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https://api.github.com/repos/huggingface/datasets/issues/4739

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https://github.com/huggingface/datasets/pull/4739

PR_kwDODunzps48BHdE

Deprecate metrics

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Deprecate metrics: - deprecate public functions: `load_metric`, `list_metrics` and `inspect_metric`: docstring and warning - test deprecation warnings are issues - deprecate metrics in all docs - remove mentions to metrics in docs and README - deprecate internal functions/classes Maybe we should also stop testing metrics?

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PR_kwDODunzps479hq4

Use CI unit/integration tests

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This PR: - Implements separate unit/integration tests - A fail in integration tests does not cancel the rest of the jobs - We should implement more robust integration tests: work in progress in a subsequent PR - For the moment, test involving network requests are marked as integration: to be evolved

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I_kwDODunzps5OYXm8

Download error on scene_parse_150

closed

NONE

``` from datasets import load_dataset dataset = load_dataset("scene_parse_150", "scene_parsing") FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip ```

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I_kwDODunzps5OYEUc

Dataset Viewer issue for deepklarity/huggingface-spaces-dataset

closed

NONE

### Link https://huggingface.co./datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train ### Description Hi Team, I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not. ``` Status code: 400 Exception: Status400Error Message: The split is being processed. Retry later. ``` Is there any explicit step to be taken to get the viewer to work? ### Owner Yes

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PR_kwDODunzps477CuP

Pin rouge_score test dependency

closed

MEMBER

Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed. Fix #4734

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I_kwDODunzps5OWZuW

Package rouge-score cannot be imported

closed

MEMBER

## Describe the bug After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true ``` FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge ``` with errors: ``` > from rouge_score import rouge_scorer E ModuleNotFoundError: No module named 'rouge_score' ``` ``` E ImportError: To be able to use rouge, you need to install the following dependency: rouge_score. E Please install it using 'pip install rouge_score' for instance' ```

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I_kwDODunzps5OWV4A

rouge metric

closed

NONE

## Describe the bug A clear and concise description of what the bug is. Loading Rouge metric gives error after latest rouge-score==0.0.7 release. Downgrading rougemetric==0.0.4 works fine. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results A clear and concise description of the expected results. from rouge_score import rouge_scorer, scoring should run ## Actual results Specify the actual results or traceback. File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module> from rouge_score import rouge_scorer, scoring ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux - Python version:3.9 - PyArrow version:

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I_kwDODunzps5OV7fu

Document better that loading a dataset passing its name does not use the local script

closed

MEMBER

As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it. What he did: - he installed `datasets` from source - he modified locally `datasets/the_pile/the_pile.py` loading script - he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")` - as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245: - the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that. He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co./docs/datasets/installation)) CC: @stevhliu

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PR_kwDODunzps474dlZ

docs: ✏️ fix TranslationVariableLanguages example

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I_kwDODunzps5OSTfP

Loading imagenet-1k validation split takes much more RAM than expected

closed

CONTRIBUTOR

## Describe the bug Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("imagenet-1k", split="validation") print(dataset) """prints Dataset({ features: ['image', 'label'], num_rows: 50000 }) """ pipe_inputs = dataset["image"] # and wait :-) ``` ## Expected results Use only < 10 GB RAM when loading the images. ## Actual results  ``` Using custom data configuration default Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc) Killed ``` ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80

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PR_kwDODunzps473GmR

Refactor Hub tests

closed

MEMBER

This PR refactors `test_upstream_hub` by removing unittests and using the following pytest Hub fixtures: - `ci_hub_config` - `set_ci_hub_access_token`: to replace setUp/tearDown - `temporary_repo` context manager: to replace `try... finally` - `cleanup_repo`: to delete repo accidentally created if one of the tests fails This is a preliminary work done to manage unit/integration tests separately.

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I_kwDODunzps5OQTmu

load_dataset gives "403" error when using Financial Phrasebank

closed

NONE

I tried both codes below to download the financial phrasebank dataset (https://huggingface.co./datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud. ``` from datasets import load_dataset, DownloadMode load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD) ``` ``` from datasets import load_dataset, DownloadMode load_dataset(path='financial_phrasebank',name='sentences_allagree') ``` **Error** ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)

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I_kwDODunzps5OPWEP

Dataset Viewer issue for TheNoob3131/mosquito-data

closed

NONE

### Link https://huggingface.co./datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test ### Description Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits. ### Owner _No response_

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PR_kwDODunzps47ykPI

Fix broken link to the Hub

closed

MEMBER

The Markdown link fails to render if it is in the same line as the `<span>`. This PR implements @mishig25's fix by using `<a href=" ">` instead. 

