Datasets:

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MDExOlB1bGxSZXF1ZXN0NjI5NjQwNTMx

Add SLR52, SLR53 and SLR54 to OpenSLR

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CONTRIBUTOR

Add large speech datasets for Sinhala, Bengali and Nepali.

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MDExOlB1bGxSZXF1ZXN0NjI5NTEwNTg5

Update README.md

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Provides description of data instances and dataset features

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https://github.com/huggingface/datasets/pull/2309

MDExOlB1bGxSZXF1ZXN0NjI5MTU4NjQx

Fix conda release

closed

MEMBER

There were a few issues with conda releases (they've been failing for a while now). To fix this I had to: - add the --single-version-externally-managed tag to the build stage (suggestion from [here](https://stackoverflow.com/a/64825075)) - set the python version of the conda build stage to 3.8 since 3.9 isn't supported - sync the evrsion requirement of `huggingface_hub` With these changes I'm working on uploading all missing versions until 1.6.2 to conda EDIT: I managed to build and upload all missing versions until 1.6.2 to conda :)

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MDU6SXNzdWU4NzQ1NTk4NDY=

Add COCO evaluation metrics

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CONTRIBUTOR

I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https://github.com/facebookresearch/detr/blob/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5/datasets/coco_eval.py#L22) and [here](https://github.com/facebookresearch/detr/blob/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5/datasets/panoptic_eval.py#L13) respectively). Running these in a notebook gives you nice summaries like this:  It would be great if we could import these metrics from the Datasets library, something like this: ``` import datasets metric = datasets.load_metric('coco') for model_input, gold_references in evaluation_dataset: model_predictions = model(model_inputs) metric.add_batch(predictions=model_predictions, references=gold_references) final_score = metric.compute() ``` I think this would be great for object detection and semantic/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier. However, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.

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https://github.com/huggingface/datasets/pull/2302

MDExOlB1bGxSZXF1ZXN0NjI4NjIzMDQ3

Add SubjQA dataset

closed

MEMBER

Hello datasetters 🙂! Here's an interesting dataset about extractive question-answering on _subjective_ product / restaurant reviews. It's quite challenging for models fine-tuned on SQuAD and provides a nice example of domain adaptation (i.e. fine-tuning a SQuAD model on this domain gives better performance). I found a bug in the start/end indices that I've proposed a fix for here: https://github.com/megagonlabs/SubjQA/pull/2 Unfortunately, the dataset creators are unresponsive, so for now I am using my fork as the source. Will update the URL if/when the creators respond.

