \| 0.096126 / 0.023109 (0.073017) \| 0.556455 / 0.275898 (0.280557) \| 0.579302 / 0.323480 (0.255822) \| 0.009222 / 0.007986 (0.001236) \| 0.006128 / 0.004328 (0.001800) \| 0.099725 / 0.004250 (0.095475) \| 0.075642 / 0.037052 (0.038589) \| 0.556645 / 0.258489 (0.298156) \| 0.615898 / 0.293841 (0.322057) \| 0.057728 / 0.128546 (-0.070818) \| 0.016746 / 0.075646 (-0.058900) \| 0.098053 / 0.419271 (-0.321219) \| 0.066676 / 0.043533 (0.023143) \| 0.534156 / 0.255139 (0.279017) \| 0.590020 / 0.283200 (0.306820) \| 0.038782 / 0.141683 (-0.102901) \| 1.952301 / 1.452155 (0.500146) \| 2.104255 / 1.492716 (0.611539) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305945 / 0.018006 (0.287939) \| 0.643915 / 0.000490 (0.643426) \| 0.006268 / 0.000200 (0.006068) \| 0.000156 / 0.000054 (0.000102) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039891 / 0.037411 (0.002479) \| 0.117888 / 0.014526 (0.103363) \| 0.134230 / 0.176557 (-0.042326) \| 0.212544 / 0.737135 (-0.524591) \| 0.128858 / 0.296338 (-0.167480) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.718165 / 0.215209 (0.502955) \| 7.023867 / 2.077655 (4.946212) \| 3.391344 / 1.504120 (1.887224) \| 3.021248 / 1.541195 (1.480053) \| 3.010217 / 1.468490 (1.541727) \| 0.932608 / 4.584777 (-3.652169) \| 5.787536 / 3.745712 (2.041824) \| 5.221305 / 5.269862 (-0.048557) \| 3.282552 / 4.565676 (-1.283125) \| 0.105486 / 0.424275 (-0.318789) \| 0.009800 / 0.007607 (0.002193) \| 0.839358 / 0.226044 (0.613314) \| 8.279712 / 2.268929 (6.010784) \| 4.118466 / 55.444624 (-51.326158) \| 3.407738 / 6.876477 (-3.468739) \| 3.632538 / 2.142072 (1.490466) \| 1.109673 / 4.805227 (-3.695555) \| 0.216541 / 6.500664 (-6.284123) \| 0.094031 / 0.075469 (0.018562) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.983979 / 1.841788 (0.142191) \| 27.125882 / 8.074308 (19.051573) \| 24.714002 / 10.191392 (14.522610) \| 0.264417 / 0.680424 (-0.416007) \| 0.034783 / 0.534201 (-0.499418) \| 0.533304 / 0.579283 (-0.045979) \| 0.647798 / 0.434364 (0.213434) \| 0.588680 / 0.540337 (0.048343) \| 0.854250 / 1.386936 (-0.532686) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#491604b46b1fd8d6fd1b7531f7917ccd657665a6 \"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) \| 0.006664 / 0.011353 (-0.004689) \| 0.004164 / 0.011008 (-0.006844) \| 0.085192 / 0.038508 (0.046684) \| 0.073578 / 0.023109 (0.050469) \| 0.356379 / 0.275898 (0.080481) \| 0.389381 / 0.323480 (0.065902) \| 0.005527 / 0.007986 (-0.002459) \| 0.003488 / 0.004328 (-0.000840) \| 0.065640 / 0.004250 (0.061390) \| 0.055013 / 0.037052 (0.017960) \| 0.358002 / 0.258489 (0.099513) \| 0.400663 / 0.293841 (0.106822) \| 0.030937 / 0.128546 (-0.097609) \| 0.008838 / 0.075646 (-0.066808) \| 0.287488 / 0.419271 (-0.131784) \| 0.051503 / 0.043533 (0.007971) \| 0.353945 / 0.255139 (0.098806) \| 0.388778 / 0.283200 (0.105579) \| 0.023346 / 0.141683 (-0.118337) \| 1.479621 / 1.452155 (0.027466) \| 1.559164 / 1.492716 (0.066448) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245160 / 0.018006 (0.227154) \| 0.561890 / 0.000490 (0.561400) \| 0.004339 / 0.000200 (0.004139) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028460 / 0.037411 (-0.008952) \| 0.082046 / 0.014526 (0.067520) \| 0.098005 / 0.176557 (-0.078552) \| 0.154171 / 0.737135 (-0.582965) \| 0.097632 / 0.296338 (-0.198707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.389993 / 0.215209 (0.174784) \| 3.893287 / 2.077655 (1.815632) \| 1.885668 / 1.504120 (0.381549) \| 1.715055 / 1.541195 (0.173860) \| 1.778008 / 1.468490 (0.309518) \| 0.482818 / 4.584777 (-4.101959) \| 3.572153 / 3.745712 (-0.173559) \| 3.267666 / 5.269862 (-2.002196) \| 2.088394 / 4.565676 (-2.477282) \| 0.056961 / 0.424275 (-0.367314) \| 0.007784 / 0.007607 (0.000177) \| 0.466586 / 0.226044 (0.240542) \| 4.652505 / 2.268929 (2.383576) \| 2.491392 / 55.444624 (-52.953233) \| 2.127600 / 6.876477 (-4.748877) \| 2.296778 / 2.142072 (0.154705) \| 0.582332 / 4.805227 (-4.222895) \| 0.134372 / 6.500664 (-6.366292) \| 0.061737 / 0.075469 (-0.013732) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253647 / 1.841788 (-0.588140) \| 19.802353 / 8.074308 (11.728045) \| 14.262815 / 10.191392 (4.071423) \| 0.169489 / 0.680424 (-0.510935) \| 0.018108 / 0.534201 (-0.516093) \| 0.391711 / 0.579283 (-0.187572) \| 0.406169 / 0.434364 (-0.028195) \| 0.456728 / 0.540337 (-0.083609) \| 0.633538 / 1.386936 (-0.753398) \|\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) \| 0.006661 / 0.011353 (-0.004692) \| 0.004181 / 0.011008 (-0.006827) \| 0.064945 / 0.038508 (0.026437) \| 0.073965 / 0.023109 (0.050856) \| 0.406549 / 0.275898 (0.130651) \| 0.441568 / 0.323480 (0.118089) \| 0.005579 / 0.007986 (-0.002407) \| 0.003523 / 0.004328 (-0.000805) \| 0.065270 / 0.004250 (0.061019) \| 0.055596 / 0.037052 (0.018544) \| 0.407701 / 0.258489 (0.149212) \| 0.444609 / 0.293841 (0.150768) \| 0.031749 / 0.128546 (-0.096797) \| 0.008680 / 0.075646 (-0.066966) \| 0.071154 / 0.419271 (-0.348117) \| 0.047376 / 0.043533 (0.003843) \| 0.406409 / 0.255139 (0.151270) \| 0.420477 / 0.283200 (0.137278) \| 0.023707 / 0.141683 (-0.117976) \| 1.484516 / 1.452155 (0.032361) \| 1.568493 / 1.492716 (0.075777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266534 / 0.018006 (0.248528) \| 0.573806 / 0.000490 (0.573316) \| 0.006247 / 0.000200 (0.006048) \| 0.000165 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033436 / 0.037411 (-0.003976) \| 0.091947 / 0.014526 (0.077421) \| 0.105556 / 0.176557 (-0.071000) \| 0.162094 / 0.737135 (-0.575041) \| 0.107879 / 0.296338 (-0.188459) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429126 / 0.215209 (0.213917) \| 4.281329 / 2.077655 (2.203675) \| 2.295406 / 1.504120 (0.791286) \| 2.123336 / 1.541195 (0.582141) \| 2.190804 / 1.468490 (0.722314) \| 0.492972 / 4.584777 (-4.091805) \| 3.638485 / 3.745712 (-0.107227) \| 3.304576 / 5.269862 (-1.965285) \| 2.063694 / 4.565676 (-2.501983) \| 0.058549 / 0.424275 (-0.365726) \| 0.007591 / 0.007607 (-0.000016) \| 0.504268 / 0.226044 (0.278223) \| 5.031990 / 2.268929 (2.763061) \| 2.773173 / 55.444624 (-52.671451) \| 2.430789 / 6.876477 (-4.445688) \| 2.699900 / 2.142072 (0.557828) \| 0.593220 / 4.805227 (-4.212007) \| 0.133710 / 6.500664 (-6.366954) \| 0.059840 / 0.075469 (-0.015629) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.351158 / 1.841788 (-0.490629) \| 20.176310 / 8.074308 (12.102002) \| 14.933202 / 10.191392 (4.741810) \| 0.169920 / 0.680424 (-0.510503) \| 0.020156 / 0.534201 (-0.514045) \| 0.397440 / 0.579283 (-0.181843) \| 0.409395 / 0.434364 (-0.024969) \| 0.471066 / 0.540337 (-0.069271) \| 0.642670 / 1.386936 (-0.744266) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf90ca7fbfd9c4639cc3faf0a349eb26490e38fc \"CML watermark\")\n" ]	2023-09-07T14:43:51Z	2023-09-07T15:46:10Z	2023-09-07T15:37:20Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6224.diff", "html_url": "https://github.com/huggingface/datasets/pull/6224", "merged_at": "2023-09-07T15:37:20Z", "patch_url": "https://github.com/huggingface/datasets/pull/6224.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6224" }	`save_to_disk` creates this file, but also [`HugginFaceDatasetSever`](https://github.com/gradio-app/gradio/blob/26fef8c7f85a006c7e25cdbed1792df19c512d02/gradio/flagging.py#L214), so this is needed to avoid issues such as [this one](https://discord.com/channels/879548962464493619/1149295819938349107/1149295819938349107).	{ "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/pull/6223	6,223	Update README.md	{ "avatar_url": "https://avatars.githubusercontent.com/u/95188570?v=4", "events_url": "https://api.github.com/users/NinoRisteski/events{/privacy}", "followers_url": "https://api.github.com/users/NinoRisteski/followers", "following_url": "https://api.github.com/users/NinoRisteski/following{/other_user}", "gists_url": "https://api.github.com/users/NinoRisteski/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/NinoRisteski", "id": 95188570, "login": "NinoRisteski", "node_id": "U_kgDOBax2Wg", "organizations_url": "https://api.github.com/users/NinoRisteski/orgs", "received_events_url": "https://api.github.com/users/NinoRisteski/received_events", "repos_url": "https://api.github.com/users/NinoRisteski/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NinoRisteski/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NinoRisteski/subscriptions", "type": "User", "url": "https://api.github.com/users/NinoRisteski", "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) \| 0.006757 / 0.011353 (-0.004596) \| 0.004233 / 0.011008 (-0.006775) \| 0.084123 / 0.038508 (0.045614) \| 0.077513 / 0.023109 (0.054404) \| 0.357024 / 0.275898 (0.081126) \| 0.392956 / 0.323480 (0.069476) \| 0.005408 / 0.007986 (-0.002577) \| 0.003363 / 0.004328 (-0.000966) \| 0.064395 / 0.004250 (0.060145) \| 0.054711 / 0.037052 (0.017659) \| 0.367287 / 0.258489 (0.108798) \| 0.402934 / 0.293841 (0.109093) \| 0.031845 / 0.128546 (-0.096701) \| 0.008646 / 0.075646 (-0.067000) \| 0.288740 / 0.419271 (-0.130531) \| 0.053171 / 0.043533 (0.009638) \| 0.360711 / 0.255139 (0.105572) \| 0.388707 / 0.283200 (0.105507) \| 0.025321 / 0.141683 (-0.116361) \| 1.500684 / 1.452155 (0.048529) \| 1.585747 / 1.492716 (0.093030) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207329 / 0.018006 (0.189323) \| 0.465304 / 0.000490 (0.464814) \| 0.003229 / 0.000200 (0.003029) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028752 / 0.037411 (-0.008659) \| 0.085327 / 0.014526 (0.070802) \| 0.332210 / 0.176557 (0.155653) \| 0.178779 / 0.737135 (-0.558356) \| 0.097765 / 0.296338 (-0.198573) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403710 / 0.215209 (0.188501) \| 4.027069 / 2.077655 (1.949414) \| 2.053451 / 1.504120 (0.549331) \| 1.906647 / 1.541195 (0.365452) \| 1.992507 / 1.468490 (0.524017) \| 0.490203 / 4.584777 (-4.094574) \| 3.696569 / 3.745712 (-0.049143) \| 3.319919 / 5.269862 (-1.949943) \| 2.072794 / 4.565676 (-2.492883) \| 0.057893 / 0.424275 (-0.366383) \| 0.007723 / 0.007607 (0.000116) \| 0.485400 / 0.226044 (0.259355) \| 4.842891 / 2.268929 (2.573963) \| 2.578949 / 55.444624 (-52.865675) \| 2.229217 / 6.876477 (-4.647259) \| 2.468017 / 2.142072 (0.325945) \| 0.595236 / 4.805227 (-4.209992) \| 0.135641 / 6.500664 (-6.365023) \| 0.061232 / 0.075469 (-0.014237) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.307059 / 1.841788 (-0.534729) \| 20.108581 / 8.074308 (12.034273) \| 14.438985 / 10.191392 (4.247593) \| 0.168878 / 0.680424 (-0.511545) \| 0.018208 / 0.534201 (-0.515993) \| 0.395986 / 0.579283 (-0.183297) \| 0.427440 / 0.434364 (-0.006924) \| 0.459917 / 0.540337 (-0.080421) \| 0.631379 / 1.386936 (-0.755557) \|\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) \| 0.007002 / 0.011353 (-0.004351) \| 0.004120 / 0.011008 (-0.006888) \| 0.064817 / 0.038508 (0.026309) \| 0.081297 / 0.023109 (0.058188) \| 0.405598 / 0.275898 (0.129700) \| 0.442360 / 0.323480 (0.118880) \| 0.005475 / 0.007986 (-0.002511) \| 0.003483 / 0.004328 (-0.000845) \| 0.064750 / 0.004250 (0.060499) \| 0.058111 / 0.037052 (0.021059) \| 0.410154 / 0.258489 (0.151665) \| 0.445137 / 0.293841 (0.151296) \| 0.033314 / 0.128546 (-0.095232) \| 0.008747 / 0.075646 (-0.066899) \| 0.071595 / 0.419271 (-0.347676) \| 0.048894 / 0.043533 (0.005361) \| 0.409162 / 0.255139 (0.154023) \| 0.428877 / 0.283200 (0.145677) \| 0.024127 / 0.141683 (-0.117556) \| 1.521369 / 1.452155 (0.069214) \| 1.573505 / 1.492716 (0.080789) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233199 / 0.018006 (0.215193) \| 0.455619 / 0.000490 (0.455129) \| 0.003688 / 0.000200 (0.003488) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033186 / 0.037411 (-0.004225) \| 0.100528 / 0.014526 (0.086003) \| 0.105617 / 0.176557 (-0.070940) \| 0.159437 / 0.737135 (-0.577698) \| 0.108064 / 0.296338 (-0.188274) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435509 / 0.215209 (0.220300) \| 4.339920 / 2.077655 (2.262265) \| 2.368983 / 1.504120 (0.864863) \| 2.211761 / 1.541195 (0.670566) \| 2.301701 / 1.468490 (0.833211) \| 0.495144 / 4.584777 (-4.089633) \| 3.768882 / 3.745712 (0.023170) \| 3.348940 / 5.269862 (-1.920922) \| 2.081142 / 4.565676 (-2.484534) \| 0.058184 / 0.424275 (-0.366091) \| 0.007597 / 0.007607 (-0.000010) \| 0.508806 / 0.226044 (0.282762) \| 5.089226 / 2.268929 (2.820297) \| 2.851930 / 55.444624 (-52.592694) \| 2.512144 / 6.876477 (-4.364332) \| 2.724461 / 2.142072 (0.582388) \| 0.593446 / 4.805227 (-4.211781) \| 0.134908 / 6.500664 (-6.365756) \| 0.060811 / 0.075469 (-0.014658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.362279 / 1.841788 (-0.479508) \| 20.548216 / 8.074308 (12.473908) \| 15.179181 / 10.191392 (4.987789) \| 0.170249 / 0.680424 (-0.510175) \| 0.020772 / 0.534201 (-0.513429) \| 0.398737 / 0.579283 (-0.180546) \| 0.441487 / 0.434364 (0.007124) \| 0.480096 / 0.540337 (-0.060242) \| 0.645825 / 1.386936 (-0.741111) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6fb8b9a833afb25311da395c6e0d9bf770ca2c7 \"CML watermark\")\n" ]	2023-09-07T11:33:20Z	2023-09-13T22:32:31Z	2023-09-13T22:23:42Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6223.diff", "html_url": "https://github.com/huggingface/datasets/pull/6223", "merged_at": "2023-09-13T22:23:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/6223.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6223" }	fixed a few typos	{ "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/6223/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6222	6,222	fix typo in Audio dataset documentation	{ "avatar_url": "https://avatars.githubusercontent.com/u/3224332?v=4", "events_url": "https://api.github.com/users/prassanna-ravishankar/events{/privacy}", "followers_url": "https://api.github.com/users/prassanna-ravishankar/followers", "following_url": "https://api.github.com/users/prassanna-ravishankar/following{/other_user}", "gists_url": "https://api.github.com/users/prassanna-ravishankar/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/prassanna-ravishankar", "id": 3224332, "login": "prassanna-ravishankar", "node_id": "MDQ6VXNlcjMyMjQzMzI=", "organizations_url": "https://api.github.com/users/prassanna-ravishankar/orgs", "received_events_url": "https://api.github.com/users/prassanna-ravishankar/received_events", "repos_url": "https://api.github.com/users/prassanna-ravishankar/repos", "site_admin": false, "starred_url": "https://api.github.com/users/prassanna-ravishankar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/prassanna-ravishankar/subscriptions", "type": "User", "url": "https://api.github.com/users/prassanna-ravishankar", "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) \| 0.006655 / 0.011353 (-0.004698) \| 0.004115 / 0.011008 (-0.006893) \| 0.083895 / 0.038508 (0.045387) \| 0.072770 / 0.023109 (0.049661) \| 0.311401 / 0.275898 (0.035503) \| 0.341079 / 0.323480 (0.017599) \| 0.005488 / 0.007986 (-0.002497) \| 0.003530 / 0.004328 (-0.000799) \| 0.064691 / 0.004250 (0.060441) \| 0.053096 / 0.037052 (0.016044) \| 0.314969 / 0.258489 (0.056480) \| 0.358245 / 0.293841 (0.064404) \| 0.030789 / 0.128546 (-0.097757) \| 0.008868 / 0.075646 (-0.066779) \| 0.288022 / 0.419271 (-0.131249) \| 0.052092 / 0.043533 (0.008559) \| 0.310061 / 0.255139 (0.054922) \| 0.345369 / 0.283200 (0.062170) \| 0.024100 / 0.141683 (-0.117582) \| 1.520573 / 1.452155 (0.068418) \| 1.593750 / 1.492716 (0.101033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242520 / 0.018006 (0.224514) \| 0.567963 / 0.000490 (0.567473) \| 0.003183 / 0.000200 (0.002983) \| 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) \| 0.029473 / 0.037411 (-0.007939) \| 0.083012 / 0.014526 (0.068486) \| 0.262386 / 0.176557 (0.085830) \| 0.155131 / 0.737135 (-0.582004) \| 0.099880 / 0.296338 (-0.196458) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382388 / 0.215209 (0.167179) \| 3.816538 / 2.077655 (1.738884) \| 1.863422 / 1.504120 (0.359302) \| 1.694652 / 1.541195 (0.153457) \| 1.738738 / 1.468490 (0.270248) \| 0.477073 / 4.584777 (-4.107704) \| 3.539244 / 3.745712 (-0.206468) \| 3.238469 / 5.269862 (-2.031392) \| 2.026154 / 4.565676 (-2.539523) \| 0.056111 / 0.424275 (-0.368164) \| 0.007615 / 0.007607 (0.000008) \| 0.460620 / 0.226044 (0.234576) \| 4.596383 / 2.268929 (2.327455) \| 2.348645 / 55.444624 (-53.095979) \| 1.977465 / 6.876477 (-4.899011) \| 2.222828 / 2.142072 (0.080755) \| 0.588065 / 4.805227 (-4.217162) \| 0.132175 / 6.500664 (-6.368489) \| 0.061322 / 0.075469 (-0.014147) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260623 / 1.841788 (-0.581164) \| 19.976475 / 8.074308 (11.902167) \| 14.346488 / 10.191392 (4.155096) \| 0.145614 / 0.680424 (-0.534810) \| 0.018309 / 0.534201 (-0.515892) \| 0.393644 / 0.579283 (-0.185639) \| 0.405355 / 0.434364 (-0.029009) \| 0.458355 / 0.540337 (-0.081982) \| 0.630147 / 1.386936 (-0.756789) \|\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) \| 0.006769 / 0.011353 (-0.004584) \| 0.004172 / 0.011008 (-0.006836) \| 0.064863 / 0.038508 (0.026355) \| 0.076831 / 0.023109 (0.053722) \| 0.419391 / 0.275898 (0.143493) \| 0.439912 / 0.323480 (0.116432) \| 0.006249 / 0.007986 (-0.001737) \| 0.003571 / 0.004328 (-0.000757) \| 0.064877 / 0.004250 (0.060626) \| 0.056023 / 0.037052 (0.018971) \| 0.419899 / 0.258489 (0.161410) \| 0.459334 / 0.293841 (0.165493) \| 0.032217 / 0.128546 (-0.096329) \| 0.008628 / 0.075646 (-0.067019) \| 0.071089 / 0.419271 (-0.348183) \| 0.047463 / 0.043533 (0.003930) \| 0.414961 / 0.255139 (0.159822) \| 0.431408 / 0.283200 (0.148209) \| 0.022406 / 0.141683 (-0.119277) \| 1.511890 / 1.452155 (0.059735) \| 1.580268 / 1.492716 (0.087551) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280805 / 0.018006 (0.262799) \| 0.553766 / 0.000490 (0.553276) \| 0.006155 / 0.000200 (0.005955) \| 0.000102 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032980 / 0.037411 (-0.004431) \| 0.092981 / 0.014526 (0.078456) \| 0.108820 / 0.176557 (-0.067737) \| 0.161709 / 0.737135 (-0.575426) \| 0.109772 / 0.296338 (-0.186566) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433659 / 0.215209 (0.218450) \| 4.328577 / 2.077655 (2.250923) \| 2.316899 / 1.504120 (0.812779) \| 2.142645 / 1.541195 (0.601451) \| 2.245518 / 1.468490 (0.777028) \| 0.489448 / 4.584777 (-4.095329) \| 3.630074 / 3.745712 (-0.115638) \| 3.322749 / 5.269862 (-1.947112) \| 2.062307 / 4.565676 (-2.503370) \| 0.058153 / 0.424275 (-0.366122) \| 0.007453 / 0.007607 (-0.000154) \| 0.507234 / 0.226044 (0.281190) \| 5.071830 / 2.268929 (2.802902) \| 2.839374 / 55.444624 (-52.605250) \| 2.429583 / 6.876477 (-4.446893) \| 2.671940 / 2.142072 (0.529868) \| 0.588256 / 4.805227 (-4.216972) \| 0.135135 / 6.500664 (-6.365530) \| 0.060963 / 0.075469 (-0.014506) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.337462 / 1.841788 (-0.504326) \| 20.292912 / 8.074308 (12.218604) \| 14.871809 / 10.191392 (4.680417) \| 0.169214 / 0.680424 (-0.511209) \| 0.020450 / 0.534201 (-0.513751) \| 0.397094 / 0.579283 (-0.182189) \| 0.411623 / 0.434364 (-0.022741) \| 0.471560 / 0.540337 (-0.068777) \| 0.647293 / 1.386936 (-0.739643) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a068dbf3b446417ffd89d32857608394ec699e6 \"CML watermark\")\n" ]	2023-09-06T23:17:24Z	2023-10-03T14:18:41Z	2023-09-07T15:39:09Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6222.diff", "html_url": "https://github.com/huggingface/datasets/pull/6222", "merged_at": "2023-09-07T15:39:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/6222.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6222" }	There is a typo in the section of the documentation dedicated to creating an audio dataset. The Dataset is incorrectly suffixed with a `Config` https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/blob/main/librivox-indonesia.py#L59	{ "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/6222/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6221	6,221	Support saving datasets with custom 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" }	[]	open	false	[ "Not a fan of pickling this sort of stuff either.\r\nNote that users can also share the code in their dataset documentation." ]	2023-09-06T16:03:32Z	2023-09-06T18:32:07Z	null	COLLABORATOR	null	null	Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036. I am not sure if supporting this is the best idea for the following reasons: >For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea. @lhoestq WDYT?	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/6221/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/6220	6,220	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_6220). 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) \| 0.005950 / 0.011353 (-0.005403) \| 0.003578 / 0.011008 (-0.007431) \| 0.079327 / 0.038508 (0.040819) \| 0.057862 / 0.023109 (0.034752) \| 0.317288 / 0.275898 (0.041390) \| 0.358210 / 0.323480 (0.034730) \| 0.004685 / 0.007986 (-0.003301) \| 0.002879 / 0.004328 (-0.001450) \| 0.062355 / 0.004250 (0.058105) \| 0.045093 / 0.037052 (0.008041) \| 0.322520 / 0.258489 (0.064031) \| 0.367114 / 0.293841 (0.073273) \| 0.027233 / 0.128546 (-0.101313) \| 0.007941 / 0.075646 (-0.067705) \| 0.260511 / 0.419271 (-0.158761) \| 0.044355 / 0.043533 (0.000822) \| 0.332993 / 0.255139 (0.077854) \| 0.351363 / 0.283200 (0.068163) \| 0.020784 / 0.141683 (-0.120899) \| 1.429044 / 1.452155 (-0.023111) \| 1.489355 / 1.492716 (-0.003362) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180903 / 0.018006 (0.162897) \| 0.421566 / 0.000490 (0.421077) \| 0.003259 / 0.000200 (0.003059) \| 0.000068 / 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) \| 0.023765 / 0.037411 (-0.013646) \| 0.072815 / 0.014526 (0.058289) \| 0.084592 / 0.176557 (-0.091965) \| 0.143556 / 0.737135 (-0.593579) \| 0.083591 / 0.296338 (-0.212748) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401896 / 0.215209 (0.186687) \| 4.006344 / 2.077655 (1.928689) \| 2.092280 / 1.504120 (0.588160) \| 1.937828 / 1.541195 (0.396633) \| 2.026901 / 1.468490 (0.558411) \| 0.499999 / 4.584777 (-4.084778) \| 3.008715 / 3.745712 (-0.736997) \| 2.789735 / 5.269862 (-2.480127) \| 1.827319 / 4.565676 (-2.738358) \| 0.057413 / 0.424275 (-0.366862) \| 0.006716 / 0.007607 (-0.000891) \| 0.473061 / 0.226044 (0.247016) \| 4.733256 / 2.268929 (2.464327) \| 2.403922 / 55.444624 (-53.040702) \| 2.017466 / 6.876477 (-4.859011) \| 2.209710 / 2.142072 (0.067638) \| 0.590813 / 4.805227 (-4.214414) \| 0.124760 / 6.500664 (-6.375904) \| 0.060976 / 0.075469 (-0.014494) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229172 / 1.841788 (-0.612616) \| 17.924644 / 8.074308 (9.850336) \| 13.697347 / 10.191392 (3.505955) \| 0.128258 / 0.680424 (-0.552166) \| 0.016780 / 0.534201 (-0.517421) \| 0.329301 / 0.579283 (-0.249982) \| 0.344527 / 0.434364 (-0.089837) \| 0.379482 / 0.540337 (-0.160855) \| 0.513851 / 1.386936 (-0.873085) \|\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) \| 0.005962 / 0.011353 (-0.005391) \| 0.003613 / 0.011008 (-0.007396) \| 0.062428 / 0.038508 (0.023920) \| 0.058151 / 0.023109 (0.035042) \| 0.452926 / 0.275898 (0.177027) \| 0.489740 / 0.323480 (0.166260) \| 0.006137 / 0.007986 (-0.001848) \| 0.002890 / 0.004328 (-0.001438) \| 0.062880 / 0.004250 (0.058629) \| 0.046175 / 0.037052 (0.009123) \| 0.452416 / 0.258489 (0.193927) \| 0.486047 / 0.293841 (0.192206) \| 0.028517 / 0.128546 (-0.100029) \| 0.008102 / 0.075646 (-0.067544) \| 0.068251 / 0.419271 (-0.351020) \| 0.040569 / 0.043533 (-0.002964) \| 0.461306 / 0.255139 (0.206167) \| 0.477675 / 0.283200 (0.194475) \| 0.020944 / 0.141683 (-0.120739) \| 1.414300 / 1.452155 (-0.037855) \| 1.502108 / 1.492716 (0.009391) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217786 / 0.018006 (0.199780) \| 0.410757 / 0.000490 (0.410267) \| 0.002981 / 0.000200 (0.002781) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026846 / 0.037411 (-0.010565) \| 0.080098 / 0.014526 (0.065572) \| 0.090591 / 0.176557 (-0.085965) \| 0.144674 / 0.737135 (-0.592461) \| 0.091287 / 0.296338 (-0.205052) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458224 / 0.215209 (0.243015) \| 4.590541 / 2.077655 (2.512886) \| 2.511251 / 1.504120 (1.007131) \| 2.329165 / 1.541195 (0.787970) \| 2.379187 / 1.468490 (0.910696) \| 0.507485 / 4.584777 (-4.077292) \| 3.135011 / 3.745712 (-0.610701) \| 2.805913 / 5.269862 (-2.463948) \| 1.851382 / 4.565676 (-2.714295) \| 0.057981 / 0.424275 (-0.366294) \| 0.006557 / 0.007607 (-0.001050) \| 0.532496 / 0.226044 (0.306452) \| 5.348802 / 2.268929 (3.079874) \| 2.993379 / 55.444624 (-52.451245) \| 2.636372 / 6.876477 (-4.240104) \| 2.753219 / 2.142072 (0.611147) \| 0.591989 / 4.805227 (-4.213238) \| 0.126691 / 6.500664 (-6.373973) \| 0.062359 / 0.075469 (-0.013110) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345498 / 1.841788 (-0.496290) \| 18.335767 / 8.074308 (10.261458) \| 15.115449 / 10.191392 (4.924057) \| 0.147382 / 0.680424 (-0.533041) \| 0.017729 / 0.534201 (-0.516472) \| 0.334337 / 0.579283 (-0.244946) \| 0.359035 / 0.434364 (-0.075329) \| 0.386319 / 0.540337 (-0.154019) \| 0.536378 / 1.386936 (-0.850558) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b028fd83d74e7701e7b8f2d87e740a989505a7 \"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) \| 0.009136 / 0.011353 (-0.002216) \| 0.005567 / 0.011008 (-0.005442) \| 0.120320 / 0.038508 (0.081812) \| 0.078082 / 0.023109 (0.054973) \| 0.405579 / 0.275898 (0.129681) \| 0.459714 / 0.323480 (0.136234) \| 0.006327 / 0.007986 (-0.001659) \| 0.007187 / 0.004328 (0.002859) \| 0.084373 / 0.004250 (0.080122) \| 0.059727 / 0.037052 (0.022675) \| 0.418918 / 0.258489 (0.160429) \| 0.486767 / 0.293841 (0.192927) \| 0.047715 / 0.128546 (-0.080831) \| 0.014417 / 0.075646 (-0.061229) \| 0.379847 / 0.419271 (-0.039425) \| 0.067472 / 0.043533 (0.023939) \| 0.419304 / 0.255139 (0.164166) \| 0.466260 / 0.283200 (0.183060) \| 0.036872 / 0.141683 (-0.104811) \| 1.876273 / 1.452155 (0.424119) \| 2.043856 / 1.492716 (0.551140) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296266 / 0.018006 (0.278260) \| 0.601843 / 0.000490 (0.601354) \| 0.005663 / 0.000200 (0.005463) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033272 / 0.037411 (-0.004139) \| 0.098839 / 0.014526 (0.084313) \| 0.124658 / 0.176557 (-0.051899) \| 0.190226 / 0.737135 (-0.546909) \| 0.119288 / 0.296338 (-0.177051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.600878 / 0.215209 (0.385668) \| 6.011749 / 2.077655 (3.934095) \| 2.611809 / 1.504120 (1.107689) \| 2.314985 / 1.541195 (0.773790) \| 2.398988 / 1.468490 (0.930498) \| 0.835577 / 4.584777 (-3.749200) \| 5.482848 / 3.745712 (1.737136) \| 4.965393 / 5.269862 (-0.304469) \| 3.082420 / 4.565676 (-1.483256) \| 0.098048 / 0.424275 (-0.326227) \| 0.009148 / 0.007607 (0.001541) \| 0.725721 / 0.226044 (0.499676) \| 7.297429 / 2.268929 (5.028501) \| 3.558050 / 55.444624 (-51.886575) \| 2.815884 / 6.876477 (-4.060593) \| 3.094103 / 2.142072 (0.952031) \| 1.023617 / 4.805227 (-3.781610) \| 0.222453 / 6.500664 (-6.278211) \| 0.081707 / 0.075469 (0.006238) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.788327 / 1.841788 (-0.053461) \| 25.285829 / 8.074308 (17.211521) \| 21.878811 / 10.191392 (11.687419) \| 0.215494 / 0.680424 (-0.464930) \| 0.032050 / 0.534201 (-0.502151) \| 0.505210 / 0.579283 (-0.074073) \| 0.623545 / 0.434364 (0.189181) \| 0.583342 / 0.540337 (0.043005) \| 0.826497 / 1.386936 (-0.560439) \|\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) \| 0.009640 / 0.011353 (-0.001713) \| 0.005479 / 0.011008 (-0.005529) \| 0.088940 / 0.038508 (0.050432) \| 0.084186 / 0.023109 (0.061077) \| 0.552290 / 0.275898 (0.276392) \| 0.583296 / 0.323480 (0.259816) \| 0.006999 / 0.007986 (-0.000987) \| 0.004597 / 0.004328 (0.000269) \| 0.089407 / 0.004250 (0.085157) \| 0.067210 / 0.037052 (0.030157) \| 0.554968 / 0.258489 (0.296479) \| 0.595635 / 0.293841 (0.301794) \| 0.052245 / 0.128546 (-0.076301) \| 0.015914 / 0.075646 (-0.059733) \| 0.097037 / 0.419271 (-0.322235) \| 0.063954 / 0.043533 (0.020421) \| 0.533752 / 0.255139 (0.278614) \| 0.573789 / 0.283200 (0.290589) \| 0.036526 / 0.141683 (-0.105157) \| 1.867713 / 1.452155 (0.415558) \| 1.996901 / 1.492716 (0.504185) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414967 / 0.018006 (0.396961) \| 0.632367 / 0.000490 (0.631877) \| 0.064061 / 0.000200 (0.063861) \| 0.000565 / 0.000054 (0.000510) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035953 / 0.037411 (-0.001458) \| 0.112603 / 0.014526 (0.098077) \| 0.126227 / 0.176557 (-0.050330) \| 0.196881 / 0.737135 (-0.540255) \| 0.127635 / 0.296338 (-0.168704) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.674735 / 0.215209 (0.459526) \| 6.614578 / 2.077655 (4.536923) \| 3.208198 / 1.504120 (1.704078) \| 2.870412 / 1.541195 (1.329217) \| 2.979358 / 1.468490 (1.510868) \| 0.872589 / 4.584777 (-3.712187) \| 5.501771 / 3.745712 (1.756059) \| 4.865191 / 5.269862 (-0.404671) \| 3.075281 / 4.565676 (-1.490396) \| 0.098048 / 0.424275 (-0.326227) \| 0.009121 / 0.007607 (0.001514) \| 0.801639 / 0.226044 (0.575595) \| 8.062040 / 2.268929 (5.793111) \| 3.996693 / 55.444624 (-51.447931) \| 3.343770 / 6.876477 (-3.532706) \| 3.555977 / 2.142072 (1.413904) \| 1.035050 / 4.805227 (-3.770177) \| 0.227552 / 6.500664 (-6.273112) \| 0.097733 / 0.075469 (0.022264) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.897210 / 1.841788 (0.055422) \| 25.762459 / 8.074308 (17.688151) \| 22.771290 / 10.191392 (12.579898) \| 0.252650 / 0.680424 (-0.427773) \| 0.032534 / 0.534201 (-0.501667) \| 0.521047 / 0.579283 (-0.058236) \| 0.620850 / 0.434364 (0.186486) \| 0.612750 / 0.540337 (0.072413) \| 0.837486 / 1.386936 (-0.549451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f522e5bdd73c45f7ba0a03f2ecd4e7de7351f2e \"CML watermark\")\n" ]	2023-09-06T15:40:33Z	2023-09-06T15:52:33Z	2023-09-06T15:41:13Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6220.diff", "html_url": "https://github.com/huggingface/datasets/pull/6220", "merged_at": "2023-09-06T15:41:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6220.