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I_kwDODunzps5OMh0Y

the_pile datasets URL broken.

closed

NONE

https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again. Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find?

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PR_kwDODunzps47vLrP

Download and prepare as Parquet for cloud storage

closed

MEMBER

Download a dataset as Parquet in a cloud storage can be useful for streaming mode and to use with spark/dask/ray. This PR adds support for `fsspec` URIs like `s3://...`, `gcs://...` etc. and ads the `file_format` to save as parquet instead of arrow: ```python from datasets import * cache_dir = "s3://..." builder = load_dataset_builder("crime_and_punish", cache_dir=cache_dir) builder.download_and_prepare(file_format="parquet") ``` EDIT: actually changed the API to ```python from datasets import * builder = load_dataset_builder("crime_and_punish") builder.download_and_prepare("s3://...", file_format="parquet") ``` credentials to cloud storage can be passed using the `storage_options` argument in For consistency with the BeamBasedBuilder, I name the parquet files `{builder.name}-{split}-xxxxx-of-xxxxx.parquet`. I think this is fine since we'll need to implement parquet sharding after this PR, so that a dataset can be used efficiently with dask for example. Note that images/audio files are not embedded yet in the parquet files, this will added in a subsequent PR TODO: - [x] docs - [x] tests

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PR_kwDODunzps47uoSj

Refactor conftest fixtures

closed

MEMBER

Previously, fixture modules `hub_fixtures` and `s3_fixtures`: - were both at the root test directory - were imported using `import *` - as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest` This PR: - puts both fixture modules in a dedicated directory `fixtures` - renames both to: `fixtures.hub` and `fixtures.s3` - imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *` - additionally creates a new fixture module `fixtures.files` with all file-related fixtures

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PR_kwDODunzps47t_HJ

Docs: Fix same-page haslinks

closed

CONTRIBUTOR

`href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co./docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION) to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element

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I_kwDODunzps5OGOHw

PyArrow Dataset error when calling `load_dataset`

open

NONE

## Describe the bug I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error: ```sh File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main raw_datasets = load_dataset( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split for key, table in logging.tqdm( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables for batch_idx, record_batch in enumerate( File "pyarrow/_parquet.pyx", line 1309, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ## Steps to reproduce the bug I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`. When creating the dataset, I do something like this: ```python import json from datasets import Dataset, Audio # manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text for line in manifest_lines: line = line.strip() if line: line_dict = json.loads(line) manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}") manifest_dict["duration"].append(line_dict["duration"]) manifest_dict["transcription"].append(line_dict["text"]) # Create a HF dataset dataset = Dataset.from_dict(manifest_dict).cast_column( "audio", Audio(sampling_rate=16_000), ) # From the docs for saving to disk # https://huggingface.co./docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk def read_audio_file(example): with open(example["audio"]["path"], "rb") as f: return {"audio": {"bytes": f.read()}} dataset = dataset.map(read_audio_file, num_proc=70) dataset.save_to_disk(f"/audio-data/hf/{artifact_name}") dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True) ``` Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace. I am able to load the dataset fine if I use `load_from_disk()`. ## Expected results `load_dataset()` should behave just like `load_from_disk()` and not cause any errors. ## Actual results See above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> I am using the `huggingface/transformers-pytorch-gpu:latest` image - `datasets` version: 2.3.0 - Platform: Docker/Ubuntu 20.04 - Python version: 3.8 - PyArrow version: 8.0.0

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I_kwDODunzps5OFLYj

Dataset Viewer issue for shamikbose89/lancaster_newsbooks

closed

NONE

### Link https://huggingface.co./datasets/shamikbose89/lancaster_newsbooks ### Description Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load ### Owner Yes

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I_kwDODunzps5OEssc

Issue loading TheNoob3131/mosquito-data dataset

closed

NONE

 So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank. Here is the error below: ValueError Traceback (most recent call last) Input In [8], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train") File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1678 # Download and prepare data -> 1679 builder_instance.download_and_prepare( 1680 download_config=download_config, 1681 download_mode=download_mode, 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, 1684 use_auth_token=use_auth_token, 1685 ) 1687 # Build dataset for splits 1688 keep_in_memory = ( 1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1690 ) Is the dataset in the wrong format or is there some security permission that I should enable?

