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PR_kwDODunzps4tsVQJ

Deprecate prepare_module

closed

MEMBER

In version 1.13, `prepare_module` was deprecated. This PR adds a deprecation warning and removes it from all the library, using `dataset_module_factory` or `metric_module_factory` instead. Fix #3165.

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I_kwDODunzps49xvTm

Deprecate prepare_module

closed

MEMBER

In version 1.13, `prepare_module` was deprecated. Add deprecation warning and remove its usage from all the library.

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I_kwDODunzps49uvXu

Add raw data files to the Hub with GitHub LFS for canonical dataset

closed

NONE

I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!

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PR_kwDODunzps4tpI44

Add Image feature

open

CONTRIBUTOR

Adds the Image feature. This feature is heavily inspired by the recently added Audio feature (#2324). Currently, this PR is pretty simple. Some considerations that need further discussion: * I've decided to use `Pillow`/`PIL` as the image decoding library. Another candidate I considered is `torchvision`, mostly because of its `accimage` backend, which should be faster for loading `jpeg` images than `Pillow`. However, `torchvision`'s io module only supports png and jpeg images, has `torch` as a hard dependency, and requires magic to work with image bytes ( `torch.ByteTensor(torch.ByteStorage.from_buffer(image_bytes)))`). * Currently, I'm converting `PIL`'s `Image` type to `np.ndarray`. The vision models in Transformers such as ViT prefer the raw `Image` type and not the decoded tensors, so there is a small overhead due to [this conversion](https://github.com/huggingface/transformers/blob/3e8761ab8077e3bb243fe2f78b2a682bd2257cf1/src/transformers/image_utils.py#L62-L73). IMO this is justified to keep this part aligned with the Audio feature, which also returns `np.ndarray`. What do you think? * Still have to work on the channel decoding logic: * PyTorch prefers the channel-first ordering (C, H, W); TF and Flax the channel-last ordering (H, W, C). One cool feature would be adjusting the channel order based on the selected formatter (`torch`, `tf`, `jax`). * By default, `Image.open` returns images of shape (H, W, C). However, ViT's feature extractor expects the format (C, H, W) if the image is passed as an array (explained [here](https://huggingface.co/transformers/model_doc/vit.html#transformers.ViTFeatureExtractor.__call__)), so I'm more inclined to the format (C, H, W). Which one do you prefer, (C, H, W) or (H, W, C)? * Are there any options you'd like to see? (the user could change those via `cast_column`, such as `sampling_rate` in the Audio feature) TODOs: * [ ] tests * in subsequent PRs: * docs - a section in the docs, which gives some additional info on the Image and Audio feature and compares them to `ArrayND` * streaming (waiting for #3129 and #3133 to get merged first) * update the image tasks and the datasets to use the new feature * Image/Audio formatting [Colab Notebook](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c?usp=sharing) where you can play with this feature. I'm also adding a link to the [Image](https://github.com/tensorflow/datasets/blob/7ac7d506488d46038a5854961d068926b3f93c7f/tensorflow_datasets/core/features/image_feature.py#L155) feature in TFDS because one of our goals is to parse TFDS scripts eventually, so our Image feature has to (at least) support all the formats theirs does. Feel free to cc anyone who might be interested. P.S. Please ignore the changes in the `datasets/**/*.py` files 😄.

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I_kwDODunzps49t-X4

`datasets-cli test` should work with datasets without scripts

open

NONE

It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not). I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!

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PR_kwDODunzps4tpCsm

Add riddle_sense dataset

closed

CONTRIBUTOR

Adding a new dataset for QA with riddles. I'm confused about the tagging process because it looks like the streamlit app loads data from the current repo, so is it something that should be done after merging or off my fork?

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PR_kwDODunzps4tofO0

Better error msg if `len(predictions)` doesn't match `len(references)` in metrics

closed

CONTRIBUTOR

Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`. cc: @BramVanroy (feel free to test this code on your examples and review this PR)

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PR_kwDODunzps4toKD5

Make inspect.get_dataset_config_names always return a non-empty list

closed

MEMBER

Make all named configs cases, so that no special unnamed config case needs to be handled differently. Fix #3135.

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PR_kwDODunzps4toGpe

Fix string encoding for Value type

closed

MEMBER

Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans Here is an example code that didn't work previously, but that works with this fix: ```python import datasets # Note that 'id' is an integer while the SQuAD metric uses strings predictions = [{'prediction_text': '1976', 'id': 5}] references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}] squad_metric = datasets.load_metric("squad") squad_metric.add_batch(predictions=predictions, references=references) results = squad_metric.compute() # {'exact_match': 100.0, 'f1': 100.0} ``` cc @sgugger @philschmid

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PR_kwDODunzps4tm3_I

Fixed: duplicate parameter and missing parameter in docstring

closed

CONTRIBUTOR

changing duplicate parameter `data_files` in `DatasetBuilder.__init__` to the missing parameter `data_dir`

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I_kwDODunzps49qOT8

Rouge and Meteor for multiple references

open

NONE

Hi, Currently rogue and meteor supports only single references. Can we use these metrics to calculate for multiple references?