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6220" }	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/6220/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6219	6,219	Release: 2.14.5	{ "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" }	[]	closed	false	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6219). 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) \| 0.009523 / 0.011353 (-0.001830) \| 0.005105 / 0.011008 (-0.005903) \| 0.122664 / 0.038508 (0.084156) \| 0.084688 / 0.023109 (0.061579) \| 0.412057 / 0.275898 (0.136159) \| 0.449690 / 0.323480 (0.126210) \| 0.006627 / 0.007986 (-0.001358) \| 0.004150 / 0.004328 (-0.000178) \| 0.082079 / 0.004250 (0.077829) \| 0.065289 / 0.037052 (0.028237) \| 0.432934 / 0.258489 (0.174445) \| 0.492068 / 0.293841 (0.198227) \| 0.048317 / 0.128546 (-0.080229) \| 0.015582 / 0.075646 (-0.060064) \| 0.372050 / 0.419271 (-0.047222) \| 0.070649 / 0.043533 (0.027116) \| 0.431754 / 0.255139 (0.176615) \| 0.473349 / 0.283200 (0.190149) \| 0.037293 / 0.141683 (-0.104390) \| 1.807537 / 1.452155 (0.355382) \| 1.923073 / 1.492716 (0.430357) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271214 / 0.018006 (0.253208) \| 0.592961 / 0.000490 (0.592471) \| 0.004062 / 0.000200 (0.003862) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034766 / 0.037411 (-0.002645) \| 0.093014 / 0.014526 (0.078488) \| 0.131332 / 0.176557 (-0.045225) \| 0.188110 / 0.737135 (-0.549025) \| 0.117617 / 0.296338 (-0.178722) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668223 / 0.215209 (0.453013) \| 6.707031 / 2.077655 (4.629376) \| 3.040178 / 1.504120 (1.536058) \| 2.641776 / 1.541195 (1.100581) \| 2.524057 / 1.468490 (1.055567) \| 0.893592 / 4.584777 (-3.691185) \| 5.535848 / 3.745712 (1.790136) \| 4.867067 / 5.269862 (-0.402794) \| 2.999933 / 4.565676 (-1.565743) \| 0.103602 / 0.424275 (-0.320673) \| 0.008887 / 0.007607 (0.001280) \| 0.822214 / 0.226044 (0.596169) \| 8.028476 / 2.268929 (5.759547) \| 3.708895 / 55.444624 (-51.735730) \| 2.858314 / 6.876477 (-4.018163) \| 3.101727 / 2.142072 (0.959655) \| 1.083136 / 4.805227 (-3.722091) \| 0.219588 / 6.500664 (-6.281076) \| 0.080151 / 0.075469 (0.004682) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.645819 / 1.841788 (-0.195969) \| 24.407887 / 8.074308 (16.333579) \| 22.371901 / 10.191392 (12.180509) \| 0.219557 / 0.680424 (-0.460867) \| 0.037867 / 0.534201 (-0.496334) \| 0.484136 / 0.579283 (-0.095147) \| 0.620546 / 0.434364 (0.186182) \| 0.562272 / 0.540337 (0.021934) \| 0.774256 / 1.386936 (-0.612680) \|\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) \| 0.009381 / 0.011353 (-0.001972) \| 0.005565 / 0.011008 (-0.005444) \| 0.091057 / 0.038508 (0.052549) \| 0.078085 / 0.023109 (0.054975) \| 0.538929 / 0.275898 (0.263031) \| 0.555155 / 0.323480 (0.231675) \| 0.007007 / 0.007986 (-0.000978) \| 0.004268 / 0.004328 (-0.000060) \| 0.086618 / 0.004250 (0.082368) \| 0.064117 / 0.037052 (0.027065) \| 0.523788 / 0.258489 (0.265299) \| 0.586451 / 0.293841 (0.292610) \| 0.050804 / 0.128546 (-0.077742) \| 0.013964 / 0.075646 (-0.061682) \| 0.096008 / 0.419271 (-0.323263) \| 0.062242 / 0.043533 (0.018709) \| 0.530398 / 0.255139 (0.275259) \| 0.568527 / 0.283200 (0.285327) \| 0.032456 / 0.141683 (-0.109227) \| 1.894975 / 1.452155 (0.442820) \| 2.084172 / 1.492716 (0.591455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295539 / 0.018006 (0.277533) \| 0.588804 / 0.000490 (0.588314) \| 0.006445 / 0.000200 (0.006245) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033965 / 0.037411 (-0.003447) \| 0.111743 / 0.014526 (0.097217) \| 0.128805 / 0.176557 (-0.047752) \| 0.185013 / 0.737135 (-0.552123) \| 0.129400 / 0.296338 (-0.166938) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.749784 / 0.215209 (0.534575) \| 7.091075 / 2.077655 (5.013420) \| 3.424517 / 1.504120 (1.920397) \| 3.069103 / 1.541195 (1.527908) \| 3.122431 / 1.468490 (1.653941) \| 0.949277 / 4.584777 (-3.635500) \| 5.648731 / 3.745712 (1.903019) \| 4.937684 / 5.269862 (-0.332178) \| 3.198027 / 4.565676 (-1.367650) \| 0.100289 / 0.424275 (-0.323987) \| 0.009411 / 0.007607 (0.001803) \| 0.862604 / 0.226044 (0.636559) \| 8.615410 / 2.268929 (6.346482) \| 4.306428 / 55.444624 (-51.138196) \| 3.591404 / 6.876477 (-3.285073) \| 3.823899 / 2.142072 (1.681827) \| 1.108006 / 4.805227 (-3.697221) \| 0.215330 / 6.500664 (-6.285334) \| 0.080755 / 0.075469 (0.005286) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.774914 / 1.841788 (-0.066873) \| 25.360983 / 8.074308 (17.286675) \| 23.624044 / 10.191392 (13.432652) \| 0.226887 / 0.680424 (-0.453537) \| 0.032625 / 0.534201 (-0.501576) \| 0.499730 / 0.579283 (-0.079553) \| 0.647819 / 0.434364 (0.213455) \| 0.592239 / 0.540337 (0.051901) \| 0.805751 / 1.386936 (-0.581185) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0daa82428a0529478801574bcc68e1ed32051f3a \"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) \| 0.008656 / 0.011353 (-0.002697) \| 0.005545 / 0.011008 (-0.005463) \| 0.107936 / 0.038508 (0.069428) \| 0.077436 / 0.023109 (0.054327) \| 0.391412 / 0.275898 (0.115514) \| 0.452811 / 0.323480 (0.129331) \| 0.004883 / 0.007986 (-0.003103) \| 0.005125 / 0.004328 (0.000796) \| 0.080006 / 0.004250 (0.075755) \| 0.054425 / 0.037052 (0.017373) \| 0.399667 / 0.258489 (0.141178) \| 0.458099 / 0.293841 (0.164258) \| 0.047302 / 0.128546 (-0.081244) \| 0.014153 / 0.075646 (-0.061493) \| 0.337281 / 0.419271 (-0.081991) \| 0.062153 / 0.043533 (0.018620) \| 0.399927 / 0.255139 (0.144788) \| 0.407186 / 0.283200 (0.123987) \| 0.036759 / 0.141683 (-0.104924) \| 1.825935 / 1.452155 (0.373780) \| 1.852238 / 1.492716 (0.359522) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274163 / 0.018006 (0.256157) \| 0.615624 / 0.000490 (0.615134) \| 0.003782 / 0.000200 (0.003582) \| 0.000115 / 0.000054 (0.000060) \|\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 (-0.011026) \| 0.101151 / 0.014526 (0.086625) \| 0.106115 / 0.176557 (-0.070442) \| 0.161253 / 0.737135 (-0.575882) \| 0.108861 / 0.296338 (-0.187478) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.587079 / 0.215209 (0.371870) \| 6.141743 / 2.077655 (4.064089) \| 2.727199 / 1.504120 (1.223079) \| 2.526827 / 1.541195 (0.985632) \| 2.598321 / 1.468490 (1.129831) \| 0.904706 / 4.584777 (-3.680071) \| 5.227742 / 3.745712 (1.482030) \| 4.621627 / 5.269862 (-0.648234) \| 2.931792 / 4.565676 (-1.633885) \| 0.089538 / 0.424275 (-0.334737) \| 0.008281 / 0.007607 (0.000674) \| 0.675773 / 0.226044 (0.449729) \| 7.212869 / 2.268929 (4.943941) \| 3.541569 / 55.444624 (-51.903056) \| 2.804034 / 6.876477 (-4.072443) \| 3.080192 / 2.142072 (0.938120) \| 1.034577 / 4.805227 (-3.770650) \| 0.218727 / 6.500664 (-6.281937) \| 0.084548 / 0.075469 (0.009079) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.528974 / 1.841788 (-0.312814) \| 21.754329 / 8.074308 (13.680021) \| 20.359808 / 10.191392 (10.168416) \| 0.234719 / 0.680424 (-0.445705) \| 0.026182 / 0.534201 (-0.508019) \| 0.448956 / 0.579283 (-0.130327) \| 0.577015 / 0.434364 (0.142651) \| 0.513675 / 0.540337 (-0.026662) \| 0.729780 / 1.386936 (-0.657156) \|\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) \| 0.010427 / 0.011353 (-0.000926) \| 0.005126 / 0.011008 (-0.005882) \| 0.082759 / 0.038508 (0.044251) \| 0.084892 / 0.023109 (0.061783) \| 0.543826 / 0.275898 (0.267927) \| 0.603050 / 0.323480 (0.279570) \| 0.006667 / 0.007986 (-0.001319) \| 0.004036 / 0.004328 (-0.000292) \| 0.079534 / 0.004250 (0.075283) \| 0.067523 / 0.037052 (0.030471) \| 0.544845 / 0.258489 (0.286356) \| 0.578823 / 0.293841 (0.284982) \| 0.054786 / 0.128546 (-0.073760) \| 0.014888 / 0.075646 (-0.060759) \| 0.095696 / 0.419271 (-0.323576) \| 0.064908 / 0.043533 (0.021375) \| 0.558087 / 0.255139 (0.302948) \| 0.593919 / 0.283200 (0.310719) \| 0.039190 / 0.141683 (-0.102493) \| 1.828680 / 1.452155 (0.376526) \| 1.908891 / 1.492716 (0.416174) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298926 / 0.018006 (0.280920) \| 0.589467 / 0.000490 (0.588977) \| 0.005276 / 0.000200 (0.005076) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034300 / 0.037411 (-0.003111) \| 0.096990 / 0.014526 (0.082464) \| 0.109347 / 0.176557 (-0.067209) \| 0.171312 / 0.737135 (-0.565823) \| 0.121736 / 0.296338 (-0.174603) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.641619 / 0.215209 (0.426410) \| 6.365556 / 2.077655 (4.287901) \| 2.947989 / 1.504120 (1.443869) \| 2.631680 / 1.541195 (1.090485) \| 2.602762 / 1.468490 (1.134272) \| 0.812767 / 4.584777 (-3.772010) \| 5.185753 / 3.745712 (1.440041) \| 4.589897 / 5.269862 (-0.679964) \| 2.833020 / 4.565676 (-1.732656) \| 0.097782 / 0.424275 (-0.326493) \| 0.008625 / 0.007607 (0.001018) \| 0.741613 / 0.226044 (0.515568) \| 7.662905 / 2.268929 (5.393976) \| 3.533753 / 55.444624 (-51.910871) \| 2.898929 / 6.876477 (-3.977547) \| 3.042616 / 2.142072 (0.900544) \| 0.933932 / 4.805227 (-3.871296) \| 0.195710 / 6.500664 (-6.304954) \| 0.066954 / 0.075469 (-0.008515) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.745353 / 1.841788 (-0.096434) \| 23.820840 / 8.074308 (15.746532) \| 20.892645 / 10.191392 (10.701253) \| 0.234853 / 0.680424 (-0.445571) \| 0.029149 / 0.534201 (-0.505051) \| 0.458953 / 0.579283 (-0.120330) \| 0.594278 / 0.434364 (0.159914) \| 0.522929 / 0.540337 (-0.017409) \| 0.753731 / 1.386936 (-0.633205) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"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) \| 0.005976 / 0.011353 (-0.005377) \| 0.003636 / 0.011008 (-0.007372) \| 0.079946 / 0.038508 (0.041437) \| 0.060143 / 0.023109 (0.037034) \| 0.314752 / 0.275898 (0.038854) \| 0.353714 / 0.323480 (0.030234) \| 0.004706 / 0.007986 (-0.003280) \| 0.002862 / 0.004328 (-0.001466) \| 0.061988 / 0.004250 (0.057737) \| 0.045907 / 0.037052 (0.008855) \| 0.316118 / 0.258489 (0.057629) \| 0.358488 / 0.293841 (0.064647) \| 0.027377 / 0.128546 (-0.101170) \| 0.007970 / 0.075646 (-0.067677) \| 0.261677 / 0.419271 (-0.157594) \| 0.045289 / 0.043533 (0.001757) \| 0.307931 / 0.255139 (0.052792) \| 0.341364 / 0.283200 (0.058165) \| 0.021021 / 0.141683 (-0.120662) \| 1.440002 / 1.452155 (-0.012153) \| 1.502904 / 1.492716 (0.010187) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201746 / 0.018006 (0.183740) \| 0.451114 / 0.000490 (0.450624) \| 0.003351 / 0.000200 (0.003151) \| 0.000067 / 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) \| 0.024233 / 0.037411 (-0.013178) \| 0.075042 / 0.014526 (0.060516) \| 0.085636 / 0.176557 (-0.090920) \| 0.144699 / 0.737135 (-0.592436) \| 0.085222 / 0.296338 (-0.211117) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.389464 / 0.215209 (0.174255) \| 3.889072 / 2.077655 (1.811417) \| 1.908307 / 1.504120 (0.404187) \| 1.738914 / 1.541195 (0.197719) \| 1.866869 / 1.468490 (0.398379) \| 0.500536 / 4.584777 (-4.084240) \| 3.050155 / 3.745712 (-0.695557) \| 2.832259 / 5.269862 (-2.437602) \| 1.886657 / 4.565676 (-2.679020) \| 0.059214 / 0.424275 (-0.365062) \| 0.006711 / 0.007607 (-0.000896) \| 0.467753 / 0.226044 (0.241709) \| 4.666939 / 2.268929 (2.398011) \| 2.471168 / 55.444624 (-52.973456) \| 2.223508 / 6.876477 (-4.652968) \| 2.176543 / 2.142072 (0.034470) \| 0.593461 / 4.805227 (-4.211766) \| 0.126216 / 6.500664 (-6.374448) \| 0.061495 / 0.075469 (-0.013974) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301279 / 1.841788 (-0.540509) \| 18.317461 / 8.074308 (10.243153) \| 13.877813 / 10.191392 (3.686421) \| 0.143510 / 0.680424 (-0.536914) \| 0.016826 / 0.534201 (-0.517375) \| 0.328735 / 0.579283 (-0.250548) \| 0.342272 / 0.434364 (-0.092092) \| 0.375768 / 0.540337 (-0.164570) \| 0.517600 / 1.386936 (-0.869336) \|\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) \| 0.006215 / 0.011353 (-0.005138) \| 0.003587 / 0.011008 (-0.007422) \| 0.062248 / 0.038508 (0.023740) \| 0.059830 / 0.023109 (0.036721) \| 0.443278 / 0.275898 (0.167380) \| 0.481279 / 0.323480 (0.157799) \| 0.004773 / 0.007986 (-0.003213) \| 0.002870 / 0.004328 (-0.001459) \| 0.062730 / 0.004250 (0.058480) \| 0.049422 / 0.037052 (0.012369) \| 0.444196 / 0.258489 (0.185707) \| 0.498614 / 0.293841 (0.204773) \| 0.028477 / 0.128546 (-0.100069) \| 0.008009 / 0.075646 (-0.067638) \| 0.067919 / 0.419271 (-0.351352) \| 0.040416 / 0.043533 (-0.003117) \| 0.439460 / 0.255139 (0.184321) \| 0.470529 / 0.283200 (0.187329) \| 0.020767 / 0.141683 (-0.120916) \| 1.478223 / 1.452155 (0.026068) \| 1.538580 / 1.492716 (0.045863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271321 / 0.018006 (0.253315) \| 0.456436 / 0.000490 (0.455946) \| 0.011817 / 0.000200 (0.011617) \| 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) \| 0.026355 / 0.037411 (-0.011056) \| 0.081681 / 0.014526 (0.067155) \| 0.091699 / 0.176557 (-0.084858) \| 0.146115 / 0.737135 (-0.591021) \| 0.094376 / 0.296338 (-0.201963) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471677 / 0.215209 (0.256468) \| 4.702909 / 2.077655 (2.625254) \| 2.664882 / 1.504120 (1.160762) \| 2.504106 / 1.541195 (0.962911) \| 2.573226 / 1.468490 (1.104736) \| 0.509679 / 4.584777 (-4.075097) \| 3.034970 / 3.745712 (-0.710742) \| 2.894704 / 5.269862 (-2.375157) \| 1.915148 / 4.565676 (-2.650528) \| 0.058312 / 0.424275 (-0.365963) \| 0.006615 / 0.007607 (-0.000993) \| 0.545339 / 0.226044 (0.319295) \| 5.462261 / 2.268929 (3.193332) \| 3.101482 / 55.444624 (-52.343143) \| 2.755417 / 6.876477 (-4.121060) \| 2.931440 / 2.142072 (0.789368) \| 0.597521 / 4.805227 (-4.207707) \| 0.125676 / 6.500664 (-6.374988) \| 0.061798 / 0.075469 (-0.013671) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356208 / 1.841788 (-0.485579) \| 18.912492 / 8.074308 (10.838184) \| 14.830128 / 10.191392 (4.638736) \| 0.145992 / 0.680424 (-0.534432) \| 0.019121 / 0.534201 (-0.515080) \| 0.331534 / 0.579283 (-0.247749) \| 0.361712 / 0.434364 (-0.072652) \| 0.387532 / 0.540337 (-0.152805) \| 0.536075 / 1.386936 (-0.850861) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"CML watermark\")\n" ]	2023-09-06T15:17:10Z	2023-09-06T15:46:20Z	2023-09-06T15:18:51Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6219.diff", "html_url": "https://github.com/huggingface/datasets/pull/6219", "merged_at": "2023-09-06T15:18:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/6219.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6219" }	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": 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https://github.com/huggingface/datasets/pull/6218	6,218	Rename old push_to_hub configs to "default" in dataset_infos	{ "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) \| 0.006529 / 0.011353 (-0.004823) \| 0.004010 / 0.011008 (-0.006998) \| 0.086258 / 0.038508 (0.047750) \| 0.073775 / 0.023109 (0.050666) \| 0.307573 / 0.275898 (0.031675) \| 0.337091 / 0.323480 (0.013611) \| 0.004251 / 0.007986 (-0.003735) \| 0.003886 / 0.004328 (-0.000443) \| 0.068238 / 0.004250 (0.063987) \| 0.057000 / 0.037052 (0.019948) \| 0.321751 / 0.258489 (0.063262) \| 0.359227 / 0.293841 (0.065386) \| 0.030841 / 0.128546 (-0.097705) \| 0.008569 / 0.075646 (-0.067078) \| 0.299523 / 0.419271 (-0.119748) \| 0.052563 / 0.043533 (0.009030) \| 0.312806 / 0.255139 (0.057667) \| 0.342273 / 0.283200 (0.059074) \| 0.025725 / 0.141683 (-0.115958) \| 1.479263 / 1.452155 (0.027108) \| 1.554975 / 1.492716 (0.062259) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316328 / 0.018006 (0.298322) \| 0.598993 / 0.000490 (0.598503) \| 0.004548 / 0.000200 (0.004348) \| 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) \| 0.027399 / 0.037411 (-0.010013) \| 0.081683 / 0.014526 (0.067157) \| 0.096968 / 0.176557 (-0.079589) \| 0.151559 / 0.737135 (-0.585576) \| 0.096558 / 0.296338 (-0.199781) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383117 / 0.215209 (0.167908) \| 3.818634 / 2.077655 (1.740979) \| 1.878112 / 1.504120 (0.373992) \| 1.729031 / 1.541195 (0.187836) \| 1.770259 / 1.468490 (0.301769) \| 0.484061 / 4.584777 (-4.100716) \| 3.596998 / 3.745712 (-0.148715) \| 3.246846 / 5.269862 (-2.023016) \| 2.019481 / 4.565676 (-2.546195) \| 0.057279 / 0.424275 (-0.366996) \| 0.007455 / 0.007607 (-0.000152) \| 0.465002 / 0.226044 (0.238958) \| 4.644669 / 2.268929 (2.375741) \| 2.346415 / 55.444624 (-53.098209) \| 2.039686 / 6.876477 (-4.836791) \| 2.172822 / 2.142072 (0.030750) \| 0.582925 / 4.805227 (-4.222302) \| 0.134246 / 6.500664 (-6.366418) \| 0.060093 / 0.075469 (-0.015376) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249033 / 1.841788 (-0.592755) \| 19.585949 / 8.074308 (11.511641) \| 14.100681 / 10.191392 (3.909289) \| 0.147138 / 0.680424 (-0.533286) \| 0.018307 / 0.534201 (-0.515894) \| 0.397939 / 0.579283 (-0.181344) \| 0.413916 / 0.434364 (-0.020448) \| 0.465688 / 0.540337 (-0.074650) \| 0.642140 / 1.386936 (-0.744797) \|\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) \| 0.006627 / 0.011353 (-0.004726) \| 0.004173 / 0.011008 (-0.006835) \| 0.063850 / 0.038508 (0.025342) \| 0.074733 / 0.023109 (0.051623) \| 0.398111 / 0.275898 (0.122213) \| 0.426344 / 0.323480 (0.102864) \| 0.006261 / 0.007986 (-0.001725) \| 0.003507 / 0.004328 (-0.000822) \| 0.064511 / 0.004250 (0.060260) \| 0.056508 / 0.037052 (0.019456) \| 0.401750 / 0.258489 (0.143261) \| 0.437081 / 0.293841 (0.143240) \| 0.031815 / 0.128546 (-0.096732) \| 0.008703 / 0.075646 (-0.066943) \| 0.071411 / 0.419271 (-0.347861) \| 0.048153 / 0.043533 (0.004620) \| 0.399221 / 0.255139 (0.144082) \| 0.429312 / 0.283200 (0.146112) \| 0.022157 / 0.141683 (-0.119526) \| 1.485656 / 1.452155 (0.033502) \| 1.550967 / 1.492716 (0.058250) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.330575 / 0.018006 (0.312569) \| 0.525553 / 0.000490 (0.525064) \| 0.004574 / 0.000200 (0.004374) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031871 / 0.037411 (-0.005541) \| 0.091819 / 0.014526 (0.077293) \| 0.105542 / 0.176557 (-0.071015) \| 0.158210 / 0.737135 (-0.578926) \| 0.107167 / 0.296338 (-0.189172) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430226 / 0.215209 (0.215017) \| 4.293456 / 2.077655 (2.215801) \| 2.289538 / 1.504120 (0.785418) \| 2.122255 / 1.541195 (0.581060) \| 2.181840 / 1.468490 (0.713350) \| 0.498529 / 4.584777 (-4.086248) \| 3.686636 / 3.745712 (-0.059077) \| 3.287279 / 5.269862 (-1.982582) \| 2.068397 / 4.565676 (-2.497280) \| 0.058775 / 0.424275 (-0.365500) \| 0.007583 / 0.007607 (-0.000024) \| 0.507165 / 0.226044 (0.281121) \| 5.072330 / 2.268929 (2.803401) \| 2.796396 / 55.444624 (-52.648228) \| 2.409946 / 6.876477 (-4.466531) \| 2.657322 / 2.142072 (0.515250) \| 0.597744 / 4.805227 (-4.207483) \| 0.133803 / 6.500664 (-6.366861) \| 0.060231 / 0.075469 (-0.015238) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.333130 / 1.841788 (-0.508658) \| 20.545936 / 8.074308 (12.471627) \| 14.875020 / 10.191392 (4.683628) \| 0.168873 / 0.680424 (-0.511551) \| 0.020316 / 0.534201 (-0.513885) \| 0.397203 / 0.579283 (-0.182080) \| 0.412412 / 0.434364 (-0.021952) \| 0.479952 / 0.540337 (-0.060385) \| 0.657155 / 1.386936 (-0.729781) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13fbee4ca8742460e9baab86a89d9100a294df3e \"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) \| 0.007885 / 0.011353 (-0.003468) \| 0.005221 / 0.011008 (-0.005787) \| 0.099457 / 0.038508 (0.060949) \| 0.085867 / 0.023109 (0.062758) \| 0.359922 / 0.275898 (0.084024) \| 0.406479 / 0.323480 (0.082999) \| 0.005001 / 0.007986 (-0.002985) \| 0.003678 / 0.004328 (-0.000650) \| 0.075647 / 0.004250 (0.071396) \| 0.064318 / 0.037052 (0.027265) \| 0.372180 / 0.258489 (0.113691) \| 0.419206 / 0.293841 (0.125365) \| 0.040438 / 0.128546 (-0.088108) \| 0.010008 / 0.075646 (-0.065638) \| 0.340911 / 0.419271 (-0.078360) \| 0.063326 / 0.043533 (0.019793) \| 0.359015 / 0.255139 (0.103876) \| 0.408601 / 0.283200 (0.125402) \| 0.029828 / 0.141683 (-0.111855) \| 1.767822 / 1.452155 (0.315667) \| 1.829079 / 1.492716 (0.336363) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234455 / 0.018006 (0.216449) \| 0.507786 / 0.000490 (0.507297) \| 0.004009 / 0.000200 (0.003809) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033374 / 0.037411 (-0.004038) \| 0.100817 / 0.014526 (0.086291) \| 0.113415 / 0.176557 (-0.063141) \| 0.180368 / 0.737135 (-0.556768) \| 0.115446 / 0.296338 (-0.180893) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488976 / 0.215209 (0.273767) \| 4.911354 / 2.077655 (2.833699) \| 2.623525 / 1.504120 (1.119405) \| 2.424400 / 1.541195 (0.883206) \| 2.497580 / 1.468490 (1.029089) \| 0.561106 / 4.584777 (-4.023671) \| 4.265649 / 3.745712 (0.519937) \| 3.830267 / 5.269862 (-1.439595) \| 2.404727 / 4.565676 (-2.160949) \| 0.067303 / 0.424275 (-0.356972) \| 0.009177 / 0.007607 (0.001570) \| 0.588433 / 0.226044 (0.362388) \| 5.871573 / 2.268929 (3.602645) \| 3.087845 / 55.444624 (-52.356779) \| 2.765381 / 6.876477 (-4.111096) \| 3.007863 / 2.142072 (0.865791) \| 0.687327 / 4.805227 (-4.117901) \| 0.157687 / 6.500664 (-6.342977) \| 0.071291 / 0.075469 (-0.004178) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.510931 / 1.841788 (-0.330857) \| 22.129590 / 8.074308 (14.055282) \| 16.780479 / 10.191392 (6.589087) \| 0.168297 / 0.680424 (-0.512127) \| 0.021294 / 0.534201 (-0.512907) \| 0.464535 / 0.579283 (-0.114748) \| 0.480041 / 0.434364 (0.045677) \| 0.549185 / 0.540337 (0.008848) \| 0.739438 / 1.386936 (-0.647498) \|\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) \| 0.007834 / 0.011353 (-0.003518) \| 0.004576 / 0.011008 (-0.006432) \| 0.073331 / 0.038508 (0.034823) \| 0.084688 / 0.023109 (0.061579) \| 0.486367 / 0.275898 (0.210469) \| 0.523127 / 0.323480 (0.199647) \| 0.006278 / 0.007986 (-0.001708) \| 0.003792 / 0.004328 (-0.000537) \| 0.075416 / 0.004250 (0.071166) \| 0.064053 / 0.037052 (0.027001) \| 0.491908 / 0.258489 (0.233419) \| 0.529177 / 0.293841 (0.235336) \| 0.038483 / 0.128546 (-0.090063) \| 0.009560 / 0.075646 (-0.066087) \| 0.083431 / 0.419271 (-0.335841) \| 0.057114 / 0.043533 (0.013581) \| 0.486316 / 0.255139 (0.231177) \| 0.512384 / 0.283200 (0.229185) \| 0.028452 / 0.141683 (-0.113231) \| 1.788886 / 1.452155 (0.336731) \| 1.893834 / 1.492716 (0.401118) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.343018 / 0.018006 (0.325011) \| 0.513673 / 0.000490 (0.513183) \| 0.056778 / 0.000200 (0.056578) \| 0.001799 / 0.000054 (0.001745) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038530 / 0.037411 (0.001119) \| 0.109286 / 0.014526 (0.094760) \| 0.122812 / 0.176557 (-0.053745) \| 0.187780 / 0.737135 (-0.549355) \| 0.124083 / 0.296338 (-0.172255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.509839 / 0.215209 (0.294630) \| 5.085840 / 2.077655 (3.008186) \| 2.746695 / 1.504120 (1.242575) \| 2.542283 / 1.541195 (1.001088) \| 2.650243 / 1.468490 (1.181753) \| 0.592801 / 4.584777 (-3.991976) \| 4.316721 / 3.745712 (0.571009) \| 3.811672 / 5.269862 (-1.458189) \| 2.433982 / 4.565676 (-2.131695) \| 0.066861 / 0.424275 (-0.357414) \| 0.008633 / 0.007607 (0.001026) \| 0.590482 / 0.226044 (0.364437) \| 5.923484 / 2.268929 (3.654556) \| 3.282293 / 55.444624 (-52.162332) \| 2.882716 / 6.876477 (-3.993761) \| 3.139581 / 2.142072 (0.997509) \| 0.690702 / 4.805227 (-4.114525) \| 0.156781 / 6.500664 (-6.343883) \| 0.071487 / 0.075469 (-0.003982) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.604557 / 1.841788 (-0.237231) \| 24.000026 / 8.074308 (15.925718) \| 17.548685 / 10.191392 (7.357293) \| 0.174883 / 0.680424 (-0.505541) \| 0.023812 / 0.534201 (-0.510389) \| 0.473522 / 0.579283 (-0.105761) \| 0.494683 / 0.434364 (0.060319) \| 0.593352 / 0.540337 (0.053015) \| 0.771852 / 1.386936 (-0.615084) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b61c96a806fa97800bc8a66607fb0c78a5d04146 \"CML watermark\")\n", "thanks! i wonder if we should also fix (change config name) all the old `dataset_infos.json` on the 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) \| 0.006388 / 0.011353 (-0.004965) \| 0.003876 / 0.011008 (-0.007132) \| 0.083960 / 0.038508 (0.045452) \| 0.068328 / 0.023109 (0.045219) \| 0.337958 / 0.275898 (0.062060) \| 0.370783 / 0.323480 (0.047303) \| 0.003925 / 0.007986 (-0.004060) \| 0.004221 / 0.004328 (-0.000107) \| 0.064198 / 0.004250 (0.059947) \| 0.052681 / 0.037052 (0.015629) \| 0.348890 / 0.258489 (0.090401) \| 0.389038 / 0.293841 (0.095197) \| 0.031133 / 0.128546 (-0.097413) \| 0.008566 / 0.075646 (-0.067080) \| 0.288169 / 0.419271 (-0.131102) \| 0.053290 / 0.043533 (0.009757) \| 0.344654 / 0.255139 (0.089515) \| 0.381287 / 0.283200 (0.098087) \| 0.022350 / 0.141683 (-0.119333) \| 1.459933 / 1.452155 (0.007778) \| 1.543097 / 1.492716 (0.050380) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212592 / 0.018006 (0.194586) \| 0.461863 / 0.000490 (0.461373) \| 0.003468 / 0.000200 (0.003268) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026849 / 0.037411 (-0.010563) \| 0.081059 / 0.014526 (0.066533) \| 0.093986 / 0.176557 (-0.082571) \| 0.150328 / 0.737135 (-0.586807) \| 0.094253 / 0.296338 (-0.202085) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382198 / 0.215209 (0.166989) \| 3.813878 / 2.077655 (1.736224) \| 1.855686 / 1.504120 (0.351566) \| 1.672995 / 1.541195 (0.131800) \| 1.697705 / 1.468490 (0.229215) \| 0.479920 / 4.584777 (-4.104857) \| 3.608305 / 3.745712 (-0.137407) \| 3.216712 / 5.269862 (-2.053149) \| 1.984781 / 4.565676 (-2.580896) \| 0.056801 / 0.424275 (-0.367475) \| 0.007499 / 0.007607 (-0.000108) \| 0.454155 / 0.226044 (0.228110) \| 4.531147 / 2.268929 (2.262218) \| 2.296149 / 55.444624 (-53.148475) \| 1.968701 / 6.876477 (-4.907775) \| 2.144286 / 2.142072 (0.002213) \| 0.599254 / 4.805227 (-4.205973) \| 0.138150 / 6.500664 (-6.362514) \| 0.060118 / 0.075469 (-0.015351) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.282486 / 1.841788 (-0.559301) \| 19.127792 / 8.074308 (11.053483) \| 14.116521 / 10.191392 (3.925129) \| 0.163792 / 0.680424 (-0.516632) \| 0.018116 / 0.534201 (-0.516085) \| 0.390789 / 0.579283 (-0.188494) \| 0.409241 / 0.434364 (-0.025123) \| 0.457824 / 0.540337 (-0.082513) \| 0.624390 / 1.386936 (-0.762546) \|\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) \| 0.006637 / 0.011353 (-0.004716) \| 0.003932 / 0.011008 (-0.007076) \| 0.063456 / 0.038508 (0.024948) \| 0.070062 / 0.023109 (0.046953) \| 0.410570 / 0.275898 (0.134672) \| 0.436700 / 0.323480 (0.113220) \| 0.005324 / 0.007986 (-0.002662) \| 0.003263 / 0.004328 (-0.001065) \| 0.063590 / 0.004250 (0.059340) \| 0.054823 / 0.037052 (0.017770) \| 0.408720 / 0.258489 (0.150231) \| 0.441493 / 0.293841 (0.147652) \| 0.031655 / 0.128546 (-0.096891) \| 0.008421 / 0.075646 (-0.067225) \| 0.070657 / 0.419271 (-0.348614) \| 0.047370 / 0.043533 (0.003837) \| 0.408217 / 0.255139 (0.153078) \| 0.422178 / 0.283200 (0.138978) \| 0.022282 / 0.141683 (-0.119401) \| 1.511417 / 1.452155 (0.059262) \| 1.570337 / 1.492716 (0.077620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224334 / 0.018006 (0.206327) \| 0.447589 / 0.000490 (0.447099) \| 0.004227 / 0.000200 (0.004027) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030797 / 0.037411 (-0.006615) \| 0.091276 / 0.014526 (0.076750) \| 0.102665 / 0.176557 (-0.073892) \| 0.155423 / 0.737135 (-0.581712) \| 0.103779 / 0.296338 (-0.192560) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434509 / 0.215209 (0.219300) \| 4.328910 / 2.077655 (2.251255) \| 2.311424 / 1.504120 (0.807304) \| 2.138380 / 1.541195 (0.597185) \| 2.196293 / 1.468490 (0.727803) \| 0.482123 / 4.584777 (-4.102654) \| 3.597870 / 3.745712 (-0.147842) \| 3.222426 / 5.269862 (-2.047435) \| 1.994467 / 4.565676 (-2.571210) \| 0.057517 / 0.424275 (-0.366758) \| 0.007336 / 0.007607 (-0.000271) \| 0.504968 / 0.226044 (0.278923) \| 5.047940 / 2.268929 (2.779012) \| 2.824014 / 55.444624 (-52.620610) \| 2.457762 / 6.876477 (-4.418714) \| 2.606970 / 2.142072 (0.464897) \| 0.580758 / 4.805227 (-4.224469) \| 0.132584 / 6.500664 (-6.368080) \| 0.059258 / 0.075469 (-0.016211) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354386 / 1.841788 (-0.487402) \| 19.738147 / 8.074308 (11.663839) \| 14.858001 / 10.191392 (4.666609) \| 0.166074 / 0.680424 (-0.514350) \| 0.020181 / 0.534201 (-0.514020) \| 0.398333 / 0.579283 (-0.180950) \| 0.406969 / 0.434364 (-0.027395) \| 0.474515 / 0.540337 (-0.065822) \| 0.649571 / 1.386936 (-0.737365) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3ac3b3a9c5f40a29fae71504574cfdeebefe349 \"CML watermark\")\n", "I would say we should delete all `dataset_infos.json` on the Hub...", "@albertvillanova @lhoestq @mariosasko should we really stop supporting it and delete from everywhere?\r\n(bc if not, I've found a bug in updating `dataset_infos.json` with `.push_to_hub` and I'd open a PR to fix it)", "We can only delete them for the datasets without namespace and open PRs for the others, so we need to keep supporting them for now" ]	2023-09-06T10:40:05Z	2023-09-07T08:31:29Z	2023-09-06T11:23:56Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6218.diff", "html_url": "https://github.com/huggingface/datasets/pull/6218", "merged_at": "2023-09-06T11:23:56Z", "patch_url": "https://github.com/huggingface/datasets/pull/6218.