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akhousker

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https://github.com/huggingface/datasets/pull/96

MDExOlB1bGxSZXF1ZXN0NDE3NjAwMjY4

lm1b

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Add lm1b dataset.

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https://github.com/huggingface/datasets/pull/95

MDExOlB1bGxSZXF1ZXN0NDE3NTY5NzA4

Replace checksums files by Dataset infos json

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### Better verifications when loading a dataset I replaced the `urls_checksums` directory that used to contain `checksums.txt` and `cached_sizes.txt`, by a single file `dataset_infos.json`. It's just a dict `config_name` -> `DatasetInfo`. It simplifies and improves how verifications of checksums and splits sizes are done, as they're all stored in `DatasetInfo` (one per config). Also, having already access to `DatasetInfo` enables to check disk space before running `download_and_prepare` for a given config. The dataset infos json file is user readable, you can take a look at the squad one that I generated in this PR. ### Renaming According to these changes, I did some renaming: `save_checksums` -> `save_infos` `ignore_checksums` -> `ignore_verifications` for example, when you are creating a dataset you have to run ```nlp-cli test path/to/my/dataset --save_infos --all_configs``` instead of ```nlp-cli test path/to/my/dataset --save_checksums --all_configs``` ### And now, the fun part We'll have to rerun the `nlp-cli test ... --save_infos --all_configs` for all the datasets ----------------- feedback appreciated !

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MDExOlB1bGxSZXF1ZXN0NDE3NDYyMTIw

Librispeech

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Add librispeech dataset and remove some useless content.

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https://github.com/huggingface/datasets/pull/93

MDExOlB1bGxSZXF1ZXN0NDE3NDIxODUy

Cleanup notebooks and various fixes

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Fixes on dataset (more flexible) metrics (fix) and general clean ups

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MDExOlB1bGxSZXF1ZXN0NDE3Mjc1ODky

[WIP] add wmt14

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WMT14 takes forever to download :-/ - WMT is the first dataset that uses an abstract class IMO, so I had to modify the `load_dataset_module` a bit.

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MDExOlB1bGxSZXF1ZXN0NDE3Mjc0MjA0

[Paracrawl] add paracrawl

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- Huge dataset - took ~1h to download - Also this PR reformats all dataset scripts and adds `datasets` to `make style`

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https://github.com/huggingface/datasets/pull/90

MDExOlB1bGxSZXF1ZXN0NDE3MjUxODE0

Add download gg drive

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We can now add datasets that download from google drive

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MDExOlB1bGxSZXF1ZXN0NDE3MjM4MjU4

Add list and inspect methods - cleanup hf_api

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Add a bunch of methods to easily list and inspect the processing scripts up-loaded on S3: ```python nlp.list_datasets() nlp.list_metrics() # Copy and prepare the scripts at `local_path` for easy inspection/modification. nlp.inspect_dataset(path, local_path) # Copy and prepare the scripts at `local_path` for easy inspection/modification. nlp.inspect_metric(path, local_path) ``` Also clean up the `HfAPI` to use `dataclasses` for better user-experience

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MDExOlB1bGxSZXF1ZXN0NDE3MjI5ODQw

Add wiki40b

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This one is a beam dataset that downloads files using tensorflow. I tested it on a small config and it works fine

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MDExOlB1bGxSZXF1ZXN0NDE3MjE1NzA0

Add Flores

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Beautiful language for sure!

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MDExOlB1bGxSZXF1ZXN0NDE3MjEwNzY2

[Load => load_dataset] change naming

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Rename leftovers @thomwolf

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https://github.com/huggingface/datasets/pull/85

MDExOlB1bGxSZXF1ZXN0NDE3MjA0ODA4

Add boolq

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I just added the dummy data for this dataset. This one was uses `tf.io.gfile.copy` to download the data but I added the support for custom download in the mock_download_manager. I also had to add a `tensorflow` dependency for tests.

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MDExOlB1bGxSZXF1ZXN0NDE3MjAxODcz

[TedHrLr] add left dummy data

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MDExOlB1bGxSZXF1ZXN0NDE2ODkyOTUz

New datasets

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MDExOlB1bGxSZXF1ZXN0NDE2ODQ1Njc5

[Datasets] add ted_hrlr

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@thomwolf - After looking at `xnli` I think it's better to leave the translation features and add a `translation` key to make them work in our framework. The result looks like this:  you can see that each split has a `translation` key which value is the nlp.features.Translation object. That's a simple change. If it's ok for you, I will add dummy data for the other configs and treat the other translation scripts in the same way.

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https://github.com/huggingface/datasets/pull/81

MDExOlB1bGxSZXF1ZXN0NDE2ODM1NzE1

add tests

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Tests for py_utils functions and for the BaseReader used to read from arrow and parquet. I also removed unused utils functions.

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https://github.com/huggingface/datasets/pull/80

MDExOlB1bGxSZXF1ZXN0NDE2ODMwNjk3

Add nbytes + nexamples check

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### Save size and number of examples Now when you do `save_checksums`, it also create `cached_sizes.txt` right next to the checksum file. This new file stores the bytes sizes and the number of examples of each split that has been prepared and stored in the cache. Example: ``` # Cached sizes: <full_config_name> <num_bytes> <num_examples> hansards/house/1.0.0/test 22906629 122290 hansards/house/1.0.0/train 191459584 947969 hansards/senate/1.0.0/test 5711686 25553 hansards/senate/1.0.0/train 40324278 182135 ``` ### Check processing output If there is a `caches_sizes.txt`, then each time we run `download_and_prepare` it will make sure that the sizes match. You can set `ignore_checksums=True` if you don't want that to happen. ### Fill Dataset Info All the split infos and the checksums are now stored correctly in DatasetInfo after `download_and_prepare` ### Check space on disk before running `download_and_prepare` Check if the space is lower than the sum of the sizes of the files in `checksums.txt` and `cached_files.txt`. This is not ideal though as it considers the files for all configs. TODO: A better way to do it would be to have save the `DatasetInfo` instead of the `checksums.txt` and `cached_sizes.txt`, in order to have one file per dataset config (and therefore consider only the sizes of the files for one config and not all of them). It can also be the occasion to factorize all the `download_and_prepare` verifications. Maybe next PR ?

