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edbeeching

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MDU6SXNzdWU5OTQwODI0MzI=

Dataset

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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MDU6SXNzdWU5OTQwMzI4MTQ=

Hug emoji

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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MDExOlB1bGxSZXF1ZXN0NzMxOTA0ODk4

Add OpenAI's HumanEval dataset

closed

CONTRIBUTOR

This PR adds OpenAI's [HumanEval](https://github.com/openai/human-eval) dataset. The dataset consists of 164 handcrafted programming problems with solutions and unittests to verify solution. This dataset is useful to evaluate code generation models.

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MDExOlB1bGxSZXF1ZXN0NzMxNzcwMTE3

add multi-proc in `to_csv`

closed

CONTRIBUTOR

This PR extends the multi-proc method used in #2747 for`to_json` to `to_csv` as well. Results on my machine post benchmarking on `ascent_kb` dataset (giving ~45% improvement when compared to num_proc = 1): ``` Time taken on 1 num_proc, 10000 batch_size 674.2055702209473 Time taken on 4 num_proc, 10000 batch_size 425.6553490161896 Time taken on 1 num_proc, 50000 batch_size 623.5897650718689 Time taken on 4 num_proc, 50000 batch_size 380.0402421951294 Time taken on 4 num_proc, 100000 batch_size 361.7168130874634 ``` This is a WIP as writing tests is pending for this PR. I'm also exploring [this](https://arrow.apache.org/docs/python/csv.html#incremental-writing) approach for which I'm using `pyarrow-5.0.0`.

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MDExOlB1bGxSZXF1ZXN0NzMxNjQ0NTY2

Use pyarrow.Table.replace_schema_metadata instead of pyarrow.Table.cast

closed

CONTRIBUTOR

This PR partially addresses #2252. ``update_metadata_with_features`` uses ``Table.cast`` which slows down ``load_from_disk`` (and possibly other methods that use it) for very large datasets. Since ``update_metadata_with_features`` is only updating the schema metadata, it makes more sense to use ``pyarrow.Table.replace_schema_metadata`` which is much faster. This PR adds a ``replace_schema_metadata`` method to all table classes, and modifies ``update_metadata_with_features`` to use it instead of ``cast``.

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MDExOlB1bGxSZXF1ZXN0NzMxNTcxODc5

Fix COUNTER dataset

closed

MEMBER

Fix filename generating `FileNotFoundError`. Related to #2866. CC: @severo.

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MDExOlB1bGxSZXF1ZXN0NzMxNTQ0NDQz

add mbpp dataset

closed

CONTRIBUTOR

This PR adds the mbpp dataset introduced by Google [here](https://github.com/google-research/google-research/tree/master/mbpp) as mentioned in #2816. The dataset contain two versions: a full and a sanitized one. They have a slightly different schema and it is current state the loading preserves the original schema. An open question is whether to harmonize the two schemas when loading the dataset or to preserve the original one. Since not all fields are overlapping the schema will not be exactly the same.

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MDU6SXNzdWU5OTMyNzQ1NzI=

Error when encoding a dataset with None objects with a Sequence feature

closed

MEMBER

There is an error when encoding a dataset with None objects with a Sequence feature To reproduce: ```python from datasets import Dataset, Features, Value, Sequence data = {"a": [[0], None]} features = Features({"a": Sequence(Value("int32"))}) dataset = Dataset.from_dict(data, features=features) ``` raises ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-24-40add67f8751> in <module> 2 data = {"a": [[0], None]} 3 features = Features({"a": Sequence(Value("int32"))}) ----> 4 dataset = Dataset.from_dict(data, features=features) [...] ~/datasets/features.py in encode_nested_example(schema, obj) 888 if isinstance(obj, str): # don't interpret a string as a list 889 raise ValueError("Got a string but expected a list instead: '{}'".format(obj)) --> 890 return [encode_nested_example(schema.feature, o) for o in obj] 891 # Object with special encoding: 892 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks TypeError: 'NoneType' object is not iterable ``` Instead, if should run without error, as if the `features` were not passed

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MDExOlB1bGxSZXF1ZXN0NzMxMzkwNjM2

Allow dynamic first dimension for ArrayXD

closed

CONTRIBUTOR

Add support for dynamic first dimension for ArrayXD features. See issue [#887](https://github.com/huggingface/datasets/issues/887). Following changes allow for `to_pylist` method of `ArrayExtensionArray` to return a list of numpy arrays where fist dimension can vary. @lhoestq Could you suggest how you want to extend test suit. For now I added only very limited testing.

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MDU6SXNzdWU5OTMwNzQxMDI=

0x290B112ED1280537B24Ee6C268a004994a16e6CE

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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MDU6SXNzdWU5OTI5NjgzODI=

Coc

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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MDU6SXNzdWU5OTI2NzY1MzU=

v1.11.1 release date

closed

NONE

Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago. When do you plan to publush v1.11.1 release?

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MDExOlB1bGxSZXF1ZXN0NzMwODg4MTU3

#2837 Use cache folder for lockfile

closed

CONTRIBUTOR

Fixes #2837 Use a cache folder directory to store the FileLock. The issue was that the lock file was in a readonly folder.

