Datasets:

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[ "Hi! We've recently updated the Wikipedia script, so these changes are only available on master and can be fetched as follows:\r\n```python\r\ndataset_wikipedia = load_dataset(\"wikipedia\", \"20220301.en\", revision=\"master\")\r\n```", "Hi, how can I load the previous \"20200501.en\" version of wikipedia which had been downloaded to the default path? Thanks!", "@JiaQiSJTU just reinstall the previous verision of the package, e.g. `!pip install -q datasets==1.0.0`" ]

"2022-04-11T13:00:14Z"

"2022-08-17T00:37:55Z"

"2022-04-21T17:04:14Z"

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[ "@lhoestq sorry, this was the only way I was able to fix the pull request ", "@lhoestq Thank you for the feedback. I wondering whether I should be passing an 'id' field in the dictionary since the 'connections' reference the 'id' of the linked messages. This 'id' would just be the same as the id_ that is in the yielded tuple.", "Yes indeed it would be cool to have the ids in the dictionary. This way the dataset can be shuffled and all without losing information about the connections. Can you add it if you don't mind ?", "Thanks :) could you also add the ids in the dictionary since they're useful for the connection links ?", "Thanks !\r\nAlso it looks like the dummy_data.zip were regenerated and are now back to being too big (300KB each).\r\nCan you reduce their sizes ? You can actually just revert to the ones you had before the last commit" ]

"2020-12-16T13:25:58Z"

"2021-01-29T10:28:53Z"

"2021-01-29T10:28:53Z"

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[ "`filter` treats the function's output as a (selection) mask - `True` keeps the sample, and `False` drops it. In your case, `bool(0)` evaluates to `False`, so dropping the first sample is the correct behavior.", "Oh gosh! 🤦 I totally misunderstood the API! My apologies!" ]

"2023-09-13T20:20:37Z"

"2023-09-17T07:05:07Z"

"2023-09-17T07:05:07Z"

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

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

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

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[ "> Awesome thank you !\r\n> \r\n> The dummy data are quite big but it was expected given that the raw files are flac files.\r\n> Given that the script doesn't even read the flac files I think we can remove them. Or maybe use empty flac files (see [here](https://hydrogenaud.io/index.php?topic=118685.0) for example). What do you think ?\r\n> \r\n> We'll find a better solution to be able to have bigger dummy_data (max 1MB instead of a few KB, maybe using git LFS.\r\n\r\nHmm, I already made the dummy data as small as possible (a single flac filie per split only). I'd like to keep them at least to have complete dummy data and don't think 500KB for all datasets together is a problem (the long-range summarization datasets are similarly heavy). The moment we allow dummy data to be loaded directly for testing, we need the flac files IMO.\r\n\r\nBut I agree that longterm, we need a better solution for the dummy data (maybe stop hosting it on github to not make the repo too heavy)" ]

"2021-01-22T14:54:37Z"

"2021-01-25T20:38:07Z"

"2021-01-25T20:37:42Z"

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[ "I think it actually comes from an issue here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/810b12f763f5cf02f2e43565b8890d278b7398cd/src/datasets/utils/file_utils.py#L575-L579\r\n\r\nand \r\n\r\nhttps://github.com/huggingface/datasets/blob/810b12f763f5cf02f2e43565b8890d278b7398cd/src/datasets/utils/streaming_download_manager.py#L386-L389\r\n\r\nThis code doesn't seem to work anymore. This can probably be fixed with\r\n\r\n```python\r\nif url.startswith(\"https://drive.google.com/\"): \r\n url += \"&confirm=t\"\r\n cookies = response.cookies \r\n```\r\n\r\nbecause Google Drive doesn't return the `download_warning` cookie anymore.", "Actually it seems that is has been fixed already in https://github.com/huggingface/datasets/pull/3787 :)\r\n\r\nI think it should have fixed the gigaword dataset loading", "@lhoestq The linked PR indeed fixes the issue. This PR is still worth merging IMO to update `gigaword`'s card." ]

"2022-02-22T12:27:16Z"

"2022-02-28T11:35:24Z"

"2022-02-28T11:35:24Z"

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"2021-08-02T05:09:05Z"

"2021-08-03T04:23:37Z"

"2021-08-03T04:23:37Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko thank you for the review! do you have any idea why `test_hash_torch_tensor` fails on \"ubuntu-latest deps-minimum\"? I removed the `torchaudio<0.12.0` test dependency so it uses the latest `torch` now, might it be connected?", "@polinaeterna The failure is due to `torch.from_numpy` not being picklable in newer versions of PyTorch. You can replace the current definition of `_save_tensor` in `utils/py_utils.py` with the following one to fix it: \r\n\r\n```python\r\n@pklregister(obj_type)\r\ndef _save_tensor(pickler, obj):\r\n # `torch.from_numpy` is not picklable in `torch>=1.11.0`\r\n def _create_tensor(np_array):\r\n return torch.from_numpy(np_array)\r\n\r\n dill_log(pickler, f\"To: {obj}\")\r\n args = (obj.detach().cpu().numpy(),)\r\n pickler.save_reduce(_create_tensor, args, obj=obj)\r\n dill_log(pickler, \"# To\")\r\n return\r\n```", "(doing a patch release now - please wait before merging ^^)", "@mariosasko génial, merci!! i've integrated all your changes, can you pls take a look one more time?", "Patch release is done (I did it from another branch than `main` anyway)", "<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) | 0.010927 / 0.011353 (-0.000426) | 0.006232 / 0.011008 (-0.004776) | 0.119815 / 0.038508 (0.081307) | 0.034138 / 0.023109 (0.011029) | 0.349945 / 0.275898 (0.074047) | 0.404967 / 0.323480 (0.081487) | 0.008672 / 0.007986 (0.000687) | 0.005010 / 0.004328 (0.000681) | 0.091931 / 0.004250 (0.087680) | 0.042534 / 0.037052 (0.005482) | 0.374701 / 0.258489 (0.116212) | 0.401027 / 0.293841 (0.107186) | 0.053523 / 0.128546 (-0.075024) | 0.019704 / 0.075646 (-0.055942) | 0.384207 / 0.419271 (-0.035064) | 0.065350 / 0.043533 (0.021817) | 0.375074 / 0.255139 (0.119935) | 0.390458 / 0.283200 (0.107259) | 0.110549 / 0.141683 (-0.031134) | 1.719812 / 1.452155 (0.267657) | 1.748906 / 1.492716 (0.256190) |\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) | 0.210051 / 0.018006 (0.192045) | 0.546503 / 0.000490 (0.546013) | 0.004078 / 0.000200 (0.003878) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030212 / 0.037411 (-0.007199) | 0.121845 / 0.014526 (0.107319) | 0.136309 / 0.176557 (-0.040247) | 0.204667 / 0.737135 (-0.532468) | 0.157327 / 0.296338 (-0.139012) |\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) | 0.672548 / 0.215209 (0.457339) | 6.239409 / 2.077655 (4.161754) | 2.462441 / 1.504120 (0.958322) | 2.063985 / 1.541195 (0.522791) | 2.098858 / 1.468490 (0.630368) | 1.262600 / 4.584777 (-3.322177) | 5.478462 / 3.745712 (1.732750) | 5.454672 / 5.269862 (0.184810) | 2.991866 / 4.565676 (-1.573810) | 0.153415 / 0.424275 (-0.270861) | 0.015061 / 0.007607 (0.007454) | 0.796115 / 0.226044 (0.570071) | 8.206858 / 2.268929 (5.937930) | 3.226395 / 55.444624 (-52.218229) | 2.503522 / 6.876477 (-4.372955) | 2.547489 / 2.142072 (0.405417) | 1.504776 / 4.805227 (-3.300451) | 0.256536 / 6.500664 (-6.244128) | 0.078543 / 0.075469 (0.003073) |\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) | 1.591109 / 1.841788 (-0.250678) | 18.153317 / 8.074308 (10.079008) | 20.465684 / 10.191392 (10.274292) | 0.229808 / 0.680424 (-0.450616) | 0.045263 / 0.534201 (-0.488938) | 0.556760 / 0.579283 (-0.022524) | 0.614985 / 0.434364 (0.180622) | 0.635675 / 0.540337 (0.095337) | 0.729817 / 1.386936 (-0.657119) |\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) | 0.011247 / 0.011353 (-0.000106) | 0.006823 / 0.011008 (-0.004185) | 0.101989 / 0.038508 (0.063481) | 0.036077 / 0.023109 (0.012968) | 0.413469 / 0.275898 (0.137571) | 0.505560 / 0.323480 (0.182080) | 0.007506 / 0.007986 (-0.000480) | 0.006369 / 0.004328 (0.002040) | 0.099597 / 0.004250 (0.095346) | 0.058115 / 0.037052 (0.021063) | 0.414735 / 0.258489 (0.156246) | 0.466801 / 0.293841 (0.172960) | 0.064771 / 0.128546 (-0.063775) | 0.021100 / 0.075646 (-0.054546) | 0.135407 / 0.419271 (-0.283864) | 0.068784 / 0.043533 (0.025251) | 0.410467 / 0.255139 (0.155328) | 0.465993 / 0.283200 (0.182794) | 0.119404 / 0.141683 (-0.022279) | 1.767107 / 1.452155 (0.314952) | 1.938342 / 1.492716 (0.445626) |\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) | 0.227038 / 0.018006 (0.209032) | 0.511389 / 0.000490 (0.510899) | 0.006723 / 0.000200 (0.006523) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033078 / 0.037411 (-0.004333) | 0.133159 / 0.014526 (0.118633) | 0.147928 / 0.176557 (-0.028629) | 0.214005 / 0.737135 (-0.523130) | 0.151655 / 0.296338 (-0.144683) |\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) | 0.634829 / 0.215209 (0.419620) | 6.578640 / 2.077655 (4.500985) | 2.673598 / 1.504120 (1.169478) | 2.338671 / 1.541195 (0.797476) | 2.389104 / 1.468490 (0.920614) | 1.274938 / 4.584777 (-3.309839) | 5.746524 / 3.745712 (2.000812) | 5.992084 / 5.269862 (0.722222) | 3.092090 / 4.565676 (-1.473587) | 0.150375 / 0.424275 (-0.273900) | 0.015470 / 0.007607 (0.007863) | 0.792962 / 0.226044 (0.566918) | 8.057491 / 2.268929 (5.788563) | 3.483966 / 55.444624 (-51.960659) | 2.715038 / 6.876477 (-4.161438) | 2.747186 / 2.142072 (0.605114) | 1.532951 / 4.805227 (-3.272276) | 0.262214 / 6.500664 (-6.238450) | 0.081308 / 0.075469 (0.005839) |\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) | 1.698448 / 1.841788 (-0.143340) | 18.590002 / 8.074308 (10.515694) | 20.584508 / 10.191392 (10.393116) | 0.227237 / 0.680424 (-0.453187) | 0.028445 / 0.534201 (-0.505756) | 0.527874 / 0.579283 (-0.051409) | 0.602844 / 0.434364 (0.168480) | 0.672948 / 0.540337 (0.132611) | 0.788103 / 1.386936 (-0.598833) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f96547708a889c09ca8a02ed7aadd8c5690503c5 \"CML watermark\")\n" ]