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MDExOlB1bGxSZXF1ZXN0NDE2ODI5NzMy

[Convert] add new pattern

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MDExOlB1bGxSZXF1ZXN0NDE2ODIwNzU5

[Tests] skip beam dataset tests for now

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For now we will skip tests for Beam Datasets

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MDExOlB1bGxSZXF1ZXN0NDE2NzQwMjAz

New datasets

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https://github.com/huggingface/datasets/pull/76

MDExOlB1bGxSZXF1ZXN0NDE2NjYyMTk2

pin flake 8

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Flake 8's new version does not like our format. Pinning the version for now.

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MDExOlB1bGxSZXF1ZXN0NDE2NjE0MzU1

WIP adding metrics

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Adding the following metrics as identified by @mariamabarham: 1. BLEU: BiLingual Evaluation Understudy: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py, https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/bleu.py (multilingual) 2. GLEU: Google-BLEU: https://github.com/cnap/gec-ranking/blob/master/scripts/compute_gleu 3. Sacrebleu: https://pypi.org/project/sacrebleu/1.4.8/ (pypi package), https://github.com/mjpost/sacrebleu (github implementation) 4. ROUGE: Recall-Oriented Understudy for Gisting Evaluation: https://github.com/google-research/google-research/tree/master/rouge, https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/rouge.py (multilingual) 5. Seqeval: https://github.com/chakki-works/seqeval (github implementation), https://pypi.org/project/seqeval/0.0.12/ (pypi package) 6. Coval: coreference evaluation package for the CoNLL and ARRAU datasets https://github.com/ns-moosavi/coval 7. SQuAD v1 evaluation script 8. SQuAD V2 evaluation script: https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/ 9. GLUE 10. XNLI Not now: 1. Perplexity: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/perplexity.py 2. Spearman: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/spearman_correlation.py 3. F1_measure: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/f1_measure.py 4. Pearson_corelation: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/pearson_correlation.py 5. AUC: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/auc.py 6. Entropy: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/entropy.py

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https://github.com/huggingface/datasets/pull/74

MDExOlB1bGxSZXF1ZXN0NDE2NjA3MDcy

fix overflow check

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I did some tests and unfortunately the test ``` pa_array.nbytes > MAX_BATCH_BYTES ``` doesn't work. Indeed for a StructArray, `nbytes` can be less 2GB even if there is an overflow (it loops...). I don't think we can do a proper overflow test for the limit of 2GB... For now I replaced it with a sanity check on the first element.

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https://github.com/huggingface/datasets/pull/73

MDExOlB1bGxSZXF1ZXN0NDE2NTMyMTg1

JSON script

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CONTRIBUTOR

Add a JSONS script to read JSON datasets from files.

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MDExOlB1bGxSZXF1ZXN0NDE2Mzc4Mjg4

[README dummy data tests] README to better understand how the dummy data structure works

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In this PR a README.md is added to tests to shine more light on how the dummy data structure works. I try to explain the different possible cases. IMO the best way to understand the logic is to checkout the dummy data structure of the different datasets I mention in the README.md since those are the "edge cases". @mariamabarham @thomwolf @lhoestq @jplu - I'd be happy to checkout the dummy data structure and get some feedback on possible improvements.

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MDExOlB1bGxSZXF1ZXN0NDE2MTUxODM4

Fix arrow writer for big datasets using writer_batch_size

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This PR fixes Yacine's bug. According to [this](https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations), it is not recommended to have pyarrow arrays bigger than 2Go. Therefore I set a default batch size of 100 000 examples per batch. In general it shouldn't exceed 2Go. If it does, I reduce the batch_size on the fly, and I notify the user with a warning.

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MDExOlB1bGxSZXF1ZXN0NDE1OTM3NDgw

adding RACE, QASC, Super_glue and Tiny_shakespear datasets

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MDExOlB1bGxSZXF1ZXN0NDE1NzYyNTQ4

fix cache dir in builder tests

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minor fix

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MDExOlB1bGxSZXF1ZXN0NDE1MzQ3NTgw

[CSV] re-add csv

closed

CONTRIBUTOR

Re-adding csv under the datasets under construction to keep circle ci happy - will have to see how to include it in the tests. @lhoestq noticed that I accidently deleted it in https://github.com/huggingface/nlp/pull/63#discussion_r422263729.