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PR_kwDODunzps47prWR

Make Extractor accept Path as input

closed

MEMBER

This PR: - Makes `Extractor` accept instance of `Path` as input - Removes unnecessary castings of `Path` to `str`

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I_kwDODunzps5ODZMj

Dataset Viewer issue for LawalAfeez/englishreview-ds-mini

closed

NONE

### Link _No response_ ### Description Unable to view the split data ### Owner _No response_

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PR_kwDODunzps47pdbh

Support "tags" yaml tag

closed

MEMBER

Added the "tags" YAML tag, so that users can specify data domain/topics keywords for dataset search

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PR_kwDODunzps47pSui

Fix POS tags

closed

MEMBER

We're now using `part-of-speech` and not `part-of-speech-tagging`, see discussion here: https://github.com/huggingface/datasets/commit/114c09aff2fa1519597b46fbcd5a8e0c0d3ae020#r78794777

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PR_kwDODunzps47o0YG

Fix named split sorting and remove unnecessary casting

closed

MEMBER

This PR: - makes `NamedSplit` sortable: so that `sorted()` can be called on them - removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set` - removes unnecessary casting of `NamedSplit` to `str`

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PR_kwDODunzps47ojC1

Document installation of sox OS dependency for audio

closed

MEMBER

The `sox` OS package needs being installed manually using the distribution package manager. This PR adds this explanation to the docs.

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PR_kwDODunzps47ohdr

Highlight non-commercial license in amazon_reviews_multi dataset card

closed

CONTRIBUTOR

Highlight that the licence granted by Amazon only covers non-commercial research use.

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I_kwDODunzps5OB96K

Document how to create a dataset loading script for audio/vision

closed

MEMBER

Currently, in our docs for Audio/Vision/Text, we explain how to: - Load data - Process data However we only explain how to *Create a dataset loading script* for text data. I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text. See, for example: - #4697 - and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492 CC: @stevhliu

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PR_kwDODunzps47ny0L

Add object detection processing tutorial

closed

CONTRIBUTOR

The following adds a quick guide on how to process object detection datasets with `albumentations`.

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I_kwDODunzps5OACgF

WMT21 & WMT22

open

CONTRIBUTOR

## Adding a Dataset - **Name:** WMT21 & WMT22 - **Description:** We are going to have three tracks: two small tasks and a large task. The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages. - **Paper:** / - **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html - **Motivation:** Many more languages than previous WMT versions - Could be very high impact Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md). I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq

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PR_kwDODunzps47lewm

Fix require torchaudio and refactor test requirements

closed

MEMBER

Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead): ```python def require_torchaudio(test_case): if find_spec("sox") is None: ... ``` The bug was introduced by: - #3685 - Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940 which moved ```python require_sndfile = pytest.mark.skipif( # In Windows and OS X, soundfile installs sndfile (sys.platform != "linux" and find_spec("soundfile") is None) # In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager or (sys.platform == "linux" and find_library("sndfile") is None), reason="Test requires 'sndfile': `pip install soundfile`; " "Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`", ) require_sox = pytest.mark.skipif( find_library("sox") is None, reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`", ) require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'") ``` to ```python def require_sndfile(test_case): """ Decorator marking a test that requires soundfile. These tests are skipped when soundfile isn't installed. """ if (sys.platform != "linux" and find_spec("soundfile") is None) or ( sys.platform == "linux" and find_library("sndfile") is None ): test_case = unittest.skip( "test requires 'sndfile': `pip install soundfile`; " "Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`", )(test_case) return test_case def require_sox(test_case): """ Decorator marking a test that requires sox. These tests are skipped when sox isn't installed. """ if find_library("sox") is None: return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")( test_case ) return test_case def require_torchaudio(test_case): """ Decorator marking a test that requires torchaudio. These tests are skipped when torchaudio isn't installed. """ if find_spec("sox") is None: return unittest.skip("test requires 'torchaudio'")(test_case) return test_case ``` This PR; - fixes the bug in `require_torchaudio` - refactors the test requirements back to using `pytest` instead of `unittest` - the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]`

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I_kwDODunzps5N-lUN

Dataset Viewer issue for TheNoob3131/mosquito-data

closed

NONE

### Link _No response_ ### Description Getting this error when trying to view dataset preview: Message: 401, message='Unauthorized', url=URL('https://huggingface.co./datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv') ### Owner _No response_

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PR_kwDODunzps47lNBg

Fix empty examples in xtreme dataset for bucc18 config

closed

MEMBER

As reported in https://huggingface.co./muibk, there are empty examples in xtreme/bucc18.de I applied your fix @mustaszewski I also used a dict to make the dataset generation much faster

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PR_kwDODunzps47lFDo

Fix crd3

closed

MEMBER

As reported in https://huggingface.co./datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode

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PR_kwDODunzps47lCFt

Skip tests only for lz4/zstd params if not installed

closed

MEMBER

Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters. This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`). Related to: - #4688

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PR_kwDODunzps47kABf

Make cast in `from_pandas` more robust

closed

CONTRIBUTOR

Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364) This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4).