"2023-02-23T19:19:44Z"

"2023-02-28T20:25:14Z"

"2023-02-28T20:16:02Z"

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[ "Thanks!" ]

"2020-09-14T23:27:19Z"

"2020-11-02T21:03:23Z"

"2020-09-15T06:19:35Z"

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[ "Hi! This behavior stems from this line: https://github.com/huggingface/datasets/blob/799b817d97590ddc97cbd38d07469403e030de8c/src/datasets/arrow_dataset.py#L1919\r\nBasically, the `Image`/`Audio` columns are decoded only if the `format_type` attribute is `None` (`set_format`/`with_format` and `set_transform`/`with_transform` assign a non-`None` value to it) and the `input_columns` param is not specified (see https://github.com/huggingface/datasets/issues/3756). We will remove these limitations soon.\r\n\r\n\r\n\r\n" ]

"2022-03-23T10:51:13Z"

"2022-12-13T16:59:06Z"

"2022-12-13T16:59:06Z"

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[ "I got the same error, and the official website for visual genome is down. Did you solve this problem? ", "I am in the same situation now :( ", "Thanks for reporting, @gulnaz-zh.\r\n\r\nI am investigating it.", "The host server is down: https://visualgenome.org/\r\n\r\nWe are contacting the dataset authors.", "Apart form data host server being down, there is an additional issue with the `datasets` library introduced by this PR:\r\n- #5238\r\n\r\nI am working to fix it.", "PR that fixes the AttributeError: https://huggingface.co/datasets/visual_genome/discussions/2", "For the issue with their data host server being down, I have opened a discussion in the \"Community\" tab of the Hub dataset: https://huggingface.co/datasets/visual_genome/discussions/3\r\nLet's continue the discussion there.", "The authors just replied to us with their new URL: https://homes.cs.washington.edu/~ranjay/visualgenome/\r\n\r\nWe have fixed the datasets loading script, which is operative again." ]

"2023-04-14T10:48:06Z"

"2023-06-30T11:31:17Z"

"2023-04-18T12:57:08Z"

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[ "Very nice !\r\nWhat do the `sentence1` and `sentence2` correspond to exactly ?\r\nAlso maybe you could use the `ClassLabel` feature type for the `label` field (see [snli](https://github.com/huggingface/datasets/blob/master/datasets/snli/snli.py) for example)", "> What do the `sentence1` and `sentence2` correspond to exactly ?\r\n\r\n`sentence1` is a question, and `sentence2` is a candidate answer sentence. The labels are [1, 2, 3, 4] defining a relation between the answer sentence and the question. For example, label 4 means that the answer sentence is inside the _long_answer_ passage AND that the _short_answer_ is within the answer sentence. All the other labels are the negatives with different characteristics. (the short_answer, long_answer terminology is borrowed from Google's NQ dataset)\r\n\r\nShould I label them simply as `question` and `answer`? I was going more with what I saw in the examples/run_glue.py script, but I realize now there is no restriction around this.\r\n\r\n> Also maybe you could use the `ClassLabel` feature type for the `label` field (see [snli](https://github.com/huggingface/datasets/blob/master/datasets/snli/snli.py) for example)\r\n\r\nI am finding it difficult to assign names to each class, but perhaps it's possible. Here's the description of each class from the paper.\r\n\r\n1. Sentences from the document that are in the long answer but do not contain the annotated short answers. It is possible that these sentences might contain the short answer.\r\n2. Sentences from the document that are not in the long answer but contain the short answer string, that is, such occurrence is purely accidental.\r\n3. Sentences from the document that are neither in the long answer nor contain the short answer.\r\n4. Sentences from the document that are in the long answer and do contain the annotated short answers.\r\n\r\nAny ideas?\r\n\r\n", "Yes it's better to have explicit feature names. Maybe go with question/answer or question/sentence.\r\nI read in the paper that 1,2 and 3 are considered negative and 4 positive.\r\nWe could have a binary classification label `label` (either positive of negative) and then two boolean fields `short_answser_in_sentence` and `sentence_in_long_answer`. What do you think ?", "> Yes it's better to have explicit feature names. Maybe go with question/answer or question/sentence.\r\n> I read in the paper that 1,2 and 3 are considered negative and 4 positive.\r\n> We could have a binary classification label `label` (either positive of negative) and then two boolean fields `short_answser_in_sentence` and `sentence_in_long_answer`. What do you think ?\r\n\r\nOk, sounds good. I went with `sentence` to keep it consistent with `short_answer_in_sentence` and `sentence_in_long_answer`. \r\n\r\nI changed it to a ClassLabel with pos and neg classes and added the two above as features. Let me know if this is not what you had in mind.\r\n\r\n" ]

"2020-10-29T23:31:56Z"

"2020-11-10T09:26:23Z"

"2020-11-10T09:26:23Z"

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[ "Hi @JieyuZhao ! Have you had a chance to add the different configurations ?\r\nThanks again for your help on this !", "> Hi @JieyuZhao ! Have you had a chance to add the different configurations ?\r\n> Thanks again for your help on this !\r\n\r\nHi @lhoestq Yes, I've updated the code. Now the configuration will have dev/test splits.", "> Cool thanks !\r\n> This looks perfect this way.\r\n> \r\n> Now we just need to update the dataset_infos.json (it contains the metadata of the dataset) and add dummy data to be able to test this script automatically.\r\n> \r\n> To update the dataset_infos.json you just need delete the current one at `./datasets/wino_biais/dataset_infos.json`, and then run this command:\r\n> \r\n> ```\r\n> datasets-cli test ./datasets/wino_biais --save_infos --all_configs --ignore_verifications\r\n> ```\r\n> \r\n> To add the dummy data there's also a tool to add them automatically.\r\n> First delete the folder at `./datasets/wino_biais/dummy` and then run\r\n> \r\n> ```\r\n> datasets-cli dummy_data ./datasets/wino_biais --auto_generate --match_text_files \"*conll\" --n_lines 15\r\n> ```\r\n> \r\n> Let me know if you have questions :)\r\n> Also don't forget to run `make style` to format the code properly.\r\n\r\nThanks for the instruction! I've updated the metadata and the dummy data and also do the formatting. Please let me know if more is needed. :)" ]

"2021-02-23T03:07:40Z"

"2021-04-07T15:24:56Z"

"2021-04-07T15:24:56Z"

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"2020-12-07T13:41:54Z"

"2020-12-08T14:44:49Z"

"2020-12-08T14:44:49Z"

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"2020-12-07T03:25:20Z"

"2020-12-07T18:05:16Z"

"2020-12-07T16:43:23Z"

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"2022-05-25T19:41:55Z"

"2022-05-27T14:51:23Z"

"2022-05-27T14:51:23Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Spent the day familiarising myself with the huggingface line of products, and happened to run into some small issues here and there. Magically, I've found exactly one small issue in `transformers`, one in `accelerate` and now one in `datasets`, hah!\r\n\r\nAs for this PR, the issue seems solved according to the [new PR documentation](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4986/en/process#map):\r\n\r\n" ]

"2022-09-16T12:41:07Z"

"2022-09-16T22:12:21Z"

"2022-09-16T17:32:14Z"

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

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

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[ "Thanks, looking at it", "Not sure what happened 😬, but it's fixed" ]

"2022-06-02T19:18:38Z"

"2022-06-07T18:50:37Z"

"2022-06-07T18:50:37Z"

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

"2022-10-03T14:23:21Z"

"2022-10-04T11:19:28Z"

"2022-10-04T11:17:32Z"

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[ "Hi, did you check the doc on `shuffle`?\r\nhttps://huggingface.co/docs/datasets/package_reference/main_classes.html?datasets.Dataset.shuffle#datasets.Dataset.shuffle", "Hi Thomas\r\nthanks for reponse, yes, I did checked it, but this does not work for me please see \r\n\r\n```\r\n(internship) rkarimi@italix17:/idiap/user/rkarimi/dev$ python \r\nPython 3.7.9 (default, Aug 31 2020, 12:42:55) \r\n[GCC 7.3.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import datasets \r\n2020-12-20 01:48:50.766004: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-20 01:48:50.766029: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n>>> data = datasets.load_dataset(\"scitail\", \"snli_format\")\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\nReusing dataset scitail (/idiap/temp/rkarimi/cache_home_1/datasets/scitail/snli_format/1.1.0/fd8ccdfc3134ce86eb4ef10ba7f21ee2a125c946e26bb1dd3625fe74f48d3b90)\r\n>>> data.shuffle(seed=2)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nTypeError: shuffle() got an unexpected keyword argument 'seed'\r\n\r\n```\r\n\r\ndatasets version\r\n`datasets 1.1.2 <pip>\r\n`\r\n", "Thanks for reporting ! \r\n\r\nIndeed it looks like an issue with `suffle` on `DatasetDict`. We're going to fix that.\r\nIn the meantime you can shuffle each split (train, validation, test) separately:\r\n```python\r\nshuffled_train_dataset = data[\"train\"].shuffle(seed=42)\r\n```\r\n" ]

"2020-12-19T20:59:39Z"

"2021-01-04T10:00:03Z"

"2021-01-04T10:00:03Z"

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"2021-02-01T15:57:44Z"

"2021-02-05T17:46:28Z"

"2021-02-05T15:49:25Z"

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

"2022-09-20T08:35:41Z"

"2022-09-20T09:27:55Z"

"2022-09-20T09:15:24Z"

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[ "> Cool thanks !\r\n> \r\n> Also cc @patrickvonplaten @anton-l it means that when calling push_to_hub, the audio bytes are embedded in the parquet files (we don't upload the audio files themselves)\r\n\r\nJust to verify quickly the size of the dataset doesn't change in this case no? E.g. if a dataset has say 20GB in size when stored in `.mp3` format it could have up to 100GB when stored in WAV. But since we are just taking the bytes here a 20GB .mp3 dataset would also have 20GB when stored in parquet no?", "@lhoestq I've addressed your comments. Additionally, I've modified `cast_storage` to account for possible null (`None`) values.\r\n\r\n@patrickvonplaten Yes, the dataset size stays the same (at least because Parquet files are compressed).", "Feel free to merge if it's all good to you :)" ]

"2022-02-07T16:47:16Z"

"2022-02-14T18:14:57Z"

"2022-02-14T18:04:58Z"

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[]

[ "Hi ! I'm getting some 503 from both the http and https addresses. Do you think we could host this data somewhere else ? (please check if there is a license and if it allows redistribution)", "Both HTTP and HTTPS links are working now.\r\n\r\nWe are closing this PR." ]

"2022-02-10T18:46:30Z"

"2022-09-23T11:52:39Z"

"2022-09-23T11:52:39Z"

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"2021-12-13T17:14:16Z"

"2021-12-14T17:30:59Z"

"2021-12-14T17:30:57Z"

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"2021-06-30T07:42:12Z"

"2021-06-30T15:31:01Z"

"2021-06-30T15:31:01Z"