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MDU6SXNzdWU4NzM5NDEyNjY=

Unable to setup dev env on Windows

closed

CONTRIBUTOR

Hi I tried installing the `".[dev]"` version on Windows 10 after cloning. Here is the error I'm facing: ```bat (env) C:\testing\datasets>pip install -e ".[dev]" Obtaining file:///C:/testing/datasets Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.5) Collecting pyarrow>=0.17.1 Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB) Requirement already satisfied: dill in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.3.1.1) Collecting pandas Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB) Requirement already satisfied: requests>=2.19.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.25.1) Requirement already satisfied: tqdm<4.50.0,>=4.27 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.49.0) Requirement already satisfied: xxhash in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.0.2) Collecting multiprocess Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB) Requirement already satisfied: fsspec in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2021.4.0) Collecting huggingface_hub<0.1.0 Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB) Requirement already satisfied: importlib_metadata in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.0.1) Requirement already satisfied: absl-py in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.12.0) Requirement already satisfied: pytest in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (6.2.3) Collecting pytest-xdist Using cached pytest_xdist-2.2.1-py3-none-any.whl (37 kB) Collecting apache-beam>=2.24.0 Using cached apache_beam-2.29.0-cp37-cp37m-win_amd64.whl (3.7 MB) Collecting elasticsearch Using cached elasticsearch-7.12.1-py2.py3-none-any.whl (339 kB) Requirement already satisfied: boto3==1.16.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.16.43) Requirement already satisfied: botocore==1.19.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.43) Collecting moto[s3]==1.3.16 Using cached moto-1.3.16-py2.py3-none-any.whl (879 kB) Collecting rarfile>=4.0 Using cached rarfile-4.0-py3-none-any.whl (28 kB) Collecting tensorflow>=2.3 Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB) Requirement already satisfied: torch in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.8.1) Requirement already satisfied: transformers in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.5.1) Collecting bs4 Using cached bs4-0.0.1-py3-none-any.whl Collecting conllu Using cached conllu-4.4-py2.py3-none-any.whl (15 kB) Collecting langdetect Using cached langdetect-1.0.8-py3-none-any.whl Collecting lxml Using cached lxml-4.6.3-cp37-cp37m-win_amd64.whl (3.5 MB) Collecting mwparserfromhell Using cached mwparserfromhell-0.6-cp37-cp37m-win_amd64.whl (101 kB) Collecting nltk Using cached nltk-3.6.2-py3-none-any.whl (1.5 MB) Collecting openpyxl Using cached openpyxl-3.0.7-py2.py3-none-any.whl (243 kB) Collecting py7zr Using cached py7zr-0.15.2-py3-none-any.whl (66 kB) Collecting tldextract Using cached tldextract-3.1.0-py2.py3-none-any.whl (87 kB) Collecting zstandard Using cached zstandard-0.15.2-cp37-cp37m-win_amd64.whl (582 kB) Collecting bert_score>=0.3.6 Using cached bert_score-0.3.9-py3-none-any.whl (59 kB) Collecting rouge_score Using cached rouge_score-0.0.4-py2.py3-none-any.whl (22 kB) Collecting sacrebleu Using cached sacrebleu-1.5.1-py3-none-any.whl (54 kB) Requirement already satisfied: scipy in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3) Collecting seqeval Using cached seqeval-1.2.2-py3-none-any.whl Collecting sklearn Using cached sklearn-0.0-py2.py3-none-any.whl Collecting jiwer Using cached jiwer-2.2.0-py3-none-any.whl (13 kB) Requirement already satisfied: toml>=0.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.10.2) Requirement already satisfied: requests_file>=1.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.5.1) Requirement already satisfied: texttable>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3) Requirement already satisfied: s3fs>=0.4.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.4.2) Requirement already satisfied: Werkzeug>=1.0.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.0.1) Collecting black Using cached black-21.4b2-py3-none-any.whl (130 kB) Collecting isort Using cached isort-5.8.0-py3-none-any.whl (103 kB) Collecting flake8==3.7.9 Using cached flake8-3.7.9-py2.py3-none-any.whl (69 kB) Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.10.0) Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.3.7) Requirement already satisfied: urllib3<1.27,>=1.25.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (1.26.4) Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (2.8.1) Collecting entrypoints<0.4.0,>=0.3.0 Using cached entrypoints-0.3-py2.py3-none-any.whl (11 kB) Collecting pyflakes<2.2.0,>=2.1.0 Using cached pyflakes-2.1.1-py2.py3-none-any.whl (59 kB) Collecting pycodestyle<2.6.0,>=2.5.0 Using cached pycodestyle-2.5.0-py2.py3-none-any.whl (51 kB) Collecting mccabe<0.7.0,>=0.6.0 Using cached mccabe-0.6.1-py2.py3-none-any.whl (8.6 kB) Requirement already satisfied: jsondiff>=1.1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.3.0) Requirement already satisfied: pytz in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2021.1) Requirement already satisfied: mock in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (4.0.3) Requirement already satisfied: MarkupSafe<2.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.1.1) Requirement already satisfied: python-jose[cryptography]<4.0.0,>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0) Requirement already satisfied: aws-xray-sdk!=0.96,>=0.93 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.8.0) Requirement already satisfied: cryptography>=2.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.4.7) Requirement already satisfied: more-itertools in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (8.7.0) Requirement already satisfied: PyYAML>=5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.4.1) Requirement already satisfied: boto>=2.36.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.49.0) Requirement already satisfied: idna<3,>=2.5 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.10) Requirement already satisfied: sshpubkeys>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.3.1) Requirement already satisfied: responses>=0.9.0 in 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moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.14.1) Requirement already satisfied: docker>=2.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.0.0) Requirement already satisfied: cfn-lint>=0.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.49.0) Requirement already satisfied: grpcio<2,>=1.29.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (1.32.0) Collecting hdfs<3.0.0,>=2.1.0 Using cached hdfs-2.6.0-py3-none-any.whl (33 kB) Collecting pyarrow>=0.17.1 Using cached pyarrow-3.0.0-cp37-cp37m-win_amd64.whl (12.6 MB) Collecting fastavro<2,>=0.21.4 Using cached fastavro-1.4.0-cp37-cp37m-win_amd64.whl (394 kB) Requirement already satisfied: httplib2<0.18.0,>=0.8 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.17.4) Collecting pymongo<4.0.0,>=3.8.0 Using cached pymongo-3.11.3-cp37-cp37m-win_amd64.whl (382 kB) Collecting crcmod<2.0,>=1.7 Using cached crcmod-1.7-py3-none-any.whl Collecting avro-python3!=1.9.2,<1.10.0,>=1.8.1 Using cached avro_python3-1.9.2.1-py3-none-any.whl Requirement already satisfied: typing-extensions<3.8.0,>=3.7.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (3.7.4.3) Requirement already satisfied: future<1.0.0,>=0.18.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.18.2) Collecting oauth2client<5,>=2.0.1 Using cached oauth2client-4.1.3-py2.py3-none-any.whl (98 kB) Collecting pydot<2,>=1.2.0 Using cached pydot-1.4.2-py2.py3-none-any.whl (21 kB) Requirement already satisfied: protobuf<4,>=3.12.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (3.15.8) Requirement already satisfied: wrapt in c:\programdata\anaconda3\envs\env\lib\site-packages (from aws-xray-sdk!=0.96,>=0.93->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.12.1) Collecting matplotlib Using cached matplotlib-3.4.1-cp37-cp37m-win_amd64.whl (7.1 MB) Requirement already satisfied: junit-xml~=1.9 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.9) Requirement already satisfied: jsonpatch in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.32) Requirement already satisfied: jsonschema~=3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0) Requirement already satisfied: networkx~=2.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.5.1) Requirement already satisfied: aws-sam-translator>=1.35.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from 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h5py-2.10.0-cp37-cp37m-win_amd64.whl (2.5 MB) Requirement already satisfied: markdown>=2.6.8 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (3.3.4) Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.8.0) Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (0.4.4) Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (0.6.0) Requirement already satisfied: google-auth<2,>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.30.0) Requirement already 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beautifulsoup4->bs4->datasets==1.5.0.dev0) (2.2.1) Collecting python-Levenshtein Using cached python-Levenshtein-0.12.2.tar.gz (50 kB) Requirement already satisfied: jsonpointer>=1.9 in c:\programdata\anaconda3\envs\env\lib\site-packages (from jsonpatch->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.1) Requirement already satisfied: pillow>=6.2.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (8.2.0) Requirement already satisfied: cycler>=0.10 in c:\programdata\anaconda3\envs\env\lib\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (0.10.0) Requirement already satisfied: kiwisolver>=1.0.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (1.3.1) Collecting multiprocess Using cached multiprocess-0.70.11-py3-none-any.whl (98 kB) Using cached multiprocess-0.70.10.zip (2.4 MB) Using cached multiprocess-0.70.9-py3-none-any.whl Requirement already satisfied: joblib in c:\programdata\anaconda3\envs\env\lib\site-packages (from nltk->datasets==1.5.0.dev0) (1.0.1) Collecting et-xmlfile Using cached et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB) Requirement already satisfied: pyzstd<0.15.0,>=0.14.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from py7zr->datasets==1.5.0.dev0) (0.14.4) Collecting pyppmd<0.13.0,>=0.12.1 Using cached pyppmd-0.12.1-cp37-cp37m-win_amd64.whl (32 kB) Collecting pycryptodome>=3.6.6 Using cached pycryptodome-3.10.1-cp35-abi3-win_amd64.whl (1.6 MB) Collecting bcj-cffi<0.6.0,>=0.5.1 Using cached bcj_cffi-0.5.1-cp37-cp37m-win_amd64.whl (21 kB) Collecting multivolumefile<0.3.0,>=0.2.0 Using cached multivolumefile-0.2.3-py3-none-any.whl (17 kB) Requirement already satisfied: iniconfig in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.1.1) Requirement already satisfied: py>=1.8.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.10.0) Requirement already satisfied: pluggy<1.0.0a1,>=0.12 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (0.13.1) Requirement already satisfied: atomicwrites>=1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.4.0) Requirement already satisfied: colorama in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (0.4.4) Collecting pytest-forked Using cached pytest_forked-1.3.0-py2.py3-none-any.whl (4.7 kB) Collecting execnet>=1.1 Using cached execnet-1.8.0-py2.py3-none-any.whl (39 kB) Requirement already satisfied: apipkg>=1.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from execnet>=1.1->pytest-xdist->datasets==1.5.0.dev0) (1.5) Collecting portalocker==2.0.0 Using cached portalocker-2.0.0-py2.py3-none-any.whl (11 kB) Requirement already satisfied: scikit-learn>=0.21.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from seqeval->datasets==1.5.0.dev0) (0.24.2) Requirement already satisfied: threadpoolctl>=2.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from scikit-learn>=0.21.3->seqeval->datasets==1.5.0.dev0) (2.1.0) Building wheels for collected packages: python-Levenshtein Building wheel for python-Levenshtein (setup.py) ... error ERROR: Command errored out with exit status 1: command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\VKC~1\AppData\Local\Temp\pip-wheel-8jh7fm18' cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\ Complete output (27 lines): running bdist_wheel running build running build_py creating build creating build\lib.win-amd64-3.7 creating build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein running egg_info writing python_Levenshtein.egg-info\PKG-INFO writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt writing entry points to python_Levenshtein.egg-info\entry_points.txt writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt writing requirements to python_Levenshtein.egg-info\requires.txt writing top-level names to python_Levenshtein.egg-info\top_level.txt reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*pyc' found anywhere in distribution warning: no previously-included files matching '*so' found anywhere in distribution warning: no previously-included files matching '.project' found anywhere in distribution warning: no previously-included files matching '.pydevproject' found anywhere in distribution writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt' copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein running build_ext building 'Levenshtein._levenshtein' extension error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ ---------------------------------------- ERROR: Failed building wheel for python-Levenshtein Running setup.py clean for python-Levenshtein Failed to build python-Levenshtein Installing collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam Running setup.py install for python-Levenshtein ... error ERROR: Command errored out with exit status 1: command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\ Complete output (27 lines): running install running build running build_py creating build creating build\lib.win-amd64-3.7 creating build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein running egg_info writing python_Levenshtein.egg-info\PKG-INFO writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt writing entry points to python_Levenshtein.egg-info\entry_points.txt writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt writing requirements to python_Levenshtein.egg-info\requires.txt writing top-level names to python_Levenshtein.egg-info\top_level.txt reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*pyc' found anywhere in distribution warning: no previously-included files matching '*so' found anywhere in distribution warning: no previously-included files matching '.project' found anywhere in distribution warning: no previously-included files matching '.pydevproject' found anywhere in distribution writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt' copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein running build_ext building 'Levenshtein._levenshtein' extension error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ ---------------------------------------- ERROR: Command errored out with exit status 1: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' Check the logs for full command output. ``` Here are conda and python versions: ```bat (env) C:\testing\datasets>conda --version conda 4.9.2 (env) C:\testing\datasets>python --version Python 3.7.10 ``` Please help me out. Thanks.

