Datasets:

abhishek-shrm

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https://github.com/huggingface/datasets/pull/4022

PR_kwDODunzps41BNeA

Replace dbpedia_14 data url

closed

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I replaced the Google Drive URL of the dataset by the FastAI one, since we've had some issues with Google Drive.

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https://github.com/huggingface/datasets/pull/4021

PR_kwDODunzps41BLAf

Fix `map` remove_columns on empty dataset

closed

MEMBER

On an empty dataset, the `remove_columns` parameter of `map` currently doesn't actually remove the columns: ```python >>> ds = datasets.load_dataset("glue", "rte") >>> ds_filtered = ds.filter(lambda x: x["label"] != -1) >>> ds_mapped = ds_filtered.map(lambda x: x, remove_columns=["label"]) >>> print(repr(ds_mapped.column_names)) { 'train': ['sentence1', 'sentence2', 'idx'], 'validation': ['sentence1', 'sentence2', 'idx'], 'test': ['sentence1', 'sentence2', 'label', 'idx'] } ``` I fixed this error and updated the tests

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PR_kwDODunzps41Am4R

Replace amazon_polarity data URL

closed

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I replaced the Google Drive URL of the dataset by the FastAI one, since we've had some issues with Google Drive.

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https://github.com/huggingface/datasets/pull/4019

PR_kwDODunzps41AlFk

Make yelp_polarity streamable

closed

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It was using `dl_manager.download_and_extract` on a TAR archive, which is not supported in streaming mode. I replaced this by `dl_manager.iter_archive`

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PR_kwDODunzps41Aj7g

Replace yelp_review_full data url

closed

MEMBER

I replaced the Google Drive URL of the Yelp review dataset by the FastAI one, since we've had some issues with Google Drive. Close https://github.com/huggingface/datasets/issues/4005

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PR_kwDODunzps41Ad_L

Support streaming scan dataset

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PR_kwDODunzps41AWBk

Support streaming blimp dataset

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I_kwDODunzps5GXSqI

Can not correctly parse the classes with imagefolder

closed

NONE

## Describe the bug I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect. ## Steps to reproduce the bug I organized my dataset (ImageNet) in the following structure: ``` - imagenet/ - train/ - n01440764/ - ILSVRC2012_val_00000293.jpg - ...... - n01695060/ - ...... - val/ - n01440764/ - n01695060/ - ...... ``` At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as: ``` from datasets import load_dataset data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'} ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification") ``` but it resulted following error (I mask my personal path as <PERSONAL_PATH>): ``` FileNotFoundError: Unable to find 'https://huggingface.co/datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main ``` Next, I followed a recent issue #3960 to load data as: ``` from datasets import load_dataset data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']} ds = load_dataset("imagefolder", data_files=data_files, task="image-classification") ``` and the data can be loaded without error as: (I copy val folder to train folder for illustration) ``` >>> ds DatasetDict({ train: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) val: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) }) ``` However, the parsed classes is wrong (should be 1000 classes): ``` >>> ds["train"].features {'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)} ``` ## Expected results I expect that the "labels" in ds["train"].features should contain 1000 classes. ## Actual results The "labels" in ds["train"].features contains only 1 wrong class. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Ubuntu 18.04 - Python version: Python 3.7.12 - PyArrow version: 7.0.0

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https://github.com/huggingface/datasets/pull/4014

PR_kwDODunzps41AGBu

Support streaming id_clickbait dataset

closed

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I_kwDODunzps5GW-Om

Cannot preview "hazal/Turkish-Biomedical-corpus-trM"

closed

NONE

## Dataset viewer issue for '*hazal/Turkish-Biomedical-corpus-trM' **Link:** *https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM* *I cannot see the dataset preview.* ``` Server Error Status code: 400 Exception: HTTPError Message: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/hazal/Turkish-Biomedical-corpus-trM?full=true ``` Am I the one who added this dataset ? Yes

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PR_kwDODunzps40_qgo

Rename wer to cer

closed

CONTRIBUTOR

wer variable changed to cer in README file

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PR_kwDODunzps40-Ho0

Fix SQuAD v2 metric docs on `references` format

open

CONTRIBUTOR

`references` it's not a list of dictionaries but a dictionary that has a list in its values.

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PR_kwDODunzps409_QV

Fix None issue with Sequence of dict

closed

MEMBER

`Features.encode_example` currently fails if it contains a sequence if dict like `Sequence({"subcolumn": Value("int32")})` and if `None` is passed instead of the dict. ```python File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 1310, in encode_example return encode_nested_example(self, example) File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 973, in encode_nested_example return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)} File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 973, in <dictcomp> return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)} File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 998, in encode_nested_example for k, (sub_schema, sub_objs) in zip_dict(schema.feature, obj): File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/utils/py_utils.py", line 207, in zip_dict yield key, tuple(d[key] for d in dicts) File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/utils/py_utils.py", line 207, in <genexpr> yield key, tuple(d[key] for d in dicts) TypeError: 'NoneType' object is not subscriptable ``` I fixed this issue and updated the tests (this case was missing in the tests)

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I_kwDODunzps5GUClz

AMI load_dataset error: sndfile library not found

closed

NONE

## Describe the bug Getting error message when loading AMI dataset. ## Steps to reproduce the bug `python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])" ` ## Expected results A clear and concise description of the expected results. ## Actual results Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset use_auth_token=use_auth_token, File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: sndfile library not found ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11 - Python version: 3.7.3 - PyArrow version: 7.0.0

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PR_kwDODunzps409Ixp

Support streaming daily_dialog dataset

closed

MEMBER

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I_kwDODunzps5GS-0d

set_format does not work with multi dimension tensor

closed

NONE

## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0

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PR_kwDODunzps408YnW

Use audio feature in ASR task template

closed

MEMBER

The AutomaticSpeechRecognition task template is outdated: it still uses the file path column as input instead of the audio column. I changed that and updated all the datasets as well as the tests. The only community dataset that will need to be updated is `facebook/multilingual_librispeech`. It has almost zero usage unfortunately (probably because users load the duplicate `multilingual_librispeech` directly instead), but it means we can update it. (this makes me think that we should deprecate `multilingual_librispeech` it and redirect users to `facebook/multilingual_librispeech`). This PR is also useful for the AudioFolder in https://github.com/huggingface/datasets/pull/3963

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I_kwDODunzps5GS7Ef

Yelp not working

closed

MEMBER

## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.

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PR_kwDODunzps408Onj

ASSIN 2 dataset: replace broken Google Drive _URLS by links on github

closed

CONTRIBUTOR

Closes #4003 . Fixes checksum error. Replaces Google Drive urls by the files hosted here: [Multilingual Transformer Ensembles for Portuguese Natural Language Tasks](https://github.com/ruanchaves/assin)

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I_kwDODunzps5GSn1d

ASSIN2 dataset checksum bug

closed

CONTRIBUTOR

## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1

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PR_kwDODunzps408Cfp

Support streaming conll2012_ontonotesv5 dataset

closed

MEMBER

Use another URL whit a single ZIP file (instead of previous one with a ZIP file inside another ZIP file).

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I_kwDODunzps5GSaS6

How to use generate this multitask dataset for SQUAD? I am getting a value error.

closed

NONE

## Dataset viewer issue for 'squad_multitask*' **Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask *short description of the issue* I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine. I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format') Error: Status code: 400 Exception: TypeError Message: argument of type 'Value' is not iterable Kindly advice.