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6218" }	Fix ```python from datasets import load_dataset_builder b = load_dataset_builder("lambdalabs/pokemon-blip-captions", "default") print(b.info) ``` which should return ``` DatasetInfo( features={'image': Image(decode=True, id=None), 'text': Value(dtype='string', id=None)}, dataset_name='pokemon-blip-captions', config_name='default', version=0.0.0, splits={'train': SplitInfo(name='train', num_bytes=119417410.0, num_examples=833, shard_lengths=None, dataset_name='pokemon-blip-captions')}, download_checksums=None, download_size=99672355, dataset_size=119417410.0, size_in_bytes=219089765.0, ... ) ``` instead of and empty dataset info. The dataset has a dataset_infos.json file with a deprecated config name "lambdalabs--pokemon-blip-captions". We switched those config names to "default" in 2.14, so the builder.info should take this into account.	{ "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/6218/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6217	6,217	`Dataset.to_dict()` ignore `decode=True` with Image feature	{ "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" }	[]	open	false	[ "We need to implement the `Image` type as a PyArrow extension type (to allow us to override the Python conversion) for this to work as expected. For now, it's best to use your approach indeed." ]	2023-09-06T09:26:16Z	2023-09-08T17:08:52Z	null	MEMBER	null	null	### Describe the bug `Dataset.to_dict` seems to ignore the decoding instruction passed in features. ### Steps to reproduce the bug ```python import datasets import numpy as np from PIL import Image img = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8) img = Image.fromarray(img) features = datasets.Features({"image": datasets.Image(decode=True)}) dataset = datasets.Dataset.from_dict({"image": [img]}, features=features) print({key: dataset[key] for key in dataset.column_names}) # {'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=5x5 at 0x7EFBC80E15B0>]} print(dataset.to_dict()) # {'image': [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x05\x00\x00\x00\x05\x08\x02\x00\x00\x00\x02\r\xb1\xb2\x00\x00\x00[IDATx\x9c\x01P\x00\xaf\xff\x01\x13\x1b<7\xe7\xe0\xdc^6\xed\x04\xc7M\xd2\x9f\x00X\x1b\xb0?\x1ba\x15\xc5 o\xd0\x80\xbe\x19/\x01\xec\x95\x1f\x9f\xffj\xfa1\xa7\xc4X\xea\xbe\xa4g\x00\xc4\x15\xdeC\xc7 \xbbaqe\xc8\xb9\xa9q\xe7\x00,?M\xc0)\xdaD`}\xb1\xdci\x1e\xafC\xa9]%.@\xa6\xf0\xb3\x00\x00\x00\x00IEND\xaeB`\x82', 'path': None}]} ``` ### Expected behavior I would expect `{key: dataset[key] for key in dataset.column_names}` and `dataset.to_dict()` to be equivalent. If the previous behavior is expected, then it should be stated [in the doc](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.to_dict). ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - Pillow 9.5.0 - numpy 1.25.2	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/6217/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/6216	6,216	Release: 2.13.2	{ "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) \| 0.007801 / 0.011353 (-0.003552) \| 0.004831 / 0.011008 (-0.006177) \| 0.123101 / 0.038508 (0.084593) \| 0.053246 / 0.023109 (0.030137) \| 0.381787 / 0.275898 (0.105889) \| 0.461822 / 0.323480 (0.138342) \| 0.004655 / 0.007986 (-0.003331) \| 0.004818 / 0.004328 (0.000490) \| 0.090865 / 0.004250 (0.086614) \| 0.070626 / 0.037052 (0.033574) \| 0.409122 / 0.258489 (0.150633) \| 0.449627 / 0.293841 (0.155787) \| 0.037477 / 0.128546 (-0.091069) \| 0.010677 / 0.075646 (-0.064970) \| 0.419970 / 0.419271 (0.000699) \| 0.064626 / 0.043533 (0.021093) \| 0.379536 / 0.255139 (0.124397) \| 0.405790 / 0.283200 (0.122590) \| 0.027290 / 0.141683 (-0.114393) \| 1.884973 / 1.452155 (0.432819) \| 1.960547 / 1.492716 (0.467831) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259393 / 0.018006 (0.241386) \| 0.502130 / 0.000490 (0.501640) \| 0.013053 / 0.000200 (0.012853) \| 0.000336 / 0.000054 (0.000281) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033459 / 0.037411 (-0.003953) \| 0.135888 / 0.014526 (0.121362) \| 0.145354 / 0.176557 (-0.031203) \| 0.213289 / 0.737135 (-0.523847) \| 0.151239 / 0.296338 (-0.145100) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.510817 / 0.215209 (0.295608) \| 5.077888 / 2.077655 (3.000234) \| 2.502991 / 1.504120 (0.998871) \| 2.275566 / 1.541195 (0.734371) \| 2.353025 / 1.468490 (0.884535) \| 0.659062 / 4.584777 (-3.925715) \| 4.411399 / 3.745712 (0.665686) \| 2.227395 / 5.269862 (-3.042467) \| 1.306771 / 4.565676 (-3.258905) \| 0.081121 / 0.424275 (-0.343154) \| 0.014252 / 0.007607 (0.006645) \| 0.635040 / 0.226044 (0.408996) \| 6.357500 / 2.268929 (4.088572) \| 3.056647 / 55.444624 (-52.387977) \| 2.671997 / 6.876477 (-4.204480) \| 2.847955 / 2.142072 (0.705883) \| 0.808163 / 4.805227 (-3.997064) \| 0.177176 / 6.500664 (-6.323488) \| 0.079984 / 0.075469 (0.004515) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.490471 / 1.841788 (-0.351317) \| 17.927433 / 8.074308 (9.853124) \| 17.744967 / 10.191392 (7.553575) \| 0.171034 / 0.680424 (-0.509390) \| 0.021432 / 0.534201 (-0.512769) \| 0.515745 / 0.579283 (-0.063538) \| 0.504746 / 0.434364 (0.070382) \| 0.630862 / 0.540337 (0.090524) \| 0.755275 / 1.386936 (-0.631662) \|\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) \| 0.008227 / 0.011353 (-0.003126) \| 0.004864 / 0.011008 (-0.006144) \| 0.092801 / 0.038508 (0.054293) \| 0.054996 / 0.023109 (0.031887) \| 0.500348 / 0.275898 (0.224450) \| 0.565028 / 0.323480 (0.241548) \| 0.004792 / 0.007986 (-0.003194) \| 0.005052 / 0.004328 (0.000723) \| 0.090640 / 0.004250 (0.086390) \| 0.074427 / 0.037052 (0.037374) \| 0.499908 / 0.258489 (0.241419) \| 0.566260 / 0.293841 (0.272419) \| 0.040011 / 0.128546 (-0.088536) \| 0.010438 / 0.075646 (-0.065208) \| 0.099385 / 0.419271 (-0.319887) \| 0.060485 / 0.043533 (0.016952) \| 0.480603 / 0.255139 (0.225464) \| 0.508807 / 0.283200 (0.225607) \| 0.025976 / 0.141683 (-0.115707) \| 1.870860 / 1.452155 (0.418705) \| 1.943460 / 1.492716 (0.450744) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227753 / 0.018006 (0.209747) \| 0.501859 / 0.000490 (0.501369) \| 0.008211 / 0.000200 (0.008011) \| 0.000127 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038329 / 0.037411 (0.000918) \| 0.148214 / 0.014526 (0.133688) \| 0.162704 / 0.176557 (-0.013852) \| 0.218543 / 0.737135 (-0.518592) \| 0.162992 / 0.296338 (-0.133347) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.553195 / 0.215209 (0.337986) \| 5.568080 / 2.077655 (3.490425) \| 2.936616 / 1.504120 (1.432496) \| 2.712624 / 1.541195 (1.171429) \| 2.713245 / 1.468490 (1.244755) \| 0.648593 / 4.584777 (-3.936184) \| 4.641361 / 3.745712 (0.895648) \| 2.207064 / 5.269862 (-3.062798) \| 1.315325 / 4.565676 (-3.250351) \| 0.080285 / 0.424275 (-0.343990) \| 0.014143 / 0.007607 (0.006536) \| 0.672467 / 0.226044 (0.446423) \| 6.730262 / 2.268929 (4.461333) \| 3.344468 / 55.444624 (-52.100157) \| 2.927837 / 6.876477 (-3.948640) \| 3.124735 / 2.142072 (0.982662) \| 0.795894 / 4.805227 (-4.009333) \| 0.170985 / 6.500664 (-6.329679) \| 0.077406 / 0.075469 (0.001937) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598059 / 1.841788 (-0.243729) \| 18.531854 / 8.074308 (10.457546) \| 18.394895 / 10.191392 (8.203503) \| 0.195702 / 0.680424 (-0.484722) \| 0.023633 / 0.534201 (-0.510568) \| 0.518110 / 0.579283 (-0.061173) \| 0.517773 / 0.434364 (0.083409) \| 0.617902 / 0.540337 (0.077565) \| 0.736459 / 1.386936 (-0.650477) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d4bb03237b74c0009043d50c5b4e4339cb98b2b \"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) \| 0.006943 / 0.011353 (-0.004410) \| 0.004524 / 0.011008 (-0.006485) \| 0.121603 / 0.038508 (0.083095) \| 0.047462 / 0.023109 (0.024353) \| 0.362393 / 0.275898 (0.086495) \| 0.440577 / 0.323480 (0.117098) \| 0.004153 / 0.007986 (-0.003832) \| 0.003778 / 0.004328 (-0.000550) \| 0.090402 / 0.004250 (0.086152) \| 0.066268 / 0.037052 (0.029216) \| 0.380721 / 0.258489 (0.122232) \| 0.442959 / 0.293841 (0.149118) \| 0.035228 / 0.128546 (-0.093318) \| 0.010217 / 0.075646 (-0.065429) \| 0.408587 / 0.419271 (-0.010684) \| 0.062609 / 0.043533 (0.019076) \| 0.372682 / 0.255139 (0.117543) \| 0.389270 / 0.283200 (0.106070) \| 0.026699 / 0.141683 (-0.114984) \| 1.760476 / 1.452155 (0.308321) \| 1.795081 / 1.492716 (0.302365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229912 / 0.018006 (0.211906) \| 0.476837 / 0.000490 (0.476348) \| 0.008178 / 0.000200 (0.007978) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031116 / 0.037411 (-0.006296) \| 0.126767 / 0.014526 (0.112241) \| 0.134242 / 0.176557 (-0.042315) \| 0.202120 / 0.737135 (-0.535016) \| 0.142777 / 0.296338 (-0.153561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470690 / 0.215209 (0.255481) \| 4.723198 / 2.077655 (2.645543) \| 2.163870 / 1.504120 (0.659750) \| 1.914177 / 1.541195 (0.372982) \| 2.034529 / 1.468490 (0.566038) \| 0.620472 / 4.584777 (-3.964305) \| 4.391008 / 3.745712 (0.645296) \| 2.100966 / 5.269862 (-3.168896) \| 1.225945 / 4.565676 (-3.339732) \| 0.076279 / 0.424275 (-0.347996) \| 0.013551 / 0.007607 (0.005944) \| 0.600989 / 0.226044 (0.374945) \| 5.946715 / 2.268929 (3.677787) \| 2.665117 / 55.444624 (-52.779508) \| 2.320004 / 6.876477 (-4.556473) \| 2.413131 / 2.142072 (0.271059) \| 0.771908 / 4.805227 (-4.033320) \| 0.165438 / 6.500664 (-6.335226) \| 0.074512 / 0.075469 (-0.000957) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.432728 / 1.841788 (-0.409060) \| 17.398133 / 8.074308 (9.323824) \| 16.819152 / 10.191392 (6.627760) \| 0.191849 / 0.680424 (-0.488575) \| 0.021557 / 0.534201 (-0.512644) \| 0.514380 / 0.579283 (-0.064903) \| 0.501453 / 0.434364 (0.067089) \| 0.634091 / 0.540337 (0.093753) \| 0.756786 / 1.386936 (-0.630150) \|\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) \| 0.007946 / 0.011353 (-0.003407) \| 0.004751 / 0.011008 (-0.006257) \| 0.090190 / 0.038508 (0.051682) \| 0.052841 / 0.023109 (0.029732) \| 0.480150 / 0.275898 (0.204252) \| 0.537509 / 0.323480 (0.214029) \| 0.004833 / 0.007986 (-0.003153) \| 0.004796 / 0.004328 (0.000467) \| 0.090616 / 0.004250 (0.086366) \| 0.074325 / 0.037052 (0.037273) \| 0.483776 / 0.258489 (0.225287) \| 0.552094 / 0.293841 (0.258254) \| 0.039240 / 0.128546 (-0.089307) \| 0.010416 / 0.075646 (-0.065230) \| 0.100275 / 0.419271 (-0.318996) \| 0.058086 / 0.043533 (0.014553) \| 0.468989 / 0.255139 (0.213850) \| 0.485502 / 0.283200 (0.202302) \| 0.027514 / 0.141683 (-0.114169) \| 1.849625 / 1.452155 (0.397470) \| 1.919515 / 1.492716 (0.426798) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248061 / 0.018006 (0.230055) \| 0.475630 / 0.000490 (0.475141) \| 0.006248 / 0.000200 (0.006048) \| 0.000105 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037746 / 0.037411 (0.000335) \| 0.141638 / 0.014526 (0.127112) \| 0.149530 / 0.176557 (-0.027026) \| 0.209255 / 0.737135 (-0.527880) \| 0.156447 / 0.296338 (-0.139892) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.544640 / 0.215209 (0.329431) \| 5.493152 / 2.077655 (3.415497) \| 2.869733 / 1.504120 (1.365613) \| 2.624216 / 1.541195 (1.083022) \| 2.710818 / 1.468490 (1.242328) \| 0.640626 / 4.584777 (-3.944151) \| 4.516130 / 3.745712 (0.770418) \| 2.128097 / 5.269862 (-3.141765) \| 1.278990 / 4.565676 (-3.286686) \| 0.077114 / 0.424275 (-0.347161) \| 0.013280 / 0.007607 (0.005673) \| 0.655552 / 0.226044 (0.429507) \| 6.526875 / 2.268929 (4.257947) \| 3.347072 / 55.444624 (-52.097553) \| 2.992435 / 6.876477 (-3.884041) \| 3.124351 / 2.142072 (0.982278) \| 0.778523 / 4.805227 (-4.026704) \| 0.161873 / 6.500664 (-6.338791) \| 0.072897 / 0.075469 (-0.002572) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.587058 / 1.841788 (-0.254730) \| 18.170612 / 8.074308 (10.096304) \| 17.220483 / 10.191392 (7.029091) \| 0.207863 / 0.680424 (-0.472561) \| 0.023746 / 0.534201 (-0.510455) \| 0.512607 / 0.579283 (-0.066676) \| 0.513258 / 0.434364 (0.078894) \| 0.597880 / 0.540337 (0.057543) \| 0.714974 / 1.386936 (-0.671962) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"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) \| 0.006224 / 0.011353 (-0.005128) \| 0.003857 / 0.011008 (-0.007151) \| 0.099786 / 0.038508 (0.061278) \| 0.037919 / 0.023109 (0.014810) \| 0.315294 / 0.275898 (0.039396) \| 0.390178 / 0.323480 (0.066698) \| 0.005358 / 0.007986 (-0.002628) \| 0.002989 / 0.004328 (-0.001340) \| 0.077834 / 0.004250 (0.073583) \| 0.053315 / 0.037052 (0.016263) \| 0.325155 / 0.258489 (0.066666) \| 0.374712 / 0.293841 (0.080871) \| 0.029176 / 0.128546 (-0.099370) \| 0.008658 / 0.075646 (-0.066988) \| 0.314245 / 0.419271 (-0.105027) \| 0.046684 / 0.043533 (0.003151) \| 0.316473 / 0.255139 (0.061334) \| 0.346119 / 0.283200 (0.062919) \| 0.022452 / 0.141683 (-0.119230) \| 1.540497 / 1.452155 (0.088343) \| 1.594888 / 1.492716 (0.102172) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204349 / 0.018006 (0.186343) \| 0.426842 / 0.000490 (0.426353) \| 0.003060 / 0.000200 (0.002860) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023611 / 0.037411 (-0.013801) \| 0.100247 / 0.014526 (0.085721) \| 0.107824 / 0.176557 (-0.068733) \| 0.166845 / 0.737135 (-0.570291) \| 0.112782 / 0.296338 (-0.183556) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423053 / 0.215209 (0.207844) \| 4.235553 / 2.077655 (2.157899) \| 1.936589 / 1.504120 (0.432469) \| 1.738519 / 1.541195 (0.197325) \| 1.787905 / 1.468490 (0.319415) \| 0.573362 / 4.584777 (-4.011414) \| 3.395272 / 3.745712 (-0.350440) \| 1.765977 / 5.269862 (-3.503884) \| 1.049596 / 4.565676 (-3.516081) \| 0.068868 / 0.424275 (-0.355407) \| 0.011028 / 0.007607 (0.003421) \| 0.532835 / 0.226044 (0.306791) \| 5.314890 / 2.268929 (3.045962) \| 2.368733 / 55.444624 (-53.075891) \| 2.033959 / 6.876477 (-4.842518) \| 2.130481 / 2.142072 (-0.011591) \| 0.689360 / 4.805227 (-4.115867) \| 0.140271 / 6.500664 (-6.360393) \| 0.068198 / 0.075469 (-0.007271) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237212 / 1.841788 (-0.604576) \| 14.182215 / 8.074308 (6.107907) \| 14.972608 / 10.191392 (4.781216) \| 0.133977 / 0.680424 (-0.546447) \| 0.016759 / 0.534201 (-0.517442) \| 0.361552 / 0.579283 (-0.217731) \| 0.394932 / 0.434364 (-0.039432) \| 0.442601 / 0.540337 (-0.097736) \| 0.535709 / 1.386936 (-0.851227) \|\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) \| 0.006327 / 0.011353 (-0.005026) \| 0.003780 / 0.011008 (-0.007228) \| 0.078358 / 0.038508 (0.039850) \| 0.037271 / 0.023109 (0.014162) \| 0.456766 / 0.275898 (0.180868) \| 0.515721 / 0.323480 (0.192241) \| 0.004770 / 0.007986 (-0.003216) \| 0.002942 / 0.004328 (-0.001387) \| 0.077383 / 0.004250 (0.073132) \| 0.051773 / 0.037052 (0.014721) \| 0.460722 / 0.258489 (0.202233) \| 0.519997 / 0.293841 (0.226157) \| 0.030461 / 0.128546 (-0.098085) \| 0.008622 / 0.075646 (-0.067024) \| 0.083271 / 0.419271 (-0.336000) \| 0.042242 / 0.043533 (-0.001291) \| 0.447691 / 0.255139 (0.192552) \| 0.481965 / 0.283200 (0.198765) \| 0.019510 / 0.141683 (-0.122173) \| 1.536718 / 1.452155 (0.084563) \| 1.588433 / 1.492716 (0.095717) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215880 / 0.018006 (0.197874) \| 0.426102 / 0.000490 (0.425612) \| 0.003976 / 0.000200 (0.003776) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026168 / 0.037411 (-0.011243) \| 0.105786 / 0.014526 (0.091260) \| 0.113772 / 0.176557 (-0.062785) \| 0.166576 / 0.737135 (-0.570559) \| 0.117560 / 0.296338 (-0.178779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.490485 / 0.215209 (0.275276) \| 4.890105 / 2.077655 (2.812450) \| 2.515099 / 1.504120 (1.010979) \| 2.306591 / 1.541195 (0.765396) \| 2.383634 / 1.468490 (0.915144) \| 0.573780 / 4.584777 (-4.010997) \| 3.474394 / 3.745712 (-0.271318) \| 1.746795 / 5.269862 (-3.523067) \| 1.044678 / 4.565676 (-3.520998) \| 0.069176 / 0.424275 (-0.355099) \| 0.011045 / 0.007607 (0.003438) \| 0.597234 / 0.226044 (0.371189) \| 5.979614 / 2.268929 (3.710685) \| 3.024203 / 55.444624 (-52.420422) \| 2.687502 / 6.876477 (-4.188975) \| 2.781637 / 2.142072 (0.639565) \| 0.690482 / 4.805227 (-4.114745) \| 0.150138 / 6.500664 (-6.350526) \| 0.077076 / 0.075469 (0.001607) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.307501 / 1.841788 (-0.534287) \| 14.366780 / 8.074308 (6.292471) \| 14.966981 / 10.191392 (4.775589) \| 0.153829 / 0.680424 (-0.526594) \| 0.018047 / 0.534201 (-0.516154) \| 0.361391 / 0.579283 (-0.217892) \| 0.398345 / 0.434364 (-0.036019) \| 0.424574 / 0.540337 (-0.115764) \| 0.517165 / 1.386936 (-0.869771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"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) \| 0.006944 / 0.011353 (-0.004409) \| 0.004504 / 0.011008 (-0.006504) \| 0.105224 / 0.038508 (0.066716) \| 0.047830 / 0.023109 (0.024721) \| 0.339723 / 0.275898 (0.063825) \| 0.419249 / 0.323480 (0.095769) \| 0.005510 / 0.007986 (-0.002476) \| 0.003574 / 0.004328 (-0.000754) \| 0.079879 / 0.004250 (0.075628) \| 0.066610 / 0.037052 (0.029557) \| 0.353818 / 0.258489 (0.095329) \| 0.397992 / 0.293841 (0.104151) \| 0.031551 / 0.128546 (-0.096995) \| 0.009037 / 0.075646 (-0.066610) \| 0.355310 / 0.419271 (-0.063961) \| 0.054931 / 0.043533 (0.011398) \| 0.335153 / 0.255139 (0.080014) \| 0.357460 / 0.283200 (0.074260) \| 0.026031 / 0.141683 (-0.115652) \| 1.546705 / 1.452155 (0.094550) \| 1.627324 / 1.492716 (0.134608) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276708 / 0.018006 (0.258701) \| 0.589402 / 0.000490 (0.588912) \| 0.009560 / 0.000200 (0.009360) \| 0.000095 / 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) \| 0.031041 / 0.037411 (-0.006370) \| 0.117219 / 0.014526 (0.102693) \| 0.125200 / 0.176557 (-0.051356) \| 0.181528 / 0.737135 (-0.555607) \| 0.131898 / 0.296338 (-0.164440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409965 / 0.215209 (0.194756) \| 4.102700 / 2.077655 (2.025045) \| 1.887578 / 1.504120 (0.383458) \| 1.696490 / 1.541195 (0.155295) \| 1.821352 / 1.468490 (0.352862) \| 0.545422 / 4.584777 (-4.039355) \| 3.933784 / 3.745712 (0.188071) \| 1.934254 / 5.269862 (-3.335607) \| 1.114935 / 4.565676 (-3.450742) \| 0.067615 / 0.424275 (-0.356660) \| 0.012004 / 0.007607 (0.004397) \| 0.522048 / 0.226044 (0.296004) \| 5.209224 / 2.268929 (2.940296) \| 2.369911 / 55.444624 (-53.074714) \| 2.032960 / 6.876477 (-4.843517) \| 2.228874 / 2.142072 (0.086802) \| 0.673172 / 4.805227 (-4.132055) \| 0.147017 / 6.500664 (-6.353647) \| 0.067020 / 0.075469 (-0.008449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281490 / 1.841788 (-0.560298) \| 16.129701 / 8.074308 (8.055393) \| 15.474730 / 10.191392 (5.283338) \| 0.143934 / 0.680424 (-0.536490) \| 0.018311 / 0.534201 (-0.515890) \| 0.435940 / 0.579283 (-0.143343) \| 0.446846 / 0.434364 (0.012482) \| 0.543943 / 0.540337 (0.003605) \| 0.648041 / 1.386936 (-0.738895) \|\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) \| 0.007380 / 0.011353 (-0.003973) \| 0.004510 / 0.011008 (-0.006499) \| 0.080741 / 0.038508 (0.042233) \| 0.050907 / 0.023109 (0.027797) \| 0.425548 / 0.275898 (0.149650) \| 0.487959 / 0.323480 (0.164479) \| 0.005887 / 0.007986 (-0.002099) \| 0.003689 / 0.004328 (-0.000639) \| 0.079588 / 0.004250 (0.075338) \| 0.071841 / 0.037052 (0.034788) \| 0.425172 / 0.258489 (0.166683) \| 0.471185 / 0.293841 (0.177344) \| 0.035768 / 0.128546 (-0.092779) \| 0.009229 / 0.075646 (-0.066418) \| 0.086021 / 0.419271 (-0.333250) \| 0.052424 / 0.043533 (0.008891) \| 0.413634 / 0.255139 (0.158495) \| 0.422310 / 0.283200 (0.139111) \| 0.026019 / 0.141683 (-0.115664) \| 1.616861 / 1.452155 (0.164707) \| 1.653660 / 1.492716 (0.160943) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280096 / 0.018006 (0.262090) \| 0.587853 / 0.000490 (0.587363) \| 0.006560 / 0.000200 (0.006360) \| 0.000181 / 0.000054 (0.000127) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033747 / 0.037411 (-0.003665) \| 0.125089 / 0.014526 (0.110564) \| 0.137995 / 0.176557 (-0.038561) \| 0.188192 / 0.737135 (-0.548943) \| 0.141438 / 0.296338 (-0.154900) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471524 / 0.215209 (0.256315) \| 4.713988 / 2.077655 (2.636334) \| 2.414785 / 1.504120 (0.910665) \| 2.226815 / 1.541195 (0.685620) \| 2.259222 / 1.468490 (0.790732) \| 0.551663 / 4.584777 (-4.033114) \| 4.031399 / 3.745712 (0.285686) \| 1.966917 / 5.269862 (-3.302945) \| 1.154487 / 4.565676 (-3.411190) \| 0.068500 / 0.424275 (-0.355775) \| 0.012127 / 0.007607 (0.004520) \| 0.579342 / 0.226044 (0.353298) \| 5.757415 / 2.268929 (3.488486) \| 2.820012 / 55.444624 (-52.624613) \| 2.521783 / 6.876477 (-4.354694) \| 2.699994 / 2.142072 (0.557921) \| 0.686152 / 4.805227 (-4.119075) \| 0.148521 / 6.500664 (-6.352143) \| 0.068478 / 0.075469 (-0.006991) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336260 / 1.841788 (-0.505528) \| 17.016935 / 8.074308 (8.942627) \| 16.406951 / 10.191392 (6.215559) \| 0.166907 / 0.680424 (-0.513517) \| 0.020166 / 0.534201 (-0.514035) \| 0.437690 / 0.579283 (-0.141593) \| 0.480337 / 0.434364 (0.045973) \| 0.518065 / 0.540337 (-0.022272) \| 0.625904 / 1.386936 (-0.761032) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"CML watermark\")\n" ]	2023-09-06T08:15:32Z	2023-09-06T08:52:18Z	2023-09-06T08:22:43Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6216.diff", "html_url": "https://github.com/huggingface/datasets/pull/6216", "merged_at": "2023-09-06T08:22:43Z", "patch_url": "https://github.com/huggingface/datasets/pull/6216.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6216" }	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": 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https://github.com/huggingface/datasets/pull/6215	6,215	Fix checking patterns to infer packaged builder	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[]	closed	false	[ "_The documentation is not available anymore as the PR was closed or merged._", "oh wow good catch", "<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) \| 0.006681 / 0.011353 (-0.004672) \| 0.003967 / 0.011008 (-0.007041) \| 0.085590 / 0.038508 (0.047082) \| 0.079285 / 0.023109 (0.056176) \| 0.311583 / 0.275898 (0.035685) \| 0.345578 / 0.323480 (0.022098) \| 0.004115 / 0.007986 (-0.003871) \| 0.004286 / 0.004328 (-0.000043) \| 0.064405 / 0.004250 (0.060155) \| 0.055084 / 0.037052 (0.018032) \| 0.316117 / 0.258489 (0.057628) \| 0.354737 / 0.293841 (0.060896) \| 0.031280 / 0.128546 (-0.097266) \| 0.008395 / 0.075646 (-0.067251) \| 0.288910 / 0.419271 (-0.130362) \| 0.051291 / 0.043533 (0.007759) \| 0.309125 / 0.255139 (0.053986) \| 0.349673 / 0.283200 (0.066473) \| 0.025016 / 0.141683 (-0.116667) \| 1.475577 / 1.452155 (0.023422) \| 1.558967 / 1.492716 (0.066251) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208504 / 0.018006 (0.190498) \| 0.462270 / 0.000490 (0.461780) \| 0.003476 / 0.000200 (0.003276) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030371 / 0.037411 (-0.007041) \| 0.086157 / 0.014526 (0.071631) \| 0.098162 / 0.176557 (-0.078395) \| 0.154649 / 0.737135 (-0.582486) \| 0.098697 / 0.296338 (-0.197642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405883 / 0.215209 (0.190674) \| 4.049614 / 2.077655 (1.971959) \| 2.075047 / 1.504120 (0.570927) \| 1.917782 / 1.541195 (0.376587) \| 2.030268 / 1.468490 (0.561778) \| 0.483974 / 4.584777 (-4.100803) \| 3.542147 / 3.745712 (-0.203566) \| 3.305999 / 5.269862 (-1.963863) \| 2.052287 / 4.565676 (-2.513390) \| 0.057246 / 0.424275 (-0.367029) \| 0.007631 / 0.007607 (0.000024) \| 0.488189 / 0.226044 (0.262144) \| 4.884784 / 2.268929 (2.615856) \| 2.576304 / 55.444624 (-52.868320) \| 2.241249 / 6.876477 (-4.635228) \| 2.490512 / 2.142072 (0.348440) \| 0.584495 / 4.805227 (-4.220733) \| 0.134741 / 6.500664 (-6.365923) \| 0.061639 / 0.075469 (-0.013830) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.317717 / 1.841788 (-0.524071) \| 20.098594 / 8.074308 (12.024286) \| 14.641051 / 10.191392 (4.449659) \| 0.165291 / 0.680424 (-0.515133) \| 0.019179 / 0.534201 (-0.515022) \| 0.399506 / 0.579283 (-0.179777) \| 0.407662 / 0.434364 (-0.026701) \| 0.457965 / 0.540337 (-0.082372) \| 0.626401 / 1.386936 (-0.760536) \|\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) \| 0.007076 / 0.011353 (-0.004277) \| 0.004125 / 0.011008 (-0.006884) \| 0.064861 / 0.038508 (0.026353) \| 0.082390 / 0.023109 (0.059281) \| 0.423227 / 0.275898 (0.147329) \| 0.452229 / 0.323480 (0.128750) \| 0.005594 / 0.007986 (-0.002392) \| 0.003465 / 0.004328 (-0.000863) \| 0.064661 / 0.004250 (0.060411) \| 0.057945 / 0.037052 (0.020892) \| 0.424572 / 0.258489 (0.166083) \| 0.465349 / 0.293841 (0.171509) \| 0.032687 / 0.128546 (-0.095859) \| 0.008573 / 0.075646 (-0.067074) \| 0.073020 / 0.419271 (-0.346251) \| 0.048423 / 0.043533 (0.004891) \| 0.413425 / 0.255139 (0.158286) \| 0.433778 / 0.283200 (0.150578) \| 0.023942 / 0.141683 (-0.117741) \| 1.495190 / 1.452155 (0.043036) \| 1.586526 / 1.492716 (0.093810) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271805 / 0.018006 (0.253799) \| 0.454922 / 0.000490 (0.454432) \| 0.015386 / 0.000200 (0.015186) \| 0.000129 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033804 / 0.037411 (-0.003607) \| 0.099317 / 0.014526 (0.084791) \| 0.107207 / 0.176557 (-0.069349) \| 0.160926 / 0.737135 (-0.576210) \| 0.108669 / 0.296338 (-0.187670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430776 / 0.215209 (0.215567) \| 4.297622 / 2.077655 (2.219967) \| 2.285918 / 1.504120 (0.781798) \| 2.109608 / 1.541195 (0.568413) \| 2.208326 / 1.468490 (0.739836) \| 0.490016 / 4.584777 (-4.094761) \| 3.570609 / 3.745712 (-0.175103) \| 3.406335 / 5.269862 (-1.863526) \| 2.070664 / 4.565676 (-2.495012) \| 0.058089 / 0.424275 (-0.366186) \| 0.007425 / 0.007607 (-0.000182) \| 0.506972 / 0.226044 (0.280927) \| 5.078643 / 2.268929 (2.809714) \| 2.858973 / 55.444624 (-52.585651) \| 2.457344 / 6.876477 (-4.419132) \| 2.687727 / 2.142072 (0.545654) \| 0.592134 / 4.805227 (-4.213093) \| 0.133966 / 6.500664 (-6.366698) \| 0.061800 / 0.075469 (-0.013669) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.337167 / 1.841788 (-0.504620) \| 20.743951 / 8.074308 (12.669643) \| 15.402686 / 10.191392 (5.211294) \| 0.164548 / 0.680424 (-0.515876) \| 0.020244 / 0.534201 (-0.513957) \| 0.399044 / 0.579283 (-0.180239) \| 0.414036 / 0.434364 (-0.020328) \| 0.474141 / 0.540337 (-0.066197) \| 0.654455 / 1.386936 (-0.732482) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de930c45a81a6dff1805bf45f59170e9f953eeb \"CML watermark\")\n" ]	2023-09-05T15:10:47Z	2023-09-06T10:34:00Z	2023-09-06T10:25:00Z	CONTRIBUTOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6215.diff", "html_url": "https://github.com/huggingface/datasets/pull/6215", "merged_at": "2023-09-06T10:25:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6215.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6215" }	Don't ignore results of pattern resolving if `self.data_files` is not None. Otherwise lines 854 and 1037 make no sense.	