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MDU6SXNzdWU4NzM5MjgxNjk=

Add VoxPopuli

open

MEMBER

## Adding a Dataset - **Name:** Voxpopuli - **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings - **Paper:** https://arxiv.org/abs/2101.00390 - **Data:** https://github.com/facebookresearch/voxpopuli - **Motivation:** biggest unlabeled speech dataset **Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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MDU6SXNzdWU4NzM5MTQ3MTc=

My iPhone

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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MDExOlB1bGxSZXF1ZXN0NjI4NDk2NjM2

Mapping in the distributed setting

closed

MEMBER

The barrier trick for distributed mapping as discussed on Thursday with @lhoestq

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MDU6SXNzdWU4NzI5NzQ5MDc=

1

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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MDExOlB1bGxSZXF1ZXN0NjI3NzY0NDk3

Create ExtractManager

closed

MEMBER

Perform refactoring to decouple extract functionality.

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MDU6SXNzdWU4NzIxMzYwNzU=

Slow #0 when using map to tokenize.

open

NONE

Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1，the process _#0_ is much slower than others. It looks like this:  It takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?

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MDExOlB1bGxSZXF1ZXN0NjI3MDQzNzQ3

imdb dataset from Don't Stop Pretraining Paper

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MDExOlB1bGxSZXF1ZXN0NjI2MjgzNTYy

Fixed typo seperate->separate

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MDExOlB1bGxSZXF1ZXN0NjI2MjcyNzE5

Don't copy recordbatches in memory during a table deepcopy

closed

MEMBER

Fix issue #2276 and hopefully #2134 The recordbatches of the `IndexedTableMixin` used to speed up queries to the table were copied in memory during a table deepcopy. This resulted in `concatenate_datasets`, `load_from_disk` and other methods to always bring the data in memory. I fixed the copy similarly to #2287 and updated the test to make sure it doesn't happen again (added a test for deepcopy + make sure that the immutable arrow objects are passed to the copied table without being copied). The issue was not caught by our tests because the total allocated bytes value in PyArrow isn't updated when deepcopying recordbatches: the copy in memory wasn't detected. This behavior looks like a bug in PyArrow, I'll open a ticket on JIRA. Thanks @samsontmr , @TaskManager91 and @mariosasko for the help

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MDExOlB1bGxSZXF1ZXN0NjI2MjEyNTIz

Bbaw egyptian

closed

CONTRIBUTOR

This is the "hieroglyph corpus" that I could unfortunately not contribute during the marathon. I re-extracted it again now, so that it is in the state as used in my paper (seee documentation). I hope it satiesfies your requirements and wish every scientist out their loads of fun deciphering a 5.000 years old language :-)

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MDExOlB1bGxSZXF1ZXN0NjI2MTg5MDU3

Allow collaborators to self-assign issues

closed

MEMBER

Allow collaborators (without write access to the repository) to self-assign issues. In order to self-assign an issue, they have to comment it with the word: `#take` or `#self-assign`.

