Datasets:

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[ "(Note that the doc is aligned with the hub validation rules, and the \"ground truth\" is the hub validation rules given that they apply to all datasets, not just the canonical ones)", "Instead of our own implementation, we now use `huggingface_hub`'s `DatasetCardData`, which has the correct type hint, so I think we can close this issue." ]

"2022-08-11T14:44:45Z"

"2023-07-21T15:38:02Z"

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"2020-05-04T09:42:11Z"

"2020-05-04T19:39:07Z"

"2020-05-04T19:39:06Z"

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"2022-01-13T23:49:36Z"

"2022-01-27T14:14:38Z"

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"2020-09-18T13:51:31Z"

"2020-09-18T17:18:31Z"

"2020-09-18T17:18:30Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4778). All of your documentation changes will be reflected on that endpoint.", "I would rather have a section in the docs that explains how to modify the script of an existing dataset (`inspect_dataset` + modification + `load_dataset`) instead of focusing on the GH datasets bundled with the source (only applicable for devs).", "Good idea! I went with @mariosasko's suggestion to use `inspect_dataset` instead of cloning a dataset repository since it's a good opportunity to show off more of the library's lesser-known functions if that's ok with everyone :)", "One advantage of cloning the repo is that it fetches potential data files referenced inside a script using relative paths, so if we decide to use `inspect_dataset`, we should at least add a tip to explain this limitation and how to circumvent it.", "Oh you're right. Calling `load_dataset` on the modified script without having the files that come with it is not ideal. I agree it should be `git clone` instead - and inspect is for inspection only ^^'" ]

"2022-08-01T20:21:07Z"

"2022-08-23T16:32:26Z"

"2022-08-23T16:32:22Z"

