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https://github.com/huggingface/datasets/pull/1180
| 1,180
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Add KorQuAD v2 Dataset
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"looks like this PR also includes the changes for the V1\r\nCould you only include the files of the V2 ?",
"hmm I have made the dummy data lighter retested on local and it passed not sure why it fails here?",
"merging since the CI is fixed on master"
] | 2020-12-05T21:33:34Z
| 2020-12-16T16:10:30Z
| 2020-12-16T16:10:30Z
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# The Korean Question Answering Dataset v2
Adding the [KorQuAD](https://korquad.github.io/) v2 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD and is an extension of [squad_kor_v1](https://github.com/huggingface/datasets/pull/1178) which is why I added it as `squad_kor_v2`.
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles. Differently from V1 it includes the html structure and markup, which makes it a different enough dataset. (doesn't share ids between v1 and v2 either)
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Edit: 🤦 looks like squad_kor_v1 commit sneaked in here too
|
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https://github.com/huggingface/datasets/pull/1179
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Small update to the doc: add flatten_indices in doc
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Small update to the doc: add flatten_indices in doc
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https://github.com/huggingface/datasets/pull/1178
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Add KorQuAD v1 Dataset
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[] | 2020-12-05T21:25:46Z
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# The Korean Question Answering Dataset
Adding the [KorQuAD](https://korquad.github.io/KorQuad%201.0/) v1 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD which is why I added it as `squad_kor_v1`. There is also a v2 which I added [here](https://github.com/huggingface/datasets/pull/1180).
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
|
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https://github.com/huggingface/datasets/pull/1177
| 1,177
|
Add Korean NER dataset
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"Closed via #1219 "
] | 2020-12-05T20:56:00Z
| 2020-12-06T20:19:48Z
| 2020-12-06T20:19:48Z
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This PR adds the [Korean named entity recognition dataset](https://github.com/kmounlp/NER). This dataset has been used in many downstream tasks, such as training [KoBERT](https://github.com/SKTBrain/KoBERT) for NER, as seen in this [KoBERT-CRF implementation](https://github.com/eagle705/pytorch-bert-crf-ner).
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https://github.com/huggingface/datasets/pull/1176
| 1,176
|
Add OpenPI Dataset
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[
"Hi @Bharat123rox ! It looks like some of the dummy data is broken or missing. Did you auto-generate it? Does the local test pass for you?",
"@yjernite requesting you to have a look as to why the tests are failing only on Windows, there seems to be a backslash error somewhere, could it be the result of `os.path.join` and what should be the fix for this?",
"This is the `black` output locally:\r\n```\r\n(datasets_env) datasets (openpi) > black --check --line-length 119 --target-version py36 datasets/openpi/\r\nAll done! ✨ 🍰 ✨\r\n1 file would be left unchanged.\r\n```",
"Can you check your version of black (should be `20.8b1`) and run `make style again`? (And don't forget to rebase before pushing ;) )\r\n\r\nThe other test was a time-out error so should be good on the next commit",
"Thanks @yjernite the CI tests finally passed!!",
"Hi @Bharat123rox did you manage to join the different config into one using the IDs ?\r\n\r\nFeel free to ping me when you're ready for the next review :) ",
"> Hi @Bharat123rox did you manage to join the different config into one using the IDs ?\n> \n> Feel free to ping me when you're ready for the next review :) \n\nNot yet @lhoestq still working on this! Meanwhile please review #1507 where I added the SelQA dataset :)",
"Ok ! Let me review SelQA then :) \r\nThanks for your help !",
"Apologies for the very late response. Here is the openpi dataset file with a single file per partition after merging `id_answers, answers.jsonl, question.jsonl , question_metadata.jsonl`\r\n\r\nhttps://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl",
"Nice thank you @nikett !",
"Hi @Bharat123rox , when you get a chance, please feel free to use the dataset from the repo ( [Link](https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl) ) . Please let me know if any file is missing! Thank you ",
"Hi @Bharat123rox are you working on this? ",
"@nikett Sorry I'm no longer working on this as I'm out of time for it, please feel free to raise a new PR for this\r\n\r\n",
"We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest to create this dataset there. Please, feel free to tell us if you need some help."
] | 2020-12-05T20:54:06Z
| 2022-10-03T09:39:54Z
| 2022-10-03T09:39:54Z
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Add the OpenPI Dataset by AI2 (AllenAI)
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https://github.com/huggingface/datasets/pull/1175
| 1,175
|
added ReDial dataset
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"merging since the CI is fixed on master"
] | 2020-12-05T20:04:18Z
| 2020-12-07T13:21:43Z
| 2020-12-07T13:21:43Z
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Updating README
Dataset link: https://redialdata.github.io/website/datasheet
|
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https://github.com/huggingface/datasets/pull/1174
| 1,174
|
Add Universal Morphologies
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"Sorry for the delay, changed the default language to \"ady\" (first alphabetical) and only downloading the relevant files for each config (dataset_infos is till 918KB though)",
"Thanks for merging it ! Looks all good\r\n\r\nLooks like I didn't reply to your last message, sorry about that.\r\nFeel free to ping me when this happens :) "
] | 2020-12-05T19:54:43Z
| 2021-01-26T16:50:16Z
| 2021-01-26T16:41:48Z
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Adding unimorph universal morphology annotations for 110 languages, pfew!!!
one lemma per row with all possible forms and annotations
https://unimorph.github.io/
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https://github.com/huggingface/datasets/pull/1173
| 1,173
|
add wikipedia biography dataset
|
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"Does anyone know why am I getting this \"Some checks were not successful\" message? For the _code_quality_ one, I have successfully run the flake8 command.",
"Ok, I need to update the README.md, but don't know if that will fix the errors",
"Hi @ACR0S , thanks for adding the dataset!\r\n\r\nIt looks like `black` is throwing the code quality error: you need to run `make style` with the latest version of `black` (`black --version` should return 20.8b1)\r\n\r\nWe also added a requirement to specify encodings when using the python `open` function (line 163 in the current version of your script)\r\n\r\nFinally, you will need to add the tags and field descriptions to the README as described here https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nLet us know if you have any further questions!",
"Also, please leave the full template of the readme with the `[More Information Needed]` paragraphs: you don't have to fill them out now but it will make it easier for us to go back to later :) ",
"Thank you for your help, @yjernite! I have updated everything (finally run the _make style_, added the tags, the ecoding to the _open_ function and put back the empty fields in the README). Hope it works now! :)",
"LGTM!",
"merging since the CI is fixed on master"
] | 2020-12-05T19:14:50Z
| 2020-12-07T11:13:14Z
| 2020-12-07T11:13:14Z
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My first PR containing the Wikipedia biographies dataset. I have followed all the steps in the [guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). It passes all the tests.
|
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https://github.com/huggingface/datasets/pull/1172
| 1,172
|
Add proto_qa dataset
|
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| 2020-12-07T11:12:24Z
| 2020-12-07T11:12:24Z
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Added dataset tags as required.
