Datasets:

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I added an error message if the first examples don't appear to be in webdataset format\r\n```\r\n\"The TAR archives of the dataset should be in Webdataset format, \"\r\n\"but the files in the archive don't share the same prefix or the same types.\"\r\n```", "@mariosasko could you review this ? I think it's fine to have webdataset as an optional dependency for now, then depending on usage and user feedbacks see if it makes sense to have our own implementation or not", "I just removed the dependency on `webdataset` @mariosasko :)", "took your comments into account, lmk if you see anything else" ]

"2023-11-08T17:31:59Z"

"2023-11-28T16:33:33Z"

"2023-11-28T16:33:10Z"

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[ "Hi @mariosasko!\r\n\r\nHas this been patched into any of the releases?", "Hi! Not yet, would you be interested in contributing a PR? I can give you some pointers if needed. ", "@mariosasko did this ever get implemented? Willing to help if you are still up for it.", "@dcruiz01 No, but here is an example of how to do this with the existing API:\r\n\r\n\r\n```python\r\nds = Dataset.from_dict({\"tokens\": [[\"a\", \"b\"], [\"c\", \"a\"], [\"c\", \"e\"]]})\r\n\r\ndef flatten_tokens(pa_table):\r\n return pa.table([pc.list_flatten(pa_table[\"tokens\"])], [\"flat_tokens\"])\r\n\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(flatten_tokens, batched=True)\r\nds = ds.with_format(None)\r\n\r\nunique_tokens = ds.unique(\"flat_tokens\")\r\n```\r\n\r\nWhen I think about it, `.unique` on `Sequence(Value(...))` should return unique sequences/arrays, not unique elements of these sequences..." ]

"2021-11-19T13:16:06Z"

"2023-05-19T14:45:40Z"

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"2022-04-07T11:07:41Z"

"2022-04-07T11:07:41Z"

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[ "Thanks for reporting @hannan72 ! I couldn't reproduce the error on my side, can you share the full stack trace please ?", "Thanks a lot for your response @lhoestq \r\nThe problem is solved accidentally today and I don't know exactly why it was happened yesterday.\r\nThe issue can be closed.", "Ok, let us know if you encounter the issue again ;)" ]

"2022-08-29T07:05:20Z"

"2022-08-30T14:43:41Z"

"2022-08-30T14:43:41Z"

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"2021-12-13T18:46:29Z"

"2021-12-13T18:57:50Z"

"2021-12-13T18:57:50Z"

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

"2022-07-22T07:18:21Z"

"2022-07-22T07:58:14Z"

"2022-07-22T07:45:18Z"

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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) | 0.006113 / 0.011353 (-0.005239) | 0.004195 / 0.011008 (-0.006813) | 0.098103 / 0.038508 (0.059595) | 0.027970 / 0.023109 (0.004860) | 0.300992 / 0.275898 (0.025094) | 0.335402 / 0.323480 (0.011922) | 0.005079 / 0.007986 (-0.002906) | 0.003516 / 0.004328 (-0.000813) | 0.077311 / 0.004250 (0.073061) | 0.037863 / 0.037052 (0.000810) | 0.302638 / 0.258489 (0.044149) | 0.346554 / 0.293841 (0.052713) | 0.025218 / 0.128546 (-0.103328) | 0.008630 / 0.075646 (-0.067017) | 0.319748 / 0.419271 (-0.099523) | 0.049182 / 0.043533 (0.005650) | 0.306233 / 0.255139 (0.051094) | 0.331040 / 0.283200 (0.047840) | 0.089203 / 0.141683 (-0.052480) | 1.496104 / 1.452155 (0.043949) | 1.567878 / 1.492716 (0.075162) |\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) | 0.215774 / 0.018006 (0.197768) | 0.436810 / 0.000490 (0.436320) | 0.000307 / 0.000200 (0.000107) | 0.000059 / 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) | 0.024102 / 0.037411 (-0.013310) | 0.095459 / 0.014526 (0.080933) | 0.106564 / 0.176557 (-0.069992) | 0.169894 / 0.737135 (-0.567241) | 0.109152 / 0.296338 (-0.187186) |\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) | 0.429066 / 0.215209 (0.213857) | 4.297385 / 2.077655 (2.219730) | 2.054854 / 1.504120 (0.550734) | 1.846844 / 1.541195 (0.305649) | 1.840807 / 1.468490 (0.372317) | 0.553193 / 4.584777 (-4.031584) | 3.366788 / 3.745712 (-0.378924) | 1.727337 / 5.269862 (-3.542525) | 0.994357 / 4.565676 (-3.571319) | 0.067790 / 0.424275 (-0.356485) | 0.012002 / 0.007607 (0.004395) | 0.533335 / 0.226044 (0.307291) | 5.341341 / 2.268929 (3.072412) | 2.543581 / 55.444624 (-52.901043) | 2.220374 / 6.876477 (-4.656103) | 2.321656 / 2.142072 (0.179583) | 0.654408 / 4.805227 (-4.150819) | 0.134693 / 6.500664 (-6.365971) | 0.066926 / 0.075469 (-0.008544) |\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) | 1.209463 / 1.841788 (-0.632325) | 13.568221 / 8.074308 (5.493913) | 13.965418 / 10.191392 (3.774026) | 0.145049 / 0.680424 (-0.535375) | 0.016936 / 0.534201 (-0.517265) | 0.371587 / 0.579283 (-0.207696) | 0.386363 / 0.434364 (-0.048001) | 0.437137 / 0.540337 (-0.103201) | 0.514779 / 1.386936 (-0.872157) |\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) | 0.006245 / 0.011353 (-0.005108) | 0.004232 / 0.011008 (-0.006776) | 0.075682 / 0.038508 (0.037174) | 0.027858 / 0.023109 (0.004749) | 0.425325 / 0.275898 (0.149427) | 0.466732 / 0.323480 (0.143253) | 0.005240 / 0.007986 (-0.002745) | 0.003506 / 0.004328 (-0.000823) | 0.075294 / 0.004250 (0.071044) | 0.041677 / 0.037052 (0.004624) | 0.426552 / 0.258489 (0.168063) | 0.469452 / 0.293841 (0.175611) | 0.025443 / 0.128546 (-0.103104) | 0.008526 / 0.075646 (-0.067120) | 0.082190 / 0.419271 (-0.337081) | 0.040906 / 0.043533 (-0.002626) | 0.428406 / 0.255139 (0.173267) | 0.446795 / 0.283200 (0.163595) | 0.093837 / 0.141683 (-0.047846) | 1.518639 / 1.452155 (0.066484) | 1.620214 / 1.492716 (0.127498) |\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) | 0.223259 / 0.018006 (0.205253) | 0.425077 / 0.000490 (0.424588) | 0.001980 / 0.000200 (0.001780) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025813 / 0.037411 (-0.011599) | 0.103062 / 0.014526 (0.088536) | 0.108958 / 0.176557 (-0.067598) | 0.161591 / 0.737135 (-0.575544) | 0.112130 / 0.296338 (-0.184209) |\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) | 0.472843 / 0.215209 (0.257634) | 4.713281 / 2.077655 (2.635626) | 2.458216 / 1.504120 (0.954096) | 2.272467 / 1.541195 (0.731273) | 2.324456 / 1.468490 (0.855965) | 0.554686 / 4.584777 (-4.030091) | 3.445079 / 3.745712 (-0.300634) | 3.451896 / 5.269862 (-1.817966) | 1.431065 / 4.565676 (-3.134612) | 0.067868 / 0.424275 (-0.356407) | 0.012093 / 0.007607 (0.004486) | 0.573571 / 0.226044 (0.347526) | 5.820452 / 2.268929 (3.551523) | 2.934858 / 55.444624 (-52.509767) | 2.602719 / 6.876477 (-4.273758) | 2.645999 / 2.142072 (0.503927) | 0.660688 / 4.805227 (-4.144540) | 0.137490 / 6.500664 (-6.363174) | 0.068311 / 0.075469 (-0.007158) |\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) | 1.321709 / 1.841788 (-0.520079) | 14.592346 / 8.074308 (6.518038) | 14.520748 / 10.191392 (4.329356) | 0.132689 / 0.680424 (-0.547735) | 0.016422 / 0.534201 (-0.517779) | 0.370071 / 0.579283 (-0.209212) | 0.397091 / 0.434364 (-0.037273) | 0.431979 / 0.540337 (-0.108358) | 0.509965 / 1.386936 (-0.876971) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"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) | 0.006182 / 0.011353 (-0.005171) | 0.004153 / 0.011008 (-0.006855) | 0.095715 / 0.038508 (0.057207) | 0.032457 / 0.023109 (0.009347) | 0.314961 / 0.275898 (0.039063) | 0.353696 / 0.323480 (0.030216) | 0.005256 / 0.007986 (-0.002729) | 0.004870 / 0.004328 (0.000541) | 0.072442 / 0.004250 (0.068192) | 0.046102 / 0.037052 (0.009050) | 0.324410 / 0.258489 (0.065921) | 0.366861 / 0.293841 (0.073020) | 0.027088 / 0.128546 (-0.101458) | 0.008572 / 0.075646 (-0.067075) | 0.325988 / 0.419271 (-0.093284) | 0.049494 / 0.043533 (0.005961) | 0.311221 / 0.255139 (0.056082) | 0.359720 / 0.283200 (0.076521) | 0.095101 / 0.141683 (-0.046581) | 1.472821 / 1.452155 (0.020667) | 1.516157 / 1.492716 (0.023441) |\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) | 0.210456 / 0.018006 (0.192450) | 0.439440 / 0.000490 (0.438950) | 0.003764 / 0.000200 (0.003564) | 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) | 0.024076 / 0.037411 (-0.013335) | 0.104886 / 0.014526 (0.090360) | 0.114164 / 0.176557 (-0.062393) | 0.167289 / 0.737135 (-0.569847) | 0.116457 / 0.296338 (-0.179882) |\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) | 0.400039 / 0.215209 (0.184830) | 3.973243 / 2.077655 (1.895588) | 1.801991 / 1.504120 (0.297871) | 1.592017 / 1.541195 (0.050822) | 1.612564 / 1.468490 (0.144074) | 0.527475 / 4.584777 (-4.057302) | 3.676246 / 3.745712 (-0.069466) | 1.806423 / 5.269862 (-3.463438) | 1.176921 / 4.565676 (-3.388756) | 0.065902 / 0.424275 (-0.358373) | 0.012245 / 0.007607 (0.004638) | 0.490883 / 0.226044 (0.264838) | 4.905270 / 2.268929 (2.636341) | 2.218694 / 55.444624 (-53.225930) | 1.903074 / 6.876477 (-4.973403) | 1.979505 / 2.142072 (-0.162567) | 0.644415 / 4.805227 (-4.160812) | 0.142433 / 6.500664 (-6.358231) | 0.063564 / 0.075469 (-0.011905) |\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) | 1.193756 / 1.841788 (-0.648032) | 14.673103 / 8.074308 (6.598795) | 13.410951 / 10.191392 (3.219559) | 0.159175 / 0.680424 (-0.521249) | 0.017076 / 0.534201 (-0.517125) | 0.388880 / 0.579283 (-0.190403) | 0.409974 / 0.434364 (-0.024390) | 0.454494 / 0.540337 (-0.085844) | 0.556873 / 1.386936 (-0.830063) |\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) | 0.006107 / 0.011353 (-0.005246) | 0.004433 / 0.011008 (-0.006575) | 0.073892 / 0.038508 (0.035384) | 0.032386 / 0.023109 (0.009277) | 0.370339 / 0.275898 (0.094441) | 0.388996 / 0.323480 (0.065516) | 0.005438 / 0.007986 (-0.002548) | 0.003875 / 0.004328 (-0.000454) | 0.073867 / 0.004250 (0.069617) | 0.048350 / 0.037052 (0.011298) | 0.380328 / 0.258489 (0.121839) | 0.411373 / 0.293841 (0.117532) | 0.028183 / 0.128546 (-0.100363) | 0.008924 / 0.075646 (-0.066723) | 0.082484 / 0.419271 (-0.336787) | 0.047321 / 0.043533 (0.003788) | 0.371702 / 0.255139 (0.116563) | 0.380535 / 0.283200 (0.097335) | 0.100772 / 0.141683 (-0.040911) | 1.475038 / 1.452155 (0.022883) | 1.564293 / 1.492716 (0.071577) |\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) | 0.214589 / 0.018006 (0.196583) | 0.437193 / 0.000490 (0.436703) | 0.003676 / 0.000200 (0.003476) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027991 / 0.037411 (-0.009421) | 0.111154 / 0.014526 (0.096628) | 0.120365 / 0.176557 (-0.056191) | 0.173601 / 0.737135 (-0.563535) | 0.126244 / 0.296338 (-0.170094) |\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) | 0.442848 / 0.215209 (0.227639) | 4.398336 / 2.077655 (2.320681) | 2.217058 / 1.504120 (0.712938) | 2.011155 / 1.541195 (0.469960) | 2.123086 / 1.468490 (0.654596) | 0.525857 / 4.584777 (-4.058920) | 3.730191 / 3.745712 (-0.015521) | 3.517680 / 5.269862 (-1.752181) | 1.557940 / 4.565676 (-3.007736) | 0.066309 / 0.424275 (-0.357967) | 0.011788 / 0.007607 (0.004181) | 0.548506 / 0.226044 (0.322462) | 5.483615 / 2.268929 (3.214687) | 2.663784 / 55.444624 (-52.780840) | 2.325744 / 6.876477 (-4.550732) | 2.344179 / 2.142072 (0.202106) | 0.644217 / 4.805227 (-4.161010) | 0.141546 / 6.500664 (-6.359118) | 0.063730 / 0.075469 (-0.011739) |\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) | 1.296032 / 1.841788 (-0.545756) | 14.903729 / 8.074308 (6.829421) | 14.505409 / 10.191392 (4.314017) | 0.170478 / 0.680424 (-0.509946) | 0.017876 / 0.534201 (-0.516325) | 0.401047 / 0.579283 (-0.178236) | 0.417855 / 0.434364 (-0.016509) | 0.472138 / 0.540337 (-0.068200) | 0.570859 / 1.386936 (-0.816077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"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) | 0.008495 / 0.011353 (-0.002858) | 0.005322 / 0.011008 (-0.005686) | 0.125471 / 0.038508 (0.086962) | 0.034604 / 0.023109 (0.011495) | 0.419831 / 0.275898 (0.143933) | 0.415707 / 0.323480 (0.092227) | 0.007471 / 0.007986 (-0.000515) | 0.005441 / 0.004328 (0.001112) | 0.095412 / 0.004250 (0.091162) | 0.053865 / 0.037052 (0.016812) | 0.375257 / 0.258489 (0.116768) | 0.438114 / 0.293841 (0.144273) | 0.046183 / 0.128546 (-0.082363) | 0.013663 / 0.075646 (-0.061984) | 0.438317 / 0.419271 (0.019045) | 0.065665 / 0.043533 (0.022133) | 0.387640 / 0.255139 (0.132501) | 0.431350 / 0.283200 (0.148150) | 0.112841 / 0.141683 (-0.028842) | 1.778639 / 1.452155 (0.326484) | 1.891948 / 1.492716 (0.399232) |\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) | 0.284371 / 0.018006 (0.266365) | 0.598247 / 0.000490 (0.597758) | 0.013674 / 0.000200 (0.013474) | 0.000483 / 0.000054 (0.000428) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032437 / 0.037411 (-0.004974) | 0.120547 / 0.014526 (0.106021) | 0.129845 / 0.176557 (-0.046711) | 0.203455 / 0.737135 (-0.533680) | 0.140039 / 0.296338 (-0.156300) |\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) | 0.596549 / 0.215209 (0.381340) | 6.138766 / 2.077655 (4.061111) | 2.515506 / 1.504120 (1.011386) | 2.124472 / 1.541195 (0.583277) | 2.160812 / 1.468490 (0.692322) | 0.898965 / 4.584777 (-3.685812) | 5.588152 / 3.745712 (1.842440) | 2.717580 / 5.269862 (-2.552282) | 1.683641 / 4.565676 (-2.882036) | 0.108045 / 0.424275 (-0.316230) | 0.014089 / 0.007607 (0.006481) | 0.749567 / 0.226044 (0.523523) | 7.518051 / 2.268929 (5.249123) | 3.198238 / 55.444624 (-52.246386) | 2.575156 / 6.876477 (-4.301321) | 2.725818 / 2.142072 (0.583745) | 1.149338 / 4.805227 (-3.655889) | 0.220443 / 6.500664 (-6.280221) | 0.081452 / 0.075469 (0.005983) |\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) | 1.624462 / 1.841788 (-0.217325) | 18.204963 / 8.074308 (10.130655) | 21.379169 / 10.191392 (11.187777) | 0.248520 / 0.680424 (-0.431903) | 0.030121 / 0.534201 (-0.504080) | 0.499542 / 0.579283 (-0.079741) | 0.599783 / 0.434364 (0.165419) | 0.597642 / 0.540337 (0.057305) | 0.681948 / 1.386936 (-0.704988) |\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) | 0.008431 / 0.011353 (-0.002921) | 0.006143 / 0.011008 (-0.004865) | 0.107531 / 0.038508 (0.069023) | 0.036308 / 0.023109 (0.013199) | 0.480555 / 0.275898 (0.204657) | 0.556407 / 0.323480 (0.232927) | 0.007614 / 0.007986 (-0.000372) | 0.004749 / 0.004328 (0.000421) | 0.105734 / 0.004250 (0.101484) | 0.051619 / 0.037052 (0.014567) | 0.514821 / 0.258489 (0.256332) | 0.562143 / 0.293841 (0.268302) | 0.042957 / 0.128546 (-0.085589) | 0.015142 / 0.075646 (-0.060505) | 0.143161 / 0.419271 (-0.276111) | 0.061910 / 0.043533 (0.018377) | 0.496923 / 0.255139 (0.241784) | 0.556302 / 0.283200 (0.273102) | 0.136700 / 0.141683 (-0.004983) | 1.886184 / 1.452155 (0.434029) | 2.004087 / 1.492716 (0.511371) |\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) | 0.235530 / 0.018006 (0.217523) | 0.600796 / 0.000490 (0.600306) | 0.009074 / 0.000200 (0.008874) | 0.000203 / 0.000054 (0.000149) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036345 / 0.037411 (-0.001066) | 0.126112 / 0.014526 (0.111586) | 0.143369 / 0.176557 (-0.033188) | 0.211381 / 0.737135 (-0.525755) | 0.151095 / 0.296338 (-0.145243) |\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) | 0.695022 / 0.215209 (0.479813) | 6.685981 / 2.077655 (4.608326) | 3.104521 / 1.504120 (1.600401) | 2.758323 / 1.541195 (1.217128) | 2.706286 / 1.468490 (1.237796) | 0.941182 / 4.584777 (-3.643595) | 5.715839 / 3.745712 (1.970127) | 5.089636 / 5.269862 (-0.180226) | 2.594739 / 4.565676 (-1.970937) | 0.112621 / 0.424275 (-0.311655) | 0.014001 / 0.007607 (0.006394) | 0.812990 / 0.226044 (0.586945) | 8.060890 / 2.268929 (5.791961) | 3.832506 / 55.444624 (-51.612119) | 3.148051 / 6.876477 (-3.728425) | 3.110096 / 2.142072 (0.968023) | 1.105050 / 4.805227 (-3.700178) | 0.219835 / 6.500664 (-6.280829) | 0.078600 / 0.075469 (0.003131) |\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) | 1.707551 / 1.841788 (-0.134237) | 19.238194 / 8.074308 (11.163885) | 22.167076 / 10.191392 (11.975684) | 0.233458 / 0.680424 (-0.446966) | 0.025131 / 0.534201 (-0.509070) | 0.525241 / 0.579283 (-0.054042) | 0.649666 / 0.434364 (0.215303) | 0.602941 / 0.540337 (0.062603) | 0.718472 / 1.386936 (-0.668464) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n" ]