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MDU6SXNzdWU4NzExMTEyMzU=

Load_dataset for local CSV files

closed

NONE

The method load_dataset fails to correctly load a dataset from csv. Moreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings. row example: ```tokens | labels ['I' , 'am', 'John'] | ['PRON', 'AUX', 'PROPN' ] ``` The method, loads each list as a string: (i.g "['I' , 'am', 'John']"). To solve this issue, I copied the Datasets.Features, created Sequence types ( instead of Value) and tried to cast the features type ``` new_features['tokens'] = Sequence(feature=Value(dtype='string', id=None)) new_features['labels'] = Sequence(feature=ClassLabel(num_classes=len(tag2idx), names=list(unique_tags))) dataset = dataset.cast(new_features) ``` but I got the following error ``` ArrowNotImplementedError: Unsupported cast from string to list using function cast_list ``` Moreover, I tried to set feature parameter in load_dataset method, to my new_features, but this fails as well. How can this be solved ?

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MDExOlB1bGxSZXF1ZXN0NjI2MTQ0MTQ3

Avoid copying table's record batches

closed

CONTRIBUTOR

Fixes #2276

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MDExOlB1bGxSZXF1ZXN0NjI2MTE5MTE2

Fix metadata validation with config names

closed

MEMBER

I noticed in https://github.com/huggingface/datasets/pull/2280 that the metadata validator doesn't parse the tags in the readme properly when then contain the tags per config.

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MDU6SXNzdWU4NzEwMDUyMzY=

Help understanding how to build a dataset for language modeling as with the old TextDataset

closed

NONE

Hello, I am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers. I would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the documents in the dataset into lines of a "tokenizable" size, as the old TextDataset class would do, where you only had to do the following, and a tokenized dataset without text loss would be available to pass to a DataCollator: ``` model_checkpoint = 'distilbert-base-uncased' from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) from transformers import TextDataset dataset = TextDataset( tokenizer=tokenizer, file_path="path/to/text_file.txt", block_size=512, ) ``` For now, what I have is the following, which, of course, throws an error because each line is longer than the maximum block size in the tokenizer: ``` import datasets dataset = datasets.load_dataset('path/to/text_file.txt') model_checkpoint = 'distilbert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"]) tokenized_datasets ``` So what would be the "standard" way of creating a dataset in the way it was done before? Thank you very much for the help :))

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MDExOlB1bGxSZXF1ZXN0NjI2MDM5MDc5

Initialize Imdb dataset as used in Don't Stop Pretraining Paper

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MDExOlB1bGxSZXF1ZXN0NjI2MDM0MDk5

Initialize imdb dataset from don't stop pretraining paper

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MDExOlB1bGxSZXF1ZXN0NjI2MDEyMzM3

Initialize imdb dataset from don't stop pretraining paper

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MDExOlB1bGxSZXF1ZXN0NjI1OTI2MjAw

Update multi_woz_v22 checksum

closed

MEMBER

Fix issue https://github.com/huggingface/datasets/issues/1876 The files were changed in https://github.com/budzianowski/multiwoz/pull/72

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MDExOlB1bGxSZXF1ZXN0NjI1OTE2Mzcy

Fixed typo seperate->separate

closed

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MDU6SXNzdWU4NzA0MzE2NjI=

Compatibility with Ubuntu 18 and GLIBC 2.27?

closed

NONE

## Describe the bug For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-29-or-higher-in-ubuntu-18-04). I'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface/datasets requires either an upgrade to Ubuntu 19/20 or a hand-rolled install of a higher version of GLIBC. ## Steps to reproduce the bug 1. clone the transformers repo 2. move to examples/pytorch/language-modeling 3. run example command: ```python run_clm.py --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir /tmp/test-clm``` ## Expected results As described in the transformers repo. ## Actual results ```Traceback (most recent call last): File "run_clm.py", line 34, in <module> from transformers import ( File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2487, in __getattr__ return super().__getattr__(name) File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/file_utils.py", line 1699, in __getattr__ module = self._get_module(self._class_to_module[name]) File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2481, in _get_module return importlib.import_module("." + module_name, self.__name__) File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/__init__.py", line 19, in <module> from . import ( File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/__init__.py", line 23, in <module> from .tokenization_layoutlm import LayoutLMTokenizer File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/tokenization_layoutlm.py", line 19, in <module> from ..bert.tokenization_bert import BertTokenizer File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/bert/tokenization_bert.py", line 23, in <module> from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils.py", line 26, in <module> from .tokenization_utils_base import ( File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 68, in <module> from tokenizers import AddedToken File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/__init__.py", line 79, in <module> from .tokenizers import ( ImportError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/tokenizers.cpython-37m-x86_64-linux-gnu.so) ``` ## Versions Paste the output of the following code: ``` - Datasets: 1.6.1 - Python: 3.7.10 (default, Feb 26 2021, 18:47:35) [GCC 7.3.0] - Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid ```

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MDU6SXNzdWU4NzAwODgwNTk=

Loss result inGptNeoForCasual

closed

NONE

Is there any way you give the " loss" and "logits" results in the gpt neo api?

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MDExOlB1bGxSZXF1ZXN0NjI1MzI5NjIz

Create CacheManager

open

MEMBER

Perform refactoring to decouple cache functionality (method `as_dataset`).

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MDU6SXNzdWU4NzAwMTA1MTE=

concatenate_datasets loads all the data into memory

closed

NONE

## Describe the bug When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk. Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.  ## Steps to reproduce the bug ```python from datasets import concatenate_datasets, load_from_disk test_sampled_pro = load_from_disk("test_sampled_pro") val_sampled_pro = load_from_disk("val_sampled_pro") big_set = concatenate_datasets([test_sampled_pro, val_sampled_pro]) # Loaded to memory big_set.save_to_disk("big_set") # Loaded to memory big_set = concatenate_datasets([big_set, val_sampled_pro]) ``` ## Expected results The data should be loaded into memory in batches and then saved directly to disk. ## Actual results The entire data set is loaded into the memory and then saved to the hard disk. ## Versions Paste the output of the following code: ```python - Datasets: 1.6.1 - Python: 3.8.8 (default, Apr 13 2021, 19:58:26) [GCC 7.3.0] - Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10 ```

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MDU6SXNzdWU4NjkzNzgzMTE=

SNLI dataset has labels of -1

closed

NONE

There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https://nlp.stanford.edu/projects/snli/) and [here](https://github.com/huggingface/datasets/tree/master/datasets/snli) don't list -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107 or 124 of the test set. It isn't clear what these labels mean. I found a [line of code](https://github.com/huggingface/datasets/blob/80e59ef178d3bb2090d091bc32315c655eb0633d/datasets/snli/snli.py#L94) that seems to put them in but it seems still unclear why they are there. The current workaround is to just drop the rows from any model being trained. Perhaps the documentation should be updated.