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https://github.com/huggingface/datasets/pull/1171
| 1,171
|
Add imdb Urdu Reviews dataset.
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"merging since the CI is fixed on master"
] | 2020-12-05T18:46:05Z
| 2020-12-07T11:11:17Z
| 2020-12-07T11:11:17Z
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Added the imdb Urdu reviews dataset. More info about the dataset over <a href="https://github.com/mirfan899/Urdu">here</a>.
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https://github.com/huggingface/datasets/pull/1170
| 1,170
|
Fix path handling for Windows
|
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[
"@lhoestq here's the fix!"
] | 2020-12-05T18:31:54Z
| 2020-12-07T10:47:23Z
| 2020-12-07T10:47:23Z
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https://github.com/huggingface/datasets/pull/1169
| 1,169
|
Add Opus fiskmo dataset for Finnish and Swedish for MT task
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| 2020-12-07T11:04:11Z
| 2020-12-07T11:04:11Z
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Adding fiskmo, a massive parallel corpus for Finnish and Swedish.
for more info : http://opus.nlpl.eu/fiskmo.php
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https://github.com/huggingface/datasets/pull/1168
| 1,168
|
Add Naver sentiment movie corpus
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| 2020-12-07T13:34:09Z
| 2020-12-07T13:34:09Z
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This PR adds the [Naver sentiment movie corpus](https://github.com/e9t/nsmc), a dataset containing Korean movie reviews from Naver, the most commonly used search engine in Korea. This dataset is often used to benchmark models on Korean NLP tasks, as seen in [this paper](https://www.aclweb.org/anthology/2020.lrec-1.199.pdf).
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https://github.com/huggingface/datasets/issues/1167
| 1,167
|
❓ On-the-fly tokenization with datasets, tokenizers, and torch Datasets and Dataloaders
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[
"We're working on adding on-the-fly transforms in datasets.\r\nCurrently the only on-the-fly functions that can be applied are in `set_format` in which we transform the data in either numpy/torch/tf tensors or pandas.\r\nFor example\r\n```python\r\ndataset.set_format(\"torch\")\r\n```\r\napplies `torch.Tensor` to the dataset entries on-the-fly.\r\n\r\nWe plan to extend this to user-defined formatting transforms.\r\nFor example\r\n```python\r\ndataset.set_format(transform=tokenize)\r\n```\r\n\r\nWhat do you think ?",
"You can now use `set_transform` to define custom formatting transforms. "
] | 2020-12-05T17:02:56Z
| 2023-07-20T15:49:42Z
| 2023-07-20T15:49:42Z
|
NONE
| null | null |
Hi there,
I have a question regarding "on-the-fly" tokenization. This question was elicited by reading the "How to train a new language model from scratch using Transformers and Tokenizers" [here](https://huggingface.co/blog/how-to-train). Towards the end there is this sentence: "If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step". I've tried coming up with a solution that would combine both `datasets` and `tokenizers`, but did not manage to find a good pattern.
I guess the solution would entail wrapping a dataset into a Pytorch dataset.
As a concrete example from the [docs](https://huggingface.co/transformers/custom_datasets.html)
```python
import torch
class SquadDataset(torch.utils.data.Dataset):
def __init__(self, encodings):
# instead of doing this beforehand, I'd like to do tokenization on the fly
self.encodings = encodings
def __getitem__(self, idx):
return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
def __len__(self):
return len(self.encodings.input_ids)
train_dataset = SquadDataset(train_encodings)
```
How would one implement this with "on-the-fly" tokenization exploiting the vectorized capabilities of tokenizers?
----
Edit: I have come up with this solution. It does what I want, but I feel it's not very elegant
```python
class CustomPytorchDataset(Dataset):
def __init__(self):
self.dataset = some_hf_dataset(...)
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
def __getitem__(self, batch_idx):
instance = self.dataset[text_col][batch_idx]
tokenized_text = self.tokenizer(instance, truncation=True, padding=True)
return tokenized_text
def __len__(self):
return len(self.dataset)
@staticmethod
def collate_fn(batch):
# batch is a list, however it will always contain 1 item because we should not use the
# batch_size argument as batch_size is controlled by the sampler
return {k: torch.tensor(v) for k, v in batch[0].items()}
torch_ds = CustomPytorchDataset()
# NOTE: batch_sampler returns list of integers and since here we have SequentialSampler
# it returns: [1, 2, 3], [4, 5, 6], etc. - check calling `list(batch_sampler)`
batch_sampler = BatchSampler(SequentialSampler(torch_ds), batch_size=3, drop_last=True)
# NOTE: no `batch_size` as now the it is controlled by the sampler!
dl = DataLoader(dataset=torch_ds, sampler=batch_sampler, collate_fn=torch_ds.collate_fn)
```
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Opus montenegrinsubs
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Opus montenegrinsubs - language pair en-me
more info : http://opus.nlpl.eu/MontenegrinSubs.php
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https://github.com/huggingface/datasets/pull/1165
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Add ar rest reviews
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[
"Copy-pasted from the Slack discussion:\r\nthe annotation and language creators should be found , not unknown\r\nthe example should go under the \"Data Instances\" paragraph, not \"Data fields\"\r\ncan you remove the abstract from the citation and add it to the dataset description? More people will see that",
"@yjernite done! thanks for the feedback",
"@lhoestq not sure why it's failing tests now, I only changed cosmetics",
"You can ignores these errors\r\n```\r\n\r\n=========================== short test summary info ===========================\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_chr_en\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_chr_en\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_great_code\r\n```\r\n\r\nthey're fixed on master",
"Feel free to ping me for the final review once you managed to change to ClassLabel :) ",
"Hey @lhoestq I was able to fix it !! I think the same errors appeared on circleCI and now it's hopefully ready to be merged?",
"@lhoestq done! thanks for your review ",
"merging since the CI is fixed on master"
] | 2020-12-05T16:56:42Z
| 2020-12-21T17:06:23Z
| 2020-12-21T17:06:23Z
|
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|
added restaurants reviews in Arabic for sentiment analysis tasks
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https://github.com/huggingface/datasets/pull/1164
| 1,164
|
Add DaNe dataset
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[
"Thanks, this looks great!\r\n\r\nFor the code quality test, it looks like `flake8` is throwing the error, so you can tun `flake8 datasets` locally and fix the errors it points out until it passes"
] | 2020-12-05T16:36:50Z
| 2020-12-08T12:50:18Z
| 2020-12-08T12:49:55Z
|
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https://github.com/huggingface/datasets/pull/1163
| 1,163
|
Added memat : Xhosa-English parallel corpora
|
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[
"The `RemoteDatasetTest` CI fail is fixed on master so it's fine",
"merging since the CI is fixed on master"
] | 2020-12-05T16:08:50Z
| 2020-12-07T10:40:24Z
| 2020-12-07T10:40:24Z
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Added memat : Xhosa-English parallel corpora
for more info : http://opus.nlpl.eu/memat.php
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https://github.com/huggingface/datasets/pull/1162
| 1,162
|
Add Mocha dataset
|
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[] | 2020-12-05T15:45:14Z
| 2020-12-07T10:09:39Z
| 2020-12-07T10:09:39Z
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More information: https://allennlp.org/mocha
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https://github.com/huggingface/datasets/pull/1161
| 1,161
|
Linguisticprobing
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[
"Thanks for your contribution, @sileod.\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nAs you already created this dataset under your organization namespace (https://huggingface.co/datasets/metaeval/linguisticprobing), I think we can safely close this PR.\r\n\r\nWe would suggest you add a dataset card with the YAML tags, to make it searchable and discoverable.\r\n\r\nPlease, feel free to tell us if you need some help."
