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https://github.com/huggingface/datasets/pull/4253

PR_kwDODunzps42-c8Q

Create metric cards for mean IOU

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-28 20:58:27

2022-04-29 17:44:47

2022-04-29 17:38:06

CONTRIBUTOR

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Proposing a metric card for mIoU :rocket: sorry for spamming you with review requests, @albertvillanova ! :hugs:

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PR_kwDODunzps429--I

Creating metric card for MAE

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-28 19:04:33

2022-04-29 16:59:11

2022-04-29 16:52:30

CONTRIBUTOR

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Initial proposal for MAE metric card

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PR_kwDODunzps4293dB

Metric card for the XTREME-S dataset

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-28 18:32:19

2022-04-29 16:46:11

2022-04-29 16:38:46

CONTRIBUTOR

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Proposing a metric card for the XTREME-S dataset :hugs:

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PR_kwDODunzps429yjN

Bump PyArrow Version to 6

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'Updated meta.yaml as well. Thanks.' "I'm OK with bumping PyArrow to version 6 to match the version in Colab, but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues." "> but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.\r\n\r\nI agree, not much attention has been payed to extension arrays in the latest developments of Arrow anyway.\r\n\r\nLet's not use them more that what we do right now, and try to remove them at one point"]

2022-04-28 18:10:50

2022-05-04 09:36:52

2022-05-04 09:29:46

CONTRIBUTOR

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Fixes #4152 This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files. This will fix ArrayND error which exists in pyarrow 5.

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PR_kwDODunzps42742y

Support streaming XGLUE dataset

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-28 10:27:23

2022-05-06 08:38:21

2022-04-28 16:08:03

MEMBER

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Support streaming XGLUE dataset. Fix #4247. CC: @severo

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I_kwDODunzps5IoDsc

conll2003 dataset loads original data.

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closed

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['Thanks for reporting @sue99.\r\n\r\nUnfortunately. I\'m not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset("conll2003")\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: [\'id\', \'tokens\', \'pos_tags\', \'chunk_tags\', \'ner_tags\'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: [\'id\', \'tokens\', \'pos_tags\', \'chunk_tags\', \'ner_tags\'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: [\'id\', \'tokens\', \'pos_tags\', \'chunk_tags\', \'ner_tags\'],\r\n num_rows: 3454\r\n })\r\n})\r\n\r\nIn [3]: dataset["train"][0]\r\nOut[3]: \r\n{\'id\': \'0\',\r\n \'tokens\': [\'EU\',\r\n \'rejects\',\r\n \'German\',\r\n \'call\',\r\n \'to\',\r\n \'boycott\',\r\n \'British\',\r\n \'lamb\',\r\n \'.\'],\r\n \'pos_tags\': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n \'chunk_tags\': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n \'ner_tags\': [3, 0, 7, 0, 0, 0, 7, 0, 0]}\r\n```\r\n\r\nJust guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub. ']

2022-04-28 09:33:31

2022-07-18 07:15:48

2022-07-18 07:15:48

NONE

## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ```

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I_kwDODunzps5Inhny

The data preview of XGLUE

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closed

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['\r\n' 'Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming. ' 'Fixed, thanks @albertvillanova !\r\n\r\nhttps://huggingface.co/datasets/xglue\r\n\r\n<img width="824" alt="Capture d’écran 2022-04-29 à 10 23 14" src="https://user-images.githubusercontent.com/1676121/165909391-9f98d98a-665a-4e57-822d-8baa2dc9b7c9.png">\r\n']

2022-04-28 07:30:50

2022-04-29 08:23:28

2022-04-28 16:08:03

NONE

It seems that something wrong with the data previvew of XGLUE

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PR_kwDODunzps427NiD

Support to load dataset with TSV files by passing only dataset name

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[]

closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-28 07:30:15

2022-05-06 08:38:28

2022-05-06 08:14:07

MEMBER

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This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`): ```python ds = load_dataset("dataset/name") ``` The refactoring allows for future builder kwargs customizations based on file extension. Related to #4238.

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PR_kwDODunzps426AUR

Add code examples for DatasetDict

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-27 22:52:22

2022-04-29 18:19:34

2022-04-29 18:13:03

MEMBER

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This PR adds code examples for `DatasetDict` in the API reference :)

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PR_kwDODunzps425Po6

task id update

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closed

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['Reverted the multi-input-text-classification tag from task_categories and added it as task_ids @lhoestq ' '_The documentation is not available anymore as the PR was closed or merged._']

2022-04-27 18:28:14

2022-05-04 10:43:53

2022-05-04 10:36:37

CONTRIBUTOR

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changed multi input text classification as task id instead of category

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PR_kwDODunzps425Gkn

WIP: Initial shades loading script and readme

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open

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[]

2022-04-27 17:45:43

2022-07-06 15:19:52

NONE

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PR_kwDODunzps425BYf

Update auth when mirroring datasets on the hub

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-27 17:22:31

2022-04-27 17:37:04

2022-04-27 17:30:42

MEMBER

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We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3.

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I_kwDODunzps5IkGlG

NonMatchingChecksumError when attempting to download GLUE

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closed

[]

['Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_dataset("glue", "rte")\r\n```' 'This appears to work. Thank you!\n\nOn Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote:\n\n> Hi :)\n>\n> I think your issue may be related to the older nlp library. I was able to\n> download glue with the latest version of datasets. Can you try updating\n> with:\n>\n> pip install -U datasets\n>\n> Then you can download:\n>\n> from datasets import load_datasetds = load_dataset("glue", "rte")\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n']

2022-04-27 14:14:21

2022-04-28 07:45:27

2022-04-28 07:45:27

NONE

## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5

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PR_kwDODunzps423xRl

Fix yield for crd3

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closed

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['I don\'t think you need to generate new dummy data, since they\'re in the same format as the original data.\r\n\r\nThe CI is failing because of this error:\r\n```python\r\n> turn["names"] = turn["NAMES"]\r\nE TypeError: tuple indices must be integers or slices, not str\r\n```\r\n\r\nDo you know what could cause this ? If I understand correctly, `turn` is supposed to be a list of dictionaries right ?' '> ``` \r\n> \r\n> Do you know what could cause this ? If I understand correctly, turn is supposed to be a list of dictionaries right ?\r\n> ```\r\n\r\nThis is strange. Let me look into this. As per https://github.com/RevanthRameshkumar/CRD3/blob/master/data/aligned%20data/c%3D2/C1E001_2_0.json TURNS is a list of dictionaries. So when we iterate over `row["TURNS]"` each `turn` is essentially a dictionary. Not sure why it\'s being considered a tuple here.']