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I_kwDODunzps49qL2V

Illegal instruction (core dumped) at datasets import

open

CONTRIBUTOR

## Describe the bug I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)" ## Steps to reproduce the bug ``` conda create --prefix path/to/env conda activate path/to/env conda install -c huggingface -c conda-forge datasets # exits with output "Illegal instruction (core dumped)" python -m datasets ``` ## Environment info When I run "datasets-cli env", I also get "Illegal instruction (core dumped)" If I run the following commands: ``` conda create --prefix path/to/another/new/env conda activate path/to/another/new/env conda install -c huggingface transformers transformers-cli env ``` Then I get: - `transformers` version: 4.11.3 - Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyTorch version (GPU?): not installed (NA) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: No - Using distributed or parallel set-up in script?: No Let me know what additional information you need in order to debug this issue. Thanks in advance!

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I_kwDODunzps49pxvO

Sacrebleu unexpected behaviour/requirement for data format

closed

CONTRIBUTOR

## Describe the bug When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153). In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error. ## Steps to reproduce the bug ```python import sacrebleu import datasets refs = [ ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'], ] hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'] expected_bleu = 48.530827 ds_bleu = datasets.load_metric("sacrebleu") bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score print(bleu_score_sb, expected_bleu) # works: 48.5308... bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"] print(bleu_score_ds, expected_bleu) # ValueError: Predictions and/or references don't match the expected format. ``` This seems to be related to how datasets forces the features format here: https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99 and then manipulates the references during the compute stage here https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122 I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229). ## Actual results Traceback (most recent call last): File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module> bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"] File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute self.add_batch(predictions=predictions, references=references) File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch raise ValueError( ValueError: Predictions and/or references don't match the expected format. Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')}, Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'], Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']] ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2 - PyArrow version: 4.0.1

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PR_kwDODunzps4tlEVE

Add TER (as implemented in sacrebleu)

closed

CONTRIBUTOR

Implements TER (Translation Edit Rate) as per its implementation in sacrebleu. Sacrebleu for BLEU scores is already implemented in `datasets` so I thought this would be a nice addition. I started from the sacrebleu implementation, as the two metrics have a lot in common. Verified with sacrebleu's [testing suite](https://github.com/mjpost/sacrebleu/blob/078c440168c6adc89ba75fe6d63f0d922d42bcfe/test/test_ter.py) that this indeed works as intended. ```python import datasets test_cases = [ (['aaaa bbbb cccc dddd'], ['aaaa bbbb cccc dddd'], 0), # perfect match (['dddd eeee ffff'], ['aaaa bbbb cccc'], 1), # no overlap ([''], ['a'], 1), # corner case, empty hypothesis (['d e f g h a b c'], ['a b c d e f g h'], 1 / 8), # a single shift fixes MT ( [ 'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit das Bild ungefähr dieselbe Größe aufweist wie die andere Größe .', 'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "', 'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie Werte für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " ein .', 'Sie können beispielsweise ein Dokument erstellen , das ein Auto über die Bühne enthält .', 'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "', ], [ 'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit die Darstellung des Bildes in einer anderen Größe beibehalten wird .', 'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "', 'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " niedrigere Werte ein .', 'Sie können beispielsweise ein Dokument erstellen , das ein Auto enthalt , das sich über die Bühne bewegt .', 'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "', ], 0.136 # realistic example from WMT dev data (2019) ), ] ter = datasets.load_metric(r"path\to\datasets\metrics\ter") predictions = ["hello there general kenobi", "foo bar foobar"] references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]] print(ter.compute(predictions=predictions, references=references)) for hyp, ref, score in test_cases: # Note the reference transformation which is different from scarebleu's input format results = ter.compute(predictions=hyp, references=[[r] for r in ref]) assert 100*score == results["score"], f"expected {100*score}, got {results['score']}" ```

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PR_kwDODunzps4tkqi-

Fix some typos in the documentation

closed

CONTRIBUTOR

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PR_kwDODunzps4tkL7t

Re-add faiss to windows testing suite

closed

CONTRIBUTOR

In recent versions, `faiss-cpu` seems to be available for Windows as well. See the [PyPi page](https://pypi.org/project/faiss-cpu/#files) to confirm. We can therefore included it for Windows in the setup file. At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works. ```python with tempfile.NamedTemporaryFile(delete=False) as tmp_file: # do stuff os.unlink(tmp_file.name) ``` closes #3150

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I_kwDODunzps49nwRq

Faiss _is_ available on Windows

closed

CONTRIBUTOR

In the setup file, I find the following: https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171 However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think. (This isn't really a bug but didn't know how else to tag.) If you agree I can do a quick PR and remove that line.