{ "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/6215/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6214	6,214	Unpin fsspec < 2023.9.0	{ "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" }	[ { "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	[]	2023-09-05T11:02:58Z	2023-09-26T15:32:52Z	2023-09-26T15:32:52Z	MEMBER	null	null	Once root issue is fixed, remove temporary pin of fsspec < 2023.9.0 introduced by: - #6210 Related to issue: - #6209 After investigation, I think the root issue is related to the new glob behavior with double asterisk `**` they have introduced in: - https://github.com/fsspec/filesystem_spec/pull/1329	{ "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/6214/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/6213	6,213	Better list array values handling in cast/embed storage	{ "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) \| 0.008451 / 0.011353 (-0.002902) \| 0.005056 / 0.011008 (-0.005952) \| 0.086367 / 0.038508 (0.047859) \| 0.068030 / 0.023109 (0.044920) \| 0.358812 / 0.275898 (0.082914) \| 0.385790 / 0.323480 (0.062310) \| 0.005608 / 0.007986 (-0.002378) \| 0.004262 / 0.004328 (-0.000067) \| 0.066618 / 0.004250 (0.062368) \| 0.053901 / 0.037052 (0.016849) \| 0.398456 / 0.258489 (0.139967) \| 0.391681 / 0.293841 (0.097840) \| 0.046743 / 0.128546 (-0.081804) \| 0.014118 / 0.075646 (-0.061528) \| 0.308479 / 0.419271 (-0.110793) \| 0.064214 / 0.043533 (0.020681) \| 0.367940 / 0.255139 (0.112801) \| 0.387204 / 0.283200 (0.104004) \| 0.036093 / 0.141683 (-0.105590) \| 1.534182 / 1.452155 (0.082027) \| 1.598357 / 1.492716 (0.105641) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265910 / 0.018006 (0.247904) \| 0.589453 / 0.000490 (0.588963) \| 0.004881 / 0.000200 (0.004681) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032540 / 0.037411 (-0.004872) \| 0.083153 / 0.014526 (0.068627) \| 0.098960 / 0.176557 (-0.077597) \| 0.162044 / 0.737135 (-0.575091) \| 0.093602 / 0.296338 (-0.202736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.517056 / 0.215209 (0.301847) \| 5.167908 / 2.077655 (3.090253) \| 2.359856 / 1.504120 (0.855736) \| 2.092448 / 1.541195 (0.551253) \| 2.100270 / 1.468490 (0.631780) \| 0.742321 / 4.584777 (-3.842456) \| 4.845010 / 3.745712 (1.099298) \| 4.361808 / 5.269862 (-0.908054) \| 2.621941 / 4.565676 (-1.943736) \| 0.094907 / 0.424275 (-0.329369) \| 0.009357 / 0.007607 (0.001750) \| 0.719859 / 0.226044 (0.493814) \| 6.929731 / 2.268929 (4.660802) \| 3.240862 / 55.444624 (-52.203763) \| 2.700817 / 6.876477 (-4.175659) \| 2.904600 / 2.142072 (0.762527) \| 0.924930 / 4.805227 (-3.880298) \| 0.194390 / 6.500664 (-6.306274) \| 0.078331 / 0.075469 (0.002862) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.539347 / 1.841788 (-0.302441) \| 22.696358 / 8.074308 (14.622050) \| 18.791692 / 10.191392 (8.600300) \| 0.221376 / 0.680424 (-0.459048) \| 0.029824 / 0.534201 (-0.504377) \| 0.455604 / 0.579283 (-0.123679) \| 0.573169 / 0.434364 (0.138805) \| 0.507109 / 0.540337 (-0.033228) \| 0.730986 / 1.386936 (-0.655950) \|\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) \| 0.009308 / 0.011353 (-0.002045) \| 0.005027 / 0.011008 (-0.005982) \| 0.074094 / 0.038508 (0.035586) \| 0.068277 / 0.023109 (0.045168) \| 0.412716 / 0.275898 (0.136818) \| 0.446883 / 0.323480 (0.123403) \| 0.005864 / 0.007986 (-0.002122) \| 0.003753 / 0.004328 (-0.000575) \| 0.072575 / 0.004250 (0.068325) \| 0.064434 / 0.037052 (0.027382) \| 0.445395 / 0.258489 (0.186906) \| 0.464520 / 0.293841 (0.170679) \| 0.045303 / 0.128546 (-0.083243) \| 0.013120 / 0.075646 (-0.062527) \| 0.077830 / 0.419271 (-0.341441) \| 0.057303 / 0.043533 (0.013770) \| 0.420845 / 0.255139 (0.165706) \| 0.431308 / 0.283200 (0.148109) \| 0.033908 / 0.141683 (-0.107775) \| 1.577667 / 1.452155 (0.125512) \| 1.677321 / 1.492716 (0.184604) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305855 / 0.018006 (0.287849) \| 0.601442 / 0.000490 (0.600953) \| 0.010722 / 0.000200 (0.010522) \| 0.000158 / 0.000054 (0.000104) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029202 / 0.037411 (-0.008209) \| 0.094576 / 0.014526 (0.080050) \| 0.106734 / 0.176557 (-0.069822) \| 0.168114 / 0.737135 (-0.569021) \| 0.107241 / 0.296338 (-0.189098) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.643634 / 0.215209 (0.428425) \| 6.391757 / 2.077655 (4.314103) \| 3.011679 / 1.504120 (1.507559) \| 2.379711 / 1.541195 (0.838517) \| 2.387444 / 1.468490 (0.918954) \| 0.823460 / 4.584777 (-3.761317) \| 4.882240 / 3.745712 (1.136528) \| 4.091170 / 5.269862 (-1.178691) \| 2.688761 / 4.565676 (-1.876915) \| 0.094555 / 0.424275 (-0.329720) \| 0.008464 / 0.007607 (0.000857) \| 0.665949 / 0.226044 (0.439905) \| 6.948237 / 2.268929 (4.679309) \| 3.384894 / 55.444624 (-52.059730) \| 2.675570 / 6.876477 (-4.200907) \| 3.073045 / 2.142072 (0.930973) \| 0.969780 / 4.805227 (-3.835447) \| 0.205859 / 6.500664 (-6.294805) \| 0.072548 / 0.075469 (-0.002922) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.563869 / 1.841788 (-0.277919) \| 22.431392 / 8.074308 (14.357084) \| 19.434811 / 10.191392 (9.243419) \| 0.255135 / 0.680424 (-0.425289) \| 0.027799 / 0.534201 (-0.506402) \| 0.427713 / 0.579283 (-0.151570) \| 0.527030 / 0.434364 (0.092666) \| 0.503660 / 0.540337 (-0.036678) \| 0.730996 / 1.386936 (-0.655940) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06c1940953807dbde4bc18af64bd3d87234edf00 \"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) \| 0.007597 / 0.011353 (-0.003756) \| 0.004492 / 0.011008 (-0.006516) \| 0.103779 / 0.038508 (0.065271) \| 0.079287 / 0.023109 (0.056178) \| 0.389651 / 0.275898 (0.113753) \| 0.421955 / 0.323480 (0.098475) \| 0.006023 / 0.007986 (-0.001963) \| 0.003727 / 0.004328 (-0.000602) \| 0.078604 / 0.004250 (0.074354) \| 0.060810 / 0.037052 (0.023758) \| 0.412170 / 0.258489 (0.153681) \| 0.436218 / 0.293841 (0.142377) \| 0.037282 / 0.128546 (-0.091264) \| 0.010341 / 0.075646 (-0.065305) \| 0.357652 / 0.419271 (-0.061620) \| 0.063320 / 0.043533 (0.019788) \| 0.389454 / 0.255139 (0.134315) \| 0.433073 / 0.283200 (0.149874) \| 0.028449 / 0.141683 (-0.113234) \| 1.894107 / 1.452155 (0.441952) \| 1.954190 / 1.492716 (0.461474) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224477 / 0.018006 (0.206471) \| 0.510878 / 0.000490 (0.510388) \| 0.005013 / 0.000200 (0.004813) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032976 / 0.037411 (-0.004436) \| 0.101073 / 0.014526 (0.086547) \| 0.113990 / 0.176557 (-0.062566) \| 0.183499 / 0.737135 (-0.553636) \| 0.114283 / 0.296338 (-0.182056) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473242 / 0.215209 (0.258033) \| 4.719800 / 2.077655 (2.642146) \| 2.318732 / 1.504120 (0.814612) \| 2.102336 / 1.541195 (0.561141) \| 2.143618 / 1.468490 (0.675128) \| 0.594122 / 4.584777 (-3.990654) \| 4.265961 / 3.745712 (0.520249) \| 3.794635 / 5.269862 (-1.475226) \| 2.394506 / 4.565676 (-2.171170) \| 0.070091 / 0.424275 (-0.354184) \| 0.009222 / 0.007607 (0.001614) \| 0.564496 / 0.226044 (0.338452) \| 5.644348 / 2.268929 (3.375419) \| 2.934395 / 55.444624 (-52.510229) \| 2.429076 / 6.876477 (-4.447401) \| 2.592010 / 2.142072 (0.449937) \| 0.713371 / 4.805227 (-4.091856) \| 0.165019 / 6.500664 (-6.335646) \| 0.075913 / 0.075469 (0.000444) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.570836 / 1.841788 (-0.270951) \| 22.569763 / 8.074308 (14.495455) \| 17.159658 / 10.191392 (6.968266) \| 0.185716 / 0.680424 (-0.494708) \| 0.021938 / 0.534201 (-0.512263) \| 0.487204 / 0.579283 (-0.092079) \| 0.472776 / 0.434364 (0.038412) \| 0.565052 / 0.540337 (0.024714) \| 0.763322 / 1.386936 (-0.623614) \|\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) \| 0.007810 / 0.011353 (-0.003543) \| 0.005140 / 0.011008 (-0.005869) \| 0.079018 / 0.038508 (0.040510) \| 0.080899 / 0.023109 (0.057790) \| 0.489213 / 0.275898 (0.213315) \| 0.525334 / 0.323480 (0.201854) \| 0.006992 / 0.007986 (-0.000994) \| 0.003729 / 0.004328 (-0.000599) \| 0.079277 / 0.004250 (0.075026) \| 0.064883 / 0.037052 (0.027831) \| 0.496718 / 0.258489 (0.238229) \| 0.534976 / 0.293841 (0.241135) \| 0.038790 / 0.128546 (-0.089756) \| 0.010122 / 0.075646 (-0.065524) \| 0.087669 / 0.419271 (-0.331603) \| 0.057959 / 0.043533 (0.014426) \| 0.490611 / 0.255139 (0.235472) \| 0.518376 / 0.283200 (0.235176) \| 0.026561 / 0.141683 (-0.115122) \| 1.843241 / 1.452155 (0.391086) \| 1.952367 / 1.492716 (0.459651) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289799 / 0.018006 (0.271792) \| 0.486999 / 0.000490 (0.486509) \| 0.017481 / 0.000200 (0.017281) \| 0.000122 / 0.000054 (0.000068) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037662 / 0.037411 (0.000250) \| 0.113238 / 0.014526 (0.098712) \| 0.123918 / 0.176557 (-0.052638) \| 0.190484 / 0.737135 (-0.546652) \| 0.126473 / 0.296338 (-0.169865) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.530622 / 0.215209 (0.315413) \| 5.292093 / 2.077655 (3.214438) \| 2.819354 / 1.504120 (1.315234) \| 2.609821 / 1.541195 (1.068626) \| 2.680090 / 1.468490 (1.211600) \| 0.603490 / 4.584777 (-3.981287) \| 4.344541 / 3.745712 (0.598828) \| 3.874001 / 5.269862 (-1.395861) \| 2.445302 / 4.565676 (-2.120375) \| 0.071173 / 0.424275 (-0.353102) \| 0.009131 / 0.007607 (0.001524) \| 0.627273 / 0.226044 (0.401229) \| 6.278637 / 2.268929 (4.009709) \| 3.433762 / 55.444624 (-52.010862) \| 2.973400 / 6.876477 (-3.903077) \| 3.188165 / 2.142072 (1.046093) \| 0.722824 / 4.805227 (-4.082404) \| 0.165154 / 6.500664 (-6.335510) \| 0.075268 / 0.075469 (-0.000202) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.652994 / 1.841788 (-0.188794) \| 23.309030 / 8.074308 (15.234722) \| 18.135649 / 10.191392 (7.944257) \| 0.177543 / 0.680424 (-0.502881) \| 0.024784 / 0.534201 (-0.509417) \| 0.489952 / 0.579283 (-0.089331) \| 0.485368 / 0.434364 (0.051004) \| 0.580583 / 0.540337 (0.040246) \| 0.787843 / 1.386936 (-0.599093) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5982039f7814a204fe532240ca6aabe72430d834 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "A bug in `FixedSizeArray.flatten` in `PyArrow<10.0.0` makes CI fail. Colab installs 9.0.0 by default, so we should be able to set the minimal version to `10.0.0` soon. Keeping this PR as a draft in the meantime.", "Closing this PR in favor of https://github.com/huggingface/datasets/pull/6283" ]	2023-09-04T16:21:23Z	2024-01-11T06:32:20Z	2023-10-05T15:24:34Z	COLLABORATOR	true	{ "diff_url": "https://github.com/huggingface/datasets/pull/6213.diff", "html_url": "https://github.com/huggingface/datasets/pull/6213", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6213.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6213" }	Use [`array.flatten`](https://arrow.apache.org/docs/python/generated/pyarrow.ListArray.html#pyarrow.ListArray.flatten) that takes `.offset` into account instead of `array.values` in array cast/embed.	{ "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/6213/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6212	6,212	Tilde (~) is not supported for data_files	{ "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/exs-avianello", "id": 128361578, "login": "exs-avianello", "node_id": "U_kgDOB6akag", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "repos_url": "https://api.github.com/users/exs-avianello/repos", "site_admin": false, "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "type": "User", "url": "https://api.github.com/users/exs-avianello", "user_view_type": "public" }	[]	open	false	[ "Hi @exs-avianello, is it really needed? Note you can alternatively use `pathlib.Path` among others as it follows:\r\n\r\n```python\r\nimport datasets\r\nfrom pathlib import Path\r\n\r\n# save a parquet file at ~/path/to/data.parquet\r\n\r\ndata_files = Path.home() / \"path/to/data.parquet\"\r\ndataset = datasets.load_dataset(\"parquet\", data_files=data_files)\r\n```", "Hi @alvarobartt ! \r\n\r\nThis is definitely just a \"nice to have\" and I am personally more than happy to just use absolute paths client-side. I just wanted to flag it up in case it can help improve the package even more 🙌 It might not be immediately obvious from the stack trace that the error is triggered by the `~` in the path" ]	2023-09-04T14:23:49Z	2023-09-05T08:28:39Z	null	NONE	null	null	### Describe the bug Attempting to `load_dataset` from a path starting with `~` (as a shorthand for the user's home directory) seems not to be fully working - at least as far as the `parquet` dataset builder is concerned. (the same file can be loaded correctly if providing its absolute path instead) I think that this is very similar to https://github.com/huggingface/datasets/issues/5757, but for `data_files` rather than `data_dir` ### Steps to reproduce the bug ```python import datasets # save a parquet file at ~/path/to/data.parquet data_files = "~/path/to/data.parquet" dataset = datasets.load_dataset("parquet", data_files=data_files) ``` ``` Downloading data files: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 12671.61it/s] Extracting data files: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 22671.91it/s] Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1949, in _prepare_split_single num_examples, num_bytes = writer.finalize() ^^^^^^^^^^^^^^^^^ File ".venv/lib/python3.11/site-packages/datasets/arrow_writer.py", line 598, in finalize raise SchemaInferenceError("Please pass `features` or at least one example when writing data") datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1813, in _prepare_split for job_id, done, content in self._prepare_split_single( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1958, 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 Can use `~` shorthand in paths when loading local (parquet) datasets. ### Environment info `datasets 2.14.3`	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/6212/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/6211	6,211	Fix empty splitinfo json	{ "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) \| 0.007756 / 0.011353 (-0.003597) \| 0.004733 / 0.011008 (-0.006275) \| 0.095874 / 0.038508 (0.057366) \| 0.081957 / 0.023109 (0.058848) \| 0.426430 / 0.275898 (0.150532) \| 0.457670 / 0.323480 (0.134190) \| 0.004448 / 0.007986 (-0.003537) \| 0.004956 / 0.004328 (0.000627) \| 0.074195 / 0.004250 (0.069945) \| 0.061101 / 0.037052 (0.024048) \| 0.435134 / 0.258489 (0.176645) \| 0.457245 / 0.293841 (0.163404) \| 0.034945 / 0.128546 (-0.093601) \| 0.010028 / 0.075646 (-0.065618) \| 0.350724 / 0.419271 (-0.068548) \| 0.064433 / 0.043533 (0.020901) \| 0.417882 / 0.255139 (0.162743) \| 0.445087 / 0.283200 (0.161887) \| 0.027576 / 0.141683 (-0.114107) \| 1.824066 / 1.452155 (0.371912) \| 1.957568 / 1.492716 (0.464852) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238568 / 0.018006 (0.220562) \| 0.505289 / 0.000490 (0.504799) \| 0.003527 / 0.000200 (0.003327) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032839 / 0.037411 (-0.004572) \| 0.096708 / 0.014526 (0.082182) \| 0.112100 / 0.176557 (-0.064456) \| 0.177215 / 0.737135 (-0.559920) \| 0.111273 / 0.296338 (-0.185066) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.475200 / 0.215209 (0.259991) \| 4.725737 / 2.077655 (2.648082) \| 2.414672 / 1.504120 (0.910552) \| 2.196357 / 1.541195 (0.655162) \| 2.329298 / 1.468490 (0.860808) \| 0.575258 / 4.584777 (-4.009519) \| 4.343630 / 3.745712 (0.597918) \| 3.837665 / 5.269862 (-1.432196) \| 2.497970 / 4.565676 (-2.067706) \| 0.066467 / 0.424275 (-0.357808) \| 0.008680 / 0.007607 (0.001073) \| 0.569923 / 0.226044 (0.343878) \| 5.634230 / 2.268929 (3.365302) \| 2.959222 / 55.444624 (-52.485402) \| 2.535954 / 6.876477 (-4.340523) \| 2.804844 / 2.142072 (0.662771) \| 0.682000 / 4.805227 (-4.123227) \| 0.158193 / 6.500664 (-6.342471) \| 0.072315 / 0.075469 (-0.003154) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.578148 / 1.841788 (-0.263639) \| 22.993419 / 8.074308 (14.919110) \| 16.524477 / 10.191392 (6.333085) \| 0.169415 / 0.680424 (-0.511009) \| 0.021520 / 0.534201 (-0.512681) \| 0.455970 / 0.579283 (-0.123313) \| 0.489022 / 0.434364 (0.054658) \| 0.535656 / 0.540337 (-0.004682) \| 0.802341 / 1.386936 (-0.584595) \|\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) \| 0.008002 / 0.011353 (-0.003351) \| 0.005577 / 0.011008 (-0.005431) \| 0.087803 / 0.038508 (0.049295) \| 0.091285 / 0.023109 (0.068176) \| 0.500514 / 0.275898 (0.224616) \| 0.549770 / 0.323480 (0.226290) \| 0.006125 / 0.007986 (-0.001861) \| 0.004031 / 0.004328 (-0.000297) \| 0.077941 / 0.004250 (0.073691) \| 0.071419 / 0.037052 (0.034367) \| 0.497570 / 0.258489 (0.239081) \| 0.542454 / 0.293841 (0.248613) \| 0.040827 / 0.128546 (-0.087719) \| 0.011029 / 0.075646 (-0.064617) \| 0.088788 / 0.419271 (-0.330484) \| 0.056970 / 0.043533 (0.013438) \| 0.523934 / 0.255139 (0.268795) \| 0.552507 / 0.283200 (0.269308) \| 0.029794 / 0.141683 (-0.111889) \| 1.817778 / 1.452155 (0.365623) \| 1.955843 / 1.492716 (0.463126) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246992 / 0.018006 (0.228986) \| 0.467879 / 0.000490 (0.467390) \| 0.005439 / 0.000200 (0.005239) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037774 / 0.037411 (0.000363) \| 0.109332 / 0.014526 (0.094806) \| 0.120103 / 0.176557 (-0.056454) \| 0.185259 / 0.737135 (-0.551876) \| 0.126189 / 0.296338 (-0.170149) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492856 / 0.215209 (0.277646) \| 5.033209 / 2.077655 (2.955554) \| 2.885551 / 1.504120 (1.381431) \| 2.480304 / 1.541195 (0.939109) \| 2.579092 / 1.468490 (1.110602) \| 0.557671 / 4.584777 (-4.027106) \| 4.352765 / 3.745712 (0.607053) \| 4.039124 / 5.269862 (-1.230738) \| 2.534342 / 4.565676 (-2.031335) \| 0.067267 / 0.424275 (-0.357008) \| 0.008891 / 0.007607 (0.001284) \| 0.591592 / 0.226044 (0.365547) \| 5.939982 / 2.268929 (3.671053) \| 3.258389 / 55.444624 (-52.186235) \| 2.843899 / 6.876477 (-4.032578) \| 3.074217 / 2.142072 (0.932144) \| 0.695065 / 4.805227 (-4.110162) \| 0.156917 / 6.500664 (-6.343747) \| 0.070185 / 0.075469 (-0.005284) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.586716 / 1.841788 (-0.255072) \| 23.405837 / 8.074308 (15.331529) \| 17.200851 / 10.191392 (7.009459) \| 0.170073 / 0.680424 (-0.510351) \| 0.023345 / 0.534201 (-0.510856) \| 0.459192 / 0.579283 (-0.120091) \| 0.477419 / 0.434364 (0.043055) \| 0.558581 / 0.540337 (0.018244) \| 0.814373 / 1.386936 (-0.572563) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#28bbe5667e6eaa1bb21685791fcf1a4ed1ef1777 \"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) \| 0.006050 / 0.011353 (-0.005303) \| 0.003661 / 0.011008 (-0.007348) \| 0.081753 / 0.038508 (0.043245) \| 0.061275 / 0.023109 (0.038166) \| 0.316278 / 0.275898 (0.040380) \| 0.350783 / 0.323480 (0.027303) \| 0.004694 / 0.007986 (-0.003291) \| 0.003003 / 0.004328 (-0.001326) \| 0.062877 / 0.004250 (0.058627) \| 0.046985 / 0.037052 (0.009933) \| 0.315698 / 0.258489 (0.057208) \| 0.364607 / 0.293841 (0.070766) \| 0.027365 / 0.128546 (-0.101181) \| 0.008016 / 0.075646 (-0.067631) \| 0.261379 / 0.419271 (-0.157893) \| 0.045173 / 0.043533 (0.001640) \| 0.313499 / 0.255139 (0.058360) \| 0.339383 / 0.283200 (0.056184) \| 0.020855 / 0.141683 (-0.120828) \| 1.429851 / 1.452155 (-0.022303) \| 1.506112 / 1.492716 (0.013396) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194872 / 0.018006 (0.176866) \| 0.451951 / 0.000490 (0.451462) \| 0.002790 / 0.000200 (0.002590) \| 0.000070 / 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) \| 0.024331 / 0.037411 (-0.013081) \| 0.073156 / 0.014526 (0.058630) \| 0.084054 / 0.176557 (-0.092502) \| 0.145656 / 0.737135 (-0.591480) \| 0.084998 / 0.296338 (-0.211340) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.391324 / 0.215209 (0.176115) \| 3.898406 / 2.077655 (1.820751) \| 1.891175 / 1.504120 (0.387055) \| 1.698738 / 1.541195 (0.157543) \| 1.774324 / 1.468490 (0.305834) \| 0.495129 / 4.584777 (-4.089648) \| 3.027027 / 3.745712 (-0.718685) \| 2.821423 / 5.269862 (-2.448439) \| 1.870761 / 4.565676 (-2.694915) \| 0.057029 / 0.424275 (-0.367246) \| 0.006715 / 0.007607 (-0.000892) \| 0.465801 / 0.226044 (0.239757) \| 4.650891 / 2.268929 (2.381962) \| 2.425097 / 55.444624 (-53.019527) \| 2.134731 / 6.876477 (-4.741745) \| 2.312854 / 2.142072 (0.170781) \| 0.589668 / 4.805227 (-4.215559) \| 0.124673 / 6.500664 (-6.375991) \| 0.060887 / 0.075469 (-0.014582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243622 / 1.841788 (-0.598166) \| 18.501640 / 8.074308 (10.427332) \| 13.853099 / 10.191392 (3.661707) \| 0.130255 / 0.680424 (-0.550168) \| 0.016824 / 0.534201 (-0.517377) \| 0.332297 / 0.579283 (-0.246986) \| 0.360346 / 0.434364 (-0.074018) \| 0.388598 / 0.540337 (-0.151739) \| 0.527551 / 1.386936 (-0.859385) \|\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) \| 0.006181 / 0.011353 (-0.005172) \| 0.003688 / 0.011008 (-0.007320) \| 0.063395 / 0.038508 (0.024887) \| 0.062531 / 0.023109 (0.039422) \| 0.446565 / 0.275898 (0.170667) \| 0.485224 / 0.323480 (0.161744) \| 0.004982 / 0.007986 (-0.003004) \| 0.002961 / 0.004328 (-0.001367) \| 0.063124 / 0.004250 (0.058874) \| 0.050234 / 0.037052 (0.013182) \| 0.449731 / 0.258489 (0.191242) \| 0.487293 / 0.293841 (0.193452) \| 0.028528 / 0.128546 (-0.100018) \| 0.008210 / 0.075646 (-0.067436) \| 0.069520 / 0.419271 (-0.349751) \| 0.041026 / 0.043533 (-0.002507) \| 0.451370 / 0.255139 (0.196231) \| 0.469151 / 0.283200 (0.185951) \| 0.021076 / 0.141683 (-0.120607) \| 1.439185 / 1.452155 (-0.012970) \| 1.492634 / 1.492716 (-0.000082) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235932 / 0.018006 (0.217926) \| 0.430070 / 0.000490 (0.429581) \| 0.007347 / 0.000200 (0.007147) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026102 / 0.037411 (-0.011309) \| 0.081333 / 0.014526 (0.066807) \| 0.090111 / 0.176557 (-0.086446) \| 0.144578 / 0.737135 (-0.592557) \| 0.091961 / 0.296338 (-0.204378) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455761 / 0.215209 (0.240552) \| 4.536345 / 2.077655 (2.458690) \| 2.496833 / 1.504120 (0.992713) \| 2.323325 / 1.541195 (0.782130) \| 2.388364 / 1.468490 (0.919873) \| 0.512010 / 4.584777 (-4.072767) \| 3.106268 / 3.745712 (-0.639444) \| 2.879224 / 5.269862 (-2.390637) \| 1.893859 / 4.565676 (-2.671818) \| 0.059131 / 0.424275 (-0.365144) \| 0.006763 / 0.007607 (-0.000844) \| 0.528205 / 0.226044 (0.302161) \| 5.296649 / 2.268929 (3.027720) \| 2.933787 / 55.444624 (-52.510838) \| 2.598258 / 6.876477 (-4.278218) \| 2.768195 / 2.142072 (0.626123) \| 0.597430 / 4.805227 (-4.207797) \| 0.125865 / 6.500664 (-6.374799) \| 0.061684 / 0.075469 (-0.013785) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.341194 / 1.841788 (-0.500594) \| 18.948225 / 8.074308 (10.873917) \| 14.912340 / 10.191392 (4.720948) \| 0.146905 / 0.680424 (-0.533519) \| 0.017952 / 0.534201 (-0.516249) \| 0.332299 / 0.579283 (-0.246984) \| 0.362733 / 0.434364 (-0.071631) \| 0.388278 / 0.540337 (-0.152060) \| 0.546436 / 1.386936 (-0.840500) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb4f8357de001df656f2ea7af27625e189c3995b \"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) \| 0.008314 / 0.011353 (-0.003038) \| 0.004904 / 0.011008 (-0.006105) \| 0.097486 / 0.038508 (0.058978) \| 0.074627 / 0.023109 (0.051518) \| 0.396395 / 0.275898 (0.120497) \| 0.440519 / 0.323480 (0.117039) \| 0.005964 / 0.007986 (-0.002022) \| 0.004203 / 0.004328 (-0.000126) \| 0.079998 / 0.004250 (0.075747) \| 0.055158 / 0.037052 (0.018106) \| 0.415439 / 0.258489 (0.156950) \| 0.476101 / 0.293841 (0.182260) \| 0.044761 / 0.128546 (-0.083785) \| 0.013966 / 0.075646 (-0.061680) \| 0.351279 / 0.419271 (-0.067993) \| 0.067250 / 0.043533 (0.023717) \| 0.414310 / 0.255139 (0.159171) \| 0.458104 / 0.283200 (0.174904) \| 0.033678 / 0.141683 (-0.108005) \| 1.730539 / 1.452155 (0.278385) \| 1.840013 / 1.492716 (0.347297) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272708 / 0.018006 (0.254702) \| 0.593563 / 0.000490 (0.593074) \| 0.005153 / 0.000200 (0.004953) \| 0.000179 / 0.000054 (0.000125) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029595 / 0.037411 (-0.007816) \| 0.087994 / 0.014526 (0.073469) \| 0.106066 / 0.176557 (-0.070491) \| 0.180491 / 0.737135 (-0.556644) \| 0.103707 / 0.296338 (-0.192631) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.566711 / 0.215209 (0.351502) \| 5.589034 / 2.077655 (3.511380) \| 2.364034 / 1.504120 (0.859914) \| 2.119050 / 1.541195 (0.577855) \| 2.103823 / 1.468490 (0.635333) \| 0.819906 / 4.584777 (-3.764871) \| 5.178464 / 3.745712 (1.432752) \| 4.433986 / 5.269862 (-0.835875) \| 2.825470 / 4.565676 (-1.740207) \| 0.096907 / 0.424275 (-0.327368) \| 0.008573 / 0.007607 (0.000966) \| 0.677607 / 0.226044 (0.451563) \| 6.811090 / 2.268929 (4.542162) \| 3.140923 / 55.444624 (-52.303701) \| 2.492251 / 6.876477 (-4.384225) \| 2.660231 / 2.142072 (0.518158) \| 0.980573 / 4.805227 (-3.824655) \| 0.209028 / 6.500664 (-6.291636) \| 0.079413 / 0.075469 (0.003944) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.578861 / 1.841788 (-0.262926) \| 22.518269 / 8.074308 (14.443961) \| 21.335916 / 10.191392 (11.144524) \| 0.211311 / 0.680424 (-0.469113) \| 0.033216 / 0.534201 (-0.500985) \| 0.473266 / 0.579283 (-0.106017) \| 0.581650 / 0.434364 (0.147286) \| 0.522442 / 0.540337 (-0.017895) \| 0.729039 / 1.386936 (-0.657897) \|\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) \| 0.008349 / 0.011353 (-0.003003) \| 0.005856 / 0.011008 (-0.005152) \| 0.077855 / 0.038508 (0.039347) \| 0.080608 / 0.023109 (0.057499) \| 0.512533 / 0.275898 (0.236635) \| 0.551862 / 0.323480 (0.228382) \| 0.007004 / 0.007986 (-0.000982) \| 0.004147 / 0.004328 (-0.000181) \| 0.086625 / 0.004250 (0.082374) \| 0.065962 / 0.037052 (0.028910) \| 0.545590 / 0.258489 (0.287101) \| 0.586313 / 0.293841 (0.292472) \| 0.048719 / 0.128546 (-0.079827) \| 0.014997 / 0.075646 (-0.060649) \| 0.089510 / 0.419271 (-0.329761) \| 0.060936 / 0.043533 (0.017404) \| 0.498455 / 0.255139 (0.243316) \| 0.535460 / 0.283200 (0.252260) \| 0.034624 / 0.141683 (-0.107059) \| 1.717401 / 1.452155 (0.265246) \| 1.808772 / 1.492716 (0.316056) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.379504 / 0.018006 (0.361497) \| 0.601756 / 0.000490 (0.601266) \| 0.061740 / 0.000200 (0.061540) \| 0.000497 / 0.000054 (0.000442) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031215 / 0.037411 (-0.006196) \| 0.097501 / 0.014526 (0.082975) \| 0.117434 / 0.176557 (-0.059122) \| 0.166014 / 0.737135 (-0.571121) \| 0.116466 / 0.296338 (-0.179873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.699444 / 0.215209 (0.484235) \| 6.329332 / 2.077655 (4.251678) \| 3.072812 / 1.504120 (1.568693) \| 2.729878 / 1.541195 (1.188683) \| 2.933785 / 1.468490 (1.465295) \| 0.935858 / 4.584777 (-3.648919) \| 5.532532 / 3.745712 (1.786820) \| 4.677139 / 5.269862 (-0.592722) \| 2.963527 / 4.565676 (-1.602149) \| 0.099661 / 0.424275 (-0.324614) \| 0.009095 / 0.007607 (0.001488) \| 0.751158 / 0.226044 (0.525114) \| 7.652588 / 2.268929 (5.383660) \| 3.802005 / 55.444624 (-51.642619) \| 3.163126 / 6.876477 (-3.713351) \| 3.401125 / 2.142072 (1.259052) \| 0.998627 / 4.805227 (-3.806600) \| 0.203310 / 6.500664 (-6.297354) \| 0.073827 / 0.075469 (-0.001642) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.662989 / 1.841788 (-0.178799) \| 23.777818 / 8.074308 (15.703510) \| 20.855378 / 10.191392 (10.663986) \| 0.279892 / 0.680424 (-0.400532) \| 0.029303 / 0.534201 (-0.504898) \| 0.473681 / 0.579283 (-0.105602) \| 0.579148 / 0.434364 (0.144784) \| 0.546931 / 0.540337 (0.006593) \| 0.769740 / 1.386936 (-0.617196) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#63114e9cb78fe02dc145f923dec13d545a8d0327 \"CML watermark\")\n" ]	2023-09-04T13:13:53Z	2023-09-04T14:58:34Z	2023-09-04T14:47:17Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6211.diff", "html_url": "https://github.com/huggingface/datasets/pull/6211", "merged_at": "2023-09-04T14:47:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/6211.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6211" }	If a split is empty, then the JSON split info should mention num_bytes = 0 and num_examples = 0. Until now they were omited because the JSON dumps ignore the fields that are equal to the default values. This is needed in datasets-server since we parse this information to the viewer	{ "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/6211/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/pull/6210	6,210	Temporarily pin fsspec < 2023.9.0	{ "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) \| 0.006494 / 0.011353 (-0.004859) \| 0.003896 / 0.011008 (-0.007112) \| 0.083940 / 0.038508 (0.045432) \| 0.068335 / 0.023109 (0.045225) \| 0.365770 / 0.275898 (0.089872) \| 0.403702 / 0.323480 (0.080222) \| 0.004005 / 0.007986 (-0.003981) \| 0.003276 / 0.004328 (-0.001052) \| 0.064877 / 0.004250 (0.060626) \| 0.053524 / 0.037052 (0.016472) \| 0.372951 / 0.258489 (0.114462) \| 0.420935 / 0.293841 (0.127094) \| 0.030656 / 0.128546 (-0.097890) \| 0.009048 / 0.075646 (-0.066599) \| 0.287607 / 0.419271 (-0.131665) \| 0.052042 / 0.043533 (0.008509) \| 0.371446 / 0.255139 (0.116307) \| 0.408781 / 0.283200 (0.125581) \| 0.024228 / 0.141683 (-0.117455) \| 1.483325 / 1.452155 (0.031170) \| 1.544321 / 1.492716 (0.051605) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212355 / 0.018006 (0.194349) \| 0.463298 / 0.000490 (0.462808) \| 0.005170 / 0.000200 (0.004970) \| 0.000087 / 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) \| 0.027824 / 0.037411 (-0.009587) \| 0.081880 / 0.014526 (0.067354) \| 0.094886 / 0.176557 (-0.081670) \| 0.150024 / 0.737135 (-0.587111) \| 0.096643 / 0.296338 (-0.199696) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388521 / 0.215209 (0.173312) \| 3.877251 / 2.077655 (1.799596) \| 1.931085 / 1.504120 (0.426965) \| 1.766525 / 1.541195 (0.225330) \| 1.814802 / 1.468490 (0.346312) \| 0.489478 / 4.584777 (-4.095299) \| 3.570973 / 3.745712 (-0.174739) \| 3.190211 / 5.269862 (-2.079651) \| 2.015670 / 4.565676 (-2.550006) \| 0.057773 / 0.424275 (-0.366503) \| 0.007611 / 0.007607 (0.000004) \| 0.462162 / 0.226044 (0.236117) \| 4.616173 / 2.268929 (2.347244) \| 2.360531 / 55.444624 (-53.084094) \| 2.053680 / 6.876477 (-4.822797) \| 2.228057 / 2.142072 (0.085985) \| 0.584921 / 4.805227 (-4.220306) \| 0.132470 / 6.500664 (-6.368194) \| 0.060482 / 0.075469 (-0.014987) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.263393 / 1.841788 (-0.578394) \| 19.416841 / 8.074308 (11.342532) \| 14.049032 / 10.191392 (3.857640) \| 0.162822 / 0.680424 (-0.517602) \| 0.018189 / 0.534201 (-0.516012) \| 0.391142 / 0.579283 (-0.188141) \| 0.409367 / 0.434364 (-0.024997) \| 0.454589 / 0.540337 (-0.085748) \| 0.632946 / 1.386936 (-0.753990) \|\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) \| 0.006568 / 0.011353 (-0.004785) \| 0.004026 / 0.011008 (-0.006982) \| 0.064522 / 0.038508 (0.026014) \| 0.071738 / 0.023109 (0.048629) \| 0.395771 / 0.275898 (0.119873) \| 0.421553 / 0.323480 (0.098073) \| 0.005291 / 0.007986 (-0.002694) \| 0.003266 / 0.004328 (-0.001063) \| 0.064464 / 0.004250 (0.060214) \| 0.054622 / 0.037052 (0.017569) \| 0.395010 / 0.258489 (0.136521) \| 0.433895 / 0.293841 (0.140054) \| 0.031670 / 0.128546 (-0.096876) \| 0.008536 / 0.075646 (-0.067111) \| 0.071059 / 0.419271 (-0.348212) \| 0.047117 / 0.043533 (0.003584) \| 0.391210 / 0.255139 (0.136071) \| 0.411685 / 0.283200 (0.128486) \| 0.022779 / 0.141683 (-0.118904) \| 1.479900 / 1.452155 (0.027746) \| 1.551853 / 1.492716 (0.059137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.332814 / 0.018006 (0.314807) \| 0.460654 / 0.000490 (0.460164) \| 0.062257 / 0.000200 (0.062057) \| 0.000374 / 0.000054 (0.000319) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031801 / 0.037411 (-0.005610) \| 0.090730 / 0.014526 (0.076204) \| 0.102955 / 0.176557 (-0.073602) \| 0.155928 / 0.737135 (-0.581207) \| 0.103028 / 0.296338 (-0.193310) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434460 / 0.215209 (0.219251) \| 4.331550 / 2.077655 (2.253895) \| 2.335990 / 1.504120 (0.831870) \| 2.183985 / 1.541195 (0.642790) \| 2.233086 / 1.468490 (0.764595) \| 0.488484 / 4.584777 (-4.096293) \| 3.603856 / 3.745712 (-0.141856) \| 3.229833 / 5.269862 (-2.040029) \| 2.007366 / 4.565676 (-2.558311) \| 0.057658 / 0.424275 (-0.366617) \| 0.007339 / 0.007607 (-0.000268) \| 0.512812 / 0.226044 (0.286768) \| 5.141497 / 2.268929 (2.872569) \| 2.847383 / 55.444624 (-52.597241) \| 2.467010 / 6.876477 (-4.409467) \| 2.644995 / 2.142072 (0.502923) \| 0.581385 / 4.805227 (-4.223842) \| 0.130755 / 6.500664 (-6.369909) \| 0.058834 / 0.075469 (-0.016635) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350162 / 1.841788 (-0.491626) \| 19.768412 / 8.074308 (11.694104) \| 15.079196 / 10.191392 (4.887804) \| 0.167083 / 0.680424 (-0.513341) \| 0.020372 / 0.534201 (-0.513829) \| 0.402685 / 0.579283 (-0.176598) \| 0.408338 / 0.434364 (-0.026026) \| 0.476788 / 0.540337 (-0.063550) \| 0.654765 / 1.386936 (-0.732171) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ff803c7e9f256c5a137c25c090e18d844f9fc6e4 \"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) \| 0.008047 / 0.011353 (-0.003305) \| 0.004662 / 0.011008 (-0.006346) \| 0.102487 / 0.038508 (0.063978) \| 0.096832 / 0.023109 (0.073723) \| 0.375298 / 0.275898 (0.099400) \| 0.420604 / 0.323480 (0.097124) \| 0.004655 / 0.007986 (-0.003330) \| 0.005699 / 0.004328 (0.001370) \| 0.077681 / 0.004250 (0.073430) \| 0.065987 / 0.037052 (0.028935) \| 0.393146 / 0.258489 (0.134657) \| 0.436324 / 0.293841 (0.142483) \| 0.036168 / 0.128546 (-0.092378) \| 0.010398 / 0.075646 (-0.065248) \| 0.347579 / 0.419271 (-0.071693) \| 0.061723 / 0.043533 (0.018190) \| 0.377439 / 0.255139 (0.122300) \| 0.416666 / 0.283200 (0.133467) \| 0.031874 / 0.141683 (-0.109809) \| 1.818885 / 1.452155 (0.366730) \| 1.904749 / 1.492716 (0.412032) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240497 / 0.018006 (0.222491) \| 0.507907 / 0.000490 (0.507417) \| 0.004574 / 0.000200 (0.004374) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033504 / 0.037411 (-0.003907) \| 0.102919 / 0.014526 (0.088393) \| 0.113014 / 0.176557 (-0.063543) \| 0.181111 / 0.737135 (-0.556024) \| 0.115047 / 0.296338 (-0.181291) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453640 / 0.215209 (0.238431) \| 4.514604 / 2.077655 (2.436949) \| 2.219758 / 1.504120 (0.715638) \| 2.004735 / 1.541195 (0.463541) \| 2.112817 / 1.468490 (0.644327) \| 0.579534 / 4.584777 (-4.005243) \| 4.095994 / 3.745712 (0.350282) \| 3.887204 / 5.269862 (-1.382658) \| 2.461755 / 4.565676 (-2.103921) \| 0.068930 / 0.424275 (-0.355345) \| 0.009102 / 0.007607 (0.001495) \| 0.540031 / 0.226044 (0.313987) \| 5.394324 / 2.268929 (3.125396) \| 2.738906 / 55.444624 (-52.705719) \| 2.332041 / 6.876477 (-4.544436) \| 2.600764 / 2.142072 (0.458692) \| 0.697859 / 4.805227 (-4.107368) \| 0.159247 / 6.500664 (-6.341417) \| 0.073339 / 0.075469 (-0.002130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.561082 / 1.841788 (-0.280706) \| 23.581031 / 8.074308 (15.506723) \| 17.011085 / 10.191392 (6.819693) \| 0.196115 / 0.680424 (-0.484308) \| 0.022050 / 0.534201 (-0.512151) \| 0.470865 / 0.579283 (-0.108418) \| 0.480539 / 0.434364 (0.046175) \| 0.546458 / 0.540337 (0.006120) \| 0.744353 / 1.386936 (-0.642583) \|\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) \| 0.007884 / 0.011353 (-0.003468) \| 0.004723 / 0.011008 (-0.006286) \| 0.076431 / 0.038508 (0.037923) \| 0.087016 / 0.023109 (0.063907) \| 0.501880 / 0.275898 (0.225982) \| 0.546286 / 0.323480 (0.222806) \| 0.006224 / 0.007986 (-0.001762) \| 0.003858 / 0.004328 (-0.000471) \| 0.076485 / 0.004250 (0.072234) \| 0.066758 / 0.037052 (0.029706) \| 0.510090 / 0.258489 (0.251601) \| 0.553935 / 0.293841 (0.260094) \| 0.037785 / 0.128546 (-0.090761) \| 0.009946 / 0.075646 (-0.065700) \| 0.084001 / 0.419271 (-0.335270) \| 0.056732 / 0.043533 (0.013199) \| 0.490724 / 0.255139 (0.235585) \| 0.528367 / 0.283200 (0.245168) \| 0.026082 / 0.141683 (-0.115601) \| 1.769200 / 1.452155 (0.317045) \| 1.847559 / 1.492716 (0.354843) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306752 / 0.018006 (0.288745) \| 0.481215 / 0.000490 (0.480725) \| 0.048231 / 0.000200 (0.048031) \| 0.000249 / 0.000054 (0.000194) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039517 / 0.037411 (0.002106) \| 0.112884 / 0.014526 (0.098359) \| 0.123858 / 0.176557 (-0.052698) \| 0.188260 / 0.737135 (-0.548875) \| 0.125819 / 0.296338 (-0.170520) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515260 / 0.215209 (0.300051) \| 5.125038 / 2.077655 (3.047383) \| 2.785122 / 1.504120 (1.281003) \| 2.590753 / 1.541195 (1.049558) \| 2.682084 / 1.468490 (1.213594) \| 0.581162 / 4.584777 (-4.003615) \| 4.241776 / 3.745712 (0.496063) \| 3.860979 / 5.269862 (-1.408883) \| 2.434203 / 4.565676 (-2.131473) \| 0.068580 / 0.424275 (-0.355695) \| 0.008700 / 0.007607 (0.001093) \| 0.604712 / 0.226044 (0.378667) \| 6.044240 / 2.268929 (3.775311) \| 3.379734 / 55.444624 (-52.064890) \| 2.968906 / 6.876477 (-3.907571) \| 3.195775 / 2.142072 (1.053703) \| 0.702431 / 4.805227 (-4.102796) \| 0.158752 / 6.500664 (-6.341912) \| 0.072795 / 0.075469 (-0.002674) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.616354 / 1.841788 (-0.225434) \| 24.258731 / 8.074308 (16.184423) \| 17.505483 / 10.191392 (7.314091) \| 0.173445 / 0.680424 (-0.506979) \| 0.023215 / 0.534201 (-0.510986) \| 0.472975 / 0.579283 (-0.106308) \| 0.478425 / 0.434364 (0.044061) \| 0.566950 / 0.540337 (0.026612) \| 0.767648 / 1.386936 (-0.619288) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1d520a5226268f2c6f0303de3e8bfd72198b082 \"CML watermark\")\n" ]	2023-09-04T07:07:07Z	2023-09-04T07:40:23Z	2023-09-04T07:30:00Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6210.diff", "html_url": "https://github.com/huggingface/datasets/pull/6210", "merged_at": "2023-09-04T07:30:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6210.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6210" }	Temporarily pin fsspec < 2023.9.0 until permanent solution is found. Hot fix #6209.	{ "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/6210/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6209	6,209	CI is broken with AssertionError: 3 failed, 12 errors	{ "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	[]	2023-09-04T06:47:05Z	2023-09-04T07:30:01Z	2023-09-04T07:30:01Z	MEMBER	null	null	Our CI is broken: 3 failed, 12 errors See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041 ``` =========================== short test summary info ============================ FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1) + where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt']) FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10 + where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10 ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv']) = 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) = ```	{ "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/6209/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/6208	6,208	Do not filter out .zip extensions from no-script datasets	{ "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) \| 0.006797 / 0.011353 (-0.004556) \| 0.003966 / 0.011008 (-0.007042) \| 0.085296 / 0.038508 (0.046788) \| 0.076873 / 0.023109 (0.053764) \| 0.355795 / 0.275898 (0.079897) \| 0.397132 / 0.323480 (0.073652) \| 0.005325 / 0.007986 (-0.002660) \| 0.003343 / 0.004328 (-0.000986) \| 0.064966 / 0.004250 (0.060716) \| 0.054519 / 0.037052 (0.017467) \| 0.357864 / 0.258489 (0.099374) \| 0.409238 / 0.293841 (0.115397) \| 0.031620 / 0.128546 (-0.096926) \| 0.008529 / 0.075646 (-0.067117) \| 0.288502 / 0.419271 (-0.130769) \| 0.053260 / 0.043533 (0.009728) \| 0.355245 / 0.255139 (0.100106) \| 0.384139 / 0.283200 (0.100939) \| 0.024507 / 0.141683 (-0.117176) \| 1.494696 / 1.452155 (0.042541) \| 1.579847 / 1.492716 (0.087130) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204011 / 0.018006 (0.186005) \| 0.451729 / 0.000490 (0.451239) \| 0.004628 / 0.000200 (0.004428) \| 0.000081 / 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) \| 0.028342 / 0.037411 (-0.009069) \| 0.084647 / 0.014526 (0.070121) \| 0.096174 / 0.176557 (-0.080383) \| 0.151753 / 0.737135 (-0.585382) \| 0.096347 / 0.296338 (-0.199991) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.387179 / 0.215209 (0.171970) \| 3.861552 / 2.077655 (1.783898) \| 1.844033 / 1.504120 (0.339913) \| 1.678811 / 1.541195 (0.137616) \| 1.793207 / 1.468490 (0.324717) \| 0.485836 / 4.584777 (-4.098941) \| 3.566274 / 3.745712 (-0.179438) \| 3.269888 / 5.269862 (-1.999974) \| 2.042850 / 4.565676 (-2.522827) \| 0.057088 / 0.424275 (-0.367187) \| 0.007627 / 0.007607 (0.000019) \| 0.460510 / 0.226044 (0.234465) \| 4.602019 / 2.268929 (2.333090) \| 2.390984 / 55.444624 (-53.053641) \| 1.976150 / 6.876477 (-4.900327) \| 2.193394 / 2.142072 (0.051322) \| 0.582775 / 4.805227 (-4.222453) \| 0.133408 / 6.500664 (-6.367256) \| 0.060577 / 0.075469 (-0.014893) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.248505 / 1.841788 (-0.593283) \| 19.771301 / 8.074308 (11.696993) \| 14.327871 / 10.191392 (4.136479) \| 0.155288 / 0.680424 (-0.525136) \| 0.018310 / 0.534201 (-0.515891) \| 0.393664 / 0.579283 (-0.185619) \| 0.410578 / 0.434364 (-0.023786) \| 0.459301 / 0.540337 (-0.081037) \| 0.631921 / 1.386936 (-0.755015) \|\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) \| 0.006827 / 0.011353 (-0.004526) \| 0.004094 / 0.011008 (-0.006915) \| 0.065299 / 0.038508 (0.026791) \| 0.079496 / 0.023109 (0.056387) \| 0.403661 / 0.275898 (0.127763) \| 0.434449 / 0.323480 (0.110969) \| 0.005398 / 0.007986 (-0.002588) \| 0.003410 / 0.004328 (-0.000919) \| 0.064832 / 0.004250 (0.060582) \| 0.056303 / 0.037052 (0.019250) \| 0.397848 / 0.258489 (0.139359) \| 0.438244 / 0.293841 (0.144403) \| 0.032637 / 0.128546 (-0.095909) \| 0.008584 / 0.075646 (-0.067063) \| 0.071406 / 0.419271 (-0.347866) \| 0.048265 / 0.043533 (0.004732) \| 0.397814 / 0.255139 (0.142675) \| 0.421601 / 0.283200 (0.138402) \| 0.023815 / 0.141683 (-0.117868) \| 1.504814 / 1.452155 (0.052659) \| 1.577185 / 1.492716 (0.084469) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231775 / 0.018006 (0.213769) \| 0.445437 / 0.000490 (0.444948) \| 0.005252 / 0.000200 (0.005052) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032777 / 0.037411 (-0.004634) \| 0.095054 / 0.014526 (0.080528) \| 0.106429 / 0.176557 (-0.070127) \| 0.160111 / 0.737135 (-0.577024) \| 0.108075 / 0.296338 (-0.188263) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426034 / 0.215209 (0.210825) \| 4.244668 / 2.077655 (2.167013) \| 2.257938 / 1.504120 (0.753818) \| 2.087993 / 1.541195 (0.546798) \| 2.170878 / 1.468490 (0.702387) \| 0.485228 / 4.584777 (-4.099549) \| 3.725912 / 3.745712 (-0.019800) \| 3.286925 / 5.269862 (-1.982937) \| 2.059929 / 4.565676 (-2.505748) \| 0.057813 / 0.424275 (-0.366462) \| 0.007518 / 0.007607 (-0.000089) \| 0.506632 / 0.226044 (0.280588) \| 5.048340 / 2.268929 (2.779411) \| 2.744756 / 55.444624 (-52.699869) \| 2.406636 / 6.876477 (-4.469841) \| 2.617552 / 2.142072 (0.475480) \| 0.588476 / 4.805227 (-4.216751) \| 0.133518 / 6.500664 (-6.367146) \| 0.060778 / 0.075469 (-0.014691) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356416 / 1.841788 (-0.485372) \| 20.467516 / 8.074308 (12.393208) \| 15.265443 / 10.191392 (5.074051) \| 0.169201 / 0.680424 (-0.511223) \| 0.020087 / 0.534201 (-0.514114) \| 0.402332 / 0.579283 (-0.176951) \| 0.414848 / 0.434364 (-0.019516) \| 0.470422 / 0.540337 (-0.069916) \| 0.647266 / 1.386936 (-0.739670) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eb001b4cee7f1d71e393c3ad489a8a5cd8119df5 \"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) \| 0.005804 / 0.011353 (-0.005549) \| 0.003519 / 0.011008 (-0.007489) \| 0.080003 / 0.038508 (0.041495) \| 0.055419 / 0.023109 (0.032309) \| 0.395254 / 0.275898 (0.119356) \| 0.432714 / 0.323480 (0.109234) \| 0.004438 / 0.007986 (-0.003548) \| 0.002832 / 0.004328 (-0.001496) \| 0.062026 / 0.004250 (0.057775) \| 0.044334 / 0.037052 (0.007282) \| 0.401278 / 0.258489 (0.142789) \| 0.451516 / 0.293841 (0.157675) \| 0.026791 / 0.128546 (-0.101755) \| 0.007946 / 0.075646 (-0.067700) \| 0.265166 / 0.419271 (-0.154106) \| 0.044119 / 0.043533 (0.000586) \| 0.399621 / 0.255139 (0.144482) \| 0.422808 / 0.283200 (0.139609) \| 0.019998 / 0.141683 (-0.121685) \| 1.433559 / 1.452155 (-0.018596) \| 1.596902 / 1.492716 (0.104186) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195662 / 0.018006 (0.177656) \| 0.423167 / 0.000490 (0.422677) \| 0.003426 / 0.000200 (0.003227) \| 0.000066 / 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) \| 0.023318 / 0.037411 (-0.014094) \| 0.072532 / 0.014526 (0.058006) \| 0.082181 / 0.176557 (-0.094375) \| 0.142214 / 0.737135 (-0.594921) \| 0.083423 / 0.296338 (-0.212915) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402270 / 0.215209 (0.187061) \| 4.027607 / 2.077655 (1.949953) \| 2.059803 / 1.504120 (0.555684) \| 1.865115 / 1.541195 (0.323920) \| 1.934976 / 1.468490 (0.466485) \| 0.502145 / 4.584777 (-4.082632) \| 2.970865 / 3.745712 (-0.774847) \| 2.784155 / 5.269862 (-2.485707) \| 1.822003 / 4.565676 (-2.743673) \| 0.057699 / 0.424275 (-0.366576) \| 0.006668 / 0.007607 (-0.000939) \| 0.471164 / 0.226044 (0.245120) \| 4.733079 / 2.268929 (2.464150) \| 2.445119 / 55.444624 (-52.999505) \| 2.132956 / 6.876477 (-4.743521) \| 2.335998 / 2.142072 (0.193926) \| 0.594881 / 4.805227 (-4.210347) \| 0.125801 / 6.500664 (-6.374863) \| 0.060780 / 0.075469 (-0.014689) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.233170 / 1.841788 (-0.608618) \| 17.942205 / 8.074308 (9.867897) \| 13.587020 / 10.191392 (3.395628) \| 0.142110 / 0.680424 (-0.538314) \| 0.016600 / 0.534201 (-0.517601) \| 0.328659 / 0.579283 (-0.250624) \| 0.347759 / 0.434364 (-0.086605) \| 0.378651 / 0.540337 (-0.161687) \| 0.523474 / 1.386936 (-0.863462) \|\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) \| 0.006028 / 0.011353 (-0.005325) \| 0.003552 / 0.011008 (-0.007456) \| 0.062175 / 0.038508 (0.023667) \| 0.057602 / 0.023109 (0.034493) \| 0.444585 / 0.275898 (0.168687) \| 0.471238 / 0.323480 (0.147758) \| 0.004562 / 0.007986 (-0.003423) \| 0.002871 / 0.004328 (-0.001457) \| 0.063101 / 0.004250 (0.058851) \| 0.046072 / 0.037052 (0.009020) \| 0.448253 / 0.258489 (0.189764) \| 0.478734 / 0.293841 (0.184893) \| 0.028463 / 0.128546 (-0.100084) \| 0.008090 / 0.075646 (-0.067557) \| 0.068142 / 0.419271 (-0.351130) \| 0.040517 / 0.043533 (-0.003016) \| 0.447145 / 0.255139 (0.192006) \| 0.469472 / 0.283200 (0.186273) \| 0.019391 / 0.141683 (-0.122291) \| 1.471195 / 1.452155 (0.019040) \| 1.532966 / 1.492716 (0.040249) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259894 / 0.018006 (0.241888) \| 0.412987 / 0.000490 (0.412497) \| 0.020780 / 0.000200 (0.020580) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026352 / 0.037411 (-0.011060) \| 0.080024 / 0.014526 (0.065498) \| 0.088041 / 0.176557 (-0.088516) \| 0.142987 / 0.737135 (-0.594148) \| 0.090108 / 0.296338 (-0.206231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458874 / 0.215209 (0.243665) \| 4.573005 / 2.077655 (2.495351) \| 2.507885 / 1.504120 (1.003765) \| 2.335432 / 1.541195 (0.794238) \| 2.379617 / 1.468490 (0.911126) \| 0.503331 / 4.584777 (-4.081446) \| 3.078284 / 3.745712 (-0.667428) \| 2.750580 / 5.269862 (-2.519282) \| 1.828100 / 4.565676 (-2.737577) \| 0.057572 / 0.424275 (-0.366703) \| 0.006553 / 0.007607 (-0.001054) \| 0.532283 / 0.226044 (0.306239) \| 5.310584 / 2.268929 (3.041656) \| 2.943559 / 55.444624 (-52.501065) \| 2.587544 / 6.876477 (-4.288932) \| 2.718261 / 2.142072 (0.576188) \| 0.590267 / 4.805227 (-4.214961) \| 0.123229 / 6.500664 (-6.377435) \| 0.060219 / 0.075469 (-0.015250) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.340773 / 1.841788 (-0.501014) \| 18.420766 / 8.074308 (10.346458) \| 14.630550 / 10.191392 (4.439158) \| 0.146666 / 0.680424 (-0.533758) \| 0.017905 / 0.534201 (-0.516296) \| 0.332483 / 0.579283 (-0.246801) \| 0.355490 / 0.434364 (-0.078874) \| 0.382618 / 0.540337 (-0.157720) \| 0.531336 / 1.386936 (-0.855600) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d438617fc577bc0222527714edafea0c52ebf239 \"CML watermark\")\n", "There were CI errors unrelated to this PR.", "<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) \| 0.008702 / 0.011353 (-0.002651) \| 0.005060 / 0.011008 (-0.005948) \| 0.097017 / 0.038508 (0.058509) \| 0.073740 / 0.023109 (0.050631) \| 0.435138 / 0.275898 (0.159240) \| 0.512776 / 0.323480 (0.189296) \| 0.006186 / 0.007986 (-0.001800) \| 0.003970 / 0.004328 (-0.000358) \| 0.089523 / 0.004250 (0.085273) \| 0.054441 / 0.037052 (0.017389) \| 0.447415 / 0.258489 (0.188926) \| 0.464851 / 0.293841 (0.171010) \| 0.050264 / 0.128546 (-0.078283) \| 0.016643 / 0.075646 (-0.059004) \| 0.350565 / 0.419271 (-0.068707) \| 0.071220 / 0.043533 (0.027687) \| 0.432531 / 0.255139 (0.177392) \| 0.472994 / 0.283200 (0.189795) \| 0.040229 / 0.141683 (-0.101454) \| 1.743431 / 1.452155 (0.291276) \| 1.778653 / 1.492716 (0.285936) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261992 / 0.018006 (0.243986) \| 0.571979 / 0.000490 (0.571489) \| 0.006270 / 0.000200 (0.006071) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027821 / 0.037411 (-0.009590) \| 0.081874 / 0.014526 (0.067348) \| 0.103725 / 0.176557 (-0.072831) \| 0.170593 / 0.737135 (-0.566542) \| 0.108749 / 0.296338 (-0.187590) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.690774 / 0.215209 (0.475565) \| 6.770902 / 2.077655 (4.693247) \| 2.887218 / 1.504120 (1.383098) \| 2.456226 / 1.541195 (0.915032) \| 2.509422 / 1.468490 (1.040932) \| 0.768451 / 4.584777 (-3.816326) \| 4.988933 / 3.745712 (1.243221) \| 4.151460 / 5.269862 (-1.118402) \| 2.640472 / 4.565676 (-1.925205) \| 0.093522 / 0.424275 (-0.330753) \| 0.008614 / 0.007607 (0.001007) \| 0.696281 / 0.226044 (0.470237) \| 6.721077 / 2.268929 (4.452149) \| 3.229760 / 55.444624 (-52.214864) \| 2.668521 / 6.876477 (-4.207956) \| 2.866420 / 2.142072 (0.724347) \| 0.945328 / 4.805227 (-3.859899) \| 0.197645 / 6.500664 (-6.303019) \| 0.074442 / 0.075469 (-0.001027) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.630468 / 1.841788 (-0.211320) \| 22.991661 / 8.074308 (14.917353) \| 19.816919 / 10.191392 (9.625527) \| 0.257410 / 0.680424 (-0.423014) \| 0.027228 / 0.534201 (-0.506973) \| 0.444515 / 0.579283 (-0.134768) \| 0.597067 / 0.434364 (0.162703) \| 0.528151 / 0.540337 (-0.012186) \| 0.771276 / 1.386936 (-0.615660) \|\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) \| 0.009154 / 0.011353 (-0.002199) \| 0.004648 / 0.011008 (-0.006360) \| 0.073054 / 0.038508 (0.034546) \| 0.077146 / 0.023109 (0.054037) \| 0.481659 / 0.275898 (0.205761) \| 0.516985 / 0.323480 (0.193505) \| 0.007447 / 0.007986 (-0.000538) \| 0.003890 / 0.004328 (-0.000438) \| 0.078701 / 0.004250 (0.074450) \| 0.059183 / 0.037052 (0.022131) \| 0.475350 / 0.258489 (0.216861) \| 0.547834 / 0.293841 (0.253993) \| 0.058440 / 0.128546 (-0.070106) \| 0.013563 / 0.075646 (-0.062083) \| 0.084320 / 0.419271 (-0.334951) \| 0.065965 / 0.043533 (0.022433) \| 0.483541 / 0.255139 (0.228402) \| 0.513940 / 0.283200 (0.230740) \| 0.042889 / 0.141683 (-0.098794) \| 1.676050 / 1.452155 (0.223895) \| 1.759206 / 1.492716 (0.266489) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274848 / 0.018006 (0.256841) \| 0.588965 / 0.000490 (0.588475) \| 0.006312 / 0.000200 (0.006112) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033871 / 0.037411 (-0.003540) \| 0.104013 / 0.014526 (0.089487) \| 0.118457 / 0.176557 (-0.058099) \| 0.178268 / 0.737135 (-0.558868) \| 0.116972 / 0.296338 (-0.179366) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.609952 / 0.215209 (0.394743) \| 5.788754 / 2.077655 (3.711100) \| 2.812166 / 1.504120 (1.308046) \| 2.362861 / 1.541195 (0.821666) \| 2.641295 / 1.468490 (1.172804) \| 0.767601 / 4.584777 (-3.817176) \| 5.027439 / 3.745712 (1.281727) \| 4.612511 / 5.269862 (-0.657351) \| 2.654364 / 4.565676 (-1.911312) \| 0.103100 / 0.424275 (-0.321175) \| 0.012233 / 0.007607 (0.004626) \| 0.749283 / 0.226044 (0.523238) \| 7.511093 / 2.268929 (5.242165) \| 3.585867 / 55.444624 (-51.858757) \| 3.255110 / 6.876477 (-3.621366) \| 3.260174 / 2.142072 (1.118102) \| 0.958422 / 4.805227 (-3.846806) \| 0.209096 / 6.500664 (-6.291568) \| 0.075014 / 0.075469 (-0.000455) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.728283 / 1.841788 (-0.113504) \| 25.411147 / 8.074308 (17.336839) \| 21.335202 / 10.191392 (11.143810) \| 0.199090 / 0.680424 (-0.481334) \| 0.031288 / 0.534201 (-0.502913) \| 0.449226 / 0.579283 (-0.130057) \| 0.555570 / 0.434364 (0.121206) \| 0.570297 / 0.540337 (0.029960) \| 0.758673 / 1.386936 (-0.628263) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa696b4b4f0d11c5b8592eb31cb1d54a707e3d33 \"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) \| 0.006862 / 0.011353 (-0.004491) \| 0.003959 / 0.011008 (-0.007049) \| 0.087219 / 0.038508 (0.048711) \| 0.078335 / 0.023109 (0.055226) \| 0.319019 / 0.275898 (0.043121) \| 0.342871 / 0.323480 (0.019391) \| 0.004065 / 0.007986 (-0.003921) \| 0.004346 / 0.004328 (0.000017) \| 0.065243 / 0.004250 (0.060993) \| 0.056698 / 0.037052 (0.019646) \| 0.326906 / 0.258489 (0.068417) \| 0.354323 / 0.293841 (0.060482) \| 0.031252 / 0.128546 (-0.097295) \| 0.008587 / 0.075646 (-0.067060) \| 0.300323 / 0.419271 (-0.118948) \| 0.052810 / 0.043533 (0.009277) \| 0.323866 / 0.255139 (0.068727) \| 0.346011 / 0.283200 (0.062811) \| 0.025584 / 0.141683 (-0.116099) \| 1.464475 / 1.452155 (0.012320) \| 1.530868 / 1.492716 (0.038152) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208927 / 0.018006 (0.190921) \| 0.454147 / 0.000490 (0.453657) \| 0.003945 / 0.000200 (0.003746) \| 0.000081 / 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) \| 0.029901 / 0.037411 (-0.007511) \| 0.088889 / 0.014526 (0.074363) \| 0.098181 / 0.176557 (-0.078375) \| 0.156787 / 0.737135 (-0.580349) \| 0.099015 / 0.296338 (-0.197324) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384981 / 0.215209 (0.169772) \| 3.831040 / 2.077655 (1.753386) \| 1.858312 / 1.504120 (0.354192) \| 1.686846 / 1.541195 (0.145651) \| 1.771509 / 1.468490 (0.303019) \| 0.485618 / 4.584777 (-4.099159) \| 3.430961 / 3.745712 (-0.314751) \| 3.264489 / 5.269862 (-2.005372) \| 2.040125 / 4.565676 (-2.525551) \| 0.057218 / 0.424275 (-0.367057) \| 0.007640 / 0.007607 (0.000033) \| 0.468072 / 0.226044 (0.242027) \| 4.677214 / 2.268929 (2.408286) \| 2.348425 / 55.444624 (-53.096199) \| 1.994352 / 6.876477 (-4.882125) \| 2.217020 / 2.142072 (0.074948) \| 0.587467 / 4.805227 (-4.217760) \| 0.133550 / 6.500664 (-6.367114) \| 0.060571 / 0.075469 (-0.014898) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.271003 / 1.841788 (-0.570785) \| 19.986365 / 8.074308 (11.912057) \| 14.574046 / 10.191392 (4.382654) \| 0.146212 / 0.680424 (-0.534212) \| 0.018320 / 0.534201 (-0.515881) \| 0.394524 / 0.579283 (-0.184759) \| 0.399707 / 0.434364 (-0.034657) \| 0.458965 / 0.540337 (-0.081372) \| 0.619940 / 1.386936 (-0.766996) \|\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) \| 0.006982 / 0.011353 (-0.004371) \| 0.004061 / 0.011008 (-0.006947) \| 0.064520 / 0.038508 (0.026012) \| 0.076828 / 0.023109 (0.053719) \| 0.402989 / 0.275898 (0.127090) \| 0.439697 / 0.323480 (0.116217) \| 0.005511 / 0.007986 (-0.002475) \| 0.003378 / 0.004328 (-0.000950) \| 0.064727 / 0.004250 (0.060477) \| 0.058114 / 0.037052 (0.021062) \| 0.402054 / 0.258489 (0.143565) \| 0.442377 / 0.293841 (0.148536) \| 0.032808 / 0.128546 (-0.095738) \| 0.008604 / 0.075646 (-0.067043) \| 0.070994 / 0.419271 (-0.348278) \| 0.048738 / 0.043533 (0.005205) \| 0.399786 / 0.255139 (0.144647) \| 0.423537 / 0.283200 (0.140338) \| 0.022397 / 0.141683 (-0.119286) \| 1.504613 / 1.452155 (0.052458) \| 1.571064 / 1.492716 (0.078348) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226876 / 0.018006 (0.208870) \| 0.451477 / 0.000490 (0.450987) \| 0.004511 / 0.000200 (0.004311) \| 0.000095 / 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) \| 0.032998 / 0.037411 (-0.004413) \| 0.095843 / 0.014526 (0.081317) \| 0.105684 / 0.176557 (-0.070873) \| 0.158175 / 0.737135 (-0.578960) \| 0.107297 / 0.296338 (-0.189041) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434912 / 0.215209 (0.219703) \| 4.326394 / 2.077655 (2.248740) \| 2.287310 / 1.504120 (0.783190) \| 2.127987 / 1.541195 (0.586793) \| 2.202485 / 1.468490 (0.733995) \| 0.494305 / 4.584777 (-4.090472) \| 3.575176 / 3.745712 (-0.170536) \| 3.354358 / 5.269862 (-1.915504) \| 2.074293 / 4.565676 (-2.491383) \| 0.058967 / 0.424275 (-0.365308) \| 0.007712 / 0.007607 (0.000105) \| 0.513734 / 0.226044 (0.287690) \| 5.107538 / 2.268929 (2.838610) \| 2.776190 / 55.444624 (-52.668434) \| 2.425051 / 6.876477 (-4.451426) \| 2.666715 / 2.142072 (0.524643) \| 0.598844 / 4.805227 (-4.206383) \| 0.134186 / 6.500664 (-6.366478) \| 0.062403 / 0.075469 (-0.013066) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346730 / 1.841788 (-0.495058) \| 20.533190 / 8.074308 (12.458882) \| 15.174443 / 10.191392 (4.983051) \| 0.167204 / 0.680424 (-0.513219) \| 0.020619 / 0.534201 (-0.513582) \| 0.399033 / 0.579283 (-0.180250) \| 0.394428 / 0.434364 (-0.039936) \| 0.468792 / 0.540337 (-0.071545) \| 0.640122 / 1.386936 (-0.746814) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2c4c2b529e2a262a5006e4caa55fbc003378006a \"CML watermark\")\n" ]	2023-09-04T06:07:12Z	2023-09-04T09:22:19Z	2023-09-04T09:13:32Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6208.diff", "html_url": "https://github.com/huggingface/datasets/pull/6208", "merged_at": "2023-09-04T09:13:32Z", "patch_url": "https://github.com/huggingface/datasets/pull/6208.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6208" }	This PR is a hotfix of: - #6207 That PR introduced the filtering out of `.zip` extensions. This PR reverts that. Hot fix #6207. Maybe we should do patch releases: the bug was introduced in 2.13.1. CC: @lhoestq	{ "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/6208/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6207	6,207	No-script datasets with ZIP files do not load	{ "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	[]	2023-09-04T05:50:27Z	2023-09-04T09:13:33Z	2023-09-04T09:13:33Z	MEMBER	null	null	While investigating an issue on a Hub dataset, I have discovered the no-script datasets containing ZIP files do not load. For example, that no-script dataset containing ZIP files, raises NonMatchingSplitsSizesError: ```python In [2]: ds = load_dataset("sidovic/LearningQ-qg") NonMatchingSplitsSizesError: [ { 'expected': SplitInfo(name='train', num_bytes=0, num_examples=188660, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') }, { 'expected': SplitInfo(name='validation', num_bytes=0, num_examples=20630, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') }, { 'expected': SplitInfo(name='test', num_bytes=0, num_examples=18227, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') } ] ``` As another example, a no-script dataset containing just a (CSV)-ZIP file, raises a DatasetGenerationError: ``` > num_examples, num_bytes = writer.finalize() src/datasets/builder.py:1949: > raise SchemaInferenceError("Please pass `features` or at least one example when writing data") E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data src/datasets/arrow_writer.py:598: SchemaInferenceError The above exception was the direct cause of the following exception: src/datasets/load.py:2143: in load_dataset builder_instance.download_and_prepare( src/datasets/builder.py:954: in download_and_prepare self._download_and_prepare( src/datasets/builder.py:1049: in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) src/datasets/builder.py:1813: in _prepare_split for job_id, done, content in self._prepare_split_single( > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset src/datasets/builder.py:1958: DatasetGenerationError ``` After investigating, I think this bug was introduced in this PR: - #5972 Related to: - https://huggingface.co/datasets/sidovic/LearningQ-qg/discussions/1 CC: @lhoestq	{ "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/6207/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/6206	6,206	When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays	{ "avatar_url": "https://avatars.githubusercontent.com/u/51043929?v=4", "events_url": "https://api.github.com/users/aihao2000/events{/privacy}", "followers_url": "https://api.github.com/users/aihao2000/followers", "following_url": "https://api.github.com/users/aihao2000/following{/other_user}", "gists_url": "https://api.github.com/users/aihao2000/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aihao2000", "id": 51043929, "login": "aihao2000", "node_id": "MDQ6VXNlcjUxMDQzOTI5", "organizations_url": "https://api.github.com/users/aihao2000/orgs", "received_events_url": "https://api.github.com/users/aihao2000/received_events", "repos_url": "https://api.github.com/users/aihao2000/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aihao2000/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aihao2000/subscriptions", "type": "User", "url": "https://api.github.com/users/aihao2000", "user_view_type": "public" }	[]	closed	false	[ "I solved the problem by modifying the \"self DEFAULT_WRITER_BATCH_SIZE\" in \"class MyDataset (datasets. GeneratorBasedBuilder) : __init__\"", "same problem, and this solution worked me also - you can set this var by setting the keyword argument `writer_batch_size=...` in `load_dataset(...,writer_batch_size=...)