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MDExOlB1bGxSZXF1ZXN0NjI0NTkyMjQx

Always update metadata in arrow schema

closed

MEMBER

We store a redundant copy of the features in the metadata of the schema of the arrow table. This is used to recover the features when doing `Dataset.from_file`. These metadata are updated after each transfor, that changes the feature types. For each function that transforms the feature types of the dataset, I added a step in the tests to make sure the metadata in the arrow schema are up to date. I also added a line to update the metadata directly in the Dataset.__init__ method. This way even a dataset instantiated with __init__ will have a table with the right metadata. cc @mariosasko

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MDExOlB1bGxSZXF1ZXN0NjI0NDcxODc1

Added CUAD metrics

closed

CONTRIBUTOR

`EM`, `F1`, `AUPR`, `Precision@80%Recall`, and `Precision@90%Recall` metrics supported for CUAD

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MDU6SXNzdWU4NjkwMTc5Nzc=

Bug in Dataset.class_encode_column

closed

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## Describe the bug All the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded. ## Expected results All the original columns should be kept. This needs regression tests.

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MDU6SXNzdWU4NjkwMDIxNDE=

Synchronize table metadata with features

open

MEMBER

**Is your feature request related to a problem? Please describe.** As pointed out in this [comment](https://github.com/huggingface/datasets/pull/2145#discussion_r621326767): > Metadata stored in the schema is just a redundant information regarding the feature types. It is used when calling Dataset.from_file to know which feature types to use. These metadata are stored in the schema of the pyarrow table by using `update_metadata_with_features`. However this something that's almost never tested properly. **Describe the solution you'd like** We should find a way to always make sure that the metadata (in `self.data.schema.metadata`) are synced with the actual feature types (in `self.info.features`).

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MDExOlB1bGxSZXF1ZXN0NjI0MzU5Njky

Fix iterable interface expected by numpy

closed

MEMBER

Numpy expects the old iterable interface with `__getitem__` instead of `__iter__`.

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MDExOlB1bGxSZXF1ZXN0NjI0MzMwNDA3

Fix query table with iterable

closed

MEMBER

The benchmark runs are failing on master because it tries to use an iterable to query the dataset. However there's currently an issue caused by the use of `np.array` instead of `np.fromiter` on the iterable. This PR fixes it

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MDExOlB1bGxSZXF1ZXN0NjI0MjQyODg1

Don't use pyarrow 4.0.0 since it segfaults when casting a sliced ListArray of integers

closed

MEMBER

This test `tests/test_table.py::test_concatenation_table_cast` segfaults with the latest update of pyarrow 4.0.0. Setting `pyarrow<4.0.0` for now. I'll open an issue on JIRA once I know more about the origin of the issue

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MDU6SXNzdWU4NjgyOTExMjk=

DatasetDict save load Failing test in 1.6 not in 1.5

open

NONE

## Describe the bug We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema. Downgrading to `>1.6` -- fixes the problem. ## Steps to reproduce the bug ```python ### Load a dataset dict from jsonl path = '/test/foo' ds_dict.save_to_disk(path) ds_from_disk = DatasetDict.load_from_disk(path). ## <-- this is where I see the error on 1.6 ``` ## Expected results Upgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk. ## Actual results ``` # Infer features if None inferred_features = Features.from_arrow_schema(arrow_table.schema) if self.info.features is None: self.info.features = inferred_features # Infer fingerprint if None if self._fingerprint is None: self._fingerprint = generate_fingerprint(self) # Sanity checks assert self.features is not None, "Features can't be None in a Dataset object" assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object" if self.info.features.type != inferred_features.type: > raise ValueError( "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format( self.info.features, self.info.features.type, inferred_features, inferred_features.type ) ) E ValueError: External features info don't match the dataset: E Got E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]} E with type E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>> E E but expected something like E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]} E with type E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>> ../../../../../.virtualenvs/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:274: ValueError ``` ## Versions - Datasets: 1.6.1 - Python: 3.8.5 (default, Jan 26 2021, 10:01:04) [Clang 12.0.0 (clang-1200.0.32.2)] - Platform: macOS-10.15.7-x86_64-i386-64bit ```

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MDExOlB1bGxSZXF1ZXN0NjIzNDY1OTI5

Make tests run faster

closed

MEMBER

From 7min to 2min to run pytest. Ideally we should keep the whole CI run time below 10min. In this PR I removed the remote tests that were never used. I also replaced nested parametrized tests with unit tests. This makes me think that we could still add more high level tests to check for a few combinations of parameters (but not all of them since there are too many of them). Let me know what you think Finally in another PR we can also separate in two circleci jobs: - the tests of the code code of the lib - the tests of the all the dataset/metric scripts.

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MDExOlB1bGxSZXF1ZXN0NjIzMTUyOTg5

Update black

closed

MEMBER

Latest black version 21.4b0 requires to reformat most dataset scripts and also the core code of the lib. This makes the CI currently fail on master

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MDExOlB1bGxSZXF1ZXN0NjIzMTQwODA1

Fix memory issue in multiprocessing: Don't pickle table index

closed

MEMBER

The table index is currently being pickled when doing multiprocessing, which brings all the record batches of the dataset in memory. I fixed that by not pickling the index attributes. Therefore each process has to rebuild the index when unpickling the table. Fix issue #2256 We'll do a patch release asap !

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MDExOlB1bGxSZXF1ZXN0NjIzMDk0NTcy

test data added, dataset_infos updated

closed

CONTRIBUTOR

Fixes #2262. Thanks for pointing out issue with dataset @jinmang2!