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PR_kwDODunzps4tjuUt

Add CMU Hinglish DoG Dataset for MT

open

CONTRIBUTOR

Address part of #2841 Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py) The dummy data part is not working properly, it shows ``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` when I run without ``--auto_generate``. Please let me know how I can fix that. Thanks

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I_kwDODunzps49nMjY

Streaming with num_workers != 0

open

NONE

## Describe the bug When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch. The code owner is likely @lhoestq ## Steps to reproduce the bug For your convenience, we've prepped a colab notebook that reproduces the bug https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing ```python !pip install datasets==1.14.0 should_freeze_forever = True # ^-- set this to True in order to freeze forever, set to False in order to work normally import torch from datasets import load_dataset data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True) data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True) data = data.shuffle(100, seed=1337) data = data.with_format("torch") loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0) # v-- the code should freeze forever at this line for i, row in enumerate(loader): print(row) if i > 10: break print("DONE!") ``` ## Expected results The code should not freeze forever with num_workers=2 ## Actual results The code freezes forever with num_workers=2 ## Environment info - `datasets` version: 1.14.0 (also found in previous versions) - Platform: google colab (also locally) - Python version: 3.7, (also 3.8) - PyArrow version: 3.0.0

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PR_kwDODunzps4tjRHG

Fix CLI test to ignore verfications when saving infos

closed

MEMBER

Fix #3146.

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I_kwDODunzps49m5M7

CLI test command throws NonMatchingSplitsSizesError when saving infos

closed

MEMBER

When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown: ``` $ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs Testing builder 'Alittihad' (1/10) Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4... Traceback (most recent call last): File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module> sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')()) File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main service.run() File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run builder.download_and_prepare( File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare self._download_and_prepare( File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}] ``` This is due because a previous run generated a wrong `dataset_info.json`. This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`.

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I_kwDODunzps49my3p

[when Image type will exist] provide a way to get the data as binary + filename

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web. Note: this issue would apply exactly the same for the `Audio` type. **Describe the solution you'd like** If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as: ```python filename: str data: bytes ``` **Describe alternatives you've considered** A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.

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I_kwDODunzps49mxWA

Infer the features if missing

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Some datasets, in particular community datasets, have no info file, thus no features. **Describe the solution you'd like** If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type. Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result. **Describe alternatives you've considered** The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal.

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I_kwDODunzps49mwV3

Provide a way to check if the features (in info) match with the data of a split

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I understand that currently the data loaded has not always the type described in the info features **Describe the solution you'd like** Provide a way to check if the rows have the type described by info features **Describe alternatives you've considered** Always check it, and raise an error when loading the data if their type doesn't match the features.

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I_kwDODunzps49mvdS

Provide a way to write a streamed dataset to the disk

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again. **Describe the solution you'd like** Provide a way to write the streamed rows of a dataset on the disk, and to load from it later. **Describe alternatives you've considered** Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.

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PR_kwDODunzps4tjGYz

Fix caching bugs

closed

CONTRIBUTOR

This PR fixes some caching bugs (most likely introduced in the latest refactor): * remove ")" added by accident in the dataset dir name * correctly pass the namespace kwargs in `CachedDatasetModuleFactory` * improve the warning message if `HF_DATASETS_OFFLINE is `True`

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I_kwDODunzps49mlOk

Add DER metric

open

MEMBER

Add DER metric for speaker diarization task. This is used by SUPERB beenchmark, for example.

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I_kwDODunzps49mlNv

Fix file/directory deletion on Windows

open

CONTRIBUTOR

Currently, on Windows, some attempts to delete a dataset file/directory will fail with the `PerimissionError`. Examples: - download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset ```python from datasets import load_dataset dset = load_dataset("sst", split="train") dset = load_dataset("sst", split="train", download_mode="force_redownload") ``` - try to clean up the cache files while keeping a reference to those files (via the mapped dataset): ```python from datasets import load_dataset dset = load_dataset("sst", split="train") dset_mapped = dset.map(lambda _: {"dummy_col": 1}) dset.cleanup_cache_files() ``` We should fix those.

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I_kwDODunzps49mCCd

More fine-grained taxonomy of error types

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did **Describe the solution you'd like** Give a specific exception type for every group of similar errors **Describe alternatives you've considered** Rely on the error message, using regex

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PR_kwDODunzps4tievk

Fix numpy deprecation warning for ragged tensors

closed

MEMBER

Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`. Fix #3084 cc @Rocketknight1

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PR_kwDODunzps4tieFi

Fix script of Arabic Billion Words dataset to return all data

closed

MEMBER

The script has a bug and only parses and generates a portion of the entire dataset. This PR fixes the loading script so that is properly parses the entire dataset. Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one: - For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027) | | Number of examples | Number of examples according to the source | |:---------------|-------------------:|-----:| | Alittihad | 349342 |349342 | | Almasryalyoum | 291723 |291723 | | Almustaqbal | 446873 |446873 | | Alqabas | 817274 |817274 | | Echoroukonline | 139732 |139732 | | Ryiadh | 858188 | 858188 | | Sabanews | 92149 |92149 | | SaudiYoum | 888068 |888068 | | Techreen | 314597 |314597 | | Youm7 | 1172136 |1025027 | Fix #3126.