] | 2020-12-05T15:35:18Z
| 2022-10-03T09:40:04Z
| 2022-10-03T09:40:04Z
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Adding Linguistic probing datasets from
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
https://www.aclweb.org/anthology/P18-1198/
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|
https://github.com/huggingface/datasets/pull/1160
| 1,160
|
adding TabFact dataset
|
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[
"FYI you guys are on GitHub's homepage 😍\r\n\r\n<img width=\"1589\" alt=\"Screenshot 2020-12-09 at 12 34 28\" src=\"https://user-images.githubusercontent.com/326577/101624883-a0ecc700-39e8-11eb-8a97-11af0d036536.png\">\r\n",
"Yeayy 😍 🔥"
] | 2020-12-05T13:05:52Z
| 2020-12-09T11:41:39Z
| 2020-12-09T09:12:41Z
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Adding TabFact: A Large-scale Dataset for Table-based Fact Verification.
https://github.com/wenhuchen/Table-Fact-Checking
- The tables are stored as individual csv files, so need to download 16,573 🤯 csv files. As a result the `datasets_infos.json` file is huge (6.62 MB).
- Original dataset has nested structure where, where table is one example and each table has multiple statements,
flattening the structure here so that each statement is one example.
|
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|
https://github.com/huggingface/datasets/pull/1159
| 1,159
|
Add Roman Urdu dataset
|
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[] | 2020-12-05T11:36:43Z
| 2020-12-07T13:41:21Z
| 2020-12-07T09:59:03Z
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This PR adds the [Roman Urdu dataset](https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set#).
|
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https://github.com/huggingface/datasets/pull/1158
| 1,158
|
Add BBC Hindi NLI Dataset
|
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[
"Hi @avinsit123 !\r\nDid you manage to rename the dataset and apply the suggestion I mentioned for the data fields ?\r\nFeel free to ping me when you're ready for a review :) ",
"Hi @avinsit123 ! Have you had a chance to take a look at my suggestions ?\r\nLet me know if you have questions or if I can help",
"@lhoestq sorry I completely forgot about this pr. I will complete it ASAP.",
"@lhoestq I have fixed the code to resolve all your comments. Pls do check. I also don't seem to know why the CI tests are failing as I ran all the tests in CONTRIBUTING.md on my local pc and they passed.",
"@lhoestq thanks for ur patient review :) . I also wish to add similar 3 more NLI hindi datasets. Hope to do within this week.",
"@lhoestq would this be merged to master?",
"Yes of course ;)\r\nmerging now !"
] | 2020-12-05T11:25:34Z
| 2021-02-05T09:48:31Z
| 2021-02-05T09:48:31Z
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# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : "https://www.aclweb.org/anthology/2020.aacl-main.71"
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
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https://github.com/huggingface/datasets/pull/1157
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Add dataset XhosaNavy English -Xhosa
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[] | 2020-12-05T11:19:54Z
| 2020-12-07T09:11:33Z
| 2020-12-07T09:11:33Z
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Add dataset XhosaNavy English -Xhosa
More info : http://opus.nlpl.eu/XhosaNavy.php
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https://github.com/huggingface/datasets/pull/1156
| 1,156
|
add telugu-news corpus
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[] | 2020-12-05T11:07:56Z
| 2020-12-07T09:08:48Z
| 2020-12-07T09:08:48Z
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Adding Telugu News Corpus to datasets.
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https://github.com/huggingface/datasets/pull/1155
| 1,155
|
Add BSD
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[
"Glad to have more Japanese data! Couple of comments:\r\n- the abbreviation might confuse some people as there is also an OPUS BSD corpus, would you mind renaming it as `bsd_ja_en`?\r\n- `flake8` is throwing some errors, you can run it locally (`flake8 datasets`) and fix what it tells you until it's happy :)\r\n- We're not using `os.path.join` for URLs as it's unstable across systems (introduces backslashes on Windows). Can you write the URLs explicitly instead?\r\n\r\nThanks!",
"Fantastic, looks great!",
"> Fantastic, looks great!\r\n\r\nThanks for your help @yjernite, really appreciate it!",
"The RemoteDatasetTest is fixed on master so it's fine",
"merging since the CI is fixed on master"
] | 2020-12-05T10:43:48Z
| 2020-12-07T09:27:46Z
| 2020-12-07T09:27:46Z
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This PR adds BSD, the Japanese-English business dialogue corpus by
[Rikters et al., 2020](https://www.aclweb.org/anthology/D19-5204.pdf).
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https://github.com/huggingface/datasets/pull/1154
| 1,154
|
Opus sardware
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[] | 2020-12-05T10:38:02Z
| 2020-12-05T17:05:45Z
| 2020-12-05T17:05:45Z
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Added Opus sardware dataset for machine translation English to Sardinian.
for more info : http://opus.nlpl.eu/sardware.php
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https://github.com/huggingface/datasets/pull/1153
| 1,153
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Adding dataset for proto_qa in huggingface datasets library
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| 2020-12-05T18:53:10Z
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Added dataset for ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Followed all steps for adding a new dataset.