`" ]	2023-09-04T04:14:00Z	2024-04-17T15:53:29Z	2023-09-04T06:05:49Z	NONE	null	null	### Describe the bug When calling load_dataset, raise error ``` Traceback (most recent call last): File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1694, in _pre pare_split_single writer.write(example, key) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 559, in write_batch self.write_table(pa_table, writer_batch_size) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 571, in write_table pa_table = pa_table.combine_chunks() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/table.pxi", line 3439, in pyarrow.lib.Table.combine_chunks 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: offset overflow while concatenating arrays The above exception was the direct cause of the following exception: Traceback (most recent call last): dataset = load_dataset( ^^^^^^^^^^^^^ File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_da taset builder_instance.download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 954, in downl oad_and_prepare self._download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _dow nload_and_prepare super()._download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _dow nload_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _pre pare_split for job_id, done, content in self._prepare_split_single( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _pre pare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard. 09/04/2023 12:02:04 - WARNING - datasets.builder - Setting num_proc from 8 back to 1 for the train split to dis able multiprocessing as it only contains one shard. ``` ### Steps to reproduce the bug Call load_dataset with the large image as feature ### Expected behavior no error ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/51043929?v=4", "events_url": "https://api.github.com/users/aihao2000/events{/privacy}", "followers_url": "https://api.github.com/users/aihao2000/followers", "following_url": "https://api.github.com/users/aihao2000/following{/other_user}", "gists_url": "https://api.github.com/users/aihao2000/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aihao2000", "id": 51043929, "login": "aihao2000", "node_id": "MDQ6VXNlcjUxMDQzOTI5", "organizations_url": "https://api.github.com/users/aihao2000/orgs", "received_events_url": "https://api.github.com/users/aihao2000/received_events", "repos_url": "https://api.github.com/users/aihao2000/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aihao2000/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aihao2000/subscriptions", "type": "User", "url": "https://api.github.com/users/aihao2000", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6206/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/6203	6,203	Support loading from a DVC remote repository	{ "avatar_url": "https://avatars.githubusercontent.com/u/16692099?v=4", "events_url": "https://api.github.com/users/bilelomrani1/events{/privacy}", "followers_url": "https://api.github.com/users/bilelomrani1/followers", "following_url": "https://api.github.com/users/bilelomrani1/following{/other_user}", "gists_url": "https://api.github.com/users/bilelomrani1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bilelomrani1", "id": 16692099, "login": "bilelomrani1", "node_id": "MDQ6VXNlcjE2NjkyMDk5", "organizations_url": "https://api.github.com/users/bilelomrani1/orgs", "received_events_url": "https://api.github.com/users/bilelomrani1/received_events", "repos_url": "https://api.github.com/users/bilelomrani1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bilelomrani1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bilelomrani1/subscriptions", "type": "User", "url": "https://api.github.com/users/bilelomrani1", "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	[ "(cross-posting from the linked DVC issue)\r\n\r\nI think this should already work out of the box with the current `datasets` and `dvc.api` releases by passing the correct `storage_options` into the datasets calls. `storage_options` is essentially just the kwargs dict that gets passed to the fsspec fs constructor.\r\n\r\nThe main thing to note here is that the fsspec DVCFileSystem URL should be `dvc://folder/file.json` (i.e. this should be the DVCFileSystem path that is relative to the DVC repo root). You cannot use a URL like `https://gitlab.com/user/repo/folder/file.json`.\r\n\r\nI think something like this should work for you (in a venv where both DVC and datasets are installed):\r\n```python\r\nimport datasets\r\n\r\n# load a dataset from Git/DVC repository where Git repo is located at https://gitlab.com/user/repo.git\r\n# and path to dataset (relative to git/dvc repo root) is 'folder/file.json'\r\ndatasets.load_from_disk(\r\n \"dvc://folder/file.json\",\r\n storage_options={\"url\": \"https://gitlab.com/user/repo.git\"},\r\n)\r\n```\r\n\r\nbasically the `dvc://` is what tells fsspec to create a `DVCFileSystem` and it will construct it like\r\n```python\r\nfs = DVCFileSystem(*storage_options)\r\n```\r\n\r\nThen the subsequent calls use the rest of the `dvc://...` URL like \r\n```python\r\nfs.exists(\"folder/file.json\")\r\n```", "Hi @pmrowla Thank you for your help, that's very helpful, I was indeed using `fsspec` incorrectly here. There is still an issue with `datasets`:\r\n\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"json\", data_files=\"dvc://folder/file.jsonl\", storage_options={\"url\": \"https://gitlab.com/repo/folder/\"})\r\n```\r\n\r\nresults in the following exception:\r\n\r\n```\r\nTraceback (most recent call last): \r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/fs.py\", line 217, in info\r\n ret = self.trie.info(key)\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/git/objects.py\", line 141, in info\r\n obj = self.trie[key]\r\n ~~~~~~~~~^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/pygtrie.py\", line 937, in __getitem__\r\n node, _ = self._get_node(key_or_slice)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/pygtrie.py\", line 630, in _get_node\r\n raise KeyError(key)\r\nKeyError: ('dvc:', 'datasets', 'spider', 'train.jsonl')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 2129, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 1815, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 1430, in dataset_module_factory\r\n ).get_module()\r\n ^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 958, in get_module\r\n data_files = DataFilesDict.from_patterns(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 674, in from_patterns\r\n DataFilesList.from_patterns(\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 589, in from_patterns\r\n origin_metadata = _get_origin_metadata(data_files, download_config=download_config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 504, in _get_origin_metadata\r\n return thread_map(\r\n ^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, iterables, *tqdm_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map\r\n return list(tqdm_class(ex.map(fn, iterables, chunksize=chunksize), *kwargs))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 619, in result_iterator\r\n yield _result_or_cancel(fs.pop())\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 317, in _result_or_cancel\r\n return fut.result(timeout)\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 456, in result\r\n return self.__get_result()\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 401, in __get_result\r\n raise self._exception\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(self.args, self.kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 491, in _get_single_origin_metadata\r\n info = fs.info(data_file)\r\n ^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc/fs/dvc.py\", line 357, in info\r\n return self._info(key, path, ignore_subrepos=ignore_subrepos)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc/fs/dvc.py\", line 377, in _info\r\n fs_info = fs.info(fs_path)\r\n ^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc_objects/fs/base.py\", line 501, in info\r\n return self.fs.info(path, kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/fs.py\", line 221, in info\r\n raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path)\r\nFileNotFoundError: [Errno 2] No such file or directory: '/dvc:/folder/file.jsonl'\r\n```\r\n\r\nSomehow the URL gets turned into `/dvc:/folder/file.jsonl` inside `datasets`. Otherwise I can confirm that using `fsspec` properly with DVC works as expected.\r\n", "For the record, there was a `dvc.api.DVCFileSystem` bug which is fixed in DVC `main` and will be available in the next DVC release.\r\n\r\nTo use DVC with `datasets` you just need to pass the Git/DVC repo `url` in `storage_options` as discussed above.\r\n\r\n(note that this requires having both `datasets` and `dvc` installed in your python environment)\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\r\n... \"json\",\r\n... data_files=\"dvc://eval/metrics.json\",\r\n... storage_options={\"url\": \"https://github.com/iterative/example-get-started.git\"},\r\n... )\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['avg_prec', 'roc_auc'],\r\n num_rows: 1\r\n })\r\n})\r\n```\r\n\r\nAny additional `DVCFileSystem` args can be passed in the same way, so to get a specific branch/tag/commit from the DVC repo you just need to specify the `rev` in `storage_options` like\r\n```\r\nstorage_options={\"url\": \"https://github.com/iterative/example-get-started.git\", \"rev\": \"main\"}\r\n```\r\n\r\nI think this issue can probably be closed now.", "Thank you for your help, closing." ]	2023-09-01T14:04:52Z	2023-09-15T15:11:27Z	2023-09-15T15:11:27Z	NONE	null	null	### Feature request Adding support for loading a file from a DVC repository, tracked remotely on a SCM. ### Motivation DVC is a popular version control system to version and manage datasets. The files are stored on a remote object storage platform, but they are tracked using Git. Integration with DVC is possible through the `DVCFileSystem`. I have a Gitlab repository where multiple files are tracked using DVC and stored in a GCP bucket. I would like to be able to load these files using `datasets` directly using an URL. My goal is to write a generic code that abstracts the storage layer, such that my users will only have to pass in an `fsspec`-compliant URL and the corresponding files will be loaded. ### Your contribution I managed to instantiate a `DVCFileSystem` pointing to a Gitlab repo from a `fsspec` chained URL in [this pull request](https://github.com/iterative/dvc/pull/9903) to DVC. ```python from fsspec.core import url_to_fs fs, _ = url_to_fs("dvc::https://gitlab.com/repository/group/my-repo") ``` From now I'm not sure how to continue, it seems that `datasets` expects the URL to be fully qualified like so: `dvc::https://gitlab.com/repository/group/my-repo/my-folder/my-file.json` but this fails because `DVCFileSystem` expects the URL to point to the root of an SCM repo. Is there a way to make this work with `datasets`?	{ "avatar_url": "https://avatars.githubusercontent.com/u/16692099?v=4", "events_url": "https://api.github.com/users/bilelomrani1/events{/privacy}", "followers_url": "https://api.github.com/users/bilelomrani1/followers", "following_url": "https://api.github.com/users/bilelomrani1/following{/other_user}", "gists_url": "https://api.github.com/users/bilelomrani1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bilelomrani1", "id": 16692099, "login": "bilelomrani1", "node_id": "MDQ6VXNlcjE2NjkyMDk5", "organizations_url": "https://api.github.com/users/bilelomrani1/orgs", "received_events_url": "https://api.github.com/users/bilelomrani1/received_events", "repos_url": "https://api.github.com/users/bilelomrani1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bilelomrani1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bilelomrani1/subscriptions", "type": "User", "url": "https://api.github.com/users/bilelomrani1", "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/6203/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/6202	6,202	avoid downgrading jax version	{ "avatar_url": "https://avatars.githubusercontent.com/u/1332458?v=4", "events_url": "https://api.github.com/users/chrisflesher/events{/privacy}", "followers_url": "https://api.github.com/users/chrisflesher/followers", "following_url": "https://api.github.com/users/chrisflesher/following{/other_user}", "gists_url": "https://api.github.com/users/chrisflesher/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/chrisflesher", "id": 1332458, "login": "chrisflesher", "node_id": "MDQ6VXNlcjEzMzI0NTg=", "organizations_url": "https://api.github.com/users/chrisflesher/orgs", "received_events_url": "https://api.github.com/users/chrisflesher/received_events", "repos_url": "https://api.github.com/users/chrisflesher/repos", "site_admin": false, "starred_url": "https://api.github.com/users/chrisflesher/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/chrisflesher/subscriptions", "type": "User", "url": "https://api.github.com/users/chrisflesher", "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	[ "https://github.com/huggingface/datasets/blob/main/setup.py#L236\r\nCurrently has the highest version at 0.3.25; Not sure if there is any reason for this, other than that was the tested version?" ]	2023-09-01T02:57:57Z	2023-10-12T16:28:59Z	2023-10-12T16:28:59Z	NONE	null	null	### Feature request Whenever I `pip install datasets[jax]` it downgrades jax to version 0.3.25. I seem to be able to install this library first then upgrade jax back to version 0.4.13. ### Motivation It would be nice to not overwrite currently installed version of jax if possible. ### Your contribution I would be willing to beta test. Or maybe write some code if I could get pointed in the right direction, I'm not super familiar with this codebase.	{ "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": 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/6202/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/6201	6,201	Fix to_json ValueError and remove pandas pin	{ "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) \| 0.006852 / 0.011353 (-0.004501) \| 0.004195 / 0.011008 (-0.006813) \| 0.095008 / 0.038508 (0.056500) \| 0.073469 / 0.023109 (0.050360) \| 0.350170 / 0.275898 (0.074272) \| 0.394309 / 0.323480 (0.070829) \| 0.004391 / 0.007986 (-0.003595) \| 0.003432 / 0.004328 (-0.000896) \| 0.072849 / 0.004250 (0.068599) \| 0.058595 / 0.037052 (0.021543) \| 0.372335 / 0.258489 (0.113846) \| 0.410616 / 0.293841 (0.116775) \| 0.034477 / 0.128546 (-0.094069) \| 0.009426 / 0.075646 (-0.066220) \| 0.329262 / 0.419271 (-0.090009) \| 0.057941 / 0.043533 (0.014408) \| 0.358624 / 0.255139 (0.103485) \| 0.413803 / 0.283200 (0.130604) \| 0.025845 / 0.141683 (-0.115837) \| 1.684289 / 1.452155 (0.232134) \| 1.791567 / 1.492716 (0.298850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222731 / 0.018006 (0.204724) \| 0.511615 / 0.000490 (0.511126) \| 0.004163 / 0.000200 (0.003963) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033260 / 0.037411 (-0.004152) \| 0.091685 / 0.014526 (0.077159) \| 0.105655 / 0.176557 (-0.070901) \| 0.167973 / 0.737135 (-0.569163) \| 0.105458 / 0.296338 (-0.190880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441789 / 0.215209 (0.226580) \| 4.404803 / 2.077655 (2.327148) \| 2.163739 / 1.504120 (0.659620) \| 1.956828 / 1.541195 (0.415633) \| 2.042183 / 1.468490 (0.573693) \| 0.552221 / 4.584777 (-4.032556) \| 3.951769 / 3.745712 (0.206057) \| 3.591983 / 5.269862 (-1.677878) \| 2.225058 / 4.565676 (-2.340619) \| 0.064528 / 0.424275 (-0.359747) \| 0.008403 / 0.007607 (0.000796) \| 0.528830 / 0.226044 (0.302786) \| 5.233686 / 2.268929 (2.964757) \| 2.681156 / 55.444624 (-52.763468) \| 2.261188 / 6.876477 (-4.615289) \| 2.470037 / 2.142072 (0.327964) \| 0.661793 / 4.805227 (-4.143434) \| 0.150138 / 6.500664 (-6.350527) \| 0.068663 / 0.075469 (-0.006807) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.463086 / 1.841788 (-0.378701) \| 21.408232 / 8.074308 (13.333924) \| 15.521718 / 10.191392 (5.330326) \| 0.164587 / 0.680424 (-0.515837) \| 0.021035 / 0.534201 (-0.513166) \| 0.445466 / 0.579283 (-0.133817) \| 0.462489 / 0.434364 (0.028125) \| 0.517733 / 0.540337 (-0.022604) \| 0.724242 / 1.386936 (-0.662694) \|\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) \| 0.007117 / 0.011353 (-0.004236) \| 0.004230 / 0.011008 (-0.006778) \| 0.072186 / 0.038508 (0.033678) \| 0.076758 / 0.023109 (0.053648) \| 0.452606 / 0.275898 (0.176708) \| 0.491872 / 0.323480 (0.168392) \| 0.005989 / 0.007986 (-0.001996) \| 0.003611 / 0.004328 (-0.000717) \| 0.072642 / 0.004250 (0.068392) \| 0.058985 / 0.037052 (0.021933) \| 0.463414 / 0.258489 (0.204925) \| 0.497538 / 0.293841 (0.203697) \| 0.036325 / 0.128546 (-0.092221) \| 0.009814 / 0.075646 (-0.065832) \| 0.078745 / 0.419271 (-0.340527) \| 0.054308 / 0.043533 (0.010775) \| 0.468210 / 0.255139 (0.213071) \| 0.476434 / 0.283200 (0.193234) \| 0.023683 / 0.141683 (-0.118000) \| 1.706457 / 1.452155 (0.254302) \| 1.775855 / 1.492716 (0.283139) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241599 / 0.018006 (0.223592) \| 0.483859 / 0.000490 (0.483370) \| 0.006432 / 0.000200 (0.006233) \| 0.000177 / 0.000054 (0.000123) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034723 / 0.037411 (-0.002688) \| 0.104420 / 0.014526 (0.089894) \| 0.121071 / 0.176557 (-0.055486) \| 0.174899 / 0.737135 (-0.562237) \| 0.119587 / 0.296338 (-0.176751) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492731 / 0.215209 (0.277522) \| 4.898621 / 2.077655 (2.820967) \| 2.710931 / 1.504120 (1.206811) \| 2.513889 / 1.541195 (0.972694) \| 2.578073 / 1.468490 (1.109583) \| 0.548318 / 4.584777 (-4.036459) \| 4.048603 / 3.745712 (0.302891) \| 3.637654 / 5.269862 (-1.632208) \| 2.263682 / 4.565676 (-2.301994) \| 0.065786 / 0.424275 (-0.358489) \| 0.008119 / 0.007607 (0.000512) \| 0.578693 / 0.226044 (0.352649) \| 5.780619 / 2.268929 (3.511691) \| 3.224625 / 55.444624 (-52.220000) \| 2.838750 / 6.876477 (-4.037726) \| 2.970276 / 2.142072 (0.828204) \| 0.654423 / 4.805227 (-4.150805) \| 0.148696 / 6.500664 (-6.351969) \| 0.066469 / 0.075469 (-0.009000) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.574772 / 1.841788 (-0.267015) \| 21.822356 / 8.074308 (13.748048) \| 16.504127 / 10.191392 (6.312735) \| 0.183357 / 0.680424 (-0.497067) \| 0.022759 / 0.534201 (-0.511442) \| 0.453746 / 0.579283 (-0.125537) \| 0.447037 / 0.434364 (0.012673) \| 0.536562 / 0.540337 (-0.003775) \| 0.731063 / 1.386936 (-0.655873) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a9027eb4d9c5b3fa60a18daa7aef121428964d90 \"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) \| 0.008542 / 0.011353 (-0.002811) \| 0.005481 / 0.011008 (-0.005527) \| 0.100122 / 0.038508 (0.061614) \| 0.078968 / 0.023109 (0.055858) \| 0.403751 / 0.275898 (0.127853) \| 0.457559 / 0.323480 (0.134079) \| 0.006152 / 0.007986 (-0.001834) \| 0.003805 / 0.004328 (-0.000523) \| 0.072787 / 0.004250 (0.068536) \| 0.054794 / 0.037052 (0.017741) \| 0.419815 / 0.258489 (0.161326) \| 0.437453 / 0.293841 (0.143612) \| 0.044641 / 0.128546 (-0.083905) \| 0.013755 / 0.075646 (-0.061892) \| 0.374683 / 0.419271 (-0.044589) \| 0.071442 / 0.043533 (0.027909) \| 0.395814 / 0.255139 (0.140675) \| 0.439042 / 0.283200 (0.155842) \| 0.034596 / 0.141683 (-0.107087) \| 1.655056 / 1.452155 (0.202902) \| 1.826410 / 1.492716 (0.333694) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278667 / 0.018006 (0.260661) \| 0.617354 / 0.000490 (0.616864) \| 0.004111 / 0.000200 (0.003911) \| 0.000138 / 0.000054 (0.000083) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025905 / 0.037411 (-0.011506) \| 0.084721 / 0.014526 (0.070195) \| 0.099737 / 0.176557 (-0.076819) \| 0.163016 / 0.737135 (-0.574119) \| 0.095104 / 0.296338 (-0.201234) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.531589 / 0.215209 (0.316380) \| 5.455303 / 2.077655 (3.377649) \| 2.495112 / 1.504120 (0.990992) \| 2.234139 / 1.541195 (0.692944) \| 2.295090 / 1.468490 (0.826599) \| 0.777627 / 4.584777 (-3.807150) \| 5.053069 / 3.745712 (1.307357) \| 4.488715 / 5.269862 (-0.781147) \| 2.775991 / 4.565676 (-1.789686) \| 0.094175 / 0.424275 (-0.330100) \| 0.008681 / 0.007607 (0.001074) \| 0.668174 / 0.226044 (0.442130) \| 6.631876 / 2.268929 (4.362948) \| 3.118055 / 55.444624 (-52.326569) \| 2.480355 / 6.876477 (-4.396122) \| 2.706643 / 2.142072 (0.564571) \| 0.927173 / 4.805227 (-3.878054) \| 0.217385 / 6.500664 (-6.283279) \| 0.067110 / 0.075469 (-0.008359) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.517926 / 1.841788 (-0.323861) \| 21.420546 / 8.074308 (13.346238) \| 21.108266 / 10.191392 (10.916874) \| 0.222449 / 0.680424 (-0.457975) \| 0.027969 / 0.534201 (-0.506232) \| 0.459484 / 0.579283 (-0.119799) \| 0.582629 / 0.434364 (0.148265) \| 0.520971 / 0.540337 (-0.019366) \| 0.694270 / 1.386936 (-0.692666) \|\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) \| 0.008257 / 0.011353 (-0.003096) \| 0.004511 / 0.011008 (-0.006497) \| 0.075031 / 0.038508 (0.036523) \| 0.070526 / 0.023109 (0.047416) \| 0.445595 / 0.275898 (0.169697) \| 0.512312 / 0.323480 (0.188832) \| 0.005933 / 0.007986 (-0.002052) \| 0.003814 / 0.004328 (-0.000515) \| 0.073553 / 0.004250 (0.069302) \| 0.058174 / 0.037052 (0.021121) \| 0.472307 / 0.258489 (0.213818) \| 0.519679 / 0.293841 (0.225838) \| 0.046027 / 0.128546 (-0.082520) \| 0.011757 / 0.075646 (-0.063889) \| 0.084883 / 0.419271 (-0.334388) \| 0.056476 / 0.043533 (0.012943) \| 0.475608 / 0.255139 (0.220469) \| 0.507588 / 0.283200 (0.224388) \| 0.031661 / 0.141683 (-0.110022) \| 1.673183 / 1.452155 (0.221028) \| 1.736836 / 1.492716 (0.244120) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.350887 / 0.018006 (0.332881) \| 0.589796 / 0.000490 (0.589306) \| 0.023066 / 0.000200 (0.022867) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030764 / 0.037411 (-0.006647) \| 0.116967 / 0.014526 (0.102441) \| 0.102760 / 0.176557 (-0.073796) \| 0.167690 / 0.737135 (-0.569445) \| 0.111350 / 0.296338 (-0.184988) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584565 / 0.215209 (0.369356) \| 5.898081 / 2.077655 (3.820426) \| 2.770374 / 1.504120 (1.266254) \| 2.467519 / 1.541195 (0.926324) \| 2.463319 / 1.468490 (0.994829) \| 0.794294 / 4.584777 (-3.790483) \| 5.272285 / 3.745712 (1.526573) \| 4.514830 / 5.269862 (-0.755032) \| 2.937259 / 4.565676 (-1.628417) \| 0.093702 / 0.424275 (-0.330574) \| 0.008012 / 0.007607 (0.000405) \| 0.772371 / 0.226044 (0.546327) \| 7.574941 / 2.268929 (5.306013) \| 3.710965 / 55.444624 (-51.733659) \| 2.927964 / 6.876477 (-3.948513) \| 3.256036 / 2.142072 (1.113964) \| 1.051649 / 4.805227 (-3.753578) \| 0.203055 / 6.500664 (-6.297609) \| 0.081072 / 0.075469 (0.005603) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.574251 / 1.841788 (-0.267537) \| 22.340801 / 8.074308 (14.266493) \| 20.497769 / 10.191392 (10.306377) \| 0.228725 / 0.680424 (-0.451699) \| 0.029095 / 0.534201 (-0.505106) \| 0.452460 / 0.579283 (-0.126823) \| 0.586419 / 0.434364 (0.152055) \| 0.571237 / 0.540337 (0.030900) \| 0.745069 / 1.386936 (-0.641867) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b23b028dfc72c297391c5f670342732b9bd9fe \"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) \| 0.006529 / 0.011353 (-0.004824) \| 0.004062 / 0.011008 (-0.006946) \| 0.083712 / 0.038508 (0.045204) \| 0.072378 / 0.023109 (0.049269) \| 0.358779 / 0.275898 (0.082881) \| 0.387216 / 0.323480 (0.063736) \| 0.004038 / 0.007986 (-0.003948) \| 0.003316 / 0.004328 (-0.001013) \| 0.065207 / 0.004250 (0.060956) \| 0.054439 / 0.037052 (0.017386) \| 0.370689 / 0.258489 (0.112200) \| 0.411008 / 0.293841 (0.117167) \| 0.031133 / 0.128546 (-0.097413) \| 0.008600 / 0.075646 (-0.067047) \| 0.287753 / 0.419271 (-0.131518) \| 0.051845 / 0.043533 (0.008312) \| 0.360327 / 0.255139 (0.105188) \| 0.394791 / 0.283200 (0.111591) \| 0.025139 / 0.141683 (-0.116544) \| 1.488151 / 1.452155 (0.035996) \| 1.556776 / 1.492716 (0.064059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209462 / 0.018006 (0.191456) \| 0.459168 / 0.000490 (0.458678) \| 0.006037 / 0.000200 (0.005837) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028444 / 0.037411 (-0.008967) \| 0.082974 / 0.014526 (0.068448) \| 0.094919 / 0.176557 (-0.081638) \| 0.151875 / 0.737135 (-0.585260) \| 0.096143 / 0.296338 (-0.200195) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402675 / 0.215209 (0.187466) \| 4.014787 / 2.077655 (1.937133) \| 2.015793 / 1.504120 (0.511673) \| 1.838976 / 1.541195 (0.297782) \| 1.931733 / 1.468490 (0.463243) \| 0.489435 / 4.584777 (-4.095342) \| 3.581662 / 3.745712 (-0.164050) \| 3.315392 / 5.269862 (-1.954469) \| 2.053369 / 4.565676 (-2.512307) \| 0.057749 / 0.424275 (-0.366526) \| 0.007720 / 0.007607 (0.000113) \| 0.483388 / 0.226044 (0.257343) \| 4.820798 / 2.268929 (2.551870) \| 2.544264 / 55.444624 (-52.900361) \| 2.170513 / 6.876477 (-4.705963) \| 2.416976 / 2.142072 (0.274903) \| 0.588351 / 4.805227 (-4.216876) \| 0.136988 / 6.500664 (-6.363676) \| 0.062294 / 0.075469 (-0.013175) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.263807 / 1.841788 (-0.577980) \| 19.888202 / 8.074308 (11.813894) \| 14.352977 / 10.191392 (4.161585) \| 0.167200 / 0.680424 (-0.513224) \| 0.018449 / 0.534201 (-0.515752) \| 0.393262 / 0.579283 (-0.186021) \| 0.407854 / 0.434364 (-0.026510) \| 0.455852 / 0.540337 (-0.084485) \| 0.629024 / 1.386936 (-0.757912) \|\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) \| 0.006642 / 0.011353 (-0.004710) \| 0.004041 / 0.011008 (-0.006967) \| 0.065823 / 0.038508 (0.027315) \| 0.076810 / 0.023109 (0.053701) \| 0.397680 / 0.275898 (0.121782) \| 0.430104 / 0.323480 (0.106624) \| 0.006035 / 0.007986 (-0.001951) \| 0.003389 / 0.004328 (-0.000939) \| 0.066056 / 0.004250 (0.061806) \| 0.054222 / 0.037052 (0.017170) \| 0.397964 / 0.258489 (0.139475) \| 0.439277 / 0.293841 (0.145436) \| 0.032394 / 0.128546 (-0.096152) \| 0.008586 / 0.075646 (-0.067060) \| 0.072538 / 0.419271 (-0.346734) \| 0.048346 / 0.043533 (0.004813) \| 0.399631 / 0.255139 (0.144492) \| 0.418684 / 0.283200 (0.135484) \| 0.022570 / 0.141683 (-0.119113) \| 1.519788 / 1.452155 (0.067633) \| 1.581457 / 1.492716 (0.088740) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243443 / 0.018006 (0.225436) \| 0.453095 / 0.000490 (0.452606) \| 0.009940 / 0.000200 (0.009740) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032293 / 0.037411 (-0.005118) \| 0.091681 / 0.014526 (0.077155) \| 0.103729 / 0.176557 (-0.072827) \| 0.156361 / 0.737135 (-0.580775) \| 0.105034 / 0.296338 (-0.191305) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427761 / 0.215209 (0.212551) \| 4.266044 / 2.077655 (2.188390) \| 2.285161 / 1.504120 (0.781041) \| 2.118652 / 1.541195 (0.577457) \| 2.203469 / 1.468490 (0.734979) \| 0.494587 / 4.584777 (-4.090190) \| 3.676706 / 3.745712 (-0.069006) \| 3.252478 / 5.269862 (-2.017383) \| 2.027432 / 4.565676 (-2.538245) \| 0.057856 / 0.424275 (-0.366419) \| 0.007279 / 0.007607 (-0.000328) \| 0.502767 / 0.226044 (0.276723) \| 5.031409 / 2.268929 (2.762480) \| 2.741767 / 55.444624 (-52.702858) \| 2.408480 / 6.876477 (-4.467997) \| 2.607193 / 2.142072 (0.465121) \| 0.590787 / 4.805227 (-4.214440) \| 0.133633 / 6.500664 (-6.367031) \| 0.061195 / 0.075469 (-0.014274) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.342824 / 1.841788 (-0.498964) \| 20.137195 / 8.074308 (12.062887) \| 14.986743 / 10.191392 (4.795351) \| 0.168218 / 0.680424 (-0.512206) \| 0.020209 / 0.534201 (-0.513992) \| 0.397446 / 0.579283 (-0.181837) \| 0.427496 / 0.434364 (-0.006868) \| 0.475058 / 0.540337 (-0.065279) \| 0.648439 / 1.386936 (-0.738497) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b54cbd6c01f52139dedbcf209ff41f0c88b9aa5 \"CML watermark\")\n" ]	2023-08-31T10:38:08Z	2023-09-05T11:07:07Z	2023-09-05T10:58:21Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6201.diff", "html_url": "https://github.com/huggingface/datasets/pull/6201", "merged_at": "2023-09-05T10:58:21Z", "patch_url": "https://github.com/huggingface/datasets/pull/6201.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6201" }	This PR fixes the root cause of the issue: - #6197 This PR also removes the temporary pin of `pandas` introduced by: - #6200 Note that for orient in ['records', 'values'], index value is ignored but - in `pandas` < 2.1.0, a ValueError is raised if not index and orient not in ['split', 'table'] - for orient = 'records', we need index = True - default index value is True - in `pandas` = 2.1.0, a ValueError is raised if index is True and orient in ['records', 'values'] - for orient = 'records', we need index = False or None - default index value is None This PR fixes the issue by not passing index and thus using default index value (valid for all pandas versions), unless orient is 'split' or 'table' (where we pass index = False, as it was done before this fix).	