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[ "Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree \"x\"` command)?", "Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because \"train\" was in the name of one of the mp3 files. \r\nmy_dataset\r\n├── data\r\n│   ├── VHA_Innovation_Stories_-_Day_2-123.mp3\r\n│   ├── VHA_Innovation_Stories_-_Day_2-124.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3\r\n│   ├── Your_Impact\\357\\274\\232_Neurosurgery_equipment-5.mp3\r\n│   └── Your_Impact\\357\\274\\232_Neurosurgery_equipment-6.mp3\r\n└── metadata.csv\r\n\r\nHere's a few of the 13 files recognized by the dataset:\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3" ]

"2023-03-05T00:14:45Z"

"2023-03-12T00:02:57Z"

"2023-03-12T00:02:57Z"

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[ "@lhoestq, should I raise a PR for this? Just a minor change while reading labels text file", "Sure feel free to open a PR thanks !" ]

"2020-12-24T13:11:54Z"

"2020-12-30T17:18:49Z"

"2020-12-30T17:18:49Z"

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[ "Hi @XingxingZhang, \r\n\r\nWe have already fixed this. You should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nDuplicate of:\r\n- #3773", "thanks @albertvillanova . by upgrading to 1.18.4 and using `load_dataset(\"...\", download_mode=\"force_redownload\")` fixed \r\n the bug.\r\n\r\nusing the following as you suggested in another thread can also fixed the bug\r\n```\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\n", "The latter solution (installing from GitHub) was proposed because the fix was not released yet. But last week we made the 1.18.4 patch release (with the fix), so no longer necessary to install from GitHub.\r\n\r\nYou can now install from PyPI, as usual:\r\n```shell\r\npip install -U datasets\r\n```\r\n" ]

"2022-03-16T15:23:30Z"

"2022-03-16T15:57:43Z"

"2022-03-16T15:39:25Z"

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"2020-12-06T14:09:07Z"

"2020-12-06T17:41:28Z"

"2020-12-06T17:41:28Z"

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[ "Hi @richarddwang,\r\n\r\n\r\nIndeed, we have recently updated the loading script of that dataset (and fixed that bug as well):\r\n- #4002\r\n\r\nThat fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:\r\n- installing `datasets` from our GitHub repo:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\n- forcing the data files to be redownloaded\r\n```python\r\nds = load_dataset('conll2012_ontonotesv5', 'english_v4', split=\"test\", download_mode=\"force_redownload\")\r\n```\r\n\r\nFeel free to re-open this issue if the problem persists. \r\n\r\nDuplicate of:\r\n- #4031" ]

"2022-04-01T11:34:28Z"

"2022-04-01T13:59:10Z"

"2022-04-01T13:59:10Z"

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[ "Circle CI shuold be green :-) " ]

"2020-06-18T16:25:23Z"

"2020-06-22T08:09:25Z"

"2020-06-22T08:09:24Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6424). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.005323 / 0.011353 (-0.006030) | 0.003560 / 0.011008 (-0.007448) | 0.062572 / 0.038508 (0.024064) | 0.049549 / 0.023109 (0.026440) | 0.236522 / 0.275898 (-0.039376) | 0.260601 / 0.323480 (-0.062879) | 0.002887 / 0.007986 (-0.005099) | 0.003225 / 0.004328 (-0.001103) | 0.048210 / 0.004250 (0.043960) | 0.038783 / 0.037052 (0.001731) | 0.242506 / 0.258489 (-0.015983) | 0.273906 / 0.293841 (-0.019935) | 0.027202 / 0.128546 (-0.101344) | 0.010577 / 0.075646 (-0.065069) | 0.211669 / 0.419271 (-0.207603) | 0.035727 / 0.043533 (-0.007806) | 0.242303 / 0.255139 (-0.012836) | 0.260468 / 0.283200 (-0.022732) | 0.020109 / 0.141683 (-0.121573) | 1.089603 / 1.452155 (-0.362552) | 1.149899 / 1.492716 (-0.342817) |\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) | 0.088768 / 0.018006 (0.070761) | 0.300300 / 0.000490 (0.299810) | 0.000212 / 0.000200 (0.000013) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018758 / 0.037411 (-0.018653) | 0.060097 / 0.014526 (0.045571) | 0.074060 / 0.176557 (-0.102496) | 0.119977 / 0.737135 (-0.617158) | 0.075298 / 0.296338 (-0.221040) |\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) | 0.278640 / 0.215209 (0.063431) | 2.715574 / 2.077655 (0.637919) | 1.466644 / 1.504120 (-0.037476) | 1.344470 / 1.541195 (-0.196725) | 1.386984 / 1.468490 (-0.081506) | 0.575796 / 4.584777 (-4.008981) | 2.392324 / 3.745712 (-1.353388) | 2.826284 / 5.269862 (-2.443578) | 1.758997 / 4.565676 (-2.806679) | 0.062474 / 0.424275 (-0.361801) | 0.004930 / 0.007607 (-0.002678) | 0.332595 / 0.226044 (0.106551) | 3.240076 / 2.268929 (0.971147) | 1.785283 / 55.444624 (-53.659341) | 1.527594 / 6.876477 (-5.348882) | 1.562840 / 2.142072 (-0.579233) | 0.655474 / 4.805227 (-4.149754) | 0.116682 / 6.500664 (-6.383983) | 0.042664 / 0.075469 (-0.032805) |\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) | 0.936306 / 1.841788 (-0.905481) | 11.561239 / 8.074308 (3.486931) | 10.341918 / 10.191392 (0.150526) | 0.140602 / 0.680424 (-0.539822) | 0.013857 / 0.534201 (-0.520344) | 0.294241 / 0.579283 (-0.285042) | 0.268359 / 0.434364 (-0.166005) | 0.326344 / 0.540337 (-0.213993) | 0.430936 / 1.386936 (-0.956000) |\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) | 0.005197 / 0.011353 (-0.006156) | 0.003543 / 0.011008 (-0.007465) | 0.049051 / 0.038508 (0.010542) | 0.052742 / 0.023109 (0.029633) | 0.277032 / 0.275898 (0.001134) | 0.300799 / 0.323480 (-0.022681) | 0.003922 / 0.007986 (-0.004064) | 0.002573 / 0.004328 (-0.001755) | 0.047270 / 0.004250 (0.043019) | 0.039782 / 0.037052 (0.002730) | 0.282780 / 0.258489 (0.024291) | 0.308858 / 0.293841 (0.015017) | 0.028641 / 0.128546 (-0.099905) | 0.010516 / 0.075646 (-0.065131) | 0.056367 / 0.419271 (-0.362904) | 0.032346 / 0.043533 (-0.011186) | 0.277591 / 0.255139 (0.022452) | 0.298539 / 0.283200 (0.015339) | 0.018168 / 0.141683 (-0.123515) | 1.104331 / 1.452155 (-0.347823) | 1.187691 / 1.492716 (-0.305025) |\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) | 0.089511 / 0.018006 (0.071505) | 0.301309 / 0.000490 (0.300820) | 0.000213 / 0.000200 (0.000013) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021466 / 0.037411 (-0.015945) | 0.069917 / 0.014526 (0.055391) | 0.081105 / 0.176557 (-0.095452) | 0.119619 / 0.737135 (-0.617516) | 0.083928 / 0.296338 (-0.212410) |\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) | 0.296471 / 0.215209 (0.081262) | 2.912139 / 2.077655 (0.834484) | 1.588861 / 1.504120 (0.084741) | 1.452148 / 1.541195 (-0.089047) | 1.475388 / 1.468490 (0.006898) | 0.555779 / 4.584777 (-4.028998) | 2.425599 / 3.745712 (-1.320113) | 2.792848 / 5.269862 (-2.477013) | 1.718757 / 4.565676 (-2.846919) | 0.077687 / 0.424275 (-0.346588) | 0.007522 / 0.007607 (-0.000085) | 0.348254 / 0.226044 (0.122210) | 3.439315 / 2.268929 (1.170386) | 1.925907 / 55.444624 (-53.518717) | 1.646163 / 6.876477 (-5.230314) | 1.662148 / 2.142072 (-0.479924) | 0.637277 / 4.805227 (-4.167950) | 0.116159 / 6.500664 (-6.384505) | 0.041518 / 0.075469 (-0.033952) |\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) | 0.966358 / 1.841788 (-0.875430) | 12.125201 / 8.074308 (4.050892) | 10.629939 / 10.191392 (0.438547) | 0.132439 / 0.680424 (-0.547984) | 0.015622 / 0.534201 (-0.518579) | 0.288824 / 0.579283 (-0.290459) | 0.277634 / 0.434364 (-0.156730) | 0.327200 / 0.540337 (-0.213138) | 0.549679 / 1.386936 (-0.837257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0850f663f5498e0f296461e99a345dfd65e3358f \"CML watermark\")\n" ]

"2023-11-15T17:28:14Z"

"2023-11-30T17:29:55Z"

"2023-11-30T17:23:46Z"

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[ "I want to be able to create folders in a model.", "How to set new split names, instead of train/test/validation? For example, I have a local dataset, consists of several subsets, named \"A\", \"B\", and \"C\". How can I create a huggingface dataset, with splits A/B/C ?\r\n\r\nThe document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local?", "> The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local?\r\n\r\nIt works the same - you just need to use local paths instead of URLs" ]

"2022-06-27T16:55:11Z"

"2023-06-14T15:43:05Z"

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[ "Updating `fsspec>=2023.10.0` did solve the issue.", "May be it should be pinned somewhere?", "> Maybe this should go in datasets directly... anyways you can easily fix this error by updating datasets>=2.15.1.dev0.\r\n\r\n@lhoestq @mariosasko for what I understand this is a bug fixed in `datasets` already, right? No need to do anything in `huggingface_hub`?", "I've opened a PR with a fix in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/1875", "Thanks! PR is merged and will be shipped in next release of `huggingface_hub`." ]

"2023-11-29T01:57:36Z"

"2023-11-29T15:39:03Z"

"2023-11-29T02:02:38Z"

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[ "The arrow writer was supposing that the columns were always in the sorted order. I just pushed a fix to reorder the arrays accordingly to the schema. It was failing for many datasets like squad", "and obviously it broke everything", "Feel free to revert my commit. I can investigate this in the coming days", "@lhoestq I do not understand when you say:\r\n> It was failing for many datasets like squad\r\n\r\nAll the tests were green after my last commit.", "> All the tests were green after my last commit.\r\n\r\nYes but loading the actual squad dataset was failing :/\r\n" ]

"2021-06-07T16:26:38Z"

"2021-06-15T18:05:36Z"

"2021-06-15T15:43:48Z"

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[]

[ "pan" ]

"2020-12-03T20:55:29Z"

"2020-12-04T16:36:39Z"

"2020-12-04T09:15:20Z"

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[ "We should also update the `bookcorpusopen` download url (see #3561) , no? ", "For `the_pile_openwebtext2` and `the_pile_stack_exchange` I did not regenerate the JSON files, but instead I just changed the download_checksums URL. ", "Seems like the mystic URL is now broken and the original should be used. ", "Also if I git clone and edit the repo or reset it before this PR it is still trying to pull using mystic? Why is this? " ]

"2022-01-25T08:11:28Z"

"2022-07-20T20:54:42Z"