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https://github.com/huggingface/datasets/pull/1152
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hindi discourse analysis dataset commit
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[
"That's a great dataset to have! We need a couple more things to be good to go:\r\n- you should `make style` and `flake8 datasets` before pushing to make the code quality check happy :) \r\n- the dataset will need some dummy data which you should be able to auto-generate and test locally: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata\r\n- there's some good information in your current README, but we need the format to follow the template [here](https://github.com/huggingface/datasets/blob/master/templates/README.md) and to have YAML tags at the top, as described in the guide: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nLEt us know if you need any help!",
"Hi @yjernite \r\nI was successfully able to generate the dataset_info.json file using the command \r\npython datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs\r\n\r\nBut unfortunately, could not generate the dummy data\r\n\r\nWhile running the command \r\npython datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate\r\nI got an error as \r\n\r\nValueError: Couldn't parse columns ['0', '1', '2', '3', '4', ......, '9982']. Maybe specify which json field must be used to read the data with --json_field <my_field>.\r\n\r\nThe thing is the dataset I am trying to upload is of the format \r\n{\r\n '0': {'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'},\r\n '1': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode},\r\n .......,\r\n '9982': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode}\r\n}\r\n\r\nCan you please suggest any errors I am making in the _generate_examples method?\r\n\r\nThanks!",
"The dummy data generator doesn't support this kind of json format yet.\r\nCan you create the dummy data manually please ? You can get the instructions by running the \r\n```\r\ndatasets-cli dummy_data ./datasets/dataset_name\r\n```\r\ncommand.",
"Hi, I created the dummy data manually but the tests are still failing it seems.\r\nCan you suggest the format of JSON which is supported by dummy data generator?\r\nI will have to modify my _generate_examples method accordingly.\r\nPlease advice on the same.\r\nThanks much.\r\n",
"Can you run `make style` to format the code for the CI please ?\r\n\r\nAlso about the dummy data, here is how to generate them:\r\n\r\nWe need a dummy_data.zip file in ./datasets/hindiDiscourse/dummy/1.0.0 (or replace hindiDiscourse by hindi_discourse since we have to rename the folder anyway)\r\nTo create the zip file, first go in this directory and create a folder named dummy_data.\r\nThen inside the dummy_data folder create a file `discourse_dataset.json` and fill it with something like 5 examples.\r\nFinally zip the dummy_data folder to end up with the dummy_data.zip file\r\n\r\nOnce it's done you can check if the dummy data test passes with \r\n```\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_hindi_discourse\r\n```\r\n\r\nIf it passes you can then remove the dummy_data folder to keep only the dummy_data.zip file",
"Hi @duttahritwik did you manage to make the dummy data ?\r\nFeel free to ping me if you have questions or if we can help",
"The error `tests/test_file_utils.py::TempSeedTest::test_tensorflow` just appeared because of tensorflow's update.\r\nOnce it's fixed on master we'll be free to merge this one",
"Ci is green on master :) ",
"merging since the CI is fixed on master"
] | 2020-12-05T09:24:01Z
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https://github.com/huggingface/datasets/pull/1151
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adding psc dataset
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adding dyk dataset
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https://github.com/huggingface/datasets/pull/1149
| 1,149
|
Fix typo in the comment in _info function
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https://github.com/huggingface/datasets/pull/1148
| 1,148
|
adding polemo2 dataset
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https://github.com/huggingface/datasets/pull/1147
| 1,147
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Vinay/add/telugu books
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[] | 2020-12-05T01:17:02Z
| 2020-12-05T16:36:04Z
| 2020-12-05T16:36:04Z
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Real data tests are failing as this dataset needs to be manually downloaded
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https://github.com/huggingface/datasets/pull/1146
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Add LINNAEUS
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https://github.com/huggingface/datasets/pull/1145
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Add Species-800
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[
"thanks @lhoestq ! I probably need to do the same change in the `SplitGenerator`s (lines 107, 110 and 113). I'll open a new PR for that",
"Yes indeed ! Good catch 👍 \r\nFeel free to open a PR and ping me",
"Hi , theres a issue pulling species_800 dataset , throws google drive error \r\n\r\nerror: \r\n\r\n```\r\nraise ConnectionError(f\"Couldn't reach {url} ({repr(head_error)})\")\r\nConnectionError: Couldn't reach https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/ (ReadTimeout(ReadTimeoutError(\"HTTPSConnectionPool(host='drive.google.com', port=443): Read timed out. (read timeout=10)\")))\r\n```\r\ncode: \r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"species_800\")\r\n```",
"Hi @obonyojimmy! I am running the same commands and they work for me. Did you check your internet connection?"
] | 2020-12-04T23:44:51Z
| 2022-01-13T03:09:20Z
| 2020-12-05T16:35:01Z
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https://github.com/huggingface/datasets/pull/1144
| 1,144
|
Add JFLEG
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[
"Hi @j-chim ! You're right it does feel redundant: your option works better, but I'd even suggest having the references in a Sequence feature, which you can declare as:\r\n```\r\n\t features=datasets.Features(\r\n {\r\n \"sentence\": datasets.Value(\"string\"),\r\n \"corrections\": datasets.Sequence(datasets.Value(\"string\")),\r\n }\r\n ),\r\n```\r\n\r\nTo create the dummy data, you just need to tell the generator which files it should use, which you can do with:\r\n`python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate --match_text_files \"train*,dev*,test*\"`\r\n",
"Many thanks for this @yjernite! I've incorporated your feedback and sorted out the dummy data."
] | 2020-12-04T22:36:38Z
| 2020-12-06T18:16:04Z
| 2020-12-06T18:16:04Z
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This PR adds [JFLEG ](https://www.aclweb.org/anthology/E17-2037/), an English grammatical error correction benchmark.
The tests were successful on real data, although it would be great if I can get some guidance on the **dummy data**. Basically, **for each source sentence there are 4 possible gold standard target sentences**. The original dataset comprise files in a flat structure, labelled by split then by source/target (e.g., dev.src, dev.ref0, ..., dev.ref3). Not sure what is the best way of adding this.
I imagine I can treat each distinct source-target pair as its own split? But having so many copies of the source sentence feels redundant, and it would make it less convenient to end-users who might want to access multiple gold standard targets simultaneously.
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https://github.com/huggingface/datasets/pull/1143
| 1,143
|
Add the Winograd Schema Challenge
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[] | 2020-12-04T22:26:59Z
| 2020-12-09T15:11:31Z
| 2020-12-09T09:32:34Z
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Adds the Winograd Schema Challenge, including configs for the more canonical wsc273 as well as wsc285 with 12 new examples.
- https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
The data format was a bit of a nightmare but I think I got it to a workable format.