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MDU6SXNzdWU5OTI1MzQ2MzI=

Hj

closed

NONE

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MDU6SXNzdWU5OTIxNjA1NDQ=

Adding an Elastic Search index to a Dataset

open

NONE

## Describe the bug When trying to index documents from the squad dataset, the connection to ElasticSearch seems to break: Reusing dataset squad (/Users/andreasmotz/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453) 90%|████████████████████████████████████████████▉ | 9501/10570 [00:01<00:00, 6335.61docs/s] No error is thrown, but the indexing breaks ~90%. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset from elasticsearch import Elasticsearch es = Elasticsearch() squad = load_dataset('squad', split='validation') index_name = "corpus" es_config = { "settings": { "number_of_shards": 1, "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}}, }, "mappings": { "properties": { "idx" : {"type" : "keyword"}, "title" : {"type" : "keyword"}, "text": { "type": "text", "analyzer": "standard", "similarity": "BM25" }, } }, } class IndexBuilder: """ Elastic search indexing of a corpus """ def __init__( self, *args, #corpus : None, dataset : squad, index_name = str, query = str, config = dict, **kwargs, ): #instantiate HuggingFace dataset self.dataset = dataset #instantiate ElasticSearch config self.config = config self.es = Elasticsearch() self.index_name = index_name self.query = query def elastic_index(self): print(self.es.info) self.es.indices.delete(index=self.index_name, ignore=[400, 404]) search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config) return search_index def exact_match_method(self, index): scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1) return scores, retrieved_examples if __name__ == "__main__": print(type(squad)) Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config) search_index = Index.elastic_index() scores, examples = Index.exact_match_method(search_index) print(scores, examples) for name in squad.column_names: print(type(squad[name])) ``` ## Environment info We run the code in Poetry. This might be the issue, since the script runs successfully in our local environment. Poetry: - Python version: 3.8 - PyArrow: 4.0.1 - Elasticsearch: 7.13.4 - datasets: 1.10.2 Local: - Python version: 3.8 - PyArrow: 3.0.0 - Elasticsearch: 7.7.1 - datasets: 1.7.0

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MDExOlB1bGxSZXF1ZXN0NzMwNTA4MTE1

Add IC, SI, ER tasks to SUPERB

closed

MEMBER

This PR adds 3 additional classification tasks to SUPERB #### Intent Classification Dataset URL seems to be down at the moment :( See the note below. S3PRL source: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/fluent_commands/dataset.py Instructions: https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#ic-intent-classification---fluent-speech-commands #### Speaker Identification Manual download script: ``` mkdir VoxCeleb1 cd VoxCeleb1 wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partab wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partac wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partad cat vox1_dev* > vox1_dev_wav.zip unzip vox1_dev_wav.zip wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_test_wav.zip unzip vox1_test_wav.zip # download the official SUPERB train-dev-test split wget https://raw.githubusercontent.com/s3prl/s3prl/master/s3prl/downstream/voxceleb1/veri_test_class.txt ``` S3PRL source: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/voxceleb1/dataset.py Instructions: https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sid-speaker-identification #### Intent Classification Manual download requires going through a slow application process, see the note below. S3PRL source: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/emotion/IEMOCAP_preprocess.py Instructions: https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#er-emotion-recognition #### :warning: Note These datasets either require manual downloads or have broken/unstable links. You can get all necessary archives in this repo: https://huggingface.co/datasets/anton-l/superb_source_data_dumps/tree/main

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MDExOlB1bGxSZXF1ZXN0NzMwMzYzNTQz

Fix data URLs and metadata in DocRED dataset

closed

MEMBER

The host of `docred` dataset has updated the `dev` data file. This PR: - Updates the dev URL - Updates dataset metadata This PR also fixes the URL of the `train_distant` split, which was wrong. Fix #2882.

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MDU6SXNzdWU5OTE4MDAxNDE=

`load_dataset('docred')` results in a `NonMatchingChecksumError`

closed

NONE

## Describe the bug I get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`. ## Steps to reproduce the bug It is quasi only this code: ```python import datasets data = datasets.load_dataset('docred') ``` ## Expected results The DocRED dataset should be loaded without any problems. ## Actual results ``` NonMatchingChecksumError Traceback (most recent call last) <ipython-input-4-b1b83f25a16c> in <module> ----> 1 d = datasets.load_dataset('docred') ~/anaconda3/lib/python3.8/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, task, streaming, **config_kwargs) 845 846 # Download and prepare data --> 847 builder_instance.download_and_prepare( 848 download_config=download_config, 849 download_mode=download_mode, ~/anaconda3/lib/python3.8/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) 613 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 614 if not downloaded_from_gcs: --> 615 self._download_and_prepare( 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 617 ) ~/anaconda3/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 673 # Checksums verification 674 if verify_infos: --> 675 verify_checksums( 676 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 677 ) ~/anaconda3/lib/python3.8/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7'] ``` ## Environment info - `datasets` version: 1.11.0 - Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 5.0.0 This error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`. ## Remarks - I have already called `rm -rf /home/<user>/.cache/huggingface`, i.e., I have tried clearing the cache. - The problem does not exist for other datasets, i.e., it seems to be DocRED-specific.

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MDExOlB1bGxSZXF1ZXN0NzMwMDc1OTAy

Add BIOSSES dataset

closed

CONTRIBUTOR

Adding the biomedical semantic sentence similarity dataset, BIOSSES, listed in "Biomedical Datasets - BigScience Workshop 2021"

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https://github.com/huggingface/datasets/pull/2880

MDExOlB1bGxSZXF1ZXN0NzI5NDIzMDMy

Extend support for streaming datasets that use pathlib.Path stem/suffix

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `pathlib`, by patching the properties `pathlib.Path.stem` and `pathlib.Path.suffix`. Related to #2876, #2874, #2866. CC: @severo

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MDU6SXNzdWU5OTAyNTc0MDQ=

In v1.4.1, all TIMIT train transcripts are "Would such an act of refusal be useful?"

closed

NONE

## Describe the bug Using version 1.4.1 of `datasets`, TIMIT transcripts are all the same. ## Steps to reproduce the bug I was following this tutorial - https://huggingface.co/blog/fine-tune-wav2vec2-english But here's a distilled repro: ```python !pip install datasets==1.4.1 from datasets import load_dataset timit = load_dataset("timit_asr", cache_dir="./temp") unique_transcripts = set(timit["train"]["text"]) print(unique_transcripts) assert len(unique_transcripts) > 1 ``` ## Expected results Expected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it. ## Actual results Every train transcript was "Would such an act of refusal be useful?" Every test transcript was "The bungalow was pleasantly situated near the shore." ## Environment info - `datasets` version: 1.4.1 - Platform: Darwin-18.7.0-x86_64-i386-64bit - Python version: 3.7.9 - PyTorch version (GPU?): 1.9.0 (False) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: tried both - Using distributed or parallel set-up in script?: no -