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MDExOlB1bGxSZXF1ZXN0NDE1Mjc5NjI0

[Tests] Test files locally

closed

CONTRIBUTOR

This PR adds a `aws` and a `local` decorator to the tests so that tests now run on the local datasets. By default, the `aws` is deactivated and `local` is activated and `slow` is deactivated, so that only 1 test per dataset runs on circle ci. **When local is activated all folders in `./datasets` are tested.** **Important** When adding a dataset, we should no longer upload it to AWS. The steps are: 1. Open a PR 2. Add a dataset as described in `datasets/README.md` 3. If all tests pass, push to master Currently we have 49 functional datasets in our code base. We have 6 datasets "under-construction" that don't pass the tests - so I put them in a folder "datasets_under_construction" - it would be nice to open a PR to fix them and put them in the `datasets` folder. **Important** when running tests locally, the datasets are cached so to rerun them delete your local cache via: `rm -r ~/.cache/huggingface/datasets/*` @thomwolf @mariamabarham @lhoestq

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MDExOlB1bGxSZXF1ZXN0NDE1MjM5Njgy

[Datasets] ReadME

closed

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MDExOlB1bGxSZXF1ZXN0NDE1MjM4MDEw

fix math dataset and xcopa

closed

CONTRIBUTOR

- fixes math dataset and xcopa, uploaded both of the to S3

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MDExOlB1bGxSZXF1ZXN0NDE1MjMwMjYy

[Datasets] Make master ready for datasets adding

closed

CONTRIBUTOR

Add all relevant files so that datasets can now be added on master

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MDExOlB1bGxSZXF1ZXN0NDE1MTczODU5

[Dataset scripts] add all datasets scripts

closed

CONTRIBUTOR

As mentioned, we can have the canonical datasets in the master. For now I also want to include all the data as present on S3 to make the synchronization easier when uploading new datastes. @mariamabarham @lhoestq @thomwolf - what do you think? If this is ok for you, I can sync up the master with the `add_dataset` branch: https://github.com/huggingface/nlp/pull/37 so that master is up to date.

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MDExOlB1bGxSZXF1ZXN0NDE1MTQ1NDAx

[Cached Path] Better error message

closed

CONTRIBUTOR

IMO returning `None` in this function only leads to confusion and is never helpful.

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MDExOlB1bGxSZXF1ZXN0NDE1MTI3MTU4

[Load] rename setup_module to prepare_module

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rename setup_module to prepare_module due to issues with pytests `setup_module` function. See: PR #59.

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MDExOlB1bGxSZXF1ZXN0NDE0OTQyNjEy

Update to simplify some datasets conversion

closed

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This PR updates the encoding of `Values` like `integers`, `boolean` and `float` to use python casting and avoid having to cast in the dataset scripts, as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r420176626 We could also change (not included in this PR yet): - `supervized_keys` to make them a NamedTuple instead of a dataclass, and - handle specifically the `Translation` features. as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r421740236 @patrickvonplaten @mariamabarham tell me if you want these two last changes as well.

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MDExOlB1bGxSZXF1ZXN0NDE0OTM3NTgx

Fix tests

closed

MEMBER

@patrickvonplaten I've broken a bit the tests with #25 while simplifying and re-organizing the `load.py` and `download_manager.py` scripts. I'm trying to fix them here but I have a weird error, do you think you can have a look? ```bash (datasets) MacBook-Pro-de-Thomas:datasets thomwolf$ python -m pytest -sv ./tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ============================================================================= test session starts ============================================================================= platform darwin -- Python 3.7.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 -- /Users/thomwolf/miniconda2/envs/datasets/bin/python cachedir: .pytest_cache rootdir: /Users/thomwolf/Documents/GitHub/datasets plugins: xdist-1.31.0, forked-1.1.3 collected 1 item tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ERROR =================================================================================== ERRORS ==================================================================================== ____________________________________________________________ ERROR at setup of DatasetTest.test_builder_class_snli ____________________________________________________________ file_path = <module 'tests.test_dataset_common' from '/Users/thomwolf/Documents/GitHub/datasets/tests/test_dataset_common.py'> download_config = DownloadConfig(cache_dir=None, force_download=False, resume_download=False, local_files_only=False, proxies=None, user_agent=None, extract_compressed_file=True, force_extract=True) download_kwargs = {} def setup_module(file_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs,) -> DatasetBuilder: r""" Download/extract/cache a dataset to add to the lib from a path or url which can be: - a path to a local directory containing the dataset processing python script - an url to a S3 directory with a dataset processing python script Dataset codes are cached inside the lib to allow easy import (avoid ugly sys.path tweaks) and using cloudpickle (among other things). Return: tuple of the unique id associated to the dataset the local path to the dataset """ if download_config is None: download_config = DownloadConfig(**download_kwargs) download_config.extract_compressed_file = True download_config.force_extract = True > name = list(filter(lambda x: x, file_path.split("/")))[-1] + ".py" E AttributeError: module 'tests.test_dataset_common' has no attribute 'split' src/nlp/load.py:169: AttributeError ============================================================================== warnings summary =============================================================================== /Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15 /Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp -- Docs: https://docs.pytest.org/en/latest/warnings.html =========================================================================== short test summary info =========================================================================== ERROR tests/test_dataset_common.py::DatasetTest::test_builder_class_snli - AttributeError: module 'tests.test_dataset_common' has no attribute 'split' ========================================================================= 1 warning, 1 error in 3.63s ========================================================================= ```

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MDExOlB1bGxSZXF1ZXN0NDE0OTM0NTY4