2022-04-27 12:31:36

2022-04-29 12:41:41

2022-04-29 12:41:41

CONTRIBUTOR

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Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example Modified the features accordingly ``` "turns": [ { "names": datasets.features.Sequence(datasets.Value("string")), "utterances": datasets.features.Sequence(datasets.Value("string")), "number": datasets.Value("int32"), } ], } ``` I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this?

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PR_kwDODunzps423tZr

Small fixes in ROC AUC docs

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-27 12:15:50

2022-05-02 13:28:57

2022-05-02 13:22:03

CONTRIBUTOR

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The list of use cases did not render on GitHub with the prepended spacing. Additionally, some typo's we're fixed.

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I_kwDODunzps5IjIL7

Dataset caching policy

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['Hi @loretoparisi, thanks for reporting.\r\n\r\nThere is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`.\r\n\r\nPlease, let me know if this fixes your problem.\r\n\r\nI can confirm you that your dataset loads without any problem for me:\r\n```python\r\nIn [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\\t", column_names=[\'label\', \'text\'])\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: [\'label\', \'text\'],\r\n num_rows: 8256449\r\n })\r\n test: Dataset({\r\n features: [\'label\', \'text\'],\r\n num_rows: 2061204\r\n })\r\n})\r\n``` ' '@albertvillanova thank you, it seems it still does not work using:\r\n\r\n```python\r\nsentences = load_dataset(\r\n "loretoparisi/tatoeba-sentences",\r\n data_files=data_files,\r\n delimiter=\'\\t\', \r\n column_names=[\'label\', \'text\'],\r\n download_mode="force_redownload"\r\n)\r\n```\r\n[This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook!\r\n\r\nThe problem is that the download file\'s revision for `test.csv` is not correctly parsed\r\n\r\n\r\n\r\nIf you download that file `test.csv` from the repo, the line `\\\\N` is not there anymore (it was there at the first file upload).\r\n\r\nMy impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\\\N`) since are binary files:\r\n\r\n```\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow\r\n!head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json\r\n```\r\n' 'SOLVED! The problem was the with the file itself, using caching parameter helped indeed.\r\nThanks for helping!']

2022-04-27 10:42:11

2022-04-27 16:29:25

2022-04-27 16:28:50

NONE

## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```

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I_kwDODunzps5Ii8sU

Common Voice 8 doesn't show datasets viewer

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closed

[]

['Thanks for reporting. I understand it\'s an error in the dataset script. To reproduce:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********")\r\nDownloading builder script: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.9k/10.9k [00:00<00:00, 10.9MB/s]\r\nDownloading extra modules: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 3.36MB/s]\r\nDownloading extra modules: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 53.1k/53.1k [00:00<00:00, 650kB/s]\r\nNo config specified, defaulting to: common_voice/en\r\nTraceback (most recent call last):\r\n File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators\r\n self._log_download(self.config.name, bundle_version, hf_auth_token)\r\n File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download\r\n email = HfApi().whoami(auth_token)["email"]\r\nKeyError: \'email\'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File "<stdin>", line 1, in <module>\r\n File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info\r\n raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```' 'Thanks for reporting @patrickvonplaten and thanks for the investigation @severo.\r\n\r\nUnfortunately I\'m not able to reproduce the error.\r\n\r\nI think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139\r\n```python\r\nfrom huggingface_hub import HfApi, HfFolder\r\n\r\nif isinstance(auth_token, bool):\r\n email = HfApi().whoami(auth_token)\r\nemail = HfApi().whoami(auth_token)["email"]\r\n```\r\n\r\nCould you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?' 'OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email!\r\n\r\n```python\r\n>>> from huggingface_hub import HfApi, HfFolder\r\n>>> auth_token = "hf_app_******"\r\n>>> t = HfApi().whoami(auth_token)\r\n>>> t\r\n{\'type\': \'app\', \'name\': \'dataset-preview-backend\'}\r\n>>> t["email"]\r\nTraceback (most recent call last):\r\n File "<stdin>", line 1, in <module>\r\nKeyError: \'email\'\r\n```\r\n\r\nNote also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key.\r\n\r\n@SBrandeis @julien-c: do you think the app token should have an email associated, like the users?' 'We can workaround this with\r\n```python\r\nemail = HfApi().whoami(auth_token).get("email", "system@huggingface.co")\r\n```\r\nin the common voice scripts' 'Hmmm, does this mean that any person who downloads the common voice dataset will be logged as "system@huggingface.co"? If so, it would defeat the purpose of sending the user\'s email to the commonvoice API, right?' 'I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy.\r\n\r\nAdditionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nCC: @patrickvonplaten @lhoestq @SBrandeis @julien-c ' 'Hmm I don\'t agree here. \r\n\r\nAnybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it\'s trivial to tweak the code to not send the "correct" email but to just whatever and it would work.\r\n\r\nNote that someone only has visibility on the code after having "signed" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. \r\n\r\nI\'m fine with both @lhoestq\'s solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?' "> Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nYes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism.\r\n\r\nCurrently the script as it is already requires the user being logged in:\r\n```python\r\nHfApi().whoami(auth_token)\r\n```\r\nthrows an exception if None/invalid auth_token is passed.\r\n\r\nOn the other hand, we should agree on the way to allow the viewer to stream the data." 'The preview is back now, thanks !']

2022-04-27 10:05:20

2022-05-10 12:17:05

2022-05-10 12:17:04

MEMBER

https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0

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PR_kwDODunzps423MOc

Replace data URL in big_patent dataset and support streaming

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'I first uploaded the data files to the Hub: I think it is a good option because we have git lfs to track versions and changes. Moreover people will be able to make PRs to propose updates on the data files.\r\n- I would have preferred to upload it it to the "data" org namespace, but it is already taken (although not used): might be possible to take it?\r\n\r\nAs an alternative (and to be consistent with previous datasets), I also uploaded the data files to our AWS bucket.\r\n\r\nWe should decide which to use (now and for future datasets) and set it here before merging. We should remove the data files for the non-chosen option.\r\n\r\nCC: @lhoestq @mariosasko @polinaeterna ' "Would it make sense to make the dataset a community one (so, create an organization for it) and store the script and the data in a single repository? Just as it is for most of the datasets. That way we can also access the data using a relative path inside the repo (that's not the point though). The point is that to me it seems a bit more straightforward to store everything in one place. \r\n\r\nI guess the strong argument against this logic is that in this case the canonical version won't work... But maybe there is some redirecting mechanism I don't know about? :)\r\n\r\nAnyway, I'm in favor of hosting data on the Hub instead of AWS :) " 'I also think storing everything in one place/single repository is the best option.\r\n\r\n@polinaeterna Canonical datasets also support data files (see the [`red_caps` repo](https://huggingface.co/datasets/red_caps/tree/main) for instance) ' 'Thanks @polinaeterna and @mariosasko for your comments.\r\n\r\nYes, definitely it is much better to have everything in the same repo. \r\n\r\nI\'m transferring their data files to the Hub under "big_patent" and deleting them from the other repo and AWS.']

