KRadim/github-issues-minimal huggingface dataset:
Add script_version suggestion when dataset/metric not found
State: closed

Adds a helpful prompt to the error message when a dataset/metric is not found, suggesting the user might need to pass `script_version="master"` if the dataset was added recently. The whole error looks like:

> Couldn't find file locally at blah/blah.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1/metrics/blah/blah.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/met
rics/blah/blah.py.
If the dataset was added recently, you may need to to pass script_version="master" to find the loading script on the master branch.
add best2009
State: closed

`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly.
Use passed --cache_dir for modules cache
State: open

When passed `--cache_dir` arg:
```shell
python datasets-cli test datasets/<my-dataset-folder> --save_infos --all_configs --cache_dir <my-cache-dir>
```
it is not used for caching the modules, which are cached in the default location at `.cache/huggingface/modules`.

With this fix, the modules will be cached at `<my-cache-dir>/modules`.
Add Mkqa dataset
State: closed

# MKQA: Multilingual Knowledge Questions & Answers Dataset
Adding the [MKQA](https://github.com/apple/ml-mkqa) dataset as part of the sprint 🎉

There is no official data splits so I added just a `train` split.
 
differently from the original:
- answer:type field is a ClassLabel (I thought it might be possible to train on this as a label for categorizing questions)
- answer:entity field has a default value of empty string '' (since this key is not available for all in original)
- answer:alias has default value of []

- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
add wisesight1000
State: closed

`wisesight1000` contains Thai social media texts randomly drawn from the full `wisesight-sentiment`, tokenized by human annotators. Out of the labels `neg` (negative), `neu` (neutral), `pos` (positive), `q` (question), 250 samples each. Some texts are removed because they look like spam.Because these samples are representative of real world content, we believe having these annotaed samples will allow the community to robustly evaluate tokenization algorithms.
Add spanish billion words
State: closed

Add an unannotated corpus of the Spanish language of nearly 1.5 billion words, compiled from different resources from the web.

The dataset needs 10 GB (download: 1.89 GiB, generated: 8.34 GiB, post-processed: Unknown size, total: 10.22 GiB), the test using dummy data pass but my laptop isn't able to run it on the real data (I left it running for over 8 hours and it didn't finish).
❓ Sharing ElasticSearch indexed dataset 
State: open
Labels: name: dataset request

Hi there,

First of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.

**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering

- how can I know where it has been saved?

- how can I share the indexed dataset with others?

I tried to dig into the docs, but could not find anything about that.

Thank you very much for your help.

Best,
Pietro

Edit: apologies for the wrong label
Add CC-News dataset of English language articles
State: closed

Adds [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/) dataset. It contains 708241 English language news articles. Although each article has a language field these tags are not reliable. I've used Spacy language detection [pipeline](https://spacy.io/universe/project/spacy-langdetect) to confirm that the article language is indeed English. 

The prepared dataset is temporarily hosted on my private Google Storage [bucket](https://storage.googleapis.com/hf_datasets/cc_news.tar.gz). We can move it to HF storage and update this PR before merging.
ethos first commit
State: closed

Ethos passed all the tests except from this one: 
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your-dataset-name>

with this error: 
E               OSError: Cannot find data file. 
E               Original error:
E               [Errno 2] No such file or directory:
Allow GitHub releases as dataset source
State: closed

# Summary

Providing a GitHub release URL to `DownloadManager.download()` currently throws a `ConnectionError: Couldn't reach [DOWNLOAD_URL]`. This PR fixes this problem by adding an exception for GitHub releases in `datasets.utils.file_utils.get_from_cache()`.

# Reproduce

```
import datasets
url = 'http://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz'
result = datasets.utils.file_utils.get_from_cache(url)

# Returns: ConnectionError: Couldn't reach http://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz
```

# Cause

GitHub releases returns a HTTP status 403 (FOUND), indicating that the request is being redirected (to AWS S3, in this case). `get_from_cache()` checks whether the status is 200 (OK) or if it is part of two exceptions (Google Drive or Firebase), otherwise the mentioned error is thrown.