{ "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", 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https://github.com/huggingface/datasets/pull/6200	6,200	Temporarily pin pandas < 2.1.0	{ "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) \| 0.008978 / 0.011353 (-0.002375) \| 0.005143 / 0.011008 (-0.005865) \| 0.104787 / 0.038508 (0.066279) \| 0.077069 / 0.023109 (0.053960) \| 0.427703 / 0.275898 (0.151805) \| 0.469865 / 0.323480 (0.146386) \| 0.004618 / 0.007986 (-0.003368) \| 0.004074 / 0.004328 (-0.000255) \| 0.088656 / 0.004250 (0.084405) \| 0.059798 / 0.037052 (0.022746) \| 0.465906 / 0.258489 (0.207417) \| 0.510281 / 0.293841 (0.216440) \| 0.051192 / 0.128546 (-0.077354) \| 0.013623 / 0.075646 (-0.062024) \| 0.379339 / 0.419271 (-0.039932) \| 0.077393 / 0.043533 (0.033860) \| 0.445165 / 0.255139 (0.190026) \| 0.473577 / 0.283200 (0.190378) \| 0.038125 / 0.141683 (-0.103558) \| 1.858635 / 1.452155 (0.406480) \| 1.869033 / 1.492716 (0.376316) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209011 / 0.018006 (0.191004) \| 0.550978 / 0.000490 (0.550488) \| 0.004904 / 0.000200 (0.004704) \| 0.000106 / 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) \| 0.031418 / 0.037411 (-0.005993) \| 0.089623 / 0.014526 (0.075098) \| 0.103491 / 0.176557 (-0.073066) \| 0.178158 / 0.737135 (-0.558978) \| 0.108515 / 0.296338 (-0.187824) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.648293 / 0.215209 (0.433084) \| 6.332361 / 2.077655 (4.254707) \| 2.469076 / 1.504120 (0.964956) \| 2.286228 / 1.541195 (0.745033) \| 2.257408 / 1.468490 (0.788918) \| 0.918027 / 4.584777 (-3.666750) \| 5.229539 / 3.745712 (1.483827) \| 4.676150 / 5.269862 (-0.593712) \| 3.220411 / 4.565676 (-1.345266) \| 0.095863 / 0.424275 (-0.328413) \| 0.008696 / 0.007607 (0.001089) \| 0.722356 / 0.226044 (0.496312) \| 7.796690 / 2.268929 (5.527762) \| 3.715044 / 55.444624 (-51.729581) \| 2.852696 / 6.876477 (-4.023780) \| 2.891838 / 2.142072 (0.749766) \| 1.195536 / 4.805227 (-3.609691) \| 0.246908 / 6.500664 (-6.253756) \| 0.079454 / 0.075469 (0.003984) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.652740 / 1.841788 (-0.189047) \| 23.791791 / 8.074308 (15.717482) \| 22.778999 / 10.191392 (12.587607) \| 0.253878 / 0.680424 (-0.426546) \| 0.031367 / 0.534201 (-0.502834) \| 0.509460 / 0.579283 (-0.069823) \| 0.603085 / 0.434364 (0.168721) \| 0.603890 / 0.540337 (0.063553) \| 0.826606 / 1.386936 (-0.560330) \|\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) \| 0.010407 / 0.011353 (-0.000946) \| 0.004751 / 0.011008 (-0.006257) \| 0.086761 / 0.038508 (0.048253) \| 0.087281 / 0.023109 (0.064172) \| 0.498409 / 0.275898 (0.222511) \| 0.560727 / 0.323480 (0.237247) \| 0.006563 / 0.007986 (-0.001423) \| 0.004078 / 0.004328 (-0.000251) \| 0.086383 / 0.004250 (0.082133) \| 0.065915 / 0.037052 (0.028862) \| 0.521871 / 0.258489 (0.263382) \| 0.582281 / 0.293841 (0.288440) \| 0.057189 / 0.128546 (-0.071357) \| 0.015514 / 0.075646 (-0.060133) \| 0.102574 / 0.419271 (-0.316697) \| 0.069155 / 0.043533 (0.025622) \| 0.525000 / 0.255139 (0.269861) \| 0.557968 / 0.283200 (0.274769) \| 0.036934 / 0.141683 (-0.104749) \| 1.919335 / 1.452155 (0.467181) \| 1.870948 / 1.492716 (0.378231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241932 / 0.018006 (0.223926) \| 0.560136 / 0.000490 (0.559646) \| 0.006438 / 0.000200 (0.006238) \| 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) \| 0.036192 / 0.037411 (-0.001220) \| 0.106829 / 0.014526 (0.092303) \| 0.128667 / 0.176557 (-0.047890) \| 0.200514 / 0.737135 (-0.536621) \| 0.127542 / 0.296338 (-0.168797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.754556 / 0.215209 (0.539347) \| 7.237324 / 2.077655 (5.159670) \| 3.267424 / 1.504120 (1.763304) \| 2.789601 / 1.541195 (1.248407) \| 2.875728 / 1.468490 (1.407238) \| 0.894274 / 4.584777 (-3.690503) \| 5.394556 / 3.745712 (1.648844) \| 4.818523 / 5.269862 (-0.451338) \| 2.965827 / 4.565676 (-1.599850) \| 0.101967 / 0.424275 (-0.322308) \| 0.008506 / 0.007607 (0.000899) \| 0.803476 / 0.226044 (0.577432) \| 8.614426 / 2.268929 (6.345497) \| 4.169113 / 55.444624 (-51.275511) \| 3.346346 / 6.876477 (-3.530130) \| 3.418206 / 2.142072 (1.276134) \| 1.111718 / 4.805227 (-3.693509) \| 0.211302 / 6.500664 (-6.289362) \| 0.072524 / 0.075469 (-0.002945) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.792705 / 1.841788 (-0.049083) \| 24.442484 / 8.074308 (16.368176) \| 23.375008 / 10.191392 (13.183616) \| 0.227946 / 0.680424 (-0.452478) \| 0.034376 / 0.534201 (-0.499825) \| 0.489260 / 0.579283 (-0.090023) \| 0.563220 / 0.434364 (0.128856) \| 0.617405 / 0.540337 (0.077068) \| 0.850577 / 1.386936 (-0.536359) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4413ea1eaca5023ace1e62ddf1070de2d41b4f6 \"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) \| 0.006594 / 0.011353 (-0.004759) \| 0.004366 / 0.011008 (-0.006642) \| 0.084241 / 0.038508 (0.045733) \| 0.071876 / 0.023109 (0.048767) \| 0.321604 / 0.275898 (0.045706) \| 0.343501 / 0.323480 (0.020021) \| 0.004069 / 0.007986 (-0.003917) \| 0.003311 / 0.004328 (-0.001017) \| 0.065079 / 0.004250 (0.060829) \| 0.053754 / 0.037052 (0.016702) \| 0.326199 / 0.258489 (0.067710) \| 0.356552 / 0.293841 (0.062711) \| 0.031568 / 0.128546 (-0.096979) \| 0.008581 / 0.075646 (-0.067065) \| 0.289170 / 0.419271 (-0.130101) \| 0.053097 / 0.043533 (0.009564) \| 0.309678 / 0.255139 (0.054539) \| 0.345717 / 0.283200 (0.062517) \| 0.024144 / 0.141683 (-0.117539) \| 1.497351 / 1.452155 (0.045196) \| 1.584691 / 1.492716 (0.091975) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206357 / 0.018006 (0.188351) \| 0.459611 / 0.000490 (0.459121) \| 0.002586 / 0.000200 (0.002386) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027459 / 0.037411 (-0.009952) \| 0.082197 / 0.014526 (0.067671) \| 0.095004 / 0.176557 (-0.081553) \| 0.151063 / 0.737135 (-0.586072) \| 0.095107 / 0.296338 (-0.201231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384363 / 0.215209 (0.169154) \| 3.836187 / 2.077655 (1.758533) \| 1.898312 / 1.504120 (0.394192) \| 1.727310 / 1.541195 (0.186115) \| 1.803579 / 1.468490 (0.335089) \| 0.485946 / 4.584777 (-4.098831) \| 3.619134 / 3.745712 (-0.126578) \| 3.255274 / 5.269862 (-2.014588) \| 2.004603 / 4.565676 (-2.561074) \| 0.057107 / 0.424275 (-0.367168) \| 0.007601 / 0.007607 (-0.000006) \| 0.456545 / 0.226044 (0.230500) \| 4.556857 / 2.268929 (2.287929) \| 2.379954 / 55.444624 (-53.064671) \| 2.045874 / 6.876477 (-4.830603) \| 2.203090 / 2.142072 (0.061018) \| 0.585400 / 4.805227 (-4.219827) \| 0.133018 / 6.500664 (-6.367646) \| 0.059457 / 0.075469 (-0.016012) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.292581 / 1.841788 (-0.549207) \| 19.360057 / 8.074308 (11.285749) \| 14.105359 / 10.191392 (3.913967) \| 0.166028 / 0.680424 (-0.514396) \| 0.018243 / 0.534201 (-0.515958) \| 0.392026 / 0.579283 (-0.187257) \| 0.412735 / 0.434364 (-0.021629) \| 0.459791 / 0.540337 (-0.080547) \| 0.624539 / 1.386936 (-0.762397) \|\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) \| 0.006677 / 0.011353 (-0.004676) \| 0.003897 / 0.011008 (-0.007111) \| 0.064139 / 0.038508 (0.025631) \| 0.071346 / 0.023109 (0.048237) \| 0.431180 / 0.275898 (0.155282) \| 0.470870 / 0.323480 (0.147390) \| 0.005562 / 0.007986 (-0.002423) \| 0.003405 / 0.004328 (-0.000924) \| 0.064532 / 0.004250 (0.060282) \| 0.055317 / 0.037052 (0.018265) \| 0.434667 / 0.258489 (0.176178) \| 0.475765 / 0.293841 (0.181924) \| 0.032392 / 0.128546 (-0.096154) \| 0.008418 / 0.075646 (-0.067228) \| 0.071069 / 0.419271 (-0.348203) \| 0.047963 / 0.043533 (0.004430) \| 0.440225 / 0.255139 (0.185086) \| 0.454860 / 0.283200 (0.171661) \| 0.022653 / 0.141683 (-0.119029) \| 1.489444 / 1.452155 (0.037289) \| 1.556913 / 1.492716 (0.064196) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226733 / 0.018006 (0.208727) \| 0.452005 / 0.000490 (0.451516) \| 0.004715 / 0.000200 (0.004515) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032042 / 0.037411 (-0.005369) \| 0.091226 / 0.014526 (0.076700) \| 0.103639 / 0.176557 (-0.072917) \| 0.157772 / 0.737135 (-0.579363) \| 0.105466 / 0.296338 (-0.190872) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439751 / 0.215209 (0.224542) \| 4.357102 / 2.077655 (2.279448) \| 2.362857 / 1.504120 (0.858737) \| 2.180559 / 1.541195 (0.639364) \| 2.279601 / 1.468490 (0.811111) \| 0.495161 / 4.584777 (-4.089616) \| 3.729199 / 3.745712 (-0.016513) \| 3.334839 / 5.269862 (-1.935023) \| 2.099315 / 4.565676 (-2.466362) \| 0.058178 / 0.424275 (-0.366097) \| 0.007303 / 0.007607 (-0.000304) \| 0.506968 / 0.226044 (0.280924) \| 5.078600 / 2.268929 (2.809671) \| 2.846420 / 55.444624 (-52.598204) \| 2.480644 / 6.876477 (-4.395833) \| 2.693204 / 2.142072 (0.551132) \| 0.590118 / 4.805227 (-4.215109) \| 0.132900 / 6.500664 (-6.367764) \| 0.060053 / 0.075469 (-0.015416) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356713 / 1.841788 (-0.485075) \| 20.380573 / 8.074308 (12.306265) \| 15.066507 / 10.191392 (4.875115) \| 0.180655 / 0.680424 (-0.499769) \| 0.020954 / 0.534201 (-0.513247) \| 0.399638 / 0.579283 (-0.179645) \| 0.420694 / 0.434364 (-0.013670) \| 0.476124 / 0.540337 (-0.064213) \| 0.647192 / 1.386936 (-0.739744) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f8c58002481568eb1aa4f6f86c4509cf476800a \"CML watermark\")\n" ]	2023-08-31T09:45:17Z	2023-08-31T10:33:24Z	2023-08-31T10:24:38Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6200.diff", "html_url": "https://github.com/huggingface/datasets/pull/6200", "merged_at": "2023-08-31T10:24:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6200.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6200" }	Temporarily pin `pandas` < 2.1.0 until permanent solution is found. Hot fix #6197.	{ "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/6200/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6199	6,199	Use load_dataset for local json files, but it not works	{ "avatar_url": "https://avatars.githubusercontent.com/u/50519434?v=4", "events_url": "https://api.github.com/users/Garen-in-bush/events{/privacy}", "followers_url": "https://api.github.com/users/Garen-in-bush/followers", "following_url": "https://api.github.com/users/Garen-in-bush/following{/other_user}", "gists_url": "https://api.github.com/users/Garen-in-bush/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Garen-in-bush", "id": 50519434, "login": "Garen-in-bush", "node_id": "MDQ6VXNlcjUwNTE5NDM0", "organizations_url": "https://api.github.com/users/Garen-in-bush/orgs", "received_events_url": "https://api.github.com/users/Garen-in-bush/received_events", "repos_url": "https://api.github.com/users/Garen-in-bush/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Garen-in-bush/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Garen-in-bush/subscriptions", "type": "User", "url": "https://api.github.com/users/Garen-in-bush", "user_view_type": "public" }	[]	open	false	[ "Hugging Face's datasets library may prioritize remote configurations. Make sure there are no conflicting configurations causing the library to prefer downloading data\r\nMay be try debugging\r\nraw_datasets = load_dataset('json', data_files=data_files)\r\nprint(raw_datasets)\r\n", "It doesn't download them but writes them to the local HF cache. The logging could indeed be better. Does loading the dataset succeed? If it doesn't, can you share the error stack trace?" ]	2023-08-31T09:42:34Z	2023-08-31T19:05:07Z	null	NONE	null	null	### Describe the bug when I use load_dataset to load my local datasets，it always goes to Hugging Face to download the data instead of loading the local dataset. ### Steps to reproduce the bug `raw_datasets = load_dataset( ‘json’, data_files=data_files)` ### Expected behavior ![image](https://github.com/huggingface/datasets/assets/50519434/add3747f-6481-4da7-b374-8f81c5a6472c) ### Environment info python version 3.8.5 datasets version 2.12 os version unbuntu 18.04	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/6199/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/6198	6,198	Preserve split order in DataFilesDict	{ "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) \| 0.007621 / 0.011353 (-0.003732) \| 0.004534 / 0.011008 (-0.006475) \| 0.099834 / 0.038508 (0.061326) \| 0.083029 / 0.023109 (0.059920) \| 0.387559 / 0.275898 (0.111661) \| 0.422453 / 0.323480 (0.098973) \| 0.006070 / 0.007986 (-0.001916) \| 0.003725 / 0.004328 (-0.000604) \| 0.075923 / 0.004250 (0.071672) \| 0.060578 / 0.037052 (0.023525) \| 0.403569 / 0.258489 (0.145079) \| 0.444991 / 0.293841 (0.151150) \| 0.035847 / 0.128546 (-0.092699) \| 0.009872 / 0.075646 (-0.065774) \| 0.335506 / 0.419271 (-0.083766) \| 0.060509 / 0.043533 (0.016976) \| 0.381034 / 0.255139 (0.125895) \| 0.426938 / 0.283200 (0.143738) \| 0.027662 / 0.141683 (-0.114021) \| 1.729565 / 1.452155 (0.277410) \| 1.842082 / 1.492716 (0.349366) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230371 / 0.018006 (0.212365) \| 0.518216 / 0.000490 (0.517726) \| 0.003897 / 0.000200 (0.003697) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031942 / 0.037411 (-0.005470) \| 0.096609 / 0.014526 (0.082083) \| 0.112707 / 0.176557 (-0.063850) \| 0.178849 / 0.737135 (-0.558286) \| 0.112793 / 0.296338 (-0.183546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445896 / 0.215209 (0.230687) \| 4.451173 / 2.077655 (2.373519) \| 2.183380 / 1.504120 (0.679260) \| 1.991583 / 1.541195 (0.450388) \| 2.096219 / 1.468490 (0.627729) \| 0.566692 / 4.584777 (-4.018085) \| 4.078278 / 3.745712 (0.332566) \| 3.787950 / 5.269862 (-1.481911) \| 2.372651 / 4.565676 (-2.193025) \| 0.065500 / 0.424275 (-0.358775) \| 0.008918 / 0.007607 (0.001311) \| 0.535589 / 0.226044 (0.309545) \| 5.364130 / 2.268929 (3.095201) \| 2.805381 / 55.444624 (-52.639244) \| 2.350769 / 6.876477 (-4.525708) \| 2.592887 / 2.142072 (0.450814) \| 0.675475 / 4.805227 (-4.129752) \| 0.153907 / 6.500664 (-6.346757) \| 0.071138 / 0.075469 (-0.004331) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.498236 / 1.841788 (-0.343552) \| 22.810460 / 8.074308 (14.736152) \| 16.275035 / 10.191392 (6.083643) \| 0.200242 / 0.680424 (-0.480182) \| 0.021553 / 0.534201 (-0.512648) \| 0.469437 / 0.579283 (-0.109846) \| 0.477752 / 0.434364 (0.043388) \| 0.537411 / 0.540337 (-0.002927) \| 0.741730 / 1.386936 (-0.645206) \|\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) \| 0.008009 / 0.011353 (-0.003344) \| 0.004626 / 0.011008 (-0.006382) \| 0.074871 / 0.038508 (0.036363) \| 0.085214 / 0.023109 (0.062105) \| 0.478057 / 0.275898 (0.202159) \| 0.522038 / 0.323480 (0.198558) \| 0.007055 / 0.007986 (-0.000931) \| 0.003813 / 0.004328 (-0.000515) \| 0.076238 / 0.004250 (0.071988) \| 0.065738 / 0.037052 (0.028686) \| 0.484391 / 0.258489 (0.225902) \| 0.524425 / 0.293841 (0.230584) \| 0.038375 / 0.128546 (-0.090171) \| 0.009964 / 0.075646 (-0.065682) \| 0.084027 / 0.419271 (-0.335245) \| 0.056979 / 0.043533 (0.013447) \| 0.486910 / 0.255139 (0.231771) \| 0.501185 / 0.283200 (0.217985) \| 0.027000 / 0.141683 (-0.114683) \| 1.767378 / 1.452155 (0.315224) \| 1.870511 / 1.492716 (0.377795) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267067 / 0.018006 (0.249061) \| 0.501714 / 0.000490 (0.501224) \| 0.012379 / 0.000200 (0.012179) \| 0.000129 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036706 / 0.037411 (-0.000706) \| 0.110064 / 0.014526 (0.095538) \| 0.124896 / 0.176557 (-0.051660) \| 0.186730 / 0.737135 (-0.550405) \| 0.123501 / 0.296338 (-0.172837) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.510793 / 0.215209 (0.295583) \| 5.133056 / 2.077655 (3.055401) \| 2.776456 / 1.504120 (1.272336) \| 2.595557 / 1.541195 (1.054362) \| 2.717922 / 1.468490 (1.249432) \| 0.578333 / 4.584777 (-4.006444) \| 4.169935 / 3.745712 (0.424223) \| 3.800078 / 5.269862 (-1.469784) \| 2.385866 / 4.565676 (-2.179810) \| 0.068114 / 0.424275 (-0.356161) \| 0.008771 / 0.007607 (0.001164) \| 0.597894 / 0.226044 (0.371850) \| 5.970293 / 2.268929 (3.701364) \| 3.352715 / 55.444624 (-52.091909) \| 2.972062 / 6.876477 (-3.904415) \| 3.179232 / 2.142072 (1.037160) \| 0.689838 / 4.805227 (-4.115389) \| 0.154890 / 6.500664 (-6.345774) \| 0.072321 / 0.075469 (-0.003148) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.613666 / 1.841788 (-0.228121) \| 23.441538 / 8.074308 (15.367230) \| 17.105417 / 10.191392 (6.914025) \| 0.171449 / 0.680424 (-0.508975) \| 0.023257 / 0.534201 (-0.510944) \| 0.466724 / 0.579283 (-0.112559) \| 0.470835 / 0.434364 (0.036471) \| 0.561860 / 0.540337 (0.021523) \| 0.759048 / 1.386936 (-0.627888) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f3b6eaf69d3352394d3bf3c4d6ed01dd2af5860 \"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) \| 0.007557 / 0.011353 (-0.003796) \| 0.004211 / 0.011008 (-0.006797) \| 0.096243 / 0.038508 (0.057735) \| 0.083603 / 0.023109 (0.060493) \| 0.367114 / 0.275898 (0.091216) \| 0.415182 / 0.323480 (0.091702) \| 0.005796 / 0.007986 (-0.002189) \| 0.003791 / 0.004328 (-0.000537) \| 0.073505 / 0.004250 (0.069254) \| 0.060335 / 0.037052 (0.023283) \| 0.392182 / 0.258489 (0.133693) \| 0.421315 / 0.293841 (0.127474) \| 0.036128 / 0.128546 (-0.092419) \| 0.009953 / 0.075646 (-0.065693) \| 0.338965 / 0.419271 (-0.080307) \| 0.061006 / 0.043533 (0.017473) \| 0.372317 / 0.255139 (0.117178) \| 0.414367 / 0.283200 (0.131167) \| 0.026970 / 0.141683 (-0.114713) \| 1.730381 / 1.452155 (0.278227) \| 1.808340 / 1.492716 (0.315624) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222622 / 0.018006 (0.204615) \| 0.474064 / 0.000490 (0.473574) \| 0.004817 / 0.000200 (0.004617) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032528 / 0.037411 (-0.004883) \| 0.097457 / 0.014526 (0.082931) \| 0.112273 / 0.176557 (-0.064283) \| 0.177953 / 0.737135 (-0.559182) \| 0.112358 / 0.296338 (-0.183981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442601 / 0.215209 (0.227392) \| 4.442065 / 2.077655 (2.364410) \| 2.156813 / 1.504120 (0.652694) \| 1.970289 / 1.541195 (0.429094) \| 2.052878 / 1.468490 (0.584388) \| 0.562661 / 4.584777 (-4.022116) \| 4.255529 / 3.745712 (0.509817) \| 3.767650 / 5.269862 (-1.502212) \| 2.431078 / 4.565676 (-2.134598) \| 0.065624 / 0.424275 (-0.358651) \| 0.008738 / 0.007607 (0.001131) \| 0.546839 / 0.226044 (0.320795) \| 5.362863 / 2.268929 (3.093934) \| 2.695924 / 55.444624 (-52.748701) \| 2.334589 / 6.876477 (-4.541888) \| 2.530757 / 2.142072 (0.388685) \| 0.675991 / 4.805227 (-4.129236) \| 0.153852 / 6.500664 (-6.346813) \| 0.069189 / 0.075469 (-0.006280) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.522916 / 1.841788 (-0.318872) \| 21.515907 / 8.074308 (13.441599) \| 16.411708 / 10.191392 (6.220316) \| 0.168245 / 0.680424 (-0.512179) \| 0.021165 / 0.534201 (-0.513036) \| 0.461838 / 0.579283 (-0.117446) \| 0.488867 / 0.434364 (0.054503) \| 0.536278 / 0.540337 (-0.004059) \| 0.766690 / 1.386936 (-0.620246) \|\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) \| 0.007683 / 0.011353 (-0.003670) \| 0.004401 / 0.011008 (-0.006608) \| 0.075463 / 0.038508 (0.036955) \| 0.081737 / 0.023109 (0.058628) \| 0.466469 / 0.275898 (0.190571) \| 0.514909 / 0.323480 (0.191429) \| 0.006106 / 0.007986 (-0.001880) \| 0.003936 / 0.004328 (-0.000393) \| 0.076773 / 0.004250 (0.072523) \| 0.061025 / 0.037052 (0.023973) \| 0.473348 / 0.258489 (0.214858) \| 0.525326 / 0.293841 (0.231485) \| 0.038224 / 0.128546 (-0.090322) \| 0.009559 / 0.075646 (-0.066087) \| 0.080847 / 0.419271 (-0.338424) \| 0.056738 / 0.043533 (0.013205) \| 0.475116 / 0.255139 (0.219977) \| 0.494689 / 0.283200 (0.211490) \| 0.029364 / 0.141683 (-0.112319) \| 1.796681 / 1.452155 (0.344527) \| 1.850600 / 1.492716 (0.357884) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.327126 / 0.018006 (0.309119) \| 0.469186 / 0.000490 (0.468696) \| 0.050600 / 0.000200 (0.050400) \| 0.000439 / 0.000054 (0.000385) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036710 / 0.037411 (-0.000701) \| 0.108669 / 0.014526 (0.094143) \| 0.119808 / 0.176557 (-0.056748) \| 0.181501 / 0.737135 (-0.555634) \| 0.121487 / 0.296338 (-0.174852) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.509076 / 0.215209 (0.293867) \| 5.056970 / 2.077655 (2.979316) \| 2.775958 / 1.504120 (1.271838) \| 2.592548 / 1.541195 (1.051353) \| 2.654381 / 1.468490 (1.185890) \| 0.557407 / 4.584777 (-4.027370) \| 4.418232 / 3.745712 (0.672519) \| 3.698072 / 5.269862 (-1.571790) \| 2.380607 / 4.565676 (-2.185069) \| 0.066242 / 0.424275 (-0.358034) \| 0.008350 / 0.007607 (0.000743) \| 0.572354 / 0.226044 (0.346309) \| 5.857637 / 2.268929 (3.588709) \| 3.242512 / 55.444624 (-52.202112) \| 2.891144 / 6.876477 (-3.985332) \| 3.217987 / 2.142072 (1.075915) \| 0.676049 / 4.805227 (-4.129178) \| 0.155515 / 6.500664 (-6.345149) \| 0.068616 / 0.075469 (-0.006853) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.670048 / 1.841788 (-0.171740) \| 22.629573 / 8.074308 (14.555265) \| 16.887676 / 10.191392 (6.696284) \| 0.168571 / 0.680424 (-0.511853) \| 0.023361 / 0.534201 (-0.510840) \| 0.463358 / 0.579283 (-0.115925) \| 0.463278 / 0.434364 (0.028914) \| 0.602397 / 0.540337 (0.062060) \| 0.793249 / 1.386936 (-0.593687) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eee318573aba6574a43d457aa0347348c1f3e4aa \"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) \| 0.006693 / 0.011353 (-0.004660) \| 0.004100 / 0.011008 (-0.006908) \| 0.084166 / 0.038508 (0.045658) \| 0.074469 / 0.023109 (0.051360) \| 0.356092 / 0.275898 (0.080194) \| 0.392389 / 0.323480 (0.068909) \| 0.003996 / 0.007986 (-0.003990) \| 0.004020 / 0.004328 (-0.000308) \| 0.064997 / 0.004250 (0.060747) \| 0.053897 / 0.037052 (0.016845) \| 0.362942 / 0.258489 (0.104453) \| 0.408694 / 0.293841 (0.114854) \| 0.031656 / 0.128546 (-0.096890) \| 0.008713 / 0.075646 (-0.066933) \| 0.289306 / 0.419271 (-0.129966) \| 0.053067 / 0.043533 (0.009534) \| 0.358740 / 0.255139 (0.103601) \| 0.393347 / 0.283200 (0.110147) \| 0.025430 / 0.141683 (-0.116253) \| 1.486114 / 1.452155 (0.033959) \| 1.572698 / 1.492716 (0.079981) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215423 / 0.018006 (0.197417) \| 0.467694 / 0.000490 (0.467204) \| 0.003965 / 0.000200 (0.003765) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027936 / 0.037411 (-0.009475) \| 0.084235 / 0.014526 (0.069709) \| 0.136275 / 0.176557 (-0.040282) \| 0.151154 / 0.737135 (-0.585982) \| 0.185592 / 0.296338 (-0.110747) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393784 / 0.215209 (0.178575) \| 3.927878 / 2.077655 (1.850223) \| 1.961216 / 1.504120 (0.457096) \| 1.802264 / 1.541195 (0.261069) \| 1.971186 / 1.468490 (0.502696) \| 0.487981 / 4.584777 (-4.096796) \| 3.649046 / 3.745712 (-0.096666) \| 3.302471 / 5.269862 (-1.967391) \| 2.058075 / 4.565676 (-2.507602) \| 0.057072 / 0.424275 (-0.367203) \| 0.007624 / 0.007607 (0.000017) \| 0.470139 / 0.226044 (0.244095) \| 4.697711 / 2.268929 (2.428783) \| 2.494813 / 55.444624 (-52.949811) \| 2.133084 / 6.876477 (-4.743393) \| 2.329740 / 2.142072 (0.187667) \| 0.585857 / 4.805227 (-4.219371) \| 0.134442 / 6.500664 (-6.366223) \| 0.060860 / 0.075469 (-0.014609) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.248504 / 1.841788 (-0.593283) \| 19.448427 / 8.074308 (11.374119) \| 14.446139 / 10.191392 (4.254747) \| 0.168081 / 0.680424 (-0.512342) \| 0.018028 / 0.534201 (-0.516173) \| 0.395061 / 0.579283 (-0.184222) \| 0.418777 / 0.434364 (-0.015587) \| 0.454509 / 0.540337 (-0.085828) \| 0.628488 / 1.386936 (-0.758448) \|\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) \| 0.006946 / 0.011353 (-0.004406) \| 0.004096 / 0.011008 (-0.006912) \| 0.065322 / 0.038508 (0.026813) \| 0.074336 / 0.023109 (0.051227) \| 0.405327 / 0.275898 (0.129429) \| 0.436878 / 0.323480 (0.113398) \| 0.006083 / 0.007986 (-0.001902) \| 0.003345 / 0.004328 (-0.000984) \| 0.065725 / 0.004250 (0.061474) \| 0.056398 / 0.037052 (0.019345) \| 0.406906 / 0.258489 (0.148417) \| 0.443330 / 0.293841 (0.149489) \| 0.033036 / 0.128546 (-0.095510) \| 0.008503 / 0.075646 (-0.067144) \| 0.071865 / 0.419271 (-0.347406) \| 0.048956 / 0.043533 (0.005423) \| 0.404579 / 0.255139 (0.149440) \| 0.424904 / 0.283200 (0.141704) \| 0.021786 / 0.141683 (-0.119897) \| 1.491868 / 1.452155 (0.039713) \| 1.565252 / 1.492716 (0.072536) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231363 / 0.018006 (0.213357) \| 0.454962 / 0.000490 (0.454472) \| 0.004680 / 0.000200 (0.004480) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032569 / 0.037411 (-0.004843) \| 0.094928 / 0.014526 (0.080402) \| 0.108096 / 0.176557 (-0.068461) \| 0.158727 / 0.737135 (-0.578409) \| 0.106951 / 0.296338 (-0.189387) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431469 / 0.215209 (0.216260) \| 4.283929 / 2.077655 (2.206274) \| 2.283891 / 1.504120 (0.779771) \| 2.118172 / 1.541195 (0.576977) \| 2.192628 / 1.468490 (0.724138) \| 0.492026 / 4.584777 (-4.092751) \| 3.692126 / 3.745712 (-0.053587) \| 3.269827 / 5.269862 (-2.000035) \| 2.028948 / 4.565676 (-2.536728) \| 0.057932 / 0.424275 (-0.366344) \| 0.007301 / 0.007607 (-0.000306) \| 0.508411 / 0.226044 (0.282367) \| 5.072803 / 2.268929 (2.803875) \| 2.756532 / 55.444624 (-52.688092) \| 2.432192 / 6.876477 (-4.444285) \| 2.654864 / 2.142072 (0.512791) \| 0.589458 / 4.805227 (-4.215769) \| 0.133924 / 6.500664 (-6.366740) \| 0.060764 / 0.075469 (-0.014705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350737 / 1.841788 (-0.491051) \| 20.265217 / 8.074308 (12.190909) \| 14.969039 / 10.191392 (4.777647) \| 0.164226 / 0.680424 (-0.516198) \| 0.020090 / 0.534201 (-0.514111) \| 0.397010 / 0.579283 (-0.182273) \| 0.412927 / 0.434364 (-0.021437) \| 0.473931 / 0.540337 (-0.066406) \| 0.653462 / 1.386936 (-0.733474) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00cb5cc57337cdff338d7a54396bf25c5c5abd67 \"CML watermark\")\n" ]	2023-08-31T09:00:26Z	2023-08-31T13:57:31Z	2023-08-31T13:48:42Z	MEMBER	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6198.diff", "html_url": "https://github.com/huggingface/datasets/pull/6198", "merged_at": "2023-08-31T13:48:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/6198.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6198" }	After investigation, I have found that this copy forces the splits to be sorted alphabetically: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/builder.py#L556 This PR removes the alphabetically sort of `DataFilesDict` keys. - Note that for a `dict`, the order of keys is relevant when hashing: ```python hash1 = Hasher.hash({'train': 'train.csv', 'test': 'test.csv'}) hash2 = Hasher.hash({'test': 'test.csv', 'train': 'train.csv'}) assert hash1 != hash2 ``` - The `DataFilesDict` is a subclass of `dict`, thus the order should be relevant as well ```python hash1 = Hasher.hash(DataFilesDict({'train': 'train.csv', 'test': 'test.csv'})) hash2 = Hasher.hash(DataFilesDict({'test': 'test.csv', 'train': 'train.csv'})) assert hash1 != hash2 ``` Fix #6196.	{ "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/6198/reactions" }	null	null	null	true
https://github.com/huggingface/datasets/issues/6197	6,197	ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'	{ "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/exs-avianello", "id": 128361578, "login": "exs-avianello", "node_id": "U_kgDOB6akag", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "repos_url": "https://api.github.com/users/exs-avianello/repos", "site_admin": false, "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "type": "User", "url": "https://api.github.com/users/exs-avianello", "user_view_type": "public" }	[]	closed	false	[ "Thanks for reporting. We are investigating it.", "This issue is caused by latest `pandas` release 2.1.0 (released yesterday Aug 30).\r\n\r\nSee: https://github.com/huggingface/datasets/actions/runs/6035484010/job/16375932085?pr=6198\r\n", "People using previous releases of `datasets` should pin `pandas` in their local environment:\r\n```\r\npython -m pip install 'pandas<2.1.0'\r\n```" ]	2023-08-31T08:51:50Z	2023-09-01T10:35:10Z	2023-08-31T10:24:40Z	NONE	null	null	### Describe the bug Saving a dataset `.to_json()` fails with a `ValueError` since the latest `pandas` [release](https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html) (`2.1.0`) In their latest release we have: > Improved error handling when using [DataFrame.to_json()](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json) with incompatible index and orient arguments ([GH 52143](https://github.com/pandas-dev/pandas/issues/52143)) i.e. an error is now raised for invalid combinations of `index` and `orient`. This means that unfortunately the custom logic at this line might sometimes lead to contradictions: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/io/json.py#L96 e.g. for the default case `orient=records` leads to `index=True`, which now raises a `ValueError` ### Steps to reproduce the bug ```python import datasets if __name__ == '__main__': dataset = datasets.Dataset.from_dict({"A": [1, 2, 3], "B": [4, 5, 6]}) dataset.to_json("dataset.json") ``` ```shell >>> ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'. ``` ### Expected behavior The dataset is successfully saved as `.json` ### Environment info `python >= 3.9` `pandas >= 2.1.0`	{ "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" }	{ "+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/6197/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/6196	6,196	Split order is not preserved	{ "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	[]	2023-08-31T08:47:16Z	2023-08-31T13:48:43Z	2023-08-31T13:48:43Z	MEMBER	null	null	I have noticed that in some cases the split order is not preserved. For example, consider a no-script dataset with configs: ```yaml configs: - config_name: default data_files: - split: train path: train.csv - split: test path: test.csv ``` - Note the defined split order is [train, test] Once the dataset is loaded, the split order is not preserved: ```python In [16]: ds Out[16]: DatasetDict({ test: Dataset({ features: ['text', 'label'], num_rows: 1 }) train: Dataset({ features: ['text', 'label'], num_rows: 2 }) }) ``` - Note the obtained split order is [test, train]	{ "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/6196/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/6195	6,195	Force to reuse cache at given path	{ "avatar_url": "https://avatars.githubusercontent.com/u/43507393?v=4", "events_url": "https://api.github.com/users/Luosuu/events{/privacy}", "followers_url": "https://api.github.com/users/Luosuu/followers", "following_url": "https://api.github.com/users/Luosuu/following{/other_user}", "gists_url": "https://api.github.com/users/Luosuu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Luosuu", "id": 43507393, "login": "Luosuu", "node_id": "MDQ6VXNlcjQzNTA3Mzkz", "organizations_url": "https://api.github.com/users/Luosuu/orgs", "received_events_url": "https://api.github.com/users/Luosuu/received_events", "repos_url": "https://api.github.com/users/Luosuu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Luosuu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Luosuu/subscriptions", "type": "User", "url": "https://api.github.com/users/Luosuu", "user_view_type": "public" }	[]	closed	false	[ "realized that need to pass the path at `cache_file_name` like\r\n\r\n```python\r\ntokenized_datasets = raw_datasets[\"train\"].map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=[text_column_name],\r\n load_from_cache_file=True,\r\n desc=\"Running tokenizer on dataset line_by_line\",\r\n # cache_file_names= {\"train\": \"cache-1982fea76aa54a13.arrow\"}\r\n cache_file_name=\"/project/huggingface_cache/datasets/..../cache-1982fea76aa54a13.arrow\",\r\n new_fingerprint=\"1982fea76aa54a13\"\r\n )\r\n```", "Thank you so much! I went through a lot of issues before finding similar experiences here. I have to say that the [docs](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Dataset.map) of `.map()` is really misleading, probably making people think that just assigning the file name to cache_file_name is enough." ]	2023-08-30T18:44:54Z	2023-11-03T10:14:21Z	2023-08-30T19:00:45Z	NONE	null	null	### Describe the bug I have run the official example of MLM like: ```bash python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name togethercomputer/RedPajama-Data-1T \ --dataset_config_name arxiv \ --per_device_train_batch_size 10 \ --preprocessing_num_workers 20 \ --validation_split_percentage 0 \ --cache_dir /project/huggingface_cache/datasets \ --line_by_line \ --do_train \ --pad_to_max_length \ --output_dir /project/huggingface_cache/test-mlm ``` it successfully runs and at my cache folder has `cache-1982fea76aa54a13_00001_of_00020.arrow`..... `cache-1982fea76aa54a13_00020_of_00020.arrow ` as tokenization cache of `map` method. And the cache works fine every time I run the command above. However, when I switched to jupyter notebook (since I do not want to load datasets every time when I changed other parameters not related to the dataloading). It is not recognizing the cache files and starts to re-run the entire tokenization process. I changed my code to ```python tokenized_datasets = raw_datasets["train"].map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=True, desc="Running tokenizer on dataset line_by_line", # cache_file_names= {"train": "cache-1982fea76aa54a13.arrow"} cache_file_name="cache-1982fea76aa54a13.arrow", new_fingerprint="1982fea76aa54a13" ) ``` it still does not recognize the previously cached files and trying to re-run the tokenization process. ### Steps to reproduce the bug use jupyter notebook for dataset map function. ### Expected behavior the map function accepts the given cache_file_name and new_fingerprint then load the previously cached files. ### Environment info - `datasets` version: 2.14.4.dev0 - Platform: Linux-3.10.0-1160.59.1.