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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) | 0.006770 / 0.011353 (-0.004583) | 0.004143 / 0.011008 (-0.006865) | 0.098928 / 0.038508 (0.060420) | 0.044893 / 0.023109 (0.021783) | 0.302630 / 0.275898 (0.026732) | 0.368173 / 0.323480 (0.044693) | 0.005631 / 0.007986 (-0.002354) | 0.003397 / 0.004328 (-0.000931) | 0.075748 / 0.004250 (0.071497) | 0.062582 / 0.037052 (0.025530) | 0.329586 / 0.258489 (0.071097) | 0.362625 / 0.293841 (0.068784) | 0.033250 / 0.128546 (-0.095296) | 0.008880 / 0.075646 (-0.066766) | 0.329683 / 0.419271 (-0.089588) | 0.054426 / 0.043533 (0.010893) | 0.297940 / 0.255139 (0.042801) | 0.319796 / 0.283200 (0.036597) | 0.023296 / 0.141683 (-0.118387) | 1.462142 / 1.452155 (0.009987) | 1.495796 / 1.492716 (0.003079) |\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) | 0.201771 / 0.018006 (0.183765) | 0.454514 / 0.000490 (0.454024) | 0.003333 / 0.000200 (0.003133) | 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) | 0.028084 / 0.037411 (-0.009327) | 0.109452 / 0.014526 (0.094926) | 0.119200 / 0.176557 (-0.057357) | 0.180302 / 0.737135 (-0.556834) | 0.125653 / 0.296338 (-0.170686) |\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) | 0.409819 / 0.215209 (0.194610) | 4.055117 / 2.077655 (1.977462) | 1.855279 / 1.504120 (0.351159) | 1.655281 / 1.541195 (0.114086) | 1.687938 / 1.468490 (0.219448) | 0.528352 / 4.584777 (-4.056425) | 3.750250 / 3.745712 (0.004538) | 3.386741 / 5.269862 (-1.883121) | 1.572036 / 4.565676 (-2.993640) | 0.065125 / 0.424275 (-0.359150) | 0.011259 / 0.007607 (0.003652) | 0.513449 / 0.226044 (0.287405) | 5.139421 / 2.268929 (2.870492) | 2.316973 / 55.444624 (-53.127651) | 1.984109 / 6.876477 (-4.892368) | 2.127915 / 2.142072 (-0.014158) | 0.653238 / 4.805227 (-4.151989) | 0.142686 / 6.500664 (-6.357978) | 0.063666 / 0.075469 (-0.011803) |\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) | 1.185174 / 1.841788 (-0.656614) | 14.790282 / 8.074308 (6.715974) | 13.089222 / 10.191392 (2.897830) | 0.146055 / 0.680424 (-0.534369) | 0.017835 / 0.534201 (-0.516366) | 0.399598 / 0.579283 (-0.179685) | 0.425296 / 0.434364 (-0.009068) | 0.478552 / 0.540337 (-0.061786) | 0.579702 / 1.386936 (-0.807234) |\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) | 0.006750 / 0.011353 (-0.004603) | 0.004156 / 0.011008 (-0.006853) | 0.074948 / 0.038508 (0.036440) | 0.043368 / 0.023109 (0.020259) | 0.355389 / 0.275898 (0.079491) | 0.429167 / 0.323480 (0.105687) | 0.003911 / 0.007986 (-0.004075) | 0.004340 / 0.004328 (0.000012) | 0.075940 / 0.004250 (0.071689) | 0.054293 / 0.037052 (0.017241) | 0.400317 / 0.258489 (0.141827) | 0.432001 / 0.293841 (0.138160) | 0.032340 / 0.128546 (-0.096206) | 0.008876 / 0.075646 (-0.066770) | 0.082284 / 0.419271 (-0.336987) | 0.050819 / 0.043533 (0.007286) | 0.351994 / 0.255139 (0.096855) | 0.375917 / 0.283200 (0.092717) | 0.022466 / 0.141683 (-0.119217) | 1.538824 / 1.452155 (0.086669) | 1.563995 / 1.492716 (0.071279) |\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) | 0.227330 / 0.018006 (0.209323) | 0.446380 / 0.000490 (0.445890) | 0.000408 / 0.000200 (0.000208) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028534 / 0.037411 (-0.008878) | 0.113467 / 0.014526 (0.098941) | 0.123590 / 0.176557 (-0.052966) | 0.174309 / 0.737135 (-0.562827) | 0.130631 / 0.296338 (-0.165707) |\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) | 0.441020 / 0.215209 (0.225811) | 4.386564 / 2.077655 (2.308909) | 2.100704 / 1.504120 (0.596584) | 1.901484 / 1.541195 (0.360289) | 1.963494 / 1.468490 (0.495004) | 0.536838 / 4.584777 (-4.047939) | 3.739071 / 3.745712 (-0.006642) | 3.278981 / 5.269862 (-1.990881) | 1.515476 / 4.565676 (-3.050201) | 0.066388 / 0.424275 (-0.357887) | 0.011857 / 0.007607 (0.004250) | 0.545507 / 0.226044 (0.319463) | 5.441479 / 2.268929 (3.172550) | 2.602144 / 55.444624 (-52.842480) | 2.235583 / 6.876477 (-4.640894) | 2.293458 / 2.142072 (0.151385) | 0.658535 / 4.805227 (-4.146692) | 0.141327 / 6.500664 (-6.359337) | 0.063726 / 0.075469 (-0.011743) |\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) | 1.247819 / 1.841788 (-0.593968) | 15.234524 / 8.074308 (7.160216) | 14.592700 / 10.191392 (4.401308) | 0.141952 / 0.680424 (-0.538472) | 0.017747 / 0.534201 (-0.516454) | 0.396819 / 0.579283 (-0.182465) | 0.415902 / 0.434364 (-0.018462) | 0.464619 / 0.540337 (-0.075718) | 0.560866 / 1.386936 (-0.826070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"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) | 0.008278 / 0.011353 (-0.003075) | 0.005044 / 0.011008 (-0.005964) | 0.123382 / 0.038508 (0.084874) | 0.054039 / 0.023109 (0.030929) | 0.382338 / 0.275898 (0.106440) | 0.453287 / 0.323480 (0.129807) | 0.006342 / 0.007986 (-0.001644) | 0.003930 / 0.004328 (-0.000398) | 0.094039 / 0.004250 (0.089789) | 0.076525 / 0.037052 (0.039472) | 0.394066 / 0.258489 (0.135577) | 0.445600 / 0.293841 (0.151759) | 0.039348 / 0.128546 (-0.089199) | 0.010485 / 0.075646 (-0.065161) | 0.433730 / 0.419271 (0.014459) | 0.082671 / 0.043533 (0.039138) | 0.375250 / 0.255139 (0.120111) | 0.416269 / 0.283200 (0.133070) | 0.038397 / 0.141683 (-0.103286) | 1.864834 / 1.452155 (0.412680) | 2.010453 / 1.492716 (0.517737) |\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) | 0.240008 / 0.018006 (0.222002) | 0.470975 / 0.000490 (0.470485) | 0.004001 / 0.000200 (0.003801) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031107 / 0.037411 (-0.006304) | 0.129371 / 0.014526 (0.114846) | 0.141559 / 0.176557 (-0.034997) | 0.205571 / 0.737135 (-0.531564) | 0.144611 / 0.296338 (-0.151728) |\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) | 0.506972 / 0.215209 (0.291763) | 5.055951 / 2.077655 (2.978296) | 2.397438 / 1.504120 (0.893318) | 2.170435 / 1.541195 (0.629240) | 2.240296 / 1.468490 (0.771806) | 0.641559 / 4.584777 (-3.943218) | 4.644772 / 3.745712 (0.899060) | 4.064200 / 5.269862 (-1.205662) | 1.946991 / 4.565676 (-2.618685) | 0.086413 / 0.424275 (-0.337862) | 0.015082 / 0.007607 (0.007475) | 0.670413 / 0.226044 (0.444369) | 6.331346 / 2.268929 (4.062418) | 2.965813 / 55.444624 (-52.478812) | 2.547952 / 6.876477 (-4.328524) | 2.718390 / 2.142072 (0.576318) | 0.796657 / 4.805227 (-4.008571) | 0.173229 / 6.500664 (-6.327435) | 0.079606 / 0.075469 (0.004137) |\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) | 1.568761 / 1.841788 (-0.273026) | 18.485432 / 8.074308 (10.411124) | 15.758513 / 10.191392 (5.567121) | 0.170427 / 0.680424 (-0.509997) | 0.021421 / 0.534201 (-0.512780) | 0.518623 / 0.579283 (-0.060660) | 0.525887 / 0.434364 (0.091523) | 0.640331 / 0.540337 (0.099993) | 0.766748 / 1.386936 (-0.620188) |\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) | 0.007680 / 0.011353 (-0.003673) | 0.005289 / 0.011008 (-0.005719) | 0.093773 / 0.038508 (0.055265) | 0.054997 / 0.023109 (0.031888) | 0.456277 / 0.275898 (0.180379) | 0.500642 / 0.323480 (0.177162) | 0.005935 / 0.007986 (-0.002050) | 0.004375 / 0.004328 (0.000047) | 0.094131 / 0.004250 (0.089881) | 0.063399 / 0.037052 (0.026347) | 0.470546 / 0.258489 (0.212057) | 0.504989 / 0.293841 (0.211148) | 0.038541 / 0.128546 (-0.090006) | 0.010403 / 0.075646 (-0.065244) | 0.102469 / 0.419271 (-0.316802) | 0.063105 / 0.043533 (0.019572) | 0.466005 / 0.255139 (0.210866) | 0.458677 / 0.283200 (0.175477) | 0.028407 / 0.141683 (-0.113276) | 1.893829 / 1.452155 (0.441675) | 1.917954 / 1.492716 (0.425238) |\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) | 0.272760 / 0.018006 (0.254754) | 0.476159 / 0.000490 (0.475669) | 0.008467 / 0.000200 (0.008267) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035755 / 0.037411 (-0.001656) | 0.145038 / 0.014526 (0.130512) | 0.148322 / 0.176557 (-0.028235) | 0.210193 / 0.737135 (-0.526943) | 0.156547 / 0.296338 (-0.139792) |\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) | 0.541204 / 0.215209 (0.325995) | 5.382746 / 2.077655 (3.305091) | 2.704229 / 1.504120 (1.200109) | 2.468422 / 1.541195 (0.927227) | 2.522672 / 1.468490 (1.054182) | 0.644899 / 4.584777 (-3.939878) | 4.654401 / 3.745712 (0.908689) | 2.159223 / 5.269862 (-3.110638) | 1.280098 / 4.565676 (-3.285578) | 0.080053 / 0.424275 (-0.344222) | 0.014383 / 0.007607 (0.006776) | 0.662770 / 0.226044 (0.436725) | 6.617651 / 2.268929 (4.348722) | 3.234347 / 55.444624 (-52.210277) | 2.861417 / 6.876477 (-4.015059) | 2.888928 / 2.142072 (0.746856) | 0.792854 / 4.805227 (-4.012374) | 0.172553 / 6.500664 (-6.328111) | 0.078402 / 0.075469 (0.002933) |\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) | 1.565351 / 1.841788 (-0.276436) | 18.681916 / 8.074308 (10.607608) | 17.264473 / 10.191392 (7.073081) | 0.168461 / 0.680424 (-0.511963) | 0.021353 / 0.534201 (-0.512848) | 0.517843 / 0.579283 (-0.061440) | 0.519907 / 0.434364 (0.085543) | 0.623687 / 0.540337 (0.083350) | 0.761796 / 1.386936 (-0.625140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"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) | 0.006750 / 0.011353 (-0.004603) | 0.004268 / 0.011008 (-0.006741) | 0.098644 / 0.038508 (0.060136) | 0.044643 / 0.023109 (0.021534) | 0.309420 / 0.275898 (0.033522) | 0.379294 / 0.323480 (0.055815) | 0.005729 / 0.007986 (-0.002256) | 0.003615 / 0.004328 (-0.000714) | 0.076086 / 0.004250 (0.071835) | 0.068994 / 0.037052 (0.031942) | 0.325653 / 0.258489 (0.067164) | 0.375187 / 0.293841 (0.081347) | 0.032546 / 0.128546 (-0.096000) | 0.009089 / 0.075646 (-0.066557) | 0.329905 / 0.419271 (-0.089366) | 0.066832 / 0.043533 (0.023300) | 0.299247 / 0.255139 (0.044108) | 0.323460 / 0.283200 (0.040260) | 0.034226 / 0.141683 (-0.107457) | 1.475659 / 1.452155 (0.023505) | 1.556234 / 1.492716 (0.063518) |\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) | 0.292305 / 0.018006 (0.274299) | 0.542584 / 0.000490 (0.542094) | 0.003047 / 0.000200 (0.002847) | 0.000082 / 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) | 0.030096 / 0.037411 (-0.007315) | 0.112341 / 0.014526 (0.097815) | 0.124965 / 0.176557 (-0.051591) | 0.183159 / 0.737135 (-0.553976) | 0.131885 / 0.296338 (-0.164453) |\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) | 0.426437 / 0.215209 (0.211228) | 4.260984 / 2.077655 (2.183330) | 2.078358 / 1.504120 (0.574238) | 1.877644 / 1.541195 (0.336449) | 2.044036 / 1.468490 (0.575546) | 0.532980 / 4.584777 (-4.051797) | 3.749573 / 3.745712 (0.003860) | 1.944155 / 5.269862 (-3.325706) | 1.090307 / 4.565676 (-3.475370) | 0.065445 / 0.424275 (-0.358830) | 0.011237 / 0.007607 (0.003630) | 0.521448 / 0.226044 (0.295403) | 5.213118 / 2.268929 (2.944189) | 2.507829 / 55.444624 (-52.936795) | 2.177179 / 6.876477 (-4.699297) | 2.351161 / 2.142072 (0.209088) | 0.656775 / 4.805227 (-4.148452) | 0.141207 / 6.500664 (-6.359457) | 0.063286 / 0.075469 (-0.012183) |\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) | 1.190281 / 1.841788 (-0.651506) | 15.327424 / 8.074308 (7.253116) | 13.300695 / 10.191392 (3.109303) | 0.190484 / 0.680424 (-0.489939) | 0.017984 / 0.534201 (-0.516217) | 0.405714 / 0.579283 (-0.173569) | 0.435915 / 0.434364 (0.001551) | 0.494083 / 0.540337 (-0.046254) | 0.600616 / 1.386936 (-0.786320) |\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) | 0.006740 / 0.011353 (-0.004613) | 0.004289 / 0.011008 (-0.006719) | 0.076532 / 0.038508 (0.038024) | 0.043305 / 0.023109 (0.020196) | 0.356111 / 0.275898 (0.080213) | 0.434121 / 0.323480 (0.110641) | 0.005599 / 0.007986 (-0.002387) | 0.003461 / 0.004328 (-0.000868) | 0.077097 / 0.004250 (0.072847) | 0.055369 / 0.037052 (0.018317) | 0.367093 / 0.258489 (0.108604) | 0.418801 / 0.293841 (0.124960) | 0.032057 / 0.128546 (-0.096489) | 0.009048 / 0.075646 (-0.066599) | 0.082897 / 0.419271 (-0.336374) | 0.050287 / 0.043533 (0.006754) | 0.352060 / 0.255139 (0.096921) | 0.376278 / 0.283200 (0.093078) | 0.023924 / 0.141683 (-0.117759) | 1.522780 / 1.452155 (0.070626) | 1.578938 / 1.492716 (0.086222) |\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) | 0.287317 / 0.018006 (0.269311) | 0.508490 / 0.000490 (0.508000) | 0.000431 / 0.000200 (0.000231) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031139 / 0.037411 (-0.006272) | 0.113927 / 0.014526 (0.099401) | 0.128147 / 0.176557 (-0.048409) | 0.179712 / 0.737135 (-0.557424) | 0.134364 / 0.296338 (-0.161975) |\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) | 0.452834 / 0.215209 (0.237625) | 4.507944 / 2.077655 (2.430289) | 2.287758 / 1.504120 (0.783638) | 2.091145 / 1.541195 (0.549951) | 2.196228 / 1.468490 (0.727738) | 0.539306 / 4.584777 (-4.045471) | 3.838941 / 3.745712 (0.093228) | 1.908801 / 5.269862 (-3.361060) | 1.139235 / 4.565676 (-3.426442) | 0.066677 / 0.424275 (-0.357599) | 0.011422 / 0.007607 (0.003815) | 0.562966 / 0.226044 (0.336921) | 5.633712 / 2.268929 (3.364784) | 2.788622 / 55.444624 (-52.656002) | 2.438465 / 6.876477 (-4.438012) | 2.523479 / 2.142072 (0.381407) | 0.668730 / 4.805227 (-4.136498) | 0.143977 / 6.500664 (-6.356687) | 0.064661 / 0.075469 (-0.010808) |\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) | 1.291708 / 1.841788 (-0.550080) | 15.573316 / 8.074308 (7.499008) | 14.435099 / 10.191392 (4.243707) | 0.147745 / 0.680424 (-0.532679) | 0.017602 / 0.534201 (-0.516599) | 0.401560 / 0.579283 (-0.177723) | 0.429861 / 0.434364 (-0.004502) | 0.469800 / 0.540337 (-0.070538) | 0.567515 / 1.386936 (-0.819421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]