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I_kwDODunzps5GQ73I

load_dataset error: sndfile library not found

closed

NONE

## Describe the bug Can't load ami dataset ## Steps to reproduce the bug ``` python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])" ``` ## Expected results ## Actual results Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e... AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1. 100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s] 100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s] 100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s] 100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s] 100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s] 100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s] Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset use_auth_token=use_auth_token, File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: sndfile library not found ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11 - Python version: 3.7.3 - PyArrow version: 7.0.0

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PR_kwDODunzps406WN_

Docs maintenance

closed

MEMBER

This PR links some functions to the API reference. These functions previously only showed up in code format because the path to the actual API was incorrect.

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PR_kwDODunzps406KyA

Fix Audio.encode_example() when writing an array

closed

CONTRIBUTOR

Closes #3996

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PR_kwDODunzps4058xr

Sync Features dictionaries

open

CONTRIBUTOR

This PR adds a wrapper to the `Features` class to keep the secondary dict, `_column_requires_decoding`, aligned with the main dict (as discussed in https://github.com/huggingface/datasets/pull/3723#discussion_r806912731). A more elegant approach would be to subclass `UserDict` and override `__setitem__` and `__delitem__`, but this PR doesn't implement it for the following reasons: * it requires replacing all occurrences of `isinstance(obj, dict)` with `isinstance(obj, Mapping)`, which is five times slower than `isinstance(obj, dict)` on my machine, in `features.py` * is a breaking change, i.e., `isinstance(Features(...), dict)` would return `False` after it * IMO, it makes sense to be consistent in the user-facing API and subclass either `dict` or `UserDict`. The problem with the latter is that it can't be used for `DatasetDict` because `DatasetDict` exposes the `data` property, which is also used by `UserDict`, so this would result in a collision.

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I_kwDODunzps5GPTMh

Audio.encode_example() throws an error when writing example from array

closed

CONTRIBUTOR

## Describe the bug When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error: `TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>` ## Steps to reproduce the bug ### Sample code to reproduce the bug ```python # download sample file !wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3 arr, sr = librosa.load("common_voice_vi_21824030.mp3") Audio().encode_example({ "path": "common_voice_vi_21824030.mp3", "array": arr, "sampling_rate":sr }) ``` ## Expected results An encoded example (`{"bytes": b'....', "path": 'path'}`) ## Actual results ```python TypeError Traceback (most recent call last) Input In [3], in <module> 1 arr, sr = librosa.load("common_voice_vi_21824030.mp3") ----> 3 Audio().encode_example({ 4 "path": "common_voice_vi_21824030.mp3", 5 "array": arr, 6 "sampling_rate":sr 7 }) File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value) 73 elif isinstance(value, dict) and "array" in value: 74 buffer = BytesIO() ---> 75 sf.write(buffer, value["array"], value["sampling_rate"]) 76 return {"bytes": buffer.getvalue(), "path": value.get("path")} 77 elif value.get("bytes") is not None or value.get("path") is not None: File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd) 312 else: 313 channels = data.shape[1] --> 314 with SoundFile(file, 'w', samplerate, channels, 315 subtype, endian, format, closefd) as f: 316 f.write(data) File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd) 625 mode_int = _check_mode(mode) 626 self._mode = mode --> 627 self._info = _create_info_struct(file, mode, samplerate, channels, 628 format, subtype, endian) 629 self._file = self._open(file, mode_int, closefd) 630 if set(mode).issuperset('r+') and self.seekable(): 631 # Move write position to 0 (like in Python file objects) File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian) 1414 original_format = format 1415 if format is None: -> 1416 format = _get_format_from_filename(file, mode) 1417 assert isinstance(format, (_unicode, str)) 1418 else: File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode) 1455 pass 1456 if format.upper() not in _formats and 'r' not in mode: -> 1457 raise TypeError("No format specified and unable to get format from " 1458 "file extension: {0!r}".format(file)) 1459 return format TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets master - Platform: Ubuntu 20.04 - Python version: python 3.8.12 - PyArrow version: 6.0.1 ## Solution I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this: ```python sf.write(buffer, value["array"], value["sampling_rate"], format="wav") ``` BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this: ```python from datasets import load_dataset, Features, Audio ds = load_dataset("common_voice", "vi", split="test") ds = ds.remove_columns("audio") ds.select(range(3)) # 3 samples just for testing def load_mp3_with_librosa(example): arr, sr = librosa.load(example["path"]) example["audio"] = { "path": example["path"], "array": arr, "sampling_rate": sr } return example updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example), features=Features( {"audio": Audio(decode=False)} )) ``` @lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? 🤗

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PR_kwDODunzps404054

Close `PIL.Image` file handler in `Image.decode_example`

closed

CONTRIBUTOR

Closes the file handler of the PIL image object in `Image.decode_example` to avoid the `Too many open files` error. To pass [the image equality checks](https://app.circleci.com/pipelines/github/huggingface/datasets/10774/workflows/d56670e6-16bb-4c64-b601-a152c5acf5ed/jobs/65825) in CI, `Image.decode_example` calls `image.load()` regardless of how the image object is created (not only for the `PIL.Image.open(local_path)` case). This is needed because `load()` sets the `readonly` attribute of a `PIL.Image` object to 0 (it's 1 after `PIL.Image.open(file_like)`), and in the older PIL versions (only fixed on main), that attribute is considered in `PIL.Image.__eq__`. More info can be found here: https://github.com/python-pillow/Pillow/issues/5926. Fix #3985

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PR_kwDODunzps404wWu

Change audio column from string path to Audio feature in ASR task

closed

CONTRIBUTOR

Will fix #3990

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I_kwDODunzps5GOe2X

Streaming dataset + interleave + DataLoader hangs with multiple workers

open

NONE

## Describe the bug Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers. ## Steps to reproduce the bug ```python from datasets import interleave_datasets, load_dataset from torch.utils.data import DataLoader en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True) it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True) de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True) multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset]) multilingual_dataset = multilingual_dataset.with_format('torch') next(iter(multilingual_dataset)) # works fairly fast dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4) for batch in dataloader: print(len(batch)) # prints nothing after 30 min of waiting dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0) for batch in dataloader: print(len(batch)) # prints right away ``` ## Expected results It should be able to iterate the dataset with multiple workers. ## Actual results Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0` but it prints nothing with `num_workers=4` or any number above 0. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.1.dev0 - `pytorch` version: 1.10.0+cu113 - Python version: 3.7 - PyArrow version: 6.0.1

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I_kwDODunzps5GNggp

Image column is not decoded in map when using with with_transform

open

NONE

## Describe the bug Image column is not _decoded_ in **map** when using with `with_transform` ## Steps to reproduce the bug ```python from datasets import Image, Dataset def add_C(batch): batch["C"] = batch["A"] return batch ds = Dataset.from_dict({"A": ["image.png"]}).cast_column("A", Image()) ds = ds.with_transform(lambda x: x) # <= This line causes the problem ds = ds.map(add_C, batched=True) print(ds[0]) ``` ## Expected results ``` {'C': <PIL.PngImagePlugin.PngImageFile>, ...} ``` ## Actual results ``` {'C': {'bytes': None, 'path': 'image.png'}, ...} ``` If we remove the `with_transform` line, we get the expected result. ## Environment info - `datasets` version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0

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I_kwDODunzps5GLSHV

Add Lung Image Database Consortium image collection (LIDC-IDRI) dataset

open

NONE

## Adding a Dataset - **Name:** *Lung Image Database Consortium image collection (LIDC-IDRI)* - **Description:** *Consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.* - **Data:** *[link to the Github repository or current dataset location](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)* - **Motivation:** *Key dataset in the healthcare community* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). FYI @osanseviero @abidlabs

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I_kwDODunzps5GJzt3

Improve AutomaticSpeechRecognition task template

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** [AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created). **Describe the solution you'd like** Change audio columns from string path to Audio feature.

