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[ "So maybe a summary here: \r\nIf I could fit a large model with batch_size = X into memory, is there a way I could train this model for huge datasets with keeping setting the same? thanks ", "The `datastets` library doesn't load datasets into memory. Therefore you can load a dataset that is terabytes big without filling up your RAM.\r\n\r\nThe only thing that's loaded into memory during training is the batch used in the training step.\r\nSo as long as your model works with batch_size = X, then you can load an even bigger dataset and it will work as well with the same batch_size.\r\n\r\nNote that you still have to take into account that some batches take more memory than others, depending on the texts lengths. If it works for a batch with batch_size = X and with texts of maximum length, then it will work for all batches.\r\n\r\nIn your case I guess that there are a few long sentences in the dataset. For those long sentences you get a memory error on your GPU because they're too long. By passing `max_train_samples` you may have taken a subset of the dataset that only contain short sentences. That's probably why in your case it worked only when you set `max_train_samples`.\r\nI'd suggest you to reduce the batch size so that the batches with long sentences can be loaded on the GPU.\r\n\r\nLet me know if that helps or if you have other questions" ]

"2021-03-08T12:35:26Z"

"2021-08-04T18:02:25Z"

"2021-08-04T18:02:25Z"

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"2021-03-09T13:36:59Z"

"2021-03-09T14:06:04Z"

"2021-03-09T14:06:03Z"

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[ "Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do you think about this ?\r\n\r\nIf you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !", "People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ? \r\n", "@lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?", "Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.", "@lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?", "We're still working on this :) This will be available soon\r\nUsers will be able to put their processed arrow files on the Hub", "Hi! You can now use `Dataset.push_to_hub` to store preprocessed files on the Hub.\r\n\r\nAnd to avoid downloading preprocessed files, you can use streaming by setting `streaming=True` in `load_dataset`." ]

"2021-03-21T04:50:07Z"

"2022-06-01T16:49:52Z"

"2022-06-01T16:49:52Z"

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[ "tagging @nreimers ", "The dataset is sadly not really compatible to be loaded with `load_dataset`. So far it is better to git clone it and to use the files directly.\r\n\r\nA data loading script would be needed to be added to this dataset. But this was too much overhead / not really intuitive how to create it.", "Since the dataset is a bunch of TSVs we should not need a dataset script I think.\r\n\r\nBy default it tries to load all the TSVs at once, which fails here because they don't all have the same columns (pd.read_csv uses the first line as header by default). But those files have no header ! So, to properly load any TSV file in this repo, one has to pass `names=[...]` for pd.read_csv to know which column names to use.\r\n\r\nTo fix this situation, we can either do\r\n1. replace the TSVs by TSV with column names\r\n2. OR specify the pd.read_csv kwargs as YAML in the dataset card - and `datasets` would use that by default\r\n\r\nWDTY ?", "There are more issues in the dataset.\r\nTo load OpenSubtitles I have to provide this (see `skiprows`):\r\n\r\n```python\r\ndf_os = pd.read_csv(\r\n \"./parallel-sentences/OpenSubtitles/OpenSubtitles-en-de-train.tsv.gz\", \r\n sep=\"\\t\", \r\n quoting=csv.QUOTE_NONE,\r\n header=None,\r\n names=[\"en\", \"de\"],\r\n skiprows=[540344, 9151700, 10040173, 10040199, 11314673, 11338258, 11869223, 12159297, 12251078, 12303334],\r\n)\r\n```", "What's wrong with those lines exactly ?\r\nMaybe passing `error_bad_lines=False` (and maybe `warn_bad_lines=True`) can be helpful", "> What's wrong with those lines exactly ? \r\n\r\nStuff like this: `ParserError: Error tokenizing data. C error: Expected 2 fields in line 540345, saw 3`\r\n\r\n", "> Maybe passing error_bad_lines=False (and maybe warn_bad_lines=True) can be helpful\r\n\r\nYes. That would hide the issue but not solve it.", "@nreimers WDYT about the two options mentioned above ?" ]

"2022-10-06T10:47:51Z"

"2022-10-11T10:00:48Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Ahahah :D not sur how i broke something by updating the README :D ", "Thanks for the fix @PierreColombo. \r\n\r\nOnce a README is modified, our CI runs tests on it, requiring additional quality fixes, so that all READMEs are progressively improved and have some minimal tags/sections/information.\r\n\r\nFor this specific README file, the additional quality requirements of the CI are: https://github.com/huggingface/datasets/runs/7819924428?check_suite_focus=true\r\n```\r\nE The following issues were found for the README at `/home/runner/work/datasets/datasets/datasets/miam/README.md`:\r\nE -\tSection `Additional Information` is missing subsection: `Dataset Curators`.\r\nE -\tSection `Additional Information` is missing subsection: `Contributions`.\r\nE -\t`Additional Information` has an extra subsection: `Benchmark Curators`. Skipping further validation checks for this subsection as expected structure is unknown.\r\n```", "Thanks a lot Albert :)))" ]

"2022-08-13T14:38:55Z"

"2022-08-17T00:50:04Z"

"2022-08-14T10:26:08Z"

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

"2022-06-21T17:05:50Z"

"2022-06-28T10:46:51Z"

"2022-06-28T10:36:10Z"

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"2021-01-13T09:55:09Z"

"2021-01-14T10:42:18Z"

"2021-01-14T10:42:18Z"

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[ "Hi @mariosasko unless someone's already working on it, I guess I can tackle it!", "Hi! Sure, feel free to tackle this.", "#self-assign", "I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?" ]

"2023-07-27T19:08:37Z"

"2023-07-28T15:28:26Z"

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[ "Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\r\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188", "Good to hear.\r\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\r\nIt will be a release if you can deal with it at the backend.\r\nThanks.", "Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows:\r\n```python\r\nds1 = ds1.rename_column(\"text\", \"src\")\r\nds2 = ds2.rename_column(\"text\", \"tgt\")\r\nds = datasets.concatenate_datasets([\"ds1\", \"ds2\"], axis=1)\r\n```" ]

"2021-01-24T01:26:06Z"

"2022-06-01T15:43:15Z"

"2022-06-01T15:43:15Z"

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

"2022-04-22T07:47:08Z"

"2022-04-26T17:00:32Z"

"2022-04-26T13:38:26Z"

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[ "Hi ! Thanks for reporting, I'm working on it ;)" ]

"2023-04-26T10:50:23Z"

"2023-06-13T15:57:06Z"

"2023-06-13T15:57:06Z"

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[ "Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\r\n\r\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\r\nThe fix will be available in the next release of `datasets` in a few days" ]

"2021-02-04T05:55:23Z"

"2022-06-01T15:38:42Z"

"2022-06-01T15:38:42Z"

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[ "Hi @Breakend, yes – we'll propose a solution today", "Thanks so much, I appreciate it!", "Thanks so much for adding the docs. I was able to successfully hide the viewer using the \r\n```\r\nviewer: false\r\n```\r\nflag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again!", "Awesome! Thanks for confirming. cc @severo ", "Just for the record:\r\n\r\n- the doc\r\n \r\n<img width=\"1430\" alt=\"Capture d’écran 2022-06-27 à 09 29 27\" src=\"https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png\">\r\n\r\n- the dataset main page\r\n\r\n<img width=\"1134\" alt=\"Capture d’écran 2022-06-27 à 09 29 05\" src=\"https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png\">\r\n\r\n- the dataset viewer page\r\n\r\n<img width=\"567\" alt=\"Capture d’écran 2022-06-27 à 09 29 16\" src=\"https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png\">\r\n" ]

"2022-06-22T02:48:40Z"

"2022-06-27T07:30:23Z"

"2022-06-26T22:26:22Z"

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[ "Thanks for fixing!" ]

"2021-10-14T23:49:40Z"

"2021-10-15T14:40:05Z"

"2021-10-15T10:11:04Z"

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"2021-09-14T13:10:35Z"

"2021-10-13T11:27:49Z"

"2021-10-13T11:27:49Z"

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"2021-08-17T18:01:48Z"

"2022-01-19T14:15:35Z"