"2023-06-20T14:28:35Z"

"2023-06-21T13:39:50Z"

"2023-06-21T13:32:22Z"

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[ "Sure, it's on the roadmap.", "Closing in favor of https://github.com/huggingface/datasets-server/issues/39." ]

"2022-01-19T19:29:26Z"

"2022-09-26T08:04:43Z"

"2022-09-26T08:04:43Z"

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[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012516 / 0.011353 (0.001163) | 0.004725 / 0.011008 (-0.006283) | 0.112245 / 0.038508 (0.073736) | 0.079146 / 0.023109 (0.056037) | 0.386415 / 0.275898 (0.110517) | 0.420441 / 0.323480 (0.096961) | 0.005682 / 0.007986 (-0.002304) | 0.004169 / 0.004328 (-0.000160) | 0.077847 / 0.004250 (0.073597) | 0.055763 / 0.037052 (0.018711) | 0.385529 / 0.258489 (0.127040) | 0.422711 / 0.293841 (0.128870) | 0.047212 / 0.128546 (-0.081334) | 0.013711 / 0.075646 (-0.061935) | 0.342856 / 0.419271 (-0.076416) | 0.066788 / 0.043533 (0.023255) | 0.380728 / 0.255139 (0.125589) | 0.416241 / 0.283200 (0.133041) | 0.034676 / 0.141683 (-0.107007) | 1.679661 / 1.452155 (0.227506) | 1.838014 / 1.492716 (0.345297) |\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) | 0.219556 / 0.018006 (0.201550) | 0.524728 / 0.000490 (0.524238) | 0.005045 / 0.000200 (0.004845) | 0.000124 / 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) | 0.025475 / 0.037411 (-0.011936) | 0.085937 / 0.014526 (0.071412) | 0.099245 / 0.176557 (-0.077311) | 0.158995 / 0.737135 (-0.578141) | 0.101504 / 0.296338 (-0.194835) |\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) | 0.582200 / 0.215209 (0.366991) | 5.794340 / 2.077655 (3.716685) | 2.473635 / 1.504120 (0.969515) | 2.168135 / 1.541195 (0.626941) | 2.215886 / 1.468490 (0.747396) | 0.855599 / 4.584777 (-3.729178) | 5.003067 / 3.745712 (1.257354) | 4.503566 / 5.269862 (-0.766295) | 2.912248 / 4.565676 (-1.653428) | 0.103267 / 0.424275 (-0.321008) | 0.012114 / 0.007607 (0.004507) | 0.712240 / 0.226044 (0.486196) | 7.131946 / 2.268929 (4.863017) | 3.280052 / 55.444624 (-52.164573) | 2.583472 / 6.876477 (-4.293004) | 2.820758 / 2.142072 (0.678686) | 1.132097 / 4.805227 (-3.673131) | 0.232191 / 6.500664 (-6.268473) | 0.082966 / 0.075469 (0.007497) |\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) | 1.581125 / 1.841788 (-0.260662) | 22.723878 / 8.074308 (14.649570) | 19.969347 / 10.191392 (9.777955) | 0.234365 / 0.680424 (-0.446059) | 0.030245 / 0.534201 (-0.503956) | 0.470843 / 0.579283 (-0.108440) | 0.558069 / 0.434364 (0.123705) | 0.534878 / 0.540337 (-0.005460) | 0.801025 / 1.386936 (-0.585911) |\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) | 0.008524 / 0.011353 (-0.002829) | 0.005083 / 0.011008 (-0.005925) | 0.078054 / 0.038508 (0.039546) | 0.082025 / 0.023109 (0.058915) | 0.458027 / 0.275898 (0.182129) | 0.498232 / 0.323480 (0.174752) | 0.005938 / 0.007986 (-0.002048) | 0.003776 / 0.004328 (-0.000553) | 0.080413 / 0.004250 (0.076163) | 0.060485 / 0.037052 (0.023433) | 0.462816 / 0.258489 (0.204327) | 0.513970 / 0.293841 (0.220129) | 0.047574 / 0.128546 (-0.080973) | 0.013424 / 0.075646 (-0.062222) | 0.087707 / 0.419271 (-0.331565) | 0.065007 / 0.043533 (0.021474) | 0.465844 / 0.255139 (0.210705) | 0.498474 / 0.283200 (0.215274) | 0.033518 / 0.141683 (-0.108164) | 1.737507 / 1.452155 (0.285352) | 1.848291 / 1.492716 (0.355574) |\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) | 0.316710 / 0.018006 (0.298703) | 0.504415 / 0.000490 (0.503925) | 0.042128 / 0.000200 (0.041928) | 0.000171 / 0.000054 (0.000117) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032097 / 0.037411 (-0.005314) | 0.099371 / 0.014526 (0.084845) | 0.109311 / 0.176557 (-0.067246) | 0.177373 / 0.737135 (-0.559762) | 0.110753 / 0.296338 (-0.185585) |\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) | 0.688060 / 0.215209 (0.472851) | 6.255219 / 2.077655 (4.177564) | 2.696845 / 1.504120 (1.192725) | 2.395424 / 1.541195 (0.854230) | 2.414870 / 1.468490 (0.946380) | 0.865704 / 4.584777 (-3.719073) | 5.086828 / 3.745712 (1.341116) | 4.648107 / 5.269862 (-0.621754) | 3.091119 / 4.565676 (-1.474558) | 0.101787 / 0.424275 (-0.322489) | 0.008829 / 0.007607 (0.001222) | 0.772398 / 0.226044 (0.546354) | 7.700366 / 2.268929 (5.431438) | 3.608632 / 55.444624 (-51.835992) | 2.923309 / 6.876477 (-3.953168) | 2.952141 / 2.142072 (0.810069) | 1.093006 / 4.805227 (-3.712221) | 0.224363 / 6.500664 (-6.276301) | 0.074927 / 0.075469 (-0.000542) |\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) | 1.638414 / 1.841788 (-0.203374) | 23.486781 / 8.074308 (15.412473) | 21.129104 / 10.191392 (10.937712) | 0.259955 / 0.680424 (-0.420469) | 0.027305 / 0.534201 (-0.506895) | 0.464448 / 0.579283 (-0.114835) | 