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PR_kwDODunzps400l1S

Remove old wikipedia leftovers

closed

MEMBER

After updating Wikipedia dataset, remove old wikipedia leftovers from doc.

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PR_kwDODunzps400RGb

More consistent references in docs

closed

CONTRIBUTOR

Aligns the internal references with style discussed in https://github.com/huggingface/datasets/pull/3980. cc @stevhliu

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PR_kwDODunzps40zAxF

Fix Faiss custom_index device

closed

MEMBER

Currently, if both `custom_index` and `device` are passed to `FaissIndex`, `device` is silently ignored. This PR fixes this by raising a ValueError if both arguments are passed. Alternatively, the `custom_index` could be transferred to the target `device`.

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I_kwDODunzps5GHuP9

Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)

open

NONE

## Describe the bug Dataset loads indefinitely after modifying cache path (~/.cache/huggingface) If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script) ** Update: Transformer modules faces the same issue as well during loading ## A clear and concise description of what the bug is. Issue: - Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory - No error code, had to terminate the process - There are some files created in the cache directory: ``` custom_cache_dir | -- modules | -- __init__.py | -- datasets_modules | -- __init__.py | -- datasets | -- __init__.py | -- script.py (Dataset loading script) | -- script.lock ``` There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk. ## Steps to reproduce the bug What I've tried: - Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703) - Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html) - Modifying cache_dir param during runtime ```python >>> from datasets import load_dataset >>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache') ``` - Disabling dataset cache ```python >>> from datasets import set_caching_enabled >>> set_caching_enabled(False) ``` ## Expected results Datasets should load / cache as usual with the only exception that cache directory is different ## Actual results Any actions taken above to change the cache directory results in loading indefinitely without terminating. ## Environment info - `transformers` version: 4.18.0.dev0 - Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - Huggingface_hub version: 0.4.0 - PyTorch version (GPU?): 1.8.1+cu102 (True) - Tensorflow version (GPU?): 2.4.1 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No

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I_kwDODunzps5GGBNZ

[image feature] Too many files open error when image feature is returned as a path

closed

MEMBER

## Describe the bug PR in context: #3967. If I load the dataset in this PR (TextVQA), and do a simple list comprehension on the dataset, I get `Too many open files error`. This is happening due to the way we are loading the image feature when a str path is returned from the `_generate_examples`. Specifically at https://github.com/huggingface/datasets/blob/508eb4ab5d52f590baa677b4f64b1cc069139f7b/src/datasets/features/image.py#L110, we are open the file handle to the image but never closing it. This in my understanding is causing the issue. ## Steps to reproduce the bug Pull the PR locally and run the following code ```python from datasets import load_dataset dataset = load_dataset("./datasets/textvqa")["train"] data = [item for item in dataset] # Error happens ``` ## Expected results List comprehension should work smoothly ## Actual results `Too many open files error` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.1.dev0 - Platform: macOS-12.2-arm64-arm-64bit - Python version: 3.10.0 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5GFZ8l

Local and automatic tests fail

open

NONE

## Describe the bug Running the tests from CircleCI on a PR or locally fails, even with no changes. Tests seem to fail on `test_metric_common.py` ## Steps to reproduce the bug ```shell git clone https://huggingface/datasets.git cd datasets ``` ```python python -m pip install -e . pytest ``` ## Expected results All tests passing ## Actual results ``` tests/test_metric_common.py:91: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../.pyenv/versions/3.8.5/lib/python3.8/doctest.py:1336: in __run exec(compile(example.source, filename, "single", <doctest datasets_modules.metrics.ter.c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155.ter.Ter[3]>:1: in <module> ??? ../datasets/src/datasets/metric.py:430: in compute output = self._compute(**inputs, **compute_kwargs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = Metric(name: "ter", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Val...ences=references) >>> print(results) {'score': 0.0, 'num_edits': 0, 'ref_length': 6.5} """, stored examples: 0) predictions = ['hello there general kenobi', 'foo bar foobar'] references = [['hello there general kenobi', 'hello there !'], ['foo bar foobar', 'foo bar foobar']] normalized = False, no_punct = False, asian_support = False, case_sensitive = False def _compute( self, predictions, references, normalized: bool = False, no_punct: bool = False, asian_support: bool = False, case_sensitive: bool = False, ): references_per_prediction = len(references[0]) if any(len(refs) != references_per_prediction for refs in references): raise ValueError("Sacrebleu requires the same number of references for each prediction") transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)] > sb_ter = TER(normalized, no_punct, asian_support, case_sensitive) E TypeError: __init__() takes 2 positional arguments but 5 were given /tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/metrics/ter/c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155/ter.py:130: TypeError ------------------------------ Captured stdout call ------------------------------- Trying: predictions = ["hello there general kenobi", "foo bar foobar"] Expecting nothing ok Trying: references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]] Expecting nothing ok Trying: ter = datasets.load_metric("ter") Expecting nothing ok Trying: results = ter.compute(predictions=predictions, references=references) Expecting nothing ================================ warnings summary ================================= ../.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15 /home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses from imp import load_source ../datasets/src/datasets/commands/test.py:35 /home/markussagen/datasets/src/datasets/commands/test.py:35: PytestCollectionWarning: cannot collect test class 'TestCommand' because it has a __init__ constructor (from: tests/commands/test_test.py) class TestCommand(BaseDatasetsCLICommand): tests/commands/test_test.py:33 /home/markussagen/mydataset/tests/commands/test_test.py:33: PytestCollectionWarning: cannot collect test class 'TestCommandArgs' because it has a __new__ constructor (from: tests/commands/test_test.py) class TestCommandArgs: tests/test_arrow_dataset.py: 760 warnings tests/test_formatting.py: 60 warnings tests/test_search.py: 31 warnings tests/features/test_array_xd.py: 117 warnings /home/markussagen/datasets/src/datasets/formatting/formatting.py:197: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) tests/test_arrow_dataset.py: 154 warnings tests/features/test_array_xd.py: 1 warning /home/markussagen/datasets/src/datasets/formatting/formatting.py:201: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) tests/test_arrow_dataset.py: 60 warnings /home/markussagen/datasets/src/datasets/arrow_dataset.py:3105: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations elif np.issubdtype(values.dtype, np.str): tests/test_arrow_dataset.py: 138 warnings tests/test_formatting.py: 21 warnings /home/markussagen/datasets/src/datasets/formatting/tf_formatter.py:69: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations data_struct.dtype == np.object tests/test_arrow_dataset.py: 240 warnings tests/test_formatting.py: 20 warnings /home/markussagen/datasets/src/datasets/formatting/torch_formatter.py:49: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects tests/test_arrow_dataset.py: 12 warnings tests/test_search.py: 2 warnings tests/features/test_array_xd.py: 6 warnings tests/features/test_image.py: 4 warnings /home/markussagen/datasets/src/datasets/features/features.py:1129: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations [0] + [len(arr) for arr in l_arr], dtype=np.object tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_banking77 /tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/banking77/aec0289529599d4572d76ab00c8944cb84f88410ad0c9e7da26189d31f62a55b/banking77.py:24: DeprecationWarning: invalid escape sequence \~ _CITATION = """\ tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_universal_dependencies /tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/universal_dependencies/065e728dfe9a8371434a6e87132c2386a6eacab1a076d3a12aa417b994e6ef7d/universal_dependencies.py:6: DeprecationWarning: invalid escape sequence \= _CITATION = """\ tests/test_filesystem.py: 105 warnings /home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/responses/__init__.py:398: DeprecationWarning: stream argument is deprecated. Use stream parameter in request directly warn( tests/test_formatting.py::FormatterTest::test_jax_formatter tests/test_formatting.py::FormatterTest::test_jax_formatter tests/test_formatting.py::FormatterTest::test_jax_formatter tests/test_formatting.py::FormatterTest::test_jax_formatter tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs /home/markussagen/datasets/src/datasets/formatting/jax_formatter.py:57: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if data_struct.dtype == np.object: # jax arrays cannot be instantied from an array of objects tests/test_formatting.py::FormatterTest::test_jax_formatter tests/test_formatting.py::FormatterTest::test_jax_formatter tests/test_formatting.py::FormatterTest::test_jax_formatter /home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3567: UserWarning: Explicitly requested dtype <class 'jax._src.numpy.lax_numpy.int64'> requested in array is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. lax._check_user_dtype_supported(dtype, "array") tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore /home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/apscheduler/util.py:95: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html if obj.zone == 'local': tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features _audio /home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/librosa/core/constantq.py:1059: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. To silence this warning, use `complex` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.complex128` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations dtype=np.complex, tests/features/test_array_xd.py::test_array_xd_with_none /home/markussagen/mydataset/tests/features/test_array_xd.py:338: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations assert isinstance(arr, np.ndarray) and arr.dtype == np.object and arr.shape == (3,) -- Docs: https://docs.pytest.org/en/stable/warnings.html ============================= short test summary info ============================= FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bleurt - I... FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_chrf - Att... FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_code_eval FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_comet - Im... FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_coval - Im... FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_ter - Type... ``` ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-5.16.11-76051611-generic-x86_64-with-glibc2.33 - Python version: 3.8.5 - PyArrow version: 5.0.0