"2023-05-30T14:59:48Z"

"2023-05-30T18:03:10Z"

"2023-05-30T17:53:29Z"

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"2020-12-01T23:44:01Z"

"2020-12-01T23:57:58Z"

"2020-12-01T23:57:58Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/974.diff", "html_url": "https://github.com/huggingface/datasets/pull/974", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/974.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/974" }

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[ "Hi @haonan-li , thank you for the report! It seems to be a bug on the [`huggingface_hub`](https://github.com/huggingface/huggingface_hub) site, there is even no such dataset as `mbzuai/bactrian-x` on the Hub. I opened and [issue](https://github.com/huggingface/huggingface_hub/issues/1453) there.", "I think `load_dataset(\"mbzuai/bactrian-x\")` shouldn't be loaded at all and raise an error but because of [this fallback](https://github.com/huggingface/datasets/blob/main/src/datasets/load.py#L1194) to packaged loaders when no other options are applicable, it loads the dataset with standard `json` loader instead of the custom loading script." ]

"2023-04-26T18:19:04Z"

"2023-04-27T11:56:58Z"

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[ "thanks!" ]

"2020-09-16T00:27:41Z"

"2020-09-21T16:31:11Z"

"2020-09-16T06:52:44Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/632.diff", "html_url": "https://github.com/huggingface/datasets/pull/632", "merged_at": "2020-09-16T06:52:44Z", "patch_url": "https://github.com/huggingface/datasets/pull/632.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/632" }

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[ "<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) | 0.008735 / 0.011353 (-0.002618) | 0.004514 / 0.011008 (-0.006494) | 0.099348 / 0.038508 (0.060840) | 0.030060 / 0.023109 (0.006951) | 0.302189 / 0.275898 (0.026291) | 0.339535 / 0.323480 (0.016055) | 0.007053 / 0.007986 (-0.000933) | 0.003420 / 0.004328 (-0.000909) | 0.076967 / 0.004250 (0.072717) | 0.034484 / 0.037052 (-0.002568) | 0.304349 / 0.258489 (0.045860) | 0.354032 / 0.293841 (0.060191) | 0.033552 / 0.128546 (-0.094995) | 0.011405 / 0.075646 (-0.064241) | 0.324773 / 0.419271 (-0.094498) | 0.041103 / 0.043533 (-0.002429) | 0.313559 / 0.255139 (0.058420) | 0.333251 / 0.283200 (0.050052) | 0.087580 / 0.141683 (-0.054103) | 1.460324 / 1.452155 (0.008169) | 1.552239 / 1.492716 (0.059523) |\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) | 0.183759 / 0.018006 (0.165753) | 0.413274 / 0.000490 (0.412784) | 0.001684 / 0.000200 (0.001484) | 0.000076 / 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) | 0.023341 / 0.037411 (-0.014071) | 0.098368 / 0.014526 (0.083842) | 0.105522 / 0.176557 (-0.071034) | 0.151581 / 0.737135 (-0.585554) | 0.108980 / 0.296338 (-0.187358) |\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) | 0.417856 / 0.215209 (0.202647) | 4.167570 / 2.077655 (2.089915) | 1.843669 / 1.504120 (0.339549) | 1.643130 / 1.541195 (0.101936) | 1.717587 / 1.468490 (0.249097) | 0.696392 / 4.584777 (-3.888384) | 3.427617 / 3.745712 (-0.318096) | 2.816486 / 5.269862 (-2.453376) | 1.539519 / 4.565676 (-3.026157) | 0.082112 / 0.424275 (-0.342163) | 0.012425 / 0.007607 (0.004818) | 0.525325 / 0.226044 (0.299281) | 5.251710 / 2.268929 (2.982781) | 2.273641 / 55.444624 (-53.170983) | 1.931002 / 6.876477 (-4.945474) | 1.977253 / 2.142072 (-0.164819) | 0.804794 / 4.805227 (-4.000434) | 0.147324 / 6.500664 (-6.353340) | 0.064966 / 0.075469 (-0.010503) |\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) | 1.193173 / 1.841788 (-0.648615) | 13.705127 / 8.074308 (5.630819) | 14.348408 / 10.191392 (4.157016) | 0.165374 / 0.680424 (-0.515050) | 0.028288 / 0.534201 (-0.505913) | 0.402546 / 0.579283 (-0.176737) | 0.413503 / 0.434364 (-0.020861) | 0.473298 / 0.540337 (-0.067039) | 0.567571 / 1.386936 (-0.819365) |\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) | 0.006735 / 0.011353 (-0.004618) | 0.004601 / 0.011008 (-0.006407) | 0.077414 / 0.038508 (0.038906) | 0.027402 / 0.023109 (0.004293) | 0.353469 / 0.275898 (0.077571) | 0.381697 / 0.323480 (0.058218) | 0.005076 / 0.007986 (-0.002910) | 0.004665 / 0.004328 (0.000336) | 0.076210 / 0.004250 (0.071960) | 0.039114 / 0.037052 (0.002061) | 0.354980 / 0.258489 (0.096491) | 0.389648 / 0.293841 (0.095807) | 0.031674 / 0.128546 (-0.096872) | 0.011752 / 0.075646 (-0.063894) | 0.086330 / 0.419271 (-0.332942) | 0.041530 / 0.043533 (-0.002003) | 0.343002 / 0.255139 (0.087863) | 0.365959 / 0.283200 (0.082760) | 0.091848 / 0.141683 (-0.049835) | 1.519427 / 1.452155 (0.067272) | 1.591529 / 1.492716 (0.098813) |\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) | 0.216458 / 0.018006 (0.198452) | 0.403326 / 0.000490 (0.402836) | 0.000432 / 0.000200 (0.000232) | 0.000059 / 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) | 0.025106 / 0.037411 (-0.012305) | 0.101113 / 0.014526 (0.086588) | 0.108104 / 0.176557 (-0.068453) | 0.142342 / 0.737135 (-0.594794) | 0.112012 / 0.296338 (-0.184326) |\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) | 0.443128 / 0.215209 (0.227919) | 4.434707 / 2.077655 (2.357052) | 2.115434 / 1.504120 (0.611315) | 1.902865 / 1.541195 (0.361670) | 1.996981 / 1.468490 (0.528491) | 0.702485 / 4.584777 (-3.882292) | 3.419151 / 3.745712 (-0.326561) | 1.911977 / 5.269862 (-3.357884) | 1.178195 / 4.565676 (-3.387481) | 0.082985 / 0.424275 (-0.341290) | 0.012415 / 0.007607 (0.004808) | 0.546188 / 0.226044 (0.320144) | 5.463592 / 2.268929 (3.194664) | 2.574911 / 55.444624 (-52.869713) | 2.232883 / 6.876477 (-4.643594) | 2.284391 / 2.142072 (0.142319) | 0.807389 / 4.805227 (-3.997839) | 0.151461 / 6.500664 (-6.349203) | 0.067831 / 0.075469 (-0.007638) |\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) | 1.286605 / 1.841788 (-0.555183) | 14.230328 / 8.074308 (6.156020) | 13.944645 / 10.191392 (3.753253) | 0.153725 / 0.680424 (-0.526699) | 0.016876 / 0.534201 (-0.517325) | 0.386109 / 0.579283 (-0.193174) | 0.401798 / 0.434364 (-0.032566) | 0.467883 / 0.540337 (-0.072454) | 0.557788 / 1.386936 (-0.829148) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c07f5c9268ce55d0e2022b018d5f44cfcedf1e43 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009305 / 0.011353 (-0.002048) | 0.004978 / 0.011008 (-0.006031) | 0.101687 / 0.038508 (0.063179) | 0.035339 / 0.023109 (0.012230) | 0.294770 / 0.275898 (0.018872) | 0.355491 / 0.323480 (0.032011) | 0.008183 / 0.007986 (0.000197) | 0.004076 / 0.004328 (-0.000253) | 0.077552 / 0.004250 (0.073302) | 0.042891 / 0.037052 (0.005838) | 0.305727 / 0.258489 (0.047238) | 0.336508 / 0.293841 (0.042667) | 0.038525 / 0.128546 (-0.090022) | 0.011878 / 0.075646 (-0.063768) | 0.334136 / 0.419271 (-0.085136) | 0.047548 / 0.043533 (0.004015) | 0.301749 / 0.255139 (0.046610) | 0.318221 / 0.283200 (0.035022) | 0.099172 / 0.141683 (-0.042511) | 1.440638 / 1.452155 (-0.011516) | 1.503505 / 1.492716 (0.010789) |\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) | 0.202748 / 0.018006 (0.184742) | 0.433670 / 0.000490 (0.433181) | 0.003139 / 0.000200 (0.002939) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025555 / 0.037411 (-0.011856) | 0.107156 / 0.014526 (0.092631) | 0.116706 / 0.176557 (-0.059851) | 0.153165 / 0.737135 (-0.583970) | 0.122614 / 0.296338 (-0.173724) |\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) | 0.398912 / 0.215209 (0.183703) | 3.965048 / 2.077655 (1.887394) | 1.894678 / 1.504120 (0.390558) | 1.706925 / 1.541195 (0.165730) | 1.745264 / 1.468490 (0.276774) | 0.691174 / 4.584777 (-3.893603) | 3.824583 / 3.745712 (0.078871) | 3.876806 / 5.269862 (-1.393055) | 1.898991 / 4.565676 (-2.666685) | 0.083687 / 0.424275 (-0.340588) | 0.012122 / 0.007607 (0.004514) | 0.510870 / 0.226044 (0.284825) | 5.094523 / 2.268929 (2.825594) | 2.265557 / 55.444624 (-53.179067) | 1.930882 / 6.876477 (-4.945594) | 2.016090 / 2.142072 (-0.125983) | 0.833108 / 4.805227 (-3.972119) | 0.164804 / 6.500664 (-6.335860) | 0.062864 / 0.075469 (-0.012605) |\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) | 1.192673 / 1.841788 (-0.649115) | 14.730393 / 8.074308 (6.656085) | 14.550736 / 10.191392 (4.359344) | 0.154451 / 0.680424 (-0.525973) | 0.029222 / 0.534201 (-0.504979) | 0.440939 / 0.579283 (-0.138345) | 0.442772 / 0.434364 (0.008409) | 0.543948 / 0.540337 (0.003610) | 0.638113 / 1.386936 (-0.748824) |\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) | 0.007589 / 0.011353 (-0.003764) | 0.005208 / 0.011008 (-0.005800) | 0.073797 / 0.038508 (0.035289) | 0.034021 / 0.023109 (0.010912) | 0.366120 / 0.275898 (0.090222) | 0.397105 / 0.323480 (0.073625) | 0.005837 / 0.007986 (-0.002148) | 0.004028 / 0.004328 (-0.000301) | 0.073502 / 0.004250 (0.069252) | 0.051233 / 0.037052 (0.014181) | 0.359849 / 0.258489 (0.101360) | 0.397476 / 0.293841 (0.103635) | 0.036727 / 0.128546 (-0.091819) | 0.012249 / 0.075646 (-0.063397) | 0.086600 / 0.419271 (-0.332671) | 0.051156 / 0.043533 (0.007623) | 0.343441 / 0.255139 (0.088302) | 0.389672 / 0.283200 (0.106472) | 0.105180 / 0.141683 (-0.036503) | 1.439719 / 1.452155 (-0.012435) | 1.537779 / 1.492716 (0.045062) |\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) | 0.199429 / 0.018006 (0.181422) | 0.440837 / 0.000490 (0.440347) | 0.005333 / 0.000200 (0.005133) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029581 / 0.037411 (-0.007830) | 0.113789 / 0.014526 (0.099263) | 0.123799 / 0.176557 (-0.052758) | 0.163772 / 0.737135 (-0.573363) | 0.127156 / 0.296338 (-0.169183) |\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) | 0.422803 / 0.215209 (0.207594) | 4.192400 / 2.077655 (2.114745) | 1.994561 / 1.504120 (0.490441) | 1.807085 / 1.541195 (0.265890) | 1.927539 / 1.468490 (0.459049) | 0.708804 / 4.584777 (-3.875973) | 3.790662 / 3.745712 (0.044950) | 3.667207 / 5.269862 (-1.602655) | 1.985107 / 4.565676 (-2.580570) | 0.086609 / 0.424275 (-0.337666) | 0.012613 / 0.007607 (0.005006) | 0.520167 / 0.226044 (0.294122) | 5.208657 / 2.268929 (2.939729) | 2.500383 / 55.444624 (-52.944241) | 2.129817 / 6.876477 (-4.746660) | 2.181205 / 2.142072 (0.039133) | 0.847925 / 4.805227 (-3.957303) | 0.168293 / 6.500664 (-6.332372) | 0.065066 / 0.075469 (-0.010403) |\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) | 1.261053 / 1.841788 (-0.580735) | 15.091644 / 8.074308 (7.017336) | 14.126139 / 10.191392 (3.934747) | 0.184956 / 0.680424 (-0.495468) | 0.017909 / 0.534201 (-0.516292) | 0.428918 / 0.579283 (-0.150365) | 0.429637 / 0.434364 (-0.004727) | 0.530900 / 0.540337 (-0.009437) | 0.627966 / 1.386936 (-0.758970) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a72fd153d3499a5c5eda783673073c9f557f11e0 \"CML watermark\")\n", "I think we should also suggest installing `resampy` in the error message thrown by the Audio feature when `librosa` is not installed.", "exploring a better solution at https://github.com/huggingface/datasets/pull/5556" ]