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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) | 0.009262 / 0.011353 (-0.002091) | 0.006157 / 0.011008 (-0.004851) | 0.125960 / 0.038508 (0.087451) | 0.036213 / 0.023109 (0.013104) | 0.399331 / 0.275898 (0.123433) | 0.453597 / 0.323480 (0.130117) | 0.006990 / 0.007986 (-0.000995) | 0.007320 / 0.004328 (0.002991) | 0.100321 / 0.004250 (0.096070) | 0.048870 / 0.037052 (0.011818) | 0.396284 / 0.258489 (0.137795) | 0.475619 / 0.293841 (0.181778) | 0.052329 / 0.128546 (-0.076217) | 0.019564 / 0.075646 (-0.056083) | 0.430942 / 0.419271 (0.011670) | 0.063224 / 0.043533 (0.019692) | 0.391717 / 0.255139 (0.136578) | 0.448342 / 0.283200 (0.165142) | 0.114055 / 0.141683 (-0.027628) | 1.793204 / 1.452155 (0.341049) | 1.895151 / 1.492716 (0.402435) |\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) | 0.283699 / 0.018006 (0.265693) | 0.597194 / 0.000490 (0.596704) | 0.007143 / 0.000200 (0.006944) | 0.000602 / 0.000054 (0.000548) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034761 / 0.037411 (-0.002651) | 0.124555 / 0.014526 (0.110030) | 0.149126 / 0.176557 (-0.027430) | 0.220335 / 0.737135 (-0.516801) | 0.153109 / 0.296338 (-0.143229) |\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) | 0.620210 / 0.215209 (0.405001) | 6.229937 / 2.077655 (4.152282) | 2.615203 / 1.504120 (1.111083) | 2.239337 / 1.541195 (0.698143) | 2.262138 / 1.468490 (0.793648) | 1.196498 / 4.584777 (-3.388279) | 5.609932 / 3.745712 (1.864220) | 3.031347 / 5.269862 (-2.238515) | 2.025530 / 4.565676 (-2.540146) | 0.139828 / 0.424275 (-0.284447) | 0.015476 / 0.007607 (0.007869) | 0.768964 / 0.226044 (0.542920) | 7.728677 / 2.268929 (5.459748) | 3.336407 / 55.444624 (-52.108217) | 2.700055 / 6.876477 (-4.176422) | 2.765223 / 2.142072 (0.623151) | 1.409073 / 4.805227 (-3.396155) | 0.246849 / 6.500664 (-6.253815) | 0.081231 / 0.075469 (0.005762) |\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) | 1.593836 / 1.841788 (-0.247952) | 18.020525 / 8.074308 (9.946216) | 21.766822 / 10.191392 (11.575430) | 0.258615 / 0.680424 (-0.421809) | 0.026895 / 0.534201 (-0.507306) | 0.529823 / 0.579283 (-0.049460) | 0.623470 / 0.434364 (0.189106) | 0.628171 / 0.540337 (0.087833) | 0.745249 / 1.386936 (-0.641687) |\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) | 0.008624 / 0.011353 (-0.002729) | 0.006317 / 0.011008 (-0.004691) | 0.097315 / 0.038508 (0.058807) | 0.035217 / 0.023109 (0.012108) | 0.440197 / 0.275898 (0.164299) | 0.473863 / 0.323480 (0.150383) | 0.006722 / 0.007986 (-0.001264) | 0.006444 / 0.004328 (0.002116) | 0.102056 / 0.004250 (0.097806) | 0.047142 / 0.037052 (0.010089) | 0.452476 / 0.258489 (0.193986) | 0.487619 / 0.293841 (0.193778) | 0.052456 / 0.128546 (-0.076090) | 0.018735 / 0.075646 (-0.056911) | 0.114656 / 0.419271 (-0.304616) | 0.062577 / 0.043533 (0.019044) | 0.444471 / 0.255139 (0.189332) | 0.494264 / 0.283200 (0.211065) | 0.117112 / 0.141683 (-0.024571) | 1.848965 / 1.452155 (0.396810) | 1.984008 / 1.492716 (0.491292) |\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) | 0.290494 / 0.018006 (0.272488) | 0.588415 / 0.000490 (0.587925) | 0.000459 / 0.000200 (0.000259) | 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) | 0.032873 / 0.037411 (-0.004538) | 0.131139 / 0.014526 (0.116614) | 0.140268 / 0.176557 (-0.036289) | 0.204561 / 0.737135 (-0.532574) | 0.147443 / 0.296338 (-0.148895) |\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) | 0.636899 / 0.215209 (0.421690) | 6.236139 / 2.077655 (4.158484) | 2.801468 / 1.504120 (1.297348) | 2.398808 / 1.541195 (0.857613) | 2.493150 / 1.468490 (1.024659) | 1.228845 / 4.584777 (-3.355932) | 5.675874 / 3.745712 (1.930162) | 3.084939 / 5.269862 (-2.184922) | 2.061310 / 4.565676 (-2.504367) | 0.142285 / 0.424275 (-0.281990) | 0.014972 / 0.007607 (0.007365) | 0.786599 / 0.226044 (0.560555) | 7.876036 / 2.268929 (5.607107) | 3.476136 / 55.444624 (-51.968489) | 2.847922 / 6.876477 (-4.028555) | 3.040326 / 2.142072 (0.898253) | 1.448538 / 4.805227 (-3.356690) | 0.257230 / 6.500664 (-6.243434) | 0.085137 / 0.075469 (0.009668) |\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) | 1.668173 / 1.841788 (-0.173615) | 18.668520 / 8.074308 (10.594212) | 20.535542 / 10.191392 (10.344150) | 0.244580 / 0.680424 (-0.435844) | 0.026364 / 0.534201 (-0.507837) | 0.531753 / 0.579283 (-0.047530) | 0.616578 / 0.434364 (0.182214) | 0.618906 / 0.540337 (0.078569) | 0.738785 / 1.386936 (-0.648151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7265cafa3103d77d6d52aa897088faefcd96659 \"CML watermark\")\n" ]

"2023-04-19T14:32:57Z"

"2023-04-21T06:45:13Z"

"2023-04-21T06:35:27Z"

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[ "Cool, love it ! :)\r\n\r\nFeel free to add a paragraph saying how to load the dataset:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"stevhliu/demo\")\r\n\r\n# or to separate each csv file into several splits\r\ndata_files = {\"train\": \"train.csv\", \"test\": \"test.csv\"}\r\ndataset = load_dataset(\"stevhliu/demo\", data_files=data_files)\r\nprint(dataset[\"train\"][0])\r\n```", "Perfect, feel free to mark this PR ready for review :)\r\n\r\ncc @albertvillanova do you have any comment ? You can check the tutorial here:\r\nhttps://47389-250213286-gh.circle-artifacts.com/0/docs/_build/html/no_code_upload.html\r\n\r\nMaybe we can just add a list of supported file types:\r\n- csv\r\n- json\r\n- json lines\r\n- text\r\n- parquet", "I just added a mention of the login for private datasets. Don't hesitate to edit or comment.\r\n\r\nOtherwise I think it's all good, feel free to merge it @stevhliu if you don't have other changes to make :)" ]

"2021-09-15T18:54:42Z"

"2021-09-27T17:51:55Z"

"2021-09-27T17:51:55Z"

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[ "I have an error on windows:\r\n```python\r\naiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]\r\n```\r\nat the `fsspec` call in `xglob`:\r\n```python\r\nfs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options)\r\n```\r\n\r\nLooks like the windows CI has an SSL issue... ", "I can reproduce it on my windows machine. On linux it works fine though", "I'm just skipping the windows test for now", "The Windows CI failure seems unrelated to this PR\r\n```python\r\nERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3\r\n```" ]

"2021-10-06T18:16:36Z"

"2021-10-12T15:25:48Z"

"2021-10-12T15:25:46Z"

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[ "Hello @hugoabonizio, thanks for the contribution.\r\nRegarding the fake data, you can generate it manually.\r\nRunning the `python datasets-cli dummy_data datasets/offcombr` should give you instructions on how to manually create the dummy data.\r\nFor reference, here is a spec for `.arff` files : https://www.cs.waikato.ac.nz/ml/weka/arff.html", "@lhoestq again the failing tests doesn't seem to be related", "merging since the CI is fixed on master" ]

"2020-12-09T19:38:08Z"

"2020-12-14T18:06:45Z"

"2020-12-14T16:51:10Z"

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[ "Sorry for using multiple github accounts, I didn't notice I was using my professional account to commit/push. Please consider this @JAugusto97 account as the correct one.", "Will remake the PR with the correct github account." ]

"2022-02-04T17:18:29Z"

"2022-02-07T03:23:24Z"

"2022-02-04T17:36:40Z"

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"2021-08-16T04:26:15Z"

"2021-08-20T14:31:33Z"

"2021-08-19T12:48:06Z"

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[ "OK, apparently it is a lot more complicated than simply changing the URL? Going to make an issue." ]

"2021-02-08T12:26:35Z"

"2021-02-08T12:42:50Z"

"2021-02-08T12:42:50Z"

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"2022-08-17T19:57:37Z"

"2022-08-17T19:57:37Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3852). All of your documentation changes will be reflected on that endpoint." ]

"2022-03-08T05:57:05Z"

"2022-03-08T16:54:36Z"

"2022-03-08T16:54:36Z"

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[ "I agree this should be documented" ]

"2023-03-21T05:25:35Z"

"2023-03-24T16:36:23Z"

"2023-03-24T16:36:23Z"

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

"2022-08-12T09:51:06Z"

"2022-08-12T13:13:55Z"

"2022-08-12T12:59:39Z"

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

"2023-01-16T16:20:09Z"

"2023-01-16T16:51:42Z"

"2023-01-16T16:49:03Z"