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I_kwDODunzps5GFKo0

Infinitely attempting lock

open

NONE

I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`. Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS). ``` %sh python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \ --model_name_or_path t5-small \ --do_train \ --do_eval \ --dataset_name cnn_dailymail \ --dataset_config "3.0.0" \ --source_prefix "summarize: " \ --output_dir /dbfs/transformers/tmp/tst-summarization \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate \ --log_level debug \ --cache_dir /dbfs/transformers/cache ``` All goes well until acquiring a lock -- ``` 03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... ``` and so on. I imagine this has to do with DBFS -- is there a way to tackle this?

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PR_kwDODunzps40vrR_

Exclude Google Drive tests of the CI

closed

MEMBER

These tests make the CI spam the Google Drive API, the CI now gets banned by Google Drive very often. I think we can just skip these tests from the CI for now. In the future we could have a CI job that runs only once a day or once a week for such cases cc @albertvillanova @mariosasko @severo Close #3415 

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PR_kwDODunzps40vfra

Add TER metric card

closed

CONTRIBUTOR

Add TER metric card This card is still missing content for the following sections: - **Limitations & Biases** - **Values from Papers** If anyone has any ideas for either of the above, feel free to either add them or point me to them and I'll add them!

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PR_kwDODunzps40vdcH

Add tip on how to speed up loading with ImageFolder

closed

CONTRIBUTOR

This PR does two things: * adds a tip on how to speed up loading of a large number of files with ImageFolder (motivated by [this issue](https://github.com/huggingface/datasets/issues/3960)) * replaces the current references to the `Dataset` methods in the Image Processing doc with their fully qualified counterparts (to align it with the Audio Processing doc) cc @stevhliu

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PR_kwDODunzps40u8NY

Fix google drive streaming for small files

closed

MEMBER

Google drive did another change recently, following #3787 #3843 . In particular Google Drive now returns 403 for GET requests with `confirm=t` when a files doesn't have a virus warning message. I fixed this by passing `confirm=t` if and only if when there is one (i.e. when status code is 200 for HEAD)

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I_kwDODunzps5GDIhY

I can't view HFcallback dataset for ASR Space

open

NONE

## Dataset viewer issue for '*Urdu-ASR-flags*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)* *I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.* Am I the one who added this dataset ? Yes

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I_kwDODunzps5GCdbH

Adapt `docs/README.md` for datasets

open

CONTRIBUTOR

## Describe the bug Currently `docs/README.md` is a direct copy from `transformers`, we should probably adapt this file for `datasets`.

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PR_kwDODunzps40uOY6

Fix main classes reference in docs

open

CONTRIBUTOR

Currently the section index (on the page's right side) of the [main classes reference](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes) incorrectly displays `Tensor returned:`, this PR fixes this issue by wrapping code examples in this page with markdown code block. There are other examples in datasets library having this issue.

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PR_kwDODunzps40tKdS

Update many missing tags to dataset README's

closed

NONE

I've started to go through the datasets available and noticed that there are 127 datasets that does not have all the tags so I started filling them in; starting with some of the most common and QA datasets Not 100% certain that the task_id is correct for SuperGLUE If anyone is browsing the issues and would like to help make Hugging face datasets even more feature complete and awesome, feel free to use this tool I wrote to find the missing tags in the [datacards](https://github.com/Hugging-Face-Supporter/datacards)

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PR_kwDODunzps40ssrA

Add XFUN dataset

open

CONTRIBUTOR

This PR adds XFUN dataset. Home page and repository: https://github.com/doc-analysis/XFUND Source code: https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/data/datasets/xfun.py

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I_kwDODunzps5GAMSH

ConnectionError and SSLError

closed

NONE

code ``` from datasets import load_dataset dataset = load_dataset('oscar', 'unshuffled_deduplicated_it') ``` bug report ``` --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module> ----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it') D:\DataScience\PythonSet\IDES\anaconda\lib\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, revision, use_auth_token, task, streaming, **config_kwargs) 1658 1659 # Create a dataset builder -> 1660 builder_instance = load_dataset_builder( 1661 path=path, 1662 name=name, D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1484 download_config = download_config.copy() if download_config else DownloadConfig() 1485 download_config.use_auth_token = use_auth_token -> 1486 dataset_module = dataset_module_factory( 1487 path, 1488 revision=revision, D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1237 ) from None -> 1238 raise e1 from None 1239 else: 1240 raise FileNotFoundError( D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now 1174 # TODO(QL): use a Hub dataset module factory instead of GitHub -> 1175 return GithubDatasetModuleFactory( 1176 path, 1177 revision=revision, D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self) 531 revision = self.revision 532 try: --> 533 local_path = self.download_loading_script(revision) 534 except FileNotFoundError: 535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None: D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision) 511 if download_config.download_desc is None: 512 download_config.download_desc = "Downloading builder script" --> 513 return cached_path(file_path, download_config=download_config) 514 515 def download_dataset_infos_file(self, revision: Optional[str]) -> str: D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 232 if is_remote_url(url_or_filename): 233 # URL, so get it from the cache (downloading if necessary) --> 234 output_path = get_from_cache( 235 url_or_filename, 236 cache_dir=cache_dir, D:\DataScience\PythonSet\IDES\anaconda\lib\site-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) 580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 581 if head_error is not None: --> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") 583 elif response is not None: 584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))"))) ``` It may be caused by Caused by SSLError(in China?) because it works well on google colab. So how can I download this dataset manually?

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PR_kwDODunzps40sdVu

Adding Roman Urdu Hate Speech dataset

closed

CONTRIBUTOR

This Pull request will add the Roman Urdu Hate speech Dataset.

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PR_kwDODunzps40sS4W

Applied index-filters on scores in search.py.

open

NONE

Updated search.py to resolve the issue mentioned in https://github.com/huggingface/datasets/issues/3961. Applied index-filters on scores in get_nearest_examples and get_nearest_examples_batch methods of search.py.