"2023-02-20T18:15:43Z"

"2023-09-24T10:07:29Z"

"2023-02-21T12:43:38Z"

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

"2022-04-26T15:10:46Z"

"2022-05-03T14:27:45Z"

"2022-05-03T14:20:47Z"

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[ "Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`", "`ade_corpus_v2` was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `ade_corpus_v2` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"ade_corpus_v2\", \"Ade_corpos_v2_drug_ade_relation\")\r\n```\r\n\r\n(looks like there is a typo in the configuration name, we'll fix it for the v2.0 release of `datasets` soon)" ]

"2020-12-23T10:58:14Z"

"2021-08-03T05:08:54Z"

"2021-08-03T05:08:54Z"

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"2021-10-29T16:56:35Z"

"2021-11-02T10:25:38Z"

"2021-11-02T10:25:37Z"

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"2021-12-09T18:03:38Z"

"2022-01-12T14:13:47Z"

"2022-01-12T14:13:47Z"

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[ "I just noticed the [docs](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2881C11-L2881C200) say:\r\n\r\n>If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.\r\n\r\nso maybe this is a bug then.", "All the values in a batch must be of the same length. So one solution is dropping all the input columns:\r\n```python\r\ndata = data.map(lambda samples: tokenizer(samples[\"text\"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nAnother is padding/transforming the input columns to the tokenizer output's length (447). " ]

"2023-06-29T22:15:21Z"

"2023-07-03T17:58:52Z"

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"2021-10-08T12:04:40Z"

"2021-10-11T07:46:43Z"

"2021-10-11T07:46:42Z"

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

"2022-09-22T11:41:59Z"

"2022-09-23T12:04:37Z"

"2022-09-23T12:02:45Z"