"2022-02-14T08:40:58Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> I am not sure why the case distinction between local and remote filesystems is even necessary for DatasetDict when saving to disk. Imo this could be removed (leaving only fs.makedirs(dataset_dict_path, exist_ok=True)).\r\n\r\nIndeed. But it's better to address this in a separate PR.", "<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) | 0.007696 / 0.011353 (-0.003656) | 0.004497 / 0.011008 (-0.006511) | 0.099302 / 0.038508 (0.060794) | 0.083360 / 0.023109 (0.060251) | 0.393483 / 0.275898 (0.117585) | 0.450505 / 0.323480 (0.127025) | 0.004610 / 0.007986 (-0.003376) | 0.003637 / 0.004328 (-0.000692) | 0.075752 / 0.004250 (0.071501) | 0.064034 / 0.037052 (0.026982) | 0.397785 / 0.258489 (0.139296) | 0.462948 / 0.293841 (0.169107) | 0.035902 / 0.128546 (-0.092644) | 0.009640 / 0.075646 (-0.066007) | 0.342299 / 0.419271 (-0.076973) | 0.059586 / 0.043533 (0.016053) | 0.404918 / 0.255139 (0.149779) | 0.440889 / 0.283200 (0.157690) | 0.028981 / 0.141683 (-0.112702) | 1.775380 / 1.452155 (0.323226) | 1.866663 / 1.492716 (0.373946) |\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) | 0.249080 / 0.018006 (0.231074) | 0.456460 / 0.000490 (0.455970) | 0.028145 / 0.000200 (0.027945) | 0.000402 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030373 / 0.037411 (-0.007038) | 0.088562 / 0.014526 (0.074036) | 0.122837 / 0.176557 (-0.053720) | 0.167122 / 0.737135 (-0.570014) | 0.103953 / 0.296338 (-0.192385) |\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) | 0.431714 / 0.215209 (0.216505) | 4.182224 / 2.077655 (2.104570) | 2.025650 / 1.504120 (0.521530) | 1.838905 / 1.541195 (0.297710) | 1.868710 / 1.468490 (0.400219) | 0.538422 / 4.584777 (-4.046355) | 4.038941 / 3.745712 (0.293228) | 3.717695 / 5.269862 (-1.552166) | 2.313197 / 4.565676 (-2.252479) | 0.061060 / 0.424275 (-0.363215) | 0.008248 / 0.007607 (0.000641) | 0.497438 / 0.226044 (0.271394) | 4.946663 / 2.268929 (2.677734) | 2.571841 / 55.444624 (-52.872784) | 2.155894 / 6.876477 (-4.720583) | 2.183180 / 2.142072 (0.041107) | 0.639810 / 4.805227 (-4.165417) | 0.153273 / 6.500664 (-6.347391) | 0.068606 / 0.075469 (-0.006863) |\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) | 1.376152 / 1.841788 (-0.465635) | 20.747088 / 8.074308 (12.672780) | 15.200311 / 10.191392 (5.008919) | 0.166380 / 0.680424 (-0.514043) | 0.021417 / 0.534201 (-0.512784) | 0.435677 / 0.579283 (-0.143606) | 0.460412 / 0.434364 (0.026048) | 0.509978 / 0.540337 (-0.030359) | 0.702506 / 1.386936 (-0.684430) |\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) | 0.007378 / 0.011353 (-0.003975) | 0.003938 / 0.011008 (-0.007070) | 0.067095 / 0.038508 (0.028587) | 0.082252 / 0.023109 (0.059143) | 0.420317 / 0.275898 (0.144419) | 0.477496 / 0.323480 (0.154017) | 0.006259 / 0.007986 (-0.001727) | 0.003513 / 0.004328 (-0.000816) | 0.072107 / 0.004250 (0.067856) | 0.061737 / 0.037052 (0.024684) | 0.444142 / 0.258489 (0.185653) | 0.488926 / 0.293841 (0.195085) | 0.033623 / 0.128546 (-0.094923) | 0.008091 / 0.075646 (-0.067555) | 0.073997 / 0.419271 (-0.345274) | 0.051295 / 0.043533 (0.007762) | 0.442551 / 0.255139 (0.187412) | 0.462713 / 0.283200 (0.179513) | 0.023115 / 0.141683 (-0.118568) | 1.645759 / 1.452155 (0.193604) | 1.758121 / 1.492716 (0.265405) |\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) | 0.233450 / 0.018006 (0.215444) | 0.445384 / 0.000490 (0.444894) | 0.006412 / 0.000200 (0.006212) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032446 / 0.037411 (-0.004965) | 0.098515 / 0.014526 (0.083989) | 0.109095 / 0.176557 (-0.067462) | 0.167645 / 0.737135 (-0.569490) | 0.110403 / 0.296338 (-0.185936) |\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) | 0.470189 / 0.215209 (0.254980) | 4.663224 / 2.077655 (2.585569) | 2.504474 / 1.504120 (1.000354) | 2.282867 / 1.541195 (0.741673) | 2.331598 / 1.468490 (0.863108) | 0.554421 / 4.584777 (-4.030356) | 4.078657 / 3.745712 (0.332945) | 3.516339 / 5.269862 (-1.753523) | 2.239134 / 4.565676 (-2.326542) | 0.062690 / 0.424275 (-0.361585) | 0.008406 / 0.007607 (0.000799) | 0.533827 / 0.226044 (0.307782) | 5.423984 / 2.268929 (3.155055) | 2.972784 / 55.444624 (-52.471840) | 2.699056 / 6.876477 (-4.177421) | 2.844403 / 2.142072 (0.702331) | 0.639194 / 4.805227 (-4.166033) | 0.142097 / 6.500664 (-6.358567) | 0.064646 / 0.075469 (-0.010823) |\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) | 1.544640 / 1.841788 (-0.297148) | 21.453429 / 8.074308 (13.379121) | 15.610723 / 10.191392 (5.419331) | 0.207796 / 0.680424 (-0.472628) | 0.021912 / 0.534201 (-0.512289) | 0.430472 / 0.579283 (-0.148811) | 0.467530 / 0.434364 (0.033166) | 0.541339 / 0.540337 (0.001002) | 0.721976 / 1.386936 (-0.664960) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#691c4bc65888005f3aadea5c104fdbc87694882d \"CML watermark\")\n" ]

"2023-07-29T20:50:45Z"

"2023-10-27T14:14:11Z"

"2023-10-27T14:04:36Z"

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[ "this doesn't work. I can decompress the file after download locally." ]

"2020-08-11T02:14:52Z"

"2020-08-11T02:22:28Z"

"2020-08-11T02:22:12Z"