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PR_kwDODunzps40sSfx

Apply index-filters on scores in get_nearest_examples and get_nearest…

closed

NONE

Updated search.py to resolve the issue mentioned in https://github.com/huggingface/datasets/issues/3961. Applied index-filters on scores in get_nearest_examples and get_nearest_examples_batch methods of search.py.

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I_kwDODunzps5F_f8g

Cannot preview cnn_dailymail dataset

open

NONE

## Dataset viewer issue for '*cnn_dailymail*' **Link:** https://huggingface.co/datasets/cnn_dailymail *short description of the issue* Am I the one who added this dataset ? Yes-No

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I_kwDODunzps5F_Mcq

Cannot preview 'indonesian-nlp/eli5_id' dataset

closed

CONTRIBUTOR

## Dataset viewer issue for '*indonesian-nlp/eli5_id*' **Link:** https://huggingface.co/datasets/indonesian-nlp/eli5_id I can not see the dataset preview. ``` Server Error Status code: 400 Exception: Status400Error Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist. ``` Am I the one who added this dataset ? Yes

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PR_kwDODunzps40rpny

[feat] Add TextVQA dataset

open

MEMBER

This would be the first classification-based vision-and-language dataset in the datasets library. Currently, the dataset downloads everything you need beforehand. See the [paper](https://arxiv.org/abs/1904.08920) for more details. Test Plan: - Ran the full and the dummy data test locally

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PR_kwDODunzps40rBNE

Create metric card for BERTScore

closed

CONTRIBUTOR

Proposing a metric card for BERTScore

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I_kwDODunzps5F9V_D

TypeError: Couldn't cast array of type for JSONLines dataset

open

MEMBER

## Describe the bug One of the [course participants](https://discuss.huggingface.co/t/chapter-5-questions/11744/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below). This reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again. ## Steps to reproduce the bug ```python from datasets import load_dataset from huggingface_hub import hf_hub_url import pandas as pd # returns 'https://huggingface.co/datasets/Evan/spaCy-github-issues/resolve/main/spacy-issues.jsonl' data_files = hf_hub_url(repo_id="Evan/spaCy-github-issues", filename="spacy-issues.jsonl", repo_type="dataset") # throws TypeError: Couldn't cast array of type dset = load_dataset("json", data_files=data_files, split="test") # no problem with pandas - note this take a while as the file is >2GB df = pd.read_json(data_files, orient="records", lines=True) df.head() ``` ## Expected results I can load any line-separated JSON file, similar to pandas. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 683, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 1136, in _prepare_split writer.write_table(table) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1121, in table_cast return cast_table_to_features(table, Features.from_arrow_schema(schema)) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1102, in cast_table_to_features arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1102, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 944, in wrapper return func(array, *args, **kwargs) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 918, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 918, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1086, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 944, in wrapper return func(array, *args, **kwargs) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 920, in wrapper return func(array, *args, **kwargs) File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1019, in array_cast raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}") TypeError: Couldn't cast array of type struct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]> to null ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.9.7 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5F8y5B

Add default Audio Loader

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Writing a custom loading dataset script might be a bit challenging for users. **Describe the solution you'd like** Add default Audio loader (analogous to ImageFolder) for small datasets with standard directory structure. **Describe alternatives you've considered** Create a custom loading script? that's what users doing now.

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PR_kwDODunzps40puyZ

Add Audio Folder

open

CONTRIBUTOR

Would resolve #3964 AudioFolder loads a .txt file with transcriptions and creates a dataset with all audiofiles in provided directory that has a transcription (independently of the directory structure) as a single split (train). Can be loaded via: ```python # for local dirs dataset = load_dataset("audiofolder", data_dir="/path/to/folder", transcripts_filename="transcripts.txt") ``` ```python # for local and remote zip archives dataset = load_dataset("audiofolder", data_files="path/to/archive/archive.zip", transcripts_filename="transcripts.txt") ``` default transcriptions filename is `transcripts.txt`. it should have the following structure: ``` audio_id_1 transcription text 1 audio_id_1 transcription text 1 ``` separator is `\t`! --- sorry for first old commits from other branch, don't know how that happened...

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PR_kwDODunzps40psq2

Fix flatten of Sequence feature type

closed

MEMBER

The `Sequence` features type is not correctly flattened if it contains a dictionary. This PR fixes this, and I added a test case for this. Close https://github.com/huggingface/datasets/issues/3795

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I_kwDODunzps5F7fau

Scores from Index at extra positions are not filtered out

open

NONE

If a FAISS index has fewer records than the requested number of top results (k), then it returns -1 in indices for the additional positions. The get_nearest_examples method only filters out the extra results from the dataset samples. It would be better to filter out extra scores too. Reference: https://github.com/huggingface/datasets/blob/2.0.0/src/datasets/search.py#L693

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I_kwDODunzps5F7NTU

Load local dataset error

open

NONE

When i used the datasets==1.11.0， it's all right. Util update the latest version, it get the error like this: ``` >>> from datasets import load_dataset >>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train'], 'validation': ['/ssd/datasets/imagenet/pytorch/val']} >>> ds = load_dataset('nateraw/image-folder', data_files=data_files, cache_dir='./', task='image-classification') [] https://huggingface.co/datasets/nateraw/image-folder/resolve/main/ /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1671, in load_dataset **config_kwargs, File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1521, in load_dataset_builder **config_kwargs, File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 1031, in __init__ super().__init__(*args, **kwargs) File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 255, in __init__ sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 584, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 546, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 196, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 146, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/ssd/datasets/imagenet/pytorch/train' at /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main ``` I need some help to solve the problem, thanks!