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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) | 0.007617 / 0.011353 (-0.003736) | 0.004580 / 0.011008 (-0.006428) | 0.100913 / 0.038508 (0.062405) | 0.087703 / 0.023109 (0.064594) | 0.424159 / 0.275898 (0.148261) | 0.467195 / 0.323480 (0.143715) | 0.006890 / 0.007986 (-0.001096) | 0.003765 / 0.004328 (-0.000564) | 0.077513 / 0.004250 (0.073262) | 0.064889 / 0.037052 (0.027837) | 0.422349 / 0.258489 (0.163860) | 0.477391 / 0.293841 (0.183550) | 0.036025 / 0.128546 (-0.092522) | 0.009939 / 0.075646 (-0.065707) | 0.342409 / 0.419271 (-0.076862) | 0.061568 / 0.043533 (0.018035) | 0.431070 / 0.255139 (0.175931) | 0.462008 / 0.283200 (0.178809) | 0.027480 / 0.141683 (-0.114203) | 1.802271 / 1.452155 (0.350116) | 1.861336 / 1.492716 (0.368620) |\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) | 0.255806 / 0.018006 (0.237800) | 0.507969 / 0.000490 (0.507479) | 0.010060 / 0.000200 (0.009860) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032286 / 0.037411 (-0.005125) | 0.104468 / 0.014526 (0.089942) | 0.112707 / 0.176557 (-0.063850) | 0.181285 / 0.737135 (-0.555850) | 0.113180 / 0.296338 (-0.183158) |\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) | 0.449265 / 0.215209 (0.234056) | 4.465941 / 2.077655 (2.388287) | 2.177889 / 1.504120 (0.673769) | 1.969864 / 1.541195 (0.428669) | 2.077502 / 1.468490 (0.609011) | 0.561607 / 4.584777 (-4.023170) | 4.281873 / 3.745712 (0.536161) | 4.975352 / 5.269862 (-0.294510) | 2.907121 / 4.565676 (-1.658555) | 0.070205 / 0.424275 (-0.354070) | 0.009164 / 0.007607 (0.001557) | 0.581921 / 0.226044 (0.355876) | 5.538667 / 2.268929 (3.269739) | 2.798853 / 55.444624 (-52.645771) | 2.314015 / 6.876477 (-4.562462) | 2.584836 / 2.142072 (0.442763) | 0.672333 / 4.805227 (-4.132894) | 0.153828 / 6.500664 (-6.346836) | 0.069757 / 0.075469 (-0.005712) |\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) | 1.559670 / 1.841788 (-0.282118) | 23.994639 / 8.074308 (15.920331) | 16.856160 / 10.191392 (6.664768) | 0.195555 / 0.680424 (-0.484869) | 0.021586 / 0.534201 (-0.512615) | 0.469295 / 0.579283 (-0.109989) | 0.481582 / 0.434364 (0.047218) | 0.588667 / 0.540337 (0.048329) | 0.734347 / 1.386936 (-0.652589) |\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) | 0.009614 / 0.011353 (-0.001739) | 0.004616 / 0.011008 (-0.006392) | 0.077223 / 0.038508 (0.038715) | 0.103074 / 0.023109 (0.079965) | 0.447834 / 0.275898 (0.171936) | 0.524696 / 0.323480 (0.201216) | 0.007120 / 0.007986 (-0.000866) | 0.003890 / 0.004328 (-0.000438) | 0.076406 / 0.004250 (0.072156) | 0.073488 / 0.037052 (0.036436) | 0.466221 / 0.258489 (0.207732) | 0.532206 / 0.293841 (0.238365) | 0.037596 / 0.128546 (-0.090950) | 0.010029 / 0.075646 (-0.065617) | 0.084313 / 0.419271 (-0.334959) | 0.060088 / 0.043533 (0.016555) | 0.437792 / 0.255139 (0.182653) | 0.512850 / 0.283200 (0.229650) | 0.032424 / 0.141683 (-0.109259) | 1.762130 / 1.452155 (0.309975) | 1.946097 / 1.492716 (0.453381) |\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) | 0.250774 / 0.018006 (0.232768) | 0.506869 / 0.000490 (0.506379) | 0.008232 / 0.000200 (0.008032) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037779 / 0.037411 (0.000368) | 0.111933 / 0.014526 (0.097407) | 0.122385 / 0.176557 (-0.054172) | 0.190372 / 0.737135 (-0.546763) | 0.122472 / 0.296338 (-0.173866) |\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) | 0.488502 / 0.215209 (0.273293) | 4.878114 / 2.077655 (2.800459) | 2.504144 / 1.504120 (1.000024) | 2.321077 / 1.541195 (0.779883) | 2.416797 / 1.468490 (0.948307) | 0.583582 / 4.584777 (-4.001195) | 4.277896 / 3.745712 (0.532184) | 3.874780 / 5.269862 (-1.395082) | 2.540099 / 4.565676 (-2.025577) | 0.068734 / 0.424275 (-0.355541) | 0.009158 / 0.007607 (0.001550) | 0.578401 / 0.226044 (0.352357) | 5.763354 / 2.268929 (3.494426) | 3.167771 / 55.444624 (-52.276853) | 2.675220 / 6.876477 (-4.201257) | 2.920927 / 2.142072 (0.778855) | 0.673948 / 4.805227 (-4.131280) | 0.157908 / 6.500664 (-6.342756) | 0.071672 / 0.075469 (-0.003797) |\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) | 1.635120 / 1.841788 (-0.206668) | 24.853480 / 8.074308 (16.779172) | 17.162978 / 10.191392 (6.971586) | 0.209577 / 0.680424 (-0.470847) | 0.030110 / 0.534201 (-0.504091) | 0.546970 / 0.579283 (-0.032313) | 0.581912 / 0.434364 (0.147548) | 0.571460 / 0.540337 (0.031123) | 0.823411 / 1.386936 (-0.563525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"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) | 0.006674 / 0.011353 (-0.004679) | 0.004198 / 0.011008 (-0.006810) | 0.084859 / 0.038508 (0.046351) | 0.076065 / 0.023109 (0.052955) | 0.316065 / 0.275898 (0.040167) | 0.352097 / 0.323480 (0.028617) | 0.005610 / 0.007986 (-0.002376) | 0.003600 / 0.004328 (-0.000729) | 0.064921 / 0.004250 (0.060671) | 0.054493 / 0.037052 (0.017441) | 0.318125 / 0.258489 (0.059636) | 0.370183 / 0.293841 (0.076342) | 0.031141 / 0.128546 (-0.097405) | 0.008755 / 0.075646 (-0.066891) | 0.288241 / 0.419271 (-0.131030) | 0.052379 / 0.043533 (0.008846) | 0.328147 / 0.255139 (0.073008) | 0.347548 / 0.283200 (0.064348) | 0.024393 / 0.141683 (-0.117290) | 1.480646 / 1.452155 (0.028492) | 1.575867 / 1.492716 (0.083151) |\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) | 0.268978 / 0.018006 (0.250971) | 0.586470 / 0.000490 (0.585980) | 0.003190 / 0.000200 (0.002990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030595 / 0.037411 (-0.006816) | 0.083037 / 0.014526 (0.068511) | 0.103706 / 0.176557 (-0.072850) | 0.164104 / 0.737135 (-0.573031) | 0.104536 / 0.296338 (-0.191802) |\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) | 0.382274 / 0.215209 (0.167065) | 3.811878 / 2.077655 (1.734223) | 1.840098 / 1.504120 (0.335978) | 1.670949 / 1.541195 (0.129754) | 1.763755 / 1.468490 (0.295264) | 0.479526 / 4.584777 (-4.105251) | 3.544443 / 3.745712 (-0.201269) | 3.263004 / 5.269862 (-2.006858) | 2.092801 / 4.565676 (-2.472875) | 0.057167 / 0.424275 (-0.367108) | 0.007450 / 0.007607 (-0.000157) | 0.463731 / 0.226044 (0.237686) | 4.624630 / 2.268929 (2.355701) | 2.327078 / 55.444624 (-53.117546) | 1.977734 / 6.876477 (-4.898743) | 2.237152 / 2.142072 (0.095079) | 0.573210 / 4.805227 (-4.232018) | 0.132095 / 6.500664 (-6.368569) | 0.060283 / 0.075469 (-0.015186) |\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) | 1.243404 / 1.841788 (-0.598384) | 20.306778 / 8.074308 (12.232470) | 14.561660 / 10.191392 (4.370268) | 0.170826 / 0.680424 (-0.509598) | 0.018574 / 0.534201 (-0.515627) | 0.392367 / 0.579283 (-0.186916) | 0.402918 / 0.434364 (-0.031446) | 0.476629 / 0.540337 (-0.063708) | 0.653709 / 1.386936 (-0.733227) |\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) | 0.006562 / 0.011353 (-0.004791) | 0.004092 / 0.011008 (-0.006916) | 0.065951 / 0.038508 (0.027443) | 0.078090 / 0.023109 (0.054981) | 0.369679 / 0.275898 (0.093781) | 0.411442 / 0.323480 (0.087962) | 0.005646 / 0.007986 (-0.002339) | 0.003537 / 0.004328 (-0.000791) | 0.066024 / 0.004250 (0.061773) | 0.058947 / 0.037052 (0.021895) | 0.389219 / 0.258489 (0.130730) | 0.414200 / 0.293841 (0.120359) | 0.030372 / 0.128546 (-0.098174) | 0.008631 / 0.075646 (-0.067015) | 0.071692 / 0.419271 (-0.347580) | 0.048035 / 0.043533 (0.004502) | 0.376960 / 0.255139 (0.121821) | 0.389847 / 0.283200 (0.106648) | 0.023940 / 0.141683 (-0.117743) | 1.487633 / 1.452155 (0.035479) | 1.561680 / 1.492716 (0.068964) |\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) | 0.301467 / 0.018006 (0.283461) | 0.544159 / 0.000490 (0.543669) | 0.000408 / 0.000200 (0.000208) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030939 / 0.037411 (-0.006472) | 0.087432 / 0.014526 (0.072906) | 0.103263 / 0.176557 (-0.073293) | 0.154551 / 0.737135 (-0.582585) | 0.104631 / 0.296338 (-0.191707) |\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) | 0.422348 / 0.215209 (0.207139) | 4.206003 / 2.077655 (2.128348) | 2.212619 / 1.504120 (0.708499) | 2.049616 / 1.541195 (0.508421) | 2.139093 / 1.468490 (0.670603) | 0.489647 / 4.584777 (-4.095130) | 3.523291 / 3.745712 (-0.222422) | 3.277657 / 5.269862 (-1.992205) | 2.111353 / 4.565676 (-2.454324) | 0.057597 / 0.424275 (-0.366679) | 0.007675 / 0.007607 (0.000068) | 0.493068 / 0.226044 (0.267023) | 4.939493 / 2.268929 (2.670565) | 2.695995 / 55.444624 (-52.748630) | 2.374904 / 6.876477 (-4.501573) | 2.600110 / 2.142072 (0.458038) | 0.586306 / 4.805227 (-4.218921) | 0.134137 / 6.500664 (-6.366527) | 0.061897 / 0.075469 (-0.013572) |\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) | 1.330628 / 1.841788 (-0.511160) | 20.557964 / 8.074308 (12.483656) | 14.251632 / 10.191392 (4.060240) | 0.148772 / 0.680424 (-0.531652) | 0.018383 / 0.534201 (-0.515817) | 0.392552 / 0.579283 (-0.186731) | 0.403959 / 0.434364 (-0.030405) | 0.462154 / 0.540337 (-0.078184) | 0.608832 / 1.386936 (-0.778104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"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) | 0.007659 / 0.011353 (-0.003694) | 0.004500 / 0.011008 (-0.006508) | 0.100379 / 0.038508 (0.061871) | 0.079731 / 0.023109 (0.056622) | 0.381788 / 0.275898 (0.105890) | 0.416524 / 0.323480 (0.093044) | 0.004446 / 0.007986 (-0.003539) | 0.003752 / 0.004328 (-0.000577) | 0.074956 / 0.004250 (0.070706) | 0.062885 / 0.037052 (0.025832) | 0.383849 / 0.258489 (0.125360) | 0.433906 / 0.293841 (0.140065) | 0.036079 / 0.128546 (-0.092468) | 0.009927 / 0.075646 (-0.065719) | 0.343879 / 0.419271 (-0.075393) | 0.061055 / 0.043533 (0.017523) | 0.376703 / 0.255139 (0.121564) | 0.428111 / 0.283200 (0.144911) | 0.028667 / 0.141683 (-0.113016) | 1.777755 / 1.452155 (0.325600) | 1.878283 / 1.492716 (0.385567) |\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) | 0.220829 / 0.018006 (0.202823) | 0.506406 / 0.000490 (0.505916) | 0.005550 / 0.000200 (0.005350) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034928 / 0.037411 (-0.002483) | 0.103873 / 0.014526 (0.089347) | 0.114352 / 0.176557 (-0.062204) | 0.188218 / 0.737135 (-0.548918) | 0.117343 / 0.296338 (-0.178995) |\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) | 0.459148 / 0.215209 (0.243939) | 4.582092 / 2.077655 (2.504437) | 2.275603 / 1.504120 (0.771483) | 2.058155 / 1.541195 (0.516960) | 2.163886 / 1.468490 (0.695396) | 0.573033 / 4.584777 (-4.011744) | 4.414891 / 3.745712 (0.669178) | 7.280433 / 5.269862 (2.010572) | 4.119414 / 4.565676 (-0.446262) | 0.067432 / 0.424275 (-0.356843) | 0.008687 / 0.007607 (0.001080) | 0.556029 / 0.226044 (0.329984) | 5.557192 / 2.268929 (3.288264) | 2.921596 / 55.444624 (-52.523028) | 2.520249 / 6.876477 (-4.356228) | 2.778965 / 2.142072 (0.636893) | 0.684765 / 4.805227 (-4.120462) | 0.159228 / 6.500664 (-6.341436) | 0.074015 / 0.075469 (-0.001454) |\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) | 1.534470 / 1.841788 (-0.307318) | 23.630693 / 8.074308 (15.556385) | 17.058142 / 10.191392 (6.866750) | 0.200909 / 0.680424 (-0.479515) | 0.021637 / 0.534201 (-0.512564) | 0.467417 / 0.579283 (-0.111866) | 0.460456 / 0.434364 (0.026092) | 0.541131 / 0.540337 (0.000793) | 0.728560 / 1.386936 (-0.658376) |\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) | 0.007625 / 0.011353 (-0.003727) | 0.004495 / 0.011008 (-0.006513) | 0.076373 / 0.038508 (0.037865) | 0.085260 / 0.023109 (0.062151) | 0.475778 / 0.275898 (0.199880) | 0.504604 / 0.323480 (0.181124) | 0.006733 / 0.007986 (-0.001253) | 0.003751 / 0.004328 (-0.000578) | 0.074993 / 0.004250 (0.070743) | 0.064704 / 0.037052 (0.027652) | 0.490072 / 0.258489 (0.231583) | 0.507560 / 0.293841 (0.213719) | 0.036765 / 0.128546 (-0.091781) | 0.009955 / 0.075646 (-0.065692) | 0.082452 / 0.419271 (-0.336820) | 0.057131 / 0.043533 (0.013598) | 0.467664 / 0.255139 (0.212525) | 0.482143 / 0.283200 (0.198943) | 0.025396 / 0.141683 (-0.116287) | 1.807587 / 1.452155 (0.355433) | 1.853355 / 1.492716 (0.360639) |\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) | 0.250543 / 0.018006 (0.232537) | 0.495685 / 0.000490 (0.495196) | 0.000415 / 0.000200 (0.000215) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035795 / 0.037411 (-0.001616) | 0.105954 / 0.014526 (0.091428) | 0.120158 / 0.176557 (-0.056399) | 0.181714 / 0.737135 (-0.555422) | 0.121242 / 0.296338 (-0.175097) |\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) | 0.488241 / 0.215209 (0.273032) | 4.866916 / 2.077655 (2.789262) | 2.531530 / 1.504120 (1.027410) | 2.360642 / 1.541195 (0.819448) | 2.457320 / 1.468490 (0.988830) | 0.571224 / 4.584777 (-4.013553) | 4.339042 / 3.745712 (0.593330) | 3.672812 / 5.269862 (-1.597050) | 2.364535 / 4.565676 (-2.201142) | 0.067004 / 0.424275 (-0.357271) | 0.009019 / 0.007607 (0.001412) | 0.563751 / 0.226044 (0.337707) | 5.664917 / 2.268929 (3.395989) | 3.043316 / 55.444624 (-52.401308) | 2.682722 / 6.876477 (-4.193755) | 2.863482 / 2.142072 (0.721409) | 0.666171 / 4.805227 (-4.139056) | 0.151862 / 6.500664 (-6.348802) | 0.071199 / 0.075469 (-0.004271) |\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) | 1.601880 / 1.841788 (-0.239907) | 23.069073 / 8.074308 (14.994765) | 16.918377 / 10.191392 (6.726985) | 0.173614 / 0.680424 (-0.506810) | 0.021843 / 0.534201 (-0.512358) | 0.470531 / 0.579283 (-0.108753) | 0.471152 / 0.434364 (0.036788) | 0.550968 / 0.540337 (0.010631) | 0.718869 / 1.386936 (-0.668067) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9e6eea46fc9503765c125395e30e26c1ae2e084 \"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) | 0.007530 / 0.011353 (-0.003823) | 0.004151 / 0.011008 (-0.006858) | 0.098490 / 0.038508 (0.059982) | 0.086955 / 0.023109 (0.063846) | 0.362133 / 0.275898 (0.086235) | 0.391402 / 0.323480 (0.067922) | 0.006274 / 0.007986 (-0.001712) | 0.003711 / 0.004328 (-0.000618) | 0.073519 / 0.004250 (0.069269) | 0.066170 / 0.037052 (0.029118) | 0.379057 / 0.258489 (0.120568) | 0.398132 / 0.293841 (0.104291) | 0.033936 / 0.128546 (-0.094610) | 0.009977 / 0.075646 (-0.065670) | 0.323766 / 0.419271 (-0.095505) | 0.078615 / 0.043533 (0.035082) | 0.352403 / 0.255139 (0.097264) | 0.386607 / 0.283200 (0.103407) | 0.036579 / 0.141683 (-0.105103) | 1.691899 / 1.452155 (0.239745) | 1.819396 / 1.492716 (0.326680) |\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) | 0.216888 / 0.018006 (0.198882) | 0.465781 / 0.000490 (0.465291) | 0.006197 / 0.000200 (0.005997) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032870 / 0.037411 (-0.004542) | 0.096026 / 0.014526 (0.081500) | 0.111093 / 0.176557 (-0.065464) | 0.185982 / 0.737135 (-0.551154) | 0.106967 / 0.296338 (-0.189371) |\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) | 0.441567 / 0.215209 (0.226358) | 4.353813 / 2.077655 (2.276158) | 2.176034 / 1.504120 (0.671914) | 1.969631 / 1.541195 (0.428437) | 2.048821 / 1.468490 (0.580330) | 0.549144 / 4.584777 (-4.035633) | 4.016166 / 3.745712 (0.270453) | 3.764249 / 5.269862 (-1.505613) | 2.293995 / 4.565676 (-2.271681) | 0.065227 / 0.424275 (-0.359048) | 0.008303 / 0.007607 (0.000695) | 0.513783 / 0.226044 (0.287738) | 5.247617 / 2.268929 (2.978689) | 2.782114 / 55.444624 (-52.662510) | 2.342776 / 6.876477 (-4.533701) | 2.621569 / 2.142072 (0.479497) | 0.679336 / 4.805227 (-4.125891) | 0.152061 / 6.500664 (-6.348603) | 0.070294 / 0.075469 (-0.005175) |\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) | 1.471778 / 1.841788 (-0.370010) | 22.714904 / 8.074308 (14.640596) | 15.607991 / 10.191392 (5.416599) | 0.172592 / 0.680424 (-0.507832) | 0.021799 / 0.534201 (-0.512402) | 0.462740 / 0.579283 (-0.116543) | 0.490885 / 0.434364 (0.056521) | 0.552997 / 0.540337 (0.012660) | 0.763784 / 1.386936 (-0.623152) |\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) | 0.007466 / 0.011353 (-0.003886) | 0.004322 / 0.011008 (-0.006686) | 0.074331 / 0.038508 (0.035823) | 0.085315 / 0.023109 (0.062206) | 0.409284 / 0.275898 (0.133386) | 0.464584 / 0.323480 (0.141104) | 0.005651 / 0.007986 (-0.002335) | 0.003577 / 0.004328 (-0.000751) | 0.070250 / 0.004250 (0.066000) | 0.059780 / 0.037052 (0.022727) | 0.419668 / 0.258489 (0.161179) | 0.462984 / 0.293841 (0.169143) | 0.034159 / 0.128546 (-0.094387) | 0.008999 / 0.075646 (-0.066647) | 0.076302 / 0.419271 (-0.342969) | 0.052274 / 0.043533 (0.008741) | 0.425938 / 0.255139 (0.170799) | 0.430399 / 0.283200 (0.147200) | 0.025017 / 0.141683 (-0.116666) | 1.680697 / 1.452155 (0.228542) | 1.774677 / 1.492716 (0.281960) |\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) | 0.291514 / 0.018006 (0.273508) | 0.461175 / 0.000490 (0.460685) | 0.023061 / 0.000200 (0.022861) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033950 / 0.037411 (-0.003462) | 0.100032 / 0.014526 (0.085506) | 0.118308 / 0.176557 (-0.058249) | 0.183601 / 0.737135 (-0.553535) | 0.116936 / 0.296338 (-0.179402) |\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) | 0.478779 / 0.215209 (0.263570) | 4.709505 / 2.077655 (2.631850) | 2.457442 / 1.504120 (0.953322) | 2.213737 / 1.541195 (0.672542) | 2.340642 / 1.468490 (0.872152) | 0.567187 / 4.584777 (-4.017590) | 3.923061 / 3.745712 (0.177349) | 3.752989 / 5.269862 (-1.516873) | 2.324028 / 4.565676 (-2.241649) | 0.064471 / 0.424275 (-0.359804) | 0.008845 / 0.007607 (0.001238) | 0.547447 / 0.226044 (0.321402) | 5.599435 / 2.268929 (3.330506) | 2.980547 / 55.444624 (-52.464077) | 2.754908 / 6.876477 (-4.121569) | 2.832978 / 2.142072 (0.690906) | 0.635059 / 4.805227 (-4.170168) | 0.153478 / 6.500664 (-6.347187) | 0.067146 / 0.075469 (-0.008323) |\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) | 1.555588 / 1.841788 (-0.286200) | 22.828906 / 8.074308 (14.754597) | 16.211008 / 10.191392 (6.019616) | 0.168009 / 0.680424 (-0.512415) | 0.021966 / 0.534201 (-0.512235) | 0.464872 / 0.579283 (-0.114411) | 0.460429 / 0.434364 (0.026065) | 0.530498 / 0.540337 (-0.009839) | 0.705020 / 1.386936 (-0.681916) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#deb9e703237c8310c5a6db04f54d54368e951edd \"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) | 0.005964 / 0.011353 (-0.005389) | 0.003644 / 0.011008 (-0.007364) | 0.079607 / 0.038508 (0.041099) | 0.058387 / 0.023109 (0.035278) | 0.312226 / 0.275898 (0.036328) | 0.349206 / 0.323480 (0.025726) | 0.004715 / 0.007986 (-0.003271) | 0.002869 / 0.004328 (-0.001460) | 0.061668 / 0.004250 (0.057417) | 0.045694 / 0.037052 (0.008642) | 0.313516 / 0.258489 (0.055027) | 0.357543 / 0.293841 (0.063702) | 0.027179 / 0.128546 (-0.101367) | 0.007961 / 0.075646 (-0.067686) | 0.262473 / 0.419271 (-0.156798) | 0.045588 / 0.043533 (0.002055) | 0.313102 / 0.255139 (0.057963) | 0.368686 / 0.283200 (0.085486) | 0.020556 / 0.141683 (-0.121127) | 1.447258 / 1.452155 (-0.004897) | 1.527319 / 1.492716 (0.034602) |\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) | 0.199417 / 0.018006 (0.181411) | 0.422155 / 0.000490 (0.421665) | 0.004972 / 0.000200 (0.004772) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023539 / 0.037411 (-0.013872) | 0.073055 / 0.014526 (0.058529) | 0.083631 / 0.176557 (-0.092926) | 0.145923 / 0.737135 (-0.591212) | 0.083820 / 0.296338 (-0.212518) |\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) | 0.396305 / 0.215209 (0.181096) | 3.967065 / 2.077655 (1.889410) | 2.101109 / 1.504120 (0.596989) | 1.958817 / 1.541195 (0.417622) | 2.037894 / 1.468490 (0.569404) | 0.496955 / 4.584777 (-4.087822) | 3.078948 / 3.745712 (-0.666764) | 3.363655 / 5.269862 (-1.906207) | 2.087659 / 4.565676 (-2.478018) | 0.057171 / 0.424275 (-0.367104) | 0.006410 / 0.007607 (-0.001197) | 0.470535 / 0.226044 (0.244491) | 4.715259 / 2.268929 (2.446330) | 2.355510 / 55.444624 (-53.089114) | 2.025270 / 6.876477 (-4.851207) | 2.210401 / 2.142072 (0.068329) | 0.580538 / 4.805227 (-4.224689) | 0.125068 / 6.500664 (-6.375596) | 0.059871 / 0.075469 (-0.015598) |\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) | 1.245468 / 1.841788 (-0.596320) | 18.322042 / 8.074308 (10.247734) | 13.609726 / 10.191392 (3.418334) | 0.143623 / 0.680424 (-0.536801) | 0.017068 / 0.534201 (-0.517133) | 0.330758 / 0.579283 (-0.248525) | 0.339946 / 0.434364 (-0.094418) | 0.377861 / 0.540337 (-0.162476) | 0.524593 / 1.386936 (-0.862343) |\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) | 0.006049 / 0.011353 (-0.005304) | 0.003737 / 0.011008 (-0.007271) | 0.062816 / 0.038508 (0.024308) | 0.063768 / 0.023109 (0.040658) | 0.362001 / 0.275898 (0.086103) | 0.395251 / 0.323480 (0.071772) | 0.004823 / 0.007986 (-0.003163) | 0.002881 / 0.004328 (-0.001447) | 0.061987 / 0.004250 (0.057737) | 0.049950 / 0.037052 (0.012898) | 0.362442 / 0.258489 (0.103953) | 0.399321 / 0.293841 (0.105480) | 0.027616 / 0.128546 (-0.100930) | 0.007965 / 0.075646 (-0.067681) | 0.068584 / 0.419271 (-0.350687) | 0.044700 / 0.043533 (0.001168) | 0.361011 / 0.255139 (0.105872) | 0.386007 / 0.283200 (0.102807) | 0.024621 / 0.141683 (-0.117061) | 1.441497 / 1.452155 (-0.010657) | 1.533145 / 1.492716 (0.040429) |\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) | 0.223446 / 0.018006 (0.205440) | 0.411147 / 0.000490 (0.410657) | 0.001821 / 0.000200 (0.001621) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025661 / 0.037411 (-0.011751) | 0.077838 / 0.014526 (0.063313) | 0.086148 / 0.176557 (-0.090408) | 0.140386 / 0.737135 (-0.596750) | 0.088793 / 0.296338 (-0.207546) |\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) | 0.425209 / 0.215209 (0.210000) | 4.250723 / 2.077655 (2.173068) | 2.403437 / 1.504120 (0.899317) | 2.283584 / 1.541195 (0.742390) | 2.326870 / 1.468490 (0.858380) | 0.504781 / 4.584777 (-4.079996) | 3.017042 / 3.745712 (-0.728670) | 4.643068 / 5.269862 (-0.626794) | 2.535710 / 4.565676 (-2.029967) | 0.058520 / 0.424275 (-0.365755) | 0.006766 / 0.007607 (-0.000841) | 0.500664 / 0.226044 (0.274620) | 5.017073 / 2.268929 (2.748145) | 2.668661 / 55.444624 (-52.775963) | 2.335486 / 6.876477 (-4.540991) | 2.486518 / 2.142072 (0.344445) | 0.598795 / 4.805227 (-4.206432) | 0.126395 / 6.500664 (-6.374269) | 0.063154 / 0.075469 (-0.012315) |\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) | 1.358059 / 1.841788 (-0.483728) | 18.615724 / 8.074308 (10.541416) | 13.670934 / 10.191392 (3.479542) | 0.134650 / 0.680424 (-0.545774) | 0.016941 / 0.534201 (-0.517260) | 0.335215 / 0.579283 (-0.244068) | 0.356118 / 0.434364 (-0.078246) | 0.393109 / 0.540337 (-0.147228) | 0.534165 / 1.386936 (-0.852771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7d3b557665f34e84cd151ffe9d80b45a19fe33 \"CML watermark\")\n" ]