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I_kwDODunzps5N81mT

Domain specific dataset discovery on the Hugging Face hub

open

MEMBER

**Is your feature request related to a problem? Please describe.** ## The problem The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data). There are various ways of identifying datasets that may be relevant for a particular use case: - searching - various filters Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'. The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co./biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search. This is also related to some existing issues on Github related to metadata on the hub: - https://github.com/huggingface/datasets/issues/3625 - https://github.com/huggingface/datasets/issues/3877 **Describe the solution you'd like** ### Some possible solutions that may help with this: #### Enable domain tags (from a controlled vocabulary) - This would add metadata field to the YAML for the domain a dataset relates to - Advantages: - the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co./space/huggingface/datasets-tagging) - the controlled vocabulary could align with an existing controlled vocabulary - this additional metadata can be used to perform filtering by domain - disadvantages - choosing the best controlled vocab may be difficult - there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain #### Enable topic tags (user-generated) Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project. #### Collections This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include: Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections. This is fairly common on other data reposotiores for example the following collections: <img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png"> all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en). There are different models one could use for how these collections could be created: - only within an org - for any dataset/model - the owner or a dataset/model has to agree to be added to a collection - a collection owner can have people suggest additions to their collection - other models.... These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront. There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets. There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection. This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform. **Additional context** Add any other context about the feature request here. I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova

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PR_kwDODunzps47jeE9

Added more information in the README about contributors of the Arabic Speech Corpus

closed

CONTRIBUTOR

Added more information in the README about contributors and encouraged reading the thesis for more infos

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PR_kwDODunzps47jKNx

Support extract lz4 compressed data files

closed

MEMBER

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PR_kwDODunzps47jA6Z

Fix Authentification Error while streaming

closed

NONE

I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub. ``` from datasets import load_dataset dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True) for d in dataset['train']: print(d) break # this is for checking ``` This code is an example for streaming private datasets. when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this, ``` /usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self) 1007 status=self.status, 1008 message=self.reason, → 1009 headers=self.headers, 1010 ) 1011 ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co./datasets/.../train-00000-of-00001-168b451062c67c34.parquet') ``` (this is an example on the dataset has `parquet` extenstion) It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co./~". so I add three lines. With this change, there is no error anymore(but this code is ad-hoc).

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PR_kwDODunzps47i9gN

Enable streaming dataset to use the "all" split

open

CONTRIBUTOR

Fixes #4637

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I_kwDODunzps5N7E6d

Trouble with streaming frgfm/imagenette vision dataset with TAR archive

closed

NONE

### Link https://huggingface.co./datasets/frgfm/imagenette ### Description Hello there :wave: Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile: Basically, I get a: ``` Status code: 400 Exception: NotImplementedError Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead. ``` I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far! Could you point me in the right direction please? :pray: ### Owner Yes

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I_kwDODunzps5N6gf7

Cannot load LinCE dataset

closed

NONE

## Describe the bug Cannot load LinCE dataset due to a connection error ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("lince", "ner_spaeng") ``` A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2 ## Expected results It should load the dataset ## Actual results ```python --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-2-fc551ddcebef> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("lince", "ner_spaeng") 10 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, revision, use_auth_token, task, streaming, **config_kwargs) 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, -> 1684 use_auth_token=use_auth_token, 1685 ) 1686 /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) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1219 1220 def _download_and_prepare(self, dl_manager, verify_infos): -> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1222 1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 772 773 # Checksums verification /root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager) 481 def _split_generators(self, dl_manager): 482 """Returns SplitGenerators.""" --> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip") 484 data_dir = os.path.join(lince_dir, self.config.data_dir) 485 return [ /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) 432 433 def get_recorded_sizes_checksums(self): /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls) 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), --> 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time /usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 346 # Singleton 347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 348 return function(data_struct) 349 350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled() /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config) 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) 336 337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]): /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, --> 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): /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, ignore_url_params, download_desc) 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: --> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") 534 elif response is not None: 535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))"))) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps47hobQ

Add MANtIS dataset

closed

CONTRIBUTOR

This PR adds MANtIS dataset. Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639) Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS) README and dataset tags are WIP.

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I_kwDODunzps5N5pos

Distributed data parallel training for streaming datasets

open

NONE

### Feature request Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training? ### Motivation Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation? ### Your contribution Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do?