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I_kwDODunzps49ltHb

Make inspect.get_dataset_config_names always return a non-empty list of configs

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to **Describe the solution you'd like** In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`). https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161

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I_kwDODunzps49liur

Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py

open

NONE

datasets version: 1.12.1 `metric = datasets.load_metric('rouge')` The error: > ConnectionError Traceback (most recent call last) > <ipython-input-3-dd10a0c5212f> in <module> > ----> 1 metric = datasets.load_metric('rouge') > > /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs) > 613 download_config=download_config, > 614 download_mode=download_mode, > --> 615 dataset=False, > 616 ) > 617 metric_cls = import_main_class(module_path, dataset=False) > > /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs) > 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) > 329 try: > --> 330 local_path = cached_path(file_path, download_config=download_config) > 331 except FileNotFoundError: > 332 if script_version is not None: > > /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) > 296 use_etag=download_config.use_etag, > 297 max_retries=download_config.max_retries, > --> 298 use_auth_token=download_config.use_auth_token, > 299 ) > 300 elif os.path.exists(url_or_filename): > > /usr/local/lib/python3.6/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://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py Is there any remedy to solve the connection issue ?

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PR_kwDODunzps4tftyZ

Support Audio feature in streaming mode

closed

MEMBER

Fix #3132.

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I_kwDODunzps49ishW

Support Audio feature in streaming mode

closed

MEMBER

Currently, Audio feature is only supported for non-streaming datasets. Due to the large size of many speech datasets, we should also support Audio feature in streaming mode.

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I_kwDODunzps49h8xp

Add ADE20k

open

NONE

## Adding a Dataset - **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k) - **Description:** A semantic segmentation dataset, consisting of 150 classes. - **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf - **Data:** http://sceneparsing.csail.mit.edu/ - **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers. 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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PR_kwDODunzps4tfBJU

Create SECURITY.md

closed

NONE

To let the repository confirm feedback@huggingface.co as its security contact.

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PR_kwDODunzps4tezlA

Support Audio feature for TAR archives in sequential access

open

MEMBER

Add Audio feature support for TAR archived files in sequential access. Fix #3128.

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I_kwDODunzps49hiaO

Support Audio feature for TAR archives in sequential access

open

MEMBER

Currently, Audio feature accesses each audio file by their file path. However, streamed TAR archive files do not allow random access to their archived files. Therefore, we should enhance the Audio feature to support TAR archived files in sequential access.

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I_kwDODunzps49hJsF

datasets-cli: convertion of a tfds dataset to a huggingface one.

open

NONE

### Discussed in https://github.com/huggingface/datasets/discussions/3079 <div type='discussions-op-text'> ^{Originally posted by **vitalyshalumov** October 14, 2021} I'm trying to convert a tfds dataset to a huggingface one. I've tried: 1. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/ 2. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/ and other permutations. The script appears to be running and finishing without an error but when looking in the huggingface/datasets/ folder nothing is created. </div>

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I_kwDODunzps49hH1_

"arabic_billion_words" dataset does not create the full dataset

closed

NONE

## Describe the bug When running: raw_dataset = load_dataset('arabic_billion_words','Alittihad') the correct dataset file is pulled from the url. But, the generated dataset includes just a small portion of the data included in the file. This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....) ## Steps to reproduce the bug ```python # Sample code to reproduce the bug raw_dataset = load_dataset('arabic_billion_words','Alittihad') #The screen message Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB) ## Expected results over 100K sentences ## Actual results only 11K sentences ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.0 - Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 4.0.1

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PR_kwDODunzps4teNPC

Add SLR83 to OpenSLR

closed

CONTRIBUTOR

The PR resolves #3119, adding SLR83 (UK and Ireland dialects) to the previously created OpenSLR dataset.

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PR_kwDODunzps4td-5w

More efficient nested features encoding

closed

CONTRIBUTOR

Nested encoding of features wastes a lot of time on operations which are effectively doing nothing when lists are used. For example, if in the input we have a list of integers, `encoded_nested_example` will iterate over it and apply `encoded_nested_example` on every element even though it just return the int as is. A similar issue is handled at an earlier stage when casting pytorch/tensorflow/pandas objects to python lists/numpy arrays: https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L149-L156 https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L212-L228 In this pull request I suggest to use the same approach in `encoded_nested_example`. In my setup there was a major speedup with this change: loading the data was at least x4 faster.