"2023-07-26T10:44:23Z"

"2023-07-27T12:51:51Z"

"2023-07-27T12:42:57Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6072.diff", "html_url": "https://github.com/huggingface/datasets/pull/6072", "merged_at": "2023-07-27T12:42:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/6072.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6072" }

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[]

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"2023-05-02T07:19:34Z"

"2023-05-02T07:21:30Z"

"2023-05-02T07:21:30Z"

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"2020-07-17T16:19:45Z"

"2020-07-21T09:13:46Z"

"2020-07-21T09:13:45Z"

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[ "merging this one then :) " ]

"2020-12-18T12:41:31Z"

"2020-12-22T15:34:07Z"

"2020-12-22T15:34:07Z"

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

"2020-12-02T08:30:32Z"

"2020-12-04T13:27:41Z"

"2020-12-04T13:27:41Z"

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{ "closed_at": "2021-07-09T05:50:07Z", "closed_issues": 12, "created_at": "2021-05-31T16:13:06Z", "creator": { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }, "description": "Next minor release", "due_on": "2021-07-08T07:00:00Z", "html_url": "https://github.com/huggingface/datasets/milestone/5", "id": 6808903, "labels_url": "https://api.github.com/repos/huggingface/datasets/milestones/5/labels", "node_id": "MDk6TWlsZXN0b25lNjgwODkwMw==", "number": 5, "open_issues": 0, "state": "closed", "title": "1.9", "updated_at": "2021-07-12T14:12:00Z", "url": "https://api.github.com/repos/huggingface/datasets/milestones/5" }

[ "i need some help with this" ]

"2021-06-12T14:17:34Z"

"2021-07-06T16:54:52Z"

"2021-06-14T07:53:23Z"

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4102). All of your documentation changes will be reflected on that endpoint.", "Closing in favor of https://github.com/huggingface/datasets/pull/4106" ]

"2022-04-05T19:21:52Z"

"2023-09-24T10:01:14Z"

"2022-04-12T08:41:46Z"

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[ "Thank you for this welcome change guys!" ]

"2021-06-08T17:14:23Z"

"2021-06-08T18:04:14Z"

"2021-06-08T17:55:43Z"

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

"2022-09-08T10:04:39Z"

"2022-09-08T14:54:47Z"

"2022-09-08T14:52:05Z"

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[]

"2020-10-27T16:00:47Z"

"2020-12-03T13:37:44Z"

"2020-12-03T13:37:44Z"