Aborted PR - Fix tests

closed

MEMBER

@patrickvonplaten I've broken a bit the tests with #25 while simplifying and re-organizing the `load.py` and `download_manager.py` scripts. I'm trying to fix them here but I have a weird error, do you think you can have a look? ```bash (datasets) MacBook-Pro-de-Thomas:datasets thomwolf$ python -m pytest -sv ./tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ============================================================================= test session starts ============================================================================= platform darwin -- Python 3.7.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 -- /Users/thomwolf/miniconda2/envs/datasets/bin/python cachedir: .pytest_cache rootdir: /Users/thomwolf/Documents/GitHub/datasets plugins: xdist-1.31.0, forked-1.1.3 collected 1 item tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ERROR =================================================================================== ERRORS ==================================================================================== ____________________________________________________________ ERROR at setup of DatasetTest.test_builder_class_snli ____________________________________________________________ file_path = <module 'tests.test_dataset_common' from '/Users/thomwolf/Documents/GitHub/datasets/tests/test_dataset_common.py'> download_config = DownloadConfig(cache_dir=None, force_download=False, resume_download=False, local_files_only=False, proxies=None, user_agent=None, extract_compressed_file=True, force_extract=True) download_kwargs = {} def setup_module(file_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs,) -> DatasetBuilder: r""" Download/extract/cache a dataset to add to the lib from a path or url which can be: - a path to a local directory containing the dataset processing python script - an url to a S3 directory with a dataset processing python script Dataset codes are cached inside the lib to allow easy import (avoid ugly sys.path tweaks) and using cloudpickle (among other things). Return: tuple of the unique id associated to the dataset the local path to the dataset """ if download_config is None: download_config = DownloadConfig(**download_kwargs) download_config.extract_compressed_file = True download_config.force_extract = True > name = list(filter(lambda x: x, file_path.split("/")))[-1] + ".py" E AttributeError: module 'tests.test_dataset_common' has no attribute 'split' src/nlp/load.py:169: AttributeError ============================================================================== warnings summary =============================================================================== /Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15 /Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp -- Docs: https://docs.pytest.org/en/latest/warnings.html =========================================================================== short test summary info =========================================================================== ERROR tests/test_dataset_common.py::DatasetTest::test_builder_class_snli - AttributeError: module 'tests.test_dataset_common' has no attribute 'split' ========================================================================= 1 warning, 1 error in 3.63s ========================================================================= ```

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https://github.com/huggingface/datasets/pull/57

MDExOlB1bGxSZXF1ZXN0NDE0ODUzMDM5

Better cached path

closed

MEMBER

### Changes: - The `cached_path` no longer returns None if the file is missing/the url doesn't work. Instead, it can raise `FileNotFoundError` (missing file), `ConnectionError` (no cache and unreachable url) or `ValueError` (parsing error) - Fix requests to firebase API that doesn't handle HEAD requests... - Allow custom download in datasets script: it allows to use `tf.io.gfile.copy` for example, to download from google storage. I added an example: the `boolq` script

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MDExOlB1bGxSZXF1ZXN0NDE0ODMyODY4

[Dataset] Tester add mock function

closed

CONTRIBUTOR

need to add an empty `extract()` function to make `hansard` dataset test work.

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MDExOlB1bGxSZXF1ZXN0NDE0NjE0MjE1

Beam datasets

closed

MEMBER

# Beam datasets ## Intro Beam Datasets are using beam pipelines for preprocessing (basically lots of `.map` over objects called PCollections). The advantage of apache beam is that you can choose which type of runner you want to use to preprocess your data. The main runners are: - the `DirectRunner` to run the pipeline locally (default). However I encountered memory issues for big datasets (like the french or english wikipedia). Small dataset work fine - Google Dataflow. I didn't play with it. - Spark or Flink, two well known data processing frameworks. I tried to use the Spark/Flink local runners provided by apache beam for python and wasn't able to make them work properly though... ## From tfds beam datasets to our own beam datasets Tensorflow datasets used beam and a complicated pipeline to shard the TFRecords files. To allow users to download beam datasets and not having to preprocess them, they also allow to download the already preprocessed datasets from their google storage (the beam pipeline doesn't run in that case). On our side, we replace TFRecords by something else. Arrow or Parquet do the job but I chose Parquet as: 1) there is a builtin apache beam parquet writer that is quite convenient, and 2) reading parquet from the pyarrow library is also simple and effective (there is a mmap option !) Moreover we don't shard datasets in many many files like tfds (they were doing probably doing that mainly because of the limit of 2Gb per TFRecord file). Therefore we have a simpler pipeline that saves each split into one parquet file. We also removed the utilities to use their google storage (for now maybe ? we'll have to discuss it). ## Main changes - Added a BeamWriter to save the output of beam pipelines into parquet files and fill dataset infos - Create a ParquetReader and refactor a bit the arrow_reader.py \> **With this, we can now try to add beam datasets from tfds** I already added the wikipedia one, and I will also try to add the Wiki40b dataset ## Test the wikipedia script You can download and run the beam pipeline for wikipedia (using the `DirectRunner` by default) like this: ``` >>> import nlp >>> nlp.load("datasets/nlp/wikipedia", dataset_config="20200501.frr") ``` This wikipedia dataset (lang: frr, North Frisian) is a small one (~10Mb), but feel free to try bigger ones (and fill 20Gb of swap memory if you try the english one lol) ## Next Should we allow to download preprocessed datasets from the tfds google storage ? Should we try to optimize the beam pipelines to run locally without memory issues ? Should we try other data processing frameworks for big datasets, like spark ? ## About this PR It should be merged after #25 ----------------- I'd be happy to have your feedback and your ideas to improve the processing of big datasets like wikipedia :)

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MDExOlB1bGxSZXF1ZXN0NDE0MjUyODkw

[Tests] Improved Error message for dummy folder structure

closed

CONTRIBUTOR

Improved Error message

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MDExOlB1bGxSZXF1ZXN0NDE0MTkwMzkz

[Features] Typo in generate_from_dict

closed

CONTRIBUTOR

Change `isinstance` test in features when generating features from dict.

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MDExOlB1bGxSZXF1ZXN0NDE0MTEyMDAy

allow dummy folder structure to handle dict of lists

closed

CONTRIBUTOR

`esnli.py` needs that extension of the dummy data testing.