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I_kwDODunzps49f-o3

Segmentation fault when loading datasets from file

closed

MEMBER

## Describe the bug Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/ ## Steps to reproduce the bug Download an example file: ``` wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl ``` Then in Python: ``` import datasets tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000) ``` ## Expected results a `tiny_kelm` functional dataset ## Actual results ☠️ `Segmentation fault (core dumped)` ☠️ ## Environment info - `datasets` version: 1.14.0 - Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps49f9P1

OSError with a custom dataset loading script

open

NONE

## Describe the bug I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag. ## Steps to reproduce the bug ```python dataset = datasets.load_dataset('classla/janes_tag', split='validation') ``` ## Expected results Dataset correctly loaded. ## Actual results Traceback (most recent call last): File "C:/mypath/test.py", line 91, in <module> load_and_print('janes_tag') File "C:/mypath/test.py", line 32, in load_and_print dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation') File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: [Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.5 - PyArrow version: 3.0.0

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PR_kwDODunzps4tc_6q

Use huggingface_hub.HfApi to list datasets/metrics

closed

CONTRIBUTOR

Delete `datasets.inspect.HfApi` and use `huggingface_hub.HfApi` instead. WIP until https://github.com/huggingface/huggingface_hub/pull/429 is merged, then wait for the new release of `huggingface_hub`, update the `huggingface_hub` version in `setup.py` and merge this PR. cc: @lhoestq

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PR_kwDODunzps4tcril

Correctly update metadata to preserve features when concatenating datasets with axis=1

closed

CONTRIBUTOR

This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`). Fixes #3111

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I_kwDODunzps49eNEs

Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** *openslr** - **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.* - **Paper:** *https://www.openslr.org/resources/83/about.html* - **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/* - **Motivation:** *Increase english ASR data with UK and Irish dialects* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). The *openslr* dataset already exists, this will add additional subset, *SLR83*.

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PR_kwDODunzps4tb0LY

Fix CI error at each release commit

closed

MEMBER

Fix test_load_dataset_canonical at release commit. Fix #3117.

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I_kwDODunzps49eIMz

CI error at each release commit

closed

MEMBER

After 1.12.0, there is a recurrent CI error at each release commit: https://app.circleci.com/pipelines/github/huggingface/datasets/8289/workflows/665d954d-e409-4602-8202-e678594d2946/jobs/51110 ``` ____________________ LoadTest.test_load_dataset_canonical _____________________ [gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical> def test_load_dataset_canonical(self): scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION) with self.assertRaises(FileNotFoundError) as context: datasets.load_dataset("_dummy") self.assertIn( f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py", > str(context.exception), ) E AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py" tests\test_load.py:358: AssertionError ```

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PR_kwDODunzps4tbr6g

Update doc links to point to new docs

closed

CONTRIBUTOR

This PR: * updates the README links and the ADD_NEW_DATASET template to point to the new docs (the new docs don't have a section with the list of all the possible features, so I added that info to the `Features` docstring, which is then referenced in the ADD_NEW_DATASET template) * fixes some broken links in the `.rst` files (fixed with the `make linkcheck` tool)

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PR_kwDODunzps4tZ-Vr

Fill in dataset card for NCBI disease dataset

closed

CONTRIBUTOR

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I_kwDODunzps49byEK

load_from_disk in DatasetsDict/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem

open

NONE

## Describe the bug Passing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter. ## Steps to reproduce the bug The documentation for the `fs` parameter states: ``` fs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``): Instance of the remote filesystem used to download the files from. ``` `PyArrowHDFS` from [fsspec](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error. ```python from fsspec.implementations.hdfs import PyArrowHDFS ... transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/" fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket) dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True) ``` ## Expected results Previous to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing: ```python transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/" fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket) my_datasets.save_to_disk(transformed_corpus_path, fs=fs) ``` As I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS: ```sh $ hadoop fs -ls "/user/my_user/clickbait/transformed_ds/" Found 4 items -rw------- 3 my_user users 43 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/dataset_dict.json drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/test drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/train drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/validation ``` I would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above. ## Actual results However, when trying to recover the saved datasets, I get this error: ``` ... File "/home/fperez/dev/neuromancer/neuromancer/corpus.py", line 186, in load_transformed_corpus_from_disk dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True) File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/dataset_dict.py", line 748, in load_from_disk dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory) File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1048, in load_from_disk fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True) File "pyarrow/_hdfsio.pyx", line 438, in pyarrow._hdfsio.HadoopFileSystem.download TypeError: download() got an unexpected keyword argument 'recursive' ``` Examining the [signature of the download method in pyarrow 5.0.0](https://github.com/apache/arrow/blob/54d2bd89c99df72fa091b025452f85dd5d88e3cf/python/pyarrow/_hdfsio.pyx#L438) we can see that there's no download parameter: ```python def download(self, path, stream, buffer_size=None): with self.open(path, 'rb') as f: f.download(stream, buffer_size=buffer_size) ``` ## Environment info - `datasets` version: 1.13.3 - Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps49br0b

Loading Data from HDF files

open

NONE

**Is your feature request related to a problem? Please describe.** More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset. **Describe the solution you'd like** I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc). **Describe alternatives you've considered** Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible. **Additional context** HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.