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[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "Added env var", "@lhoestq would you or another maintainer be able to review please? :)", "I removed the env var", "<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) | 0.005771 / 0.011353 (-0.005582) | 0.004086 / 0.011008 (-0.006922) | 0.097170 / 0.038508 (0.058661) | 0.027464 / 0.023109 (0.004355) | 0.305425 / 0.275898 (0.029527) | 0.343869 / 0.323480 (0.020389) | 0.004899 / 0.007986 (-0.003087) | 0.003294 / 0.004328 (-0.001034) | 0.074710 / 0.004250 (0.070459) | 0.034982 / 0.037052 (-0.002070) | 0.306063 / 0.258489 (0.047574) | 0.343115 / 0.293841 (0.049274) | 0.025155 / 0.128546 (-0.103392) | 0.008429 / 0.075646 (-0.067217) | 0.318680 / 0.419271 (-0.100591) | 0.043304 / 0.043533 (-0.000229) | 0.306703 / 0.255139 (0.051564) | 0.335535 / 0.283200 (0.052335) | 0.087428 / 0.141683 (-0.054255) | 1.483769 / 1.452155 (0.031614) | 1.538753 / 1.492716 (0.046037) |\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) | 0.203313 / 0.018006 (0.185307) | 0.413864 / 0.000490 (0.413375) | 0.003186 / 0.000200 (0.002986) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022862 / 0.037411 (-0.014550) | 0.097306 / 0.014526 (0.082780) | 0.102823 / 0.176557 (-0.073733) | 0.162803 / 0.737135 (-0.574333) | 0.106311 / 0.296338 (-0.190028) |\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) | 0.451710 / 0.215209 (0.236501) | 4.508520 / 2.077655 (2.430865) | 2.181118 / 1.504120 (0.676998) | 1.977607 / 1.541195 (0.436412) | 2.008366 / 1.468490 (0.539876) | 0.565388 / 4.584777 (-4.019389) | 3.439318 / 3.745712 (-0.306394) | 1.747512 / 5.269862 (-3.522349) | 1.102124 / 4.565676 (-3.463553) | 0.069212 / 0.424275 (-0.355063) | 0.011926 / 0.007607 (0.004318) | 0.553414 / 0.226044 (0.327370) | 5.548959 / 2.268929 (3.280031) | 2.628769 / 55.444624 (-52.815856) | 2.301003 / 6.876477 (-4.575473) | 2.341744 / 2.142072 (0.199672) | 0.673092 / 4.805227 (-4.132135) | 0.137722 / 6.500664 (-6.362942) | 0.066909 / 0.075469 (-0.008560) |\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) | 1.196854 / 1.841788 (-0.644934) | 13.421776 / 8.074308 (5.347468) | 13.839760 / 10.191392 (3.648368) | 0.140557 / 0.680424 (-0.539867) | 0.016619 / 0.534201 (-0.517582) | 0.357985 / 0.579283 (-0.221298) | 0.387018 / 0.434364 (-0.047346) | 0.452798 / 0.540337 (-0.087540) | 0.542085 / 1.386936 (-0.844851) |\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) | 0.005868 / 0.011353 (-0.005484) | 0.004103 / 0.011008 (-0.006905) | 0.076126 / 0.038508 (0.037618) | 0.027744 / 0.023109 (0.004635) | 0.357257 / 0.275898 (0.081359) | 0.387981 / 0.323480 (0.064501) | 0.004807 / 0.007986 (-0.003178) | 0.003337 / 0.004328 (-0.000991) | 0.075486 / 0.004250 (0.071236) | 0.035121 / 0.037052 (-0.001931) | 0.361385 / 0.258489 (0.102896) | 0.399346 / 0.293841 (0.105505) | 0.025263 / 0.128546 (-0.103284) | 0.008571 / 0.075646 (-0.067075) | 0.081815 / 0.419271 (-0.337457) | 0.041114 / 0.043533 (-0.002418) | 0.362840 / 0.255139 (0.107701) | 0.380926 / 0.283200 (0.097727) | 0.092728 / 0.141683 (-0.048955) | 1.517647 / 1.452155 (0.065492) | 1.534914 / 1.492716 (0.042198) |\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) | 0.199669 / 0.018006 (0.181663) | 0.399070 / 0.000490 (0.398580) | 0.002014 / 0.000200 (0.001814) | 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) | 0.024541 / 0.037411 (-0.012870) | 0.099676 / 0.014526 (0.085151) | 0.106503 / 0.176557 (-0.070054) | 0.153755 / 0.737135 (-0.583380) | 0.108564 / 0.296338 (-0.187775) |\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) | 0.443842 / 0.215209 (0.228633) | 4.441158 / 2.077655 (2.363503) | 2.159496 / 1.504120 (0.655376) | 1.955358 / 1.541195 (0.414163) | 1.973864 / 1.468490 (0.505374) | 0.550467 / 4.584777 (-4.034310) | 3.381831 / 3.745712 (-0.363881) | 2.561192 / 5.269862 (-2.708670) | 1.361684 / 4.565676 (-3.203992) | 0.068140 / 0.424275 (-0.356135) | 0.012005 / 0.007607 (0.004398) | 0.551921 / 0.226044 (0.325877) | 5.503591 / 2.268929 (3.234662) | 2.591609 / 55.444624 (-52.853015) | 2.246681 / 6.876477 (-4.629796) | 2.290941 / 2.142072 (0.148868) | 0.655212 / 4.805227 (-4.150015) | 0.136013 / 6.500664 (-6.364651) | 0.066995 / 0.075469 (-0.008474) |\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) | 1.300438 / 1.841788 (-0.541350) | 13.866224 / 8.074308 (5.791916) | 13.932624 / 10.191392 (3.741232) | 0.144345 / 0.680424 (-0.536079) | 0.016623 / 0.534201 (-0.517578) | 0.357629 / 0.579283 (-0.221654) | 0.389759 / 0.434364 (-0.044605) | 0.417704 / 0.540337 (-0.122633) | 0.501358 / 1.386936 (-0.885578) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89f775226321ba94e5bf4670a323c0fb44f5f65c \"CML watermark\")\n", "Thank you!" ]

"2023-05-05T20:22:40Z"

"2023-05-25T17:45:54Z"

"2023-05-25T08:46:15Z"

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[ "Hi ! You're completely right, this must be mentioned in the dataset card.\r\nIf you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the \"Licensing Information\" section at https://github.com/huggingface/datasets/blob/master/datasets/trivia_qa/README.md" ]

"2021-11-23T08:00:15Z"

"2021-11-29T11:24:21Z"

"2021-11-29T11:24:21Z"

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"2021-11-17T16:22:53Z"

"2021-11-17T16:23:09Z"

"2021-11-17T16:23:08Z"

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"2021-01-29T14:26:06Z"

"2021-01-29T18:51:10Z"

"2021-01-29T17:05:07Z"

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"2020-11-10T14:50:40Z"

"2020-12-03T13:37:57Z"

"2020-12-03T13:37:57Z"

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[ "Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account", "I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful.", "For anyone interested, I see that with `dill==0.3.6` the workaround I suggested doesn't work anymore.\r\nI opened an issue about it: https://github.com/uqfoundation/dill/issues/572.\r\n\r\n " ]

"2021-11-19T08:18:36Z"

"2023-01-30T12:40:17Z"

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[ "There was an issue in the format, thanks.\r\nNow you can do\r\n```python3\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```\r\n\r\nand the expected format is \r\n```\r\nArgs:\r\n predictions: List of question-answers dictionaries with the following key-values:\r\n - 'id': id of the question-answer pair as given in the references (see below)\r\n - 'prediction_text': the text of the answer\r\n references: List of question-answers dictionaries with the following key-values:\r\n - 'id': id of the question-answer pair (see above),\r\n - 'answers': a Dict {'text': list of possible texts for the answer, as a list of strings}\r\n```" ]

"2020-05-16T12:06:37Z"

"2020-05-22T13:38:52Z"

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

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"2021-11-09T10:54:15Z"

"2022-02-14T15:46:00Z"

"2021-11-09T13:49:28Z"

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[ "Hi @Tudyx, \r\n\r\nPlease note that in Python, the boolean NOT operator (`not`) has lower precedence than comparison operators (`<=`, `<`), thus the expression you mention is equivalent to:\r\n```python\r\n not (-1 <= example_data < self.num_classes)\r\n```\r\n\r\nAlso note that as expected, the exception is raised if:\r\n- `example_data < -1`\r\n- or `example_data >= self.num_classes`\r\n\r\nThe raise of the exception is expected when `example_data` equals 4 and `self.num_classes` equals 4 too." ]

"2022-02-14T11:44:35Z"

"2022-02-14T15:09:36Z"

"2022-02-14T15:07:43Z"

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[ "Hi,\r\n\r\nI found a small typo in the code example of create imagefolder dataset. It confused me a little when I first saw it.\r\n\r\nBest Regards.\r\n\r\nXin" ]

"2023-08-04T13:36:59Z"

"2023-08-04T13:45:32Z"

"2023-08-04T13:41:43Z"

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[ "hii @lhoestq can you assign this issue to me?\r\n", "You can reply \"#self-assign\" to this issue to automatically get assigned to it :)\r\nLet me know if you have any questions or if I can help", "#2289 ", "Actually i am quite new to figure it out how everything goes and done \r\n\r\n> You can reply \"#self-assign\" to this issue to automatically get assigned to it :)\r\n> Let me know if you have any questions or if I can help\r\n\r\nwhen i wrote #self-assign it automatically got converted to some number is it correct or i have done it some wrong way, I am quite new to open source thus wanna try to learn and explore it", "#2289 #self-assign ", "Ah yea github tries to replace the #self-assign with an issue link. I guess you can try to copy-paste instead to see if it works\r\n\r\nAnyway let me assign you manually", "thanks a lot @lhoestq ! though i have a very lil idea of the issue, i am new. as i said before, but gonna try my best shot to do it.\r\ncan you please suggest some tips or anything from your side, how basically we approach it will be really helpfull.\r\nWill try my best!", "The HfFileSystem from the `huggingface_hub` package can already read the HTTP_PROXY and HTTPS_PROXY environment variables. So the remaining thing missing is the `user_agent` that the user may include in a `DownloadConfig` object. The user agent can be used for regular http calls but also calls to the HfFileSystem.\r\n\r\n- for http, the `user_agent` isn't passed from `DownloadConfig` to `get_datasets_user_agent` in `_prepare_single_hop_path_and_storage_options` in `streaming_download_manager.py` so we need to include it\r\n- for HfFileSystem I think it requires a PR in https://github.com/huggingface/huggingface_hub to include it in the `HfFileSystem.__init__`" ]

"2023-07-17T16:39:26Z"

"2023-10-09T13:49:14Z"

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[ "Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.", "Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one)." ]

"2023-01-23T10:58:54Z"

"2023-01-24T01:45:48Z"

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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) | 0.008337 / 0.011353 (-0.003016) | 0.005588 / 0.011008 (-0.005421) | 0.110259 / 0.038508 (0.071751) | 0.038928 / 0.023109 (0.015819) | 0.350441 / 0.275898 (0.074543) | 0.378473 / 0.323480 (0.054993) | 0.006369 / 0.007986 (-0.001616) | 0.005730 / 0.004328 (0.001401) | 0.083042 / 0.004250 (0.078792) | 0.048686 / 0.037052 (0.011634) | 0.367561 / 0.258489 (0.109072) | 0.398073 / 0.293841 (0.104232) | 0.043247 / 0.128546 (-0.085299) | 0.013862 / 0.075646 (-0.061785) | 0.386745 / 0.419271 (-0.032527) | 0.060107 / 0.043533 (0.016574) | 0.345450 / 0.255139 (0.090311) | 0.371269 / 0.283200 (0.088069) | 0.117508 / 0.141683 (-0.024175) | 1.689345 / 1.452155 (0.237191) | 1.777119 / 1.492716 (0.284402) |\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) | 0.248248 / 0.018006 (0.230242) | 0.505200 / 0.000490 (0.504710) | 0.015354 / 0.000200 (0.015155) | 0.000794 / 0.000054 (0.000740) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030179 / 0.037411 (-0.007232) | 0.118583 / 0.014526 (0.104057) | 0.131546 / 0.176557 (-0.045010) | 0.196173 / 0.737135 (-0.540962) | 0.140532 / 0.296338 (-0.155807) |\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) | 0.470733 / 0.215209 (0.255524) | 4.758868 / 2.077655 (2.681213) | 2.246731 / 1.504120 (0.742611) | 1.995232 / 1.541195 (0.454037) | 2.057596 / 1.468490 (0.589106) | 0.819227 / 4.584777 (-3.765550) | 4.472093 / 3.745712 (0.726381) | 2.428154 / 5.269862 (-2.841708) | 1.748023 / 4.565676 (-2.817654) | 0.101965 / 0.424275 (-0.322310) | 0.014706 / 0.007607 (0.007098) | 0.600593 / 0.226044 (0.374548) | 5.869565 / 2.268929 (3.600637) | 2.764890 / 55.444624 (-52.679735) | 2.332112 / 6.876477 (-4.544364) | 2.486190 / 2.142072 (0.344118) | 0.979123 / 4.805227 (-3.826104) | 0.199543 / 6.500664 (-6.301121) | 0.075906 / 0.075469 (0.000436) |\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) | 1.397694 / 1.841788 (-0.444094) | 16.910500 / 8.074308 (8.836192) | 16.174131 / 10.191392 (5.982739) | 0.173975 / 0.680424 (-0.506449) | 0.021403 / 0.534201 (-0.512798) | 0.496187 / 0.579283 (-0.083096) | 0.487369 / 0.434364 (0.053005) | 0.565924 / 0.540337 (0.025587) | 0.684965 / 1.386936 (-0.701971) |\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) | 0.008253 / 0.011353 (-0.003100) | 0.005745 / 0.011008 (-0.005263) | 0.085848 / 0.038508 (0.047340) | 0.038753 / 0.023109 (0.015644) | 0.401278 / 0.275898 (0.125379) | 0.433132 / 0.323480 (0.109652) | 0.006112 / 0.007986 (-0.001874) | 0.005973 / 0.004328 (0.001644) | 0.085339 / 0.004250 (0.081088) | 0.053297 / 0.037052 (0.016244) | 0.400265 / 0.258489 (0.141776) | 0.455155 / 0.293841 (0.161314) | 0.043116 / 0.128546 (-0.085430) | 0.013957 / 0.075646 (-0.061689) | 0.099507 / 0.419271 (-0.319764) | 0.058858 / 0.043533 (0.015325) | 0.398030 / 0.255139 (0.142891) | 0.418171 / 0.283200 (0.134971) | 0.114392 / 0.141683 (-0.027291) | 1.683102 / 1.452155 (0.230947) | 1.801427 / 1.492716 (0.308711) |\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) | 0.242271 / 0.018006 (0.224265) | 0.494920 / 0.000490 (0.494430) | 0.007328 / 0.000200 (0.007128) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034061 / 0.037411 (-0.003351) | 0.146417 / 0.014526 (0.131891) | 0.161079 / 0.176557 (-0.015477) | 0.213999 / 0.737135 (-0.523137) | 0.166704 / 0.296338 (-0.129634) |\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) | 0.491214 / 0.215209 (0.276005) | 4.846946 / 2.077655 (2.769291) | 2.352595 / 1.504120 (0.848475) | 2.114055 / 1.541195 (0.572860) | 2.213537 / 1.468490 (0.745047) | 0.799625 / 4.584777 (-3.785152) | 4.440519 / 3.745712 (0.694807) | 4.476103 / 5.269862 (-0.793758) | 2.249384 / 4.565676 (-2.316292) | 0.098807 / 0.424275 (-0.325468) | 0.014463 / 0.007607 (0.006856) | 0.611793 / 0.226044 (0.385748) | 6.045710 / 2.268929 (3.776782) | 2.865957 / 55.444624 (-52.578667) | 2.454052 / 6.876477 (-4.422425) | 2.606153 / 2.142072 (0.464080) | 0.969057 / 4.805227 (-3.836170) | 0.198499 / 6.500664 (-6.302166) | 0.077012 / 0.075469 (0.001543) |\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) | 1.497020 / 1.841788 (-0.344767) | 17.834277 / 8.074308 (9.759969) | 16.413792 / 10.191392 (6.222400) | 0.201979 / 0.680424 (-0.478445) | 0.020627 / 0.534201 (-0.513574) | 0.499767 / 0.579283 (-0.079516) | 0.496982 / 0.434364 (0.062618) | 0.579554 / 0.540337 (0.039216) | 0.693287 / 1.386936 (-0.693649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a3fee942ae159ff6cfe6a23b343605e7e12f55 \"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) | 0.007461 / 0.011353 (-0.003892) | 0.005341 / 0.011008 (-0.005668) | 0.099252 / 0.038508 (0.060744) | 0.034723 / 0.023109 (0.011614) | 0.300980 / 0.275898 (0.025082) | 0.353860 / 0.323480 (0.030380) | 0.006100 / 0.007986 (-0.001885) | 0.004149 / 0.004328 (-0.000180) | 0.074765 / 0.004250 (0.070514) | 0.052226 / 0.037052 (0.015174) | 0.305098 / 0.258489 (0.046609) | 0.357445 / 0.293841 (0.063604) | 0.036129 / 0.128546 (-0.092417) | 0.012482 / 0.075646 (-0.063165) | 0.333321 / 0.419271 (-0.085951) | 0.050489 / 0.043533 (0.006956) | 0.294728 / 0.255139 (0.039589) | 0.322722 / 0.283200 (0.039523) | 0.101226 / 0.141683 (-0.040456) | 1.436787 / 1.452155 (-0.015367) | 1.515784 / 1.492716 (0.023068) |\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) | 0.291836 / 0.018006 (0.273830) | 0.550735 / 0.000490 (0.550245) | 0.003828 / 0.000200 (0.003628) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028490 / 0.037411 (-0.008922) | 0.109543 / 0.014526 (0.095017) | 0.119451 / 0.176557 (-0.057105) | 0.176721 / 0.737135 (-0.560415) | 0.126711 / 0.296338 (-0.169628) |\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) | 0.418863 / 0.215209 (0.203654) | 4.179167 / 2.077655 (2.101512) | 1.965126 / 1.504120 (0.461006) | 1.775544 / 1.541195 (0.234349) | 1.882667 / 1.468490 (0.414177) | 0.709201 / 4.584777 (-3.875576) | 3.754780 / 3.745712 (0.009068) | 2.175324 / 5.269862 (-3.094538) | 1.477454 / 4.565676 (-3.088223) | 0.085527 / 0.424275 (-0.338748) | 0.012685 / 0.007607 (0.005078) | 0.514276 / 0.226044 (0.288231) | 5.140518 / 2.268929 (2.871589) | 2.436011 / 55.444624 (-53.008614) | 2.114355 / 6.876477 (-4.762122) | 2.278893 / 2.142072 (0.136821) | 0.847825 / 4.805227 (-3.957402) | 0.169579 / 6.500664 (-6.331086) | 0.065306 / 0.075469 (-0.010163) |\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) | 1.190376 / 1.841788 (-0.651411) | 14.756581 / 8.074308 (6.682272) | 14.622610 / 10.191392 (4.431218) | 0.168186 / 0.680424 (-0.512238) | 0.017527 / 0.534201 (-0.516674) | 0.427808 / 0.579283 (-0.151475) | 0.437278 / 0.434364 (0.002914) | 0.509242 / 0.540337 (-0.031095) | 0.602500 / 1.386936 (-0.784436) |\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) | 0.007331 / 0.011353 (-0.004022) | 0.005703 / 0.011008 (-0.005305) | 0.074992 / 0.038508 (0.036484) | 0.034069 / 0.023109 (0.010960) | 0.343513 / 0.275898 (0.067615) | 0.369061 / 0.323480 (0.045582) | 0.006034 / 0.007986 (-0.001951) | 0.004344 / 0.004328 (0.000016) | 0.074678 / 0.004250 (0.070428) | 0.052262 / 0.037052 (0.015210) | 0.364758 / 0.258489 (0.106269) | 0.401130 / 0.293841 (0.107289) | 0.037635 / 0.128546 (-0.090912) | 0.012599 / 0.075646 (-0.063047) | 0.086935 / 0.419271 (-0.332337) | 0.058161 / 0.043533 (0.014628) | 0.338727 / 0.255139 (0.083589) | 0.355957 / 0.283200 (0.072757) | 0.111607 / 0.141683 (-0.030076) | 1.454357 / 1.452155 (0.002202) | 1.591529 / 1.492716 (0.098813) |\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) | 0.284379 / 0.018006 (0.266373) | 0.550720 / 0.000490 (0.550230) | 0.002868 / 0.000200 (0.002668) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028876 / 0.037411 (-0.008535) | 0.110892 / 0.014526 (0.096366) | 0.122519 / 0.176557 (-0.054038) | 0.169774 / 0.737135 (-0.567361) | 0.129381 / 0.296338 (-0.166957) |\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) | 0.429181 / 0.215209 (0.213972) | 4.251016 / 2.077655 (2.173361) | 2.056778 / 1.504120 (0.552658) | 1.860458 / 1.541195 (0.319264) | 1.958923 / 1.468490 (0.490432) | 0.712667 / 4.584777 (-3.872110) | 3.856910 / 3.745712 (0.111198) | 3.374535 / 5.269862 (-1.895327) | 1.846744 / 4.565676 (-2.718932) | 0.087238 / 0.424275 (-0.337037) | 0.012718 / 0.007607 (0.005111) | 0.524654 / 0.226044 (0.298609) | 5.209756 / 2.268929 (2.940827) | 2.494882 / 55.444624 (-52.949743) | 2.201150 / 6.876477 (-4.675327) | 2.274189 / 2.142072 (0.132117) | 0.844728 / 4.805227 (-3.960499) | 0.167467 / 6.500664 (-6.333197) | 0.064018 / 0.075469 (-0.011451) |\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) | 1.273284 / 1.841788 (-0.568503) | 15.104413 / 8.074308 (7.030105) | 15.134025 / 10.191392 (4.942633) | 0.147568 / 0.680424 (-0.532856) | 0.017429 / 0.534201 (-0.516772) | 0.422052 / 0.579283 (-0.157231) | 0.425786 / 0.434364 (-0.008578) | 0.491753 / 0.540337 (-0.048584) | 0.585091 / 1.386936 (-0.801845) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3d26e74898e0a9dc0d78490104e2e173269ef5b \"CML watermark\")\n" ]