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PR_kwDODunzps47go-F

update `samsum` script

closed

CONTRIBUTOR

update `samsum` script after #4672 was merged (citation is also updated)

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I_kwDODunzps5N4UgQ

Unable to cast a column with `Image()` by using the `cast_column()` feature

closed

NONE

## Describe the bug A clear and concise description of what the bug is. When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ``` When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected. ## Steps to reproduce the bug ```python from datasets import Dataset, Image data_dict = { "img_path": ["https://picsum.photos/200/300"] } dataset = Dataset.from_dict(data_dict) #NOTE Comment out this line and use cast_column and it works properly dataset = dataset.add_column("yeet", [1]) #NOTE This line fails to execute properly if `add_column` is called before dataset = dataset.cast_column("img_path", Image()) # #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While # # running this, make sure to comment out the `cast_column` line # new_features = dataset.features.copy() # new_features["img_path"] = Image() # dataset = dataset.cast(new_features) print(dataset) print(dataset.features) print(dataset[0]) ``` ## Expected results A clear and concise description of the expected results. Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step ## Actual results Specify the actual results or traceback. ``` Traceback (most recent call last): File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module> dataset = dataset.cast_column("img_path", Image()) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column dataset._data = dataset._data.cast(dataset.features.arrow_schema) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast new_tables.append(subtable.cast(subschema, *args, **kwargs)) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast return InMemoryTable(table_cast(self.table, *args, **kwargs)) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast return cast_table_to_schema(table, schema) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Ubuntu 20.04.3 LTS - Python version: 3.9.7 - PyArrow version: 7.0.0

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I_kwDODunzps5N3eyY

Dataset Viewer issue for rajistics/indian_food_images

closed

NONE

### Link https://huggingface.co./datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train ### Description I have a train/test split in my dataset <img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png"> t The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea. The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory ### Owner Yes

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PR_kwDODunzps47fG6w

Refactor base extractors

closed

MEMBER

This PR: - Refactors base extractors as subclasses of `BaseExtractor`: - this is an abstract class defining the interface with: - `is_extractable`: abstract class method - `extract`: abstract static method - Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`): - this has a default implementation of `is_extractable` - this improves performance (reducing the number of file reads) by allowing passing already read `magic_number` - Refactors `Extractor`: - reads magic number from file only once This PR deprecates: ```python is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True) self.extractor.extract(input_path, output_path, extractor=extractor) ``` and uses more Pythonic instead: ```python extractor_format = self.extractor.infer_extractor_format(input_path) self.extractor.extract(input_path, output_path, extractor_format) ```

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PR_kwDODunzps47eyw5

Test extractors for all compression formats

closed

MEMBER

This PR: - Adds all compression formats to `test_extractor` - Tests each base extractor for all compression formats Note that all compression formats are tested except "rar".

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PR_kwDODunzps47eW6C

Skip test_extractor only for zstd param if zstandard not installed

closed

MEMBER

Currently, if `zstandard` is not installed, `test_extractor` is skipped for all compression format parameters. This PR fixes `test_extractor` so that if `zstandard` is not installed, `test_extractor` is skipped only for the `zstd` compression parameter, that is, it is not skipped for all the other compression parameters (`gzip`, `xz`,...).

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PR_kwDODunzps47eF_E

Trigger CI also on push to main

closed

MEMBER

Currently, new CI (on GitHub Actions) is only triggered on pull requests branches when the base branch is main. This PR also triggers the CI when a PR is merged to main branch.

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PR_kwDODunzps47d8Jf

Align logging with Transformers (again)

closed

CONTRIBUTOR

Fix #2832

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PR_kwDODunzps47dju8

Fix mock fsspec

closed

MEMBER

This PR: - Removes an unused method from `DummyTestFS` - Refactors `mock_fsspec` to make it simpler

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I_kwDODunzps5N0S7e

How to assign new values to Dataset?

closed

NONE

 Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it? For example, I want to change all the labels of the SST2 dataset to `0`: ```python from datasets import load_dataset data = load_dataset('glue','sst2') data['train']['label'] = [0]*len(data) ``` I will get the error: ``` TypeError: 'Dataset' object does not support item assignment ```

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PR_kwDODunzps47cLkm

Update create dataset card docs

closed

MEMBER

This PR proposes removing the [online dataset card creator](https://huggingface.co./datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co./spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input. Let me know what you think! :)

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I_kwDODunzps5NxXz3

weird issue/bug with columns (dataset iterable/stream mode)

open

CONTRIBUTOR

I have a dataset online (CloverSearch/cc-news-mutlilingual) that has a bunch of columns, two of which are "score_title_maintext" and "score_title_description". the original files are jsonl formatted. I was trying to iterate through via streaming mode and grab all "score_title_description" values, but I kept getting key not found after a certain point of iteration. I found that some json objects in the file don't have "score_title_description". And in SOME cases, this returns a NONE and in others it just gets a key error. Why is there an inconsistency here and how can I fix it?