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MDExOlB1bGxSZXF1ZXN0NDE0MDUyOTYw

[Testing] Improved testing structure

closed

CONTRIBUTOR

This PR refactors the test design a bit and puts the mock download manager in the `utils` files as it is just a test helper class. as @mariamabarham pointed out, creating a dummy folder structure can be quite hard to grasp. This PR tries to change that to some extent. It follows the following logic for the `dummy` folder structure now: 1.) The data bulider has no config -> the `dummy` folder structure is: `dummy/<version>/dummy_data.zip` 2) The data builder has >= 1 configs -> the `dummy` folder structure is: `dummy/<config_name_1>/<version>/dummy_data.zip` `dummy/<config_name_2>/<version>/dummy_data.zip` Now, the difficult part is how to create the `dummy_data.zip` file. There are two cases: A) The `data_urs` parameter inserted into the `download_and_extract` fn is a **string**: -> the `dummy_data.zip` file zips the folder: `dummy_data/<relative_path_of_folder_structure_of_url>` B) The `data_urs` parameter inserted into the `download_and_extract` fn is a **dict**: -> the `dummy_data.zip` file zips the folder: `dummy_data/<relative_path_of_folder_structure_of_url_behind _key_1>` `dummy_data/<relative_path_of_folder_structure_of_url_behind _key_2>` By relative folder structure I mean `url_path.split('./')[-1]`. As an example the dataset **xquad** by deepmind has the following url path behind the key `de`: `https://github.com/deepmind/xquad/blob/master/xquad.de.json` -> This means that the relative url path should be `xquad.de.json`. @mariamabarham B) is a change from how is was before and I think is makes more sense. While before the `dummy_data.zip` file for xquad with config `de` looked like: `dummy_data/de` it would now look like `dummy_data/xquad.de.json`. I think this is better and easier to understand. Therefore there are currently 6 tests that would have to have changed their dummy folder structure, but which can easily be done (30min). I also added a function: `print_dummy_data_folder_structure` that prints out the expected structures when testing which should be quite helpful.

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MDExOlB1bGxSZXF1ZXN0NDEzNTAwMjE0

[Tests] test only for fast test as a default

closed

CONTRIBUTOR

Test only for one config on circle ci to speed up testing. Add all config test as a slow test. @mariamabarham @thomwolf

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MDExOlB1bGxSZXF1ZXN0NDEzNDY5ODg0

fix flatten nested

closed

MEMBER

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MDExOlB1bGxSZXF1ZXN0NDEzNDM2MTgz

[Command Convert] remove tensorflow import

closed

CONTRIBUTOR

Remove all tensorflow import statements.

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MDExOlB1bGxSZXF1ZXN0NDEzMzg5MDc1

[PyArrow Feature] fix py arrow bool

closed

CONTRIBUTOR

To me it seems that `bool` can only be accessed with `bool_` when looking at the pyarrow types: https://arrow.apache.org/docs/python/api/datatypes.html.

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MDExOlB1bGxSZXF1ZXN0NDEzMzUxNTY0

[Features] Strip str key before dict look-up

closed

CONTRIBUTOR

The dataset `anli.py` currently fails because it tries to look up a key `1\n` in a dict that only has the key `1`. Added an if statement to strip key if it cannot be found in dict.

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MDExOlB1bGxSZXF1ZXN0NDEzMzQzMjAy

[Load] Separate Module kwargs and builder kwargs.

closed

CONTRIBUTOR

Kwargs for the `load_module` fn should be passed with `module_xxxx` to `builder_kwargs` of `load` fn. This is a follow-up PR of: https://github.com/huggingface/nlp/pull/41

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MDExOlB1bGxSZXF1ZXN0NDEyOTUwMzU1

[Tests] Fix tests for datasets with no config

closed

CONTRIBUTOR

Forgot to fix `None` problem for datasets that have no config this in PR: https://github.com/huggingface/nlp/pull/42

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MDExOlB1bGxSZXF1ZXN0NDEyODcxNTE5

[Checksums] If no configs exist prevent to run over empty list

closed

CONTRIBUTOR

`movie_rationales` e.g. has no configs.

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MDExOlB1bGxSZXF1ZXN0NDEyODU1OTE2

[Tests] allow tests for builders without config

closed

CONTRIBUTOR

Some dataset scripts have no configs - the tests have to be adapted for this case. In this case the dummy data will be saved as: - natural_questions -> dummy -> -> 1.0.0 (version num) -> -> -> dummy_data.zip

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MDExOlB1bGxSZXF1ZXN0NDEyODQzNDQy

[Load module] allow kwargs into load module

closed

CONTRIBUTOR

Currenly it is not possible to force a re-download of the dataset script. This simple change allows to pass ``force_reload=True`` as ``builder_kwargs`` in the ``load.py`` function.

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https://github.com/huggingface/datasets/pull/40

MDExOlB1bGxSZXF1ZXN0NDEyODI4NzU2

Update remote checksums instead of overwrite

closed

MEMBER

When the user uploads a dataset on S3, checksums are also uploaded with the `--upload_checksums` parameter. If the user uploads the dataset in several steps, then the remote checksums file was previously overwritten. Now it's going to be updated with the new checksums.