"2023-03-15T20:02:54Z"

"2023-03-16T14:20:44Z"

"2023-03-16T14:12:55Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/5644.diff", "html_url": "https://github.com/huggingface/datasets/pull/5644", "merged_at": "2023-03-16T14:12:55Z", "patch_url": "https://github.com/huggingface/datasets/pull/5644.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5644" }

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"2021-09-10T16:07:29Z"

"2021-09-10T16:27:45Z"

"2021-09-10T16:27:44Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/2894.diff", "html_url": "https://github.com/huggingface/datasets/pull/2894", "merged_at": "2021-09-10T16:27:44Z", "patch_url": "https://github.com/huggingface/datasets/pull/2894.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2894" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I noticed this bug the other day and was going to look into it! \"Where are these processes coming from?\" ;-)" ]

"2022-11-25T15:37:42Z"

"2022-12-07T12:55:39Z"

"2022-12-07T12:52:51Z"

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[ "upgraded to php 3.9.2 and it works!" ]

"2021-02-19T22:13:05Z"

"2021-02-19T22:41:11Z"

"2021-02-19T22:40:28Z"

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[]

[ "Hi, thanks for reporting! We can add a `sources: datasets.Value(\"string\")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?", "Hi Mario,\r\n\r\nThanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric:\r\n```\r\n features=datasets.Features(\r\n {\r\n \"sources\": datasets.Value(\"string\", id=\"sequence\"),\r\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\r\n \"references\": datasets.Sequence(datasets.Value(\"string\", id=\"sequence\"), id=\"references\"),\r\n }\r\n ),\r\n```\r\n\r\nBut that only avoids a failure in `encode_batch` in the `add_batch` method:\r\n```\r\n batch = {\"predictions\": predictions, \"references\": references}\r\n batch = self.info.features.encode_batch(batch)\r\n```\r\n\r\nThe real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param:\r\n```\r\ndef add_batch(self, *, predictions=None, references=None):\r\ndef add(self, *, prediction=None, reference=None):\r\ndef compute(self, *, predictions=None, references=None, **kwargs)\r\n```\r\n\r\nAnd then, it fails:\r\n`TypeError: add_batch() got an unexpected keyword argument sources`\r\n\r\nI need this for adding any metric based on SARI or alike, not only for sari.py :)\r\n\r\nLet me know if I understood correctly the proposed solution.\r\n", "The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR." ]

"2022-03-03T18:57:54Z"

"2022-03-04T18:04:21Z"

"2022-03-04T18:04:21Z"

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"2020-12-24T12:40:14Z"

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

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

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[ "Hi @abdulelahsm did you manage to fix your issue ?\r\nFeel free to ping me if you have questions or if you're ready for a review", "@lhoestq fixed it! ready to merge. I hope haha", "merging since the CI is fixed on master" ]

"2020-12-05T22:08:46Z"

"2020-12-21T09:53:51Z"

"2020-12-21T09:53:51Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6510). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "CI errors are unrelated to the changes, so I'm merging.", "<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) | 0.005161 / 0.011353 (-0.006192) | 0.003494 / 0.011008 (-0.007515) | 0.062601 / 0.038508 (0.024093) | 0.052876 / 0.023109 (0.029767) | 0.255595 / 0.275898 (-0.020303) | 0.283108 / 0.323480 (-0.040371) | 0.003856 / 0.007986 (-0.004130) | 0.002686 / 0.004328 (-0.001642) | 0.048604 / 0.004250 (0.044353) | 0.037886 / 0.037052 (0.000834) | 0.252902 / 0.258489 (-0.005587) | 0.286906 / 0.293841 (-0.006935) | 0.028570 / 0.128546 (-0.099976) | 0.010684 / 0.075646 (-0.064962) | 0.208154 / 0.419271 (-0.211118) | 0.036169 / 0.043533 (-0.007364) | 0.276026 / 0.255139 (0.020887) | 0.272274 / 0.283200 (-0.010925) | 0.017690 / 0.141683 (-0.123993) | 1.202400 / 1.452155 (-0.249755) | 1.231223 / 1.492716 (-0.261494) |\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) | 0.095229 / 0.018006 (0.077222) | 0.302205 / 0.000490 (0.301716) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018877 / 0.037411 (-0.018534) | 0.062286 / 0.014526 (0.047760) | 0.075191 / 0.176557 (-0.101366) | 0.121419 / 0.737135 (-0.615716) | 0.075641 / 0.296338 (-0.220697) |\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) | 0.282914 / 0.215209 (0.067705) | 2.769156 / 2.077655 (0.691501) | 1.480219 / 1.504120 (-0.023901) | 1.355742 / 1.541195 (-0.185453) | 1.399740 / 1.468490 (-0.068750) | 0.556365 / 4.584777 (-4.028412) | 2.399679 / 3.745712 (-1.346033) | 2.850510 / 5.269862 (-2.419351) | 1.781428 / 4.565676 (-2.784249) | 0.063045 / 0.424275 (-0.361230) | 0.004931 / 0.007607 (-0.002676) | 0.343743 / 0.226044 (0.117698) | 3.374907 / 2.268929 (1.105978) | 1.857774 / 55.444624 (-53.586851) | 1.577154 / 6.876477 (-5.299323) | 1.626597 / 2.142072 (-0.515475) | 0.653991 / 4.805227 (-4.151236) | 0.121306 / 6.500664 (-6.379358) | 0.042131 / 0.075469 (-0.033339) |\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) | 0.948826 / 1.841788 (-0.892962) | 11.922497 / 8.074308 (3.848188) | 10.592334 / 10.191392 (0.400942) | 0.129145 / 0.680424 (-0.551279) | 0.014652 / 0.534201 (-0.519549) | 0.286074 / 0.579283 (-0.293210) | 0.265338 / 0.434364 (-0.169026) | 0.346872 / 0.540337 (-0.193466) | 0.450480 / 1.386936 (-0.936456) |\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) | 0.005305 / 0.011353 (-0.006048) | 0.003583 / 0.011008 (-0.007426) | 0.049855 / 0.038508 (0.011347) | 0.052882 / 0.023109 (0.029773) | 0.268429 / 0.275898 (-0.007469) | 0.293375 / 0.323480 (-0.030105) | 0.004052 / 0.007986 (-0.003934) | 0.002685 / 0.004328 (-0.001644) | 0.049206 / 0.004250 (0.044955) | 0.040187 / 0.037052 (0.003135) | 0.270112 / 0.258489 (0.011623) | 0.306380 / 0.293841 (0.012539) | 0.029161 / 0.128546 (-0.099386) | 0.010948 / 0.075646 (-0.064698) | 0.057721 / 0.419271 (-0.361550) | 0.032628 / 0.043533 (-0.010905) | 0.267458 / 0.255139 (0.012319) | 0.291905 / 0.283200 (0.008705) | 0.018096 / 0.141683 (-0.123587) | 1.112744 / 1.452155 (-0.339410) | 1.161962 / 1.492716 (-0.330754) |\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) | 0.097449 / 0.018006 (0.079443) | 0.304270 / 0.000490 (0.303780) | 0.000235 / 0.000200 (0.000035) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023550 / 0.037411 (-0.013861) | 0.078246 / 0.014526 (0.063720) | 0.091229 / 0.176557 (-0.085327) | 0.130624 / 0.737135 (-0.606511) | 0.092767 / 0.296338 (-0.203571) |\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) | 0.284962 / 0.215209 (0.069753) | 2.761090 / 2.077655 (0.683435) | 1.545409 / 1.504120 (0.041289) | 1.424573 / 1.541195 (-0.116622) | 1.438869 / 1.468490 (-0.029621) | 0.571281 / 4.584777 (-4.013496) | 2.419493 / 3.745712 (-1.326219) | 2.802611 / 5.269862 (-2.467251) | 1.749880 / 4.565676 (-2.815796) | 0.062566 / 0.424275 (-0.361709) | 0.005243 / 0.007607 (-0.002364) | 0.344653 / 0.226044 (0.118608) | 3.367488 / 2.268929 (1.098559) | 1.925871 / 55.444624 (-53.518754) | 1.624258 / 6.876477 (-5.252219) | 1.663742 / 2.142072 (-0.478330) | 0.634553 / 4.805227 (-4.170675) | 0.116745 / 6.500664 (-6.383919) | 0.041734 / 0.075469 (-0.033735) |\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) | 1.006808 / 1.841788 (-0.834980) | 12.499711 / 8.074308 (4.425403) | 10.956260 / 10.191392 (0.764868) | 0.132393 / 0.680424 (-0.548031) | 0.015924 / 0.534201 (-0.518277) | 0.289837 / 0.579283 (-0.289446) | 0.281565 / 0.434364 (-0.152799) | 0.337393 / 0.540337 (-0.202945) | 0.560385 / 1.386936 (-0.826551) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f699ab27ef2c0c23dc3a514b5bb155485ff6913 \"CML watermark\")\n" ]