0.553737 / 0.434364 (0.119373) | 0.571318 / 0.540337 (0.030981) | 0.772917 / 1.386936 (-0.614019) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ec5ee9e78b464364796651d995823c7ecb0f951 \"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) | 0.009093 / 0.011353 (-0.002260) | 0.005283 / 0.011008 (-0.005725) | 0.112299 / 0.038508 (0.073791) | 0.081341 / 0.023109 (0.058232) | 0.363799 / 0.275898 (0.087901) | 0.409261 / 0.323480 (0.085781) | 0.006400 / 0.007986 (-0.001586) | 0.003965 / 0.004328 (-0.000363) | 0.074389 / 0.004250 (0.070139) | 0.060654 / 0.037052 (0.023602) | 0.391046 / 0.258489 (0.132557) | 0.430514 / 0.293841 (0.136673) | 0.054900 / 0.128546 (-0.073646) | 0.017972 / 0.075646 (-0.057675) | 0.410875 / 0.419271 (-0.008396) | 0.067405 / 0.043533 (0.023873) | 0.371468 / 0.255139 (0.116329) | 0.435061 / 0.283200 (0.151861) | 0.038063 / 0.141683 (-0.103620) | 1.733509 / 1.452155 (0.281354) | 1.833899 / 1.492716 (0.341182) |\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) | 0.243230 / 0.018006 (0.225224) | 0.605636 / 0.000490 (0.605146) | 0.004890 / 0.000200 (0.004690) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027624 / 0.037411 (-0.009787) | 0.084799 / 0.014526 (0.070273) | 0.104405 / 0.176557 (-0.072152) | 0.165383 / 0.737135 (-0.571752) | 0.102083 / 0.296338 (-0.194255) |\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) | 0.578334 / 0.215209 (0.363125) | 5.369520 / 2.077655 (3.291866) | 2.294174 / 1.504120 (0.790055) | 2.054195 / 1.541195 (0.513000) | 2.007304 / 1.468490 (0.538814) | 0.839283 / 4.584777 (-3.745494) | 5.262288 / 3.745712 (1.516576) | 4.363346 / 5.269862 (-0.906516) | 2.854903 / 4.565676 (-1.710773) | 0.096975 / 0.424275 (-0.327300) | 0.008237 / 0.007607 (0.000630) | 0.646746 / 0.226044 (0.420702) | 6.250621 / 2.268929 (3.981693) | 2.900377 / 55.444624 (-52.544247) | 2.283238 / 6.876477 (-4.593239) | 2.443785 / 2.142072 (0.301713) | 0.991719 / 4.805227 (-3.813508) | 0.189755 / 6.500664 (-6.310909) | 0.067906 / 0.075469 (-0.007563) |\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) | 1.515563 / 1.841788 (-0.326225) | 21.956499 / 8.074308 (13.882191) | 19.161750 / 10.191392 (8.970358) | 0.238199 / 0.680424 (-0.442225) | 0.026771 / 0.534201 (-0.507430) | 0.450195 / 0.579283 (-0.129088) | 0.585168 / 0.434364 (0.150804) | 0.522945 / 0.540337 (-0.017393) | 0.776244 / 1.386936 (-0.610693) |\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) | 0.007997 / 0.011353 (-0.003356) | 0.005021 / 0.011008 (-0.005988) | 0.087308 / 0.038508 (0.048800) | 0.077760 / 0.023109 (0.054650) | 0.425313 / 0.275898 (0.149415) | 0.451470 / 0.323480 (0.127990) | 0.006848 / 0.007986 (-0.001137) | 0.004812 / 0.004328 (0.000484) | 0.071198 / 0.004250 (0.066947) | 0.058325 / 0.037052 (0.021273) | 0.427411 / 0.258489 (0.168922) | 0.466069 / 0.293841 (0.172228) | 0.048686 / 0.128546 (-0.079861) | 0.011841 / 0.075646 (-0.063806) | 0.086225 / 0.419271 (-0.333047) | 0.060500 / 0.043533 (0.016967) | 0.435580 / 0.255139 (0.180441) | 0.456919 / 0.283200 (0.173719) | 0.035094 / 0.141683 (-0.106588) | 1.582805 / 1.452155 (0.130650) | 1.717838 / 1.492716 (0.225122) |\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) | 0.283967 / 0.018006 (0.265960) | 0.517496 / 0.000490 (0.517006) | 0.014747 / 0.000200 (0.014547) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027870 / 0.037411 (-0.009541) | 0.083835 / 0.014526 (0.069309) | 0.099157 / 0.176557 (-0.077400) | 0.173210 / 0.737135 (-0.563925) | 0.094212 / 0.296338 (-0.202127) |\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) | 0.535720 / 0.215209 (0.320511) | 5.273730 / 2.077655 (3.196075) | 2.422560 / 1.504120 (0.918440) | 2.131416 / 1.541195 (0.590222) | 2.192000 / 1.468490 (0.723510) | 0.708469 / 4.584777 (-3.876308) | 4.758092 / 3.745712 (1.012380) | 3.940729 / 5.269862 (-1.329133) | 2.553093 / 4.565676 (-2.012583) | 0.084895 / 0.424275 (-0.339380) | 0.008730 / 0.007607 (0.001123) | 0.646975 / 0.226044 (0.420930) | 6.294811 / 2.268929 (4.025883) | 3.293964 / 55.444624 (-52.150660) | 2.568985 / 6.876477 (-4.307492) | 2.743786 / 2.142072 (0.601713) | 0.899733 / 4.805227 (-3.905494) | 0.193484 / 6.500664 (-6.307181) | 0.070012 / 0.075469 (-0.005457) |\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) | 1.502255 / 1.841788 (-0.339532) | 20.690234 / 8.074308 (12.615926) | 18.375791 / 10.191392 (8.184399) | 0.200135 / 0.680424 (-0.480289) | 0.029434 / 0.534201 (-0.504767) | 0.477267 / 0.579283 (-0.102016) | 0.566869 / 0.434364 (0.132505) | 0.543756 / 0.540337 (0.003418) | 0.700476 / 1.386936 (-0.686460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef17d9fd6c648bb41d43ba301c3de4d7b6f833d8 \"CML watermark\")\n" ]