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https://github.com/huggingface/datasets/pull/1142
| 1,142
|
Fix PerSenT
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[] | 2020-12-04T21:21:02Z
| 2020-12-14T13:39:34Z
| 2020-12-14T13:39:34Z
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New PR for dataset PerSenT
|
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https://github.com/huggingface/datasets/pull/1141
| 1,141
|
Add GitHub version of ETH Py150 Corpus
|
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[
"The `RemoteDatasetTest` is fixed on master so it's fine",
"thanks for rebasing :)\r\n\r\nCI is green now, merging"
] | 2020-12-04T21:16:08Z
| 2020-12-09T18:32:44Z
| 2020-12-07T10:00:24Z
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Add the redistributable version of **ETH Py150 Corpus**
|
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https://github.com/huggingface/datasets/pull/1140
| 1,140
|
Add Urdu Sentiment Corpus (USC).
|
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[
"@lhoestq have made the suggested changes in the README file.",
"@lhoestq Created a new PR #1231 with only the relevant files.\r\nclosing this one :)"
] | 2020-12-04T20:55:27Z
| 2020-12-07T03:27:23Z
| 2020-12-07T03:27:23Z
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Added Urdu Sentiment Corpus. More details about the dataset over <a href="https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus">here</a>.
|
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https://github.com/huggingface/datasets/pull/1139
| 1,139
|
Add ReFreSD dataset
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[
"Cool dataset! Replying in-line:\r\n\r\n> This PR adds the **ReFreSD dataset**.\r\n> The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.\r\n> \r\n> Need feedback on:\r\n> \r\n> * I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.\r\n\r\nyou can use `--match_text_files` in the dummy data generation:\r\n`python datasets-cli dummy_data datasets/refresd --auto_generate --match_text_files \"REFreSD_rationale\"`\r\n\r\n> * The feature names.\r\n> \r\n> * I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.\r\n\r\nIt would actually be even better to use the `Translation` feature here to replace best:\r\n`\"sentence_pair\": datasets.Translation(languages=['en', 'fr']),`\r\n\r\nThen during `_generate_examples` this filed should look like\"\r\n`{\"sentence_pair\": {\"fr\": french, \"en\": english}}`\r\n\r\n> * There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.\r\nLooks good!\r\n\r\n> * The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.\r\n\r\nHaving the feature declared as `\"rationale_en\": datasets.Sequence(datasets.Value(\"int32\"))` should work\r\n\r\n> \r\n> Thanks in advance\r\n\r\nHope that helps you out! Don't forget to `make style`, rebase from master, and run all the tests before pushing again! You will also need to add a `README.md` as described in the guide:\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card",
"Thanks a lot for the answer, that does help a lot !\r\nI opened a PR for a License in the original repo so I was waiting for that for the model card. If there is no news on Monday, I'll add it without License. ",
"Looks good! It looks like it might need a rebase to pass the tests. Once you do that, should be good to go!"
] | 2020-12-04T20:45:11Z
| 2020-12-16T16:01:18Z
| 2020-12-16T16:01:18Z
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|
This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance
|
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https://github.com/huggingface/datasets/pull/1138
| 1,138
|
updated after the class name update
|
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[] | 2020-12-04T20:19:43Z
| 2020-12-05T15:43:32Z
| 2020-12-05T15:43:32Z
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@lhoestq <---
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https://github.com/huggingface/datasets/pull/1137
| 1,137
|
add wmt mlqe 2020 shared task
|
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[
"re-created in #1218 because this was too messy"
] | 2020-12-04T19:45:34Z
| 2020-12-06T19:59:44Z
| 2020-12-06T19:53:46Z
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First commit for Shared task 1 (wmt_mlqw_task1) of WMT20 MLQE (quality estimation of machine translation)
Note that I copied the tags in the README for only one (of the 7 configurations): `en-de`.
There is one configuration for each pair of languages.
|
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https://github.com/huggingface/datasets/pull/1136
| 1,136
|
minor change in description in paws-x.py and updated dataset_infos
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[] | 2020-12-04T19:17:49Z
| 2020-12-06T18:02:57Z
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https://github.com/huggingface/datasets/pull/1135
| 1,135
|
added paws
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[] | 2020-12-04T18:52:38Z
| 2020-12-09T17:17:13Z
| 2020-12-09T17:17:13Z
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Updating README and tags for dataset card in a while
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https://github.com/huggingface/datasets/pull/1134
| 1,134
|
adding xquad-r dataset
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| 2020-12-05T16:50:47Z
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https://github.com/huggingface/datasets/pull/1133
| 1,133
|
Adding XQUAD-R Dataset
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[] | 2020-12-04T18:22:29Z
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https://github.com/huggingface/datasets/pull/1132
| 1,132
|
Add Urdu Sentiment Corpus (USC).
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[] | 2020-12-04T18:12:24Z
| 2020-12-04T20:52:48Z
| 2020-12-04T20:52:48Z
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Added Urdu Sentiment Corpus. More details about the dataset over <a href="https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus">here</a>.
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https://github.com/huggingface/datasets/pull/1131
| 1,131
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Adding XQUAD-R Dataset
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[] | 2020-12-04T17:35:43Z
| 2020-12-04T18:27:22Z
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https://github.com/huggingface/datasets/pull/1130
| 1,130
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adding discovery
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[
"merging since the CI is fixed on master"
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https://github.com/huggingface/datasets/pull/1129
| 1,129
|
Adding initial version of cord-19 dataset
|
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[
"Hi @ggdupont !\r\nHave you had a chance to take a look at my suggestions ?\r\nFeel free to ping me if you have questions or when you're ready for a review",
"> Hi @ggdupont !\r\n> Have you had a chance to take a look at my suggestions ?\r\n> Feel free to ping me if you have questions or when you're ready for a review\r\n\r\nYes I did, just busy period (and no time on weekend right now ;-) )",
"With some delay, reduced the dummy data and had t rebase",
"Thanks !\r\n\r\nIt looks like the rebase messed up the github diff for this PR (2.000+ files changed)\r\nCould you create another branch and another PR please ?",
"Cleaned PR: https://github.com/huggingface/datasets/pull/1850"
] | 2020-12-04T17:03:17Z
| 2021-02-09T10:22:35Z
| 2021-02-09T10:18:06Z
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Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### TODO:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding
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https://github.com/huggingface/datasets/pull/1128
| 1,128
|
Add xquad-r dataset
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[] | 2020-12-04T16:48:53Z
| 2020-12-04T18:14:30Z
| 2020-12-04T18:14:26Z
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https://github.com/huggingface/datasets/pull/1127
| 1,127
|
Add wikiqaar dataset
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| 2020-12-07T16:39:41Z
| 2020-12-07T16:39:41Z
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Arabic Wiki Question Answering Corpus.