# Solution

Just like the exceptions for Google Drive and Firebase, add a condition for GitHub releases URLs that return the HTTP status 403. If this is the case, continue normally.
Add HateSpeech Corpus for Polish
State: closed

This PR adds a HateSpeech Corpus for Polish, containing offensive language examples.

- **Homepage:** http://zil.ipipan.waw.pl/HateSpeech
- **Paper:** http://www.qualitativesociologyreview.org/PL/Volume38/PSJ_13_2_Troszynski_Wawer.pdf
Add OffensEval-TR 2020 Dataset
State: closed

This PR adds the OffensEval-TR 2020 dataset which is a Turkish offensive language corpus by me and @basakbuluz. The corpus consist of randomly sampled tweets and annotated in a similar way to [OffensEval](https://sites.google.com/site/offensevalsharedtask/) and [GermEval](https://projects.fzai.h-da.de/iggsa/).

- **Homepage:** [offensive-turkish](https://coltekin.github.io/offensive-turkish/)
- **Paper:** [A Corpus of Turkish Offensive Language on Social Media](https://coltekin.github.io/offensive-turkish/troff.pdf)
- **Point of Contact:** [Çağrı Çöltekin](ccoltekin@sfs.uni-tuebingen.de)
add W&I + LOCNESS dataset (BEA-2019 workshop shared task on GEC)
State: closed

- **Name:** W&I + LOCNESS dataset (from the BEA-2019 workshop shared task on GEC)
- **Description:** https://www.cl.cam.ac.uk/research/nl/bea2019st/#data
- **Paper:** https://www.aclweb.org/anthology/W19-4406/
- **Motivation:** This is a recent dataset (actually two in one) for grammatical error correction and is used for benchmarking in this field of NLP.

### Checkbox

- [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 : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
can't load "german_legal_entity_recognition" dataset
State: closed

FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition/german_legal_entity_recognition.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py
imdb dataset cannot be downloaded
State: closed

hi
please find error below getting imdb train spli:
thanks

`
datasets.load_dataset>>> datasets.load_dataset("imdb", split="train")`


errors


```
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets/downloads
Traceback (most recent call last):        
  File "<stdin>", line 1, in <module>
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
    ignore_verifications=ignore_verifications,
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]


```
'iwslt2017-ro-nl', cannot be downloaded 
State: closed
Labels: name: dataset bug

Hi
I am trying 
`>>> datasets.load_dataset("iwslt2017", 'iwslt2017-ro-nl', split="train")`

getting this error thank you for your help
```
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset iwsl_t217/iwslt2017-ro-nl (download: 314.07 MiB, generated: 39.92 MiB, post-processed: Unknown size, total: 354.00 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/iwsl_t217/iwslt2017-ro-nl/1.0.0/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd...
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir  /idiap/temp/rkarimi/cache_home_1/datasets/downloads
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
    ignore_verifications=ignore_verifications,
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/iwslt2017/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd/iwslt2017.py", line 118, in _split_generators
    dl_dir = dl_manager.download_and_extract(MULTI_URL)
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
    num_proc=download_config.num_proc,
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
    return function(data_struct)
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
    use_etag=download_config.use_etag,
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz

```
[libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted
State: closed

Hi
I am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help

{'epoch': 20.0}                                                                                                                                             
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:16<00:00,  1.22it/s]
12/08/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs/experiment/joint/finetune/lr-2e-5
12/08/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}
12/08/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***
12/08/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:37<00:00,  1.19s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): 
terminate called after throwing an instance of 'google::protobuf::FatalException'
  what():  CHECK failed: (index) >= (0): 
Aborted
boolq does not work 
State: closed