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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) | 0.006334 / 0.011353 (-0.005018) | 0.004447 / 0.011008 (-0.006561) | 0.099287 / 0.038508 (0.060779) | 0.027426 / 0.023109 (0.004317) | 0.322638 / 0.275898 (0.046740) | 0.370501 / 0.323480 (0.047021) | 0.004775 / 0.007986 (-0.003210) | 0.003289 / 0.004328 (-0.001040) | 0.076531 / 0.004250 (0.072280) | 0.037485 / 0.037052 (0.000432) | 0.335634 / 0.258489 (0.077145) | 0.384031 / 0.293841 (0.090190) | 0.031258 / 0.128546 (-0.097288) | 0.011619 / 0.075646 (-0.064027) | 0.326309 / 0.419271 (-0.092963) | 0.042513 / 0.043533 (-0.001020) | 0.340817 / 0.255139 (0.085678) | 0.369846 / 0.283200 (0.086646) | 0.084904 / 0.141683 (-0.056779) | 1.481739 / 1.452155 (0.029584) | 1.566593 / 1.492716 (0.073877) |\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) | 0.186424 / 0.018006 (0.168418) | 0.400879 / 0.000490 (0.400389) | 0.003520 / 0.000200 (0.003320) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023287 / 0.037411 (-0.014124) | 0.097767 / 0.014526 (0.083241) | 0.103271 / 0.176557 (-0.073286) | 0.165414 / 0.737135 (-0.571722) | 0.106437 / 0.296338 (-0.189901) |\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) | 0.422711 / 0.215209 (0.207502) | 4.221382 / 2.077655 (2.143727) | 1.906807 / 1.504120 (0.402687) | 1.709595 / 1.541195 (0.168400) | 1.720452 / 1.468490 (0.251962) | 0.699477 / 4.584777 (-3.885300) | 3.415840 / 3.745712 (-0.329873) | 2.835669 / 5.269862 (-2.434192) | 1.501775 / 4.565676 (-3.063901) | 0.082896 / 0.424275 (-0.341379) | 0.012855 / 0.007607 (0.005248) | 0.514373 / 0.226044 (0.288329) | 5.190000 / 2.268929 (2.921071) | 2.302539 / 55.444624 (-53.142086) | 1.963410 / 6.876477 (-4.913067) | 2.020944 / 2.142072 (-0.121128) | 0.805919 / 4.805227 (-3.999308) | 0.150604 / 6.500664 (-6.350060) | 0.065977 / 0.075469 (-0.009492) |\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) | 1.206487 / 1.841788 (-0.635300) | 13.631513 / 8.074308 (5.557205) | 13.800258 / 10.191392 (3.608866) | 0.146914 / 0.680424 (-0.533509) | 0.016454 / 0.534201 (-0.517747) | 0.377752 / 0.579283 (-0.201532) | 0.384312 / 0.434364 (-0.050052) | 0.434912 / 0.540337 (-0.105425) | 0.522507 / 1.386936 (-0.864429) |\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) | 0.006328 / 0.011353 (-0.005025) | 0.004406 / 0.011008 (-0.006602) | 0.077951 / 0.038508 (0.039443) | 0.026716 / 0.023109 (0.003607) | 0.337303 / 0.275898 (0.061405) | 0.372036 / 0.323480 (0.048556) | 0.004800 / 0.007986 (-0.003185) | 0.003153 / 0.004328 (-0.001175) | 0.076823 / 0.004250 (0.072573) | 0.035873 / 0.037052 (-0.001179) | 0.340243 / 0.258489 (0.081754) | 0.380183 / 0.293841 (0.086342) | 0.032185 / 0.128546 (-0.096361) | 0.011545 / 0.075646 (-0.064101) | 0.086887 / 0.419271 (-0.332384) | 0.041560 / 0.043533 (-0.001973) | 0.338716 / 0.255139 (0.083577) | 0.363080 / 0.283200 (0.079881) | 0.088375 / 0.141683 (-0.053308) | 1.499004 / 1.452155 (0.046850) | 1.585904 / 1.492716 (0.093188) |\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) | 0.211645 / 0.018006 (0.193639) | 0.403707 / 0.000490 (0.403218) | 0.000415 / 0.000200 (0.000215) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024972 / 0.037411 (-0.012440) | 0.097996 / 0.014526 (0.083470) | 0.105941 / 0.176557 (-0.070616) | 0.155521 / 0.737135 (-0.581615) | 0.108246 / 0.296338 (-0.188092) |\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) | 0.442316 / 0.215209 (0.227107) | 4.417977 / 2.077655 (2.340322) | 2.078324 / 1.504120 (0.574205) | 1.863678 / 1.541195 (0.322483) | 1.917149 / 1.468490 (0.448659) | 0.697628 / 4.584777 (-3.887149) | 3.412810 / 3.745712 (-0.332902) | 1.866473 / 5.269862 (-3.403389) | 1.155923 / 4.565676 (-3.409754) | 0.082831 / 0.424275 (-0.341444) | 0.012367 / 0.007607 (0.004760) | 0.540018 / 0.226044 (0.313974) | 5.420472 / 2.268929 (3.151544) | 2.508540 / 55.444624 (-52.936084) | 2.166397 / 6.876477 (-4.710080) | 2.153486 / 2.142072 (0.011414) | 0.804860 / 4.805227 (-4.000367) | 0.151178 / 6.500664 (-6.349486) | 0.067870 / 0.075469 (-0.007599) |\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) | 1.310387 / 1.841788 (-0.531400) | 13.908916 / 8.074308 (5.834608) | 14.136895 / 10.191392 (3.945503) | 0.139389 / 0.680424 (-0.541035) | 0.016687 / 0.534201 (-0.517514) | 0.379624 / 0.579283 (-0.199659) | 0.382634 / 0.434364 (-0.051730) | 0.439632 / 0.540337 (-0.100706) | 0.524913 / 1.386936 (-0.862023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8f2143b4ed39b58ed415029e7838d767662da91 \"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) | 0.006365 / 0.011353 (-0.004988) | 0.004457 / 0.011008 (-0.006551) | 0.097989 / 0.038508 (0.059481) | 0.027686 / 0.023109 (0.004577) | 0.357412 / 0.275898 (0.081514) | 0.368573 / 0.323480 (0.045093) | 0.004859 / 0.007986 (-0.003127) | 0.003262 / 0.004328 (-0.001066) | 0.076487 / 0.004250 (0.072237) | 0.035526 / 0.037052 (-0.001527) | 0.332862 / 0.258489 (0.074373) | 0.369334 / 0.293841 (0.075493) | 0.030750 / 0.128546 (-0.097796) | 0.011503 / 0.075646 (-0.064143) | 0.323289 / 0.419271 (-0.095982) | 0.042302 / 0.043533 (-0.001231) | 0.334009 / 0.255139 (0.078870) | 0.354150 / 0.283200 (0.070951) | 0.082895 / 0.141683 (-0.058788) | 1.499727 / 1.452155 (0.047572) | 1.574123 / 1.492716 (0.081407) |\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) | 0.192583 / 0.018006 (0.174577) | 0.408136 / 0.000490 (0.407646) | 0.001272 / 0.000200 (0.001072) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022883 / 0.037411 (-0.014528) | 0.095710 / 0.014526 (0.081185) | 0.106545 / 0.176557 (-0.070011) | 0.165784 / 0.737135 (-0.571352) | 0.108594 / 0.296338 (-0.187744) |\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) | 0.429483 / 0.215209 (0.214274) | 4.292338 / 2.077655 (2.214683) | 1.917759 / 1.504120 (0.413639) | 1.711489 / 1.541195 (0.170294) | 1.735668 / 1.468490 (0.267178) | 0.707602 / 4.584777 (-3.877175) | 3.369643 / 3.745712 (-0.376070) | 1.874517 / 5.269862 (-3.395344) | 1.248560 / 4.565676 (-3.317117) | 0.083247 / 0.424275 (-0.341028) | 0.012606 / 0.007607 (0.004999) | 0.519342 / 0.226044 (0.293297) | 5.225462 / 2.268929 (2.956533) | 2.433230 / 55.444624 (-53.011394) | 2.006005 / 6.876477 (-4.870471) | 2.093156 / 2.142072 (-0.048916) | 0.809372 / 4.805227 (-3.995855) | 0.151691 / 6.500664 (-6.348973) | 0.066680 / 0.075469 (-0.008789) |\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) | 1.226283 / 1.841788 (-0.615505) | 13.604338 / 8.074308 (5.530030) | 13.953245 / 10.191392 (3.761853) | 0.132904 / 0.680424 (-0.547520) | 0.016420 / 0.534201 (-0.517781) | 0.395316 / 0.579283 (-0.183967) | 0.385003 / 0.434364 (-0.049361) | 0.483303 / 0.540337 (-0.057034) | 0.578459 / 1.386936 (-0.808477) |\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) | 0.006218 / 0.011353 (-0.005135) | 0.004451 / 0.011008 (-0.006557) | 0.076892 / 0.038508 (0.038384) | 0.027017 / 0.023109 (0.003908) | 0.356976 / 0.275898 (0.081078) | 0.396083 / 0.323480 (0.072603) | 0.005510 / 0.007986 (-0.002476) | 0.003265 / 0.004328 (-0.001063) | 0.075771 / 0.004250 (0.071521) | 0.037117 / 0.037052 (0.000064) | 0.362181 / 0.258489 (0.103692) | 0.401771 / 0.293841 (0.107931) | 0.032062 / 0.128546 (-0.096484) | 0.011453 / 0.075646 (-0.064194) | 0.085773 / 0.419271 (-0.333498) | 0.041679 / 0.043533 (-0.001854) | 0.355120 / 0.255139 (0.099981) | 0.390170 / 0.283200 (0.106970) | 0.088210 / 0.141683 (-0.053473) | 1.526434 / 1.452155 (0.074279) | 1.586019 / 1.492716 (0.093302) |\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) | 0.196836 / 0.018006 (0.178830) | 0.401161 / 0.000490 (0.400671) | 0.002880 / 0.000200 (0.002680) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024445 / 0.037411 (-0.012966) | 0.100187 / 0.014526 (0.085661) | 0.106391 / 0.176557 (-0.070165) | 0.159764 / 0.737135 (-0.577372) | 0.109828 / 0.296338 (-0.186511) |\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) | 0.444228 / 0.215209 (0.229018) | 4.420769 / 2.077655 (2.343114) | 2.069437 / 1.504120 (0.565318) | 1.862587 / 1.541195 (0.321392) | 1.934627 / 1.468490 (0.466137) | 0.699681 / 4.584777 (-3.885095) | 3.352540 / 3.745712 (-0.393172) | 2.613172 / 5.269862 (-2.656689) | 1.445116 / 4.565676 (-3.120561) | 0.083086 / 0.424275 (-0.341189) | 0.012715 / 0.007607 (0.005108) | 0.537450 / 0.226044 (0.311405) | 5.403052 / 2.268929 (3.134123) | 2.506703 / 55.444624 (-52.937921) | 2.170198 / 6.876477 (-4.706279) | 2.201909 / 2.142072 (0.059837) | 0.799555 / 4.805227 (-4.005672) | 0.150825 / 6.500664 (-6.349839) | 0.067234 / 0.075469 (-0.008235) |\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) | 1.293097 / 1.841788 (-0.548691) | 13.817133 / 8.074308 (5.742825) | 14.247231 / 10.191392 (4.055839) | 0.128422 / 0.680424 (-0.552002) | 0.016541 / 0.534201 (-0.517660) | 0.382466 / 0.579283 (-0.196817) | 0.380560 / 0.434364 (-0.053804) | 0.439061 / 0.540337 (-0.101276) | 0.521865 / 1.386936 (-0.865071) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69e60be438c334919f590512fd664436bd6b3667 \"CML watermark\")\n", "I also took the liberty of removing `_hf_hub_fixes.py` completely :)\r\n\r\n> Do you think this is really necessary and convenient? I would naively say that 5% of the users is not a negligible number...\r\n\r\nI think it's ok. Most of them are using old versions of `datasets` anyway.\r\n\r\n", "merging, but lmk if you have other concerns", "<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) | 0.006810 / 0.011353 (-0.004543) | 0.004683 / 0.011008 (-0.006325) | 0.100889 / 0.038508 (0.062381) | 0.030135 / 0.023109 (0.007026) | 0.356407 / 0.275898 (0.080509) | 0.389175 / 0.323480 (0.065695) | 0.005358 / 0.007986 (-0.002627) | 0.004760 / 0.004328 (0.000432) | 0.075904 / 0.004250 (0.071654) | 0.040341 / 0.037052 (0.003288) | 0.357363 / 0.258489 (0.098874) | 0.394185 / 0.293841 (0.100344) | 0.031322 / 0.128546 (-0.097224) | 0.011636 / 0.075646 (-0.064010) | 0.327327 / 0.419271 (-0.091944) | 0.042494 / 0.043533 (-0.001039) | 0.338079 / 0.255139 (0.082940) | 0.363388 / 0.283200 (0.080189) | 0.087102 / 0.141683 (-0.054581) | 1.505686 / 1.452155 (0.053531) | 1.562112 / 1.492716 (0.069396) |\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) | 0.203630 / 0.018006 (0.185624) | 0.425986 / 0.000490 (0.425496) | 0.003786 / 0.000200 (0.003586) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024138 / 0.037411 (-0.013274) | 0.101752 / 0.014526 (0.087226) | 0.105436 / 0.176557 (-0.071121) | 0.165385 / 0.737135 (-0.571750) | 0.114510 / 0.296338 (-0.181828) |\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) | 0.447561 / 0.215209 (0.232352) | 4.449212 / 2.077655 (2.371557) | 2.169472 / 1.504120 (0.665352) | 1.989025 / 1.541195 (0.447831) | 2.036267 / 1.468490 (0.567776) | 0.698647 / 4.584777 (-3.886130) | 3.483281 / 3.745712 (-0.262431) | 1.949306 / 5.269862 (-3.320555) | 1.290313 / 4.565676 (-3.275363) | 0.083079 / 0.424275 (-0.341196) | 0.012759 / 0.007607 (0.005152) | 0.540944 / 0.226044 (0.314899) | 5.473391 / 2.268929 (3.204463) | 2.632037 / 55.444624 (-52.812587) | 2.327396 / 6.876477 (-4.549081) | 2.428880 / 2.142072 (0.286808) | 0.808918 / 4.805227 (-3.996309) | 0.153283 / 6.500664 (-6.347381) | 0.068325 / 0.075469 (-0.007145) |\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) | 1.212527 / 1.841788 (-0.629260) | 14.306444 / 8.074308 (6.232136) | 14.904980 / 10.191392 (4.713588) | 0.142796 / 0.680424 (-0.537628) | 0.016829 / 0.534201 (-0.517372) | 0.384806 / 0.579283 (-0.194477) | 0.390505 / 0.434364 (-0.043859) | 0.441734 / 0.540337 (-0.098603) | 0.526159 / 1.386936 (-0.860777) |\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) | 0.006950 / 0.011353 (-0.004403) | 0.004647 / 0.011008 (-0.006362) | 0.078925 / 0.038508 (0.040417) | 0.028081 / 0.023109 (0.004971) | 0.343420 / 0.275898 (0.067522) | 0.380567 / 0.323480 (0.057087) | 0.005286 / 0.007986 (-0.002700) | 0.004816 / 0.004328 (0.000487) | 0.077332 / 0.004250 (0.073081) | 0.042131 / 0.037052 (0.005078) | 0.345371 / 0.258489 (0.086882) | 0.390232 / 0.293841 (0.096392) | 0.032395 / 0.128546 (-0.096152) | 0.011669 / 0.075646 (-0.063978) | 0.087649 / 0.419271 (-0.331622) | 0.042465 / 0.043533 (-0.001068) | 0.342863 / 0.255139 (0.087724) | 0.368947 / 0.283200 (0.085748) | 0.091725 / 0.141683 (-0.049958) | 1.477435 / 1.452155 (0.025280) | 1.563449 / 1.492716 (0.070733) |\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) | 0.208016 / 0.018006 (0.190010) | 0.428387 / 0.000490 (0.427898) | 0.000443 / 0.000200 (0.000243) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026963 / 0.037411 (-0.010449) | 0.103854 / 0.014526 (0.089328) | 0.109068 / 0.176557 (-0.067488) | 0.160107 / 0.737135 (-0.577028) | 0.112843 / 0.296338 (-0.183496) |\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) | 0.437161 / 0.215209 (0.221952) | 4.396178 / 2.077655 (2.318523) | 2.067597 / 1.504120 (0.563477) | 1.875247 / 1.541195 (0.334053) | 1.962451 / 1.468490 (0.493961) | 0.701427 / 4.584777 (-3.883350) | 3.459564 / 3.745712 (-0.286148) | 1.959482 / 5.269862 (-3.310380) | 1.191866 / 4.565676 (-3.373810) | 0.083243 / 0.424275 (-0.341032) | 0.012740 / 0.007607 (0.005133) | 0.535236 / 0.226044 (0.309191) | 5.351715 / 2.268929 (3.082786) | 2.490868 / 55.444624 (-52.953756) | 2.195680 / 6.876477 (-4.680797) | 2.233854 / 2.142072 (0.091781) | 0.809041 / 4.805227 (-3.996187) | 0.151498 / 6.500664 (-6.349166) | 0.068297 / 0.075469 (-0.007172) |\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) | 1.303596 / 1.841788 (-0.538192) | 14.712746 / 8.074308 (6.638438) | 14.778412 / 10.191392 (4.587020) | 0.147093 / 0.680424 (-0.533331) | 0.017105 / 0.534201 (-0.517096) | 0.381687 / 0.579283 (-0.197596) | 0.402435 / 0.434364 (-0.031929) | 0.453538 / 0.540337 (-0.086800) | 0.538866 / 1.386936 (-0.848070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10f637c3a598c8042865b31f779e315a3da5337e \"CML watermark\")\n" ]