"2023-12-18T15:34:19Z"

"2023-12-18T15:39:22Z"

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[ "Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch.\r\n\r\nHowever it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`." ]

"2021-03-17T13:51:49Z"

"2021-08-04T17:57:16Z"

"2021-08-04T17:57:16Z"

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[ "Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\r\n- `error_bad_lines` : bool, default True\r\n Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will be dropped from the DataFrame that is returned.\r\n\r\nYou could try:\r\n```python\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\r\n```\r\n" ]

"2021-02-05T00:49:03Z"

"2021-02-09T17:39:27Z"

"2021-02-09T17:39:27Z"

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[ "Should we close this PR?", "since the original issue has to do with metrics that have been moved to `evaludate` I think we can close this one", "_The documentation is not available anymore as the PR was closed or merged._" ]

"2021-12-20T16:50:49Z"

"2023-09-25T10:28:30Z"

"2023-09-25T09:20:28Z"

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[ "Thanks for the PR @iliemihai . \r\n\r\nFew comments - \r\n\r\nCan you run - \r\n`python datasets-cli dummy_data ./datasets/ronec --auto_generate` to generate dummy data.\r\n\r\nAlso, before committing files run : \r\n`make style`\r\n`flake8 datasets`\r\nthen you can add and commit files.", "> Thanks for the PR @iliemihai .\r\n> \r\n> Few comments -\r\n> \r\n> Can you run -\r\n> `python datasets-cli dummy_data ./datasets/ronec --auto_generate` to generate dummy data.\r\n> \r\n> Also, before committing files run :\r\n> `make style`\r\n> `flake8 datasets`\r\n> then you can add and commit files.\r\n\r\nSorry, forgot to generate dummy data. I will do it now :D", "Awesome, good job @iliemihai !\r\nI think the PR is ready to merge.\r\n@lhoestq would you mind double-checking this ?", "Had to regenerate the dummy data since I just found out they were empty files" ]

"2020-12-11T22:14:50Z"

"2020-12-21T14:48:56Z"

"2020-12-21T14:48:56Z"

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

"2022-06-07T10:40:54Z"

"2022-06-08T07:24:54Z"

"2022-06-08T07:16:17Z"

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"2022-02-18T00:47:55Z"

"2022-02-18T00:59:15Z"

"2022-02-18T00:59:15Z"

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[ "I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.", "Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1" ]

"2022-07-19T17:47:37Z"

"2022-07-20T06:46:57Z"

"2022-07-20T06:46:02Z"

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"2022-11-14T13:17:02Z"

"2022-11-14T15:43:01Z"

"2022-11-14T13:41:12Z"

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

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

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

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4640). All of your documentation changes will be reflected on that endpoint." ]

"2022-07-06T08:56:38Z"

"2022-07-06T15:19:55Z"

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[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note this issue was already fixed in `datasets` 2.6.0 version:\r\n- #5040\r\n\r\nIf you update your `datasets` version, you will be able to load the dataset:\r\n```\r\npip install -U datasets\r\n```", "Oops, missed that I needed to upgrade. Thanks!" ]

"2023-01-26T00:43:23Z"

"2023-01-27T05:44:31Z"

"2023-01-26T07:41:58Z"

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"2023-12-05T15:35:34Z"

"2023-12-06T08:17:34Z"

"2023-12-05T18:08:43Z"

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"2022-01-05T06:44:20Z"

"2022-01-17T15:54:36Z"

"2022-01-17T15:54:36Z"

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[ "Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)" ]

"2022-05-03T17:44:56Z"

"2022-05-04T06:58:35Z"

"2022-05-04T06:58:35Z"

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"2023-12-05T15:35:34Z"

"2023-12-06T08:17:34Z"

"2023-12-05T18:08:43Z"

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[]

[ "It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for [\"word\"](https://en.wiktionary.org/wiki/word),\r\n\r\nPronunciation\r\n([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/\r\n([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/", "Hi @i-am-neo, thanks for reporting.\r\n\r\nNormally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public.\r\n\r\nAlso note that last commit \"Add metadata\" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well.\r\n\r\n@i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.", "Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!", "All datasets are private now. \r\n\r\nRe:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)", "Thanks a lot, @cakiki.\r\n\r\n@i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets. ", "Thanks for letting me know, @albertvillanova @cakiki.\r\nAny chance of having a subset alpha version in the meantime? \r\nI only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes.\r\n\r\nWould like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. \r\n\r\n(Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!", "Hey @i-am-neo,\r\n\r\nCool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)", "@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/)\r\nYou're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file", "thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!", "thanks @patrickvonplaten. will do - getting my observations together." ]

"2022-05-02T20:34:25Z"

"2022-05-06T15:53:30Z"

"2022-05-03T11:23:48Z"

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"2020-09-07T15:59:57Z"

"2020-09-09T09:00:09Z"

"2020-09-09T09:00:08Z"

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[ "Looks great to me! ", "The new download manager is ready. I removed the old folder and I fixed a few remaining dependencies.\r\nI tested it on squad and a few others from the dataset folder and it works fine.\r\n\r\nThe only impact of these changes is that it breaks the `download_and_prepare` script that was used to register the checksums when we create a dataset, as the checksum logic is not implemented.\r\n\r\nLet me know if you have remarks", "Ok merged it (a bit fast for you to update the copyright, now I see that. but it's ok, we'll do a pass on these doc/copyright before releasing anyway)", "Actually two additional things here @lhoestq (I merged too fast sorry, let's make a new PR for additional developments):\r\n- I think we can remove some dependencies now (e.g. `promises`) in setup.py, can you have a look?\r\n- also, I think we can remove the boto3 dependency like here: https://github.com/huggingface/transformers/pull/3968" ]

"2020-04-23T16:08:07Z"

"2021-05-05T18:25:24Z"

"2020-04-25T21:25:10Z"

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"2021-10-21T08:23:01Z"

"2021-11-17T17:42:07Z"

"2021-11-17T17:42:07Z"

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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) | 0.006760 / 0.011353 (-0.004593) | 0.003918 / 0.011008 (-0.007091) | 0.084016 / 0.038508 (0.045508) | 0.069927 / 0.023109 (0.046818) | 0.307898 / 0.275898 (0.032000) | 0.337453 / 0.323480 (0.013973) | 0.004132 / 0.007986 (-0.003854) | 0.003248 / 0.004328 (-0.001081) | 0.064526 / 0.004250 (0.060275) | 0.056424 / 0.037052 (0.019371) | 0.316313 / 0.258489 (0.057824) | 0.356302 / 0.293841 (0.062461) | 0.030634 / 0.128546 (-0.097912) | 0.008467 / 0.075646 (-0.067180) | 0.286676 / 0.419271 (-0.132595) | 0.051813 / 0.043533 (0.008280) | 0.309874 / 0.255139 (0.054735) | 0.332513 / 0.283200 (0.049313) | 0.023919 / 0.141683 (-0.117764) | 1.509033 / 1.452155 (0.056878) | 1.549636 / 1.492716 (0.056920) |\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) | 0.221464 / 0.018006 (0.203458) | 0.447873 / 0.000490 (0.447384) | 0.002408 / 0.000200 (0.002208) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027634 / 0.037411 (-0.009777) | 0.081802 / 0.014526 (0.067276) | 0.781489 / 0.176557 (0.604933) | 0.165184 / 0.737135 (-0.571951) | 0.121526 / 0.296338 (-0.174813) |\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) | 0.408215 / 0.215209 (0.193006) | 4.091192 / 2.077655 (2.013538) | 2.062608 / 1.504120 (0.558488) | 1.895747 / 1.541195 (0.354552) | 1.873682 / 1.468490 (0.405192) | 0.484184 / 4.584777 (-4.100593) | 3.469096 / 3.745712 (-0.276616) | 3.365325 / 5.269862 (-1.904537) | 2.000333 / 4.565676 (-2.565343) | 0.056661 / 0.424275 (-0.367614) | 0.007100 / 0.007607 (-0.000507) | 0.478587 / 0.226044 (0.252542) | 4.768703 / 2.268929 (2.499774) | 2.472432 / 55.444624 (-52.972192) | 2.133611 / 6.876477 (-4.742865) | 2.154296 / 2.142072 (0.012223) | 0.582293 / 4.805227 (-4.222934) | 0.131932 / 6.500664 (-6.368732) | 0.060259 / 0.075469 (-0.015211) |\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) | 1.259167 / 1.841788 (-0.582620) | 18.465604 / 8.074308 (10.391296) | 14.024528 / 10.191392 (3.833136) | 0.162320 / 0.680424 (-0.518104) | 0.018144 / 0.534201 (-0.516057) | 0.389931 / 0.579283 (-0.189352) | 0.396456 / 0.434364 (-0.037908) | 0.454734 / 0.540337 (-0.085603) | 0.636406 / 1.386936 (-0.750530) |\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) | 0.006565 / 0.011353 (-0.004788) | 0.004008 / 0.011008 (-0.007000) | 0.064526 / 0.038508 (0.026018) | 0.071963 / 0.023109 (0.048854) | 0.415456 / 0.275898 (0.139557) | 0.441199 / 0.323480 (0.117719) | 0.005619 / 0.007986 (-0.002366) | 0.003261 / 0.004328 (-0.001067) | 0.064817 / 0.004250 (0.060567) | 0.055349 / 0.037052 (0.018296) | 0.425172 / 0.258489 (0.166683) | 0.452629 / 0.293841 (0.158788) | 0.031676 / 0.128546 (-0.096870) | 0.008432 / 0.075646 (-0.067214) | 0.071752 / 0.419271 (-0.347519) | 0.047176 / 0.043533 (0.003643) | 0.408641 / 0.255139 (0.153502) | 0.428579 / 0.283200 (0.145380) | 0.021548 / 0.141683 (-0.120135) | 1.495153 / 1.452155 (0.042999) | 1.557933 / 1.492716 (0.065217) |\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) | 0.212749 / 0.018006 (0.194743) | 0.441263 / 0.000490 (0.440773) | 0.005831 / 0.000200 (0.005631) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031844 / 0.037411 (-0.005567) | 0.091590 / 0.014526 (0.077064) | 0.102859 / 0.176557 (-0.073697) | 0.155859 / 0.737135 (-0.581276) | 0.104717 / 0.296338 (-0.191622) |\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) | 0.425924 / 0.215209 (0.210715) | 4.292829 / 2.077655 (2.215174) | 2.314350 / 1.504120 (0.810230) | 2.163087 / 1.541195 (0.621892) | 2.217310 / 1.468490 (0.748820) | 0.490889 / 4.584777 (-4.093887) | 3.498287 / 3.745712 (-0.247425) | 3.224980 / 5.269862 (-2.044881) | 1.987739 / 4.565676 (-2.577938) | 0.057486 / 0.424275 (-0.366790) | 0.007199 / 0.007607 (-0.000408) | 0.501194 / 0.226044 (0.275149) | 5.015202 / 2.268929 (2.746273) | 2.816307 / 55.444624 (-52.628318) | 2.474593 / 6.876477 (-4.401884) | 2.649510 / 2.142072 (0.507437) | 0.597167 / 4.805227 (-4.208060) | 0.131199 / 6.500664 (-6.369465) | 0.059532 / 0.075469 (-0.015938) |\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) | 1.384053 / 1.841788 (-0.457734) | 18.964201 / 8.074308 (10.889893) | 14.336209 / 10.191392 (4.144817) | 0.187522 / 0.680424 (-0.492902) | 0.020201 / 0.534201 (-0.514000) | 0.394778 / 0.579283 (-0.184505) | 0.408393 / 0.434364 (-0.025971) | 0.470965 / 0.540337 (-0.069373) | 0.667974 / 1.386936 (-0.718962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b3333d790800ddaa3bf386ee71dc800258c921c \"CML watermark\")\n" ]

"2023-10-19T07:53:56Z"

"2023-10-20T14:06:13Z"

"2023-10-20T13:57:39Z"

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

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

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

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

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[ "Thanks for reporting, @lewtun.\r\n\r\nIt works locally in streaming mode:\r\n```\r\n{'bid': 27681,\r\n 'is_aggregate': True,\r\n 'source': 'cliffnotes',\r\n 'chapter_path': 'all_chapterized_books/27681-chapters/chapters_1_to_2.txt',\r\n 'summary_path': 'finished_summaries/cliffnotes/The Last of the Mohicans/section_1_part_0.txt',\r\n 'book_id': 'The Last of the Mohicans.chapters 1-2',\r\n 'summary_id': 'chapters 1-2',\r\n 'content': None,\r\n 'summary': '{\"name\": \"Chapters 1-2\", \"url\": \"https://web.archive.org/web/20201101053205/https://www.cliffsnotes.com/literature/l/the-last-of-the-mohicans/summary-and-analysis/chapters-12\", \"summary\": \"Before any characters appear, the time and geography are made clear. Though it is the last war that England and France waged for a country that neither would retain, the wilderness between the forces still has to be...\r\n```\r\n\r\nI'm forcing the refresh of the preview. ", "The preview appears as expected once the refresh forced.", "Thank you @albertvillanova 🤗 !" ]

"2022-07-06T10:38:16Z"

"2022-07-06T13:25:28Z"

"2022-07-06T11:58:06Z"

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