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[ "(@patrickvonplaten )", "Also cc @lhoestq here", "The documentation in the code is definitely outdated - thanks for letting me know, I'll remove it in https://github.com/huggingface/datasets/pull/4184 .\r\n\r\nYou're exactly right `audio` `array` already decodes the audio file to the correct waveform. This is done on the fly, which is also why one should **not** do `ds[\"audio\"][\"array\"][0]` as this will decode all dataset samples, but instead `ds[0][\"audio\"][\"array\"]` see: https://huggingface.co/docs/datasets/audio_process#audio-datasets\r\n\r\n", "So, again to clarify: On disk, only the raw flac file content is stored? Is this also the case after `save_to_disk`?\r\n\r\nAnd is it simple to also store it re-encoded as ogg or mp3 instead?\r\n", "Hey, \r\n\r\nSorry yeah I was just about to look into this! We actually had an outdated version of Librispeech ASR that didn't save any files, but instead converted the audio files to a byte string, then was then decoded on-the-fly. This however is not very user-friendly so we recently decided to instead show the full path of the audio files with the `path` parameter.\r\n\r\nI'm currently changing this for Librispeech here: https://github.com/huggingface/datasets/pull/4184 .\r\nYou should be able to see the audio file in the original `flac` format under `path` then. I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ? ", "> I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ?\r\n\r\nSure, I would expect that `load_dataset(\"librispeech_asr\")` would give you the original (not re-encoded) data (flac or already decoded). So such re-encoding logic would be some separate generic function. So I could do sth like `dataset.reencode_as_ogg(**ogg_encode_opts).save_to_disk(...)` or so.\r\n", "A follow-up question: I wonder whether a Parquet dataset is maybe more what we actually want to have? (Following also my comment here: https://github.com/huggingface/datasets/pull/4184#issuecomment-1105045491.) Because I think we actually would prefer to embed the data content in the dataset.\r\n\r\nSo, instead of `save_to_disk`/`load_from_disk`, we would use `to_parquet`,`from_parquet`? Is there any downside? Are arrow files more efficient?\r\n\r\nRelated is also the doc update in #4193.\r\n", "`save_to_disk` saves the dataset as an Arrow file, which is the format we use to load a dataset using memory mapping. This way the dataset does not fill your RAM, but is read from your disk instead.\r\n\r\nTherefore you can directly reload a dataset saved with `save_to_disk` using `load_from_disk`.\r\n\r\nParquet files are used for cold storage: to use memory mapping on a Parquet dataset, you first have to convert it to Arrow. We use Parquet to reduce the I/O when pushing/downloading data from the Hugging face Hub. When you load a Parquet file from the Hub, it is converted to Arrow on the fly during the download." ]

"2022-04-20T09:35:55Z"

"2022-04-21T11:00:53Z"

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"2023-01-17T06:49:57Z"

"2023-01-18T06:33:24Z"

"2023-01-17T08:51:18Z"

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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) | 0.006761 / 0.011353 (-0.004592) | 0.004307 / 0.011008 (-0.006701) | 0.084682 / 0.038508 (0.046174) | 0.083994 / 0.023109 (0.060885) | 0.316612 / 0.275898 (0.040714) | 0.346157 / 0.323480 (0.022678) | 0.004490 / 0.007986 (-0.003495) | 0.003699 / 0.004328 (-0.000629) | 0.066144 / 0.004250 (0.061894) | 0.057958 / 0.037052 (0.020906) | 0.319018 / 0.258489 (0.060529) | 0.367597 / 0.293841 (0.073756) | 0.031146 / 0.128546 (-0.097401) | 0.008814 / 0.075646 (-0.066832) | 0.290971 / 0.419271 (-0.128301) | 0.052769 / 0.043533 (0.009236) | 0.313125 / 0.255139 (0.057986) | 0.330473 / 0.283200 (0.047273) | 0.025922 / 0.141683 (-0.115760) | 1.494989 / 1.452155 (0.042834) | 1.556140 / 1.492716 (0.063423) |\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) | 0.310580 / 0.018006 (0.292574) | 0.563600 / 0.000490 (0.563110) | 0.012344 / 0.000200 (0.012144) | 0.000382 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031468 / 0.037411 (-0.005943) | 0.084856 / 0.014526 (0.070331) | 0.101371 / 0.176557 (-0.075186) | 0.158735 / 0.737135 (-0.578400) | 0.102451 / 0.296338 (-0.193888) |\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) | 0.402288 / 0.215209 (0.187079) | 4.001351 / 2.077655 (1.923696) | 2.022710 / 1.504120 (0.518590) | 1.850236 / 1.541195 (0.309041) | 1.946779 / 1.468490 (0.478289) | 0.485828 / 4.584777 (-4.098949) | 3.584925 / 3.745712 (-0.160787) | 3.400815 / 5.269862 (-1.869046) | 2.123187 / 4.565676 (-2.442490) | 0.057373 / 0.424275 (-0.366902) | 0.007383 / 0.007607 (-0.000224) | 0.479773 / 0.226044 (0.253729) | 4.805342 / 2.268929 (2.536414) | 2.530151 / 55.444624 (-52.914473) | 2.190136 / 6.876477 (-4.686341) | 2.463666 / 2.142072 (0.321593) | 0.583512 / 4.805227 (-4.221715) | 0.134205 / 6.500664 (-6.366459) | 0.062021 / 0.075469 (-0.013448) |\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) | 1.239532 / 1.841788 (-0.602255) | 20.252941 / 8.074308 (12.178633) | 14.265697 / 10.191392 (4.074305) | 0.158745 / 0.680424 (-0.521679) | 0.018605 / 0.534201 (-0.515596) | 0.394246 / 0.579283 (-0.185037) | 0.415260 / 0.434364 (-0.019104) | 0.462636 / 0.540337 (-0.077701) | 0.645318 / 1.386936 (-0.741618) |\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) | 0.007063 / 0.011353 (-0.004290) | 0.004388 / 0.011008 (-0.006621) | 0.064997 / 0.038508 (0.026489) | 0.085135 / 0.023109 (0.062026) | 0.424349 / 0.275898 (0.148451) | 0.456033 / 0.323480 (0.132553) | 0.005745 / 0.007986 (-0.002241) | 0.003705 / 0.004328 (-0.000624) | 0.065835 / 0.004250 (0.061585) | 0.058366 / 0.037052 (0.021314) | 0.421654 / 0.258489 (0.163165) | 0.460334 / 0.293841 (0.166493) | 0.032828 / 0.128546 (-0.095718) | 0.008974 / 0.075646 (-0.066673) | 0.072524 / 0.419271 (-0.346747) | 0.048558 / 0.043533 (0.005025) | 0.413546 / 0.255139 (0.158407) | 0.435765 / 0.283200 (0.152565) | 0.023754 / 0.141683 (-0.117929) | 1.476884 / 1.452155 (0.024730) | 