"2023-03-15T18:26:07Z"

"2023-03-20T12:34:09Z"

"2023-03-20T12:26:58Z"

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[ "Thanks for reporting !\r\nIt uses a temporary file to write the data.\r\nHowever it looks like the temporary file is not placed in the right directory during the processing", "Well actually I just tested and the temporary file is placed in the same directory, so it should work as expected.\r\nWhich version of `datasets` are you using ?", "`datasets-1.0.1`\r\nHere you can reproduce it here:\r\nhttps://colab.research.google.com/drive/1O0KcepTFsmpkBbrbLLMq42iwTKmQh8d5?usp=sharing\r\n", "It looks like a pyarrow issue with google colab.\r\nFor some reason this code increases the disk usage of google colab while it actually writes into google drive:\r\n\r\n```python\r\nimport pyarrow as pa\r\n\r\nstream = pa.OSFile(\"/content/drive/My Drive/path/to/file.arrow\", \"wb\")\r\nwriter = pa.RecordBatchStreamWriter(stream, schema=pa.schema({\"text\": pa.string()}))\r\nwriter.write_table(pa.Table.from_pydict({\"text\": [\"a\"*511 + \"\\n\"] * ((1 << 30) // 512)})) # 1GiB\r\nwriter.close()\r\nstream.close()\r\n```\r\n\r\nMoreover if I `rm` the file on google drive, it frees disk space on google colab.", "It looks like replacing `pa.OSFile` by `open` fixes it, I'm going to open a PR", "Ok. Thank you so much!", "Actually I did more tests it doesn't >.<\r\nI'll let you know if I find a way to fix that", "Actually I also have the issue when writing a regular text file\r\n\r\n```python\r\nf = open(\"/content/drive/My Drive/path/to/file\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) // 512)) # 1GiB\r\nf.close()\r\n```\r\n\r\nIs that supposed to happen ?", "The code you wrote should write a 1GB file in the Google Drive folder. Doesn't it? ", "Yes it does, but the disk usage of google colab also increases by 1GB", "I could check it and as you say as I write to te Drive disk the colab disk also increases...", "To reproduce it: \r\n```bash\r\n!df -h | grep sda1\r\n```\r\n```python\r\nf = open(\"/content/drive/My Drive/test_to_remove.txt\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) // 512)) # 1GiB\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) // 512)) # 1GiB\r\nf.close()\r\n```\r\n```bash\r\n!ls -lh /content/drive/My\\ Drive/test_to_remove.txt\r\n\r\n!df -h | grep sda1\r\n\r\n!rm -rf /content/drive/My\\ Drive/test_to_remove.txt\r\n\r\n```\r\n[Colab](https://colab.research.google.com/drive/1D0UiweCYQwwWZ65EEhuqqbaDDbhJYXfm?usp=sharing)\r\n\r\n\r\n", "Apparently, Colab uses a local cache of the data files read/written from Google Drive. See:\r\n- https://github.com/googlecolab/colabtools/issues/2087#issuecomment-860818457\r\n- https://github.com/googlecolab/colabtools/issues/1915#issuecomment-804234540\r\n- https://github.com/googlecolab/colabtools/issues/2147#issuecomment-885052636" ]

"2020-09-18T15:41:26Z"

"2022-02-16T14:53:29Z"

"2022-02-16T14:53:29Z"

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[ "Hi ! AFAIK a dataset must be present on a local disk to be able to efficiently memory map the datasets Arrow files. What makes you think that it is possible to load from a cloud storage and have good performance ?\r\n\r\nAnyway it's already possible to download_and_prepare a dataset as Arrow files in a cloud storage with:\r\n```python\r\nbuilder = load_dataset_builder(..., cache_dir=\"/temp/dir\")\r\nbuilder.download_and_prepare(\"/cloud_dir\")\r\n```\r\n\r\nbut then \r\n```python\r\nds = builder.as_dataset()\r\n```\r\nwould fail if \"/cloud_dir\" is not a local directory.", "In my use case, I am trying to mount the S3 bucket as local system with S3FS-FUSE / [goofys](https://github.com/kahing/goofys). I want to use S3 to save the download data and save checkpoint for training for persistent. Setting the s3 location as cache directory is not fast enough. That is why I want to set a work directory for temp data for memory map and only save the final result to s3 cache. ", "You can try setting `HF_DATASETS_DOWNLOADED_DATASETS_PATH` and `HF_DATASETS_EXTRACTED_DATASETS_PATH` to S3, and `HF_DATASETS_CACHE` to your local disk.\r\n\r\nThis way all your downloaded and extracted data are on your mounted S3, but the datasets Arrow files are on your local disk", "If we hope to also persist the Arrow files on the mounted S3 but work with the efficiency of local disk, is there any recommended way to do this, other than copying the Arrow files from local disk to S3?" ]

"2023-03-29T07:21:09Z"

"2023-04-12T22:30:25Z"

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"2020-12-09T22:59:19Z"

"2020-12-11T13:57:32Z"

"2020-12-11T13:57:32Z"

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[ "Hi @lhoestq,\r\nInitially I tried int(-1) only in place of nan labels and missing values but I kept on getting this error ```pyarrow.lib.ArrowTypeError: Expected bytes, got a 'int' object``` maybe because I'm sending int values (-1) to objects which are string type" ]

"2020-12-01T12:37:44Z"

"2020-12-01T23:11:33Z"

"2020-12-01T23:11:33Z"

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[ "Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.", "Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction.create_exapmles_from_document` can be applied to dataset object other than `TextDatasetForNextSentencePrediction` e.g. a `Dataset` object which is loaded by `datasets.load_dataset`?", "It would probably require a bit of tweaking, but you can apply it to a dataset, yes.\r\nThis should give you a new dataset with sentence pairs you can train a model on.\r\n\r\nYou can find the documentation about dataset processing here:\r\nhttps://huggingface.co/docs/datasets/processing.html#processing-data-with-map", "Thank you for detail information.\r\n\r\nI'll try to apply `create_examples_from_document` to `Dataset` object.\r\n" ]

"2021-03-29T06:48:03Z"

"2021-04-01T04:58:40Z"

"2021-04-01T04:58:40Z"

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[]

[ "related to #5444 ", "We now log these messages instead of printing them (addressed in #6019), so I'm closing this issue." ]

"2023-03-16T20:30:07Z"

"2023-07-21T14:20:25Z"

"2023-07-21T14:20:24Z"

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[ "As the survey is finished, can we close this issue, @LysandreJik ?", "Yes! I'll post a public summary on the forums shortly.", "Is the summary available? I would be interested in reading your findings." ]

"2022-12-09T14:48:02Z"

"2023-06-02T20:24:44Z"

"2023-01-25T19:35:40Z"

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[ "Thanks for reporting ! #2852 fixed this error\r\n\r\nWe'll do a new release of `datasets` soon :)" ]

"2021-08-20T12:15:15Z"

"2021-08-31T13:13:02Z"

"2021-08-31T13:12:09Z"

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"2021-09-27T09:09:08Z"

"2021-09-27T12:59:59Z"

"2021-09-27T12:59:59Z"

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[ "It looks like the dataset isn't available anymore on appen.com\r\n\r\nThe CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?" ]

"2021-11-09T09:26:42Z"

"2021-12-14T14:38:29Z"

"2021-12-14T14:38:29Z"

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[ "This is perfect, @albertvillanova - thank you! Tested it to work.\r\n\r\nMight it be a good idea to document the args to `to_json`?\r\n\r\nand also even a very basic progress bar? took 10min for 8M large records for `openwebtext` so perhaps some indication of it's being alive every min or so?", "@lhoestq I added tests for both `lines` and `orient`." ]

"2021-05-12T08:19:25Z"

"2021-05-21T09:01:14Z"

"2021-05-21T09:01:13Z"

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"2020-05-14T14:27:19Z"

"2020-07-12T05:34:20Z"

"2020-05-14T20:23:32Z"

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

"2022-04-08T13:33:35Z"

"2022-04-13T14:07:41Z"

"2022-04-13T14:01:16Z"

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[ "Hi @j-chim ! You're right it does feel redundant: your option works better, but I'd even suggest having the references in a Sequence feature, which you can declare as:\r\n```\r\n\t features=datasets.Features(\r\n {\r\n \"sentence\": datasets.Value(\"string\"),\r\n \"corrections\": datasets.Sequence(datasets.Value(\"string\")),\r\n }\r\n ),\r\n```\r\n\r\nTo create the dummy data, you just need to tell the generator which files it should use, which you can do with:\r\n`python datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate --match_text_files \"train*,dev*,test*\"`\r\n", "Many thanks for this @yjernite! I've incorporated your feedback and sorted out the dummy data." ]

"2020-12-04T22:36:38Z"

"2020-12-06T18:16:04Z"