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MDExOlB1bGxSZXF1ZXN0NDEyODIxNTA4

[Test] improve slow testing

closed

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MDU6SXNzdWU2MTE2Nzc2NTY=

[Checksums] Error for some datasets

closed

CONTRIBUTOR

The checksums command works very nicely for `squad`. But for `crime_and_punish` and `xnli`, the same bug happens: When running: ``` python nlp-cli nlp-cli test xnli --save_checksums ``` leads to: ``` File "nlp-cli", line 33, in <module> service.run() File "/home/patrick/python_bin/nlp/commands/test.py", line 61, in run ignore_checksums=self._ignore_checksums, File "/home/patrick/python_bin/nlp/builder.py", line 383, in download_and_prepare self._download_and_prepare(dl_manager=dl_manager, download_config=download_config) File "/home/patrick/python_bin/nlp/builder.py", line 627, in _download_and_prepare dl_manager=dl_manager, max_examples_per_split=download_config.max_examples_per_split, File "/home/patrick/python_bin/nlp/builder.py", line 431, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/patrick/python_bin/nlp/datasets/xnli/8bf4185a2da1ef2a523186dd660d9adcf0946189e7fa5942ea31c63c07b68a7f/xnli.py", line 95, in _split_generators dl_dir = dl_manager.download_and_extract(_DATA_URL) File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 246, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 186, in download self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 166, in _record_sizes_checksums self._recorded_sizes_checksums[url] = get_size_checksum(path) File "/home/patrick/python_bin/nlp/utils/checksums_utils.py", line 81, in get_size_checksum with open(path, "rb") as f: TypeError: expected str, bytes or os.PathLike object, not tuple ```

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MDExOlB1bGxSZXF1ZXN0NDEyNzg5MjQ4

[Datasets ToDo-List] add datasets

closed

CONTRIBUTOR

## Description This PR acts as a dashboard to see which datasets are added to the library and work. Cicle-ci should always be green so that we can be sure that newly added datasets are functional. This PR should not be merged. ## Progress **For the following datasets the test commands**: ``` RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_<your-dataset-name> ``` and ``` RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_dataset_all_configs_<your-dataset-name> ``` **passes**. - [x] Squad - [x] Sentiment140 - [x] XNLI - [x] Crime_and_Punish - [x] movie_rationales - [x] ai2_arc - [x] anli - [x] event2Mind - [x] Fquad - [x] blimp - [x] empathetic_dialogues - [x] cosmos_qa - [x] xquad - [x] blog_authorship_corpus - [x] SNLI - [x] break_data - [x] SQuAD v2 - [x] cfq - [x] eraser_multi_rc - [x] Glue - [x] Tydiqa - [x] wiki_qa - [x] wikitext - [x] winogrande - [x] wiqa - [x] esnli - [x] civil_comments - [x] commonsense_qa - [x] com_qa - [x] coqa - [x] wiki_split - [x] cos_e - [x] xcopa - [x] quarel - [x] quartz - [x] squad_it - [x] quoref - [x] squad_pt - [x] cornell_movie_dialog - [x] SciQ - [x] Scifact - [x] hellaswag - [x] ted_multi (in translate) - [x] Aeslc (summarization) - [x] drop - [x] gap - [x] hansard - [x] opinosis - [x] MLQA - [x] math_dataset ## How-To-Add a dataset **Before adding a dataset make sure that your branch is up to date**: 1. `git checkout add_datasets` 2. `git pull` **Add a dataset via the `convert_dataset.sh` bash script:** Running `bash convert_dataset.sh <file/to/tfds/datascript.py>` (*e.g.* `bash convert_dataset.sh ../tensorflow-datasets/tensorflow_datasets/text/movie_rationales.py`) will automatically run all the steps mentioned in **Add a dataset manually** below. Make sure that you run `convert_dataset.sh` from the root folder of `nlp`. The conversion script should work almost always for step 1): "convert dataset script from tfds to nlp format" and 2) "create checksum file" and step 3) "make style". It can also sometimes automatically run step 4) "create the correct dummy data from tfds", but this will only work if a) there is either no config name or only one config name and b) the `tfds testing/test_data/fake_example` is in the correct form. Nevertheless, the script should always be run in the beginning until an error occurs to be more efficient. If the conversion script does not work or fails at some step, then you can run the steps manually as follows: **Add a dataset manually** Make sure you run all of the following commands from the root of your `nlp` git clone. Also make sure that you changed to this branch: ``` git checkout add_datasets ``` 1) the tfds datascript file should be converted to `nlp` style: ``` python nlp-cli convert --tfds_path <path/to/tensorflow_datasets/text/your_dataset_name>.py --nlp_directory datasets/nlp ``` This will convert the tdfs script and create a folder with the correct name. 2) the checksum file should be added. Use the command: ``` python nlp-cli test datasets/nlp/<your-dataset-folder> --save_checksums --all_configs ``` A checksums.txt file should be created in your folder and the structure should look as follows: squad/ ├── squad.py/ └── urls_checksums/ ...........└── checksums.txt Delete the created `*.lock` file afterward - it should not be uploaded to AWS. 3) run black and isort on your newly added datascript files so that they look nice: ``` make style ``` 4) the dummy data should be added. For this it might be useful to take a look into the structure of other examples as shown in the PR here and at `<path/to/tensorflow_datasets/testing/test_data/test_data/fake_examples>` whether the same data can be used. 5) the data can be uploaded to AWS using the command ``` aws s3 cp datasets/nlp/<your-dataset-folder> s3://datasets.huggingface.co/nlp/<your-dataset-folder> --recursive ``` 6) check whether all works as expected using: ``` RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_<your-dataset-name> ``` and ``` RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_dataset_all_configs_<your-dataset-name> ``` 7) push to this PR and rerun the circle ci workflow to check whether circle ci stays green. 8) Edit this commend and tick off your newly added dataset :-) ## TODO-list Maybe we can add a TODO-list here for everybody that feels like adding new datasets so that we will not add the same datasets. Here a link to available datasets: https://docs.google.com/spreadsheets/d/1zOtEqOrnVQwdgkC4nJrTY6d-Av02u0XFzeKAtBM2fUI/edit#gid=0 Patrick: - [ ] boolq - *weird download link* - [ ] c4 - *beam dataset*

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MDExOlB1bGxSZXF1ZXN0NDEyNjgwOTk1