1.560011 / 1.492716 (0.067294) |\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) | 0.318279 / 0.018006 (0.300272) | 0.544990 / 0.000490 (0.544501) | 0.007118 / 0.000200 (0.006918) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033352 / 0.037411 (-0.004059) | 0.092921 / 0.014526 (0.078395) | 0.109028 / 0.176557 (-0.067528) | 0.161433 / 0.737135 (-0.575703) | 0.108445 / 0.296338 (-0.187893) |\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) | 0.438925 / 0.215209 (0.223716) | 4.400714 / 2.077655 (2.323059) | 2.403727 / 1.504120 (0.899607) | 2.236472 / 1.541195 (0.695277) | 2.319219 / 1.468490 (0.850729) | 0.490159 / 4.584777 (-4.094618) | 3.647474 / 3.745712 (-0.098238) | 3.433144 / 5.269862 (-1.836718) | 2.145367 / 4.565676 (-2.420310) | 0.057994 / 0.424275 (-0.366281) | 0.007452 / 0.007607 (-0.000155) | 0.513808 / 0.226044 (0.287763) | 5.130792 / 2.268929 (2.861863) | 2.861691 / 55.444624 (-52.582934) | 2.473292 / 6.876477 (-4.403185) | 2.756445 / 2.142072 (0.614372) | 0.586783 / 4.805227 (-4.218444) | 0.134170 / 6.500664 (-6.366494) | 0.061149 / 0.075469 (-0.014320) |\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) | 1.350144 / 1.841788 (-0.491644) | 21.003528 / 8.074308 (12.929220) | 15.174314 / 10.191392 (4.982922) | 0.186535 / 0.680424 (-0.493888) | 0.020821 / 0.534201 (-0.513380) | 0.399210 / 0.579283 (-0.180073) | 0.431942 / 0.434364 (-0.002422) | 0.475395 / 0.540337 (-0.064942) | 0.677457 / 1.386936 (-0.709479) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6aa5fc278324a253eab43ad1bc048e822e4ae5c7 \"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) | 0.007062 / 0.011353 (-0.004291) | 0.004299 / 0.011008 (-0.006710) | 0.086019 / 0.038508 (0.047511) | 0.085166 / 0.023109 (0.062057) | 0.355804 / 0.275898 (0.079906) | 0.381056 / 0.323480 (0.057577) | 0.005500 / 0.007986 (-0.002486) | 0.003496 / 0.004328 (-0.000833) | 0.064866 / 0.004250 (0.060615) | 0.057399 / 0.037052 (0.020346) | 0.357914 / 0.258489 (0.099425) | 0.395387 / 0.293841 (0.101546) | 0.031763 / 0.128546 (-0.096784) | 0.008665 / 0.075646 (-0.066981) | 0.290097 / 0.419271 (-0.129175) | 0.053297 / 0.043533 (0.009765) | 0.355659 / 0.255139 (0.100520) | 0.378232 / 0.283200 (0.095032) | 0.026015 / 0.141683 (-0.115668) | 1.437121 / 1.452155 (-0.015034) | 1.538798 / 1.492716 (0.046082) |\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) | 0.243518 / 0.018006 (0.225511) | 0.461361 / 0.000490 (0.460871) | 0.009529 / 0.000200 (0.009329) | 0.000473 / 0.000054 (0.000419) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030379 / 0.037411 (-0.007032) | 0.089851 / 0.014526 (0.075325) | 0.098278 / 0.176557 (-0.078278) | 0.157077 / 0.737135 (-0.580058) | 0.098997 / 0.296338 (-0.197341) |\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) | 0.382415 / 0.215209 (0.167206) | 3.801964 / 2.077655 (1.724309) | 1.887680 / 1.504120 (0.383560) | 1.775903 / 1.541195 (0.234709) | 1.851338 / 1.468490 (0.382848) | 0.483616 / 4.584777 (-4.101161) | 3.612977 / 3.745712 (-0.132736) | 3.397700 / 5.269862 (-1.872162) | 2.114572 / 4.565676 (-2.451105) | 0.057250 / 0.424275 (-0.367025) | 0.007362 / 0.007607 (-0.000245) | 0.456873 / 0.226044 (0.230829) | 4.567319 / 2.268929 (2.298391) | 2.399476 / 55.444624 (-53.045148) | 2.054542 / 6.876477 (-4.821935) | 2.343432 / 2.142072 (0.201359) | 0.582319 / 4.805227 (-4.222908) | 0.134045 / 6.500664 (-6.366619) | 0.062726 / 0.075469 (-0.012743) |\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) | 1.283390 / 1.841788 (-0.558398) | 20.358511 / 8.074308 (12.284202) | 14.933989 / 10.191392 (4.742597) | 0.164960 / 0.680424 (-0.515464) | 0.018625 / 0.534201 (-0.515576) | 0.394087 / 0.579283 (-0.185196) | 0.416761 / 0.434364 (-0.017603) | 0.466669 / 0.540337 (-0.073669) | 0.643161 / 1.386936 (-0.743775) |\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) | 0.007141 / 0.011353 (-0.004212) | 0.004185 / 0.011008 (-0.006824) | 0.066097 / 0.038508 (0.027588) | 0.088436 / 0.023109 (0.065327) | 0.401189 / 0.275898 (0.125291) | 0.440402 / 0.323480 (0.116922) | 0.005729 / 0.007986 (-0.002257) | 0.003527 / 0.004328 (-0.000801) | 0.065278 / 0.004250 (0.061027) | 0.060866 / 0.037052 (0.023813) | 0.407035 / 0.258489 (0.148546) | 0.443923 / 0.293841 (0.150083) | 0.032922 / 0.128546 (-0.095625) | 0.008739 / 0.075646 (-0.066907) | 0.071800 / 0.419271 (-0.347472) | 0.048994 / 0.043533 (0.005461) | 0.403736 / 0.255139 (0.148597) | 0.419566 / 0.283200 (0.136366) | 0.025369 / 0.141683 (-0.116314) | 1.474980 / 1.452155 (0.022825) | 1.553500 / 1.492716 (0.060784) |\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) | 0.225224 / 0.018006 (0.207218) | 0.462891 / 0.000490 (0.462401) | 0.006958 / 0.000200 (0.006758) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034431 / 0.037411 (-0.002980) | 0.100021 / 0.014526 (0.085495) | 0.108339 / 0.176557 (-0.068217) | 0.162762 / 0.737135 (-0.574374) | 0.108951 / 0.296338 (-0.187388) |\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) | 0.435966 / 0.215209 (0.220757) | 4.351744 / 2.077655 (2.274089) | 2.372307 / 1.504120 (0.868187) | 2.192146 / 1.541195 (0.650951) | 2.326839 / 1.468490 (0.858349) | 0.488292 / 4.584777 (-4.096485) | 3.745227 / 3.745712 (-0.000485) | 3.456306 / 5.269862 (-1.813556) | 2.159771 / 4.565676 (-2.405906) | 0.057953 / 0.424275 (-0.366322) | 0.007469 / 0.007607 (-0.000138) | 0.515116 / 0.226044 (0.289071) | 5.162871 / 2.268929 (2.893942) | 2.850336 / 55.444624 (-52.594288) | 2.514700 / 6.876477 (-4.361777) | 2.748843 / 2.142072 (0.606770) | 0.587687 / 4.805227 (-4.217540) | 0.134333 / 6.500664 (-6.366331) | 0.062097 / 0.075469 (-0.013372) |\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) | 1.377082 / 1.841788 (-0.464705) | 21.103127 / 8.074308 (13.028819) | 15.325275 / 10.191392 (5.133883) | 0.166225 / 0.680424 (-0.514199) | 0.020472 / 0.534201 (-0.513729) | 0.395866 / 0.579283 (-0.183417) | 0.444964 / 0.434364 (0.010600) | 0.475367 / 0.540337 (-0.064970) | 0.693325 / 1.386936 (-0.693611) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79b5bbbd52ffd90dd958c05b333d7c90a03756cc \"CML watermark\")\n" ]