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https://github.com/huggingface/datasets/pull/1126
| 1,126
|
Adding babi dataset
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[
"This is ok now @lhoestq\r\n\r\nI've included the tweak to `dummy_data` to only use the data transmitted to `_generate_examples` by default (it only do that if it can find at least one path to an existing file in the `gen_kwargs` and this can be unactivated with a flag).\r\n\r\nShould I extract it in another PR or is it ok like this?",
"Nice !\r\nCould you add the dummy data generation trick in another PR ?\r\nI think we can also extend it to make it work not only with data files paths but also with data directories (sometimes it's one of the parent directory that is passed to gen_kwargs, not the actual path to the file).\r\nThis will help a lot to make the dummy data lighter !",
"This PR can be closed due to #2053 @lhoestq\r\n\r\n"
] | 2020-12-04T15:42:34Z
| 2021-03-30T09:44:04Z
| 2021-03-30T09:44:04Z
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MEMBER
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Adding the English version of bAbI.
Samples are taken from ParlAI for consistency with the main users at the moment.
Supersede #945 (problem with the rebase) and adresses the issues mentioned in the review (dummy data are smaller now and code comments are fixed).
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https://github.com/huggingface/datasets/pull/1125
| 1,125
|
Add Urdu fake news dataset.
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[
"@lhoestq looks like a lot of files were updated... shall I create a new PR?",
"Hi @chaitnayabasava ! you can try rebasing and see if that fixes the number of files changed, otherwise please do open a new PR with only the relevant files and close this one :) ",
"Created a new PR #1230.\r\nclosing this one :)"
] | 2020-12-04T15:38:17Z
| 2020-12-07T03:21:05Z
| 2020-12-07T03:21:05Z
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Added Urdu fake news dataset. More information about the dataset can be found <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>.
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https://github.com/huggingface/datasets/pull/1124
| 1,124
|
Add Xitsonga Ner
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[
"looks like this PR includes changes about many files other than the ones related to xitsonga NER\r\n\r\ncould you create another branch and another PR please ?"
] | 2020-12-04T15:27:44Z
| 2020-12-06T18:31:35Z
| 2020-12-06T18:31:35Z
|
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Clean Xitsonga Ner PR
|
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https://github.com/huggingface/datasets/pull/1123
| 1,123
|
adding cdt dataset
|
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[
"the `ms_terms` formatting CI fails is fixed on master",
"merging since the CI is fixed on master"
] | 2020-12-04T15:19:36Z
| 2020-12-04T17:05:56Z
| 2020-12-04T17:05:56Z
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https://github.com/huggingface/datasets/pull/1122
| 1,122
|
Add Urdu fake news.
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| 2020-12-04T15:20:07Z
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Added Urdu fake news dataset. More information about the dataset can be found <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>.
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https://github.com/huggingface/datasets/pull/1121
| 1,121
|
adding cdt dataset
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https://github.com/huggingface/datasets/pull/1120
| 1,120
|
Add conda environment activation
|
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[] | 2020-12-04T14:59:43Z
| 2020-12-04T18:34:48Z
| 2020-12-04T16:40:57Z
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|
Added activation of Conda environment before installing.
|
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https://github.com/huggingface/datasets/pull/1119
| 1,119
|
Add Google Great Code Dataset
|
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| 2020-12-06T17:33:14Z
| 2020-12-06T17:33:13Z
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|
https://github.com/huggingface/datasets/pull/1118
| 1,118
|
Add Tashkeela dataset
|
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[
"Sorry @lhoestq for the trouble, sometime I forget to change the names :/",
"> Sorry @lhoestq for the trouble, sometime I forget to change the names :/\r\n\r\nhaha it's ok ;)"
] | 2020-12-04T14:26:18Z
| 2020-12-04T15:47:01Z
| 2020-12-04T15:46:51Z
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|
Arabic Vocalized Words Dataset.
|
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https://github.com/huggingface/datasets/pull/1117
| 1,117
|
Fix incorrect MRQA train+SQuAD URL
|
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[
"Thanks ! could you regenerate the dataset_infos.json file ?\r\n\r\n```\r\ndatasets-cli test ./datasets/mrqa --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\nalso cc @VictorSanh ",
"Oooops, good catch @jimmycode ",
"> Thanks ! could you regenerate the dataset_infos.json file ?\r\n> \r\n> ```\r\n> datasets-cli test ./datasets/mrqa --save_infos --all_configs --ignore_verifications\r\n> ```\r\n> \r\n> also cc @VictorSanh\r\n\r\nUpdated the `dataset_infos.json` file."
] | 2020-12-04T14:14:26Z
| 2020-12-06T17:14:11Z
| 2020-12-06T17:14:10Z
|
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|
Fix issue #1115
|
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https://github.com/huggingface/datasets/pull/1116
| 1,116
|
add dbpedia_14 dataset
|
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[
"Thanks for the review. \r\nCheers!",
"Hi @hfawaz, this week we are doing the 🤗 `datasets` sprint (see some details [here](https://discuss.huggingface.co/t/open-to-the-community-one-week-team-effort-to-reach-v2-0-of-hf-datasets-library/2176)).\r\n\r\nNothing more to do on your side but it means that if you register on the thread I linked above, you can have some goodies for the present dataset that you have already added (and a special goodie if you want to spend more time and add 2 other datasets as well).\r\n\r\nIf you want to join, just tell me (or post on the thread on the HuggingFace forum: https://discuss.huggingface.co/t/open-to-the-community-one-week-team-effort-to-reach-v2-0-of-hf-datasets-library/2176)",
"Hello @thomwolf \r\nThanks for the feedback and for this invitation, indeed I would be glad to join you guys (you can add me). \r\nI will see if I have the time to implement a couple of datasets. \r\nCheers! ",
"@hfawaz invited you to the slack with your uha email.\r\n\r\nCheck your spam folder if you can't find the invitation :)",
"Oh thanks, but can you invite me on my gmail: hassanismailfawaz@gmail.com \r\nUHA is my old organization, I haven't had the time to update my online profiles yet.\r\nThank you "
] | 2020-12-04T14:13:59Z
| 2020-12-07T10:06:54Z
| 2020-12-05T15:36:23Z
|
CONTRIBUTOR
| false
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This dataset corresponds to the DBpedia dataset requested in https://github.com/huggingface/datasets/issues/353.
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https://github.com/huggingface/datasets/issues/1115
| 1,115
|
Incorrect URL for MRQA SQuAD train subset
|
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[
"good catch !"