Metrics - refactoring, adding support for download and distributed metrics

closed

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Refactoring metrics to have a similar loading API than the datasets and improving the import system. # Import system The import system has ben upgraded. There are now three types of imports allowed: 1. `library` imports (identified as "absolute imports") ```python import seqeval ``` => we'll test all the imports before running the scripts and if one cannot be imported we'll display an error message like this one: `ImportError: To be able to use this metric/dataset, you need to install the following dependencies ['seqeval'] using 'pip install seqeval' for instance'` 2. `internal` imports (identified as "relative imports") ```python import .c4_utils ``` => we'll assume this point to a file in the same directory/S3-directory as the main script and download this file. 2. `external` imports (identified as "relative imports" with a comment starting with `# From:`) ```python from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py ``` => we'll assume this point to the URL of a python script (if it's a link to a github file, we'll take the raw file automatically). => the script is downloaded and renamed to the import name (here above renamed from `bleu.py` to `nmt_bleu.py`). Renaming the file can be necessary if the distant file has the same name as the dataset/metric processing script. If you forgot to rename the distant script and it has the same name as the dataset/metric, you'll have an explicit error message asking to rename the import anyway. # Hosting metrics Metrics are hosted on a S3 bucket like the dataset processing scripts. # Metrics scripts Metrics scripts have a lot in common with datasets processing scripts. They also have a `metric.info` including citations, descriptions and links to relevant pages. Metrics have more documentation to supply to ensure they are used well. Four examples are already included for reference in [./metrics](./metrics): BLEU, ROUGE, SacreBLEU and SeqEVAL. # Automatic support for distributed/multi-processing metric computation We've also added support for automatic distributed/multi-processing metric computation (e.g. when using DistributedDataParallel). We leverage our own dataset format for smart caching in this case. Here is a quick gist of a standard use of metrics (the simplest usage): ```python import nlp bleu_metric = nlp.load_metric('bleu') # If you only have a single iteration, you can easily compute the score like this predictions = model(inputs) score = bleu_metric.compute(predictions, references) # If you have a loop, you can "add" your predictions and references at each iteration instead of having to save them yourself (the metric object store them efficiently for you) for batch in dataloader: model_input, targets = batch predictions = model(model_inputs) bleu.add(predictions, targets) score = bleu_metric.compute() # Compute the score from all the stored predictions/references ``` Here is a quick gist of a use in a distributed torch setup (should work for any python multi-process setup actually). It's pretty much identical to the second example above: ```python import nlp # You need to give the total number of parallel python processes (num_process) and the id of each process (process_id) bleu = nlp.load_metric('bleu', process_id=torch.distributed.get_rank(),b num_process=torch.distributed.get_world_size()) for batch in dataloader: model_input, targets = batch predictions = model(model_inputs) bleu.add(predictions, targets) score = bleu_metric.compute() # Compute the score on the first node by default (can be set to compute on each node as well) ```

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MDExOlB1bGxSZXF1ZXN0NDEyNjAyMTc0

[Tests] fix typo

closed

CONTRIBUTOR

@lhoestq - currently the slow test fail with: ``` _____________________________________________________________________________________ DatasetTest.test_load_real_dataset_xnli _____________________________________________________________________________________ self = <tests.test_dataset_common.DatasetTest testMethod=test_load_real_dataset_xnli>, dataset_name = 'xnli' @slow def test_load_real_dataset(self, dataset_name): with tempfile.TemporaryDirectory() as temp_data_dir: > dataset = load(dataset_name, data_dir=temp_data_dir) tests/test_dataset_common.py:153: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../../python_bin/nlp/load.py:497: in load dbuilder.download_and_prepare(**download_and_prepare_kwargs) ../../python_bin/nlp/builder.py:383: in download_and_prepare self._download_and_prepare(dl_manager=dl_manager, download_config=download_config) ../../python_bin/nlp/builder.py:627: in _download_and_prepare dl_manager=dl_manager, max_examples_per_split=download_config.max_examples_per_split, ../../python_bin/nlp/builder.py:431: in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) ../../python_bin/nlp/datasets/xnli/8bf4185a2da1ef2a523186dd660d9adcf0946189e7fa5942ea31c63c07b68a7f/xnli.py:95: in _split_generators dl_dir = dl_manager.download_and_extract(_DATA_URL) ../../python_bin/nlp/utils/download_manager.py:246: in download_and_extract return self.extract(self.download(url_or_urls)) ../../python_bin/nlp/utils/download_manager.py:186: in download self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) ../../python_bin/nlp/utils/download_manager.py:166: in _record_sizes_checksums self._recorded_sizes_checksums[url] = get_size_checksum(path) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ path = ('', '/tmp/tmpkajlg9yc/downloads/c0f7773c480a3f2d85639d777e0e17e65527460310d80760fd3fc2b2f2960556.c952a63cb17d3d46e412ceb7dbcd656ce2b15cc9ef17f50c28f81c48a7c853b5') def get_size_checksum(path: str) -> Tuple[int, str]: """Compute the file size and the sha256 checksum of a file""" m = sha256() > with open(path, "rb") as f: E TypeError: expected str, bytes or os.PathLike object, not tuple ../../python_bin/nlp/utils/checksums_utils.py:81: TypeError ``` - the checksums probably need to be updated no? And we should also think about how to write a test for the checksums.