"2023-10-11T21:51:12Z"

"2023-10-12T12:47:15Z"

"2023-10-12T12:38:19Z"

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

"2022-04-27T10:42:11Z"

"2022-04-27T16:29:25Z"

"2022-04-27T16:28:50Z"

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"2021-01-04T18:11:50Z"

"2021-01-04T18:13:14Z"

"2021-01-04T18:13:13Z"

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[ "Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized.", "Sure ! I opened an issue #1877 so we can discuss this specific aspect :)", "I am going to implement this consolidation step in #2151.", "Sounds good !", "I retake this PR once the consolidation step is already implemented by #2151." ]

"2021-02-12T15:03:46Z"

"2021-04-23T10:01:31Z"

"2021-04-23T10:01:31Z"

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[ "Some commits seem to be there twice (made the mistake of rebasing because I wasn't sure whether the doc had changed), is this an issue @lhoestq ?" ]

"2021-11-23T15:55:21Z"

"2021-11-24T11:54:13Z"

"2021-11-24T11:54:13Z"

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

"2022-04-22T14:23:48Z"

"2022-05-03T14:23:40Z"

"2022-05-03T14:16:46Z"

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[ "Initially I removed the ` - ` since there was only one `pretty_name` per config but turns out it was breaking here in `from_yaml_string`https://github.com/huggingface/datasets/blob/74751e3f98c74d22c48c6beb1fab0c13b5dfd075/src/datasets/utils/metadata.py#L197 in `/utils/metadata.py`", "@lhoestq I guess this will also need some validation?", "Looks like the parser doesn't allow things like\r\n```\r\npretty_name:\r\n config_name1: My awesome config number 1\r\n config_name2: My amazing config number 2\r\n```\r\ntherefore you had to use `-` and consider them as a list.\r\n\r\nI would be nice to add support for this case in the validator.\r\n\r\nThere's one thing though: the DatasetMetadata object currently corresponds to the yaml tags that are flattened: the config names are just ignored, and the lists are concatenated.\r\n\r\nTherefore I think we would potentially need to instantiate several `DatasetMetadata` objects: one per config. Otherwise we would end up with a list of several pretty_name while we actually need at most 1 per config.\r\n\r\nWhat do you think @gchhablani ?", "I was thinking of returning `metada_dict` (on line 193) whenever `load_dataset_card` is called (we can pass an extra parameter to `from_readme` or `from_yaml_string` for that to happen).\r\n\r\nOne just needs config_name as key for the dictionary inside `pretty_name` dict and for single config, there would be only one value to print. We can do this for other fields as well like `size_categories`, `languages` etc. This will obviate the need to flatten the YAML tags so that don't have to instantiate several DatasetMetadata objects. What do you guys think @lhoestq @gchhablani? \r\n\r\nUpdate: I was thinking of returning the whole dictionary before flattening so that user can access whatever they want with specific configs. Let's say [this](https://pastebin.com/eJ84314f) is my `metadata_dict` before flattening (the loaded YAML string), so instead of validating it and then returning the items individually we can return it just after loading the YAML string.", "Hi @lhoestq @bhavitvyamalik \r\n\r\n@bhavitvyamalik, I'm not sure I understand your approach, can you please elaborate? The `metadata_dict` is flattened before instantiating the object, do you want to remove that? Still confused.\r\n\r\nFew things come to my mind after going through this PR. They might not be entirely relevant to the current task, but I'm just trying to think about possible cases and discuss them here.\r\n\r\n1. Instead of instantiating a new `DatasetMetadata` for each config with flattened tags, why can't we make it more flexible and validate only non-dict items? However, in that case, the types wouldn't be as strict for the class attributes. It would also not work for cases that are like `Dict[str,List[Dict[str,str]]`, but I guess that won't be needed anyway in the foreseeable future?\r\n\r\n Ideally, it would be something like - Check the metadata tag type (root), do a DFS, and find the non-dict objects (leaves), and validate them. Is this an overkill to handle the problem?\r\n2. For single-config datasets, there can be slightly different validation for `pretty_names`, than for multi-config. The user shouldn't need to provide a config name for single-config datasets, wdyt @bhavitvyamalik @lhoestq? Either way, for multi-config, the validation can use the dictionary keys in the path to that leaf node to verify `pretty_names: ... (config)` as well. This will check whether the config name is same as the key (might be unnecessary but prevents typos, so less work for the reviewer(s)). For future, however, it might be beneficial to have something like this.\r\n3. Should we have a default config name for single-config datasets? People use any string they feel like. I've seen `plain_text`, `default` and the dataset name. I've used `image` for a few datasets myself AFAIR. For smarter validation (again, a future case ;-;), it'd be easier for us to have a simple rule for naming configs in single-config datasets. Wdyt @lhoestq?", "Btw, `pretty_names` can also be used to handle this during validation :P \r\n\r\n```\r\n-# Dataset Card for [Dataset Name]\r\n+# Dataset Card for Allegro Reviews\r\n```\r\n\r\nThis is where `DatasetMetadata` and `ReadMe` should be combined. But there are very few overlaps, I guess.\r\n\r\n\n@bhavitvyamalik @lhoestq What about adding a pretty name across all configs, and then config-specific names?\n\nLike\n\n```yaml\npretty_names:\n all_configs: X (dataset_name)\n config_1: X1 (config_1_name)\n config_2: X2 (config_2_name)\n```\nThen, using the `metadata_dict`, the ReadMe header can be validated against `X`.\n\nSorry if I'm throwing too many ideas at once.", "@bhavitvyamalik\r\n\r\nNow, I think I better understand what you're saying. So you want to skip validation for the unflattened metadata and just return it? And let the validation run for the flattened version?", "Exactly! Validation is important but once the YAML tags are validated I feel we shouldn't do that again while calling `load_dataset_card`. +1 for default config name for single-config datasets.", "@bhavitvyamalik\r\nActually, I made the `ReadMe` validation similar to `DatasetMetadata` validation and the class was validating the metadata during the creation. \r\n\r\nMaybe we need to have a separate validation method instead of having it in `__post_init__`? Wdyt @lhoestq? \r\n\r\nI'm sensing too many things to look into. It'd be great to discuss these sometime. \r\n\r\nBut if this PR is urgent then @bhavitvyamalik's logic seems good to me. It doesn't need major modifications in validation.", "> Maybe we need to have a separate validation method instead of having it in __post_init__? Wdyt @lhoestq?\r\n\r\nWe can definitely have a `is_valid()` method instead of doing it in the post init.\r\n\r\n> What about adding a pretty name across all configs, and then config-specific names?\r\n\r\nLet's keep things simple to starts with. If we can allow both single-config and multi-config cases it would already be great :)\r\n\r\nfor single-config:\r\n```yaml\r\npretty_name: Allegro Reviews\r\n```\r\n\r\nfor multi-config:\r\n```yaml\r\npretty_name:\r\n mrpc: Microsoft Research Paraphrase Corpus (MRPC)\r\n sst2: Stanford Sentiment Treebank\r\n ...\r\n```\r\n\r\nTo support the multi-config case I see two options:\r\n1. Don't allow DatasetMetadata to have dictionaries but instead have separate DatasetMetadata objects per config\r\n2. allow DatasetMetadata to have dictionaries. It implies to remove the flattening step. Then we could get metadata for a specific config this way for example:\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\nglue_dataset_card = load_dataset_card(\"glue\")\r\nprint(glue_dataset_card.metadata)\r\n# DatasetMetatada object with dictionaries since there are many configs\r\nprint(glue_dataset_card.metadata.get_metadata_for_config(\"mrpc\"))\r\n# DatasetMetatada object with no dictionaries since there are only the mrpc tags\r\n```\r\n\r\nLet me know what you think or if you have other ideas.", "I think Option 2 is better.\n\nJust to clarify, will `get_metadata_for_config` also return common details, like language, say?", "> Just to clarify, will get_metadata_for_config also return common details, like language, say?\r\n\r\nYes that would be more convenient IMO. For example a dataset card like this\r\n```yaml\r\nlanguages:\r\n- en\r\npretty_name:\r\n config1: Pretty Name for Config 1\r\n config3: Pretty Name for Config 2\r\n```\r\n\r\nthen `metadat.get_metadata_for_config(\"config1\")` would return something like\r\n```python\r\nDatasetMetadata(languages=[\"en\"], pretty_name=\"Pretty Name for Config 1\")\r\n```", "@lhoestq, should we do this post-processing in `load_dataset_card` by returning unflattened dictionary from `DatasetMetadata` or send this from `DatasetMetadata`? Since there isn't much to do I feel once we have the unflattened dictionary", "Not sure I understand the difference @bhavitvyamalik , could you elaborate please ?", "I was talking about this unflattened dictionary:\r\n\r\n> I was thinking of returning the whole dictionary before flattening so that user can access whatever they want with specific configs. Let's say [this](https://pastebin.com/eJ84314f) is my metadata_dict before flattening (the loaded YAML string), so instead of validating it and then returning the items individually we can return it just after loading the YAML string.\r\n\r\nPost-processing meant extracting config-specific fields from this dictionary and then return this `languages=[\"en\"], pretty_name=\"Pretty Name for Config 1\"`", "I still don't understand what you mean by \"returning unflattened dictionary from DatasetMetadata or send this from DatasetMetadata\", sorry. Can you give an example or rephrase this ?\r\n\r\nIMO load_dataset_card can return a dataset card object with a metadata field. If the metadata isn't flat (i.e. it has several configs), you can get the flat metadata of 1 specific config with `get_metadata_for_config`. But of course if you have better ideas or suggestions, we can discuss this", "@lhoestq, I think he is saying whatever `get_metadata_for_config` is doing can be done in `load_dataset_card` by taking the unflattened `metadata_dict` as input.\r\n\r\n@bhavitvyamalik, I think it'd be better to have this \"post-processing\" in `DatasetMetadata` instead of `load_dataset_card`, as @lhoestq has shown. I'll quickly get on that.\r\n\r\n---\r\nThree things that are to be changed in `DatasetMetadata`:\r\n1. Allow Non-flat elements and their validation.\r\n2. Create a method to get metadata by config name.\r\n3. Create a `validate()` method.\r\n\r\nOnce that is done, this PR can be updated and reviewed, wdys?", "Thanks @gchhablani for the help ! Now that https://github.com/huggingface/datasets/pull/2436 is merged you can remove the `-` in the pretty_name @bhavitvyamalik :)", "Thanks @bhavitvyamalik.\r\n\r\nI think this PR was superseded by these others also made by you:\r\n- #3498\r\n- #3536\r\n\r\nI'm closing this." ]

"2021-05-22T09:24:45Z"

"2022-09-23T13:29:14Z"

"2022-09-23T13:29:13Z"

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[ "Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before `push_to_hub` in a faster manner and cache it to disk:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset(...)\r\nformat = dataset.format\r\ndataset = dataset.with_format(\"arrow\")\r\ndataset = dataset.map(embed_table_storage, batched=True)\r\ndataset = dataset.with_format(**format)\r\n# push_to_hub(...)\r\n```\r\n\r\n(In Datasets 3.0, these external bytes will be written to an Arrow file when generating a dataset to avoid this \"embed\" step)", "Hi, thanks, this solution saves some time.\r\nBut can't we avoid embedding all external image files bytes with each push, skipping the images that have already been pushed into the repo?\r\n\r\nEdit: Ok I missed the part of cache it manually on the disk the first time, this solves the problem. Thank you" ]

"2023-07-19T09:25:26Z"

"2023-07-20T18:16:01Z"

"2023-07-20T18:16:00Z"

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[ "Nice :) \r\n\r\ncould you add the tags and also remove all the dummy data files that are not zipped ? The diff currently shows 800 files changes xD", "Aaaaand it's rebase time - the new one is at #1264 !", "closing this one since a new PR was created" ]

"2020-12-06T15:36:20Z"

"2023-09-24T09:51:43Z"

"2020-12-09T09:40:32Z"

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"2021-09-20T11:11:32Z"

"2021-09-20T13:18:28Z"

"2021-09-20T13:18:28Z"

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

"2022-06-29T19:09:21Z"

"2022-07-04T17:41:20Z"

"2022-07-04T17:29:41Z"

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[ "@BradleyHsu", "Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!", "I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.\r\n\r\nhttps://github.com/poloclub/diffusiondb/issues/7", "I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652\r\n\r\nI opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2", "This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.", "Awesome, big thanks to both @xiaohk and @mariosasko!", "so, if i reproduce the bug, what should i do ? with huggingface_hub0.13.3 dataset2.6.1", "huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(ARGS.model_path, trust_remote_code=True)\r\n\r\nPlease handle automatically for local path and repo name inside, otherwise users always get confused about this", "I think I'm also hitting this error, trying to load a model from a local path." ]

"2022-11-21T21:59:53Z"

"2023-05-27T00:06:14Z"

"2022-11-28T02:50:42Z"