] | 2020-12-04T14:05:24Z
| 2020-12-06T17:14:22Z
| 2020-12-06T17:14:22Z
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CONTRIBUTOR
| null | null |
https://github.com/huggingface/datasets/blob/4ef4c8f8b7a60e35c6fa21115fca9faae91c9f74/datasets/mrqa/mrqa.py#L53
The URL for `train+SQuAD` subset of MRQA points to the dev set instead of train set. It should be `https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz`.
|
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https://github.com/huggingface/datasets/pull/1114
| 1,114
|
Add sesotho ner corpus
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[] | 2020-12-04T13:59:41Z
| 2020-12-04T15:02:07Z
| 2020-12-04T15:02:07Z
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Clean Sesotho PR
|
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https://github.com/huggingface/datasets/pull/1113
| 1,113
|
add qed
|
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[] | 2020-12-04T13:47:57Z
| 2020-12-05T15:46:21Z
| 2020-12-05T15:41:57Z
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|
adding QED: Dataset for Explanations in Question Answering
https://github.com/google-research-datasets/QED
https://arxiv.org/abs/2009.06354
|
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https://github.com/huggingface/datasets/pull/1112
| 1,112
|
Initial version of cord-19 dataset from AllenAI with only the abstract
|
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[
"too ugly, I'll make a clean one"
] | 2020-12-04T13:36:39Z
| 2020-12-04T16:16:40Z
| 2020-12-04T16:16:24Z
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Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [ ] Both tests for the real data and the dummy data pass.
### TODO:
- [ ] add more metadata
- [ ] add full text
- [ ] add pre-computed document embedding
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https://github.com/huggingface/datasets/pull/1111
| 1,111
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Add Siswati Ner corpus
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[] | 2020-12-04T12:57:31Z
| 2020-12-04T14:43:01Z
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Clean Siswati PR
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https://github.com/huggingface/datasets/issues/1110
| 1,110
|
Using a feature named "_type" fails with certain operations
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[
"Thanks for reporting !\r\n\r\nIndeed this is a keyword in the library that is used to encode/decode features to a python dictionary that we can save/load to json.\r\nWe can probably change `_type` to something that is less likely to collide with user feature names.\r\nIn this case we would want something backward compatible though.\r\n\r\nFeel free to try a fix and open a PR, and to ping me if I can help :) "
] | 2020-12-04T12:56:33Z
| 2022-01-14T18:07:00Z
| 2022-01-14T18:07:00Z
|
CONTRIBUTOR
| null | null |
A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:
```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({"_type": ["whatever"]}).map()
concatenate_datasets([ds])
# or simply
Dataset(ds._data)
```
Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.
Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.
Best wishes and keep up the awesome work!
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https://github.com/huggingface/datasets/pull/1109
| 1,109
|
add woz_dialogue
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[] | 2020-12-04T12:13:07Z
| 2020-12-05T15:41:23Z
| 2020-12-05T15:40:18Z
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Adding Wizard-of-Oz task oriented dialogue dataset
https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz
https://arxiv.org/abs/1604.04562
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https://github.com/huggingface/datasets/pull/1108
| 1,108
|
Add Sepedi NER corpus
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[] | 2020-12-04T12:11:24Z
| 2020-12-04T14:39:00Z
| 2020-12-04T14:39:00Z
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|
Finally a clean PR for Sepedi
|
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https://github.com/huggingface/datasets/pull/1107
| 1,107
|
Add arsentd_lev dataset
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[
"thanks ! can you also regenerate the dataset_infos.json file please ?"
] | 2020-12-04T11:31:04Z
| 2020-12-05T15:38:09Z
| 2020-12-05T15:38:09Z
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|
Add The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV)
Paper: [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830)
Homepage: http://oma-project.com/
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https://github.com/huggingface/datasets/pull/1106
| 1,106
|
Add Urdu fake news
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[] | 2020-12-04T11:24:14Z
| 2020-12-04T14:21:12Z
| 2020-12-04T14:21:12Z
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Added Urdu fake news dataset. More information about the dataset can be found <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>.
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https://github.com/huggingface/datasets/pull/1105
| 1,105
|
add xquad_r dataset
|
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[
"looks like this PR includes changes in many files than the ones for xquad_r, could you create a new branch and a new PR ?",
"Sure, I will close this then.\r\n"
] | 2020-12-04T11:19:35Z
| 2020-12-04T16:37:00Z
| 2020-12-04T16:37:00Z
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https://github.com/huggingface/datasets/pull/1104
| 1,104
|
add TLC
|
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[] | 2020-12-04T11:14:58Z
| 2020-12-04T14:29:23Z
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Added TLC dataset
|
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https://github.com/huggingface/datasets/issues/1103
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Add support to download kaggle datasets
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[
"Hey, I think this is great idea. Any plan to integrate kaggle private datasets loading to `datasets`?",
"The workflow for downloading a Kaggle dataset and turning it into an HF dataset is pretty simple:\r\n```python\r\n!kaggle datasets download -p path\r\nds = load_dataset(path)\r\n```\r\n\r\nNative support would make our download logic even more complex, and I don't think this is a good idea considering this particular feature is not requested often. \r\n\r\nPS: Kaggle should integrate their API with `fsspec` to allow us to use a common interface if they are interested in tighter integrations"
] | 2020-12-04T11:08:37Z
| 2023-07-20T15:22:24Z
| 2023-07-20T15:22:23Z
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CONTRIBUTOR
| null | null |
We can use API key
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https://github.com/huggingface/datasets/issues/1102
| 1,102
|
Add retries to download manager
|
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[] | 2020-12-04T11:08:11Z
| 2020-12-22T15:34:06Z
| 2020-12-22T15:34:06Z
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CONTRIBUTOR
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https://github.com/huggingface/datasets/pull/1101
| 1,101
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Add Wikicorpus dataset
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[
"@lhoestq done! ;)"
] | 2020-12-04T10:57:26Z
| 2020-12-09T18:13:10Z
| 2020-12-09T18:13:09Z
|
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Add dataset.