"2020-12-06T18:16:04Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6473). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.005270 / 0.011353 (-0.006083) | 0.003471 / 0.011008 (-0.007537) | 0.061942 / 0.038508 (0.023434) | 0.052671 / 0.023109 (0.029562) | 0.250541 / 0.275898 (-0.025357) | 0.270677 / 0.323480 (-0.052803) | 0.002933 / 0.007986 (-0.005053) | 0.003264 / 0.004328 (-0.001064) | 0.048055 / 0.004250 (0.043804) | 0.037459 / 0.037052 (0.000407) | 0.254926 / 0.258489 (-0.003563) | 0.292547 / 0.293841 (-0.001294) | 0.027959 / 0.128546 (-0.100587) | 0.010762 / 0.075646 (-0.064884) | 0.204961 / 0.419271 (-0.214310) | 0.035488 / 0.043533 (-0.008045) | 0.254102 / 0.255139 (-0.001037) | 0.273654 / 0.283200 (-0.009546) | 0.018126 / 0.141683 (-0.123556) | 1.082330 / 1.452155 (-0.369825) | 1.147179 / 1.492716 (-0.345538) |\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) | 0.093223 / 0.018006 (0.075217) | 0.301912 / 0.000490 (0.301422) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018407 / 0.037411 (-0.019004) | 0.060412 / 0.014526 (0.045886) | 0.074063 / 0.176557 (-0.102494) | 0.118743 / 0.737135 (-0.618392) | 0.076484 / 0.296338 (-0.219854) |\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) | 0.289929 / 0.215209 (0.074720) | 2.825096 / 2.077655 (0.747442) | 1.511444 / 1.504120 (0.007324) | 1.394812 / 1.541195 (-0.146383) | 1.419751 / 1.468490 (-0.048739) | 0.569995 / 4.584777 (-4.014782) | 2.402586 / 3.745712 (-1.343126) | 2.826223 / 5.269862 (-2.443639) | 1.751554 / 4.565676 (-2.814123) | 0.064266 / 0.424275 (-0.360009) | 0.005047 / 0.007607 (-0.002561) | 0.341513 / 0.226044 (0.115469) | 3.372106 / 2.268929 (1.103177) | 1.872693 / 55.444624 (-53.571931) | 1.588200 / 6.876477 (-5.288276) | 1.630800 / 2.142072 (-0.511272) | 0.654266 / 4.805227 (-4.150961) | 0.124292 / 6.500664 (-6.376372) | 0.042876 / 0.075469 (-0.032593) |\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) | 0.948406 / 1.841788 (-0.893382) | 11.652947 / 8.074308 (3.578639) | 10.218195 / 10.191392 (0.026803) | 0.128447 / 0.680424 (-0.551976) | 0.014092 / 0.534201 (-0.520109) | 0.287631 / 0.579283 (-0.291652) | 0.264843 / 0.434364 (-0.169521) | 0.329997 / 0.540337 (-0.210340) | 0.439597 / 1.386936 (-0.947339) |\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) | 0.005418 / 0.011353 (-0.005935) | 0.003589 / 0.011008 (-0.007419) | 0.050074 / 0.038508 (0.011566) | 0.052566 / 0.023109 (0.029456) | 0.293447 / 0.275898 (0.017549) | 0.320518 / 0.323480 (-0.002962) | 0.004094 / 0.007986 (-0.003892) | 0.002690 / 0.004328 (-0.001639) | 0.048200 / 0.004250 (0.043949) | 0.040692 / 0.037052 (0.003640) | 0.297086 / 0.258489 (0.038597) | 0.323827 / 0.293841 (0.029986) | 0.029511 / 0.128546 (-0.099035) | 0.011079 / 0.075646 (-0.064568) | 0.058562 / 0.419271 (-0.360709) | 0.032897 / 0.043533 (-0.010636) | 0.297244 / 0.255139 (0.042105) | 0.316812 / 0.283200 (0.033612) | 0.018468 / 0.141683 (-0.123215) | 1.140948 / 1.452155 (-0.311207) | 1.195453 / 1.492716 (-0.297263) |\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) | 0.092677 / 0.018006 (0.074671) | 0.300775 / 0.000490 (0.300285) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021617 / 0.037411 (-0.015794) | 0.077135 / 0.014526 (0.062610) | 0.079848 / 0.176557 (-0.096709) | 0.118475 / 0.737135 (-0.618661) | 0.081174 / 0.296338 (-0.215164) |\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) | 0.294424 / 0.215209 (0.079215) | 2.863989 / 2.077655 (0.786334) | 1.590604 / 1.504120 (0.086484) | 1.474345 / 1.541195 (-0.066849) | 1.482120 / 1.468490 (0.013630) | 0.567829 / 4.584777 (-4.016948) | 2.493782 / 3.745712 (-1.251930) | 2.823460 / 5.269862 (-2.446402) | 1.732677 / 4.565676 (-2.833000) | 0.065518 / 0.424275 (-0.358757) | 0.004923 / 0.007607 (-0.002684) | 0.349313 / 0.226044 (0.123268) | 3.428618 / 2.268929 (1.159689) | 1.970641 / 55.444624 (-53.473983) | 1.655884 / 6.876477 (-5.220593) | 1.657151 / 2.142072 (-0.484921) | 0.661208 / 4.805227 (-4.144019) | 0.119129 / 6.500664 (-6.381535) | 0.040770 / 0.075469 (-0.034699) |\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) | 0.964865 / 1.841788 (-0.876923) | 12.050218 / 8.074308 (3.975910) | 10.458749 / 10.191392 (0.267357) | 0.141856 / 0.680424 (-0.538568) | 0.015091 / 0.534201 (-0.519109) | 0.288897 / 0.579283 (-0.290387) | 0.275343 / 0.434364 (-0.159021) | 0.328363 / 0.540337 (-0.211975) | 0.579243 / 1.386936 (-0.807693) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7721021e284859ea0952444bae6300a0d00794f \"CML watermark\")\n" ]

"2023-12-05T15:36:23Z"

"2023-12-05T18:14:50Z"

"2023-12-05T18:08:41Z"

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

"2022-04-07T07:38:46Z"

"2022-04-07T09:47:24Z"

"2022-04-07T07:57:13Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4296). All of your documentation changes will be reflected on that endpoint." ]

"2022-05-09T11:18:22Z"

"2022-07-06T15:19:53Z"