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/43507393?v=4", "events_url": "https://api.github.com/users/Luosuu/events{/privacy}", "followers_url": "https://api.github.com/users/Luosuu/followers", "following_url": "https://api.github.com/users/Luosuu/following{/other_user}", "gists_url": "https://api.github.com/users/Luosuu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Luosuu", "id": 43507393, "login": "Luosuu", "node_id": "MDQ6VXNlcjQzNTA3Mzkz", "organizations_url": "https://api.github.com/users/Luosuu/orgs", "received_events_url": "https://api.github.com/users/Luosuu/received_events", "repos_url": "https://api.github.com/users/Luosuu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Luosuu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Luosuu/subscriptions", "type": "User", "url": "https://api.github.com/users/Luosuu", "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/6195/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/6194	6,194	Support custom fingerprinting with `Dataset.from_generator`	{ "avatar_url": "https://avatars.githubusercontent.com/u/16692099?v=4", "events_url": "https://api.github.com/users/bilelomrani1/events{/privacy}", "followers_url": "https://api.github.com/users/bilelomrani1/followers", "following_url": "https://api.github.com/users/bilelomrani1/following{/other_user}", "gists_url": "https://api.github.com/users/bilelomrani1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bilelomrani1", "id": 16692099, "login": "bilelomrani1", "node_id": "MDQ6VXNlcjE2NjkyMDk5", "organizations_url": "https://api.github.com/users/bilelomrani1/orgs", "received_events_url": "https://api.github.com/users/bilelomrani1/received_events", "repos_url": "https://api.github.com/users/bilelomrani1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bilelomrani1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bilelomrani1/subscriptions", "type": "User", "url": "https://api.github.com/users/bilelomrani1", "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	[ "The `fingerprint` parameter serves a slightly different purpose - we use it to inject a new fingerprint after transforming a `Dataset` (computed from the previous fingerprint + transform + transform args), e.g., to be able to compute the cache file for a transform. There is no concept of `fingerprint` before a `Dataset` is fully initialized, but we still need to hash the args (e.g., generator func) of the \"dataset creation methods\" (`from_generator`, `from_csv`, etc.) to compute the cache directory (to store the initial version and transformed dataset versions)\r\n\r\nI agree it should be easier to bypass the hashing mechanism in this instance, too. However, we should probably first address https://github.com/huggingface/datasets/issues/5080 before solving this (e.g., maybe exposing `hash` in `load_dataset`/`load_dataset_builder`.", "Adding +1 here:\r\n\r\nIf the generator needs to access some external resources or state, then it's not always straightforward to make it pickle-able. So I'd like to be able to override how the default cache key derivation needs to pickle the generator (and of course, I'd accept responsibility for that part of cache consistency).\r\n\r\nAppears to be a recurrent roadbump: #6118 #5963 #5819 #5750 #4983 ", "Silly hack incoming:\r\n\r\n```python\r\nimport uuid\r\n\r\nclass _DatasetGeneratorPickleHack:\r\n def __init__(self, generator, generator_id=None):\r\n self.generator = generator\r\n self.generator_id = (\r\n generator_id if generator_id is not None else str(uuid.uuid4())\r\n )\r\n\r\n def __call__(self, args, kwargs):\r\n return self.generator(kwargs, *kwargs)\r\n\r\n def __reduce__(self):\r\n return (_DatasetGeneratorPickleHack_raise, (self.generator_id,))\r\n\r\n\r\ndef _DatasetGeneratorPickleHack_raise(args, **kwargs):\r\n raise AssertionError(\"cannot actually unpickle _DatasetGeneratorPickleHack!\")\r\n```\r\n\r\nNow `Dataset.from_generator(_DatasetGeneratorPickleHack(gen))` works even if `gen` is unpicklable, because Dataset just pickles the shim object that avoids actually traversing `gen`. Then, one can work out how to set `generator_id` meaningfully to allow cache reuse.", "I'd like some way to do this too. I find that sometimes the hash doesn't cover enough, and that the dataset is not regenerated even when underlying data has changed, and by supplying a custom fingerprint I could do a better job of controlling when my dataset is regenerated.", "This is what I did and it works: \r\n\r\nhttps://github.com/stevemadere/s3-datasets/blob/e475a566a16d3051656a66f8ff4d3baa4c55a66c/src/tokengenerators/text_ds_2_tokens_generator.py#L200\r\n", "I ran into the same thing - my actual generator reads from a disk source that might have new data (images) available at some point and it ends up ignoring calling the generator. Thanks for the hack @mlin 👋 ", "just wanted to pitch my support for an easy control over the generator id. requiring that generators are pickleable just to get a unique id is limiting: plenty of classes (maybe even hf.datasets own) are written with no pickle support in mind. also as mentioned above the state of a generator might extend beyond its pickle." ]	2023-08-29T22:43:13Z	2024-12-22T01:14:39Z	null	NONE	null	null	### Feature request When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`. ### Motivation Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash. ### Your contribution If validated, I can try to submit a PR for this.	null	{ "+1": 3, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/6194/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/6193	6,193	Dataset loading script method does not work with .pyc file	{ "avatar_url": "https://avatars.githubusercontent.com/u/43389071?v=4", "events_url": "https://api.github.com/users/riteshkumarumassedu/events{/privacy}", "followers_url": "https://api.github.com/users/riteshkumarumassedu/followers", "following_url": "https://api.github.com/users/riteshkumarumassedu/following{/other_user}", "gists_url": "https://api.github.com/users/riteshkumarumassedu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/riteshkumarumassedu", "id": 43389071, "login": "riteshkumarumassedu", "node_id": "MDQ6VXNlcjQzMzg5MDcx", "organizations_url": "https://api.github.com/users/riteshkumarumassedu/orgs", "received_events_url": "https://api.github.com/users/riteshkumarumassedu/received_events", "repos_url": "https://api.github.com/users/riteshkumarumassedu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/riteshkumarumassedu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/riteshkumarumassedu/subscriptions", "type": "User", "url": "https://api.github.com/users/riteshkumarumassedu", "user_view_type": "public" }	[]	open	false	[ "Before dynamically loading `.py` scripts with `importlib.import_module`, we also parse their contents to check imports, which is tricky to implement for binary `.pyc` files (requires parsing bytecode), so I don't think this is something we want to support (unless more users request it ofc) as this use case is a bit too specific.\r\n\r\n@lhoestq What's your opinion on this?", "> Before dynamically loading .py scripts with importlib.import_module, we also parse their contents to check imports, which is tricky to implement for binary .pyc files (requires parsing bytecode), so I don't think this is something we want to support (unless more users request it ofc) as this use case is a bit too specific.\r\n\r\nYes indeed. Though you can use a .py that imports a package that contains your .pyc code and that you previously installed", "Hi @lhoestq ,\r\nCould you share some example code related to the approach that you are suggesting? " ]	2023-08-29T19:35:06Z	2023-08-31T19:47:29Z	null	NONE	null	null	### Describe the bug The huggingface dataset library specifically looks for ‘.py’ file while loading the dataset using loading script approach and it does not work with ‘.pyc’ file. While deploying in production, it becomes an issue when we are restricted to use only .pyc files. Is there any work around for this ? ### Steps to reproduce the bug 1. Create a dataset loading script to read the custom data. 2. compile the code to make sure that .pyc file is created 3. Delete the loading script and re-run the code. Usually, python should make use of complied .pyc files. However, in this case, the dataset library errors out with the message that it's unable to find the data loader loading script. ### Expected behavior The code should make use of .pyc file and run without any error. ### Environment info NA	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/6193/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/6192	6,192	Set minimal fsspec version requirement to 2023.1.0	{ "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" }	[]	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) \| 0.005972 / 0.011353 (-0.005381) \| 0.003636 / 0.011008 (-0.007372) \| 0.080254 / 0.038508 (0.041746) \| 0.059564 / 0.023109 (0.036455) \| 0.310615 / 0.275898 (0.034717) \| 0.359307 / 0.323480 (0.035827) \| 0.003408 / 0.007986 (-0.004578) \| 0.002941 / 0.004328 (-0.001388) \| 0.063699 / 0.004250 (0.059449) \| 0.046072 / 0.037052 (0.009020) \| 0.318670 / 0.258489 (0.060181) \| 0.369677 / 0.293841 (0.075836) \| 0.026995 / 0.128546 (-0.101552) \| 0.007954 / 0.075646 (-0.067693) \| 0.261667 / 0.419271 (-0.157604) \| 0.045167 / 0.043533 (0.001634) \| 0.314276 / 0.255139 (0.059137) \| 0.348871 / 0.283200 (0.065672) \| 0.021748 / 0.141683 (-0.119935) \| 1.438598 / 1.452155 (-0.013557) \| 1.530119 / 1.492716 (0.037403) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196894 / 0.018006 (0.178888) \| 0.445757 / 0.000490 (0.445267) \| 0.002842 / 0.000200 (0.002642) \| 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) \| 0.024923 / 0.037411 (-0.012488) \| 0.075186 / 0.014526 (0.060661) \| 0.087193 / 0.176557 (-0.089364) \| 0.147496 / 0.737135 (-0.589639) \| 0.087083 / 0.296338 (-0.209255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423545 / 0.215209 (0.208336) \| 4.187927 / 2.077655 (2.110273) \| 2.008656 / 1.504120 (0.504536) \| 1.791313 / 1.541195 (0.250119) \| 1.849836 / 1.468490 (0.381346) \| 0.499458 / 4.584777 (-4.085318) \| 2.983206 / 3.745712 (-0.762506) \| 2.801005 / 5.269862 (-2.468856) \| 1.886207 / 4.565676 (-2.679469) \| 0.057343 / 0.424275 (-0.366932) \| 0.006666 / 0.007607 (-0.000941) \| 0.483948 / 0.226044 (0.257904) \| 4.874818 / 2.268929 (2.605890) \| 2.439393 / 55.444624 (-53.005231) \| 2.049861 / 6.876477 (-4.826616) \| 2.217050 / 2.142072 (0.074977) \| 0.589760 / 4.805227 (-4.215467) \| 0.125298 / 6.500664 (-6.375366) \| 0.061123 / 0.075469 (-0.014347) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234721 / 1.841788 (-0.607067) \| 18.193756 / 8.074308 (10.119448) \| 13.682835 / 10.191392 (3.491443) \| 0.129345 / 0.680424 (-0.551078) \| 0.016589 / 0.534201 (-0.517612) \| 0.332355 / 0.579283 (-0.246928) \| 0.358408 / 0.434364 (-0.075955) \| 0.382044 / 0.540337 (-0.158293) \| 0.535403 / 1.386936 (-0.851533) \|\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) \| 0.006193 / 0.011353 (-0.005160) \| 0.003674 / 0.011008 (-0.007335) \| 0.062481 / 0.038508 (0.023973) \| 0.062096 / 0.023109 (0.038987) \| 0.449592 / 0.275898 (0.173694) \| 0.479245 / 0.323480 (0.155765) \| 0.004793 / 0.007986 (-0.003193) \| 0.002896 / 0.004328 (-0.001433) \| 0.062887 / 0.004250 (0.058636) \| 0.050049 / 0.037052 (0.012997) \| 0.454940 / 0.258489 (0.196451) \| 0.486115 / 0.293841 (0.192274) \| 0.028585 / 0.128546 (-0.099961) \| 0.007954 / 0.075646 (-0.067692) \| 0.067744 / 0.419271 (-0.351528) \| 0.040473 / 0.043533 (-0.003060) \| 0.448408 / 0.255139 (0.193269) \| 0.472423 / 0.283200 (0.189223) \| 0.020549 / 0.141683 (-0.121133) \| 1.563618 / 1.452155 (0.111463) \| 1.520149 / 1.492716 (0.027432) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226604 / 0.018006 (0.208598) \| 0.417615 / 0.000490 (0.417126) \| 0.003386 / 0.000200 (0.003186) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027264 / 0.037411 (-0.010147) \| 0.081709 / 0.014526 (0.067184) \| 0.091793 / 0.176557 (-0.084763) \| 0.145559 / 0.737135 (-0.591576) \| 0.091869 / 0.296338 (-0.204469) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462917 / 0.215209 (0.247708) \| 4.629512 / 2.077655 (2.551857) \| 2.555715 / 1.504120 (1.051595) \| 2.388064 / 1.541195 (0.846870) \| 2.458320 / 1.468490 (0.989830) \| 0.511615 / 4.584777 (-4.073162) \| 3.124566 / 3.745712 (-0.621146) \| 2.839190 / 5.269862 (-2.430672) \| 1.894551 / 4.565676 (-2.671126) \| 0.059565 / 0.424275 (-0.364710) \| 0.006481 / 0.007607 (-0.001126) \| 0.532023 / 0.226044 (0.305979) \| 5.361507 / 2.268929 (3.092579) \| 2.982594 / 55.444624 (-52.462031) \| 2.644870 / 6.876477 (-4.231606) \| 2.831476 / 2.142072 (0.689404) \| 0.607381 / 4.805227 (-4.197846) \| 0.126067 / 6.500664 (-6.374597) \| 0.062130 / 0.075469 (-0.013339) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350442 / 1.841788 (-0.491345) \| 18.829553 / 8.074308 (10.755245) \| 14.796701 / 10.191392 (4.605309) \| 0.145393 / 0.680424 (-0.535031) \| 0.018218 / 0.534201 (-0.515983) \| 0.335500 / 0.579283 (-0.243783) \| 0.359190 / 0.434364 (-0.075174) \| 0.388377 / 0.540337 (-0.151960) \| 0.534994 / 1.386936 (-0.851942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ff7629eb72f499d841d64aa03f97e0b1707d1cc7 \"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) \| 0.006741 / 0.011353 (-0.004612) \| 0.004097 / 0.011008 (-0.006911) \| 0.084513 / 0.038508 (0.046005) \| 0.074216 / 0.023109 (0.051107) \| 0.352481 / 0.275898 (0.076583) \| 0.394806 / 0.323480 (0.071326) \| 0.005603 / 0.007986 (-0.002383) \| 0.003482 / 0.004328 (-0.000847) \| 0.065165 / 0.004250 (0.060914) \| 0.054065 / 0.037052 (0.017013) \| 0.359399 / 0.258489 (0.100910) \| 0.409776 / 0.293841 (0.115935) \| 0.030997 / 0.128546 (-0.097550) \| 0.008717 / 0.075646 (-0.066929) \| 0.288692 / 0.419271 (-0.130579) \| 0.052372 / 0.043533 (0.008840) \| 0.353867 / 0.255139 (0.098728) \| 0.391212 / 0.283200 (0.108012) \| 0.024033 / 0.141683 (-0.117650) \| 1.496552 / 1.452155 (0.044398) \| 1.567267 / 1.492716 (0.074550) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294074 / 0.018006 (0.276067) \| 0.595421 / 0.000490 (0.594931) \| 0.003826 / 0.000200 (0.003626) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028676 / 0.037411 (-0.008736) \| 0.082064 / 0.014526 (0.067538) \| 0.542399 / 0.176557 (0.365842) \| 0.217188 / 0.737135 (-0.519947) \| 0.099364 / 0.296338 (-0.196975) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384282 / 0.215209 (0.169073) \| 3.832204 / 2.077655 (1.754550) \| 1.842500 / 1.504120 (0.338380) \| 1.668192 / 1.541195 (0.126997) \| 1.745207 / 1.468490 (0.276717) \| 0.481881 / 4.584777 (-4.102896) \| 3.677819 / 3.745712 (-0.067893) \| 3.329062 / 5.269862 (-1.940799) \| 2.056882 / 4.565676 (-2.508795) \| 0.056898 / 0.424275 (-0.367377) \| 0.007624 / 0.007607 (0.000016) \| 0.459712 / 0.226044 (0.233667) \| 4.611100 / 2.268929 (2.342171) \| 2.370244 / 55.444624 (-53.074381) \| 2.032756 / 6.876477 (-4.843721) \| 2.336056 / 2.142072 (0.193984) \| 0.583503 / 4.805227 (-4.221725) \| 0.135041 / 6.500664 (-6.365623) \| 0.062245 / 0.075469 (-0.013224) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303894 / 1.841788 (-0.537894) \| 20.315185 / 8.074308 (12.240876) \| 14.388779 / 10.191392 (4.197387) \| 0.169060 / 0.680424 (-0.511364) \| 0.018609 / 0.534201 (-0.515592) \| 0.395140 / 0.579283 (-0.184143) \| 0.418231 / 0.434364 (-0.016133) \| 0.461496 / 0.540337 (-0.078842) \| 0.630298 / 1.386936 (-0.756638) \|\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) \| 0.006999 / 0.011353 (-0.004354) \| 0.004197 / 0.011008 (-0.006812) \| 0.064524 / 0.038508 (0.026016) \| 0.078791 / 0.023109 (0.055682) \| 0.397563 / 0.275898 (0.121665) \| 0.423056 / 0.323480 (0.099576) \| 0.005697 / 0.007986 (-0.002288) \| 0.003592 / 0.004328 (-0.000736) \| 0.066178 / 0.004250 (0.061928) \| 0.058114 / 0.037052 (0.021062) \| 0.398619 / 0.258489 (0.140130) \| 0.435496 / 0.293841 (0.141655) \| 0.032758 / 0.128546 (-0.095788) \| 0.008677 / 0.075646 (-0.066970) \| 0.071359 / 0.419271 (-0.347913) \| 0.048636 / 0.043533 (0.005103) \| 0.389762 / 0.255139 (0.134623) \| 0.412109 / 0.283200 (0.128910) \| 0.023511 / 0.141683 (-0.118172) \| 1.514768 / 1.452155 (0.062613) \| 1.580163 / 1.492716 (0.087446) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.370491 / 0.018006 (0.352485) \| 0.529751 / 0.000490 (0.529261) \| 0.016959 / 0.000200 (0.016759) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033361 / 0.037411 (-0.004051) \| 0.091610 / 0.014526 (0.077084) \| 0.106642 / 0.176557 (-0.069915) \| 0.160906 / 0.737135 (-0.576229) \| 0.106894 / 0.296338 (-0.189444) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429932 / 0.215209 (0.214723) \| 4.276459 / 2.077655 (2.198804) \| 2.268518 / 1.504120 (0.764398) \| 2.092512 / 1.541195 (0.551317) \| 2.182218 / 1.468490 (0.713728) \| 0.494464 / 4.584777 (-4.090313) \| 3.750731 / 3.745712 (0.005019) \| 3.352370 / 5.269862 (-1.917492) \| 2.105630 / 4.565676 (-2.460046) \| 0.058465 / 0.424275 (-0.365810) \| 0.007449 / 0.007607 (-0.000158) \| 0.506896 / 0.226044 (0.280851) \| 5.070201 / 2.268929 (2.801272) \| 2.758128 / 55.444624 (-52.686496) \| 2.408378 / 6.876477 (-4.468099) \| 2.690633 / 2.142072 (0.548561) \| 0.595662 / 4.805227 (-4.209565) \| 0.134355 / 6.500664 (-6.366309) \| 0.060113 / 0.075469 (-0.015356) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.380413 / 1.841788 (-0.461375) \| 20.691210 / 8.074308 (12.616901) \| 15.682282 / 10.191392 (5.490890) \| 0.165887 / 0.680424 (-0.514536) \| 0.020541 / 0.534201 (-0.513660) \| 0.397846 / 0.579283 (-0.181437) \| 0.425374 / 0.434364 (-0.008990) \| 0.476261 / 0.540337 (-0.064076) \| 0.648617 / 1.386936 (-0.738319) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88797b8827334674d7f78c39171c00f0a28ceed6 \"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) \| 0.008787 / 0.011353 (-0.002566) \| 0.007569 / 0.011008 (-0.003439) \| 0.103918 / 0.038508 (0.065410) \| 0.083347 / 0.023109 (0.060238) \| 0.441838 / 0.275898 (0.165940) \| 0.420202 / 0.323480 (0.096722) \| 0.007295 / 0.007986 (-0.000690) \| 0.005366 / 0.004328 (0.001037) \| 0.082659 / 0.004250 (0.078409) \| 0.059711 / 0.037052 (0.022658) \| 0.401821 / 0.258489 (0.143332) \| 0.432906 / 0.293841 (0.139065) \| 0.048662 / 0.128546 (-0.079885) \| 0.014091 / 0.075646 (-0.061555) \| 0.352583 / 0.419271 (-0.066689) \| 0.064739 / 0.043533 (0.021206) \| 0.410890 / 0.255139 (0.155751) \| 0.443450 / 0.283200 (0.160251) \| 0.035817 / 0.141683 (-0.105866) \| 1.754687 / 1.452155 (0.302532) \| 1.887338 / 1.492716 (0.394622) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209440 / 0.018006 (0.191434) \| 0.519641 / 0.000490 (0.519152) \| 0.005726 / 0.000200 (0.005526) \| 0.000107 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031027 / 0.037411 (-0.006384) \| 0.097503 / 0.014526 (0.082977) \| 0.106985 / 0.176557 (-0.069572) \| 0.178235 / 0.737135 (-0.558900) \| 0.108110 / 0.296338 (-0.188228) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.594325 / 0.215209 (0.379116) \| 6.159414 / 2.077655 (4.081759) \| 2.664892 / 1.504120 (1.160772) \| 2.363355 / 1.541195 (0.822160) \| 2.410754 / 1.468490 (0.942264) \| 0.842557 / 4.584777 (-3.742220) \| 5.112059 / 3.745712 (1.366347) \| 4.633152 / 5.269862 (-0.636709) \| 2.965891 / 4.565676 (-1.599785) \| 0.097922 / 0.424275 (-0.326353) \| 0.008602 / 0.007607 (0.000995) \| 0.773029 / 0.226044 (0.546985) \| 7.462314 / 2.268929 (5.193386) \| 3.584776 / 55.444624 (-51.859848) \| 2.752375 / 6.876477 (-4.124102) \| 2.976345 / 2.142072 (0.834272) \| 1.049423 / 4.805227 (-3.755804) \| 0.212001 / 6.500664 (-6.288663) \| 0.074095 / 0.075469 (-0.001374) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.577905 / 1.841788 (-0.263883) \| 23.280931 / 8.074308 (15.206623) \| 21.017946 / 10.191392 (10.826554) \| 0.228746 / 0.680424 (-0.451678) \| 0.027877 / 0.534201 (-0.506324) \| 0.469173 / 0.579283 (-0.110110) \| 0.567614 / 0.434364 (0.133250) \| 0.545041 / 0.540337 (0.004704) \| 0.754743 / 1.386936 (-0.632194) \|\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) \| 0.008958 / 0.011353 (-0.002395) \| 0.005077 / 0.011008 (-0.005931) \| 0.083990 / 0.038508 (0.045482) \| 0.078586 / 0.023109 (0.055476) \| 0.482164 / 0.275898 (0.206266) \| 0.525575 / 0.323480 (0.202095) \| 0.006031 / 0.007986 (-0.001955) \| 0.003922 / 0.004328 (-0.000407) \| 0.084547 / 0.004250 (0.080296) \| 0.064539 / 0.037052 (0.027487) \| 0.501256 / 0.258489 (0.242767) \| 0.531985 / 0.293841 (0.238144) \| 0.050438 / 0.128546 (-0.078109) \| 0.014004 / 0.075646 (-0.061642) \| 0.091269 / 0.419271 (-0.328003) \| 0.060825 / 0.043533 (0.017292) \| 0.492573 / 0.255139 (0.237434) \| 0.517060 / 0.283200 (0.233861) \| 0.033576 / 0.141683 (-0.108107) \| 1.775719 / 1.452155 (0.323564) \| 1.866865 / 1.492716 (0.374149) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225026 / 0.018006 (0.207020) \| 0.510715 / 0.000490 (0.510225) \| 0.005791 / 0.000200 (0.005591) \| 0.000116 / 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) \| 0.032795 / 0.037411 (-0.004616) \| 0.109206 / 0.014526 (0.094680) \| 0.121441 / 0.176557 (-0.055115) \| 0.179735 / 0.737135 (-0.557401) \| 0.115825 / 0.296338 (-0.180514) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633259 / 0.215209 (0.418050) \| 6.298084 / 2.077655 (4.220430) \| 2.892604 / 1.504120 (1.388484) \| 2.570858 / 1.541195 (1.029663) \| 2.611441 / 1.468490 (1.142951) \| 0.897801 / 4.584777 (-3.686976) \| 5.185863 / 3.745712 (1.440151) \| 4.656897 / 5.269862 (-0.612965) \| 3.078575 / 4.565676 (-1.487101) \| 0.100563 / 0.424275 (-0.323712) \| 0.008368 / 0.007607 (0.000761) \| 0.749152 / 0.226044 (0.523108) \| 7.687484 / 2.268929 (5.418556) \| 3.689238 / 55.444624 (-51.755387) \| 2.896779 / 6.876477 (-3.979698) \| 3.158688 / 2.142072 (1.016615) \| 1.083490 / 4.805227 (-3.721737) \| 0.216994 / 6.500664 (-6.283670) \| 0.074053 / 0.075469 (-0.001416) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.732812 / 1.841788 (-0.108976) \| 23.952127 / 8.074308 (15.877819) \| 22.078140 / 10.191392 (11.886748) \| 0.229491 / 0.680424 (-0.450933) \| 0.032070 / 0.534201 (-0.502131) \| 0.503344 / 0.579283 (-0.075939) \| 0.588489 / 0.434364 (0.154125) \| 0.550199 / 0.540337 (0.009861) \| 0.778203 / 1.386936 (-0.608733) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e95a508b8d1747b5331bdbbd3e1021e97602c49 \"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) \| 0.007569 / 0.011353 (-0.003784) \| 0.004447 / 0.011008 (-0.006561) \| 0.098573 / 0.038508 (0.060064) \| 0.081743 / 0.023109 (0.058634) \| 0.379912 / 0.275898 (0.104013) \| 0.411203 / 0.323480 (0.087723) \| 0.004492 / 0.007986 (-0.003494) \| 0.005627 / 0.004328 (0.001298) \| 0.075974 / 0.004250 (0.071724) \| 0.062512 / 0.037052 (0.025459) \| 0.386971 / 0.258489 (0.128482) \| 0.433299 / 0.293841 (0.139458) \| 0.035935 / 0.128546 (-0.092611) \| 0.009845 / 0.075646 (-0.065801) \| 0.342940 / 0.419271 (-0.076331) \| 0.061343 / 0.043533 (0.017810) \| 0.381984 / 0.255139 (0.126845) \| 0.417921 / 0.283200 (0.134721) \| 0.028469 / 0.141683 (-0.113214) \| 1.758472 / 1.452155 (0.306317) \| 1.847768 / 1.492716 (0.355051) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234297 / 0.018006 (0.216291) \| 0.520020 / 0.000490 (0.519531) \| 0.007375 / 0.000200 (0.007175) \| 0.000767 / 0.000054 (0.000713) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032738 / 0.037411 (-0.004673) \| 0.097656 / 0.014526 (0.083130) \| 0.112476 / 0.176557 (-0.064080) \| 0.179222 / 0.737135 (-0.557913) \| 0.113638 / 0.296338 (-0.182700) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453677 / 0.215209 (0.238467) \| 4.528143 / 2.077655 (2.450489) \| 2.243874 / 1.504120 (0.739754) \| 2.051546 / 1.541195 (0.510351) \| 2.196050 / 1.468490 (0.727560) \| 0.567345 / 4.584777 (-4.017432) \| 4.133591 / 3.745712 (0.387879) \| 3.855286 / 5.269862 (-1.414576) \| 2.393496 / 4.565676 (-2.172180) \| 0.066567 / 0.424275 (-0.357708) \| 0.009038 / 0.007607 (0.001431) \| 0.549166 / 0.226044 (0.323122) \| 5.472767 / 2.268929 (3.203839) \| 2.788012 / 55.444624 (-52.656612) \| 2.426132 / 6.876477 (-4.450345) \| 2.684856 / 2.142072 (0.542784) \| 0.680198 / 4.805227 (-4.125029) \| 0.157782 / 6.500664 (-6.342882) \| 0.073000 / 0.075469 (-0.002469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.622435 / 1.841788 (-0.219352) \| 22.965715 / 8.074308 (14.891407) \| 16.626903 / 10.191392 (6.435511) \| 0.197156 / 0.680424 (-0.483268) \| 0.025599 / 0.534201 (-0.508602) \| 0.495550 / 0.579283 (-0.083733) \| 0.466575 / 0.434364 (0.032211) \| 0.565862 / 0.540337 (0.025525) \| 0.793835 / 1.386936 (-0.593102) \|\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) \| 0.007721 / 0.011353 (-0.003632) \| 0.004652 / 0.011008 (-0.006356) \| 0.076636 / 0.038508 (0.038127) \| 0.082183 / 0.023109 (0.059074) \| 0.474665 / 0.275898 (0.198767) \| 0.511593 / 0.323480 (0.188113) \| 0.006240 / 0.007986 (-0.001746) \| 0.003750 / 0.004328 (-0.000578) \| 0.076939 / 0.004250 (0.072689) \| 0.063333 / 0.037052 (0.026281) \| 0.476469 / 0.258489 (0.217980) \| 0.512514 / 0.293841 (0.218674) \| 0.037802 / 0.128546 (-0.090744) \| 0.009975 / 0.075646 (-0.065671) \| 0.084190 / 0.419271 (-0.335081) \| 0.056705 / 0.043533 (0.013172) \| 0.475429 / 0.255139 (0.220290) \| 0.496414 / 0.283200 (0.213215) \| 0.026039 / 0.141683 (-0.115644) \| 1.796059 / 1.452155 (0.343905) \| 1.867461 / 1.492716 (0.374745) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285219 / 0.018006 (0.267213) \| 0.506311 / 0.000490 (0.505821) \| 0.018545 / 0.000200 (0.018345) \| 0.000142 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037832 / 0.037411 (0.000420) \| 0.110437 / 0.014526 (0.095911) \| 0.122953 / 0.176557 (-0.053604) \| 0.187049 / 0.737135 (-0.550087) \| 0.123539 / 0.296338 (-0.172800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.508120 / 0.215209 (0.292911) \| 5.082836 / 2.077655 (3.005182) \| 2.800411 / 1.504120 (1.296291) \| 2.579457 / 1.541195 (1.038262) \| 2.645945 / 1.468490 (1.177455) \| 0.578574 / 4.584777 (-4.006203) \| 4.163401 / 3.745712 (0.417689) \| 3.858575 / 5.269862 (-1.411286) \| 2.389892 / 4.565676 (-2.175785) \| 0.068639 / 0.424275 (-0.355636) \| 0.008779 / 0.007607 (0.001172) \| 0.598925 / 0.226044 (0.372880) \| 5.987147 / 2.268929 (3.718219) \| 3.361791 / 55.444624 (-52.082833) \| 2.910425 / 6.876477 (-3.966051) \| 3.156849 / 2.142072 (1.014776) \| 0.690945 / 4.805227 (-4.114283) \| 0.157441 / 6.500664 (-6.343223) \| 0.071596 / 0.075469 (-0.003873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.672763 / 1.841788 (-0.169025) \| 23.599525 / 8.074308 (15.525217) \| 17.520087 / 10.191392 (7.328695) \| 0.169174 / 0.680424 (-0.511250) \| 0.023470 / 0.534201 (-0.510731) \| 0.469234 / 0.579283 (-0.110050) \| 0.470020 / 0.434364 (0.035656) \| 0.579949 / 0.540337 (0.039611) \| 0.771353 / 1.386936 (-0.615583) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029227a116c14720afca71b9b22e78eb2a1c09a6 \"CML watermark\")\n" ]	2023-08-29T15:23:41Z	2023-08-30T14:01:56Z	2023-08-30T13:51:32Z	COLLABORATOR	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6192.diff", "html_url": "https://github.com/huggingface/datasets/pull/6192", "merged_at": "2023-08-30T13:51:32Z", "patch_url": "https://github.com/huggingface/datasets/pull/6192.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6192" }	Fix https://github.com/huggingface/datasets/issues/6141 Colab installs 2023.6.0, so we should be good 🙂	{ "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/6192/reactions" }	null	null	null	true

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