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I_kwDODunzps5NwuIM

IndexError when loading ImageFolder

closed

NONE

## Describe the bug Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv). ## Steps to reproduce the bug Put a csv file in a folder with images and load it: ```python import datasets datasets.load_dataset("imagefolder", data_dir=path/to/folder) ``` ## Expected results I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case. ## Actual results Here is the whole traceback: ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27 - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NwZ7y

Dataset Viewer issue for codeparrot/xlcost-text-to-code

closed

NONE

### Link https://huggingface.co./datasets/codeparrot/xlcost-text-to-code ### Description Error ``` Server Error Status code: 400 Exception: TypeError Message: 'NoneType' object is not iterable ``` Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully. Thanks! ### Owner Yes

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PR_kwDODunzps47XX67

Added method to remove excess nesting in a DatasetDict

closed

NONE

Added the ability for a DatasetDict to remove additional nested layers within its features to avoid conflicts when collating. It is meant to accompany [this PR](https://github.com/huggingface/transformers/pull/18119) to resolve the same issue [#15505](https://github.com/huggingface/transformers/issues/15505). @stas00 @lhoestq

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I_kwDODunzps5NtYP4

Cant pass streaming dataset to dataloader after take()

open

NONE

## Describe the bug I am trying to pass a streaming version of c4 to a dataloader, but it can't be passed after I call `dataset.take(n)`. Some functions such as `shuffle()` can be applied without breaking the dataloader but not take. ## Steps to reproduce the bug ```python import datasets import torch dset = datasets.load_dataset(path='c4', name='en', split="train", streaming=True) dset = dset.take(50_000) dset = dset.with_format("torch") num_workers = 8 batch_size = 512 loader = torch.utils.data.DataLoader(dataset=dset, batch_size=batch_size, num_workers=num_workers) for batch in loader: ... ``` ## Expected results No error thrown when iterating over the dataloader ## Actual results Original Traceback (most recent call last): File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch data.append(next(self.dataset_iter)) File "/root/.local/lib/python3.9/site-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py", line 48, in __iter__ for key, example in self._iter_shard(shard_idx): File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 586, in _iter_shard yield from ex_iterable.shard_data_sources(shard_idx) File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in shard_data_sources raise NotImplementedError(f"{type(self)} doesn't implement shard_data_sources yet") NotImplementedError: <class 'datasets.iterable_dataset.TakeExamplesIterable'> doesn't implement shard_data_sources yet ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.31 - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NnuMI

Random 400 Client Error when pushing dataset

closed

NONE

## Describe the bug When pushing a dataset, the client errors randomly with `Bad Request for url:...`. At the next call, a new parquet file is created for each shard. The client may fail at any random shard. ## Steps to reproduce the bug ```python dataset.push_to_hub("ORG/DATASET", private=True, branch="main") ``` ## Expected results Push all the dataset to the Hub with no duplicates. If it fails, it should retry or fail, but continue from the last failed shard. ## Actual results ``` --------------------------------------------------------------------------- HTTPError Traceback (most recent call last) testing.ipynb Cell 29 in <cell line: 1>() ----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main") File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files) 4291 warnings.warn( 4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.", 4293 FutureWarning, 4294 ) 4295 max_shard_size = shard_size -> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub( 4298 repo_id=repo_id, 4299 split=split, 4300 private=private, 4301 token=token, 4302 branch=branch, 4303 max_shard_size=max_shard_size, 4304 embed_external_files=embed_external_files, 4305 ) 4306 organization, dataset_name = repo_id.split("/") 4307 info_to_dump = self.info.copy() File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files) 4193 shard.to_parquet(buffer) 4194 uploaded_size += buffer.tell() -> 4195 _retry( 4196 api.upload_file, 4197 func_kwargs=dict( 4198 path_or_fileobj=buffer.getvalue(), 4199 path_in_repo=shard_path_in_repo, 4200 repo_id=repo_id, 4201 token=token, 4202 repo_type="dataset", 4203 revision=branch, 4204 identical_ok=False, 4205 ), 4206 exceptions=HTTPError, 4207 status_codes=[504], 4208 base_wait_time=2.0, 4209 max_retries=5, 4210 max_wait_time=20.0, 4211 ) 4212 shards_path_in_repo.append(shard_path_in_repo) 4214 # Cleanup to remove unused files File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 282 except exceptions as err: 283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): --> 284 raise err 285 else: 286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 279 while True: 280 try: --> 281 return func(*func_args, **func_kwargs) 282 except exceptions as err: 283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr) 1957 commit_message = ( 1958 commit_message 1959 if commit_message is not None 1960 else f"Upload {path_in_repo} with huggingface_hub" 1961 ) 1962 operation = CommitOperationAdd( 1963 path_or_fileobj=path_or_fileobj, 1964 path_in_repo=path_in_repo, 1965 ) -> 1967 pr_url = self.create_commit( 1968 repo_id=repo_id, 1969 repo_type=repo_type, 1970 operations=[operation], 1971 commit_message=commit_message, 1972 commit_description=commit_description, 1973 token=token, 1974 revision=revision, 1975 create_pr=create_pr, 1976 ) 1977 if pr_url is not None: 1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url) File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads) 1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}" 1838 