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I_kwDODunzps5F6J33

Medium-sized dataset conversion from pandas causes a crash

open

NONE

Hi, I am suffering from the following issue: ## Describe the bug Conversion to arrow dataset from pandas dataframe of a certain size deterministically causes the following crash: ``` File "/home/datasets_crash.py", line 7, in <module> arrow=datasets.Dataset.from_pandas(d) File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 783, in from_pandas table = InMemoryTable.from_pandas( File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/table.py", line 379, in from_pandas return cls(pa.Table.from_pandas(*args, **kwargs)) File "pyarrow/table.pxi", line 1487, in pyarrow.lib.Table.from_pandas File "pyarrow/table.pxi", line 1532, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 1181, in pyarrow.lib.Table.validate File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458) ``` ## Steps to reproduce the bug I have a dataset made from replicated single example mocking a dict representation of a publication. I copy over this example 140k times and create a pandas frame. I use 'Dataset.from_pandas' and boom ```python # Sample code to reproduce the bug import copy import datasets import pandas # serialized dict is quite long to be realistic representation of a publication content paper_as_dict=eval("{'article_id': '2020-11-05T14:25:05.321Z02bc3286-91b7-486a-9c74-4f457fbc586a', 'sections': [{'section_id': 'body.0', 'paragraphs': [{'sentences': ['11010111001000000011010011110011101110111011000100001010011100101001111010110111101011101111101010101110001111011110111010111', '1101100110110010010101010100110011000111001100100000011100010111010000011100001101111000000011010111001111001010101111110011010010111011000110100110010', '101011011000010100000010011001011011000000110011011110000101001110110000010001100110111100011100110101010010110000101', '1101101110101010101000000010101011111001111000101000110001110100111000100000011001110100110000110100111011001010110011101001001110']}]}, {'section_id': 'body.1', 'paragraphs': [{'sentences': ['11111100100100111000101001011110100110011001011011001001100110100111011010000110011000010001010100101110001001101011110111110101111100001001001000011110110010110011100110110111110011100011111000101010111010101011001110000100000001001010010010011101111100011010', '10101000110000110111110011101111000101010010001001010000001111001100000010001000001110111110010011101000000111011', '111010011111101111110011111110110001000111100101001000100110101111110000111000111111110000101001101000110011010111011101001010110110001000100000001110001111100110110001110001001100011010100110100010100111000110110100010010100101011110000110000101010010001110101100000']}, {'sentences': ['111110011110110110001111001101011110010110100011101010110101011001101110110111100000111101010110011110111101001111000101110001001010010101100111111001001000011101000100110000101', '011101101101111101001100101010000010111101100101110100101000001100010100110011010010100001101001110111100011010011011111000111111101110001010111010011010110001000010101100110000100010110101110110011001010011001100111101100001001', '1110001011011010101001100001110001110001000111111111101110100001011101101001110100000110000011010001101010101110101110101101001010100100010000000010110010010010', '11101111000111111100111110010000111101110010010101001111011001111110011000011100110001010010000100101010', '111000110110110010101100010010100001100100110010101000001000011101000100101011011010000011001011011111001101100001110010100001111110111001001010101100100110001011011100000101010010000000001100010000101100110110111101110010100010011101110110111010011011000011001010111011100000000010101001011000100000011010100011101001011001010010011110100100']}, {'sentences': ['001101111100001101001001001110000110010101011101001001111111011000111001111011101011110111000000100001110110101110001010001111110100010', '0000110010110101001100011011000011001101001110001000000110010101000011101011110110000000100111000001010000101011111011110001001100001110101010101110101011111000000011001111011110001010010111010000100100000001111001011100101111010101111001001101100101001101111000111011010110010001010010010111010000001101101111100101000111101011001000101', '00000101100101100111101010000101011100101100001100011001100100001100001010001010010011001001111001000010100010000110100111110000001000101000111100010111110011000100000111100010000100010111100010101', '111100110010100110000010010101010101110011110100000101110000000111010101111001011110010101001110000001001000010110010010011110111110010110100101110011001101110111001111100011100100011110010010100101011111111']}, {'sentences': ['1100001110101111000001011001100110001011100011110110010011001000101000011110010101010011011000111010000101010011010000000111011001000010100101000011111101000000000101111000', '1110101000100110001111000011000101110111001100101010011001100011010011111111111010101011010101010011000101001100100000110010100110110110110001101100', '00010001100100101100100111111110111111101000100110101111101111110101110001010001011100000000000011010101101001111010001110101101110011001011111101110100010000111101', '011100011101011001000110010110100100000010100010010110011000000010101110011111111101010010010001100110101010010001100010110011110001011011101010111111100100110110010111101001100101010111001', '10111000011010101111110110011010101011111001000001010010111111010010111111100100010100110100101101110100110011001000110100000111000100110000001000111010', '0010011111111011100111010001111001011101001010000010110000010111000101001101000011101110100100000000100100010010101010100011100101001000100110110000010111111110000011011101111000111010']}]}, {'section_id': 'body.2.0', 'paragraphs': [{'sentences': ['110010010011001110100100011001111100010011110111101011011011001010010010010011101011', '000110101110011011101011000000100011111000001100011011110101101011000110011010001010001101101100000111100101001011111001001101111', '1000011100100000100100100010010000111011000100110010000011110111100110110001101001010100011111010100101000111', '11110111111000110010000000000100010010110001100010001010000111011000101100011010010101110110011010110101001101110011101011101100000001000100101011010110110100101011101010010101101000011110000010101011001011000001000000001010110000100010000100011110101001111100001000100000111000001010011111111110101010100011011000010000111000110', '1001000111011000111110001111111001100001000000101000111011101101100101010110001101000000001111010111100011111000000100001001110', '100110010111010101111010100000010001110101111001010010001100001110100100100101110011010101001000100101000100100011001110001100111000010010011011000010011010010000110001000000100011110010110110011010001100111010111110011']}, {'sentences': ['10010101011100010111011111001001001010100011001001111101101001000000001111101110000111101011000001001011101110101001100010010001101111001110000100010010001001101111011111110010011011110011', '110001110010110000101111000000110010010010100000010100001111101101000101100000000110000000011111011001111000010110110001011010011011101100100110011000100110101010111010111111000111001111010110010001001110100001011011000110000000111101110000001111011011101110100000100010000110001000000110100000', '101010000000010000110110111000110000100111000001110100101101101010001010010010101010100111010110001001000101011110010011001001001110111001101101100100011110011011110101100010110111001010000001000110100000001010011111111110111010011110001001110100011011000101011000110110011011010110100100011111111011100111110110000110011011110110110011101010101111001101010110101000000001100101111010000101110', '1010100110111111111000110110111110010100000100001110101110111001011000010001110110001111111110000101001001110010001110000111010101111010111111011100100011100111111101101111000010001100101000010001100110110100110111111100100011001011000001111110010100110111000010011110111011001101100000101011111110101000011000010', '00000001110000101001110101110011101001110011000111111101111101111000010011100000101000001011001110', '101000111010010000011010011010011010010010100010110100011100100111011101010100101110100111010001000000', '01101000110001101011001101100010100011011010000000001010101000010101000110100010000000110001110001010010000000101101000011000100000110011101100001010100011111101010010110001101110101010111101100001110000011001101', '0010010111000011110010011110001010100000111100001011010100100010101010010011101101100110001001111001000110000111011110010000110101010110111111010110100000011010001001010001000110001101101000101110001011110000101101110000110010110010111001100010011011100011', '00110111110000000100110111101011000100100110001000001001101011001000010100100001100111100110000110110101111010000010101000000101000011001011101001', '0100100001000111001110110110000001000100111001101101110100100111010111110001110010110111100110011111001001000011101110100101111011000110100000111010011101']}, {'sentences': ['100001001011101111111100110111011110001101111101100001000110110000100101011000000100000', '10101001001111110101001010100110011110101101001']}]}, {'section_id': 'body.2.0.0', 'paragraphs': [{'sentences': ['1110101100001100011000101000010000100010101101010110101011100101110110110111010101001100100000000111011001000100011110101011111010100101001010000010001001101010100011110010101110011001100010000100110011000011101010001000111001000001100', '101000000011001001110101000100101010000111000111100010010001111111100110001100000100011010011010010101101111010101010000110011101001111001111011111001110001010000110101101011101111010000001100', '01100001011110010100000101001101111101010011100010011001011110110010010011100101000', '0011100111000101111000010001111100000111000101110001111010001100001000111010000101100001110101100111111', '00001100000011110001011010010110000000111110110001111000110000011011001110000000100011001010110000010000010001101010101100000010011011000101011111100010010', '1011101011101111000001100100111000011000010010011110011000110111010010111100111101100110011010000110000111000110111110101111000001000010011101111000110000100011110101101101001101000110010000001000010011011010101100', '1000010011100011100000010011011111111110101101111011101010010111000000101011000000110101111000010011', '01100000110011001110101111101101011001011101000010001100101010100011010101010100111011011110100010100111', '011011010100011011110010101000110001111110110']}]}, {'section_id': 'body.2.0.1', 'paragraphs': [{'sentences': ['00111011011101000100100111000001101001011000111100100010101001010011001011000010011111001100000100010001100101110011001000110001101011010111011111011000010011010010111010011111101000110111011100010011100111111110110111011', '011011010101101101010000001011010110011111011110100111010101010110001101000010011111000011100', '110001000110010000000111101110111110101110111000101000010001110101000101001000111000010001011101010000110001010001101001001110111110111010111010011101000101101010000', '001000111110100110000001111100000111001110111001110111001000111010001001100111001101000001001001010111000111011100001111011001111110001011000111110011111101011101000100101001111011100001000110101010101111111110011111111011000101110001000000000100111011111011001100111', '11010101100010010100010010010101001011001011000001100010101111111101001101110011001010010100000111010101', '01110000110011111000110010011010000011100000010010001111100010010100100001011011111110001100', '011101111100011101100111110101111001101010010001001110101100001101000000111000']}]}, {'section_id': 'body.2.0.2', 'paragraphs': [{'sentences': ['0111011000110100110000001011001110111000011110100111011000000001000010001111111001101111011100101110101101000111000101000010000111011010110000011101111110111110100111000111000011', '00100110111000110101100111000110100010011010010101001010011000000101000110100110011010011111000100000011000000010001010000100111101011111111101010001111010000001011100001110100000101001101101010011011101000', '000001110001010010100101010100010101001100011001001101101101110111011111101010010111010110110111011110101100001000011110111011001', '0001110010111110100110110011000001111100100100110101011010010101010100101000010101000100101000011011', '1000010010010101001100101110010111010100000110101110000000111001111111001011111010000011110001011001001001000101', '0001111100111010010100010111010110011011000000001111010010110001000011010001100111101110001110000011010101111100001000011010110100000100100001111011110110000000101000010001111001010010110101110111101101110111000100', '1000101100001000100001101110111110000100000001000010101111010011010010010111011010100011001000100100001010001100110']}]}, {'section_id': 'body.2.0.3', 'paragraphs': [{'sentences': ['1010100111100011110110101011100001011010011010100100010011000110111000001010010110111001001101111000010100100110101001010001010001000110010000001', '100010101010100111000011111101010100101110011000100011100100100111000010000011001010010111011010000101010011011110111001010110', '0110000110110110110011011000011010010000001010011000010001011110110010000100011111010100110111111010010111000101111', '10100100000011100010110110011111011011101101111000001001010100001001011010000011001010101100000', '1011111111100001001100000010000100110010101000010100111111110010110011101110000101101011101', '10001111110000011100100000101100000000010000100000011100110000011110111010011101010111101001111000100000000110000011010010001100110111100001001011101011001111110010100111001001010001010011010010010111001101110101110000101011', '101101111111101101010010000110111110000110000111001001010011111101011001011010101100010100110101101011100111100100110010001011110001110010000011101100100100001001110010000010011111100110101']}]}, {'section_id': 'body.2.1', 'paragraphs': [{'sentences': ['1010010011010011001111111001000110010001101111101011001011011000101001010101010001000110100011110101110001110110111010010010100100111000101100100101111110100000011111001101010111101010100101011011110111111110', '000010101101111100000110010110011001111100001101011101000100010001001001000000101101000001110000011010111100000010010000010101110101100010011000101110110111111001000101000111000110100001001100001010101010100011', '0000000011101110111100100010111100101010110001111101110110010000100100010000101001101111001111001001100110010011010000101001110010000000100101011101001010100100011101101001011000010111110100101010110110011001110000110010010111110110101100001011101001100111010001000010111010001010000100010010011110111100110011100011111101101000011100111110101010100110001100100000100011011010111000111110010110100010111101001001101000001100100010000111110000011101111100111101000000000']}, {'sentences': ['01011000010110011000000101101000110101011010100111011001001001100001101101111101111001101111100101111001101011011001011110110110110100001100111111010100101110111111101000101100101010110011111011100101101010100110111001111100100011001110011101000110100000001100001100110001110101001000011010000110101011010000001111100100000100101110011000001001010011011101100011000001100000011', '1001100000101000000011110100110001100001101001100011010000111111010110101111001000100111000011010100100000110110001', 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'0101101001010101001101010100011000111011001000100001110100110011100000001001010110001101010110011100111111100101101111101111011001111111110010111010011011011111011011110000101011010', '11000001110111000001100100001110000111001010000101011011101010111001011100010010010111111111000011111110010111100011100110001001100011111010100111110111001110010', '0100010110100001010101110111100011100100010111111011101001100101111110101011010010101111001000101001111000001110001100011001110010100110101100110100100000001010101101011110011001000101100111001001001110100', '100000100010011111001101010000100110011110001100000010010110110100000111111011010100101111010111001110101000100001111101001110000011010110000010100', '00100110000011100101000110110001000011101000011010101000010001111011100001111111001011100111101000001000000110110001000101111010010010001100111', '0110110100011001110011001111100010101001011111011001011001101101010010101101110101010100001000100100000111101110001001110111000110011101101010100000101', '0011111010010011011101010110100110000011000011100100101011011001110110001110001111000011010111011000110100111111011101110111000010010000011011010011011100000011101100110110100100000010110101110100110101001100111011101001010111011011110100110101110010011011010001010111110011001000010100010101010010110010010110000100110001000011010011000100101011010100100111010']}]}]}") d=pandas.DataFrame.from_records(copy.deepcopy(paper_as_dict) for _ in range(140_100)) arrow=datasets.Dataset.from_pandas(d) ``` ## Expected results The dataset should be converted without error. ## Actual results Error `pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458)` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets==1.18.4 pandas==1.3.5 - Platform: macOS 11.6 or CentOS Linux 7 (Core) - Python version: Python 3.9.7 - PyArrow version: pyarrow==3.0.0