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I_kwDODunzps49behb

OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB

open

NONE

## Describe the bug Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error : > OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB Note that I always run `batch_size=writer_batch_size` : ## Steps to reproduce the bug ```python datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla") ``` ## Introspecting CUDA memory during bug Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb `print(torch.cuda.memory_summary(device=device, abbreviated=False))` > |===========================================================================| | PyTorch CUDA memory summary, device ID 0 | |---------------------------------------------------------------------------| | CUDA OOMs: 0 | cudaMalloc retries: 0 | |===========================================================================| | Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed | |---------------------------------------------------------------------------| | Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB | | from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB | | from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB | |---------------------------------------------------------------------------| | Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB | | from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB | | from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB | |---------------------------------------------------------------------------| | GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B | | from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B | | from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B | |---------------------------------------------------------------------------| | Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB | | from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB | | from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB | |---------------------------------------------------------------------------| | Allocations | 198 | 224 | 478 | 280 | | from large pool | 74 | 75 | 75 | 1 | | from small pool | 124 | 150 | 403 | 279 | |---------------------------------------------------------------------------| | Active allocs | 198 | 224 | 478 | 280 | | from large pool | 74 | 75 | 75 | 1 | | from small pool | 124 | 150 | 403 | 279 | |---------------------------------------------------------------------------| | GPU reserved segments | 21 | 21 | 21 | 0 | | from large pool | 20 | 20 | 20 | 0 | | from small pool | 1 | 1 | 1 | 0 | |---------------------------------------------------------------------------| | Non-releasable allocs | 18 | 23 | 166 | 148 | | from large pool | 17 | 18 | 19 | 2 | | from small pool | 1 | 6 | 147 | 146 | |===========================================================================| ## Expected results Efficiently process the datasets and write it down to disk. ## Actual results -------------------------------------------------------------------------- OverflowError Traceback (most recent call last) ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2390 else: -> 2391 writer.write(example) 2392 else: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size) 367 --> 368 self.write_examples_on_file() 369 ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self) 316 if not isinstance(pa_array[0], pa.lib.FloatScalar): --> 317 raise OverflowError( 318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format( OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module> 3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon') 4 #tracker.start() ----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True, 6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False, 7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) # ~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc) 481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False): 482 if "xxx_" in column: --> 483 dataset = dataset.map(lambda example : 484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column], 485 token_type_ids=example[column.replace("input_ids","token_type_ids")], ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2034 2035 if num_proc is None or num_proc == 1: -> 2036 return self._map_single( 2037 function=function, 2038 with_indices=with_indices, ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs) 501 self: "Dataset" = kwargs.pop("self") 502 # apply actual function --> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 505 for dataset in datasets: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2425 if update_data: 2426 if writer is not None: -> 2427 writer.finalize() 2428 if tmp_file is not None: 2429 tmp_file.close() ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream) 440 # Re-intializing to empty list for next batch 441 self.hkey_record = [] --> 442 self.write_examples_on_file() 443 if self.pa_writer is None: 444 if self._schema is not None: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self) 315 # This check fails with FloatArrays with nans, which is not what we want, so account for that: 316 if not isinstance(pa_array[0], pa.lib.FloatScalar): --> 317 raise OverflowError( 318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format( 319 type(pa_array) OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0 ##Next steps Testing on Linux. @albertvillanova

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I_kwDODunzps49bbFH

concatenate_datasets removes ClassLabel typing.

closed

CONTRIBUTOR

## Describe the bug When concatenating two datasets, we lose typing of ClassLabel columns. I can work on this if this is a legitimate bug, ## Steps to reproduce the bug ```python import datasets from datasets import Dataset, ClassLabel, Value, concatenate_datasets DS_LEN = 100 my_dataset = Dataset.from_dict( { "sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)], "label": [i % 2 for i in range(DS_LEN)] } ) my_predictions = Dataset.from_dict( { "pred": [(i + 1) % 2 for i in range(DS_LEN)] } ) my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])})) print("Original") print(my_dataset) print(my_dataset.features) concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1) print("Concatenated") print(concat_ds) print(concat_ds.features) ``` ## Expected results The features of `concat_ds` should contain ClassLabel. ## Actual results On master, I get: ``` {'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)} ``` ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.11 - PyArrow version: 4.0.1

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PR_kwDODunzps4tZakS

Stream TAR-based dataset using iter_archive

closed

MEMBER

I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable. It means that around 80 datasets become streamable :)

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PR_kwDODunzps4tZXmC

Update BibTeX entry

closed

MEMBER

Update BibTeX entry.

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PR_kwDODunzps4tY8ID

Add Google BLEU (aka GLEU) metric

closed

CONTRIBUTOR

This PR adds the NLTK implementation of Google BLEU metric. This is also a part of an effort to resolve an unfortunate naming collision between GLEU for machine translation and GLEU for grammatical error correction. I used [this page](https://huggingface.co/docs/datasets/add_metric.html) for reference. Please, point me to the right direction if I missed anything.

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PR_kwDODunzps4tYyhF

Add paper BibTeX citation

closed

MEMBER

Add paper BibTeX citation to README file.