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[ "Hi @lhoestq ,\r\n\r\nI am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.\r\n\r\nRunning Transformers 4.3.2 with datasets installed from source from `master` branch.\r\n\r\n```bash\r\n(venv) sergey_mkrtchyan datasets (master) $ python\r\nPython 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)\r\n[Clang 6.0 (clang-600.0.57)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n>>> tokenizer = RagTokenizer.from_pretrained(\"facebook/rag-token-nq\")\r\n>>> retriever = RagRetriever.from_pretrained(\"facebook/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=True)\r\nUsing custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\r\nReusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\nUsing custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\r\nReusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\n 0%| | 0/10 [00:00<?, ?it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 425, in from_pretrained\r\n return cls(\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 387, in __init__\r\n self.init_retrieval()\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 458, in init_retrieval\r\n self.index.init_index()\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 284, in init_index\r\n self.dataset = load_dataset(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py\", line 750, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py\", line 734, in as_dataset\r\n datasets = utils.map_nested(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py\", line 195, in map_nested\r\n return function(data_struct)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py\", line 769, in _build_single_dataset\r\n post_processed = self._post_process(ds, resources_paths)\r\n File \"/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py\", line 205, in _post_process\r\n dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py\", line 2516, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py\", line 416, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index.add(vecs)\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py\", line 104, in replacement_add\r\n self.add_c(n, swig_ptr(x))\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py\", line 3263, in add\r\n return _swigfaiss.IndexHNSW_add(self, n, x)\r\nRuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed\r\n>>>\r\n```\r\n\r\nThe error message is hinting that it could be related to this, but I might be wrong. Any ideas?\r\n\r\n\r\nEdit: Can confirm it's working fine with datasets==1.2.0\r\n\r\nDouble Edit: Did some further digging. The issue is related to this commit: 8c5220307c33f00e01c3bf7b8. I opened a separate issue #1941 for proper tracking." ]

"2021-02-22T15:23:46Z"

"2021-02-25T01:33:48Z"

"2021-02-22T15:36:08Z"

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[ "fixed by https://github.com/huggingface/datasets/pull/4472" ]

"2022-06-10T12:26:06Z"

"2022-06-10T13:24:53Z"

"2022-06-10T13:24:53Z"

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[ { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]

[]

[ "Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.", "Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!", "Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?", "Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks", "The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)", "> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`", "Fixed.\r\n\r\n<img width=\"1542\" alt=\"Capture d’écran 2022-04-12 à 13 57 24\" src=\"https://user-images.githubusercontent.com/1676121/162957585-af96d19c-f86c-47fe-80c4-2b071083cee4.png\">\r\n" ]

"2021-11-16T16:05:19Z"

"2022-04-12T11:57:43Z"

"2022-04-12T11:57:43Z"

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[ "looks like this PR also includes changes about another dataset `covid_qa_deepset`\r\n\r\nCould you create another branch and another PR that only includes the changes for the wikitext-tl-39 dataset ?" ]

"2020-12-07T13:43:48Z"

"2020-12-08T16:00:58Z"

"2020-12-08T16:00:58Z"

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[ "1) Changed the language in a few places apart from those you mentioned in README\r\n2) Reduced the size of dummy data folder by removing all other entries except the first\r\n3) Updated YAML tags by using to the past version of `datasets-tagging` app. Will update the quick fix on that repository too in a while", "@bhavitvyamalik Thanks for adding the dataset on huggingface! Can you please add a metric also for the dataset using the squad_v2 metric file? ", "@MohammedRakib you can check [#2257](https://github.com/huggingface/datasets/pull/2257)" ]

"2021-04-13T21:05:03Z"

"2021-04-24T14:25:51Z"

"2021-04-16T08:50:44Z"

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[ "🤗 This PR closes https://github.com/huggingface/datasets/issues/4398", "_The documentation is not available anymore as the PR was closed or merged._", "Hi! Thanks for reporting and providing a fix. I made a small change to make the fix easier to understand.", "Hi, @mariosasko thanks! It makes sense, sorry I'm not that familiar with `datasets` code 😩 ", "Sure @albertvillanova I'll do that later today and ping you once done, thanks! :hugs:", "Hi again @albertvillanova! Let me know if those tests are fine 🤗 ", "Hi @alvarobartt,\r\n\r\nI think your tests are failing. I don't know why previously, after your last commit, the CI tests were not triggered. \r\n\r\nIn order to force the re-running of the CI tests, I had to edit your file using the GitHub UI.\r\n\r\nFirst I tried to do it using my terminal, but I don't have push right to your PR branch: I would ask you next time you open a PR, please mark the checkbox \"Allow edits from maintainers\": https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests", "Hi @albertvillanova, let me check those again! And regarding that checkbox I thought it was already checked so my bad there 😩 ", "@albertvillanova again it seems that the tests were not automatically triggered, but I tested those locally and now they work, as previously those were failing as I created an assertion as `self.assertEqual` over an empty list that was compared as `None` while the value was `[]` so I updated it to be `self.assertListEqual` and changed the comparison value to `[]`.", "@lhoestq any idea why the CI is not triggered?", "@alvarobartt I have tested locally and the tests continue failing.\r\n\r\nI think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n", "You're right @albertvillanova I was indeed running the tests with `datasets==2.2.0` rather than with the branch version, I'll check it again! Sorry for the inconvenience...", "> @alvarobartt I have tested locally and the tests continue failing.\r\n> \r\n> I think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n\r\nIn order to have some regressions tests for the fixed scenario, I've manually updated the value of `_format_columns` in the `ArrowDataset` so as to check whether it's updated or not right after calling `remove_columns`, and it does behave as expected, so with the latest version of this branch the reported issue doesn't occur anymore.", "Hi again @albertvillanova sorry I was on leave! I'll do that ASAP :hugs:", "@albertvillanova, does it make sense to add regression tests for `DatasetDict`? As `DatasetDict` doesn't have the attribute `_format_columns`, when we call `remove_columns` over a `DatasetDict` it removes the columns and updates the attributes of each split which is an `ArrowDataset`.\r\n\r\nSo on, we can either:\r\n- Update first the `_format_columns` attribute of each split and then remove the columns over the `DatasetDict`\r\n- Loop over the splits of `DatasetDict` and remove the columns right after updating `_format_columns` of each `ArrowDataset`.\r\n\r\nI assume that the best regression test is the one implemented (mentioned first above), let me know if there's a better way to do that 👍🏻 ", "I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?", "> I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?\r\n\r\nHi @lhoestq I can check now!", "It worked indeed @lhoestq, thanks for the proposal and the review! 🤗 ", "Oops, I forgot about `@transmit_format`'s existence. From what I see, we should also use this decorator in `flatten`, `rename_column` and `rename_columns`. \r\n\r\n@alvarobartt Let me know if you'd like to work on this (in a subsequent PR).", "Sure @mariosasko I can prepare another PR to add those too, thanks! " ]

"2022-05-26T11:40:06Z"

"2022-06-14T19:05:37Z"

"2022-06-14T16:01:56Z"