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https://github.com/huggingface/datasets/pull/1100
| 1,100
|
Urdu fake news
|
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[] | 2020-12-04T10:41:20Z
| 2020-12-04T11:19:00Z
| 2020-12-04T11:19:00Z
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Added Bend the Truth urdu fake news dataset. More inforation <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>.
|
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https://github.com/huggingface/datasets/pull/1099
| 1,099
|
Add tamilmixsentiment data
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| 2020-12-06T06:32:22Z
| 2020-12-05T16:48:33Z
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https://github.com/huggingface/datasets/pull/1098
| 1,098
|
Add ToTTo Dataset
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[] | 2020-12-04T10:07:25Z
| 2020-12-04T13:38:20Z
| 2020-12-04T13:38:19Z
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Adds a brand new table to text dataset: https://github.com/google-research-datasets/ToTTo
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https://github.com/huggingface/datasets/pull/1097
| 1,097
|
Add MSRA NER labels
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[] | 2020-12-04T09:38:16Z
| 2020-12-04T13:31:59Z
| 2020-12-04T13:31:58Z
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Fixes #940
|
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https://github.com/huggingface/datasets/pull/1096
| 1,096
|
FIX matinf link in ADD_NEW_DATASET.md
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| 2020-12-04T14:25:35Z
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https://github.com/huggingface/datasets/pull/1095
| 1,095
|
Add TupleInf Open IE Dataset
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[
"Errors are in the CI are not related to this PR (RemoteDatasetError)\r\nthe CI is fixed on master so it's fine ",
"@lhoestq Added the dataset card. Please let me know if more information needs to be added."
] | 2020-12-04T09:08:07Z
| 2020-12-04T15:40:54Z
| 2020-12-04T15:40:54Z
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For more information: https://allenai.org/data/tuple-ie
|
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https://github.com/huggingface/datasets/pull/1094
| 1,094
|
add urdu fake news dataset
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[] | 2020-12-04T08:57:38Z
| 2020-12-04T09:20:56Z
| 2020-12-04T09:20:56Z
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Added Urdu fake news dataset. The dataset can be found <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>.
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https://github.com/huggingface/datasets/pull/1093
| 1,093
|
Add NCBI Disease Corpus dataset
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https://github.com/huggingface/datasets/pull/1092
| 1,092
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Add Coached Conversation Preference Dataset
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Adding [Coached Conversation Preference Dataset](https://research.google/tools/datasets/coached-conversational-preference-elicitation/)
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https://github.com/huggingface/datasets/pull/1091
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Add Google wellformed query dataset
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"hope this works.."
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| 2020-12-06T17:43:03Z
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This pull request will add Google wellformed_query dataset. Link of dataset is https://github.com/google-research-datasets/query-wellformedness
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https://github.com/huggingface/datasets/pull/1090
| 1,090
|
add thaisum
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| 2020-12-04T11:16:06Z
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ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists. We evaluate the performance of various existing summarization models on ThaiSum dataset and analyse the characteristic of the dataset to present its difficulties.
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https://github.com/huggingface/datasets/pull/1089
| 1,089
|
add sharc_modified
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| 2020-12-04T10:41:30Z
| 2020-12-04T10:31:44Z
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Adding modified ShARC dataset https://github.com/nikhilweee/neural-conv-qa
|
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https://github.com/huggingface/datasets/pull/1088
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|
add xquad_r dataset
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| 2020-12-04T10:58:13Z
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https://github.com/huggingface/datasets/pull/1087
| 1,087
|
Add Big Patent dataset
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[
"@lhoestq reduced the dummy data size to around 19MB in total and added the dataset card.",
"@lhoestq so I ended up removing all the nested JSON objects in the gz datafile and keep only one object with minimal content: `{\"publication_number\": \"US-8230922-B2\", \"abstract\": \"dummy abstract\", \"application_number\": \"US-201113163519-A\", \"description\": \"dummy description\"}`. \r\n\r\nThey're reduced to 35KB in total (2.5KB per domain and 17.5KB for all domains), hopefully, they're small enough."
] | 2020-12-04T04:37:30Z
| 2020-12-06T17:21:00Z
| 2020-12-06T17:20:59Z
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* More info on the dataset: https://evasharma.github.io/bigpatent/
* There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later.
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https://github.com/huggingface/datasets/pull/1086
| 1,086
|
adding cdt dataset
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[
"> Thanks for adding this one !\r\n> \r\n> I left a few comments\r\n> \r\n> after the change you'll need to regenerate the dataset_infos.json file as well\r\n\r\ndataset_infos.json regenerated",
"looks like this PR includes changes to many files other that the ones for CDT\r\ncould you create another branch and another PR please ?"
] | 2020-12-04T01:28:11Z
| 2020-12-04T15:04:02Z
| 2020-12-04T15:04:02Z
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- **Name:** *Cyberbullying Detection Task*
- **Description:** *The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.*
- **Data:** *https://github.com/ptaszynski/cyberbullying-Polish*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
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https://github.com/huggingface/datasets/pull/1085
| 1,085
|
add mutual friends conversational dataset
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[
"Ready for review"
] | 2020-12-04T00:48:21Z
| 2020-12-16T15:58:31Z
| 2020-12-16T15:58:30Z
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Mutual friends dataset
WIP
TODO:
- scenario_kbs (bug with pyarrow conversion)
- download from codalab checksums bug
|
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https://github.com/huggingface/datasets/pull/1084
| 1,084
|
adding cdsc dataset
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[] | 2020-12-04T00:10:05Z
| 2020-12-04T10:41:26Z
| 2020-12-04T10:41:26Z
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- **Name**: *cdsc (domains: cdsc-e & cdsc-r)*
- **Description**: *Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource.*
- **Data**: *http://2019.poleval.pl/index.php/tasks/*
- **Motivation**: *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
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https://github.com/huggingface/datasets/pull/1083
| 1,083
|
Add the multilingual Exams dataset
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[
"Will slim down the dummy files in the morning"
] | 2020-12-04T00:06:04Z
| 2020-12-04T17:12:00Z
| 2020-12-04T17:12:00Z
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https://github.com/mhardalov/exams-qa
`multilingual` configs have all languages mixed together
`crosslingual` mixes the languages for test but separates them for train and dec, so I've made one config per language for train/dev data and one config with the joint test set
|
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https://github.com/huggingface/datasets/pull/1082
| 1,082
|
Myanmar news dataset
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[
"merging since the CI is fixed on master"
] | 2020-12-03T23:39:00Z
| 2020-12-04T10:13:38Z
| 2020-12-04T10:13:38Z
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Add news topic classification dataset in Myanmar / Burmese languagess
This data was collected in 2017 by Aye Hninn Khine , and published on GitHub with a GPL license
https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
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|
https://github.com/huggingface/datasets/pull/1081
| 1,081
|
Add Knowledge-Enhanced Language Model Pre-training (KELM)
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[] | 2020-12-03T23:30:09Z
| 2020-12-04T16:36:28Z
| 2020-12-04T16:36:28Z
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Adds the KELM dataset.
- Webpage/repo: https://github.com/google-research-datasets/KELM-corpus
- Paper: https://arxiv.org/pdf/2010.12688.pdf
|
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{
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}
| null | null | null | true
|
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