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PR_kwDODunzps4tYA6i

Fix URLs in blog_authorship_corpus dataset

closed

MEMBER

After contacting the authors of the paper "Effects of Age and Gender on Blogging", they confirmed: - the old URLs are no longer valid - there are alternative host URLs Fix #3091.

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I_kwDODunzps49Vs1b

download_mode=`force_redownload` does not work on removed datasets

open

CONTRIBUTOR

## Describe the bug If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead. ## Steps to reproduce the bug _requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981 ```python import datasets as ds dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload') ``` ## Expected results It should raise an exception, since the dataset does not exist anymore. ## Actual results It uses the cached result ``` Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub. ``` ## Environment info - `datasets` version: 1.13.4.dev0 - Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1

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I_kwDODunzps49VoVc

Missing Zenodo 1.13.3 release

closed

MEMBER

After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305 TODO: - [x] Contact Zenodo support - [x] Check it is fixed

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PR_kwDODunzps4tUzJQ

Fix project description in PyPI

closed

MEMBER

Fix project description appearing in PyPI, so that it contains the content of the README.md file (like transformers). Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/ Fix #3102.

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I_kwDODunzps49VlE2

Unsuitable project description in PyPI

closed

MEMBER

Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/

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PR_kwDODunzps4tUelE

Update SUPERB to use Audio features

closed

CONTRIBUTOR

This is the same dataset refresh as the other Audio ones: https://github.com/huggingface/datasets/pull/3081 cc @patrickvonplaten

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PR_kwDODunzps4tTwpn

Replace FSTimeoutError with parent TimeoutError

closed

MEMBER

PR #3050 introduced a dependency on `fsspec.FSTiemoutError`. Note that this error only exists from `fsspec` version `2021.06.0` (June 2021). To fix #3097, there are 2 alternatives: - Either pinning `fsspec` to versions newer or equal to `2021.06.0` - Or replacing `fsspec.FSTimeoutError` wth its parent `asyncio.TimeoutError`, which exists from Python 3.8.0 (Sep 2018). This PR implements the second approach. Fix #3097.

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I_kwDODunzps49SzGe

AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'

closed

NONE

## Describe the bug When using `pip install datasets` or use `conda install -c huggingface -c conda-forge datasets` cannot install datasets ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("sst", "default") ``` ## Actual results --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-5-fbe7981e6e21> in <module> 1 import torch 2 import transformers ----> 3 from datasets import load_dataset 4 5 dataset = load_dataset("sst", "default") ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/__init__.py in <module> 35 from .arrow_reader import ArrowReader, ReadInstruction 36 from .arrow_writer import ArrowWriter ---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder 38 from .combine import interleave_datasets 39 from .dataset_dict import DatasetDict, IterableDatasetDict ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/builder.py in <module> 42 ) 43 from .arrow_writer import ArrowWriter, BeamWriter ---> 44 from .data_files import DataFilesDict, _sanitize_patterns 45 from .dataset_dict import DatasetDict, IterableDatasetDict 46 from .fingerprint import Hasher ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/data_files.py in <module> 118 119 def _exec_patterns_in_dataset_repository( --> 120 dataset_info: huggingface_hub.hf_api.DatasetInfo, 121 patterns: List[str], 122 allowed_extensions: Optional[list] = None, AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: macOS-11.3.1-arm64-arm-64bit - Python version: 3.8.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4tSRSZ

Push to hub capabilities for `Dataset` and `DatasetDict`

open

MEMBER

This PR implements a `push_to_hub` method on `Dataset` and `DatasetDict`. This does not currently work in `IterableDatasetDict` nor `IterableDataset` as those are simple dicts and I would like your opinion on how you would like to implement this before going ahead and doing it. This implementation needs to be used with the following `huggingface_hub` branch in order to work correctly: https://github.com/huggingface/huggingface_hub/pull/415 ### Implementation The `push_to_hub` API is entirely based on HTTP requests rather than a git-based workflow: - This allows pushing changes without firstly cloning the repository, which reduces the time in half for the `push_to_hub` method. - Collaboration, as well as the system of branches/merges/rebases is IMO less straightforward than for models and spaces. In the situation where such collaboration is needed, I would *heavily* advocate for the `Repository` helper of the `huggingface_hub` to be used instead of the `push_to_hub` method which will always be, by design, limiting in that regard (even if based on a git-workflow instead of HTTP requests) In order to overcome the limit of 5GB files set by the HTTP requests, dataset sharding is used. ### Testing The test suite implemented here makes use of the moon-staging instead of the production setup. As several repositories are created and deleted, it is better to use the staging. It does not require setting an environment variable or any kind of special attention but introduces a new decorator `with_staging_testing` which patches global variables to use the staging endpoint instead of the production endpoint. ### Examples The tests cover a lot of examples and behavior.