2022-04-27 10:01:13

2022-06-10 08:10:55

2022-05-02 18:21:15

MEMBER

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This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub. Moreover, this PR makes the dataset streamable. Fix #4217.

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I_kwDODunzps5IiTlA

How to load VERY LARGE dataset?

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open

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['The `Trainer` support `IterableDataset`, not just datasets.']

2022-04-27 07:50:13

2022-04-27 11:44:17

NONE

### System Info ```shell I am using transformer trainer while meeting the issue. The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency. I wonder if there are any tricks like Sharding in huggingface trainer. Looking forward to your reply. ``` ### Who can help? Trainer: @sgugger ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction None ### Expected behavior ```shell I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html. Thanks a lot! ```

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PR_kwDODunzps422Mwn

Autoeval config

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'Related to: https://github.com/huggingface/autonlp-backend/issues/414 and https://github.com/huggingface/autonlp-backend/issues/424' "The tests are failing due to the changed metadata:\r\n\r\n```\r\ngot an unexpected keyword argument 'train-eval-index'\r\n```\r\n\r\nI think you can fix this by updating the `DatasetMetadata` class and implementing an appropriate `validate_train_eval_index()` function\r\n\r\n@lhoestq we are working with an arbitrary set of tags for `autoeval config`. See https://github.com/huggingface/autonlp-backend/issues/414\r\nI need to add a validator function though for the tests to pass. Our set is not well-defined as in the rest https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources. What's a workaround for this?" "On the question of validating the `train-eval-index` metadata, I think the simplest approach would be to validate that the required fields exist and not worry about their values (which are open-ended).\r\n\r\nFor me, the required fields include:\r\n\r\n* `config`\r\n* `task`\r\n* `task_id`\r\n* `splits` (train / validation / eval)\r\n* `col_mapping`\r\n* `metrics` (checking that each one has `type`, `name`) \r\n\r\nHere I'm using the spec defined in https://github.com/huggingface/autonlp-backend/issues/414 as a guide.\r\n\r\nWDYT @lhoestq ?" "Makes sense ! Currently the metadata type validator doesn't support subfields - let me open a PR to add it" 'I ended up improving the metadata validation in this PR x)\r\n\r\nIn particular:\r\n- I added support YAML keys with dashes instead of underscores for `train-eval-index`\r\n- I added `train-eval-index` validation with `validate_train_eval_index`. It does nothing fancy, it just checks that it is a list if it exists in the YAML, but feel free to improve it if you want\r\n\r\nLet me know if it sounds good to you ! I think we can improve `validate_train_eval_index` in another PR' "Come on windows... I didn't do anything advanced...\r\n\r\nAnyway, will try to fix this when I get back home x)" "> Come on windows... I didn't do anything advanced...\r\n> \r\n> Anyway, will try to fix this when I get back home x)\r\n\r\nHehe, thanks!" 'Thanks, @lhoestq this is great! ' 'Did I just fix it for windows and now it fails on linux ? xD' '> Did I just fix it for windows and now it fails on linux ? xD\r\n\r\nLooks like the Heisenberg uncertainty principle is at play here - you cannot simultaneously have unit tests passing in both Linux and Windows 😅 ' 'The worst is that the tests pass locally both on my windows and my linux x)' "Ok fixed it, the issue came from python 3.6 that doesn't return the right `__origin__` for Dict and List types" '> Alright thanks for adding the first Autoeval config ! :D\r\n\r\nWoohoo! Thank you so much 🤗 ' 'This is cool!']

2022-04-27 05:32:10

2022-05-06 13:20:31

2022-05-05 18:20:58

CONTRIBUTOR

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Added autoeval config to imdb as pilot

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PR_kwDODunzps421r-6

Autoeval

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closed

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4233). All of your documentation changes will be reflected on that endpoint.']

2022-04-27 01:32:09

2022-04-27 05:29:30

2022-04-27 01:32:23

CONTRIBUTOR

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PR_kwDODunzps421qz4

adding new tag to tasks.json and modified for existing datasets

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'closing in favor of https://github.com/huggingface/datasets/pull/4244']

2022-04-27 01:21:09

2022-05-03 14:23:56

2022-05-03 14:16:39

CONTRIBUTOR

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PR_kwDODunzps421pUX

Fix invalid url to CC-Aligned dataset

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-27 01:07:01

2022-05-16 17:01:13

2022-05-16 16:53:12

CONTRIBUTOR

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The CC-Aligned dataset url has changed to https://data.statmt.org/cc-aligned/, the old address http://www.statmt.org/cc-aligned/ is no longer valid

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I_kwDODunzps5IhIJN

Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?

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open

[]

['Thanks for reporting @beyondguo.\r\n\r\nIndeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip\r\nAnd that URL only contains the English version.' 'The German data requires payment\r\n\r\nThe [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states "The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00)."\r\n\r\nInflation since 2003 has also affected LDC\'s prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly.\r\n\r\nThe ACL hold copyright of this data; I\'ll mail them and anyone I can find at ECI to see if they\'ll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)\r\n']

2022-04-27 00:53:52

2022-05-12 12:23:42

NONE

 But on huggingface datasets:  Where is the German data?

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PR_kwDODunzps421mjM

new task tag

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[]

closed

[]

[]

2022-04-27 00:47:08

2022-04-27 00:48:28

2022-04-27 00:48:17

CONTRIBUTOR

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multi-input-text-classification tag for classification datasets that take more than one input

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PR_kwDODunzps421NKL

new task tag

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[]

closed

[]

[]

2022-04-26 22:00:33

2022-04-27 00:48:31

2022-04-27 00:46:31

CONTRIBUTOR

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multi-input-text-classification tag for classification datasets that take more than one input

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PR_kwDODunzps420-mc

Add f1 metric card, update docstring in py file

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-26 20:41:03

2022-05-03 12:50:23

2022-05-03 12:43:33

CONTRIBUTOR

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PR_kwDODunzps420kAv

Add pearsonr mc, update functionality to match the original docs

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'thank you @lhoestq!! :hugs: ']

2022-04-26 18:30:46

2022-05-03 17:09:24

2022-05-03 17:02:28

CONTRIBUTOR

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- adds pearsonr metric card - adds ability to return p-value - p-value was mentioned in the original docs as a return value, but there was no option to return it. I updated the _compute function slightly to have an option to return the p-value.

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PR_kwDODunzps420LNM

autoeval config

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2022-04-26 16:38:34

2022-04-27 00:48:31

2022-04-26 22:00:26

CONTRIBUTOR

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add train eval index for autoeval

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PR_kwDODunzps420KX2

autoeval config

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closed

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[]

2022-04-26 16:35:19

2022-04-26 16:36:45

2022-04-26 16:36:45

CONTRIBUTOR

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add train eval index for autoeval

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PR_kwDODunzps42z0YV

Add Accuracy Metric Card

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-26 15:10:46

2022-05-03 14:27:45

2022-05-03 14:20:47

CONTRIBUTOR

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- adds accuracy metric card - updates docstring in accuracy.py - adds .json file with metric card and docstring information

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PR_kwDODunzps42zpcd

Fix description links in dataset cards

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'Non passing tests are due to other pre-existing errors in dataset cards: not related to this PR.']

2022-04-26 14:36:25

2022-05-06 08:38:38

2022-04-26 16:52:29

MEMBER

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I noticed many links were not properly displayed (only text, no link) on the Hub because of wrong syntax, e.g.: https://huggingface.co/datasets/big_patent This PR fixes all description links in dataset cards.

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I_kwDODunzps5IeVUO

Dictionary Feature

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closed

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['Hi @jordiae,\r\n\r\nInstead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n```python\r\n"list_of_dict_feature": [\r\n {\r\n "key1_in_dict": datasets.Value("string"),\r\n "key2_in_dict": datasets.Value("int32"),\r\n ...\r\n }\r\n],\r\n```\r\n\r\nFeel free to re-open this issue if that does not work for your use case.' '> Hi @jordiae,\r\n> \r\n> Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n> \r\n> ```python\r\n> "list_of_dict_feature": [\r\n> {\r\n> "key1_in_dict": datasets.Value("string"),\r\n> "key2_in_dict": datasets.Value("int32"),\r\n> ...\r\n> }\r\n> ],\r\n> ```\r\n> \r\n> Feel free to re-open this issue if that does not work for your use case.\r\n\r\nThank you']

2022-04-26 12:50:18

2022-04-29 14:52:19

2022-04-28 17:04:58

NONE

Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance.

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PR_kwDODunzps42w5YO

Altered faiss installation comment

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'Hi ! Can you explain why this change is needed ?' 'Facebook recommends installing FAISS using conda (https://github.com/facebookresearch/faiss/blob/main/INSTALL.md). pip does not seem to have the latest version of FAISS. The latest version of faiss is 1.7.2 (https://anaconda.org/conda-forge/faiss), but the latest one available through pip is 1.5.3 (https://pypi.org/project/faiss/). ']

2022-04-26 01:20:43

2022-05-09 17:29:34

2022-05-09 17:22:09

CONTRIBUTOR

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PR_kwDODunzps42v6rE

Add F1 Metric Card

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-25 19:14:56

2022-04-26 20:44:18

2022-04-26 20:37:46

CONTRIBUTOR

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PR_kwDODunzps42vTA0

Make code for image downloading from image urls cacheable

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-25 16:17:59

2022-04-26 17:00:24

2022-04-26 13:38:26

CONTRIBUTOR

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Fix #4199

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I_kwDODunzps5IZquN

Big_Patent dataset broken

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closed

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['Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.\r\n\r\nSee related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com\r\n\r\nTo quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551):\r\n\r\n> PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.\r\n\r\n' 'We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.' 'The data owners have agreed on hosting their data on the Hub.']

2022-04-25 15:31:45

2022-05-26 06:29:43

2022-05-02 18:21:15

NONE

## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No

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PR_kwDODunzps42u1_w

Avoid recursion error in map if example is returned as dict value

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-25 14:40:32

2022-05-04 17:20:06

2022-05-04 17:12:52

CONTRIBUTOR

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I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko). This code replicates the bug: ```python from datasets import Dataset dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]}) dset.map(lambda ex: {"translation": ex}) ``` and this is the fix for it (before this PR): ```python from datasets import Dataset dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]}) dset.map(lambda ex: {"translation": dict(ex)}) ``` Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries. P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks.

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PR_kwDODunzps42uuhY

Add `drop_last_batch` to `IterableDataset.map`

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-25 14:15:19

2022-05-03 15:56:07

2022-05-03 15:48:54

CONTRIBUTOR

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Addresses this comment: https://github.com/huggingface/datasets/pull/3801#pullrequestreview-901736921

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PR_kwDODunzps42utC5

Skip checksum computation in Imagefolder by default

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-25 14:10:41

2022-05-03 15:28:32

2022-05-03 15:21:29

CONTRIBUTOR

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Avoids having to set `ignore_verifications=True` in `load_dataset("imagefolder", ...)` to skip checksum verification and speed up loading. The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part.

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PR_kwDODunzps42uft_

ETT time series dataset

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._' 'thank you!\r\n']

2022-04-25 13:26:18

2022-05-05 12:19:21

2022-05-05 12:10:35

CONTRIBUTOR

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Ready for review.

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PR_kwDODunzps42udRf

[Common Voice] Make sure bytes are correctly deleted if `path` exists

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._' 'cool that you noticed that we store unnecessary bytes again :D ']

2022-04-25 13:18:26

2022-04-26 22:54:28

2022-04-26 22:48:27

MEMBER

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`path` should be set to local path inside audio feature if exist so that bytes can correctly be deleted.

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I_kwDODunzps5IYbDt

DatasetDict containing Datasets with different features when pushed to hub gets remapped features

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closed

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['Hi @pietrolesci, thanks for reporting.\r\n\r\nPlease note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n\r\nTo handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**.\r\n\r\nHowever, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.' 'Hi @albertvillanova,\r\n\r\nThanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation.\r\n\r\nAgain, thanks for your time @albertvillanova!\r\n\r\nBest,\r\nPietro' 'Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. \r\n\r\n@albertvillanova @lhoestq wdyt?' 'I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features.\r\n\r\nNote that later you will be able to push datasets with different features as different dataset **configurations** (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon']

2022-04-25 11:22:54

2022-05-20 15:15:30

2022-05-20 15:15:30

NONE

Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```

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TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'

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closed

[]

['Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows:\r\n```python\r\nfrom datasets import load_dataset,Features,Value,ClassLabel\r\nclass_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]\r\nfeatures = Features({ \'label\': ClassLabel(names=class_names), \'text\': Value(\'string\')})\r\nnum_labels = features[\'label\'].num_classes\r\ndata_files = { "train": "train.csv", "test": "test.csv" }\r\nsentences = load_dataset(\r\n "loretoparisi/tatoeba-sentences",\r\n data_files=data_files,\r\n delimiter=\'\\t\', \r\n column_names=[\'label\', \'text\'],\r\n)\r\n# You can make this part faster with num_proc=<some int>\r\nsentences = sentences.map(lambda ex: features["label"].str2int(ex["label"]) if ex["label"] is not None else None, features=features)\r\n```\r\n\r\n@lhoestq IIRC, I suggested adding `cast_to_storage` to `ClassLabel` + `table_cast` to the packaged loaders if the `ClassLabel`/`Image`/`Audio` type is present in `features` to avoid this kind of error, but your concern was speed. IMO shouldn\'t be a problem if we do `table_cast` only when these features are present.' 'I agree packaged loaders should support `ClassLabel` feature without throwing an error.' '@albertvillanova @mariosasko thank you, with that change now I get\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-9-eeb68eeb9bec>](https://localhost:8080/#) in <module>()\r\n 11 )\r\n 12 # You can make this part faster with num_proc=<some int>\r\n---> 13 sentences = sentences.map(lambda ex: features["label"].str2int(ex["label"]) if ex["label"] is not None else None, features=features)\r\n 14 sentences = sentences.shuffle()\r\n\r\n8 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n 2193 if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)):\r\n 2194 raise TypeError(\r\n-> 2195 f"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects."\r\n 2196 )\r\n 2197 elif isinstance(indices, list) and isinstance(processed_inputs, Mapping):\r\n\r\nTypeError: Provided `function` which is applied to all elements of table returns a variable of type <class \'int\'>. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\r\n```\r\n\r\nthe error is raised by [this](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2221)\r\n\r\n```\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n```' '@mariosasko changed it like\r\n\r\n```python\r\nsentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)\r\n```\r\n\r\nto avoid the above errorr.' 'Any update on this? Is this correct ?\r\n> @mariosasko changed it like\r\n> \r\n> ```python\r\n> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)\r\n> ```\r\n> \r\n> to avoid the above errorr.\r\n\r\n']

2022-04-25 07:28:42

2022-05-31 12:16:31

2022-05-31 12:16:31

NONE

### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vědět, co je tam uvnitř. ces Kdo o tom chce slyšet? deu Tom sagte, er fühle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rá rögtön. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hören. ``` ### Expected behavior ```shell correctly load train and test files. ```

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PR_kwDODunzps42r7bW

Add CMU MoCap Dataset

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4208). All of your documentation changes will be reflected on that endpoint.' '- Updated the readme.\r\n- Added dummy_data.zip and ran the all the tests.\r\n\r\nThe dataset works for "asf/amc" and "avi" formats which have a single download link for the complete dataset. But "c3d" and "mpg" have multiple download links, can we combine and host these links on the Hub since the dataset is free to use ?' '"c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?\r\nCan we combine and host these links on the Hub since the dataset is free to use ?' '> "c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?\r\n\r\nWe store downloaded data under `~/.cache/huggingface/datasets/downloads` (by default), so these downloads are "hidden" and won\'t clutter one\'s filesystem in an "obvious way".' '> We store downloaded data under ~/.cache/huggingface/datasets/downloads (by default), so these downloads are "hidden" and won\'t clutter one\'s filesystem in an "obvious way".\r\n\r\nYes, the filesystem won\'t be clustered, but the problem is processing the dataset becomes cumbersome. For eg, for the c3d format has 5 part-downloads, so the folders will be as follows : \r\n```\r\n[\'~/.cache/huggingface/datasets/downloads/extracted/0e6bf028f490bf18c23ce572d1437c4ef32a74f630e33c26a806250d35cfcdd1\', \'~/.cache/huggingface/datasets/downloads/extracted/1b44fc5c7a6e031c904545422d449fd964f8ee795b9d1dcb0b6a76d03b50ebe6\', \'~/.cache/huggingface/datasets/downloads/extracted/137595188e96187c24ce1aa5c78200c7f78816fbd9d6c62354c01b3e6ec550c7\', \'~/.cache/huggingface/datasets/downloads/extracted/6c0c893e435f36fd79aa0f199f58fe16f01985f039644a7cb094a8c43a15ffd4\', \'~/.cache/huggingface/datasets/downloads/extracted/45e4703354cbc975e6add66f1b17b716c882b56f44575b033c5926aa5fcfb17f\']\r\n```\r\nEach of these folders have a given set of subjects, so we\'ll be need to write extra code to fetch data from each of these folders, and the mpg format has 12 part-downloads which will lead to 12 folders having certain set of subjects, so it is cumbersome to process them.' 'I have added all the changes that were suggested. We just need to handle the multi-part download for c3d and mpg formats. Easiest way would be to have just one zip for these formats.' 'But we can handle this with a simple mapping that stores the id ranges (for each config), no? And an actual file path is not important during processing.' 'I have added code to handle c3d, mpg formats as well. The data for the mpg format seems incomplete as it contains only 53 rows. I have added a note regarding this in the Data Splits section.' 'The real data test works fine and dummy_data test work fine. There were few missing files which was causing issues, I have fixed it now.\r\n' '- Reduced the dummy_data size.\r\n- Added sample dataset preprocessing code, it is not complete though.\r\n- Added all changes suggested.\r\n\r\nLet me know if anything else is required. Thank you. :)']

2022-04-24 17:31:08

2022-07-06 15:19:52

CONTRIBUTOR

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Resolves #3457 Dataset Request : Add CMU Graphics Lab Motion Capture dataset [#3457](https://github.com/huggingface/datasets/issues/3457) This PR adds the CMU MoCap Dataset. The authors didn't respond even after multiple follow ups, so I ended up crawling the website to get categories, subcategories and description information. Some of the subjects do not have category/subcategory/description as well. I am using a subject to categories, subcategories and description map (metadata file). Currently the loading of the dataset works for "asf/amc" and "avi" formats since they have a single download link. But "c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ? Any suggestions/inputs on this would be helpful. Thank you.

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PR_kwDODunzps42rmbK

[Minor edit] Fix typo in class name

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2022-04-24 09:49:37

2022-05-05 13:17:47

2022-05-05 13:17:47

CONTRIBUTOR

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Typo: `datasets.DatsetDict` -> `datasets.DatasetDict`

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PR_kwDODunzps42pJQW

Add Nerval Metric

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2022-04-22 19:45:00

2022-07-06 15:19:52

NONE

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This PR adds readme.md and ner_val.py to metrics. Nerval is a python package that helps evaluate NER models. It creates classification report and confusion matrix at entity level.

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PR_kwDODunzps42oVFE

Fix `convert_file_size_to_int` for kilobits and megabits

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-22 14:56:21

2022-05-03 15:28:42

2022-05-03 15:21:48

CONTRIBUTOR

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Minor change to fully align this function with the recent change in Transformers (https://github.com/huggingface/transformers/pull/16891)

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PR_kwDODunzps42oN0j

Add Recall Metric Card

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['_The documentation is not available anymore as the PR was closed or merged._' 'This looks good to me! ']

2022-04-22 14:24:26

2022-05-03 13:23:23

2022-05-03 13:16:24

CONTRIBUTOR

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What this PR mainly does: - add metric card for recall metric - update docs in recall python file Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)

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PR_kwDODunzps42oNrS

Add Precision Metric Card

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-22 14:23:48

2022-05-03 14:23:40

2022-05-03 14:16:46

CONTRIBUTOR

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What this PR mainly does: - add metric card for precision metric - update docs in precision python file Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)

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PR_kwDODunzps42n278

Fix some type annotation in doc

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-22 12:53:31

2022-04-22 15:03:00

2022-04-22 14:56:43

MEMBER

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PR_kwDODunzps42nIRm

Update GH template for dataset viewer issues

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['_The documentation is not available anymore as the PR was closed or merged._' 'You can see rendering at: https://github.com/huggingface/datasets/blob/6b48fedbdafe12a42c7b6edcecc32820af1a4822/.github/ISSUE_TEMPLATE/dataset-viewer.yml']

2022-04-22 09:34:44

2022-05-06 08:38:43

2022-04-26 08:45:55

MEMBER

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Update template to use new issue forms instead. With this PR we can check if this new feature is useful for us. Once validated, we can update the other templates. CC: @severo

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PR_kwDODunzps42mz0w

Add to docs how to load from local script

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-22 08:08:25

2022-05-06 08:39:25

2022-04-23 05:47:25

MEMBER

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This option was missing from the docs guide (it was only explained in the docstring of `load_dataset`). Although this is an infrequent use case, there might be some users interested in it. Related to #4192 CC: @stevhliu

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I_kwDODunzps5IPPCc

Cache miss during reload for datasets using image fetch utilities through map

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closed

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['Hi ! Maybe one of the objects in the function is not deterministic across sessions ? You can read more about it and how to investigate here: https://huggingface.co/docs/datasets/about_cache' 'Hi @apsdehal! Can you verify that replacing\r\n```python\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\r\n for _ in range(retries + 1):\r\n try:\r\n request = urllib.request.Request(\r\n image_url,\r\n data=None,\r\n headers={"user-agent": get_datasets_user_agent()},\r\n )\r\n with urllib.request.urlopen(request, timeout=timeout) as req:\r\n image = PIL.Image.open(io.BytesIO(req.read()))\r\n break\r\n except Exception:\r\n image = None\r\n return image\r\n```\r\nwith \r\n```python\r\nUSER_AGENT = get_datasets_user_agent()\r\n\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\r\n for _ in range(retries + 1):\r\n try:\r\n request = urllib.request.Request(\r\n image_url,\r\n data=None,\r\n headers={"user-agent": USER_AGENT},\r\n )\r\n with urllib.request.urlopen(request, timeout=timeout) as req:\r\n image = PIL.Image.open(io.BytesIO(req.read()))\r\n break\r\n except Exception:\r\n image = None\r\n return image\r\n```\r\nfixes the issue?' 'Thanks @mariosasko. That does fix the issue. In general, I think these image downloading utilities since they are being used by a lot of image dataset should be provided as a part of `datasets` library right to keep the logic consistent and READMEs smaller? If they already exists, that is also great, please point me to those. I saw that `http_get` does exist.' 'You can find my rationale (and a proposed solution) for why these utilities are not a part of `datasets` here: https://github.com/huggingface/datasets/pull/4100#issuecomment-1097994003.' 'Makes sense. But, I think as the number of image datasets as grow, more people are copying pasting original code from docs to work as it is while we make fixes to them later. I think we do need a central place for these to avoid that confusion as well as more easier access to image datasets. Should we restart that discussion, possible on slack?']

2022-04-22 07:47:08

2022-04-26 17:00:32

2022-04-26 13:38:26

MEMBER

## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5INVwv

There is no dataset

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2022-04-21 19:19:26

2022-05-03 11:29:05

2022-04-22 06:12:25

NONE

## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No

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PR_kwDODunzps42kyXD

Add remove_columns=True

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['_The documentation is not available anymore as the PR was closed or merged._' "Any reason why we can't just do `[inputs.copy()]` in this line for in-place operations to not have effects anymore:\r\nhttps://github.com/huggingface/datasets/blob/bf432011ff9155a5bc16c03956bc63e514baf80d/src/datasets/arrow_dataset.py#L2232.\r\n\r\n(in the `batched` case, we can also copy the inputs' values (list objects) to ignore in-place modifications to the inputs' columns)\r\n\r\nI think `remove_columns=True` has no meaning, so I'm not a fan of this change." "@mariosasko copy does have a cost associated with it ... and plus you'll have to consider `deepcopy` Imagine columnds that are list of list of list of list .... Though I have to agree that `remove_columns=True` doesn't make sense (but, IMO, neither does it in its current use-case as it should refer to `input_columns`) " 'Okay closing this PR for the following reasons:\r\n - `remove_columns=True` was expected to keep the `.update`-like operator for `.map`. I initially thought it would be a good way to ignore function side effects and only keep output of that function (cf. PR description).\r\n - expected `remove_columns=True` is a bad API according to @mariosasko and introduces unecessary changes for little gain (strictly equivalent to `remove_columns=dset.column_names`)']

2022-04-21 17:28:13

2022-04-22 14:51:41

2022-04-22 14:45:30

MEMBER

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This should fix all the issue we have with in place operations in mapping functions. This is crucial as where we do some weird things like: ``` def apply(batch): batch_size = len(batch["id"]) batch["text"] = ["potato" for _ range(batch_size)] return {} # Columns are: {"id": int} dset.map(apply, batched=True, remove_columns="text") # crashes because `text` is not in the original columns dset.map(apply, batched=True) # mapped datasets has `text` column ``` In this PR we suggest to have `remove_columns=True` so that we ignore the input completely, and just use the output to generate mapped dataset. This means that inplace operations won't have any effects anymore.

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I_kwDODunzps5IMohd

Embed image and audio files in `save_to_disk`

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2022-04-21 16:25:18

2022-04-21 16:25:18

MEMBER

Following https://github.com/huggingface/datasets/pull/4184, currently a dataset saved using `save_to_disk` doesn't actually contain the bytes of the image or audio files. Instead it stores the path to your local files. Adding `embed_external_files` and set it to True by default to save_to_disk would be kind of a breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice: - the resulting dataset is self contained, in case you want to delete your cache for example or share it with someone else - users also upload these Arrow files to cloud storage via the fs parameter, and in this case they would expect to upload a self-contained dataset - consistency with push_to_hub This can be implemented at the same time as sharding for `save_to_disk` for efficiency, and reuse the helpers from `push_to_hub` to embed the external files. cc @mariosasko

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PR_kwDODunzps42jjD3

Support lists of multi-dimensional numpy arrays

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-21 12:22:26

2022-05-12 15:16:34

2022-05-12 15:08:40

MEMBER

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Fix #4191. CC: @SaulLu

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PR_kwDODunzps42izQG

Document save_to_disk and push_to_hub on images and audio files

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['_The documentation is not available anymore as the PR was closed or merged._' 'Good catch, I updated the docstrings']

2022-04-21 09:04:36

2022-04-22 09:55:55

2022-04-22 09:49:31

MEMBER

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Following https://github.com/huggingface/datasets/pull/4187, I explained in the documentation of `save_to_disk` and `push_to_hub` how they handle image and audio data.

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I_kwDODunzps5IKbPK

load_dataset can't load local dataset,Unable to find ...

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["Hi! :)\r\n\r\nI believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please?" 'Hi @ahf876828330, \r\n\r\nAs @stevhliu pointed out, the proper way to load a dataset is not trying to load its metadata file.\r\n\r\nIn your case, as the dataset script is local, you should better point to your local loading script:\r\n```python\r\ndataset = load_dataset("dataset/opus_books.py")\r\n```\r\n\r\nPlease, feel free to re-open this issue if the previous code snippet does not work for you.' "> Hi! :)\r\n> \r\n> I believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please?\r\n\r\nYes，you are right!So if I have a metadata dataset local,How can I turn it to a dataset that can be used by the load_dataset() function？Are there some examples?" "The metadata file isn't a dataset so you can't turn it into one. You should try @albertvillanova's code snippet above (now merged in the docs [here](https://huggingface.co/docs/datasets/master/en/loading#local-loading-script)), which uses your local loading script `opus_books.py` to:\r\n\r\n1. Download the actual dataset. \r\n2. Once the dataset is downloaded, `load_dataset` will load it for you."]

2022-04-21 08:28:58

2022-04-25 16:51:57

2022-04-22 07:39:53

NONE

Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained   the code is in the model.py,why I can't use the load_dataset function to load my local dataset?

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I_kwDODunzps5IH5A6

feat: create an `Array3D` column from a list of arrays of dimension 2

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['Hi @SaulLu, thanks for your proposal.\r\n\r\nJust I got a bit confused about the dimensions...\r\n- For the 2D case, you mention it is possible to create an `Array2D` from a list of arrays of dimension 1\r\n- However, you give an example of creating an `Array2D` from arrays of dimension 2:\r\n - the values of `data_map` are arrays of dimension 2\r\n - the outer list in `prepare_dataset_2D` should not be taken into account in the dimension counting, as it is used because in `map` you pass `batched=True`\r\n\r\nNote that for the 3D alternatives you mention:\r\n- In `prepare_dataset_3D_ter`, you create an `Array3D` from arrays of dimension 3:\r\n - the array `data_map[index][np.newaxis, :, :]` has dimension 3\r\n - the outer list in `prepare_dataset_3D_ter` is the one used by `batched=True`\r\n- In `prepare_dataset_3D_bis`, you create an `Array3D` from a list of list of lists:\r\n - the value of `data_map[index].tolist()` is a list of lists\r\n - it is enclosed by another list `[data_map[index].tolist()]`, thus giving a list of list of lists\r\n - the outer list is the one used by `batched=True`\r\n\r\nTherefore, if I understand correctly, your request would be to be able to create an `Array3D` from a list of an array of dimension 2:\r\n- In `prepare_dataset_3D`, `data_map[index]` is an array of dimension 2\r\n- it is enclosed by a list `[data_map[index]]`, thus giving a list of an array of dimension 2\r\n- the outer list is the one used by `batched=True`\r\n\r\nPlease, feel free to tell me if I did not understand you correctly.' 'Hi @albertvillanova ,\r\n\r\nIndeed my message was confusing and you guessed right :smile: : I think would be interesting to be able to create an Array3D from a list of an array of dimension 2. \r\n\r\nFor the 2D case I should have given as a "similar" example:\r\n```python\r\n\r\ndata_map_1D = {\r\n 1: np.array([0.2, 0.4]),\r\n 2: np.array([0.1, 0.4]),\r\n}\r\n\r\ndef prepare_dataset_2D(batch):\r\n batch["pixel_values"] = [[data_map_1D[index]] for index in batch["id"]]\r\n return batch\r\n \r\nds_2D = ds.map(\r\n prepare_dataset_2D, \r\n batched=True, \r\n remove_columns=ds.column_names, \r\n features=features.Features({"pixel_values": features.Array2D(shape=(1, 2), dtype="float32")})\r\n)\r\n```']

2022-04-20 18:04:32

2022-05-12 15:08:40

2022-05-12 15:08:40

NONE

**Is your feature request related to a problem? Please describe.** It is possible to create an `Array2D` column from a list of arrays of dimension 1. Similarly, I think it might be nice to be able to create a `Array3D` column from a list of lists of arrays of dimension 1. To illustrate my proposal, let's take the following toy dataset t: ```python import numpy as np from datasets import Dataset, features data_map = { 1: np.array([[0.2, 0,4],[0.19, 0,3]]), 2: np.array([[0.1, 0,4],[0.19, 0,3]]), } def create_toy_ds(): my_dict = {"id":[1, 2]} return Dataset.from_dict(my_dict) ds = create_toy_ds() ``` The following 2D processing works without any errors raised: ```python def prepare_dataset_2D(batch): batch["pixel_values"] = [data_map[index] for index in batch["id"]] return batch ds_2D = ds.map( prepare_dataset_2D, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array2D(shape=(2, 3), dtype="float32")}) ) ``` The following 3D processing doesn't work: ```python def prepare_dataset_3D(batch): batch["pixel_values"] = [[data_map[index]] for index in batch["id"]] return batch ds_3D = ds.map( prepare_dataset_3D, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3, dtype="float32")}) ) ``` The error raised is: ``` --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) [<ipython-input-6-676547e4cd41>](https://localhost:8080/#) in <module>() 3 batched=True, 4 remove_columns=ds.column_names, ----> 5 features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")}) 6 ) 12 frames [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1971 new_fingerprint=new_fingerprint, 1972 disable_tqdm=disable_tqdm, -> 1973 desc=desc, 1974 ) 1975 else: [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs) 518 self: "Dataset" = kwargs.pop("self") 519 # apply actual function --> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 522 for dataset in datasets: [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs) 485 } 486 # apply actual function --> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 489 # re-apply format to the output [/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py](https://localhost:8080/#) in wrapper(*args, **kwargs) 456 # Call actual function 457 --> 458 out = func(self, *args, **kwargs) 459 460 # Update fingerprint of in-place transforms + update in-place history of transforms [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2354 writer.write_table(batch) 2355 else: -> 2356 writer.write_batch(batch) 2357 if update_data and writer is not None: 2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file [/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_batch(self, batch_examples, writer_batch_size) 505 col_try_type = try_features[col] if try_features is not None and col in try_features else None 506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 507 arrays.append(pa.array(typed_sequence)) 508 inferred_features[col] = typed_sequence.get_inferred_type() 509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() [/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in __arrow_array__(self, type) 175 storage = list_of_np_array_to_pyarrow_listarray(data, type=pa_type.value_type) 176 else: --> 177 storage = pa.array(data, pa_type.storage_dtype) 178 return pa.ExtensionArray.from_storage(pa_type, storage) 179 /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Can only convert 1-dimensional array values ``` **Describe the solution you'd like** No error in the second scenario and an identical result to the following snippets. **Describe alternatives you've considered** There are other alternatives that work such as: ```python def prepare_dataset_3D_bis(batch): batch["pixel_values"] = [[data_map[index].tolist()] for index in batch["id"]] return batch ds_3D_bis = ds.map( prepare_dataset_3D_bis, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")}) ) ``` or ```python def prepare_dataset_3D_ter(batch): batch["pixel_values"] = [data_map[index][np.newaxis, :, :] for index in batch["id"]] return batch ds_3D_ter = ds.map( prepare_dataset_3D_ter, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")}) ) ``` But both solutions require the user to be aware that `data_map[index]` is an `np.array` type. cc @lhoestq as we discuss this offline :smile:

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PR_kwDODunzps42gK3y

Deprecate `shard_size` in `push_to_hub` in favor of `max_shard_size`

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-20 16:08:01

2022-04-22 13:58:25

2022-04-22 13:52:00

CONTRIBUTOR

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This PR adds a `max_shard_size` param to `push_to_hub` and deprecates `shard_size` in favor of this new param to have a more descriptive name (a shard has at most the `shard_size` bytes in `push_to_hub`) for the param and to align the API with [Transformers](https://github.com/huggingface/transformers/blob/ff06b177917384137af2d9585697d2d76c40cdfc/src/transformers/modeling_utils.py#L1350).

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PR_kwDODunzps42gGv5

Document how to use FAISS index for special operations

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-04-20 15:51:56

2022-05-06 08:43:10

2022-05-06 08:35:52

MEMBER

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Document how to use FAISS index for special operations, by accessing the index itself. Close #4029.

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PR_kwDODunzps42fpMv

Support streaming cnn_dailymail dataset

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['_The documentation is not available anymore as the PR was closed or merged._' 'Did you run the `datasets-cli` command before merging to make sure you generate all the examples ?']

2022-04-20 14:04:36

2022-05-11 13:39:06

2022-04-20 15:52:49

MEMBER

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Support streaming cnn_dailymail dataset. Fix #3969. CC: @severo

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PR_kwDODunzps42flGp

Don't duplicate data when encoding audio or image

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['_The documentation is not available anymore as the PR was closed or merged._' 'I\'m not familiar with the concept of streaming vs non-streaming in HF datasets. I just wonder that you have the distinction here. Why doesn\'t it work to always make use of `bytes`? "using a local file - which is often required for audio" - why would that be?\r\n\r\nThe `path` would always point to some location in the `cache_dir`? I think this can be problematic. I would have expected that after I did `dataset.save_to_disk(...)` that I can remove the cache dir. But maybe just because I\'m not familiar with HF. Or maybe the docs can be improved to clarify this.\r\n' "We could always load every data file into `bytes` and save it this way the audio as bytes in `arrow` format, but the problem then would be that it makes the `file` column useless, *i.e.* people cannot inspect the audio file locally anymore or else they would need to first save bytes as a file which is not evident. This either breaks backwards compatibility or forces the user to stored 2x the required size locally. There was a longer discussion here: https://github.com/huggingface/datasets/issues/3663\r\n\r\nIt's a good argument though that `dataset.save_to_disk(...)` should save everything that is needed to the disk and should be independent of other folders, but I do think the arguments of #3663 to not break backwards compatibility and to allow people to inspect the downloaded audio files locally are a bit more important here. \r\n\r\nBut maybe, we could add a flag, `save_files_as_bytes` or `make_independent`, `make_self_contained` or a better name to `save_to_disk(...)` and `push_to_hub(...)` that would allow to make the resulting folder completely independent. " 'What do you think @mariosasko @lhoestq @polinaeterna @anton-l ?\r\n' 'For context: you can either store the path to local images or audio files, or the bytes of those files.\r\n\r\nIf your images and audio files are local files, then the arrow file from `save_to_disk` will store paths to these files.\r\nIf you want to include the bytes or your images or audio files instead, you must `read()` those files first.\r\nThis can be done by storing the "bytes" instead of the "path" of the images or audio files.\r\n\r\nOn the other hand, the resulting Parquet files from `push_to_hub` are self-contained, so that anyone can reload the dataset from the Hub. If your dataset contains image or audio data, the Parquet files will store the bytes of your images or audio files.\r\n\r\nFor now I just updated the documentation: https://github.com/huggingface/datasets/pull/4193. Maybe we can also embed the image and audio bytes in `save_to_disk` when we implement sharding, so that is can be done as efficiently as `push_to_hub`.\r\n\r\nAnyway, merging this one :)']

2022-04-20 13:50:37

2022-04-21 09:17:00

2022-04-21 09:10:47