Hi
I am getting this error when trying to load boolq, thanks for your help

ts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock
Traceback (most recent call last):
  File "finetune_t5_trainer.py", line 274, in <module>
    main()
  File "finetune_t5_trainer.py", line 147, in main
    for task in data_args.tasks]
  File "finetune_t5_trainer.py", line 147, in <listcomp>
    for task in data_args.tasks]
  File "/remote/idiap.svm/user.active/rkarimi/dev/ruse/seq2seq/tasks/tasks.py", line 58, in get_dataset
    dataset = self.load_dataset(split=split)
  File "/remote/idiap.svm/user.active/rkarimi/dev/ruse/seq2seq/tasks/tasks.py", line 54, in load_dataset
    return datasets.load_dataset(self.task.name, split=split)
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
    ignore_verifications=ignore_verifications,
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
    custom_download(url, path)
  File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
Add dutch book review dataset
State: closed

- Name: Dutch Book Review Dataset (DBRD)
- Description: The DBRD (pronounced dee-bird) dataset contains over 110k book reviews along with associated binary sentiment polarity labels and is intended as a benchmark for sentiment classification in Dutch.
- Paper: https://arxiv.org/abs/1910.00896
- Data: https://github.com/benjaminvdb/DBRD
- Motivation: A large (real-life) dataset of Dutch book reviews and sentiment polarity (positive/negative), based on the associated rating.

Checks
- [x] Create the dataset script /datasets/dbrd/dbrd.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _info(), _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 : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
add thaiqa_squad
State: closed

Example format is a little different from SQuAD since `thaiqa` always have one answer per question so I added a check to convert answers to lists if they are not already one to future-proof additional questions that might have multiple answers.

`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/).
add One Million Posts Corpus
State: closed

- **Name:** One Million Posts Corpus
- **Description:** The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).
- **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711
- **Data:** https://github.com/OFAI/million-post-corpus
- **Motivation:** Big German (real-life) dataset containing different annotations around forum moderation with expert annotations.

### Checkbox

- [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 : fill the tags and the various paragraphs
- [X] Both tests for the real data and the dummy data pass.
add DFKI SmartData Corpus
State: closed

- **Name:** DFKI SmartData Corpus
- **Description:** DFKI SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types.
- **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf
- **Data:** https://github.com/DFKI-NLP/smartdata-corpus
- **Motivation:** Contains fine-grained NER labels for German.

### Checkbox

- [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 : fill the tags and the various paragraphs
- [X] Both tests for the real data and the dummy data pass.
Added NewsPH Raw Dataset
State: closed

Added the raw version of the NewsPH dataset, which was used to automatically generate the NewsPH-NLI corpus. Dataset of news articles in Filipino from mainstream Philippine news sites on the internet. Can be used as a language modeling dataset or to reproduce the NewsPH-NLI dataset.

Paper: https://arxiv.org/abs/2010.11574
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
add thainer
State: closed

ThaiNER (v1.3) is a 6,456-sentence named entity recognition dataset created from expanding the 2,258-sentence
[unnamed dataset](http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) by
[Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/).
It is used to train NER taggers in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp).
The NER tags are annotated by [Tirasaroj and Aroonmanakun (2012)]((http://pioneer.chula.ac.th/~awirote/publications/))
for 2,258 sentences and the rest by [@wannaphong](https://github.com/wannaphong/).
The POS tags are done by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s `perceptron` engine trained on `orchid_ud`.
[@wannaphong](https://github.com/wannaphong/) is now the only maintainer of this dataset.
Add Naver sentiment movie corpus
State: closed

Supersedes #1168 

> 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).
Add AmbigQA dataset
State: closed

# AmbigQA: Answering Ambiguous Open-domain Questions Dataset
Adding the [AmbigQA](https://nlp.cs.washington.edu/ambigqa/) dataset as part of the sprint 🎉 (from Open dataset list for Dataset sprint)