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MDExOlB1bGxSZXF1ZXN0NDEyNTg0OTM0

[Tests] add slow tests

closed

CONTRIBUTOR

This PR adds a slow test that downloads the "real" dataset. The test is decorated as "slow" so that it will not automatically run on circle ci. Before uploading a dataset, one should test that this test passes, manually by running ``` RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_<your-dataset-script-name> ``` This PR should be merged after PR: #33

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MDExOlB1bGxSZXF1ZXN0NDEyMzU1ODE0

Big cleanup/refactoring for clean serialization

closed

MEMBER

This PR cleans many base classes to re-build them as `dataclasses`. We can thus use a simple serialization workflow for `DatasetInfo`, including it's `Features` and `SplitDict` based on `dataclasses` `asdict()`. The resulting code is a lot shorter, can be easily serialized/deserialized, dataset info are human-readable and we can get rid of the `dataclass_json` dependency. The scripts have breaking changes and the conversion tool is updated. Example of dataset info in SQuAD script now: ```python def _info(self): return nlp.DatasetInfo( description=_DESCRIPTION, features=nlp.Features({ "id": nlp.Value('string'), "title": nlp.Value('string'), "context": nlp.Value('string'), "question": nlp.Value('string'), "answers": nlp.Sequence({ "text": nlp.Value('string'), "answer_start": nlp.Value('int32'), }), }), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage="https://rajpurkar.github.io/SQuAD-explorer/", citation=_CITATION, ) ``` Example of serialized dataset info: ```bash { "description": "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\n", "citation": "@article{2016arXiv160605250R,\n author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n Konstantin and {Liang}, Percy},\n title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n journal = {arXiv e-prints},\n year = 2016,\n eid = {arXiv:1606.05250},\n pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": { "id": { "dtype": "string", "_type": "Value" }, "title": { "dtype": "string", "_type": "Value" }, "context": { "dtype": "string", "_type": "Value" }, "question": { "dtype": "string", "_type": "Value" }, "answers": { "feature": { "text": { "dtype": "string", "_type": "Value" }, "answer_start": { "dtype": "int32", "_type": "Value" } }, "length": -1, "_type": "Sequence" } }, "supervised_keys": null, "name": "squad", "version": { "version_str": "1.0.0", "description": "New split API (https://tensorflow.org/datasets/splits)", "nlp_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0 }, "splits": { "train": { "name": "train", "num_bytes": 79426386, "num_examples": 87599, "dataset_name": "squad" }, "validation": { "name": "validation", "num_bytes": 10491883, "num_examples": 10570, "dataset_name": "squad" } }, "size_in_bytes": 0, "download_size": 35142551, "download_checksums": [] } ```

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https://github.com/huggingface/datasets/pull/32

MDExOlB1bGxSZXF1ZXN0NDEyMTAzMzIx

Fix map caching notebooks

closed

MEMBER

Previously, caching results with `.map()` didn't work in notebooks. To reuse a result, `.map()` serializes the functions with `dill.dumps` and then it hashes it. The problem is that when using `dill.dumps` to serialize a function, it also saves its origin (filename + line no.) and the origin of all the `globals` this function needs. However for notebooks and shells, the filename looks like \<ipython-input-13-9ed2afe61d25\> and the line no. changes often. To fix the problem, I added a new dispatch function for code objects that ignore the origin of the code if it comes from a notebook or a python shell. I tested these cases in a notebook: - lambda functions - named functions - methods - classmethods - staticmethods - classes that implement `__call__` The caching now works as expected for all of them :) I also tested the caching in the demo notebook and it works fine !

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https://api.github.com/repos/huggingface/datasets/issues/31

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https://github.com/huggingface/datasets/pull/31

MDExOlB1bGxSZXF1ZXN0NDEyMDczNDE4

[Circle ci] Install a virtual env before running tests

closed

CONTRIBUTOR

Install a virtual env before running tests to not running into sudo issues when dynamically downloading files. Same number of tests now pass / fail as on my local computer: 

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https://api.github.com/repos/huggingface/datasets/issues/30

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https://github.com/huggingface/datasets/pull/30

MDExOlB1bGxSZXF1ZXN0NDExOTY4Mzk3

add metrics which require download files from github

closed

CONTRIBUTOR

To download files from github, I copied the `load_dataset_module` and its dependencies (without the builder) in `load.py` to `metrics/metric_utils.py`. I made the following changes: - copy the needed files in a folder`metric_name` - delete all other files that are not needed For metrics that require an external import, I first create a `<metric_name>_imports.py` file which contains all external urls. Then I create a `<metric_name>.py` in which I will load the external files using `<metric_name>_imports.py`

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https://api.github.com/repos/huggingface/datasets/issues/29

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https://github.com/huggingface/datasets/pull/29

MDExOlB1bGxSZXF1ZXN0NDExNzIwODMx

Hf_api small changes

closed

MEMBER

From Patrick: ```python from nlp import hf_api api = hf_api.HfApi() api.dataset_list() ``` works :-)

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https://api.github.com/repos/huggingface/datasets/issues/28

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https://github.com/huggingface/datasets/pull/28

MDExOlB1bGxSZXF1ZXN0NDExNzE5MTQy

[Circle ci] Adds circle ci config

closed

CONTRIBUTOR

@thomwolf can you take a look and set up circle ci on: https://app.circleci.com/projects/project-dashboard/github/huggingface I think for `nlp` only admins can set it up, which I guess is you :-)

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https://api.github.com/repos/huggingface/datasets/issues/27

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https://github.com/huggingface/datasets/pull/27

MDExOlB1bGxSZXF1ZXN0NDExNzA5OTc0

[Cleanup] Removes all files in testing except test_dataset_common

closed

CONTRIBUTOR

As far as I know, all files in `tests` were old `tfds test files` so I removed them. We can still look them up on the other library.

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https://github.com/huggingface/datasets/pull/26

MDExOlB1bGxSZXF1ZXN0NDExNzA2NjA2

[Tests] Clean tests

closed