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MDU6SXNzdWU4NjczMjUzNTE=

NewsPH NLI dataset script fails to access test data.

closed

NONE

In Newsph-NLI Dataset (#1192), it fails to access test data. According to the script below, the download manager will download the train data when trying to download the test data. https://github.com/huggingface/datasets/blob/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b/datasets/newsph_nli/newsph_nli.py#L71 If you download it according to the script above, you can see that train and test receive the same data as shown below. ```python >>> from datasets import load_dataset >>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py") >>> newsph_nli DatasetDict({ train: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 420000 }) test: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 420000 }) validation: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 90000 }) }) >>> newsph_nli["train"][0] {'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).', 'label': 1, 'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'} >>> newsph_nli["test"][0] {'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).', 'label': 1, 'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'} ``` In local, I modified the code of the source as below and got the correct result. ```python 71 test_path = os.path.join(download_path, "test.csv") ``` ```python >>> from datasets import load_dataset >>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py") >>> newsph_nli DatasetDict({ train: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 420000 }) test: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 9000 }) validation: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 90000 }) }) >>> newsph_nli["train"][0] {'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).', 'label': 1, 'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'} >>> newsph_nli["test"][0] {'hypothesis': '-- JAI (@JaiPaller) September 13, 2019', 'label': 1, 'premise': 'Pinag-iingat ng Konsulado ng Pilipinas sa Dubai ang publiko, partikular ang mga donor, laban sa mga scam na gumagamit ng mga charitable organization.'} ``` I don't have experience with open source pull requests, so I suggest that you reflect them in the source. Thank you for reading :)

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MDExOlB1bGxSZXF1ZXN0NjIyODIxNzQw

Improve ReadInstruction logic and update docs

closed

CONTRIBUTOR

Improve ReadInstruction logic and docs.

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MDExOlB1bGxSZXF1ZXN0NjIyNzMwODYx

GooAQ dataset added

closed

CONTRIBUTOR

@lhoestq here the dataset is stored with Git LFS. Should I add option for manual downloading of dataset using `git lfs pull` post repo cloning or can we accommodate this in the current `download_and_extract`?

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MDExOlB1bGxSZXF1ZXN0NjIyNjc2ODA0

Add support for Split.ALL

closed

CONTRIBUTOR

The title says it all.

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MDExOlB1bGxSZXF1ZXN0NjIyNjcxNTQy

Fix incorrect update_metadata_with_features calls in ArrowDataset

closed

CONTRIBUTOR

Fixes bugs in the `unpdate_metadata_with_features` calls (caused by changes in #2151)

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MDExOlB1bGxSZXF1ZXN0NjIyNTkwMDQw

added metrics for CUAD

closed

CONTRIBUTOR

For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here

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MDU6SXNzdWU4NjY3MDg2MDk=

Running `datase.map` with `num_proc > 1` uses a lot of memory

closed

NONE

## Describe the bug Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow. ## Steps to reproduce the bug ```python from datasets import load_dataset dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False) def _prepare_sample(batch): return {"input_ids": list(), "attention_mask": list()} for split_name, dataset_split in list(dstc8_datset.items()): print(f"Processing {split_name}") encoded_dataset_split = dataset_split.map( function=_prepare_sample, batched=True, num_proc=4, remove_columns=dataset_split.column_names, batch_size=10, writer_batch_size=10, keep_in_memory=False, ) print(encoded_dataset_split) path = f"./data/encoded_{split_name}" encoded_dataset_split.save_to_disk(path) ``` ## Expected results Memory usage should stay within reasonable boundaries. ## Actual results This is htop-output from running the provided script.  ## Versions ``` - Datasets: 1.6.0 - Python: 3.8.8 (default, Apr 13 2021, 19:58:26) [GCC 7.3.0] - Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10 ``` Running on WSL2

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MDExOlB1bGxSZXF1ZXN0NjIyMTc0Njg4

Task casting for text classification & question answering

closed

CONTRIBUTOR

This PR implements task preparation for a given task, in the continuation of #2143 Task taxonomy follows 🤗 Transformers's pipelines taxonomy: https://github.com/huggingface/transformers/tree/master/src/transformers/pipelines Edit by @lewtun: This PR implements support for the following tasks: * `text-classification` * `question-answering` The intended usage is as follows: ```python # Load a dataset with default column names / features ds = load_dataset("dataset_name") # Cast column names / features to schema. Casting is defined in the dataset's `DatasetInfo` ds = ds.prepare_for_task(task="text-classification") # Casting can also be realised during load ds = load_dataset("dataset_name", task="text-classification") # We can also combine shared tasks across dataset concatenation ds1 = load_dataset("dataset_name_1", task="text-classification") ds2 = load_dataset("dataset_name_2", task="text-classification") # If the tasks have the same schema, so will `ds_concat` ds_concat = concatenate_datasets([ds1, ds2]) ``` Note that the current implementation assumes that `DatasetInfo.task_templates` has been pre-defined by the user / contributor when overriding the `MyDataset(GeneratorBasedBuilder)._info` function. As pointed out by @SBrandeis, for evaluation we'll need a way to detect which datasets are already have a compatible schema so we don't have to edit hundreds of dataset scripts. One possibility is to check if the schema features are a subset of the dataset ones, e.g. ```python squad = load_dataset("./datasets/squad", split="train") qa = QuestionAnswering() schema = Features({**qa.input_schema, **qa.label_schema}) assert all(item in squad.features.items() for item in schema.items()) ```

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MDExOlB1bGxSZXF1ZXN0NjIyMTE1NDI0

Update format, fingerprint and indices after add_item

closed

MEMBER

Added fingerprint and format update wrappers + update the indices by adding the index of the newly added item in the table.

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MDExOlB1bGxSZXF1ZXN0NjIyMDA2Njg3

Perform minor refactoring: use config

closed

MEMBER

Perform minor refactoring related to `config`.

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MDU6SXNzdWU4NjU4NzA3MTA=

Slow dataloading with big datasets issue persists

open

NONE

Hi, I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122). However, the problem seems to persist. Here is the profiled results: 1) Running with 60GB ``` Action | Mean duration (s) |Num calls | Total time (s) | Percentage % | ------------------------------------------------------------------------------------------------------------------------------------ Total | - |_ | 517.96 | 100 % | ------------------------------------------------------------------------------------------------------------------------------------ model_backward | 0.26144 |100 | 26.144 | 5.0475 | model_forward | 0.11123 |100 | 11.123 | 2.1474 | get_train_batch | 0.097121 |100 | 9.7121 | 1.8751 | ``` 3) Running with 600GB, datasets==1.6.0 ``` Action | Mean duration (s) |Num calls | Total time (s) | Percentage % | ------------------------------------------------------------------------------------------------------------------------------------ Total | - |_ | 4563.2 | 100 % | ------------------------------------------------------------------------------------------------------------------------------------ get_train_batch | 5.1279 |100 | 512.79 | 11.237 | model_backward | 4.8394 |100 | 483.94 | 10.605 | model_forward | 0.12162 |100 | 12.162 | 0.26652 | ``` I see that `get_train_batch` lags when data is large. Could this be related to different issues? I would be happy to provide necessary information to investigate.