Added both the light and full versions (as seen on the dataset homepage)
The json format changes based on the value of one 'type' field, so I set the unavailable field to an empty list. This is explained in the README -> Data Fields

```py
train_light_dataset = load_dataset('./datasets/ambig_qa',"light",split="train")
val_light_dataset = load_dataset('./datasets/ambig_qa',"light",split="validation")
train_full_dataset = load_dataset('./datasets/ambig_qa',"full",split="train")
val_full_dataset = load_dataset('./datasets/ambig_qa',"full",split="validation")


for example in train_light_dataset:
    for i,t in enumerate(example['annotations']['type']):
        if t =='singleAnswer':
            # use the example['annotations']['answer'][i]
            # example['annotations']['qaPairs'][i] - > is []
            print(example['annotations']['answer'][i])
        else:
            # use the example['annotations']['qaPairs'][i]
            # example['annotations']['answer'][i] - > is []
            print(example['annotations']['qaPairs'][i])

```

- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
Add numeric fused head dataset
State: closed

Adding the [NFH: Numeric Fused Head](https://nlp.biu.ac.il/~lazary/fh/) dataset.

Everything looks sensible and I've included both the identification and resolution tasks. I haven't personally used this dataset in my research so am unable to specify what the default configuration / supervised keys should be.

I've filled out the basic info on the model card to the best of my knowledge but it's a little tricky to understand exactly what the fields represent.

Dataset author: @yanaiela
Add HKCanCor
State: closed

This PR adds the [Hong Kong Cantonese Corpus](http://compling.hss.ntu.edu.sg/hkcancor/), by [Luke and Wong 2015](http://compling.hss.ntu.edu.sg/hkcancor/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf). 

The dummy data included here was manually created, as the original dataset uses a xml-like format (see a copy hosted [here](https://github.com/fcbond/hkcancor/blob/master/sample/d1_v.txt) for example) that requires a few processing steps.
Add Korean NER dataset
State: closed

Supersedes #1177 

> 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).
adding medical-questions-pairs dataset
State: closed

This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.
Dataset : https://github.com/curai/medical-question-pair-dataset
Paper : https://drive.google.com/file/d/1CHPGBXkvZuZc8hpr46HeHU6U6jnVze-s/view
add taskmaster3
State: closed

Adding Taskmaster-3 dataset
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020.

The dataset structure almost same as original dataset with these two changes

1. In original dataset, each `apis` has a `args` filed which is a `dict`  with variable keys, which represent the name and value of the args. Here converted that to a `list` of `dict` with keys `arg_name` and `arg_value`. For ex.

```python
args = {"name.movie": "Mulan", "name.theater": ": "Mountain AMC 16"}
```
becomes 
```python
[
  {
    "arg_name": "name.movie",
    "arg_value": "Mulan"
  },
  {
    "arg_name": "name.theater",
    "arg_value": "Mountain AMC 16"
  }
]
```

2. Each `apis` has a `response` which is also a `dict` with variable keys representing response name/type and it's value. As above converted it to `list` of `dict` with keys `response_name` and `response_value`.
Add large spanish corpus
State: closed

Adds a collection of Spanish corpora that can be useful for pretraining language models. 

Following a nice suggestion from @yjernite we provide the user with three main ways to preprocess / load either 

* the whole corpus (17GB!)
* one specific sub-corpus
* the whole corpus, but return a single split. this is useful if you want to cache the whole preprocessing step once and interact with individual sub-corpora

See the dataset card for more details.

Ready for review!
[AfriBooms] Dataset exists already
State: closed

When trying to add "AfriBooms": https://docs.google.com/spreadsheets/d/12ShVow0M6RavnzbBEabm5j5dv12zBaf0y-niwEPPlo4/edit#gid=1386399609 I noticed that the dataset exists already as a config of Universal Dependencies (universal_dependencies.py). I checked and the data exactly matches so that the new data link does not give any new data.

This PR improves the config's description a bit by linking to the paper.
add lst20 with manual download
State: closed

passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```

```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
```
Medical question pairs
State: closed

This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.
Dataset : https://github.com/curai/medical-question-pair-dataset
Paper : https://drive.google.com/file/d/1CHPGBXkvZuZc8hpr46HeHU6U6jnVze-s/view
**No splits added**
Update ADD_NEW_DATASET.md
State: closed

Windows needs special treatment again: unfortunately adding `torch` to the requirements does not work well (crashing the installation). Users should first install torch manually and then continue with the other commands.