commit_resp = requests.post( 1839 url=commit_url, 1840 headers={"Authorization": f"Bearer {token}"}, 1841 json=commit_payload, 1842 params={"create_pr": 1} if create_pr else None, 1843 ) -> 1844 _raise_for_status(commit_resp) 1845 return commit_resp.json().get("pullRequestUrl", None) File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request) 76 if request.status_code == 401: 77 # The repo was not found and the user is not Authenticated 78 raise RepositoryNotFoundError( 79 f"401 Client Error: Repository Not Found for url: {request.url}. If the" 80 " repo is private, make sure you are authenticated. (Request ID:" 81 f" {request_id})" 82 ) ---> 84 _raise_with_request_id(request) File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request) 92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str): 93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:] ---> 95 raise e File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request) 88 request_id = request.headers.get("X-Request-Id") 89 try: ---> 90 request.raise_for_status() 91 except Exception as e: 92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str): File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self) 1016 http_error_msg = ( 1017 f"{self.status_code} Server Error: {reason} for url: {self.url}" 1018 ) 1020 if http_error_msg: -> 1021 raise HTTPError(http_error_msg, response=self) HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co./api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31 - Python version: 3.9.4 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5Nngas

Dataset.map gets stuck on _cast_to_python_objects

closed

NONE

## Describe the bug `Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows. Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem. This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting. This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors. ## Steps to reproduce the bug A minimal, self-contained example to reproduce is below: ```python import transformers from transformers import AutoTokenizer from datasets import load_dataset import torch import cProfile pretrained = 'distilbert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(pretrained) squad = load_dataset('squad') squad_train = squad['train'] squad_tiny = squad_train.select(range(5000)) assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast) def tokenize(ds): tokens = tokenizer(text=ds['question'], text_pair=ds['context'], add_special_tokens=True, padding='max_length', truncation='only_second', max_length=160, stride=32, return_overflowing_tokens=True, return_offsets_mapping=True, ) return tokens cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)' cProfile.run(cmd, sort='tottime') ``` ## Actual results The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit. ``` 63524075 function calls (58206482 primitive calls) in 121.836 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects) 42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance} 16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>) 5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck} 80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array} 5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__) 2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive) 5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects} 2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len} 5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding) 5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects} 1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single) 40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__) 10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects} 1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper) 11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr} 36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function} 15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__) 3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic} 77 0.020 0.000 0.020 0.000 {built-in method builtins.dir} 37 0.019 0.001 0.019 0.001 socket.py:543(send) 15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>) 432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers) 5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys) 51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers) 5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus) 3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic} 5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>) 31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects} 1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get) 5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs) 51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits) 5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus) 368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator) 26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance) 51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>) 80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature) 684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects} 4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__) ... ``` ## Environment info I observed this on both Google colab and my local workstation: ### Google colab - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 ### Local - `datasets` version: 2.3.2 - Platform: Windows-7-6.1.7601-SP1 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NneXx

Unable to use dataset with PyTorch dataloader

open

NONE

## Describe the bug When using `.with_format("torch")`, an arrow table is returned and I am unable to use it by passing it to a PyTorch DataLoader: please see the code below. ## Steps to reproduce the bug ```python from datasets import load_dataset from torch.utils.data import DataLoader ds = load_dataset( "para_crawl", name="enfr", cache_dir="/tmp/test/", split="train", keep_in_memory=True, ) dataloader = DataLoader(ds.with_format("torch"), num_workers=32) print(next(iter(dataloader))) ``` Is there something I am doing wrong? The documentation does not say much about the behavior of `.with_format()` so I feel like I am a bit stuck here :-/ Thanks in advance for your help! ## Expected results The code should run with no error ## Actual results ``` AttributeError: 'str' object has no attribute 'dtype' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.18.0-348.el8.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.4 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NkC78

Issue loading datasets -- pyarrow.lib has no attribute