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https://github.com/huggingface/datasets/pull/3958

PR_kwDODunzps40nQU2

Update Wikipedia metadata

closed

MEMBER

This PR updates: - dataset card - metadata JSON

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https://github.com/huggingface/datasets/pull/3957

PR_kwDODunzps40magW

Fix xtreme s metrics

closed

MEMBER

We in fact do need BABEL in xtreme-s

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I_kwDODunzps5F33TH

TypeError: __init__() missing 1 required positional argument: 'scheme'

closed

NONE

## Describe the bug Based on [this tutorial](https://huggingface.co/docs/datasets/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset squad = load_dataset('squad', split='validation') squad.add_elasticsearch_index("context", host="localhost", port="9200") ``` ## Expected results [Creating an elastic index based on the provided tutorial](https://huggingface.co/docs/datasets/faiss_es#elasticsearch) ## Actual results ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-8fb51aa33961> in <module> 1 from datasets import load_dataset 2 squad = load_dataset('squad', split='validation') ----> 3 squad.add_elasticsearch_index("context", host="localhost", port="9200") ~/opt/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config) 3777 """ 3778 with self.formatted_as(type=None, columns=[column]): -> 3779 super().add_elasticsearch_index( 3780 column=column, 3781 index_name=index_name, ~/opt/anaconda3/lib/python3.8/site-packages/datasets/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config) 587 """ 588 index_name = index_name if index_name is not None else column --> 589 es_index = ElasticSearchIndex( 590 host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config 591 ) ~/opt/anaconda3/lib/python3.8/site-packages/datasets/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config) 123 from elasticsearch import Elasticsearch # noqa: F811 124 --> 125 self.es_client = es_client if es_client is not None else Elasticsearch([{"host": host, "port": str(port)}]) 126 self.es_index_name = ( 127 es_index_name ~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport) 310 311 if _transport is None: --> 312 node_configs = client_node_configs( 313 hosts, 314 cloud_id=cloud_id, ~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in client_node_configs(hosts, cloud_id, **kwargs) 99 else: 100 assert hosts is not None --> 101 node_configs = hosts_to_node_configs(hosts) 102 103 # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults. ~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in hosts_to_node_configs(hosts) 142 143 elif isinstance(host, Mapping): --> 144 node_configs.append(host_mapping_to_node_config(host)) 145 else: 146 raise ValueError( ~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in host_mapping_to_node_config(host) 209 options["path_prefix"] = options.pop("url_prefix") 210 --> 211 return NodeConfig(**options) # type: ignore 212 213 TypeError: __init__() missing 1 required positional argument: 'scheme' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Mac - Python version: 3.8.0 - PyArrow version: 7.0.0 - ElaticSearch Info: { "name" : "byname", "cluster_name" : "elasticsearch_brew", "cluster_uuid" : "9xkjrltiQIG0J95ciWhqRA", "version" : { "number" : "7.10.2-SNAPSHOT", "build_flavor" : "oss", "build_type" : "tar", "build_hash" : "unknown", "build_date" : "2021-01-16T01:41:27.115673Z", "build_snapshot" : true, "lucene_version" : "8.7.0", "minimum_wire_compatibility_version" : "6.8.0", "minimum_index_compatibility_version" : "6.0.0-beta1" }, "tagline" : "You Know, for Search" }