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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==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) | 0.009044 / 0.011353 (-0.002309) | 0.004244 / 0.011008 (-0.006765) | 0.106705 / 0.038508 (0.068197) | 0.029779 / 0.023109 (0.006670) | 0.289684 / 0.275898 (0.013786) | 0.347100 / 0.323480 (0.023620) | 0.007071 / 0.007986 (-0.000915) | 0.003734 / 0.004328 (-0.000595) | 0.077971 / 0.004250 (0.073720) | 0.035323 / 0.037052 (-0.001730) | 0.334520 / 0.258489 (0.076031) | 0.375804 / 0.293841 (0.081964) | 0.049211 / 0.128546 (-0.079335) | 0.016992 / 0.075646 (-0.058654) | 0.337208 / 0.419271 (-0.082064) | 0.053700 / 0.043533 (0.010167) | 0.295750 / 0.255139 (0.040611) | 0.330157 / 0.283200 (0.046958) | 0.097017 / 0.141683 (-0.044666) | 1.379353 / 1.452155 (-0.072802) | 1.402670 / 1.492716 (-0.090047) |\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) | 0.012685 / 0.018006 (-0.005321) | 0.474541 / 0.000490 (0.474051) | 0.006752 / 0.000200 (0.006552) | 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) | 0.025735 / 0.037411 (-0.011676) | 0.092507 / 0.014526 (0.077982) | 0.100275 / 0.176557 (-0.076281) | 0.180359 / 0.737135 (-0.556777) | 0.104312 / 0.296338 (-0.192026) |\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) | 0.456558 / 0.215209 (0.241349) | 4.786667 / 2.077655 (2.709012) | 1.873169 / 1.504120 (0.369050) | 1.640935 / 1.541195 (0.099741) | 1.614543 / 1.468490 (0.146053) | 0.936144 / 4.584777 (-3.648633) | 4.699886 / 3.745712 (0.954174) | 2.398545 / 5.269862 (-2.871317) | 1.642808 / 4.565676 (-2.922868) | 0.124803 / 0.424275 (-0.299472) | 0.011848 / 0.007607 (0.004241) | 0.631684 / 0.226044 (0.405639) | 6.096052 / 2.268929 (3.827124) | 2.463052 / 55.444624 (-52.981572) | 1.928551 / 6.876477 (-4.947926) | 1.927790 / 2.142072 (-0.214283) | 1.098912 / 4.805227 (-3.706315) | 0.196343 / 6.500664 (-6.304321) | 0.063296 / 0.075469 (-0.012173) |\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) | 1.255032 / 1.841788 (-0.586755) | 13.853623 / 8.074308 (5.779315) | 16.303280 / 10.191392 (6.111888) | 0.227287 / 0.680424 (-0.453137) | 0.037527 / 0.534201 (-0.496674) | 0.449345 / 0.579283 (-0.129938) | 0.522054 / 0.434364 (0.087690) | 0.552848 / 0.540337 (0.012511) | 0.642994 / 1.386936 (-0.743942) |\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) | 0.008470 / 0.011353 (-0.002883) | 0.005167 / 0.011008 (-0.005841) | 0.077794 / 0.038508 (0.039286) | 0.029228 / 0.023109 (0.006119) | 0.340828 / 0.275898 (0.064930) | 0.400170 / 0.323480 (0.076691) | 0.005485 / 0.007986 (-0.002500) | 0.003854 / 0.004328 (-0.000475) | 0.077597 / 0.004250 (0.073346) | 0.036519 / 0.037052 (-0.000533) | 0.335522 / 0.258489 (0.077033) | 0.412622 / 0.293841 (0.118781) | 0.044587 / 0.128546 (-0.083959) | 0.016024 / 0.075646 (-0.059623) | 0.092312 / 0.419271 (-0.326960) | 0.055660 / 0.043533 (0.012127) | 0.343140 / 0.255139 (0.088001) | 0.386403 / 0.283200 (0.103203) | 0.098634 / 0.141683 (-0.043049) | 1.326126 / 1.452155 (-0.126029) | 1.430316 / 1.492716 (-0.062400) |\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) | 0.222807 / 0.018006 (0.204801) | 0.473622 / 0.000490 (0.473132) | 0.000376 / 0.000200 (0.000176) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024599 / 0.037411 (-0.012813) | 0.100743 / 0.014526 (0.086217) | 0.112086 / 0.176557 (-0.064471) | 0.198294 / 0.737135 (-0.538842) | 0.111210 / 0.296338 (-0.185129) |\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) | 0.494120 / 0.215209 (0.278911) | 5.117958 / 2.077655 (3.040303) | 2.305131 / 1.504120 (0.801011) | 2.015591 / 1.541195 (0.474396) | 2.027284 / 1.468490 (0.558794) | 1.014241 / 4.584777 (-3.570536) | 4.738836 / 3.745712 (0.993124) | 2.519718 / 5.269862 (-2.750143) | 1.706379 / 4.565676 (-2.859298) | 0.122452 / 0.424275 (-0.301824) | 0.011500 / 0.007607 (0.003893) | 0.632864 / 0.226044 (0.406820) | 6.295457 / 2.268929 (4.026529) | 2.824897 / 55.444624 (-52.619727) | 2.324359 / 6.876477 (-4.552117) | 2.281046 / 2.142072 (0.138974) | 1.173570 / 4.805227 (-3.631657) | 0.197195 / 6.500664 (-6.303469) | 0.064845 / 0.075469 (-0.010624) |\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) | 1.273224 / 1.841788 (-0.568563) | 14.531155 / 8.074308 (6.456847) | 15.892176 / 10.191392 (5.700784) | 0.208051 / 0.680424 (-0.472373) | 0.023119 / 0.534201 (-0.511082) | 0.422317 / 0.579283 (-0.156966) | 0.519946 / 0.434364 (0.085582) | 0.544517 / 0.540337 (0.004179) | 0.605955 / 1.386936 (-0.780981) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#337a4a91d0268c68f26760321c9b45bb4a98832a \"CML watermark\")\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) | 0.010806 / 0.011353 (-0.000547) | 0.005631 / 0.011008 (-0.005378) | 0.113166 / 0.038508 (0.074657) | 0.042980 / 0.023109 (0.019871) | 0.344856 / 0.275898 (0.068958) | 0.404417 / 0.323480 (0.080938) | 0.012222 / 0.007986 (0.004236) | 0.004470 / 0.004328 (0.000141) | 0.088072 / 0.004250 (0.083822) | 0.049815 / 0.037052 (0.012763) | 0.366532 / 0.258489 (0.108043) | 0.392558 / 0.293841 (0.098717) | 0.045411 / 0.128546 (-0.083135) | 0.014118 / 0.075646 (-0.061529) | 0.392894 / 0.419271 (-0.026378) | 0.067713 / 0.043533 (0.024181) | 0.353013 / 0.255139 (0.097874) | 0.378375 / 0.283200 (0.095175) | 0.123686 / 0.141683 (-0.017996) | 1.665272 / 1.452155 (0.213118) | 1.748383 / 1.492716 (0.255667) |\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) | 0.011672 / 0.018006 (-0.006335) | 0.481667 / 0.000490 (0.481178) | 0.003644 / 0.000200 (0.003444) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030436 / 0.037411 (-0.006976) | 0.122577 / 0.014526 (0.108052) | 0.135409 / 0.176557 (-0.041148) | 0.220385 / 0.737135 (-0.516750) | 0.143140 / 0.296338 (-0.153199) |\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) | 0.471146 / 0.215209 (0.255937) | 4.645023 / 2.077655 (2.567368) | 2.126783 / 1.504120 (0.622663) | 1.907905 / 1.541195 (0.366710) | 1.969561 / 1.468490 (0.501071) | 0.798670 / 4.584777 (-3.786107) | 4.394787 / 3.745712 (0.649075) | 2.353535 / 5.269862 (-2.916327) | 1.501013 / 4.565676 (-3.064664) | 0.097472 / 0.424275 (-0.326803) | 0.014015 / 0.007607 (0.006408) | 0.589365 / 0.226044 (0.363320) | 5.897331 / 2.268929 (3.628402) | 2.656198 / 55.444624 (-52.788427) | 2.256082 / 6.876477 (-4.620395) | 2.271122 / 2.142072 (0.129050) | 0.961566 / 4.805227 (-3.843661) | 0.188303 / 6.500664 (-6.312361) | 0.073258 / 0.075469 (-0.002211) |\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) | 1.445266 / 1.841788 (-0.396522) | 16.876710 / 8.074308 (8.802402) | 16.004287 / 10.191392 (5.812895) | 0.212252 / 0.680424 (-0.468172) | 0.033186 / 0.534201 (-0.501015) | 0.520564 / 0.579283 (-0.058719) | 0.516865 / 0.434364 (0.082501) | 0.638482 / 0.540337 (0.098144) | 0.761959 / 1.386936 (-0.624977) |\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) | 0.008101 / 0.011353 (-0.003252) | 0.005512 / 0.011008 (-0.005497) | 0.086138 / 0.038508 (0.047630) | 0.038605 / 0.023109 (0.015496) | 0.413082 / 0.275898 (0.137184) | 0.444016 / 0.323480 (0.120536) | 0.006196 / 0.007986 (-0.001790) | 0.005736 / 0.004328 (0.001408) | 0.086938 / 0.004250 (0.082688) | 0.052307 / 0.037052 (0.015255) | 0.415206 / 0.258489 (0.156717) | 0.481510 / 0.293841 (0.187669) | 0.041469 / 0.128546 (-0.087077) | 0.013481 / 0.075646 (-0.062165) | 0.101528 / 0.419271 (-0.317744) | 0.056507 / 0.043533 (0.012974) | 0.418166 / 0.255139 (0.163027) | 0.443834 / 0.283200 (0.160634) | 0.116434 / 0.141683 (-0.025249) | 1.651223 / 1.452155 (0.199068) | 1.746429 / 1.492716 (0.253713) |\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) | 0.242381 / 0.018006 (0.224375) | 0.478826 / 0.000490 (0.478337) | 0.000463 / 0.000200 (0.000264) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031743 / 0.037411 (-0.005668) | 0.126141 / 0.014526 (0.111616) | 0.134539 / 0.176557 (-0.042018) | 0.216546 / 0.737135 (-0.520590) | 0.143513 / 0.296338 (-0.152825) |\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) | 0.486915 / 0.215209 (0.271706) | 4.833812 / 2.077655 (2.756158) | 2.317785 / 1.504120 (0.813666) | 2.114181 / 1.541195 (0.572986) | 2.153896 / 1.468490 (0.685406) | 0.797490 / 4.584777 (-3.787287) | 4.369950 / 3.745712 (0.624238) | 2.305492 / 5.269862 (-2.964370) | 1.488860 / 4.565676 (-3.076816) | 0.098071 / 0.424275 (-0.326204) | 0.014129 / 0.007607 (0.006522) | 0.611311 / 0.226044 (0.385266) | 6.087482 / 2.268929 (3.818554) | 2.837676 / 55.444624 (-52.606948) | 2.451819 / 6.876477 (-4.424657) | 2.456763 / 2.142072 (0.314690) | 0.957637 / 4.805227 (-3.847590) | 0.190974 / 6.500664 (-6.309690) | 0.074497 / 0.075469 (-0.000972) |\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) | 1.466214 / 1.841788 (-0.375574) | 17.063925 / 8.074308 (8.989617) | 14.630326 / 10.191392 (4.438934) | 0.170570 / 0.680424 (-0.509854) | 0.023794 / 0.534201 (-0.510407) | 0.509175 / 0.579283 (-0.070108) | 0.506485 / 0.434364 (0.072121) | 0.616965 / 0.540337 (0.076628) | 0.718176 / 1.386936 (-0.668760) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4f14de325e26910d026f377756dd8a231150398 \"CML watermark\")\n" ]