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MDU6SXNzdWU4NjU4NDg3MDU=

while running run_qa.py, ran into a value error

open

NONE

command: python3 run_qa.py --model_name_or_path hyunwoongko/kobart --dataset_name squad_kor_v2 --do_train --do_eval --per_device_train_batch_size 8 --learning_rate 3e-5 --num_train_epochs 3 --max_seq_length 512 --doc_stride 128 --output_dir /tmp/debug_squad/ error: ValueError: External features info don't match the dataset: Got {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answer': {'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None), 'html_answer_start': Value(dtype='int32', id=None)}, 'url': Value(dtype='string', id=None), 'raw_html': Value(dtype='string', id=None)} with type struct<answer: struct<text: string, answer_start: int32, html_answer_start: int32>, context: string, id: string, question: string, raw_html: string, title: string, url: string> but expected something like {'answer': {'answer_start': Value(dtype='int32', id=None), 'html_answer_start': Value(dtype='int32', id=None), 'text': Value(dtype='string', id=None)}, 'context': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'raw_html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)} with type struct<answer: struct<answer_start: int32, html_answer_start: int32, text: string>, context: string, id: string, question: string, raw_html: string, title: string, url: string> I didn't encounter this error 4 hours ago. any solutions for this kind of issue? looks like gained dataset format refers to 'Data Fields', while expected refers to 'Data Instances'.

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MDU6SXNzdWU4NjU0MDI0NDk=

some issue in loading local txt file as Dataset for run_mlm.py

open

NONE

 first of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error. > FileNotFoundError: [Errno 2] No such file or directory: 'c' by removing one of the training .txt files It's fixed and although if I put all file as training it's ok   after this, my question is how could I use this defined Dataset for run_mlm.py for from scratch pretraining. by using --train_file path_to_train_file just can use one .txt , .csv or, .json file. I tried to set my defined Dataset as --dataset_name but the below issue occurs. > Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 336, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/dataset/dataset.py > During handling of the above exception, another exception occurred: > Traceback (most recent call last): File "run_mlm.py", line 486, in <module> main() File "run_mlm.py", line 242, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 719, in load_dataset use_auth_token=use_auth_token, File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 347, in prepare_module combined_path, github_file_path FileNotFoundError: Couldn't find file locally at dataset/dataset.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.6.0/datasets/dataset/dataset.py. The file is also not present on the master branch on github.

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MDExOlB1bGxSZXF1ZXN0NjIxMzU1MzE3

Allow downloading/processing/caching only specific splits

open

MEMBER

Allow downloading/processing/caching only specific splits without downloading/processing/caching the other splits. This PR implements two steps to handle only specific splits: - it allows processing/caching only specific splits into Arrow files - for some simple cases, it allows downloading only specific splits (which is more intricate as it depends on the user-defined method `_split_generators`) This PR makes several assumptions: - `DownloadConfig` contains the configuration settings for downloading - the parameter `split` passed to `load_dataset` is just a parameter for loading (from cache), not for downloading

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MDExOlB1bGxSZXF1ZXN0NjIxMDEyNzg5

Implement Dataset to JSON

closed

MEMBER

Implement `Dataset.to_json`.

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MDExOlB1bGxSZXF1ZXN0NjIwOTgzNzY3

Implement Dataset from Parquet

closed

MEMBER

Implement instantiation of Dataset from Parquet file.

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MDExOlB1bGxSZXF1ZXN0NjIwNDg3OTUw

Faster map w/ input_columns & faster slicing w/ Iterable keys

closed

CONTRIBUTOR

@lhoestq Fixes #2193 - `map` now uses `with_format` to only load needed columns in memory when `input_columns` is set - Slicing datasets with Iterables of indices now uses a new `Table.fast_gather` method, implemented with `np.searchsorted`, to find the appropriate batch indices all at once. `pa.concat_tables` is no longer used for this; we just call `pa.Table.from_batches` with a list of all the batch slices. Together these changes have sped up batched `map()` calls over subsets of columns quite considerably in my initial testing.

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MDExOlB1bGxSZXF1ZXN0NjE5NjQzMjQ3

Add `key` type and duplicates verification with hashing

closed

CONTRIBUTOR

Closes #2230 There is currently no verification for the data type and the uniqueness of the keys yielded by the `dataset_builder`. This PR is currently a work in progress with the following goals: - [x] Adding `hash_salt` to `ArrowWriter` so that the keys belonging to different splits have different hash - [x] Add `key` arrtibute to `ArrowWriter.write()` for hashing - [x] Add a hashing class which takes an input key of certain type (`str`/`int`/anything convertible to string) and produces a 128-bit hash using `hashlib.md5` - [x] Creating a function giving a custom error message when non-unique keys are found **[This will take care of type-checking for keys]** - [x] Checking for duplicate keys in `writer.write()` for each batch [**NOTE**: This PR is currently concerned with `GeneratorBasedBuilder` only, for simplification. A subsequent PR will be made in future for `ArrowBasedBuilder`] @lhoestq Thank you for the feedback. It would be great to have your guidance on this!

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MDExOlB1bGxSZXF1ZXN0NjE5NTAyODc0

Set specific cache directories per test function call

open

MEMBER

Implement specific cache directories (datasets, metrics and modules) per test function call. Currently, the cache directories are set within the temporary test directory, but they are shared across all test function calls. This PR implements specific cache directories for each test function call, so that tests are atomic and there are no side effects.