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PR_kwDODunzps40l5kG

Remove unncessary 'pylint disable' message in ReadMe

open

NONE

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I_kwDODunzps5F3XZg

The dataset preview is not available for tdklab/Hebrew_Squad_v1.1 dataset

open

NONE

## Dataset viewer issue for 'tdklab/Hebrew_Squad_v1.1' **Link:** https://huggingface.co/api/datasets/tdklab/Hebrew_Squad_v1.1?full=true The dataset preview is not available for this dataset. Am I the one who added this dataset ? Yes

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I_kwDODunzps5F3TBY

Add ImageNet Sketch

open

CONTRIBUTOR

## Adding a Dataset - **Name:** ImageNet Sketch - **Description:** ImageNet-Sketch is a dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale. - **Paper:** [Learning Robust Global Representations by Penalizing Local Predictive Power](https://arxiv.org/abs/1905.13549) - **Data:** https://github.com/HaohanWang/ImageNet-Sketch - **Motivation:** Allows for evaluating the robustness of vision models. 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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I_kwDODunzps5F2bTr

Checksum error for glue sst2, stsb, rte etc datasets

closed

NONE

## Describe the bug Checksum error for glue sst2, stsb, rte etc datasets ## Steps to reproduce the bug ```python >>> nlp.load_dataset('glue', 'sst2') Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 73.0/73.0 [00:00<00:00, 18.2kB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Library/Python/3.8/lib/python/site-packages/nlp/load.py", line 548, in load_dataset builder_instance.download_and_prepare( File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 462, in download_and_prepare self._download_and_prepare( File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 521, in _download_and_prepare verify_checksums( File "/Library/Python/3.8/lib/python/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8'] ``` ## Expected results dataset load should succeed without checksum error. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Library/Python/3.8/lib/python/site-packages/nlp/load.py", line 548, in load_dataset builder_instance.download_and_prepare( File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 462, in download_and_prepare self._download_and_prepare( File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 521, in _download_and_prepare verify_checksums( File "/Library/Python/3.8/lib/python/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8'] ``` ## Environment info - `datasets` version: '1.18.3' - Platform: Mac OS - Python version: Python 3.8.9 - PyArrow version: '7.0.0'

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I_kwDODunzps5F1Liu

Forked streaming datasets try to `open` data urls rather than use network

open

NONE

## Describe the bug Building on #3950, if you bypass the pickling problem you still can't use the dataset. Somehow something gets confused and the forked processes try to `open` urls rather than anything else. ## Steps to reproduce the bug ```python from multiprocessing import freeze_support import transformers from transformers import Trainer, AutoModelForCausalLM, TrainingArguments import datasets import torch.utils.data # work around #3950 class TorchIterableDataset(datasets.IterableDataset, torch.utils.data.IterableDataset): pass def _ensure_format(v: datasets.IterableDataset) -> datasets.IterableDataset: return TorchIterableDataset(v._ex_iterable, v.info, v.split, "torch", v._shuffling) if __name__ == '__main__': freeze_support() ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) ds = _ensure_format(ds) model = AutoModelForCausalLM.from_pretrained("distilgpt2") Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train() ``` ## Expected results I'd expect the dataset to load the url correctly and produce examples. ## Actual results ``` warnings.warn( ***** Running training ***** Num examples = 8000 Num Epochs = 9223372036854775807 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 8 Gradient Accumulation steps = 1 Total optimization steps = 1000 0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last): File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 22, in <module> Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train() File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train for step, inputs in enumerate(epoch_iterator): File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__ data = self._next_data() File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data return self._process_data(data) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data data.reraise() File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise raise exception FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0. Original Traceback (most recent call last): File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch data.append(next(self.dataset_iter)) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 497, in __iter__ for key, example in self._iter(): File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 494, in _iter yield from ex_iterable File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 87, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/Users/dlwh/.cache/huggingface/modules/datasets_modules/datasets/oscar/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2/oscar.py", line 358, in _generate_examples with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: FileNotFoundError: [Errno 2] No such file or directory: 'https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/en/en_part_1.txt.gz' Error in atexit._run_exitfuncs: Traceback (most recent call last): File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 27, in poll pid, sts = os.waitpid(self.pid, flag) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler _error_if_any_worker_fails() RuntimeError: DataLoader worker (pid 6932) is killed by signal: Terminated: 15. 0%| | 0/1000 [00:02<?, ?it/s] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.2-arm64-arm-64bit - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5F1JiJ

Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1

open

NONE

## Describe the bug Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash. ## Steps to reproduce the bug ```python import transformers from transformers import Trainer, AutoModelForCausalLM, TrainingArguments import datasets ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("torch") model = AutoModelForCausalLM.from_pretrained("distilgpt2") Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train() ``` ## Expected results For this code I'd expect a crash related to not having preprocessed the data, but instead we get a pickling error. ## Actual results ``` 0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last): File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 7, in <module> Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train() File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train for step, inputs in enumerate(epoch_iterator): File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__ return self._get_iterator() File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator return _MultiProcessingDataLoaderIter(self) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 918, in __init__ w.start() File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/process.py", line 121, in start self._popen = self._Popen(self) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 224, in _Popen return _default_context.get_context().Process._Popen(process_obj) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 284, in _Popen return Popen(process_obj) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch reduction.dump(process_obj, fp) File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) AttributeError: Can't pickle local object 'iterable_dataset.<locals>.TorchIterableDataset' 0%| | 0/1000 [00:00<?, ?it/s] ``` This immediate crash can be fixed by not using a local class to make the `TorchIterableDataset` (Note that you have to do with_format("torch") or you get an exception because the dataset has no len) However, any lambdas etc used as maps will also trigger this crash. A more permanent fix would be to move away from multiprocessing and instead use something like pathos or multiprocessing_on_dill (https://stackoverflow.com/questions/19984152/what-can-multiprocessing-and-dill-do-together) Note that if you bypass this crash you get another crash. (I'll file a separate bug). ## Environment info - `datasets` version: 2.0.0 - Platform: macOS-12.2-arm64-arm-64bit - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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PR_kwDODunzps40jia-

Remove GLEU metric

open

CONTRIBUTOR

Remove the GLEU metric as it is not actually implemented.

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PR_kwDODunzps40jg1F

Google BLEU Metric Card

closed

CONTRIBUTOR

Add metric card for Google BLEU (GLEU) metric One thing I noticed while writing this up is that, while this metric was made specifically to be better than BLEU at the sentence level instead of the corpus level, the current implementation only allows the calculation of the corpus-level statistic. I think changing this would be a good thing to put on the to do list for the future.

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PR_kwDODunzps40jfLq

BLEU metric card

closed