closed

NONE

## Describe the bug I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error: `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Steps to reproduce the bug ```python dataset = load_dataset("glue", "cola") ``` ## Expected results Download datasets without issue. ## Actual results `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 8.0.0 - Pandas version: 1.1.0

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I_kwDODunzps5Ni9eb

load_datasets on csv returns everything as a string

closed

NONE

## Describe the bug If you use: `conll_dataset.to_csv("ner_conll.csv")` It will create a csv file with all of your data as expected, however when you load it with: `conll_dataset = load_dataset("csv", data_files="ner_conll.csv")` everything is read in as a string. For example if I look at everything in 'ner_tags' I get back `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` instead of what I originally saved which was `[[3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], [5, 0]]` I think maybe there is something funky going on with the csv delimiter ## Steps to reproduce the bug ```python # Sample code to reproduce the bug #load original conll dataset orig_conll = load_dataset("conll2003") #save original conll as a csv orig_conll.to_csv("ner_conll.csv") #reload conll data as a csv new_conll = load_dataset("csv", data_files="ner_conll.csv")` ``` ## Expected results A clear and concise description of the expected results. I would expect the data be returned as the data type I saved it as. I.e. if I save a list of ints [[3, 0, 7, 0, 0, 0, 7, 0, 0]], I shouldnt get back a string ['[3 0 7 0 0 0 7 0 0]'] I also get back a string when I pass a list of strings ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'] ## Actual results A list of strings `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` A string "['EU' 'rejects' 'German' 'call' 'to' 'boycott' 'British' 'lamb' '.']" ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0

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PR_kwDODunzps47NEfV

Support extract 7-zip compressed data files

closed

MEMBER

Fix partially #3541, fix #4670.

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I_kwDODunzps5NglB1

Dataset Viewer issue for wmt16

closed

MEMBER

### Link https://huggingface.co./datasets/wmt16 ### Description [Reported](https://huggingface.co./spaces/autoevaluate/model-evaluator/discussions/12#62cb83f14c7f35284e796f9c) by a user of AutoTrain Evaluate. AFAIK this dataset was working 1-2 weeks ago, and I'm not sure how to interpret this error. ``` Status code: 400 Exception: NotImplementedError Message: This is a abstract method ``` Thanks! ### Owner No

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I_kwDODunzps5NfC92

Can't extract files from `.7z` zipfile using `download_and_extract`

closed

CONTRIBUTOR

## Describe the bug I'm adding a new dataset which is a `.7z` zip file in Google drive and contains 3 json files inside. I'm able to download the data files using `download_and_extract` but after downloading it throws this error: ``` >>> dataset = load_dataset("./datasets/mantis/") Using custom data configuration default Downloading and preparing dataset mantis/default to /Users/bhavitvyamalik/.cache/huggingface/datasets/mantis/default/1.1.0/611affa804ec53e2055a335cc1b8b213bb5a0b5142d919967729d5ee23c6bab4... Downloading data: 100%|█████████████████████████████████████████████████████████| 77.2M/77.2M [00:23<00:00, 3.28MB/s] /Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6 Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/load.py", line 1745, in load_dataset use_auth_token=use_auth_token, File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 595, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 690, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: [Errno 20] Not a directory: '/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6/merged_train.json' ``` just before generating the splits. I checked `fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6` file and it's `7z` zip file (similar to downloaded Google drive file) which means it didn't get unzip. Do I need to unzip it separately and then pass the paths for train,dev,test files in `SplitGenerator`? ## Environment info - `datasets` version: 1.18.4.dev0 - Platform: Darwin-19.6.0-x86_64-i386-64bit - Python version: 3.7.8 - PyArrow version: 5.0.0

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I_kwDODunzps5NehtD

loading oscar-corpus/OSCAR-2201 raises an error

closed

NONE

## Describe the bug load_dataset('oscar-2201', 'af') raises an error: Traceback (most recent call last): File "/usr/lib/python3.8/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset builder_instance = load_dataset_builder( File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder dataset_module = dataset_module_factory( File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py I've tried other permutations such as : oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-2201', 'af') oscar_22 = load_dataset('oscar-corpus/OSCAR-2201') with the same unfortunate result. ## Steps to reproduce the bug oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-2201', 'af') oscar_22 = load_dataset('oscar-corpus/OSCAR-2201') # Sample code to reproduce the bug ``` ## Expected results loaded data ## Actual results Traceback (most recent call last): File "/usr/lib/python3.8/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset builder_instance = load_dataset_builder( File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder dataset_module = dataset_module_factory( File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NeGVV

Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed