Datasets:

marasaki

/

github-issues

like 0

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"2020-12-09T19:49:28Z"

"2020-12-11T11:17:40Z"

"2020-12-11T11:17:40Z"

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"2020-12-29T01:05:21Z"

"2020-12-30T16:51:24Z"

"2020-12-30T16:51:24Z"

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[ "@lhoestq fixed" ]

"2020-12-08T19:42:04Z"

"2020-12-14T10:03:47Z"

"2020-12-14T10:03:46Z"

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[ "it’s failing because of `RemoteDatasetTest.test_load_dataset_orange_sum`\r\nwhich i think is not the dataset you are doing a PR for. Try rebasing with:\r\n```\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit push -u -f origin your_branch\r\n```", "> it’s failing because of `RemoteDatasetTest.test_load_dataset_orange_sum`\r\n> which i think is not the dataset you are doing a PR for. Try rebasing with:\r\n> \r\n> ```\r\n> git fetch upstream\r\n> git rebase upstream/master\r\n> git push -u -f origin your_branch\r\n> ```\r\n\r\nThanks, my branch seems to be up to date. \r\n```Current branch add-wiki-lingua-dataset is up to date.```", "Also where do the google drive urls come from ?", "looks like this PR includes changes about many other files than the ones for wiki_lingua.\r\n\r\nCan you create another branch and another PR ?\r\n(or you can try to fix this branch with rebase and push force if you're familiar with it)", "Thanks for fixing the dummy data and removing the glob call :) ", "> looks like this PR includes changes about many other files than the ones for wiki_lingua.\r\n> \r\n> Can you create another branch and another PR ?\r\n> (or you can try to fix this branch with rebase and push force if you're familiar with it)\r\n\r\nEasier to create a new branch and submit, I have submitted a new PR #1582 ", "Closing this one in favor of #1582 " ]

"2020-12-11T02:04:18Z"

"2020-12-16T15:27:13Z"

"2020-12-16T15:27:13Z"

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[ "Hmmm this one broke master. I'm fixing it.\r\n\r\nMaybe because your branch was outdated ?", "Sorry @lhoestq, I forgot to update the imports... :/", "It's fine, the CI should have caught this tbh. Not sure why it did't fail" ]

"2021-02-19T19:51:25Z"

"2021-02-22T14:56:56Z"

"2021-02-22T13:32:49Z"

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

"2022-03-25T10:11:28Z"

"2022-03-25T12:08:55Z"

"2022-03-25T12:03:52Z"

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[ "The CI error is unrelated to the change." ]

"2021-11-12T18:02:37Z"

"2021-11-16T18:18:17Z"

"2021-11-16T17:55:22Z"

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[ "Thanks for reporting, @sauravtii.\r\n\r\nUnfortunately, I'm not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"josianem/imdb\")\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 25799\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 25000\r\n })\r\n unsupervised: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 50000\r\n })\r\n})\r\n```\r\n\r\nCould you please retry to load the dataset? Maybe there was a temporary connection issue to Dropbox.", "Thanks @albertvillanova. I am facing another issue now\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"sample.py\", line 4, in <module>\r\n dataset = load_dataset(\"josianem/imdb\")\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 738, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=34501348, num_examples=25799, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nThis is my code\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\")\r\n```", "Your connection didn't work and you got an empty dataset (`num_bytes=0, num_examples=0`):\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: \r\n[\r\n {\r\n 'expected': SplitInfo(name='train', num_bytes=34501348, num_examples=25799, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }, \r\n {\r\n 'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }, \r\n {\r\n 'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }\r\n]\r\n```\r\n\r\nCould you please try the link in your browser and see if it works? https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1\r\n- If it does not work, you should contact the author of the dataset in their Community tab (https://huggingface.co/datasets/josianem/imdb/discussions) and inform them, so that they can host their data elsewhere, for example on the Hugging Face Hub itself\r\n\r\nIf the link works, you should try to load the dataset but forcing the re-download of the data files (so that the cache is refreshed with the actual data file), by passing `download_mode=\"force_redownload\"`:\r\n```python\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n```", "After pasting the link in the browser, it did start the download so it seems that the link is working. But even after including the `download_mode` in my code I am facing the same issue:\r\n\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n File \"sample.py\", line 4, in <module>\r\n dataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 704, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/saurav/.cache/huggingface/modules/datasets_modules/datasets/imdb/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f/imdb.py\", line 79, in _split_generators\r\n archive = dl_manager.download(_DOWNLOAD_URL)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 197, in map_nested\r\n return function(data_struct)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 289, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 606, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1\r\n```\r\n\r\nMy code:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n```", "I have tried again to reproduce your issue without success: the dataset loads perfectly, both in my local machine and in a Colab notebook.\r\n- See: https://colab.research.google.com/drive/1dky3T0XGFuldggy22NNQQN-UqOFqvnuY?usp=sharing\r\n\r\nI think the cause maight be that you are using a very old version of `datasets`. Please, could you update it and retry?\r\n```\r\npip install -U datasets\r\n```", "That worked!! Thanks @albertvillanova : )\r\n\r\n```\r\nDownloading builder script: 100%|███████| 4.20k/4.20k [00:00<00:00, 6.69MB/s]\r\nDownloading metadata: 100%|█████████████| 2.60k/2.60k [00:00<00:00, 3.41MB/s]\r\nDownloading readme: 100%|███████████████| 7.52k/7.52k [00:00<00:00, 12.6MB/s]\r\nDownloading and preparing dataset imdb/plain_text to /home/saurav/.cache/huggingface/datasets/josianem___imdb/plain_text/1.0.0/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f...\r\nDownloading data: 100%|███████████████████| 301M/301M [01:32<00:00, 3.25MB/s]\r\nDataset imdb downloaded and prepared to /home/saurav/.cache/huggingface/datasets/josianem___imdb/plain_text/1.0.0/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f. Subsequent calls will reuse this data.\r\n100%|█████████████████████████████████████████| 3/3 [00:00<00:00, 794.83it/s]\r\n```\r\n\r\nThe code I used:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n\r\n```\r\n\r\nBut when I remove `download_mode=\"force_redownload\"` I get the same error. Any guess on that?", "That is because the cache got the \"empty\" download file the first time you tried and got the connection error.\r\n\r\nThen, once you no longer get the connection error, you need to refresh the cache by passing `download_mode=\"force_redownload\"`." ]

"2023-04-17T09:08:18Z"

"2023-04-18T07:18:20Z"

"2023-04-18T07:18:20Z"

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[ "Hi! Thanks for the detailed analysis of adding IIIF support. I like the idea of \"using IIIF through datasets scripts\" due to its ease of use. Another approach that I like is yielding image ids and using the `piffle` library (which offers a bit more flexibility) + `map` to download + cache images. We can handle bad URLs in `map` by returning `None`. Plus, we can add a `Dataset Preprocessing` section with the code that explains this approach to the card of such datasets. WDYT?\r\n\r\n> currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.\r\n\r\nThis is why (currently) adding a new feature type would be overkill, IMO.\r\n" ]

"2022-03-28T15:19:25Z"

"2022-04-05T18:20:53Z"

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"2021-12-15T17:30:12Z"

"2021-12-20T14:22:13Z"

"2021-12-20T14:22:12Z"

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

"2022-07-04T20:24:40Z"

"2022-07-07T13:23:32Z"

"2022-07-07T13:11:47Z"

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

"2022-07-15T17:47:48Z"

"2022-07-18T08:46:56Z"

"2022-07-18T08:34:49Z"

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[ "Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?", "> Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?\r\n\r\nYes there's a few - I'll fix them tomorrow :)" ]

"2021-07-09T16:07:29Z"

"2021-07-12T18:32:59Z"

"2021-07-12T17:32:02Z"

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[ "These test errors are related to issue #4428 \r\n", "_The documentation is not available anymore as the PR was closed or merged._", "I only made a trivial modification in my commit https://github.com/huggingface/datasets/pull/4431/commits/402c893d35224d7828176717233909ac5f1e7b3e\r\n\r\nI have submitted a PR #4434 for the about issue.", "> Awesome thanks for adding this dataset :)\r\n> \r\n> I just have one comment about the licensing.\r\n> \r\n> Also it seems that you already have the dataset in https://huggingface.co/datasets/silver/personal_dialog, so it's unnecessary to add it here\r\n\r\nThank you very much for your comment.\r\n\r\nSo, should I close this PR?", "Thanks for fixing the licensing section :)\r\n\r\n> So, should I close this PR?\r\n\r\nYes you can close this PR, it's better if your dataset is under your namespace at https://huggingface.co/datasets/silver/personal_dialog :)\r\n\r\nDon't forget to update the licensing section on https://huggingface.co/datasets/silver/personal_dialog as well" ]

"2022-06-01T01:20:40Z"

"2022-06-11T12:40:23Z"

"2022-06-11T12:31:16Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006678 / 0.011353 (-0.004675) | 0.004013 / 0.011008 (-0.006995) | 0.083372 / 0.038508 (0.044864) | 0.070339 / 0.023109 (0.047230) | 0.339026 / 0.275898 (0.063128) | 0.370945 / 0.323480 (0.047465) | 0.004050 / 0.007986 (-0.003935) | 0.003283 / 0.004328 (-0.001046) | 0.064956 / 0.004250 (0.060705) | 0.055427 / 0.037052 (0.018374) | 0.341787 / 0.258489 (0.083297) | 0.385030 / 0.293841 (0.091189) | 0.031791 / 0.128546 (-0.096755) | 0.008511 / 0.075646 (-0.067135) | 0.286538 / 0.419271 (-0.132734) | 0.052893 / 0.043533 (0.009360) | 0.338522 / 0.255139 (0.083383) | 0.371821 / 0.283200 (0.088622) | 0.023731 / 0.141683 (-0.117951) | 1.485857 / 1.452155 (0.033702) | 1.515218 / 1.492716 (0.022502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232798 / 0.018006 (0.214792) | 0.446783 / 0.000490 (0.446293) | 0.007395 / 0.000200 (0.007195) | 0.000385 / 0.000054 (0.000330) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028866 / 0.037411 (-0.008545) | 0.081653 / 0.014526 (0.067127) | 0.094457 / 0.176557 (-0.082099) | 0.151761 / 0.737135 (-0.585375) | 0.095579 / 0.296338 (-0.200760) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379926 / 0.215209 (0.164717) | 3.801839 / 2.077655 (1.724184) | 1.830302 / 1.504120 (0.326182) | 1.686912 / 1.541195 (0.145717) | 1.803418 / 1.468490 (0.334928) | 0.484431 / 4.584777 (-4.100346) | 3.592748 / 3.745712 (-0.152964) | 3.402578 / 5.269862 (-1.867284) | 2.043434 / 4.565676 (-2.522242) | 0.057274 / 0.424275 (-0.367001) | 0.007211 / 0.007607 (-0.000396) | 0.462611 / 0.226044 (0.236567) | 4.610703 / 2.268929 (2.341775) | 2.397668 / 55.444624 (-53.046956) | 2.149983 / 6.876477 (-4.726494) | 2.199100 / 2.142072 (0.057028) | 0.575883 / 4.805227 (-4.229344) | 0.133421 / 6.500664 (-6.367243) | 0.061168 / 0.075469 (-0.014301) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246792 / 1.841788 (-0.594995) | 18.974385 / 8.074308 (10.900077) | 14.268859 / 10.191392 (4.077467) | 0.166340 / 0.680424 (-0.514084) | 0.018227 / 0.534201 (-0.515974) | 0.389646 / 0.579283 (-0.189637) | 0.418780 / 0.434364 (-0.015584) | 0.458063 / 0.540337 (-0.082275) | 0.635156 / 1.386936 (-0.751780) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006613 / 0.011353 (-0.004740) | 0.003977 / 0.011008 (-0.007031) | 0.064609 / 0.038508 (0.026101) | 0.070418 / 0.023109 (0.047308) | 0.395814 / 0.275898 (0.119916) | 0.424803 / 0.323480 (0.101323) | 0.005342 / 0.007986 (-0.002644) | 0.003252 / 0.004328 (-0.001076) | 0.065177 / 0.004250 (0.060927) | 0.055299 / 0.037052 (0.018247) | 0.403983 / 0.258489 (0.145494) | 0.438522 / 0.293841 (0.144681) | 0.032336 / 0.128546 (-0.096210) | 0.008524 / 0.075646 (-0.067122) | 0.071645 / 0.419271 (-0.347627) | 0.048137 / 0.043533 (0.004604) | 0.395170 / 0.255139 (0.140031) | 0.421727 / 0.283200 (0.138528) | 0.023028 / 0.141683 (-0.118655) | 1.500739 / 1.452155 (0.048584) | 1.568887 / 1.492716 (0.076170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227542 / 0.018006 (0.209536) | 0.447882 / 0.000490 (0.447393) | 0.005416 / 0.000200 (0.005216) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032954 / 0.037411 (-0.004457) | 0.091994 / 0.014526 (0.077468) | 0.105957 / 0.176557 (-0.070600) | 0.158728 / 0.737135 (-0.578407) | 0.104734 / 0.296338 (-0.191605) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436275 / 0.215209 (0.221066) | 4.344864 / 2.077655 (2.267209) | 2.304949 / 1.504120 (0.800829) | 2.123963 / 1.541195 (0.582768) | 2.189099 / 1.468490 (0.720609) | 0.492662 / 4.584777 (-4.092115) | 3.633662 / 3.745712 (-0.112051) | 3.251338 / 5.269862 (-2.018524) | 2.061378 / 4.565676 (-2.504299) | 0.058100 / 0.424275 (-0.366175) | 0.007311 / 0.007607 (-0.000297) | 0.516227 / 0.226044 (0.290183) | 5.184228 / 2.268929 (2.915300) | 2.780343 / 55.444624 (-52.664281) | 2.423428 / 6.876477 (-4.453048) | 2.617371 / 2.142072 (0.475298) | 0.590455 / 4.805227 (-4.214772) | 0.131728 / 6.500664 (-6.368936) | 0.059994 / 0.075469 (-0.015475) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354920 / 1.841788 (-0.486868) | 19.427822 / 8.074308 (11.353514) | 15.289037 / 10.191392 (5.097645) | 0.170437 / 0.680424 (-0.509987) | 0.020242 / 0.534201 (-0.513959) | 0.394921 / 0.579283 (-0.184362) | 0.426447 / 0.434364 (-0.007917) | 0.468321 / 0.540337 (-0.072017) | 0.671052 / 1.386936 (-0.715884) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bade7af74437347a760830466eb74f7a8ce0d799 \"CML watermark\")\n" ]

"2023-10-16T19:10:39Z"

"2023-10-17T15:13:37Z"

"2023-10-17T15:04:52Z"

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"2022-06-22T16:35:23Z"

"2022-06-23T10:13:44Z"

"2022-06-23T10:13:44Z"

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[ "Thanks for reporting @MoritzLaurer.\r\n\r\nCurrently, the dataset preview is working properly: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli\r\n\r\nPlease note that when a dataset is modified, it might take some time until the preview is completely updated.\r\n\r\n@severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?", "Thanks for your response. You are right, its now working well. I had waited for 30 min or so and refreshed several times and thought there was some other error. Yeah, a different error message sounds like a good idea to avoid confusion. ", "I'm closing this issue then.", "> @severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?\r\n\r\nYes, it's a known issue, and we're about to ship a better version" ]

"2022-08-19T14:55:20Z"

"2022-08-22T14:47:14Z"

"2022-08-22T06:13:20Z"

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[ "merging since the CI is fixed on master" ]

"2020-12-11T23:05:09Z"

"2020-12-17T14:38:41Z"

"2020-12-17T14:38:41Z"

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[ "Hi! \r\n\r\n\r\nYou can specify `download_config=DownloadConfig(resume_download=True))` in `load_dataset` to resume the download when re-running the code after the timeout error:\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\ndataset = load_dataset('the_pile', split='train', cache_dir='F:\\datasets', download_config=DownloadConfig(resume_download=True))\r\n```\r\n\r\n", "@mariosasko , I used your suggestion but its not saving anything , just stops and runs from the same point .\r\nbelow is the script to download and save on disk .\r\n\r\n```\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n\r\n#load the Pile dataset from Hugging Face Datasets\r\n#dataset = load_dataset('the_pile')\r\ndataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n\r\n\r\n# save each file in the dataset to disk\r\nfor i, example in enumerate(dataset['train']):\r\n filename = f'pile_file_{i}.json'\r\n with open(filename, 'w') as f:\r\n f.write(str(example))\r\n\r\nprint(\"Finished saving Pile dataset files to disk.\")\r\n```\r\n", "@mariosasko , it shows nothing in dataset folder\r\n\r\n```\r\n du -sh /mnt/nlp/hugging_face/*\r\n20K /mnt/nlp/hugging_face/datasets\r\n4.0K /mnt/nlp/hugging_face/download_pile.py\r\n```\r\n", "@mariosasko \r\n\r\n```\r\nroot@d20f0ab8f4f8:/mnt/hugging_face# python3 download_pile.py\r\nNo config specified, defaulting to: the_pile/all\r\nDownloading and preparing dataset the_pile/all to /mnt/hugging_face/datasets/the_pile/all/0.0.0/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349...\r\nDownloading data files: 0%| | 0/3 [00:00<?, ?it/s]\r\n\r\n\r\n\r\n\r\n\r\nDownloading data: 70%|████████████████████████████████████████████████████████████████████▊ | 10.7G/15.2G [12:09<11:53, 6.36MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [22:15<00:00, 7.25MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [46:17<00:00, 5.48MB/s]\r\nDownloading data: 40%|██████████████████████████████████████▏ | 6.07G/15.3G [50:49<1:17:02, 1.99MB/s]\r\nTraceback (most recent call last):██████████████████████████▊ | 6.07G/15.3G [50:49<25:35:23, 99.9kB/s]\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 444, in _error_catcher\r\n yield\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 567, in read\r\n data = self._fp_read(amt) if not fp_closed else b\"\"\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 525, in _fp_read\r\n data = self._fp.read(chunk_amt)\r\n File \"/usr/lib/python3.8/http/client.py\", line 459, in read\r\n n = self.readinto(b)\r\n File \"/usr/lib/python3.8/http/client.py\", line 503, in readinto\r\n n = self.fp.readinto(b)\r\n File \"/usr/lib/python3.8/socket.py\", line 669, in readinto\r\n return self._sock.recv_into(b)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1241, in recv_into\r\n return self.read(nbytes, buffer)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1099, in read\r\n return self._sslobj.read(len, buffer)\r\nConnectionResetError: [Errno 104] Connection reset by peer\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 816, in generate\r\n yield from self.raw.stream(chunk_size, decode_content=True)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 628, in stream\r\n data = self.read(amt=amt, decode_content=decode_content)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 593, in read\r\n raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n File \"/usr/lib/python3.8/contextlib.py\", line 131, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 461, in _error_catcher\r\n raise ProtocolError(\"Connection broken: %r\" % e, e)\r\nurllib3.exceptions.ProtocolError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_pile.py\", line 6, in <module>\r\n dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 945, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/root/.cache/huggingface/modules/datasets_modules/datasets/the_pile/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349/the_pile.py\", line 192, in _split_generators\r\n data_dir = dl_manager.download(_DATA_URLS[self.config.name])\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 427, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 443, in map_nested\r\n mapped = [\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 444, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in _single_map_nested\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in <listcomp>\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 346, in _single_map_nested\r\n return function(data_struct)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 453, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 182, in cached_path\r\n output_path = get_from_cache(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 575, in get_from_cache\r\n http_get(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 379, in http_get\r\n for chunk in response.iter_content(chunk_size=1024):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 818, in generate\r\n raise ChunkedEncodingError(e)\r\nrequests.exceptions.ChunkedEncodingError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n```\r\n", "Users with slow internet speed are doomed (4MB/s). The dataset downloads fine at minimum speed 10MB/s.\n\nAlso, when the train splits were generated and then I removed the downloads folder to save up disk space, it started redownloading the whole dataset. Is there any way to use the already generated splits instead?", "@sentialx @mariosasko , anytime on my above script , am I downloading and saving dataset correctly . Please suggest :)", "@sentialx probably worth noting that `resume_download=True` doesn't directly save the dataset to disk, but instead just helps in resuming the dataset resume on interruption as @mariosasko mentions. resolving resumptions after a crash is [an open issue](https://github.com/huggingface/datasets/issues/5380) at the moment." ]

"2023-03-03T09:52:08Z"

"2023-10-14T02:15:52Z"

"2023-03-24T12:44:25Z"

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

"2022-09-30T12:24:27Z"

"2022-10-03T11:00:11Z"

"2022-10-03T10:58:15Z"

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"2022-08-09T14:36:49Z"

"2022-08-31T11:59:08Z"

"2022-08-31T11:59:08Z"

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"2021-03-09T13:18:29Z"

"2021-03-10T10:08:37Z"

"2021-03-10T10:08:36Z"

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

"2022-04-26T18:30:46Z"

"2022-05-03T17:09:24Z"

"2022-05-03T17:02:28Z"

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"2021-07-09T00:09:17Z"

"2021-07-13T17:13:19Z"

"2021-07-13T16:08:30Z"

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[ "Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n", "Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) only to convert it back into the arrow type (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L143, and https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L35). Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!", "The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense ", "OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L698) and `generate_from_arrow_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!", "Yes you're totally right :)" ]

"2021-02-16T20:38:04Z"

"2021-02-19T18:27:11Z"

"2021-02-19T18:27:11Z"

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[ "Normally, I'm expecting errors from the validation of the README file... 😅 ", "That is:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_cards.py::test_changed_dataset_card[blog_authorship_corpus]\r\n==== 1 failed, 3182 passed, 2763 skipped, 16 warnings in 201.23s (0:03:21) =====\r\n```", "@lhoestq, apart from the dataset card, everything is OK with this PR: I tested it locally." ]

"2021-07-20T15:44:50Z"

"2021-07-21T13:11:58Z"

"2021-07-21T13:11:58Z"

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[ "I wasn't sure to file this against transformers or datasets.", "[`KeyDataset`](https://github.com/huggingface/transformers/blob/7f8b909189547944617741d8d3c6c84504701693/src/transformers/pipelines/pt_utils.py#L296) doesn't support iterable datasets, so you either need to implement a version that does (and also indexing nested (translation) fields):\r\n\r\n```python\r\nfrom torch.utils.data import Dataset, IterableDataset\r\n\r\ndef build_key_fetcher(key: str):\r\n def _key_fetcher(item):\r\n for sub_key in key.split(\".\"):\r\n item = item[sub_key]\r\n return item\r\n return _key_fetcher\r\n\r\nclass KeyDataset(Dataset):\r\n def __new__(cls, dataset: Dataset, key: str):\r\n cls = _KeyIterableDataset if isinstance(dataset, IterableDataset) else _KeyMapDataset\r\n self = object.__new__(cls)\r\n self.dataset = dataset\r\n self.key = key\r\n self._key_fetcher = build_key_fetcher(key)\r\n return self\r\n\r\nclass _KeyMapDataset(KeyDataset):\r\n def __getitem__(self, i):\r\n return self._key_fetcher(self.dataset[i])\r\n \r\n def __len__(self):\r\n return len(self.dataset)\r\n\r\n\r\nclass _KeyIterableDataset(KeyDataset):\r\n def __iter__(self):\r\n for ex in self.dataset:\r\n yield self._key_fetcher(ex)\r\n\r\nks = KeyDataset(ds, \"translation.en\")\r\n```\r\n\r\nor use `IterableDataset`'s `map`:\r\n```python\r\ndef fetch_en_translation(ex):\r\n return {\"en\": ex[\"translation\"][\"en\"]}\r\nks = ds.map(fetch_en_translation, remove_columns=ds.column_names) \r\n```\r\n\r\ncc @sgugger: Perhaps the `KeyDataset` + PyTorch `IterableDataset` case should be supported by Transformers", "@mariosasko The map snippet didn't quite work, but gave me enough of a clue to get it working. The following snippet does work:\r\n```\r\ndef en_translation(x):\r\n return {\"en\":x['translation']['en']}\r\nks = ds.map(en_translation, remove_columns=['translation'])\r\ntest=[]\r\nfor x in iter(ks):\r\n test.append(x['en'])\r\nxx= mt(test)\r\nfor x in xx:\r\n print(x)\r\n```\r\n\r\nI tried just returning `x['translation']['en`]` in the helper function instead of the dict, but that didn't give me an iterator over strings that pipeline would work with either.\r\n\r\n\r\nThe snippet as is gives the following error:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/pdb.py\", line 1704, in main\r\n pdb._runscript(mainpyfile)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/pdb.py\", line 1573, in _runscript\r\n self.run(statement)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/bdb.py\", line 580, in run\r\n exec(cmd, globals, locals)\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/jlquinn/models/hf/ende.t5.pipe.py\", line 1, in <module>\r\n from transformers import pipeline\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 335, in __call__\r\n return super().__call__(*args, **kwargs)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 138, in __call__\r\n result = super().__call__(*args, **kwargs)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/base.py\", line 1027, in __call__\r\n return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/base.py\", line 1033, in run_single\r\n model_inputs = self.preprocess(inputs, **preprocess_params)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 287, in preprocess\r\n return super()._parse_and_tokenize(*args, truncation=truncation)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 100, in _parse_and_tokenize\r\n raise ValueError(\r\nValueError: `args[0]`: <datasets.iterable_dataset.IterableDataset object at 0x7f5fd38ef1c0> have the wrong format. The should be either of type `str` or type `list`\r\nUncaught exception. Entering post mortem debugging\r\nRunning 'cont' or 'step' will restart the program\r\n```\r\n", "So perhaps there's no bug exactly, but I would love to see two things: 1) improve the documentation to better understand what's really getting returned. 2) update the example provided of using transformer pipeline with a dataset to include the oddball case that translation appears to be.", "cc @Narsil ", "Hi,\r\n\r\nfor the original snippet, the issue is that `streaming` datasets are not countable (they have no len) and therefore `KeyDataset` cannot work with them ( KeyDataset is a dataset and therefore requires a length).\r\n\r\nI modified slightly the original snippet to make it work:\r\n\r\n```python\r\nfrom transformers import pipeline\r\nfrom transformers.pipelines.pt_utils import KeyDataset\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(path=\"wmt14\", name=\"fr-en\", split=\"test\", streaming=True)\r\nbs = 1\r\nmt = pipeline(\r\n \"translation_en_to_fr\", model=\"hf-internal-testing/tiny-random-T5ForConditionalGeneration\", batch_size=bs\r\n)\r\n\r\n\r\ndef ks(ds):\r\n for item in ds:\r\n yield item[\"translation\"][\"en\"]\r\n\r\n\r\n# print(f\"{ks}\")\r\nxx = mt(ks(ds))\r\nfor x in xx:\r\n print(x)\r\n```\r\n\r\nThis is what the first example in the docs suggests to use (as it's the most flexible): https://huggingface.co/docs/transformers/v4.29.1/en/pipeline_tutorial#using-pipelines-on-a-dataset\r\n\r\n`KeyDataset` really exists only to get a `sized` dataset to work nicer with `tqdm` for instance.\r\n\r\n@sgugger should we update the docs to remove `KeyDataset` entirely ? (We can add a note to pass manually the length of the data to tqdm so that the progress bar option can still be easy to use ?)\r\n", "Maybe moving `KeyDataset` later on in the guide and specify it's mostly for streaming then? Or is it also necessary for batch_size>1 (which is what the current doc implies)?", "Hmm\r\n\r\nIterator (`yield`) :\r\n- Not countable\r\n- Super flexible\r\n- Cannot use `num_workers>1` (threading requires indexing at the correct location, iterators require to iterate in order,so each thread would iterate over the full thing being genuinely a bad idea)\r\n- Can batch\r\n- tqdm doesn't show a nice progress bar (it has no total)\r\n\r\nKeyDataset (Or any PyTorch like Dataset returning the correct object for the pipeline):\r\n- Countable\r\n- Less flexible (not applicable to datasets with streaming), can only work on single keys. But should be easy to read and write your own (like @mariosasko did)\r\n- Works with `num_workers > 1` (Every worker can fetch exactly what's needed)\r\n- Can batch \r\n- tqdm shows a nice progress bar\r\n\r\nIn the docs, if we update all the examples to use iterators, and include an example with\r\n\r\n```\r\nfor item in tqdm.tqdm(pipe(iterator(), total=len(dataset))))\r\n```\r\n\r\nWe can save the biggest feature that doesn't work out of the box with iterators which is the tqdm progress bar.\r\n\r\n`num_workers>1` we can mention it, but it tends to be an issues only on CPU intensive loads, like image (and maybe audio)\r\n" ]

"2023-05-11T21:52:38Z"

"2023-05-16T15:59:55Z"

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

"2022-06-03T13:55:50Z"

"2022-06-03T14:35:32Z"

"2022-06-03T14:27:09Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006529 / 0.011353 (-0.004824) | 0.004527 / 0.011008 (-0.006481) | 0.098051 / 0.038508 (0.059543) | 0.028058 / 0.023109 (0.004949) | 0.368543 / 0.275898 (0.092645) | 0.397126 / 0.323480 (0.073646) | 0.005072 / 0.007986 (-0.002913) | 0.003377 / 0.004328 (-0.000952) | 0.076867 / 0.004250 (0.072617) | 0.040121 / 0.037052 (0.003069) | 0.373422 / 0.258489 (0.114933) | 0.403969 / 0.293841 (0.110128) | 0.031485 / 0.128546 (-0.097061) | 0.011673 / 0.075646 (-0.063973) | 0.321837 / 0.419271 (-0.097434) | 0.042828 / 0.043533 (-0.000704) | 0.370391 / 0.255139 (0.115252) | 0.391737 / 0.283200 (0.108538) | 0.084764 / 0.141683 (-0.056919) | 1.463114 / 1.452155 (0.010959) | 1.527042 / 1.492716 (0.034325) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200964 / 0.018006 (0.182958) | 0.403967 / 0.000490 (0.403477) | 0.002439 / 0.000200 (0.002239) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023531 / 0.037411 (-0.013880) | 0.097424 / 0.014526 (0.082899) | 0.104854 / 0.176557 (-0.071703) | 0.165682 / 0.737135 (-0.571453) | 0.109416 / 0.296338 (-0.186922) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431041 / 0.215209 (0.215832) | 4.326039 / 2.077655 (2.248384) | 2.085123 / 1.504120 (0.581003) | 1.922720 / 1.541195 (0.381525) | 2.006608 / 1.468490 (0.538118) | 0.703348 / 4.584777 (-3.881428) | 3.441516 / 3.745712 (-0.304196) | 1.875244 / 5.269862 (-3.394618) | 1.181341 / 4.565676 (-3.384336) | 0.083442 / 0.424275 (-0.340833) | 0.012966 / 0.007607 (0.005359) | 0.536047 / 0.226044 (0.310002) | 5.354856 / 2.268929 (3.085927) | 2.451064 / 55.444624 (-52.993560) | 2.076110 / 6.876477 (-4.800367) | 2.196507 / 2.142072 (0.054435) | 0.811196 / 4.805227 (-3.994032) | 0.152547 / 6.500664 (-6.348118) | 0.067978 / 0.075469 (-0.007491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196169 / 1.841788 (-0.645618) | 13.697234 / 8.074308 (5.622926) | 13.966652 / 10.191392 (3.775260) | 0.143735 / 0.680424 (-0.536688) | 0.016484 / 0.534201 (-0.517717) | 0.382349 / 0.579283 (-0.196934) | 0.401507 / 0.434364 (-0.032857) | 0.447297 / 0.540337 (-0.093041) | 0.529779 / 1.386936 (-0.857157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006698 / 0.011353 (-0.004655) | 0.004608 / 0.011008 (-0.006400) | 0.076220 / 0.038508 (0.037712) | 0.027340 / 0.023109 (0.004231) | 0.344095 / 0.275898 (0.068197) | 0.374715 / 0.323480 (0.051235) | 0.004883 / 0.007986 (-0.003102) | 0.004658 / 0.004328 (0.000330) | 0.075381 / 0.004250 (0.071130) | 0.036099 / 0.037052 (-0.000953) | 0.340382 / 0.258489 (0.081893) | 0.383488 / 0.293841 (0.089647) | 0.031534 / 0.128546 (-0.097012) | 0.011735 / 0.075646 (-0.063912) | 0.085895 / 0.419271 (-0.333377) | 0.042226 / 0.043533 (-0.001306) | 0.340301 / 0.255139 (0.085162) | 0.366079 / 0.283200 (0.082879) | 0.088828 / 0.141683 (-0.052854) | 1.487880 / 1.452155 (0.035725) | 1.561318 / 1.492716 (0.068601) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226366 / 0.018006 (0.208360) | 0.408934 / 0.000490 (0.408444) | 0.000396 / 0.000200 (0.000196) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024521 / 0.037411 (-0.012891) | 0.100167 / 0.014526 (0.085641) | 0.106480 / 0.176557 (-0.070077) | 0.156377 / 0.737135 (-0.580758) | 0.111709 / 0.296338 (-0.184630) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436138 / 0.215209 (0.220928) | 4.370919 / 2.077655 (2.293265) | 2.066402 / 1.504120 (0.562282) | 1.862157 / 1.541195 (0.320962) | 1.920701 / 1.468490 (0.452211) | 0.695517 / 4.584777 (-3.889260) | 3.435558 / 3.745712 (-0.310154) | 1.864000 / 5.269862 (-3.405861) | 1.164134 / 4.565676 (-3.401543) | 0.083006 / 0.424275 (-0.341269) | 0.012751 / 0.007607 (0.005144) | 0.535405 / 0.226044 (0.309360) | 5.368530 / 2.268929 (3.099602) | 2.494197 / 55.444624 (-52.950427) | 2.161370 / 6.876477 (-4.715107) | 2.180345 / 2.142072 (0.038272) | 0.808076 / 4.805227 (-3.997151) | 0.151891 / 6.500664 (-6.348773) | 0.067643 / 0.075469 (-0.007826) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334245 / 1.841788 (-0.507543) | 14.112805 / 8.074308 (6.038497) | 14.152303 / 10.191392 (3.960911) | 0.153492 / 0.680424 (-0.526932) | 0.016542 / 0.534201 (-0.517659) | 0.376013 / 0.579283 (-0.203270) | 0.386528 / 0.434364 (-0.047836) | 0.436461 / 0.540337 (-0.103876) | 0.519278 / 1.386936 (-0.867658) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce1d1076fc55ac49277398304e551f0b56c3c9e2 \"CML watermark\")\n" ]

"2023-03-14T15:29:11Z"

"2023-03-14T16:37:06Z"

"2023-03-14T16:29:52Z"

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[ "Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default codec is `cp1252`, which causes the problem.", "Awesome, thank you. 😃 ", "@sgraaf, I have just merged the fix in the master branch.\r\n\r\nYou can either:\r\n- install `datasets` from source code\r\n- wait until we make the next release of `datasets`\r\n- set the `utf-8` codec as your default instead of `cp1252`. This can be done by activating the Python [UTF-8 mode](https://www.python.org/dev/peps/pep-0540) either by passing the command-line option `-X utf8` or by setting the environment variable `PYTHONUTF8=1`." ]

"2021-05-05T09:22:52Z"

"2021-05-05T10:57:31Z"

"2021-05-05T10:50:55Z"

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[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3936). All of your documentation changes will be reflected on that endpoint." ]

"2022-03-16T08:48:04Z"

"2022-03-16T17:04:07Z"

"2022-03-16T17:04:05Z"

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[]

[ "You can add columns that you wish to set into `torch` format using `dataset.set_format(\"torch\", ['id', 'abc'])` so that input batch of the transform only contains those columns", "Sorry, I miss `output_all_columns` args and thought after `dataset.set_format(\"torch\", columns=columns)` I can only get specific columns I assigned." ]

"2021-12-27T07:19:55Z"

"2021-12-27T09:07:07Z"

"2021-12-27T09:07:07Z"

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[]

[ "I tried to make the dataset much more smaller (100000 rows) , then the speed became `33.88it/s` from`8.62s/it`. It's nearly 200 times... Do you have any idea? Thank you!", "Similar issue: https://github.com/huggingface/transformers/issues/8818\r\n\r\nI changed `RandomSampler` to `SequentialSampler` in the `trainer.py`, but the speed didn't become faster.", "I changed\r\n```\r\ntokenized_datasets = load_from_disk(\r\n \"/pathto/dataset\"\r\n )\r\n```\r\nto\r\n```\r\ntokenized_datasets = load_from_disk(\r\n \"/pathto/dataset\", keep_in_memory=True\r\n )\r\n```\r\nand obtained normal speed. It's seems that the problem is on the os's speed limit.", "Hi ! Currently `save_to_disk` saves one big Arrow file, which causes some slow downs. This has been discussed in #3735 and we'll implement sharding pretty soon to solve this\r\n\r\nFor now you can try splitting and saving your dataset in several Arrow files. Then you can load them one by one and use `concatenate_datasets` to have your big dataset again and hopefully with a better speed" ]

"2022-05-23T14:04:37Z"

"2022-06-06T16:08:01Z"

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"2022-11-25T13:29:33Z"

"2022-11-29T08:05:13Z"

"2022-11-29T08:05:13Z"

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[ "Hi @lhoestq,\r\nI plunged into the code and it should be manageable for me to work on it!\r\n#take\r\n\r\nAlso, setting `d1`, `d2` and `d3` as you did raised a `SyntaxError: 'yield' inside list comprehension` for me, on Python 3.8.10.\r\nThe following snippet works for me though:\r\n```\r\nd1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\nd2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\nd3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n```\r\n\r\n", "Great @ylacombe thanks ! I'm assigning you this issue", "Hi @ylacombe :) Is there anything I can do to help ? Feel free to ping me if you have any question :)", "Hi @lhoestq,\r\n\r\nI actually have already wrote the code last time [on this commit](https://github.com/ylacombe/datasets/commit/84769db97facc78a33ec53f7b1b395951e1804df) but I still have to change the docs and write some tests though. I'm working on it.\r\n\r\nHowever, I still your advice on one matter. \r\nIn #4831, when using a `Dataset` list with probabilities, I had change the original behavior so that it stops as soon as one or all datasets are out of samples. By nature, this behavior can't be applied with an `IterableDataset` because one only knows an iterable dataset is out of sample when receiving a StopIteration error after calling the iterator once again. \r\nTo sum up, as it is right know, the behavior is not consistent with an `IterableDataset` list or a `Dataset` list, when using probabilities.\r\nTo be honest, I think that the current behavior with a `Dataset` list is desirable and avoid having too many samples, so I would recommand keeping that as it is, but I can understand the desire to have the same behavior for both classes. \r\nWhat do you think ? Please let me know if you need more details.\r\n\r\n\r\nEDIT:\r\nHere is an example:\r\n```\r\n>>> from tests.test_iterable_dataset import *\r\n>>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\n>>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\n>>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)\r\n>>> [x[\"a\"] for x in dataset]\r\n[10, 0, 11, 1, 2, 20, 12, 13]\r\n>>> from tests.test_arrow_dataset import *\r\n>>> d1 = Dataset.from_dict({\"a\": [0, 1, 2]})\r\n>>> d2 = Dataset.from_dict({\"a\": [10, 11, 12]})\r\n>>> d3 = Dataset.from_dict({\"a\": [20, 21, 22]})\r\n>>> interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)[\"a\"]\r\n[10, 0, 11, 1, 2]\r\n[10, 0, 11, 1, 2]\r\n```\r\n ", "Hi ! Awesome :) \r\n\r\nMaybe you can pre-load the next sample to know if the dataset is empty or not ?\r\nThis way it should be possible to have the same behavior for `IterableDataset`", "Hi @ylacombe let us know if we can help with anything :)", "Hi @lhoestq, I've finally made some advances in the matter. I've modified the `IterableDataset` behavior so that it aligns with the `Dataset` behavior as we have discussed. The documentation has been dealt with too. \r\nIt works as expected on my examples. However I'm having trouble figuring out how to test `interleave_datasets` on `test_iterable_datasets.py` as I have never worked with pytest. Could you help me on that or give me some indications? \r\n", "Thanks @ylacombe :)\r\n\r\nUsing the `pytest` command, you can run all the functions in a python file that start with \"test_*\" and make sure they return not errors:\r\n```\r\npytest tests/test_iterable_dataset.py\r\n```\r\n\r\nIn our case it can be nice to define a `test_interleave_datasets_with_oversampling` function. This function can contain the code example that we mentioned earlier in this github issue to make sure it works as expected.", "Resolved via #5036." ]

"2022-08-25T10:06:55Z"

"2022-10-03T12:37:46Z"

"2022-10-03T12:37:46Z"

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[ "Cool thanks :) Feel free to merge if it's all good for you" ]

"2021-12-08T14:58:43Z"

"2021-12-14T17:08:42Z"

"2021-12-14T17:08:42Z"

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[ "Not yet but it’s on the short/mid-term roadmap (requested by many indeed).", "oh, great, really awesome feature to have, thank you very much for the great, fabulous work", "We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)", "thanks a lot Quentin, this would be really really a great feature to have\n\nOn Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> We'll work on dataset streaming soon. This should allow you to only load\n> the examples you need ;)\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2161#issuecomment-814791922>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ>\n> .\n>\n", "Is streaming completed? On the 1.8.0 docs it is mentioned (https://huggingface.co/docs/datasets/dataset_streaming.html), but when following the example I get the following error:\r\n\r\n```\r\n>>> dataset2 = load_dataset(\"amazon_us_reviews\", \"Pet_Products_v1_00\", split='train', streaming=True)\r\n\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<ipython-input-21-1eedab26cff1> in <module>()\r\n----> 1 en_dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n\r\n3 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n 339 if value is not None:\r\n 340 if not hasattr(builder_config, key):\r\n--> 341 raise ValueError(f\"BuilderConfig {builder_config} doesn't have a '{key}' key.\")\r\n 342 setattr(builder_config, key, value)\r\n 343 \r\n\r\nValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key.\r\n```\r\n\r\nUPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package:\r\n\r\n```\r\n!pip install git+https://github.com/huggingface/datasets.git\r\n!pip install datasets[streaming]\r\n```", "Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)" ]

"2021-04-02T10:06:46Z"

"2022-10-05T13:26:51Z"

"2022-10-05T13:26:51Z"

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[ "Fix #3089.", "@albertvillanova all tests are passing now. Either you or @lhoestq can review it!" ]

"2021-10-15T09:31:14Z"

"2022-07-10T14:36:49Z"

"2021-10-22T08:23:57Z"

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[ "Hi! This is indeed a bug in our zero-copy logic.\r\n\r\nTo fix it, instead of the line:\r\nhttps://github.com/huggingface/datasets/blob/7cfac43b980ab9e4a69c2328f085770996323005/src/datasets/features/features.py#L702\r\n\r\nwe should have:\r\n```python\r\nreturn pa.types.is_primitive(pa_type) and not (pa.types.is_boolean(pa_type) or pa.types.is_temporal(pa_type))\r\n```", "@mariosasko submitted a small PR [here](https://github.com/huggingface/datasets/pull/5504)" ]

"2023-02-01T20:47:33Z"

"2023-02-08T14:33:19Z"

"2023-02-08T14:33:19Z"

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[ "Hi! Yes, that makes sense! Would you like to submit a PR to fix this?", "Created PR https://github.com/huggingface/datasets/pull/3971" ]

"2022-03-18T06:13:23Z"

"2022-04-12T14:41:58Z"

"2022-04-12T14:41:58Z"

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

"2022-03-23T14:51:48Z"

"2022-03-23T18:24:52Z"

"2022-03-23T18:19:27Z"

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

"2022-08-31T16:52:27Z"

"2022-09-22T14:34:11Z"

"2022-09-01T05:04:53Z"

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"2022-11-17T02:39:57Z"

"2022-11-18T10:53:45Z"

"2022-11-18T10:53:45Z"

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

"2022-07-08T10:49:13Z"

"2022-07-08T14:14:53Z"

"2022-07-08T14:02:42Z"

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"2021-03-29T15:34:13Z"

"2021-03-31T12:48:02Z"

"2021-03-31T12:48:01Z"

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[ "@SBrandeis all checks passing" ]

"2020-12-16T14:22:08Z"

"2020-12-17T16:07:10Z"

"2020-12-17T16:07:10Z"

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[ "@lhoestq thanks for the comments - I believe these are now addressed. I re-generated the datasets_info.json and dummy data" ]

"2021-01-31T17:38:55Z"

"2021-02-10T20:39:13Z"

"2021-02-03T10:35:54Z"

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[ "Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can share with the Arrow team for further debugging ?", "> If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nYup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.\r\n\r\n> Do you have a minimum reproducible example...\r\n\r\nNot sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?\r\nJust generate a random manifest list, I can add that to the above script if that's what you mean?\r\n", "Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.\r\n\r\nsetting `max_shard_size=\"2GB\"` should do the job (or `max_shard_size=\"1GB\"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)" ]

"2022-07-20T01:16:03Z"

"2022-07-22T14:11:47Z"

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"2021-01-06T08:26:03Z"

"2021-01-07T23:53:47Z"

"2021-01-07T14:01:22Z"

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[ "Echo'ing this observation: I have a few datasets in the neighborhood of 2GB CSVs uncompressed, and when I use something like `Dataset.save_to_disk()` it's ~18GB on disk.\r\n\r\nIf this is unexpected behavior, would be happy to help run debugging as needed.", "Thanks @ioana-blue for pointing out this problem (and thanks also @justin-yan). You are right that current implementation of the datasets caching files take too much memory. We are definitely changing this and optimizing the defaults, so that the file sizes are considerably reduced. I will come back to you as soon as this is fixed.", "Thank you! Also I noticed that the files don't seem to be cleaned after the jobs finish. Last night I had only 3 jobs running, but the cache was still at 180GB. ", "And to clarify, it's not memory, it's disk space. Thank you!", "Hi ! As Albert said they can sometimes take more space that expected but we'll fix that soon.\r\n\r\nAlso, to give more details about caching: computations on a dataset are cached by default so that you don't have to recompute them the next time you run them.\r\n\r\nSo by default the cache files stay on your disk when you job is finished (so that if you re-execute it, it will be reloaded from the cache).\r\nFeel free to clear your cache after your job has finished, or disable caching using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```", "Thanks for the tip, this is useful. ", "Hi @ioana-blue, we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.", "Thank you!" ]

"2021-03-02T14:35:53Z"

"2021-03-30T14:03:59Z"

"2021-03-16T09:44:00Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006173 / 0.011353 (-0.005180) | 0.003773 / 0.011008 (-0.007235) | 0.099499 / 0.038508 (0.060991) | 0.037918 / 0.023109 (0.014809) | 0.321329 / 0.275898 (0.045431) | 0.379739 / 0.323480 (0.056259) | 0.004664 / 0.007986 (-0.003322) | 0.002943 / 0.004328 (-0.001385) | 0.077759 / 0.004250 (0.073509) | 0.055271 / 0.037052 (0.018219) | 0.329428 / 0.258489 (0.070939) | 0.378731 / 0.293841 (0.084890) | 0.027737 / 0.128546 (-0.100810) | 0.008566 / 0.075646 (-0.067081) | 0.313220 / 0.419271 (-0.106052) | 0.047101 / 0.043533 (0.003568) | 0.316211 / 0.255139 (0.061072) | 0.341826 / 0.283200 (0.058626) | 0.020838 / 0.141683 (-0.120845) | 1.550064 / 1.452155 (0.097909) | 1.706518 / 1.492716 (0.213801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203093 / 0.018006 (0.185087) | 0.425345 / 0.000490 (0.424856) | 0.004800 / 0.000200 (0.004600) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024590 / 0.037411 (-0.012821) | 0.098115 / 0.014526 (0.083589) | 0.108274 / 0.176557 (-0.068282) | 0.170804 / 0.737135 (-0.566332) | 0.110560 / 0.296338 (-0.185778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425251 / 0.215209 (0.210042) | 4.239075 / 2.077655 (2.161421) | 1.955601 / 1.504120 (0.451481) | 1.774796 / 1.541195 (0.233602) | 1.826641 / 1.468490 (0.358150) | 0.558777 / 4.584777 (-4.026000) | 3.361697 / 3.745712 (-0.384015) | 1.764468 / 5.269862 (-3.505394) | 1.032280 / 4.565676 (-3.533396) | 0.067872 / 0.424275 (-0.356403) | 0.010998 / 0.007607 (0.003391) | 0.525682 / 0.226044 (0.299637) | 5.254356 / 2.268929 (2.985427) | 2.384332 / 55.444624 (-53.060292) | 2.045578 / 6.876477 (-4.830898) | 2.170914 / 2.142072 (0.028841) | 0.674782 / 4.805227 (-4.130445) | 0.135351 / 6.500664 (-6.365314) | 0.066591 / 0.075469 (-0.008878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209181 / 1.841788 (-0.632606) | 14.044518 / 8.074308 (5.970210) | 13.184705 / 10.191392 (2.993313) | 0.130836 / 0.680424 (-0.549588) | 0.016582 / 0.534201 (-0.517619) | 0.360005 / 0.579283 (-0.219279) | 0.379519 / 0.434364 (-0.054845) | 0.422174 / 0.540337 (-0.118164) | 0.515546 / 1.386936 (-0.871390) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006293 / 0.011353 (-0.005060) | 0.003784 / 0.011008 (-0.007224) | 0.079248 / 0.038508 (0.040739) | 0.038452 / 0.023109 (0.015343) | 0.444727 / 0.275898 (0.168829) | 0.500535 / 0.323480 (0.177055) | 0.003455 / 0.007986 (-0.004531) | 0.002873 / 0.004328 (-0.001455) | 0.077439 / 0.004250 (0.073189) | 0.047855 / 0.037052 (0.010803) | 0.448049 / 0.258489 (0.189560) | 0.509517 / 0.293841 (0.215676) | 0.028359 / 0.128546 (-0.100188) | 0.008503 / 0.075646 (-0.067143) | 0.084961 / 0.419271 (-0.334310) | 0.042880 / 0.043533 (-0.000653) | 0.436628 / 0.255139 (0.181489) | 0.456574 / 0.283200 (0.173375) | 0.019539 / 0.141683 (-0.122144) | 1.561273 / 1.452155 (0.109118) | 1.572018 / 1.492716 (0.079301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230250 / 0.018006 (0.212244) | 0.415189 / 0.000490 (0.414700) | 0.003213 / 0.000200 (0.003013) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025541 / 0.037411 (-0.011871) | 0.102326 / 0.014526 (0.087800) | 0.110258 / 0.176557 (-0.066298) | 0.162488 / 0.737135 (-0.574647) | 0.112782 / 0.296338 (-0.183556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457936 / 0.215209 (0.242727) | 4.581503 / 2.077655 (2.503848) | 2.237659 / 1.504120 (0.733540) | 2.029960 / 1.541195 (0.488765) | 2.082911 / 1.468490 (0.614421) | 0.556485 / 4.584777 (-4.028292) | 3.384418 / 3.745712 (-0.361295) | 1.748809 / 5.269862 (-3.521053) | 1.034759 / 4.565676 (-3.530917) | 0.067500 / 0.424275 (-0.356776) | 0.011425 / 0.007607 (0.003818) | 0.561340 / 0.226044 (0.335295) | 5.623629 / 2.268929 (3.354701) | 2.733587 / 55.444624 (-52.711038) | 2.401578 / 6.876477 (-4.474899) | 2.524569 / 2.142072 (0.382496) | 0.673170 / 4.805227 (-4.132057) | 0.136681 / 6.500664 (-6.363983) | 0.068060 / 0.075469 (-0.007409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318651 / 1.841788 (-0.523137) | 14.362123 / 8.074308 (6.287815) | 14.385964 / 10.191392 (4.194572) | 0.149914 / 0.680424 (-0.530510) | 0.016877 / 0.534201 (-0.517324) | 0.358406 / 0.579283 (-0.220877) | 0.394349 / 0.434364 (-0.040015) | 0.422471 / 0.540337 (-0.117866) | 0.513807 / 1.386936 (-0.873129) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006272 / 0.011353 (-0.005080) | 0.003903 / 0.011008 (-0.007105) | 0.100180 / 0.038508 (0.061672) | 0.037799 / 0.023109 (0.014690) | 0.385627 / 0.275898 (0.109729) | 0.446518 / 0.323480 (0.123038) | 0.004811 / 0.007986 (-0.003175) | 0.003032 / 0.004328 (-0.001296) | 0.077063 / 0.004250 (0.072812) | 0.055564 / 0.037052 (0.018512) | 0.397346 / 0.258489 (0.138857) | 0.443242 / 0.293841 (0.149401) | 0.027904 / 0.128546 (-0.100642) | 0.008386 / 0.075646 (-0.067260) | 0.315013 / 0.419271 (-0.104259) | 0.047943 / 0.043533 (0.004410) | 0.378443 / 0.255139 (0.123304) | 0.411472 / 0.283200 (0.128272) | 0.020465 / 0.141683 (-0.121218) | 1.526594 / 1.452155 (0.074439) | 1.547018 / 1.492716 (0.054301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219377 / 0.018006 (0.201370) | 0.430254 / 0.000490 (0.429764) | 0.003218 / 0.000200 (0.003018) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023667 / 0.037411 (-0.013744) | 0.099143 / 0.014526 (0.084617) | 0.106044 / 0.176557 (-0.070513) | 0.166186 / 0.737135 (-0.570949) | 0.108736 / 0.296338 (-0.187603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437971 / 0.215209 (0.222762) | 4.363675 / 2.077655 (2.286021) | 2.011993 / 1.504120 (0.507873) | 1.845189 / 1.541195 (0.303994) | 1.831848 / 1.468490 (0.363358) | 0.562402 / 4.584777 (-4.022375) | 3.365259 / 3.745712 (-0.380453) | 1.781491 / 5.269862 (-3.488371) | 1.023454 / 4.565676 (-3.542223) | 0.067857 / 0.424275 (-0.356418) | 0.011076 / 0.007607 (0.003469) | 0.532267 / 0.226044 (0.306223) | 5.340344 / 2.268929 (3.071415) | 2.388649 / 55.444624 (-53.055976) | 2.055373 / 6.876477 (-4.821104) | 2.205047 / 2.142072 (0.062975) | 0.672909 / 4.805227 (-4.132318) | 0.135244 / 6.500664 (-6.365420) | 0.066184 / 0.075469 (-0.009285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206838 / 1.841788 (-0.634950) | 13.967075 / 8.074308 (5.892767) | 13.143971 / 10.191392 (2.952579) | 0.143991 / 0.680424 (-0.536433) | 0.016673 / 0.534201 (-0.517527) | 0.376180 / 0.579283 (-0.203103) | 0.386550 / 0.434364 (-0.047814) | 0.440590 / 0.540337 (-0.099747) | 0.529974 / 1.386936 (-0.856962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006299 / 0.011353 (-0.005054) | 0.003784 / 0.011008 (-0.007224) | 0.077875 / 0.038508 (0.039367) | 0.038689 / 0.023109 (0.015580) | 0.421684 / 0.275898 (0.145786) | 0.472649 / 0.323480 (0.149169) | 0.003570 / 0.007986 (-0.004415) | 0.004448 / 0.004328 (0.000120) | 0.077867 / 0.004250 (0.073616) | 0.049514 / 0.037052 (0.012462) | 0.375983 / 0.258489 (0.117494) | 0.470632 / 0.293841 (0.176791) | 0.028238 / 0.128546 (-0.100308) | 0.008462 / 0.075646 (-0.067185) | 0.082452 / 0.419271 (-0.336819) | 0.043617 / 0.043533 (0.000084) | 0.400874 / 0.255139 (0.145735) | 0.426191 / 0.283200 (0.142992) | 0.020602 / 0.141683 (-0.121081) | 1.567658 / 1.452155 (0.115504) | 1.572610 / 1.492716 (0.079893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246144 / 0.018006 (0.228138) | 0.419402 / 0.000490 (0.418913) | 0.001691 / 0.000200 (0.001491) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026105 / 0.037411 (-0.011306) | 0.104734 / 0.014526 (0.090208) | 0.110257 / 0.176557 (-0.066300) | 0.161429 / 0.737135 (-0.575706) | 0.114367 / 0.296338 (-0.181972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453352 / 0.215209 (0.238143) | 4.537924 / 2.077655 (2.460269) | 2.196193 / 1.504120 (0.692073) | 2.002087 / 1.541195 (0.460892) | 2.041722 / 1.468490 (0.573231) | 0.561643 / 4.584777 (-4.023134) | 3.449108 / 3.745712 (-0.296605) | 2.862800 / 5.269862 (-2.407062) | 1.387895 / 4.565676 (-3.177782) | 0.068076 / 0.424275 (-0.356199) | 0.011568 / 0.007607 (0.003961) | 0.559279 / 0.226044 (0.333235) | 5.598738 / 2.268929 (3.329809) | 2.676649 / 55.444624 (-52.767975) | 2.334588 / 6.876477 (-4.541889) | 2.376215 / 2.142072 (0.234142) | 0.673109 / 4.805227 (-4.132118) | 0.137587 / 6.500664 (-6.363077) | 0.069131 / 0.075469 (-0.006338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307332 / 1.841788 (-0.534456) | 14.536036 / 8.074308 (6.461728) | 14.173734 / 10.191392 (3.982342) | 0.145143 / 0.680424 (-0.535281) | 0.016662 / 0.534201 (-0.517539) | 0.366901 / 0.579283 (-0.212383) | 0.394498 / 0.434364 (-0.039866) | 0.430546 / 0.540337 (-0.109792) | 0.518950 / 1.386936 (-0.867986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008122 / 0.011353 (-0.003231) | 0.005585 / 0.011008 (-0.005424) | 0.121219 / 0.038508 (0.082711) | 0.047616 / 0.023109 (0.024507) | 0.440576 / 0.275898 (0.164678) | 0.491053 / 0.323480 (0.167573) | 0.004774 / 0.007986 (-0.003211) | 0.006758 / 0.004328 (0.002430) | 0.103852 / 0.004250 (0.099602) | 0.071560 / 0.037052 (0.034508) | 0.463107 / 0.258489 (0.204618) | 0.516904 / 0.293841 (0.223063) | 0.048052 / 0.128546 (-0.080494) | 0.013679 / 0.075646 (-0.061968) | 0.428383 / 0.419271 (0.009112) | 0.069468 / 0.043533 (0.025936) | 0.432593 / 0.255139 (0.177454) | 0.471810 / 0.283200 (0.188611) | 0.037541 / 0.141683 (-0.104142) | 1.823490 / 1.452155 (0.371335) | 1.922558 / 1.492716 (0.429842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252315 / 0.018006 (0.234309) | 0.541757 / 0.000490 (0.541267) | 0.000373 / 0.000200 (0.000173) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030361 / 0.037411 (-0.007050) | 0.125928 / 0.014526 (0.111402) | 0.145102 / 0.176557 (-0.031455) | 0.209798 / 0.737135 (-0.527337) | 0.147349 / 0.296338 (-0.148990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627554 / 0.215209 (0.412345) | 5.917422 / 2.077655 (3.839767) | 2.491083 / 1.504120 (0.986963) | 2.147078 / 1.541195 (0.605883) | 2.167511 / 1.468490 (0.699021) | 0.903061 / 4.584777 (-3.681716) | 5.518537 / 3.745712 (1.772825) | 2.654348 / 5.269862 (-2.615514) | 1.645121 / 4.565676 (-2.920556) | 0.103782 / 0.424275 (-0.320493) | 0.013048 / 0.007607 (0.005441) | 0.756732 / 0.226044 (0.530687) | 7.622873 / 2.268929 (5.353945) | 3.122689 / 55.444624 (-52.321936) | 2.537735 / 6.876477 (-4.338742) | 2.640090 / 2.142072 (0.498018) | 1.128635 / 4.805227 (-3.676593) | 0.228089 / 6.500664 (-6.272575) | 0.086207 / 0.075469 (0.010738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561591 / 1.841788 (-0.280197) | 18.110299 / 8.074308 (10.035991) | 20.718017 / 10.191392 (10.526625) | 0.225741 / 0.680424 (-0.454682) | 0.031738 / 0.534201 (-0.502463) | 0.530789 / 0.579283 (-0.048495) | 0.607364 / 0.434364 (0.173000) | 0.581593 / 0.540337 (0.041256) | 0.726033 / 1.386936 (-0.660903) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009323 / 0.011353 (-0.002030) | 0.005360 / 0.011008 (-0.005649) | 0.103608 / 0.038508 (0.065100) | 0.050158 / 0.023109 (0.027049) | 0.499906 / 0.275898 (0.224008) | 0.561005 / 0.323480 (0.237525) | 0.005093 / 0.007986 (-0.002892) | 0.008285 / 0.004328 (0.003956) | 0.103446 / 0.004250 (0.099196) | 0.061478 / 0.037052 (0.024426) | 0.494016 / 0.258489 (0.235527) | 0.537550 / 0.293841 (0.243709) | 0.048829 / 0.128546 (-0.079717) | 0.017032 / 0.075646 (-0.058614) | 0.107748 / 0.419271 (-0.311524) | 0.065607 / 0.043533 (0.022074) | 0.488709 / 0.255139 (0.233570) | 0.512023 / 0.283200 (0.228823) | 0.032067 / 0.141683 (-0.109616) | 1.907585 / 1.452155 (0.455431) | 1.960994 / 1.492716 (0.468278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.018006 (0.260371) | 0.551474 / 0.000490 (0.550985) | 0.006886 / 0.000200 (0.006686) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.135179 / 0.014526 (0.120654) | 0.133703 / 0.176557 (-0.042853) | 0.198923 / 0.737135 (-0.538212) | 0.155108 / 0.296338 (-0.141231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.690566 / 0.215209 (0.475357) | 6.789594 / 2.077655 (4.711940) | 2.940668 / 1.504120 (1.436549) | 2.562431 / 1.541195 (1.021236) | 2.554232 / 1.468490 (1.085742) | 0.888470 / 4.584777 (-3.696307) | 5.672318 / 3.745712 (1.926606) | 2.741626 / 5.269862 (-2.528236) | 1.818336 / 4.565676 (-2.747340) | 0.110434 / 0.424275 (-0.313841) | 0.014114 / 0.007607 (0.006507) | 0.830632 / 0.226044 (0.604588) | 8.270787 / 2.268929 (6.001859) | 3.723486 / 55.444624 (-51.721139) | 2.993671 / 6.876477 (-3.882806) | 2.918273 / 2.142072 (0.776201) | 1.105337 / 4.805227 (-3.699891) | 0.222976 / 6.500664 (-6.277688) | 0.085290 / 0.075469 (0.009820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816027 / 1.841788 (-0.025760) | 18.496850 / 8.074308 (10.422541) | 20.457032 / 10.191392 (10.265640) | 0.243533 / 0.680424 (-0.436891) | 0.027044 / 0.534201 (-0.507157) | 0.500752 / 0.579283 (-0.078531) | 0.620963 / 0.434364 (0.186599) | 0.607995 / 0.540337 (0.067658) | 0.722915 / 1.386936 (-0.664021) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n" ]

"2023-06-22T18:23:11Z"

"2023-06-22T18:40:24Z"

"2023-06-22T18:30:16Z"

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[ "Hi @Ierezell, thanks for reporting.\r\n\r\nI'm making a PR to suppress those logs from the terminal. " ]

"2022-03-15T18:10:42Z"

"2022-03-17T08:37:24Z"

"2022-03-17T08:37:24Z"

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"2021-10-06T12:32:02Z"

"2021-10-13T10:19:11Z"

"2021-10-13T10:16:04Z"

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[ "Hi! You can avoid this error by using the preprocessed version:\r\n```python\r\nimport datasets\r\nds = datasets.load_dataset('natural_questions')\r\n```\r\n\r\nPS: Once we finish https://github.com/huggingface/datasets/pull/5364, this error will no longer be a problem.", "> Hi! You can avoid this error by using the preprocessed version:\r\n> \r\n> ```python\r\n> import datasets\r\n> ds = datasets.load_dataset('natural_questions')\r\n> ```\r\n> \r\n> PS: Once we finish #5364, this error will no longer be a problem.\r\n\r\nThanks, wish #5364 finish early" ]

"2023-05-15T02:46:04Z"

"2023-06-05T09:11:19Z"

"2023-06-05T09:11:18Z"

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"2023-07-20T09:08:52Z"

"2023-07-20T09:09:37Z"

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[ "Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments:\r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nI think we should fix that in `huggingface_hub`, will keep you posted. In the meantime please use `huggingface_hub` 0.4.0", "I'll be sending a fix for this later today on the `huggingface_hub` side.\r\n\r\nThe error would be converted to a `FutureWarning` if `datasets` uses kwargs instead of positional, for example here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nto be:\r\n\r\n``` python\r\n api.create_repo(\r\n name=dataset_name,\r\n token=token,\r\n repo_type=\"dataset\",\r\n organization=organization,\r\n private=private,\r\n )\r\n```\r\n\r\nBut `name` and `organization` are deprecated in `huggingface_hub=0.5`, and people should pass `repo_id='org/name` instead. Note that `repo_id` was introduced in 0.5 and if `datasets` wants to support older `huggingface_hub` versions (which I encourage it to do), there needs to be a helper function to do that. It can be something like:\r\n\r\n\r\n```python\r\ndef create_repo(\r\n client,\r\n name: str,\r\n token: Optional[str] = None,\r\n organization: Optional[str] = None,\r\n private: Optional[bool] = None,\r\n repo_type: Optional[str] = None,\r\n exist_ok: Optional[bool] = False,\r\n space_sdk: Optional[str] = None,\r\n) -> str:\r\n try:\r\n return client.create_repo(\r\n repo_id=f\"{organization}/{name}\",\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n except TypeError:\r\n return client.create_repo(\r\n name=name,\r\n organization=organization,\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n```\r\n\r\nin a `utils/_fixes.py` kinda file and and be used internally.\r\n\r\nI'll be sending a patch to `huggingface_hub` to convert the error reported in this issue to a `FutureWarning`.", "PR with the hotfix on the `huggingface_hub` side: https://github.com/huggingface/huggingface_hub/pull/822", "We can definitely change `push_to_hub` to use `repo_id` in `datasets` and require `huggingface_hub>=0.5.0`.\r\n\r\nLet me open a PR :)", "`huggingface_hub` 0.5.1 just got released with a fix, feel free to update `huggingface_hub` ;)" ]

"2022-04-06T08:59:57Z"

"2022-04-13T14:30:47Z"

"2022-04-13T14:30:47Z"

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[ "Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L103)", "Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \r\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\r\n\r\n\r\n`de/2017/7006210/7063319.xml.gz en/2017/7006210/7050201.xml.gz 335 339 340` (every space is actually a tab, aside from the space between `339` and `340`)\r\n\r\n\r\nWhere filenames encode the information like this: `lang/year/imdb_id/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\r\n\r\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\r\n\r\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\r\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\r\n\r\n**Note:** I did not find any official information about this encoding, but it appears to check out:\r\nhttps://www.imdb.com/title/tt7006210/, https://www.opensubtitles.org/en/subtitles/7063319 and https://www.opensubtitles.org/en/subtitles/7050201 all link to the same episode, so I guess (I hope!) it's correct.\r\n\r\n", "I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\r\nAnd for the `sentenceIds` a list of integers is fine.", "Thanks for improving it @Valahaar :) ", "Something like this? (adapted from [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L114))\r\n\r\n```python\r\nresult = (\r\n sentence_counter,\r\n {\r\n \"id\": str(sentence_counter),\r\n \"meta\": {\r\n \"year\": year,\r\n \"imdbId\": imdb_id,\r\n \"subtitleId\": {l1: l1_sub_id, l2: l2_sub_id},\r\n \"sentenceIds\": {l1: [... source_sids ...], l2: [... target_sids ...]},\r\n # or maybe src/tgt? I'd go with the first one for consistency with 'translation'\r\n \"subtitleId\": {\"src\": l1_sub_id, \"tgt\": l2_sub_id},\r\n \"sentenceIds\": {\"src\": [... source_sids ...], \"tgt\": [... target_sids ...]},\r\n },\r\n \"translation\": {l1: x, l2: y},\r\n },\r\n )\r\n```\r\nOr at top level, avoiding nesting into 'meta'?", "Merged in #1865, closing. Thanks :)" ]

"2021-02-08T13:55:13Z"

"2021-02-12T17:38:58Z"

"2021-02-12T17:38:58Z"

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"2022-02-11T08:21:03Z"

"2022-02-11T22:56:40Z"

"2022-02-11T22:56:39Z"

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"2021-05-17T13:10:37Z"

"2021-05-17T14:07:13Z"

"2021-05-17T14:07:13Z"

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

"2022-03-11T22:21:40Z"

"2022-03-21T16:10:03Z"

"2022-03-21T16:05:35Z"

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[]

[ "Hi ! I think you should avoid calling `timit_train['audio']`. Indeed by doing so you're **loading all the audio column in memory**. This is problematic in your case because the TIMIT dataset is huge.\r\n\r\nIf you want to access the audio data of some samples, you should do this instead `timit_train[:10][\"train\"]` for example.\r\n\r\nOther than that, I'm not sure why you get a `TypeError: string indices must be integers`, do you have a code snippet that reproduces the issue that you can share here ?", "I get the same error when I try to do `timit_train[0]` or really any indexing into the whole thing. \r\n\r\nReally, that IS the code snippet that reproduces the issue. If you index into other fields like 'file' or whatever, it works. As soon as one of the fields you're looking into is 'audio', you get that issue. It's a weird issue and I suspect it's Sagemaker/environment related, maybe the mix of libraries and dependencies are not good. \r\n\r\n\r\nExample code snippet with issue. \r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train[0])\r\n```", "Ok I see ! From the error you got, it looks like the `value` encoded in the arrow file of the TIMIT dataset you loaded is a string instead of a dictionary with keys \"path\" and \"bytes\" but we don't support this since 1.18\r\n\r\nCan you try regenerating the dataset with `load_dataset('timit_asr', download_mode=\"force_redownload\")` please ? I think it should fix the issue." ]

"2022-01-24T03:26:03Z"

"2023-07-25T15:20:20Z"

"2023-07-25T15:20:20Z"

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[ "also asked in https://discuss.huggingface.co/t/create-multiple-dataset-configs-with-push-to-hub-method/25480" ]

"2022-10-24T12:59:18Z"

"2022-11-04T14:55:20Z"

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[ "What's the issue exactly ?\r\n\r\nGiven an `upstream` remote repository with url `https://github.com/huggingface/datasets.git`, you can totally rebase from `upstream/master`.\r\n\r\nIt's mentioned at the beginning how to add the `upstream` remote repository\r\n\r\nhttps://github.com/huggingface/datasets/blob/987df6b4e9e20fc0c92bc9df48137d170756fd7b/ADD_NEW_DATASET.md#L10-L14", "~~What difference is there with the default `origin` remote that is set when you clone the repo?~~ I've just understood that this applies to **forks** of the repo 🤡 " ]

"2021-03-09T09:48:55Z"

"2021-03-09T11:33:31Z"

"2021-03-09T11:33:31Z"

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[ "Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But\r\n1. maybe it's the wrong default\r\n2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).\r\n", "Hi @severo,\r\n\r\nThanks for clarifying. \r\n\r\nI think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)\r\nMaybe having non-encoded labels as a default can be useful?\r\n\r\nAnyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!\r\n\r\n ", "Thanks for the 👍 in https://github.com/huggingface/datasets/issues/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?\r\n\r\n_(note for myself: one idea per comment in the future)_", "As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be \"decoded\" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.\r\n\r\nI hope this is useful", "I like the idea of \"0 (neutral)\". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.", "I like @lhoestq's idea of having grayed-out labels.", "Proposals by @gary149. Which one do you prefer? Please vote with the thumbs\r\n\r\n- 👍 \r\n\r\n \r\n\r\n- 👎 \r\n\r\n \r\n\r\n", "I like Option 1 better as it shows clearly what the user is downloading", "Thanks! ", "It's [live](https://huggingface.co/datasets/glue/viewer/cola/train):\r\n\r\n<img width=\"1126\" alt=\"Capture d’écran 2022-02-14 à 10 26 03\" src=\"https://user-images.githubusercontent.com/1676121/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png\">\r\n\r\nThanks all for the help to improve the UI!", "Love it ! thanks :)" ]

"2022-02-03T12:10:43Z"

"2022-02-16T11:22:31Z"

"2022-02-11T17:01:21Z"

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[ "Hi !\r\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\r\nIndeed you're concatenating the `input_ids` at this line:\r\n\r\nhttps://github.com/LuCeHe/GenericTools/blob/431835d8e13ec24dceb5ee4dc4ae58f0e873b091/KerasTools/lm_preprocessing.py#L134\r\n\r\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\r\n\r\nTo fix that you can instead concatenate them all using\r\n```python\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n```\r\n\r\nAlso you may need to drop the \"text\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\r\n```python\r\ndset = dset.map(\r\n tokenize_function,\r\n batched=True,\r\n remove_columns=[\"text\"]\r\n)\r\n```", "You saved my life." ]

"2021-02-04T02:30:23Z"

"2022-10-05T12:42:57Z"

"2022-10-05T12:42:57Z"

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"2022-01-04T23:48:11Z"

"2022-01-05T12:49:41Z"

"2022-01-05T12:49:40Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5364). All of your documentation changes will be reflected on that endpoint.", "Deleting `BeamPipeline` and `upload_local_to_remote` would break the existing Beam scripts, so I reverted this change.\r\n\r\nFrom what I understand, we need these components in our scripts for the pattern:\r\n```python\r\nif not pipeline.is_local():\r\n dl_manager.ship_files_with_pipeline()\r\n```\r\n\r\nI plan to address this in a subsequent PR by (implicitly) downloading the files directly to the remote storage of the non-local runners.", "I got `AttributeError: 'Pipeline' object has no attribute 'is_local'` when running\r\n```python\r\nload_dataset(\"wikipedia\", language=\"af\", date=\"20230101\", beam_runner=\"DirectRunner\")\r\n```\r\n```python\r\n~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)\r\n 965 # Use dictionary since testing mock always returns the same result.\r\n 966 downloaded_files = dl_manager.download({\"xml\": xml_urls})\r\n--> 967 if not pipeline.is_local():\r\n 968 downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)\r\n 969 \r\n\r\nAttributeError: 'Pipeline' object has no attribute 'is_local'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010649 / 0.011353 (-0.000704) | 0.006116 / 0.011008 (-0.004892) | 0.115568 / 0.038508 (0.077060) | 0.041704 / 0.023109 (0.018595) | 0.360459 / 0.275898 (0.084561) | 0.425679 / 0.323480 (0.102200) | 0.008992 / 0.007986 (0.001006) | 0.006321 / 0.004328 (0.001993) | 0.090223 / 0.004250 (0.085973) | 0.049877 / 0.037052 (0.012824) | 0.382447 / 0.258489 (0.123958) | 0.406567 / 0.293841 (0.112726) | 0.045138 / 0.128546 (-0.083409) | 0.014203 / 0.075646 (-0.061444) | 0.388897 / 0.419271 (-0.030375) | 0.057176 / 0.043533 (0.013644) | 0.358729 / 0.255139 (0.103590) | 0.386086 / 0.283200 (0.102887) | 0.119221 / 0.141683 (-0.022462) | 1.731574 / 1.452155 (0.279419) | 1.744103 / 1.492716 (0.251386) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230380 / 0.018006 (0.212373) | 0.493690 / 0.000490 (0.493201) | 0.005150 / 0.000200 (0.004950) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030771 / 0.037411 (-0.006641) | 0.123196 / 0.014526 (0.108671) | 0.134097 / 0.176557 (-0.042459) | 0.190442 / 0.737135 (-0.546693) | 0.138416 / 0.296338 (-0.157923) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469763 / 0.215209 (0.254554) | 4.682847 / 2.077655 (2.605192) | 2.076717 / 1.504120 (0.572597) | 1.843721 / 1.541195 (0.302527) | 1.923486 / 1.468490 (0.454996) | 0.817680 / 4.584777 (-3.767097) | 4.482409 / 3.745712 (0.736697) | 3.898695 / 5.269862 (-1.371167) | 2.078291 / 4.565676 (-2.487386) | 0.100285 / 0.424275 (-0.323990) | 0.014761 / 0.007607 (0.007154) | 0.611261 / 0.226044 (0.385217) | 5.926919 / 2.268929 (3.657990) | 2.685080 / 55.444624 (-52.759544) | 2.232179 / 6.876477 (-4.644298) | 2.305576 / 2.142072 (0.163504) | 0.993729 / 4.805227 (-3.811498) | 0.194491 / 6.500664 (-6.306173) | 0.074176 / 0.075469 (-0.001293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.388592 / 1.841788 (-0.453196) | 17.146945 / 8.074308 (9.072636) | 15.989570 / 10.191392 (5.798178) | 0.200147 / 0.680424 (-0.480277) | 0.034009 / 0.534201 (-0.500192) | 0.517531 / 0.579283 (-0.061753) | 0.533966 / 0.434364 (0.099602) | 0.637024 / 0.540337 (0.096687) | 0.749166 / 1.386936 (-0.637770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008240 / 0.011353 (-0.003113) | 0.006139 / 0.011008 (-0.004869) | 0.112258 / 0.038508 (0.073750) | 0.039001 / 0.023109 (0.015891) | 0.449467 / 0.275898 (0.173569) | 0.483422 / 0.323480 (0.159942) | 0.006176 / 0.007986 (-0.001810) | 0.006340 / 0.004328 (0.002012) | 0.083105 / 0.004250 (0.078855) | 0.047002 / 0.037052 (0.009950) | 0.458564 / 0.258489 (0.200075) | 0.513704 / 0.293841 (0.219863) | 0.041359 / 0.128546 (-0.087188) | 0.014515 / 0.075646 (-0.061131) | 0.392599 / 0.419271 (-0.026673) | 0.055222 / 0.043533 (0.011690) | 0.446956 / 0.255139 (0.191817) | 0.469194 / 0.283200 (0.185994) | 0.118212 / 0.141683 (-0.023471) | 1.682647 / 1.452155 (0.230492) | 1.780076 / 1.492716 (0.287360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259124 / 0.018006 (0.241117) | 0.507559 / 0.000490 (0.507069) | 0.001080 / 0.000200 (0.000880) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031969 / 0.037411 (-0.005442) | 0.126997 / 0.014526 (0.112471) | 0.139593 / 0.176557 (-0.036963) | 0.182735 / 0.737135 (-0.554400) | 0.145871 / 0.296338 (-0.150468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.530894 / 0.215209 (0.315685) | 5.284979 / 2.077655 (3.207324) | 2.592886 / 1.504120 (1.088766) | 2.407202 / 1.541195 (0.866007) | 2.434079 / 1.468490 (0.965589) | 0.829382 / 4.584777 (-3.755395) | 4.481710 / 3.745712 (0.735998) | 3.912280 / 5.269862 (-1.357581) | 1.962291 / 4.565676 (-2.603386) | 0.101840 / 0.424275 (-0.322435) | 0.014528 / 0.007607 (0.006921) | 0.639956 / 0.226044 (0.413911) | 6.414685 / 2.268929 (4.145756) | 3.240290 / 55.444624 (-52.204334) | 2.795208 / 6.876477 (-4.081269) | 2.912122 / 2.142072 (0.770050) | 0.992188 / 4.805227 (-3.813039) | 0.200701 / 6.500664 (-6.299964) | 0.074235 / 0.075469 (-0.001234) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455075 / 1.841788 (-0.386712) | 17.186669 / 8.074308 (9.112361) | 15.404357 / 10.191392 (5.212965) | 0.168267 / 0.680424 (-0.512157) | 0.020774 / 0.534201 (-0.513427) | 0.502603 / 0.579283 (-0.076680) | 0.506500 / 0.434364 (0.072136) | 0.624245 / 0.540337 (0.083907) | 0.735529 / 1.386936 (-0.651407) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]

"2022-12-15T12:38:05Z"

"2023-01-25T15:49:25Z"

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

"2022-03-15T19:24:57Z"

"2022-03-18T16:28:31Z"

"2022-03-18T16:24:33Z"

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"2020-12-28T19:09:05Z"

"2020-12-29T10:43:14Z"

"2020-12-29T10:43:14Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5196). All of your documentation changes will be reflected on that endpoint.", "@lhoestq I think we should first agree if `datasets` can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: some users may have override this.\r\n\r\nIf so, I then would suggest to initiate a deprecation cycle.", "After a discussion with the rest of the datasets team, we agreed we can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: this will have minimal impact, only for **private Hubs**. We will address eventual possible impacts in the future.\r\n\r\nAdditionally, we also ignore `config.HUB_DEFAULT_VERSION`.\r\n\r\nSee explanation in this PR description: https://github.com/huggingface/datasets/pull/5196#issue-1434401646", "I'm trying to upgrade datasets to 2.7.0 in https://github.com/huggingface/datasets-server, and the tests fail due to this change. I think it's a breaking change (that was not listed in https://github.com/huggingface/datasets/releases/tag/2.7.0) since code that previously worked (by setting `datasets.config.HUB_DATASETS_URL = CI_HUB_DATASETS_URL` for example) does not work anymore.\r\n\r\nI'm not sure what is the correct way to set up the tests; besides setting the env var \"HF_ENDPOINT\" before launching the tests (which, I think, is not a good way to do: the tests should not depend on the environment).", "OK, I re-read this thread, and https://github.com/huggingface/datasets/pull/5196#issuecomment-1307430175 explicitely states that `config.HUB_DATASETS_URL` (as well as `config.HUB_DEFAULT_VERSION`) is now ignored. I was expecting the breaking changes to be listed in the release notes: https://github.com/huggingface/datasets/releases/tag/2.7.0.", "> I'm not sure what is the correct way to set up the tests; besides setting the env var \"HF_ENDPOINT\" before launching the tests (which, I think, is not a good way to do: the tests should not depend on the environment).\r\n\r\nI think the current workaround of settings an env variable before launching the tests is \"not so bad\" when considering the fact that env variables are evaluated at import time in `huggingface_hub` (and most probable `datasets` as well). I think that when refactoring this in huggingface_hub (https://github.com/huggingface/huggingface_hub/issues/1172) I'll opt for instantiating a `Settings` object (or `Constants`) that contains all the settings variables. This way it will not be possible to import attributes individually + tests would be easier. As I see it, it would be similar to [what `Pydantic` does](https://pydantic-docs.helpmanual.io/usage/settings/) even though we most probably don't want Pydantic as a root dependency just for that. ", "You can use fixtures in your tests:\r\n```python\r\nCI_HUB_ENDPOINT = \"https://hub-ci.huggingface.co\"\r\nCI_HUB_DATASETS_URL = CI_HUB_ENDPOINT + \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nCI_HFH_HUGGINGFACE_CO_URL_TEMPLATE = CI_HUB_ENDPOINT + \"/{repo_id}/resolve/{revision}/{filename}\"\r\n\r\n@pytest.fixture\r\ndef ci_hfh_hf_hub_url(monkeypatch):\r\n monkeypatch.setattr(\r\n \"huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE\", CI_HFH_HUGGINGFACE_CO_URL_TEMPLATE\r\n )\r\n\r\n@pytest.fixture\r\ndef ci_hub_config(monkeypatch):\r\n monkeypatch.setattr(\"datasets.config.HF_ENDPOINT\", CI_HUB_ENDPOINT)\r\n monkeypatch.setattr(\"datasets.config.HUB_DATASETS_URL\", CI_HUB_DATASETS_URL)\r\n```\r\n\r\nand use `@pytest.fixture(autouse=True)` if you want to always use the CI endpoints.\r\n\r\nAnd when `huggingface-hub` and `datasets` change the way we can set the endpoint, we'll just need to update the fixtures.\r\nI think ultimately you'll only have to change the `huggingface-hub` endpoint settings\r\n", "OK.\r\n\r\nIn fact, in datasets-server we set `config.HUB_DATASETS_URL` (https://github.com/huggingface/datasets-server/blob/35a30dbcd687b26db1f02502ea8305f70c064473/workers/splits/src/splits/config.py#L26) at config time, before starting the workers. It's not an issue with how to launch the tests, but with the app in itself.\r\n\r\nI understand that for now, the only way to fix this is to setup `HF_ENDPOINT` in the env when launching the app (currently, we set the endpoint with `COMMON_HF_ENDPOINT`, a custom env var I set to be sure not to have side-effects)", "> You can use fixtures in your tests:\r\n\r\nThanks, used in https://github.com/huggingface/datasets-server/pull/644." ]

"2022-11-03T10:08:09Z"

"2022-12-06T11:38:17Z"

"2022-11-09T07:15:12Z"

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[ "Hi ! Could it be because you need to free the memory used by `tarfile` by emptying the tar `members` by any chance ?\r\n```python\r\n yield key, {\"audio\": {\"path\": audio_name, \"bytes\": audio_file_obj.read()}}\r\n audio_tarfile.members = [] # free memory\r\n key += 1\r\n```\r\n\r\nand then you can set `DEFAULT_WRITER_BATCH_SIZE` to whatever value makes more sense for your dataset.\r\n\r\nLet me know if the issue persists (which could happen, given that you managed to run your generator without RAM issues and using os.walk didn't solve the issue)", "Thanks for your reply! Tried it but the issue persists. ", "I also run out of memory when loading `mozilla-foundation/common_voice_8_0` that also uses `tarfile` via `dl_manager.iter_archive`. There seems to be some data files that stay in memory somewhere\r\n\r\nI don't have the issue with other compression formats like gzipped files", "I'm facing a similar memory leak issue when loading cv8. As you said @lhoestq \r\n\r\n`load_dataset(\"mozilla-foundation/common_voice_8_0\", \"en\", use_auth_token=True, writer_batch_size=1)`\r\n\r\nThis issue is happening on a 32GB RAM machine. \r\n\r\nAny updates on how to fix this?", "I've run a memory profiler to see where's the leak comes from:\r\n\r\n\r\n\r\n... it seems that it's related to the tarfile lib buffer reader. But I don't know why it's only happening on the huggingface script", "I have the same problem when loading video into numpy. \r\n```\r\nyield id,{ \r\n \"video\": imageio.v3.imread(video_path),\r\n \"label\": int(label)\r\n}\r\n```\r\nSince video files are heavy, it can only processes a dozen samples before OOM.", "For video datasets I think you can just define the max number of video that can stay in memory by adding this class attribute to your dataset builer:\r\n```py\r\nDEFAULT_WRITER_BATCH_SIZE = 8 # only 8 videos at a time in memory before flushing the dataset writer\r\n```", "same thing happens for me with `load_dataset(\"mozilla-foundation/common_voice_8_0\", \"en\", use_auth_token=True, writer_batch_size=1)` on azure ml. seems to fill up `tmp` and not release that memory until OOM", "I'll add that I'm encountering the same issue with\r\n`load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train')`.\r\nSame for `'es'` in place of `'ceb'`.", "> I'll add that I'm encountering the same issue with\r\n> load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train').\r\n> Same for 'es' in place of 'ceb'.\r\n\r\nThis is because the Apache Beam `DirectRunner` runs with the full data in memory unfortunately. Optimizing the `DirectRunner` is not in the scope of the `datasets` library, but rather in the Apache Beam project I believe. If you have memory issues with the `DirectRunner`, please consider switching to a machine with more RAM, or to distributed processing runtimes like Spark, Flink or DataFlow. There is a bit of documentation here: https://huggingface.co/docs/datasets/beam", "> > I'll add that I'm encountering the same issue with\r\n> > `load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train')`.\r\n> > Same for `'es'` in place of `'ceb'`.\r\n> \r\n> This is because the Apache Beam `DirectRunner` runs with the full data in memory unfortunately. Optimizing the `DirectRunner` is not in the scope of the `datasets` library, but rather in the Apache Beam project I believe. If you have memory issues with the `DirectRunner`, please consider switching to a machine with more RAM, or to distributed processing runtimes like Spark, Flink or DataFlow. There is a bit of documentation here: https://huggingface.co/docs/datasets/beam\r\n\r\nFair enough, but this line of code crashed an AWS instance with 1024GB of RAM! I have also tried with `Runner='Flink'` on an environment with 51GB of RAM, which also failed.\r\n\r\nApache Beam has tons of open tickets already - is it worth submitting one to them over this?", "> Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM!\r\n\r\nWhat, wikipedia is not even bigger than 20GB\r\n\r\ncc @albertvillanova", "> > Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM!\r\n> \r\n> What, wikipedia is not even bigger than 20GB\r\n> \r\n> cc @albertvillanova\r\n\r\nLuckily, on Colab you can watch the call stack at the bottom of the screen - much of the time and space complexity seems to come from `_parse_and_clean_wikicode()` rather than the actual download process. As far as I can tell, the script is loading the full dataset and then cleaning it all at once, which is consuming a lot of memory.", "I think we are mixing many different bugs in this Issue page:\r\n- TAR archive with audio files\r\n- video file\r\n- distributed parsing of Wikipedia using Apache Beam\r\n\r\n@dan-the-meme-man may I ask you to open a separate Issue for your problem? Then I will address it. It is important to fix it because we are currently working on a Datasets enhancement to be able to provide all Wikipedias already preprocessed.\r\n\r\nOn the other hand, I think we could keep this Issue page for the original problem: TAR archive with audio files. That is not fixed yet either.", "Is there an update on the TAR archive issue with audio files? Happy to lend a hand in fixing this :)", "I found the issue with Common Voice 8 and opened a PR to fix it: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/discussions/2\r\n\r\nBasically the `metadata` dict that contains the transcripts per audio file was continuously getting filled with bytes from `f.read()` because of this code:\r\n```python\r\nresult = metadata[path]\r\nresult[\"audio\"] = {\"path\": path, \"bytes\": f.read()}\r\n```\r\ncopying the result with `result = dict(metadata[path])` fixes it: the bytes are no longer added to `metadata`\r\n\r\nI also opened PRs to the other CV datasets", "Amazing, that's a great find! Thanks @lhoestq!", "I'm closing this one for now, but feel free to reopen if you encounter other memory issues with audio datasets" ]