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MDU6SXNzdWU4NjI5MDkzODk=

Map is slow and processes batches one after another

closed

NONE

## Describe the bug I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. I process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps. pseudo code: ```python ds = datasets.load_from_disk("path") new_dataset = ds.map(work, batched=True, ...) # fast uses all processes final_dataset = new_dataset.map(work2, batched=True, ...) # slow starts one process after another ``` ## Expected results Second stage should be as fast as the first stage. ## Versions Paste the output of the following code: - Datasets: 1.5.0 - Python: 3.8.8 (default, Feb 24 2021, 21:46:12) - Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10 Do you guys have any idea? Thanks a lot!

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MDU6SXNzdWU4NjI4NzAyMDU=

Link to datasets viwer on Quick Tour page returns "502 Bad Gateway"

closed

NONE

Link to datasets viwer (https://huggingface.co/datasets/viewer/) on Quick Tour page (https://huggingface.co/docs/datasets/quicktour.html) returns "502 Bad Gateway" The same error with https://huggingface.co/datasets/viewer/?dataset=glue&config=mrpc

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MDExOlB1bGxSZXF1ZXN0NjE5MjI0MzIw

Add SLR32 to OpenSLR

closed

CONTRIBUTOR

I would like to add SLR32 to OpenSLR. It contains four South African languages: Afrikaans, Sesotho, Setswana and isiXhosa

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MDExOlB1bGxSZXF1ZXN0NjE5MDkyODc5

Clarify how to load wikihow

closed

MEMBER

Explain clearer how to load the dataset in the manual download instructions. En relation with #2239.

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MDU6SXNzdWU4NjE5MDQzMDY=

Error loading wikihow dataset

closed

CONTRIBUTOR

## Describe the bug When attempting to load wikihow into a dataset with ```python from datasets import load_dataset dataset = load_dataset('wikihow', data_dir='./wikihow') ``` I get the message: ``` AttributeError: 'BuilderConfig' object has no attribute 'filename' ``` at the end of a [full stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2). ## Steps to reproduce the bug I have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https://huggingface.co/datasets/wikihow) says to use ```python from datasets import load_dataset dataset = load_dataset('wikihow') ``` to load the dataset. I do so and I get the message ``` AssertionError: The dataset wikihow with config all requires manual data. Please follow the manual download instructions: You need to manually download two wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset. You need to download the following two files manually: 1) https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv 2) https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv The <path/to/folder> can e.g. be "~/manual_wikihow_data". Wikihow can then be loaded using the following command `datasets.load_dataset("wikihow", data_dir="<path/to/folder>")`. . Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path/to/manual/data>') ``` So I create a directory `./wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory. Then I run ```python from datasets import load_dataset dataset = load_dataset('wikihow', data_dir='./wikihow') ``` that's when I get the [stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2) ## Expected results I expected it to load the downloaded files into a dataset. ## Actual results ```python Using custom data configuration default-data_dir=.%2Fwikihow Downloading and preparing dataset wikihow/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/azureuser/.cache/huggingface/datasets/wikihow/default-data_dir=.%2Fwikihow/0.0.0/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2... --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-9-5e4d40142f30> in <module> ----> 1 dataset = load_dataset('wikihow',data_dir='./wikihow') ~/.local/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 745 try_from_hf_gcs=try_from_hf_gcs, 746 base_path=base_path,--> 747 use_auth_token=use_auth_token, 748 ) 749 ~/.local/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 577 if not downloaded_from_gcs: 578 self._download_and_prepare( --> 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) 581 # Sync info ~/.local/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 632 split_dict = SplitDict(dataset_name=self.name) 633 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 634 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 635 636 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/wikihow/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2/wikihow.py in _split_generators(self, dl_manager) 132 133 path_to_manual_file = os.path.join( --> 134 os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename 135 ) 136 AttributeError: 'BuilderConfig' object has no attribute 'filename' ``` ## Versions Paste the output of the following code: ```python import datasets import sys import platform print(f""" - Datasets: {datasets.__version__} - Python: {sys.version} - Platform: {platform.platform()} """) ``` ``` - Datasets: 1.5.0 - Python: 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0] - Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic ```

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https://github.com/huggingface/datasets/pull/2238

MDExOlB1bGxSZXF1ZXN0NjE4MTY5NzM5

NLU evaluation data

closed

CONTRIBUTOR

New intent classification dataset from https://github.com/xliuhw/NLU-Evaluation-Data

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MDU6SXNzdWU4NjE0Mjc0Mzk=

Update Dataset.dataset_size after transformed with map

open

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After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated.

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MDU6SXNzdWU4NjEzODgxNDU=

Request to add StrategyQA dataset

open

NONE

## Request to add StrategyQA dataset - **Name:** StrategyQA - **Description:** open-domain QA [(project page)](https://allenai.org/data/strategyqa) - **Paper:** [url](https://arxiv.org/pdf/2101.02235.pdf) - **Data:** [here](https://allenai.org/data/strategyqa) - **Motivation:** uniquely-formulated dataset that also includes a question-decomposition breakdown and associated Wikipedia annotations for each step. Good for multi-hop reasoning modeling.

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MDExOlB1bGxSZXF1ZXN0NjE3Nzc0NDUw

Update README.md

closed

CONTRIBUTOR

Adding relevant citations (paper accepted at AAAI 2020 & EMNLP 2020) to the benchmark

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MDExOlB1bGxSZXF1ZXN0NjE3MzI4NDU3

Fix bash snippet formatting in ADD_NEW_DATASET.md

closed

CONTRIBUTOR

This PR indents the paragraphs around the bash snippets in ADD_NEW_DATASET.md to fix formatting.

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MDExOlB1bGxSZXF1ZXN0NjE3MDYwMTkw

Fix `xnli` dataset tuple key

closed

CONTRIBUTOR

Closes #2229 The `xnli` dataset yields a tuple key in case of `ar` which is inconsistant with the acceptable key types (str/int). The key was thus ported to `str` keeping the original information intact.

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MDExOlB1bGxSZXF1ZXN0NjE3MDQyNTI4

Start filling GLUE dataset card

closed

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The dataset card was pretty much empty. I added the descriptions (mainly from TFDS since the script is the same), and I also added the tasks tags as well as examples for a subset of the tasks. cc @sgugger

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MDExOlB1bGxSZXF1ZXN0NjE2ODYyNTEx

Fix map when removing columns on a formatted dataset

closed

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This should fix issue #2226 The `remove_columns` argument was ignored on formatted datasets

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