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I_kwDODunzps49Qjub

`ModuleNotFoundError: No module named 'fsspec.exceptions'`

closed

MEMBER

## Describe the bug I keep runnig into a fsspec ModuleNotFound error ## Steps to reproduce the bug ```python >>> from datasets import get_dataset_infos 2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module> from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module> from .utils.streaming_download_manager import StreamingDownloadManager File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module> from fsspec.exceptions import FSTimeoutError ModuleNotFoundError: No module named 'fsspec.exceptions' ``` Yet, I do have `fsspec`: ```bash hf@victor-scale:~/dev/promptsource$ pip show fsspec Name: fsspec Version: 2021.5.0 Summary: File-system specification Home-page: http://github.com/intake/filesystem_spec Author: None Author-email: None License: BSD Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages Requires: Required-by: datasets ``` With the same version of fsspec and `datasets==1.9.0`, I don't see this problem.... ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> I can't even run `datasets-cli env` actually.., but here's my env: - `datasets` version: 1.13.3 - Platform: Ubuntu 18.04 - Python version: 3.7.10 - PyArrow version: 3.0.0

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PR_kwDODunzps4tQblQ

Fix Audio feature mp3 resampling

closed

MEMBER

Issue #3095 is related to mp3 resampling, not to `cast_column`. This PR fixes Audio feature mp3 resampling. Fix #3095.

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I_kwDODunzps49PbDa

`cast_column` makes audio decoding fail

closed

MEMBER

## Describe the bug After changing the sampling rate automatic decoding fails. ## Steps to reproduce the bug ```python from datasets import load_dataset import datasets ds = load_dataset("common_voice", "ab", split="train") ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000)) print(ds[0]["audio"]) # <- this fails currently ``` yields: ``` TypeError: forward() takes 2 positional arguments but 4 were given ``` ## Expected results no failure ## 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. --> Copy-and-paste the text below in your GitHub issue. - `datasets` version: 1.13.2 (master) - Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps49O8p5

Support loading a dataset from SQLite files

open

MEMBER

As requested by @julien-c, we could eventually support loading a dataset from SQLite files, like it is the case for JSON/CSV files.

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I_kwDODunzps49Osas

Error loading json dataset with multiple splits if keys in nested dicts have a different order

open

NONE

## Describe the bug Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below. If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine. ## Steps to reproduce the bug Create two json files: train.json ``` {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} ``` test.json ``` {"a": {"b": 1, "c": 2}} {"a": {"b": 3, "c": 4}} ``` ```python from datasets import load_dataset # Loading the files individually works (even though the keys in train.json don't have the same order) load_dataset('json', data_files={"test": "test.json"}) load_dataset('json', data_files={"train": "train.json"}) # Loading both splits fails load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) ``` ## Expected results Loading both splits should not give an error whether the nested dicts are have the same order or not. ## Actual results ``` >>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) Using custom data configuration default-f1bc76fd07398c4c Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8839.42it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 477.82it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 5.0.0

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PR_kwDODunzps4tPj6e

Fix JNLBA dataset

closed

CONTRIBUTOR

As mentioned in #3089, I've added more tags and also updated the link for dataset which was earlier using a Google Drive link. I'm having problem with generating dummy data as `datasets-cli dummy_data ./datasets/jnlpba --auto_generate --match_text_files "*.iob2"` is giving `datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! ` error. I'll try to add dummy data manually.

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I_kwDODunzps49Op1K

`blog_authorship_corpus` is broken

closed

NONE

## Describe the bug The dataset `blog_authorship_corpus` is broken. By bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty. I suspect it is because the data download url is broken (http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip). ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("blog_authorship_corpus", split="train", download_mode='force_redownload') ``` ## Expected results No error. ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) /tmp/ipykernel_5237/1729238701.py in <module> 2 ds = load_dataset( 3 "blog_authorship_corpus", split="train", ----> 4 download_mode='force_redownload' 5 ) /opt/conda/lib/python3.7/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) 1115 ignore_verifications=ignore_verifications, 1116 try_from_hf_gcs=try_from_hf_gcs, -> 1117 use_auth_token=use_auth_token, 1118 ) 1119 /opt/conda/lib/python3.7/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) 635 if not downloaded_from_gcs: 636 self._download_and_prepare( --> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 638 ) 639 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 707 if verify_infos: 708 verify_checksums( --> 709 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 710 ) 711 /opt/conda/lib/python3.7/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: ['http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip'] ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11 - Python version: 3.7.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4tPEtH

Update BibTeX entry

closed

MEMBER

Update BibTeX entry.

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I_kwDODunzps49Nl6w

JNLPBA Dataset

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results The dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L81-L83) are: O, B, and I. The correct entities from the original data file are: ['O', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-cell_line', 'I-cell_line', 'B-cell_type', 'I-cell_type', 'B-protein', 'I-protein'] ## Actual results The dataset loader script needs to include the following NER names: ['O', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-cell_line', 'I-cell_line', 'B-cell_type', 'I-cell_type', 'B-protein', 'I-protein'] And the [data](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:

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PR_kwDODunzps4tOhRx

Use template column_mapping to transmit_format instead of template features

closed