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MDU6SXNzdWU5OTAwOTMzMTY=

NotADirectoryError: [WinError 267] During load_from_disk

open

NONE

## Describe the bug Trying to load saved dataset or dataset directory from Amazon S3 on a Windows machine fails. Performing the same operation succeeds on non-windows environment (AWS Sagemaker). ## Steps to reproduce the bug ```python # Followed https://huggingface.co/docs/datasets/filesystems.html#loading-a-processed-dataset-from-s3 from datasets import load_from_disk from datasets.filesystems import S3FileSystem s3_file = "output of save_to_disk" s3_filesystem = S3FileSystem() load_from_disk(s3_file, fs=s3_filesystem) ``` ## Expected results load_from_disk succeeds without error ## Actual results Seems like it succeeds in pulling the file into a windows temp directory, as it exists in my system, but fails to process it. ``` Exception ignored in: <finalize object at 0x26409231ce0; dead> Traceback (most recent call last): File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\weakref.py", line 566, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 817, in _cleanup cls._rmtree(name) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) [Previous line repeated 2 more times] File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 618, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 805, in onerror cls._rmtree(path) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 599, in _rmtree_unsafe onerror(os.scandir, path, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 596, in _rmtree_unsafe with os.scandir(path) as scandir_it: NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\grassycup\\AppData\\Local\\Temp\\tmp45f_qbma\\tests3bucket\\output\\test_output\\train\\dataset.arrow' Exception ignored in: <finalize object at 0x264091c7880; dead> Traceback (most recent call last): File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\weakref.py", line 566, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 817, in _cleanup cls._rmtree(name) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) [Previous line repeated 2 more times] File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 618, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 805, in onerror cls._rmtree(path) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 599, in _rmtree_unsafe onerror(os.scandir, path, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 596, in _rmtree_unsafe with os.scandir(path) as scandir_it: NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\grassycup\\AppData\\Local\\Temp\\tmp45f_qbma\\tests3bucket\\output\\test_output\\train\\dataset.arrow' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0

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MDU6SXNzdWU5OTAwMjcyNDk=

Don't keep the dummy data folder or dataset_infos.json when resolving data files

closed

MEMBER

When there's no dataset script, all the data files of a folder or a repository on the Hub are loaded as data files. There are already a few exceptions: - files starting with "." are ignored - the dataset card "README.md" is ignored - any file named "config.json" is ignored (currently it isn't used anywhere, but it could be used in the future to define splits or configs for example, but not 100% sure) However any data files in a folder named "dummy" should be ignored as well as they should only be used to test the dataset. Same for "dataset_infos.json" which should only be used to get the `dataset.info`

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MDExOlB1bGxSZXF1ZXN0NzI4NjU3MDc2

Extend support for streaming datasets that use pathlib.Path.glob

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `pathlib`, by patching the method `pathlib.Path.glob`. Related to #2874, #2866. CC: @severo

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MDU6SXNzdWU5ODk5MTkzOTg=

Add Congolese Swahili speech datasets

open

MEMBER

## Adding a Dataset - **Name:** Congolese Swahili speech corpora - **Data:** https://gamayun.translatorswb.org/data/ Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Also related: https://mobile.twitter.com/OktemAlp/status/1435196393631764482

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MDExOlB1bGxSZXF1ZXN0NzI4Mzg2Mjg4

Support streaming datasets that use pathlib

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `pathlib.Path`. Related to: #2866. CC: @severo

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MDExOlB1bGxSZXF1ZXN0NzI4MzA0MTMw

adding swedish_medical_ner

closed

CONTRIBUTOR

Adding the Swedish Medical NER dataset, listed in "Biomedical Datasets - BigScience Workshop 2021" Code refactored

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MDExOlB1bGxSZXF1ZXN0NzI4MTkzMjkz

adding swedish_medical_ner

closed

CONTRIBUTOR

Adding the Swedish Medical NER dataset, listed in "Biomedical Datasets - BigScience Workshop 2021"

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MDU6SXNzdWU5ODk0MzYwODg=

datasets.config.PYARROW_VERSION has no attribute 'major'

closed

CONTRIBUTOR

In the test_dataset_common.py script, line 288-289 ``` if datasets.config.PYARROW_VERSION.major < 3: packaged_datasets = [pd for pd in packaged_datasets if pd["dataset_name"] != "parquet"] ``` which throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS. ``` import datasets datasets.config.PYARROW_VERSION.major --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /var/folders/1f/0wqmlgp90qjd5mpj53fnjq440000gn/T/ipykernel_73361/2547517336.py in <module> 1 import datasets ----> 2 datasets.config.PYARROW_VERSION.major AttributeError: 'str' object has no attribute 'major' ``` ## Environment info - `datasets` version: 1.11.0 - Platform: Darwin-20.6.0-x86_64-i386-64bit - Python version: 3.7.11 - PyArrow version: 4.0.1

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MDExOlB1bGxSZXF1ZXN0NzI3MjI4Njk5

Fix three typos in two files for documentation

closed

CONTRIBUTOR

Changed "bacth_size" to "batch_size" (2x) Changed "intsructions" to "instructions"

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MDU6SXNzdWU5ODc2NzY0MjA=

TypeError: 'NoneType' object is not callable

closed

NONE

## Describe the bug TypeError: 'NoneType' object is not callable ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric dataset = datasets.load_dataset("glue", 'cola') ``` ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Platform: - Python version: 3.7 - PyArrow version:

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MDU6SXNzdWU5ODcxMzkxNDY=

Add Common Objects in 3D (CO3D)

open

CONTRIBUTOR

## Adding a Dataset - **Name:** *Common Objects in 3D (CO3D)* - **Description:** *See blog post [here](https://ai.facebook.com/blog/common-objects-in-3d-dataset-for-3d-reconstruction)* - **Paper:** *[link to paper](https://arxiv.org/abs/2109.00512)* - **Data:** *[link to data](https://ai.facebook.com/datasets/co3d-downloads/)* - **Motivation:** *excerpt from above blog post:* > As the first data set of its kind, CO3D will aptly enable reconstruction of real-life 3D objects. Indeed, CO3D already provides training data to enable our NeRFormer to tackle the new-view synthesis (NVS) task. Here, photorealistic NVS is a major step on the path to fully immersive AR/VR effects, where objects can be virtually transported across different environments, which will allow connecting users by sharing or recollecting their experiences. > > Besides practical applications in AR/VR, we hope that the data set will become a standard testbed for the recent proliferation of methods (including NeRFormer, Implicit Differentiable Renderer, NeRF, and others) that reconstruct 3D scenes by means of an implicit shape model. > 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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MDExOlB1bGxSZXF1ZXN0NzI2MTE3NzAw

Add CaSiNo dataset

closed

CONTRIBUTOR

Hi. I request you to add our dataset to the repository. This data was recently published at NAACL 2021: https://aclanthology.org/2021.naacl-main.254.pdf

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MDU6SXNzdWU5ODY3MDY2NzY=

"counter" dataset raises an error in normal mode, but not in streaming mode

closed

CONTRIBUTOR

## Describe the bug `counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode. ## Steps to reproduce the bug ```python >>> import datasets as ds >>> a = ds.load_dataset('counter', split="train", streaming=False) Using custom data configuration default Downloading and preparing dataset counter/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to /home/slesage/.cache/huggingface/datasets/counter/default/1.0.0/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9... Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 726, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 1124, in _prepare_split for key, record in utils.tqdm( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/tqdm/std.py", line 1185, in __iter__ for obj in iterable: File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/counter/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9/counter.py", line 161, in _generate_examples with derived_file.open(encoding="utf-8") as f: File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/pathlib.py", line 1222, in open return io.open(self, mode, buffering, encoding, errors, newline, File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/pathlib.py", line 1078, in _opener return self._accessor.open(self, flags, mode) FileNotFoundError: [Errno 2] No such file or directory: '/home/slesage/.cache/huggingface/datasets/downloads/extracted/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211/COUNTER/0032p.xml' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/load.py", line 1112, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 636, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 728, in _download_and_prepare raise OSError( OSError: Cannot find data file. Original error: [Errno 2] No such file or directory: '/home/slesage/.cache/huggingface/datasets/downloads/extracted/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211/COUNTER/0032p.xml' ``` ```python >>> import datasets as ds >>> b = ds.load_dataset('counter', split="train", streaming=True) Using custom data configuration default >>> list(b) [] ``` ## Expected results An exception should be raised in streaming mode ## Actual results No exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty. ## Environment info - `datasets` version: 1.11.1.dev0 - Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29 - Python version: 3.8.11 - PyArrow version: 4.0.1

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MDExOlB1bGxSZXF1ZXN0NzI1NjY1ODgx

Add MultiEURLEX dataset

closed

CONTRIBUTOR

**Add new MultiEURLEX Dataset** MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource). Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels); this is multi-label classification task (given the text, predict multiple labels).

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MDExOlB1bGxSZXF1ZXN0NzI1MzkyNjcw

Fix data URL in ToTTo dataset

closed

MEMBER

Data source host changed their data URL: google-research-datasets/ToTTo@cebeb43. Fix #2860.

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MDExOlB1bGxSZXF1ZXN0NzI1MzkwMTkx

Update dataset URL

closed

NONE

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MDU6SXNzdWU5ODU3NjMwMDE=

Only retain relevant statistics in certain metrics

open

NONE

**Is your feature request related to a problem? Please describe.** As I understand, in the `add_batch()` function, the raw predictions and references are kept (in memory?) until `compute()` is called. https://github.com/huggingface/datasets/blob/e248247518140d5b0527ce2843a1a327e2902059/src/datasets/metric.py#L423-L442 This takes O(n) memory. However, for many (most?) metrics, this is not necessary. E.g., for accuracy, only the # correct and # total need to be recorded. **Describe the solution you'd like** Probably an inheritance hierarchy where `"predictions"` and `"references"` are not always the two keys for the final metric computation. Each metric should create and maintain its own relevant statistics, again for example, `"n_correct"` and `"n_total"` for accuracy. I believe the metrics in AllenNLP (https://github.com/allenai/allennlp/tree/39c40fe38cd2fd36b3465b0b3c031f54ec824160/allennlp/training/metrics) can be used as a good reference. **Describe alternatives you've considered** At least `Metric.compute()` shouldn't hard-code `"predictions"` and `"references"` so that custom subclasses may override this behavior. https://github.com/huggingface/datasets/blob/e248247518140d5b0527ce2843a1a327e2902059/src/datasets/metric.py#L399-L400

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MDExOlB1bGxSZXF1ZXN0NzI0NDM2OTcw

fix: 🐛 be more specific when catching exceptions

closed

CONTRIBUTOR

The same specific exception is catched in other parts of the same function.

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MDU6SXNzdWU5ODUwMTMzMzk=

Cannot download TOTTO dataset

closed

NONE

Error: Couldn't find file at https://storage.googleapis.com/totto/totto_data.zip `datasets version: 1.11.0` # How to reproduce: ```py from datasets import load_dataset dataset = load_dataset('totto') ```

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MDU6SXNzdWU5ODQzMjQ1MDA=

Loading allenai/c4 in streaming mode does too many HEAD requests

closed

MEMBER

This does 60,000+ HEAD requests to get all the ETags of all the data files: ```python from datasets import load_dataset load_dataset("allenai/c4", streaming=True) ``` It makes loading the dataset completely impractical. The ETags are used to compute the config id (it must depend on the data files being used). Instead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)

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MDExOlB1bGxSZXF1ZXN0NzIzNjEzNzQ0

Fix s3fs version in CI

closed

MEMBER

The latest s3fs version has new constrains on aiobotocore, and therefore on boto3 and botocore This PR changes the constrains to avoid the new conflicts In particular it pins the version of s3fs.

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MDExOlB1bGxSZXF1ZXN0NzIzNTY5OTE4

Update: Openwebtext - update size

closed

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Update the size of the Openwebtext dataset I also regenerated the dataset_infos.json but the data file checksum didn't change, and the number of examples either (8013769 examples) related to #2839

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MDExOlB1bGxSZXF1ZXN0NzIzMzg2NzIw

fix: 🐛 remove URL's query string only if it's ?dl=1

closed

CONTRIBUTOR

A lot of URL use the query strings, for example http://opus.nlpl.eu/download.php?f=Bianet/v1/moses/en-ku.txt.zip, we must not remove it when trying to detect the protocol. We thus remove it only in the case of the query string being ?dl=1 which occurs on dropbox and dl.orangedox.com. Also: add unit tests. See https://github.com/huggingface/datasets/pull/2843 for the original discussion.

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MDExOlB1bGxSZXF1ZXN0NzIzMzcxMTIy

Fix windows CI CondaError

closed

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From this thread: https://github.com/conda/conda/issues/6057 We can fix the conda error ``` CondaError: Cannot link a source that does not exist. C:\Users\...\Anaconda3\Scripts\conda.exe ``` by doing ```bash conda update conda ``` before doing any install in the windows CI

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MDExOlB1bGxSZXF1ZXN0NzIzMjU3NDg5

Fix caching when moving script

closed

MEMBER

When caching the result of a `map` function, the hash that is computed depends on many properties of this function, such as all the python objects it uses, its code and also the location of this code. Using the full path of the python script for the location of the code makes the hash change if a script like `run_mlm.py` is moved. I changed this by simply using the base name of the script instead of the full path. Note that this change also affects the hash of the code used from imported modules, but I think it's fine. Indeed it hashes the code of the imported modules anyway, so the location of the python files of the imported modules doesn't matter when computing the hash. Close https://github.com/huggingface/datasets/issues/2825

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MDExOlB1bGxSZXF1ZXN0NzIzMjI4NDY3

Add AMI dataset

closed

CONTRIBUTOR

This is an initial commit for AMI dataset

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MDExOlB1bGxSZXF1ZXN0NzIzMTU4Mzc4

Fix: linnaeus - fix url

closed

MEMBER

The url was causing a `ConnectionError` because of the "/" at the end Close https://github.com/huggingface/datasets/issues/2821

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MDExOlB1bGxSZXF1ZXN0NzIyNDg4MDY2

Update `column_names` showed as `:func:` in exploring.st

closed

CONTRIBUTOR

Hi, One mention of `column_names` in exploring.st was showing it as `:func:` instead of `:attr:`.

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MDU6SXNzdWU5ODI2NTQ2NDQ=

Wound segmentation datasets

open

MEMBER

## Adding a Dataset - **Name:** Wound segmentation datasets - **Description:** annotated wound image dataset - **Paper:** https://www.nature.com/articles/s41598-020-78799-w - **Data:** https://github.com/uwm-bigdata/wound-segmentation - **Motivation:** Interesting simple image dataset, useful for segmentation, with visibility due to http://www.miccai.org/special-interest-groups/challenges/ and https://fusc.grand-challenge.org/ 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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MDU6SXNzdWU5ODI2MzE0MjA=

Add Open Catalyst Project Dataset

open

MEMBER

## Adding a Dataset - **Name:** Open Catalyst 2020 (OC20) Dataset - **Website:** https://opencatalystproject.org/ - **Data:** https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md 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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MDExOlB1bGxSZXF1ZXN0NzIxODYyMDQx

Update README.md

closed

CONTRIBUTOR

Changed 'Tain' to 'Train'.

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MDExOlB1bGxSZXF1ZXN0NzIxNjA3OTA0

fix regex to accept negative timezone

closed

CONTRIBUTOR

fix #2846

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MDU6SXNzdWU5ODE1ODc1OTA=

Negative timezone

closed

CONTRIBUTOR

## Describe the bug The load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex: ``` "^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+:]*)$" ``` So a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files. ## Steps to reproduce the bug ```python # Where the timestamp column has a tz of -03:00 datasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files, 'test': test_files}, cache_dir="./cache_teste/") ``` ## Expected results The -03:00 is a valid tz so the regex should accept this without raising an error. ## Actual results As this regex disaproves a valid tz it raises the following error: ```python raise ValueError( f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp." f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]" f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp" ) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Platform: Ubuntu 20.04 - Python version: 3.8 - PyArrow version: 5.0.0

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MDU6SXNzdWU5ODE0ODc4NjE=

[feature request] adding easy to remember `datasets.cache_dataset()` + `datasets.is_dataset_cached()`

open

MEMBER

Often, there is a need to prepare a dataset but not use it immediately, e.g. think tests suite setup, so it'd be really useful to be able to do: ``` if not datasets.is_dataset_cached(ds): datasets.cache_dataset(ds) ``` This can already be done with: ``` builder = load_dataset_builder(ds) if not os.path.idsir(builder.cache_dir): builder.download_and_prepare() ``` but the current way is a way less intuitive and much harder to remember than the proposed API, IMHO. One more way is to do: ``` _ = load_dataset(ds) ``` but it wastes resources loading the dataset when it's not needed. this has been discussed at https://huggingface.slack.com/archives/C01229B19EX/p1630021912025800 Thank you! @lhoestq

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MDExOlB1bGxSZXF1ZXN0NzIxNDQzMjY2

Fix: wikicorpus - fix keys

closed

MEMBER

As mentioned in https://github.com/huggingface/datasets/issues/2552, there is a duplicate keys error in `wikicorpus`. I fixed that by taking into account the file index in the keys

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MDExOlB1bGxSZXF1ZXN0NzIxMzkwODA5

Fix extraction protocol inference from urls with params

closed

MEMBER

Previously it was unable to infer the compression protocol for files at URLs like ``` https://foo.bar/train.json.gz?dl=1 ``` because of the query parameters. I fixed that, this should allow 10+ datasets to work in streaming mode: ``` "discovery", "emotion", "grail_qa", "guardian_authorship", "pragmeval", "simple_questions_v2", "versae/adobo", "w-nicole/childes_data", "w-nicole/childes_data_no_tags_", "w-nicole/childes_data_with_tags", "w-nicole/childes_data_with_tags_" ``` cc @severo

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MDU6SXNzdWU5ODA3MjU4OTk=

always requiring the username in the dataset name when there is one

closed

MEMBER

Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due. So both of us started with `stas/openwebtext-10k`, somewhere along the lines lost `stas/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k` So this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w/o it. The same in code: ``` # first run python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')" # now run immediately python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')" # the second command should fail, but it doesn't fail now. ``` Please let me know if I explained myself clearly. Thank you!

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Adding GLUECoS Hinglish and Spanglish code-switching bemchmark

open

MEMBER

## Adding a Dataset - **Name:** GLUECoS - **Description:** a Microsoft Benchmark to evaluate code-switching for only two language pairs but a variety of tasks - **Paper:** https://aclanthology.org/2020.acl-main.329/ - **Data:** https://github.com/microsoft/GLUECoS - **Motivation:** We currently only have [one other](https://huggingface.co/datasets/lince) dataset for code-switching 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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MDU6SXNzdWU5ODA0ODkwNzQ=

How can I compute BLEU-4 score use `load_metric` ?

closed

NONE

I have found the sacrebleu metric. But, I do not know the difference between it and BLEU-4. If I want to compute BLEU-4 score, what can i do?

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MDU6SXNzdWU5ODAyNzE3MTU=

OpenWebText: NonMatchingSplitsSizesError

closed

MEMBER

## Describe the bug When downloading `openwebtext`, I'm getting: ``` datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}] ``` I suspect that the file we download from has changed since the size doesn't look like to match with documentation `Downloading: 0%| | 0.00/12.9G [00:00<?, ?B/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("openwebtext", download_mode="force_redownload") ``` ## Expected results Loading is successful ## Actual results Loading throws above error. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.10.2 - Platform: linux (Redhat version 8.1) - Python version: 3.8 - PyArrow version: 4.0.1

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MDExOlB1bGxSZXF1ZXN0NzIwMzcxMDk5

Add error_bad_chunk to the JSON loader

open

MEMBER

Add the `error_bad_chunk` parameter to the JSON loader. Setting `error_bad_chunk=False` allows to skip an unparsable chunk of JSON data without raising an error. Additional note: In case of an unparsable JSON chunk, the JSON loader no longer tries to load the full JSON (which could take a lot of time in streaming mode) to get the JSON fields that the user may have forgotten to pass. Ex : for squad-like data, the user has to pass `field="data"` to tell the loader to get the list of examples from this field. TODO: update docs cc @lvwerra

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MDU6SXNzdWU5NzkyOTgyOTc=

prepare_module issue when loading from read-only fs

closed

CONTRIBUTOR

## Describe the bug When we use prepare_module from a readonly file system, we create a FileLock using the `local_path`. This path is not necessarily writable. `lock_path = local_path + ".lock"` ## Steps to reproduce the bug Run `load_dataset` on a readonly python loader file. ```python ds = load_dataset( python_loader, data_files={"train": train_path, "test": test_path} ) ``` where `python_loader` is a path to a file located in a readonly folder. ## Expected results This should work I think? ## Actual results ```python return load_dataset( File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 711, in load_dataset module_path, hash, resolved_file_path = prepare_module( File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 465, in prepare_module with FileLock(lock_path): File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 314, in __enter__ self.acquire() File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 263, in acquire self._acquire() File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 378, in _acquire fd = os.open(self._lock_file, open_mode) OSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0NzE5NjY5MDUy

Optimize Dataset.filter to only compute the indices to keep

closed

MEMBER

Optimize `Dataset.filter` to only compute the indices of the rows to keep, instead of creating a new Arrow table with the rows to keep. Creating a new table was an issue because it could take a lot of disk space. This will be useful to process audio datasets for example cc @patrickvonplaten

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MDExOlB1bGxSZXF1ZXN0NzE5NjUxOTE4

Update: timit_asr - make the dataset streamable

closed

MEMBER

The TIMIT ASR dataset had two issues that was preventing it from being streamable: 1. it was missing a call to `open` before `pd.read_csv` 2. it was using `os.path.dirname` which is not supported for streaming I made the dataset streamable by using `open` to load the CSV, and by adding the support for `os.path.dirname` in dataset scripts to stream data You can now do ```python from datasets import load_dataset timit_asr = load_dataset("timit_asr", streaming=True) print(next(iter(timit_asr["train"]))) ``` prints: ```json {"file": "zip://data/TRAIN/DR4/MMDM0/SI681.WAV::https://data.deepai.org/timit.zip", "phonetic_detail": {"start": [0, 1960, 2466, 3480, 4000, 5960, 7480, 7880, 9400, 9960, 10680, 13480, 15680, 15880, 16920, 18297, 18882, 19480, 21723, 22516, 24040, 25190, 27080, 28160, 28560, 30120, 31832, 33240, 34640, 35968, 37720], "utterance": ["h#", "w", "ix", "dcl", "s", "ah", "tcl", "ch", "ix", "n", "ae", "kcl", "t", "ix", "v", "r", "ix", "f", "y", "ux", "zh", "el", "bcl", "b", "iy", "y", "ux", "s", "f", "el", "h#"], "stop": [1960, 2466, 3480, 4000, 5960, 7480, 7880, 9400, 9960, 10680, 13480, 15680, 15880, 16920, 18297, 18882, 19480, 21723, 22516, 24040, 25190, 27080, 28160, 28560, 30120, 31832, 33240, 34640, 35968, 37720, 39920]}, "sentence_type": "SI", "id": "SI681", "speaker_id": "MMDM0", "dialect_region": "DR4", "text": "Would such an act of refusal be useful?", "word_detail": { "start": [1960, 4000, 9400, 10680, 15880, 18297, 27080, 30120], "utterance": ["would", "such", "an", "act", "of", "refusal", "be", "useful"], "stop": [4000, 9400, 10680, 15880, 18297, 27080, 30120, 37720] }} ``` cc @patrickvonplaten @vrindaprabhu

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MDExOlB1bGxSZXF1ZXN0NzE4OTE5NjQ0

Fix IndexError by ignoring empty RecordBatch

closed

MEMBER

We need to ignore the empty record batches for the interpolation search to work correctly when querying arrow tables Close #2833 cc @SaulLu

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MDU6SXNzdWU5NzgyOTYxNDA=

IndexError when accessing first element of a Dataset if first RecordBatch is empty

closed

MEMBER

The computation of the offsets of the underlying Table of a Dataset has some issues if the first RecordBatch is empty. ```python from datasets import Dataset import pyarrow as pa pa_table = pa.Table.from_pydict({"a": [1]}) pa_table2 = pa.Table.from_pydict({"a": []}, schema=pa_table.schema) ds_table = pa.concat_tables([pa_table2, pa_table]) dataset = Dataset(ds_table) print([len(b) for b in dataset.data._batches]) # [0, 1] print(dataset.data._offsets) # [0 0 1] (should be [0, 1]) dataset[0] ``` raises ```python --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/datasets/table.py in _interpolation_search(arr, x) 90 else: 91 i, j = i, k ---> 92 raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.") 93 94 IndexError: Invalid query '0' for size 1. ``` This can be fixed by ignoring empty batches when computing `table._batches` and `table._offsets` cc @SaulLu

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MDU6SXNzdWU5NzgwMTI4MDA=

Logging levels not taken into account

open

MEMBER

## Describe the bug The `logging` module isn't working as intended relative to the levels to set. ## Steps to reproduce the bug ```python from datasets import logging logging.set_verbosity_debug() logger = logging.get_logger() logger.error("ERROR") logger.warning("WARNING") logger.info("INFO") logger.debug("DEBUG" ``` ## Expected results I expect all logs to be output since I'm putting a `debug` level. ## Actual results Only the two first logs are output. ## Environment info - `datasets` version: 1.11.0 - Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33 - Python version: 3.9.6 - PyArrow version: 5.0.0 ## To go further This logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`. `transformers` sets a default `StreamHandler` [here](https://github.com/huggingface/transformers/blob/5c6eca71a983bae2589eed01e5c04fcf88ba5690/src/transformers/utils/logging.py#L86)

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MDU6SXNzdWU5Nzc4NjQ2MDA=

ArrowInvalid when mapping dataset with missing values

open

NONE

## Describe the bug I encountered an `ArrowInvalid` when mapping dataset with missing values. Here are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown). [data_small.csv](https://github.com/huggingface/datasets/files/7037838/data_small.csv) [data.csv](https://github.com/huggingface/datasets/files/7037842/data.csv) ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("csv", data_files=['data_small.csv']) datasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id']) ``` ## Expected results No error ## Actual results ``` File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Invalid null value ``` ## Environment info - `datasets` version: 1.5.0 - Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyTorch version (GPU?): 1.7.1+cpu (False) - Tensorflow version (GPU?): 2.4.1 (False) - Using GPU in script?: no - Using distributed or parallel set-up in script?: no

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MDExOlB1bGxSZXF1ZXN0NzE4MjkyMTM2

Add imagefolder dataset

open

CONTRIBUTOR

A generic imagefolder dataset inspired by `torchvision.datasets.ImageFolder`. Resolves #2508 --- Example Usage: [](https://colab.research.google.com/gist/nateraw/954fa8cba4ff806f6147a782fa9efd1a/imagefolder-official-example.ipynb)

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MDU6SXNzdWU5NzcyMzMzNjA=

Optimize streaming from TAR archives

open

MEMBER

Hi ! As you know TAR has some constraints for data streaming. While it is optimized for buffering, the files in the TAR archive **need to be streamed in order**. It means that we can't choose which file to stream from, and this notation is to be avoided for TAR archives: ``` tar://books_large_p1.txt::https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2 ``` Instead, I suggest we implement `iter_archive` for the `StreamingDownloadManager`. The regular `DownloadManager` already has it. Then we will have to update the json/txt/csv/etc. loaders to make them use `iter_archive` on TAR archives. That's also what Tensorflow Datasets is doing in this case. See this [dataset](https://github.com/tensorflow/datasets/blob/93895059c80a9e05805e8f32a2e310f66a23fc98/tensorflow_datasets/image_classification/flowers.py) for example. Therefore instead of doing ```python uncompressed = dl_manager.extract(tar_archive) filename = "books_large_p1.txt" with open(os.path.join(uncompressed, filename)) as f: for line in f: ... ``` we'll do ```python for filename, f in dl_manager.iter_archive(tar_archive): for line in f: ... ```

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MDExOlB1bGxSZXF1ZXN0NzE3OTYwODg3

Add code-mixed Kannada Hope speech dataset

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** *KanHope* - **Description:** *A code-mixed English-Kannada dataset for Hope speech detection* - **Paper:** *https://arxiv.org/abs/2108.04616* - **Data:** *https://github.com/adeepH/KanHope/tree/main/dataset* - **Motivation:** *The dataset is amongst the very few resources available for code-mixed low-resourced Dravidian languages of India*

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MDExOlB1bGxSZXF1ZXN0NzE3Nzg3MjEw

add a text classification dataset

closed

CONTRIBUTOR

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MDU6SXNzdWU5NzY5NzQyNTQ=

Add a Text Classification dataset: KanHope

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** *KanHope* - **Description:** *A code-mixed English-Kannada dataset for Hope speech detection* - **Paper:** *https://arxiv.org/abs/2108.04616* (I am the author of the paper} - **Author:** *[AdeepH](https://github.com/adeepH)* - **Data:** *https://github.com/adeepH/KanHope/tree/main/dataset* - **Motivation:** *The dataset is amongst the very few resources available for code-mixed Dravidian languages* - I tried following the steps as per the instructions. However, could not resolve an error. Any help would be appreciated. - The dataset card and the scripts for the dataset *https://github.com/adeepH/datasets/tree/multilingual-hope-speech/datasets/mhs_eval* ``` Using custom data configuration default Downloading and preparing dataset bn_hate_speech/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/bn_hate_speech/default/0.0.0/5f417ddc89777278abd29988f909f39495f0ec802090f7d8fa63b5bffb121762... --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-114-4a9cdb519e4c> in <module>() 1 from datasets import load_dataset 2 ----> 3 data = load_dataset('/content/bn') 9 frames /usr/local/lib/python3.7/dist-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, task, streaming, **config_kwargs) 850 ignore_verifications=ignore_verifications, 851 try_from_hf_gcs=try_from_hf_gcs, --> 852 use_auth_token=use_auth_token, 853 ) 854 /usr/local/lib/python3.7/dist-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) 614 if not downloaded_from_gcs: 615 self._download_and_prepare( --> 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 617 ) 618 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 691 try: 692 # Prepare split will record examples associated to the split --> 693 self._prepare_split(split_generator, **prepare_split_kwargs) 694 except OSError as e: 695 raise OSError( /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator) 1107 disable=bool(logging.get_verbosity() == logging.NOTSET), 1108 ): -> 1109 example = self.info.features.encode_example(record) 1110 writer.write(example, key) 1111 finally: /usr/local/lib/python3.7/dist-packages/datasets/features.py in encode_example(self, example) 1015 """ 1016 example = cast_to_python_objects(example) -> 1017 return encode_nested_example(self, example) 1018 1019 def encode_batch(self, batch): /usr/local/lib/python3.7/dist-packages/datasets/features.py in encode_nested_example(schema, obj) 863 if isinstance(schema, dict): 864 return { --> 865 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 866 } 867 elif isinstance(schema, (list, tuple)): /usr/local/lib/python3.7/dist-packages/datasets/features.py in <dictcomp>(.0) 863 if isinstance(schema, dict): 864 return { --> 865 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) 866 } 867 elif isinstance(schema, (list, tuple)): /usr/local/lib/python3.7/dist-packages/datasets/features.py in encode_nested_example(schema, obj) 890 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks 891 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)): --> 892 return schema.encode_example(obj) 893 # Other object should be directly convertible to a native Arrow type (like Translation and Translation) 894 return obj /usr/local/lib/python3.7/dist-packages/datasets/features.py in encode_example(self, example_data) 665 # If a string is given, convert to associated integer 666 if isinstance(example_data, str): --> 667 example_data = self.str2int(example_data) 668 669 # Allowing -1 to mean no label. /usr/local/lib/python3.7/dist-packages/datasets/features.py in str2int(self, values) 623 if value not in self._str2int: 624 value = str(value).strip() --> 625 output.append(self._str2int[str(value)]) 626 else: 627 # No names provided, try to integerize KeyError: ' ' ```

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MDU6SXNzdWU5NzY1ODQ5MjY=

The datasets.map function does not load cached dataset after moving python script

closed

NONE

## Describe the bug The datasets.map function caches the processed data to a certain directory. When the map function is called another time with totally the same parameters, the cached data are supposed to be reloaded instead of re-processing. However, it doesn't reuse cached data sometimes. I use the common data processing in different tasks, the datasets are processed again, the only difference is that I run them in different files. ## Steps to reproduce the bug Just run the following codes in different .py files. ```python if __name__ == '__main__': from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) ``` ## Expected results The map function should reload data in the second or any later runs. ## Actual results The processing happens in each run. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: linux - Python version: 3.7.6 - PyArrow version: 3.0.0 This is the first time I report a bug. If there is any problem or confusing description, please let me know 😄.

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MDExOlB1bGxSZXF1ZXN0NzE3MzIyMzY5

Fix defaults in cache_dir docstring in load.py

closed

CONTRIBUTOR

Fix defaults in the `cache_dir` docstring.

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MDU6SXNzdWU5NzYxMzUzNTU=

HF_DATASETS_CACHE variable in Windows

closed

NONE

I can't seem to use a custom Cache directory in Windows. I have tried: set HF_DATASETS_CACHE = "C:\Datasets" set HF_DATASETS_CACHE = "C:/Datasets" set HF_DATASETS_CACHE = "C:\\Datasets" set HF_DATASETS_CACHE = "r'C:\Datasets'" set HF_DATASETS_CACHE = "\Datasets" set HF_DATASETS_CACHE = "/Datasets" In each instance I get the "[WinError 123] The filename, directory name, or volume label syntax is incorrect" error when attempting to load a dataset

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MDExOlB1bGxSZXF1ZXN0NzE2ODUxMTAy

Add url prefix convention for many compression formats

closed

MEMBER

## Intro When doing dataset streaming, the uncompression of compressed files is done on the fly using `fsspec`. In particular, the download manager method `download_and_extract` doesn't return a path to the local download and extracted file, but instead a chained URL so that the uncompression can be done when the file is opened. A few examples of chained URLS: - `gz://file.txt::https://foo.bar/file.txt.gz` - `bz2://file.txt::https://foo.bar/file.txt.bz2` - `zip://::https://foo.bar/archive.zip` - `tar://::https://foo.bar/archive.tar.gz` (the TAR uncompression includes gz, bz2 etc. uncompression in `fsspec`) This syntax is highly inspired by the `fsspec` URL chaining syntax from https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining This url prefixing allows `open` to know what kind of uncompression to do in a dataset script when doing ```python def _generate_examples(self, urlpath): with open(urlpath) as f: .... ``` ## What it changes This changes the previous behavior from https://github.com/huggingface/datasets/pull/2786 , in which `open` was trying to infer the compression automatically. Infering the compression made it impossible to know whether the user wanted `open` to return compressed data (as the default behavior of the buitin open), or the uncompressed data. By adding uncompression prefixes to the URL, `open` know directly if it has to uncompress or not, and also which protocol to use. ## Additional notes This PR should close https://github.com/huggingface/datasets/issues/2813 It should also close this PR https://github.com/huggingface/datasets/pull/2811 since the oscar dataset script won't try to uncompress twice anymore Note that I had to temporarily remove the support for passing tar and zip files to `data_files` for streaming to make it work, since it makes it ambiguous whether a zip file passed as `data_files` should be uncompressed or not. IMO we can make it work again by changing the syntax to make the glob explicit: ```python load_dataset("json", data_files="zip://*.jsonl::https://foo.bar/archive.zip") ``` This is the exact same convention as fsspec and it removes all ambiguities cc @albertvillanova @lewtun

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MDU6SXNzdWU5NzU1NTYwMzI=

Cannot load linnaeus dataset

closed

NONE

## Describe the bug The [linnaeus](https://huggingface.co/datasets/linnaeus) dataset cannot be loaded. To reproduce: ``` from datasets import load_dataset datasets = load_dataset("linnaeus") ``` This results in: ``` Downloading and preparing dataset linnaeus/linnaeus (download: 17.36 MiB, generated: 8.74 MiB, post-processed: Unknown size, total: 26.10 MiB) to /root/.cache/huggingface/datasets/linnaeus/linnaeus/1.0.0/2ff05dbc256108233262f596e09e322dbc3db067202de14286913607cd9cb704... --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-4-7ef3a88f6276> in <module>() 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("linnaeus") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 603 raise FileNotFoundError("Couldn't find file at {}".format(url)) 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") --> 605 raise ConnectionError("Couldn't reach {}".format(url)) 606 607 # Try a second time ConnectionError: Couldn't reach https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/ ```

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MDU6SXNzdWU5NzUyMTA3MTI=

Downloading “reddit” dataset keeps timing out.