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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) | 0.006663 / 0.011353 (-0.004690) | 0.004091 / 0.011008 (-0.006918) | 0.084954 / 0.038508 (0.046445) | 0.071869 / 0.023109 (0.048760) | 0.314706 / 0.275898 (0.038808) | 0.352794 / 0.323480 (0.029314) | 0.004027 / 0.007986 (-0.003959) | 0.003371 / 0.004328 (-0.000957) | 0.065456 / 0.004250 (0.061205) | 0.055828 / 0.037052 (0.018775) | 0.316502 / 0.258489 (0.058013) | 0.377979 / 0.293841 (0.084138) | 0.030870 / 0.128546 (-0.097676) | 0.008616 / 0.075646 (-0.067030) | 0.288625 / 0.419271 (-0.130646) | 0.052314 / 0.043533 (0.008781) | 0.322725 / 0.255139 (0.067586) | 0.351810 / 0.283200 (0.068611) | 0.025726 / 0.141683 (-0.115957) | 1.439308 / 1.452155 (-0.012847) | 1.524484 / 1.492716 (0.031768) |\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) | 0.235212 / 0.018006 (0.217206) | 0.444926 / 0.000490 (0.444437) | 0.009887 / 0.000200 (0.009687) | 0.000402 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028956 / 0.037411 (-0.008455) | 0.084401 / 0.014526 (0.069875) | 0.339686 / 0.176557 (0.163130) | 0.186785 / 0.737135 (-0.550350) | 0.195017 / 0.296338 (-0.101322) |\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) | 0.405480 / 0.215209 (0.190271) | 4.024315 / 2.077655 (1.946661) | 2.056398 / 1.504120 (0.552278) | 1.912099 / 1.541195 (0.370904) | 1.950119 / 1.468490 (0.481629) | 0.486071 / 4.584777 (-4.098706) | 3.578501 / 3.745712 (-0.167211) | 3.268980 / 5.269862 (-2.000881) | 2.018114 / 4.565676 (-2.547563) | 0.057440 / 0.424275 (-0.366835) | 0.007281 / 0.007607 (-0.000326) | 0.474760 / 0.226044 (0.248716) | 4.746908 / 2.268929 (2.477979) | 2.550111 / 55.444624 (-52.894513) | 2.171932 / 6.876477 (-4.704544) | 2.392235 / 2.142072 (0.250162) | 0.585940 / 4.805227 (-4.219287) | 0.136445 / 6.500664 (-6.364219) | 0.062125 / 0.075469 (-0.013344) |\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) | 1.270763 / 1.841788 (-0.571025) | 19.213516 / 8.074308 (11.139208) | 13.992620 / 10.191392 (3.801228) | 0.167356 / 0.680424 (-0.513068) | 0.018261 / 0.534201 (-0.515940) | 0.392489 / 0.579283 (-0.186794) | 0.418845 / 0.434364 (-0.015519) | 0.461824 / 0.540337 (-0.078513) | 0.649661 / 1.386936 (-0.737275) |\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) | 0.006675 / 0.011353 (-0.004678) | 0.003913 / 0.011008 (-0.007096) | 0.064943 / 0.038508 (0.026435) | 0.072426 / 0.023109 (0.049317) | 0.400785 / 0.275898 (0.124887) | 0.434359 / 0.323480 (0.110879) | 0.005370 / 0.007986 (-0.002616) | 0.003290 / 0.004328 (-0.001038) | 0.065035 / 0.004250 (0.060785) | 0.054924 / 0.037052 (0.017872) | 0.404442 / 0.258489 (0.145953) | 0.439027 / 0.293841 (0.145186) | 0.032467 / 0.128546 (-0.096080) | 0.008565 / 0.075646 (-0.067081) | 0.070653 / 0.419271 (-0.348619) | 0.048034 / 0.043533 (0.004501) | 0.400869 / 0.255139 (0.145730) | 0.423048 / 0.283200 (0.139848) | 0.022757 / 0.141683 (-0.118926) | 1.516956 / 1.452155 (0.064801) | 1.581599 / 1.492716 (0.088883) |\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) | 0.214761 / 0.018006 (0.196755) | 0.440921 / 0.000490 (0.440431) | 0.007538 / 0.000200 (0.007338) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032313 / 0.037411 (-0.005099) | 0.091365 / 0.014526 (0.076839) | 0.106665 / 0.176557 (-0.069891) | 0.158637 / 0.737135 (-0.578498) | 0.104894 / 0.296338 (-0.191445) |\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) | 0.432995 / 0.215209 (0.217786) | 4.339911 / 2.077655 (2.262256) | 2.313139 / 1.504120 (0.809019) | 2.142552 / 1.541195 (0.601357) | 2.279275 / 1.468490 (0.810785) | 0.501133 / 4.584777 (-4.083644) | 3.696160 / 3.745712 (-0.049552) | 3.341886 / 5.269862 (-1.927976) | 2.105972 / 4.565676 (-2.459705) | 0.059268 / 0.424275 (-0.365008) | 0.007568 / 0.007607 (-0.000039) | 0.512546 / 0.226044 (0.286502) | 5.130219 / 2.268929 (2.861290) | 2.808292 / 55.444624 (-52.636332) | 2.478721 / 6.876477 (-4.397755) | 2.679341 / 2.142072 (0.537269) | 0.599022 / 4.805227 (-4.206206) | 0.143761 / 6.500664 (-6.356903) | 0.062061 / 0.075469 (-0.013409) |\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) | 1.430507 / 1.841788 (-0.411281) | 20.458085 / 8.074308 (12.383777) | 15.268356 / 10.191392 (5.076964) | 0.163359 / 0.680424 (-0.517065) | 0.020908 / 0.534201 (-0.513293) | 0.396870 / 0.579283 (-0.182413) | 0.432630 / 0.434364 (-0.001733) | 0.475909 / 0.540337 (-0.064429) | 0.681031 / 1.386936 (-0.705905) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd1dd6aa4c7fa7744c1c1f877573ff59f1529292 \"CML watermark\")\n", "CI failures are unrelated", "<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) | 0.005815 / 0.011353 (-0.005538) | 0.003419 / 0.011008 (-0.007589) | 0.080286 / 0.038508 (0.041778) | 0.056487 / 0.023109 (0.033377) | 0.304414 / 0.275898 (0.028516) | 0.341039 / 0.323480 (0.017559) | 0.004392 / 0.007986 (-0.003594) | 0.002852 / 0.004328 (-0.001477) | 0.062339 / 0.004250 (0.058089) | 0.044683 / 0.037052 (0.007630) | 0.311651 / 0.258489 (0.053162) | 0.357249 / 0.293841 (0.063409) | 0.027300 / 0.128546 (-0.101246) | 0.007963 / 0.075646 (-0.067683) | 0.261948 / 0.419271 (-0.157323) | 0.044952 / 0.043533 (0.001419) | 0.309990 / 0.255139 (0.054851) | 0.340735 / 0.283200 (0.057536) | 0.020786 / 0.141683 (-0.120897) | 1.471378 / 1.452155 (0.019224) | 1.517260 / 1.492716 (0.024543) |\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) | 0.245447 / 0.018006 (0.227441) | 0.418967 / 0.000490 (0.418477) | 0.007039 / 0.000200 (0.006840) | 0.000196 / 0.000054 (0.000142) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022880 / 0.037411 (-0.014532) | 0.071862 / 0.014526 (0.057337) | 0.083009 / 0.176557 (-0.093547) | 0.143414 / 0.737135 (-0.593722) | 0.082896 / 0.296338 (-0.213442) |\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) | 0.390645 / 0.215209 (0.175436) | 3.888104 / 2.077655 (1.810450) | 1.859572 / 1.504120 (0.355452) | 1.683803 / 1.541195 (0.142608) | 1.697902 / 1.468490 (0.229412) | 0.499537 / 4.584777 (-4.085239) | 3.015832 / 3.745712 (-0.729881) | 2.805696 / 5.269862 (-2.464166) | 1.830408 / 4.565676 (-2.735268) | 0.058191 / 0.424275 (-0.366085) | 0.006357 / 0.007607 (-0.001250) | 0.462486 / 0.226044 (0.236442) | 4.634951 / 2.268929 (2.366022) | 2.309364 / 55.444624 (-53.135260) | 1.979521 / 6.876477 (-4.896956) | 2.080011 / 2.142072 (-0.062062) | 0.593086 / 4.805227 (-4.212141) | 0.124856 / 6.500664 (-6.375808) | 0.060172 / 0.075469 (-0.015297) |\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) | 1.251439 / 1.841788 (-0.590349) | 17.068999 / 8.074308 (8.994691) | 13.527209 / 10.191392 (3.335817) | 0.146636 / 0.680424 (-0.533788) | 0.016866 / 0.534201 (-0.517335) | 0.333202 / 0.579283 (-0.246081) | 0.360444 / 0.434364 (-0.073920) | 0.388378 / 0.540337 (-0.151959) | 0.530519 / 1.386936 (-0.856417) |\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) | 0.006043 / 0.011353 (-0.005310) | 0.003612 / 0.011008 (-0.007396) | 0.062644 / 0.038508 (0.024135) | 0.056104 / 0.023109 (0.032995) | 0.446328 / 0.275898 (0.170430) | 0.478044 / 0.323480 (0.154564) | 0.004641 / 0.007986 (-0.003345) | 0.002896 / 0.004328 (-0.001432) | 0.062344 / 0.004250 (0.058093) | 0.046339 / 0.037052 (0.009287) | 0.454866 / 0.258489 (0.196377) | 0.484242 / 0.293841 (0.190401) | 0.028602 / 0.128546 (-0.099944) | 0.008075 / 0.075646 (-0.067571) | 0.067980 / 0.419271 (-0.351291) | 0.041339 / 0.043533 (-0.002194) | 0.452911 / 0.255139 (0.197772) | 0.474180 / 0.283200 (0.190981) | 0.019395 / 0.141683 (-0.122288) | 1.432161 / 1.452155 (-0.019993) | 1.505800 / 1.492716 (0.013083) |\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) | 0.216983 / 0.018006 (0.198977) | 0.406232 / 0.000490 (0.405743) | 0.005101 / 0.000200 (0.004902) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026295 / 0.037411 (-0.011116) | 0.080490 / 0.014526 (0.065964) | 0.088105 / 0.176557 (-0.088451) | 0.143294 / 0.737135 (-0.593841) | 0.089125 / 0.296338 (-0.207213) |\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) | 0.465512 / 0.215209 (0.250302) | 4.648656 / 2.077655 (2.571002) | 2.598225 / 1.504120 (1.094105) | 2.409588 / 1.541195 (0.868393) | 2.513745 / 1.468490 (1.045255) | 0.507425 / 4.584777 (-4.077352) | 3.130164 / 3.745712 (-0.615548) | 2.836817 / 5.269862 (-2.433045) | 1.836029 / 4.565676 (-2.729647) | 0.058829 / 0.424275 (-0.365446) | 0.006551 / 0.007607 (-0.001056) | 0.537892 / 0.226044 (0.311848) | 5.401079 / 2.268929 (3.132150) | 3.019817 / 55.444624 (-52.424807) | 2.695131 / 6.876477 (-4.181346) | 2.805321 / 2.142072 (0.663248) | 0.595681 / 4.805227 (-4.209546) | 0.124368 / 6.500664 (-6.376296) | 0.060712 / 0.075469 (-0.014757) |\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) | 1.361508 / 1.841788 (-0.480279) | 17.811373 / 8.074308 (9.737065) | 14.482705 / 10.191392 (4.291313) | 0.153193 / 0.680424 (-0.527231) | 0.018347 / 0.534201 (-0.515854) | 0.330900 / 0.579283 (-0.248383) | 0.374948 / 0.434364 (-0.059416) | 0.385615 / 0.540337 (-0.154722) | 0.568077 / 1.386936 (-0.818859) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18ef408c21f8efbb2142f050a691b5c916455af3 \"CML watermark\")\n" ]

"2023-10-12T14:58:07Z"

"2023-10-12T15:58:20Z"

"2023-10-12T15:49:54Z"

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"2021-04-08T14:42:24Z"

"2021-04-08T15:47:41Z"

"2021-04-08T15:47:40Z"

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[ "re-created in #1218 because this was too messy" ]

"2020-12-04T19:45:34Z"

"2020-12-06T19:59:44Z"

"2020-12-06T19:53:46Z"

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[ "One thing for SQuAD is that I wanted to be able to use the SQuAD dataset directly in the metrics and I'm not sure it will be possible with this format.\r\n\r\n(maybe it's not really possible in general though)", "This is kinda related to one thing I had in mind which is that we may want to be able to dump our model predictions in a `Dataset` as well so that we don't keep them in memory (and we can export them in a nice format later as well when we will have a serialization formats).\r\n\r\nMaybe this is overkill though, I haven't fully wraped my head around this.", "I'm also perfectly fine with merging this PR in the current state and working on a larger scope later.", "This is the format needed to run the official script directly. The format of the squad dataset is different from the input of the metric. \r\n\r\n> One thing for SQuAD is that I wanted to be able to use the SQuAD dataset directly in the metrics and I'm not sure it will be possible with this format.\r\n> \r\n> (maybe it's not really possible in general though)\r\n\r\nOk I see. I'll try to use the same format", "Ok with this update I changed the format to fit the squad dataset format.\r\nNow you can do:\r\n```python\r\nsquad_dset = nlp.load_dataset(\"squad\")\r\nsquad_metric = nlp.load_metric(\"/Users/quentinlhoest/Desktop/hf/nlp-bis/metrics/squad\")\r\npredictions = [\r\n {\"id\": v[\"id\"], \"prediction_text\": v[\"answers\"][\"text\"][0]} # take first possible answer\r\n for v in squad_dset[\"validation\"]\r\n]\r\nsquad_metric.compute(predictions, squad_dset[\"validation\"])\r\n```" ]

"2020-05-19T18:37:36Z"

"2020-05-22T13:36:50Z"

"2020-05-22T13:36:48Z"

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[ "Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [1/20]\r\n03/24/2022 02:20:01 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [2/20]\r\n03/24/2022 02:20:09 - ERROR - datasets.packaged_modules.json.json - Failed to read file 'gzip://file-000000000007.json::https://huggingface.co/datasets/lvwerra/codeparrot-clean-train/resolve/1d740acb9d09cf7a3307553323e2c677a6535407/file-000000000007.json.gz' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0\r\n```", "Ah, thanks! I did get errors like that. Sad that PR wasn't merged in! \r\n\r\nI'm currently just downloading 200GB of the Pile locally to avoid streaming (I have space and it's faster anyway), but that's really useful! I can probably apply the dumb patch of just commenting out the bits that raise the JSON Parse Error lol, based on your code - if I continue the loop should it be fine?", "Yup you can get some inspiration from this PR. It simply ignores the bad chunks (a chunk is ~a few MBs of data).\r\nWe'll try to merge this PR soon" ]

"2022-10-02T11:56:46Z"

"2022-10-04T17:59:03Z"

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[ "@albertvillanova Please take a review.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5197). All of your documentation changes will be reflected on that endpoint." ]

"2022-11-03T13:35:58Z"

"2022-11-03T13:45:19Z"

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"2020-07-15T12:59:55Z"

"2020-07-17T17:02:16Z"

"2020-07-17T17:02:14Z"

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"2020-08-31T09:30:10Z"

"2020-08-31T11:15:11Z"

"2020-08-31T11:15:10Z"

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"2020-12-06T21:02:54Z"

"2020-12-08T10:54:56Z"

"2020-12-08T10:54:56Z"

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[ "@lhoestq I am not sure how to test `dictionary_encode_column` (in-place version was not tested before)", "I can take a look at dictionary_encode_column tomorrow.\r\nAlthough it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik.", "Now let's update the documentation to use the new methods x)" ]

"2021-02-15T18:44:26Z"

"2021-02-24T14:54:49Z"

"2021-02-24T14:53:26Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "IMO `DatasetMetadata` shouldn't crash with attributes that it doesn't know, btw", "Yea this PR is mostly to have a validation that this field contains a list of strings.\r\n\r\nRegarding unknown fields, the tagging app currently returns an error if a field is unknown using the `DatasetMetadata`. We can change that though" ]

"2022-07-19T12:34:31Z"

"2022-07-20T13:44:50Z"

"2022-07-20T13:31:56Z"

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