This issue arises all the time when adding torch as a dependency, but because so many novice users seem to participate in adding datasets, it may be useful to add an explicit note for Windows users to ensure that they do not run into issues.
Add NewsPH_NLI dataset
State: closed

This PR adds the NewsPH-NLI Dataset, the first benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.

Link to the paper: https://arxiv.org/pdf/2010.11574.pdf

Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
Add Fake News Detection in Filipino dataset
State: closed

This PR adds the Fake News Filipino Dataset, a low-resource fake news detection corpora in Filipino. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.

Link to the paper: http://www.lrec-conf.org/proceedings/lrec2020/index.html

Link to the dataset/repo: https://github.com/jcblaisecruz02/Tagalog-fake-news
Add Dengue dataset in Filipino
State: closed

This PR adds the Dengue Dataset, a benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.

Link to the paper: https://ieeexplore.ieee.org/document/8459963

Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
Add Hate Speech Dataset in Filipino
State: closed

This PR adds the Hate Speech Dataset, a text classification dataset in Filipino, consisting 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.

Link to the paper: https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019

Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
Add Adversarial SQuAD dataset
State: closed

# Adversarial SQuAD

Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉 
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.

There are 2 splits of this dataset:

- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.

(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)

The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
ADD COVID-QA dataset
State: closed

This PR adds the COVID-QA dataset, a question answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19

Link to the paper: https://openreview.net/forum?id=JENSKEEzsoU
Link to the dataset/repo: https://github.com/deepset-ai/COVID-QA
Add  KorQuAD v2 Dataset
State: closed

# 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
Add  KorQuAD v1 Dataset
State: closed

# 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)
Add Korean NER dataset
State: closed

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).
Add Naver sentiment movie corpus
State: closed

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).
❓ On-the-fly tokenization with datasets, tokenizers, and torch Datasets and Dataloaders
State: closed
Labels: name: question, name: generic discussion

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)
```
adding TabFact dataset
State: closed

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.
Add JFLEG
State: closed

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.
Add ReFreSD dataset
State: closed

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
Adding initial version of cord-19 dataset
State: closed

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
Incorrect URL for MRQA SQuAD train subset
State: closed

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`.
Initial version of cord-19 dataset from AllenAI with only the abstract
State: closed

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
Using a feature named "_type" fails with certain operations
State: closed

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!
adding cdt dataset
State: closed

- **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.*
adding cdsc dataset
State: closed

- **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.*
Add WikiANN NER dataset
State: closed

This PR adds the full set of 176 languages from the balanced train/dev/test splits of WikiANN / PAN-X from: https://github.com/afshinrahimi/mmner

Until now, only 40 of these languages were available in `datasets` as part of the XTREME benchmark

Courtesy of the dataset author, we can now download this dataset from a Dropbox URL without needing a manual download anymore 🥳, so at some point it would be worth updating the PAN-X subset of XTREME as well 😄 

Link to gist with some snippets for producing dummy data: https://gist.github.com/lewtun/5b93294ab6dbcf59d1493dbe2cfd6bb9

P.S. @yjernite I think I was confused about needing to generate a set of YAML tags per config, so ended up just adding a single one in the README.
nkjp-ner
State: closed

- **Name:** *nkjp-ner*
- **Description:** *The NKJP-NER is based on a human-annotated part of NKJP. We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.*
- **Data:** *https://klejbenchmark.com/tasks/*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
Not support links with 302 redirect 
State: closed
Labels: name: bug, name: enhancement

I have an issue adding this download link https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz

it might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests). 

```
r.head("https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz", allow_redirects=True)                                      
# <Response [403]>
```
Add KorNLU dataset
State: closed

Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)

**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq 

- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data