"2023-02-27T17:04:46Z"

"2023-02-28T13:52:15Z"

"2023-02-28T13:44:04Z"

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"2022-08-04T12:28:51Z"

"2022-08-22T16:14:16Z"

"2022-08-22T16:14:16Z"

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"2021-06-15T14:03:54Z"

"2021-06-15T16:25:36Z"

"2021-06-15T16:25:35Z"

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[ "In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading. " ]

"2023-08-04T04:01:52Z"

"2023-08-17T17:48:42Z"

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"2020-12-07T07:53:57Z"

"2020-12-08T13:30:33Z"

"2020-12-08T13:30:33Z"

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[]

[ "Yes, having `cache_dir` as an attribute looks cleaner.\r\n\r\n\r\n\r\n", "Good job! Looking forward to this new feature! 🥂", "@lhoestq Sorry for the late reply. Yes, I'll start working on tests. Thanks for the detailed explanation of the current issues with caching (like the idea of adding the `use_caching` parameter to `load_dataset`) ", "@lhoestq Sure. I'm aware this is a high-priority issue to some extent, so feel free to take over.\r\n\r\nFew suggestions I have:\r\n* there is a slight difference between setting `use_caching` to `False` in `load_dataset` and disabling caching globally with `set_caching_enabled(False)` because the former will never execute the following code (`self._cache_dir` is always `False`): \r\nhttps://github.com/huggingface/datasets/blob/c231abdb174987419bbde3360b5b9d6a4672c736/src/datasets/arrow_dataset.py#L1807-L1824\r\n, so I'm just checking whether this is intended (if yes, maybe the docs should mention this) or not?\r\n* think we should add the `use_caching` parameter to every method that has the `keep_in_memory` (and `in_memory` 😃) parameter in its signature for better consistency, but I say let's address this in a separate PR. IMO we need one more PR that will deal exclusively with consistency in the caching logic.", "Hi @mariosasko \r\nWe discussed internally and we think that this feature might not be the direction we're doing to take for these reasons:\r\n\r\n- it goes against our simple definition of caching: on-disk == uses file cache, and in-memory == nothing is written to disk. I think it adds too much complexity just for a minimal flexibility addition\r\n- there are a few edge cases which are really confusing:\r\n - map on an in memory dataset with a cache_file_name specified by the user -> should the result be in memory or from disk ?\r\n - it would require a special cache directory just for in memory datasets, since they don’t have a preferred directory for caching\r\n- it would break a lot of stuff and would require to rewrite significant parts of the core code and the tests\r\n\r\n\r\nSo in the end we're probably going to close this PR.\r\nLet me know what you think, and thank you anyway for your help on this !", "Hi,\r\n\r\nI'm fine with that. I agree this adds too much complexity. Btw, I like the idea of reverting default in-memory for small datasets that led to this PR.", "Superseded by #2460 (to close issue #2458)." ]

"2021-05-06T19:35:32Z"

"2021-06-08T19:46:48Z"

"2021-06-08T19:06:46Z"

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[ "I also thought a bit about the fact that \"sources\" can't be added to the batch.. but changing that would require a lot more changes. And I agree that the idea of adding them as part of the references is not ideal. Conceptually they are not references.\r\n\r\nI would keep it like this for now.. And in the future, work on a more consistent batch interface." ]

"2020-12-15T08:56:00Z"

"2021-01-14T13:33:10Z"

"2021-01-14T13:33:10Z"

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[ "I will add this directly on the hub (same as #4486)—in https://huggingface.co/lapix" ]

"2022-06-24T20:07:54Z"

"2022-07-06T19:03:02Z"

"2022-07-06T19:03:02Z"

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[ "Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__` is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use the named splits API (for now):\r\n```python\r\ntrain_ds, test_ds = load_dataset(\r\n path='csv', \r\n delimiter='\\t', \r\n data_files=text_files, \r\n split=['train[:90%]', 'train[-10%:]'],\r\n)\r\n\r\ntrain_ds = train_ds.rename_column('sentence', 'text')\r\n```", "This has been fixed in #2043 , thanks @mariosasko \r\nThe fix is available on master and we'll do a new release soon :)\r\n\r\nfeel free to re-open if you still have issues" ]

"2021-03-10T09:40:38Z"

"2021-03-16T14:06:08Z"

"2021-03-16T14:05:05Z"

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[ "The problem appears to stem from the omission of the lines that you mentioned. If you add them back and try examples from [this](https://huggingface.co/docs/datasets/using_metrics.html) tutorial (sacrebleu metric example) the code you implemented works fine.\r\n\r\nI think the purpose of these lines is follows:\r\n\r\n1. Sacrebleu metrics confusingly expect a nested list of strings when you have just one reference for each hypothesis (i.e. `[[\"example1\", \"example2\", \"example3]]`), while for cases with more than one reference a _nested list of lists of strings_ (i.e. `[[\"ref1a\", \"ref1b\"], [\"ref2a\", \"ref2b\"], [\"ref3a\", \"ref3b\"]]`) is expected instead. So `transformed_references` line outputs the required single reference format for sacrebleu's ter implementation which you can't pass directly to `compute`.\r\n2. I'm assuming that an additional check is also related to that confusing format with one/many references, because it's really difficult to tell what exactly you're doing wrong if you're not aware of that issue." ]

"2021-10-23T14:26:45Z"

"2021-11-02T11:04:11Z"

"2021-11-02T11:04:11Z"

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

"2022-03-28T13:45:55Z"

"2022-03-28T16:26:48Z"

"2022-03-28T16:21:46Z"

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[ "Can you assign this task to me? @nreimers @mariosasko ", "Hi! Sure! Let us know if you need any pointers." ]

"2022-03-17T09:20:31Z"

"2022-05-23T18:05:29Z"

"2022-05-23T18:05:29Z"

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[ "Hi @phtephanx , the `amazon_polarity` dataset has not been released yet. It will be available in the coming soon v2of `datasets` :) \r\n\r\nYou can still access it now if you want, but you will need to install datasets via the master branch:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`" ]

"2020-12-16T12:51:05Z"

"2020-12-16T16:02:56Z"

"2020-12-16T16:02:56Z"

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"2023-06-17T02:19:58Z"

"2023-06-17T02:19:58Z"

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[ "Hi, thanks for reporting! I've opened a PR with the fix.", "Wow, that was quick! Thank you very much 🙏 " ]

"2022-06-16T12:36:20Z"

"2022-06-20T08:18:46Z"

"2022-06-16T17:11:40Z"

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

"2022-09-28T10:10:23Z"

"2022-09-30T12:30:48Z"

"2022-09-30T12:28:23Z"

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"2021-08-26T17:36:37Z"

"2021-08-27T08:13:24Z"

"2021-08-27T08:13:24Z"

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[ "Thanks for reporting, @kolk.\r\n\r\nWe are fixing it. " ]

"2022-02-22T00:43:33Z"

"2022-02-22T08:12:39Z"

"2022-02-22T08:12:39Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Rebasing fixed the test failures, so this should be ready to review now! There's still a failure on Win but it seems unrelated.", "Gentle ping @lhoestq ! This is a simple fix (dropping columns after loading a batch from the dataset rather than with `.remove_columns()` to make sure we don't break transforms), and tests are green so we're ready for review!", "@lhoestq Test is in!" ]

"2022-06-23T18:21:05Z"

"2022-07-04T19:00:13Z"

"2022-07-04T18:49:01Z"

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[ "Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https://github.com/budzianowski/multiwoz/pull/59\r\nI'm opening a PR to update the checksums of the data files.", "I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll be able to get the new version with\r\n```\r\npip install --upgrade datasets\r\n```", "Hi, I still meet the error when loading the datasets after upgradeing datasets.\r\n\r\nraise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']", "This must be related to https://github.com/budzianowski/multiwoz/pull/72\r\nThose files have changed, let me update the checksums for this dataset.\r\n\r\nFor now you can use `ignore_verifications=True` in `load_dataset` to skip the checksum verification." ]

"2021-02-14T19:14:48Z"

"2021-08-04T18:08:00Z"

"2021-08-04T18:08:00Z"

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"2021-05-11T20:44:33Z"

"2021-05-17T10:53:14Z"

"2021-05-17T10:48:34Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq it is now released so you can move forward with it :) ", "I took your comments into account @Wauplin :)\r\nI also bumped the requirement to 0.2.0 because we're using `set_access_token`\r\n\r\ncc @albertvillanova WDYT ? I edited the CI job to also check for our minimum supported version of hfh at the same time as the minimum pyarrow version", "@lhoestq great, thanks ! :)" ]

"2022-09-27T18:37:49Z"

"2022-09-30T09:18:24Z"

"2022-09-30T09:15:59Z"

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