"2023-08-03T14:46:04Z"

"2023-08-03T14:56:59Z"

"2023-08-03T14:46:18Z"

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[ "Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640", "The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.", "For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred. \r\n\r\nUpdating to `datasets==2.12.0` fixed the problem for me." ]

"2023-01-22T23:50:12Z"

"2023-05-23T10:35:48Z"

"2023-02-10T16:31:36Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Are the `metadata JSON file` not on their way to deprecation? 😆😇\r\n\r\nIMO, more generally than this particular PR, the contribution process should be simplified now that many validation checks happen on the hub side.\r\n\r\nKeeping this open in the meantime to get more potential feedback!" ]

"2022-08-09T10:21:14Z"

"2022-09-23T11:35:38Z"

"2022-09-23T11:33:44Z"

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"2020-12-07T13:06:12Z"

"2020-12-08T14:33:29Z"

"2020-12-08T14:33:29Z"

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"2022-01-31T09:35:07Z"

"2022-01-31T09:35:07Z"

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"2021-07-09T13:19:35Z"

"2021-07-12T14:12:57Z"

"2021-07-09T13:50:05Z"

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[ "I am highly interested performance of dataset so I ran your example as a curious user.\r\n```python\r\ntrain_dataset.cast_column(\"x\", Array3D(shape=img_shape, dtype=\"float32\"))\r\n```\r\nhave return values and \"x\" is a new column, it shoulde be\r\n```python\r\nds=train_dataset.cast_column(\"img\", Array3D(shape=(3,32,32), dtype=\"float32\"))\r\n```\r\nI rewrite your example as\r\n```python\r\ntrain_dataset = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\ntransform_func = torchvision.transforms.Compose([\r\n ToTensor(), \r\n Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] \r\n)\r\n \r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"img\": transform_func(x[\"img\"])},\r\n)\r\nds=train_dataset.cast_column(\"img\", Array3D(shape=(3,32,32), dtype=\"float32\"))\r\nfor i in tqdm(ds):\r\n pass\r\n```\r\nthat require ~11s in my environment. While\r\n```python\r\nds = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\n\r\nfor i in tqdm(ds):\r\n pass\r\n```\r\nonly need ~6s. (So I guess it's still undesirable)" ]

"2023-05-15T16:43:58Z"

"2023-05-16T03:27:38Z"

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[ "Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?", "Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how the `ArrowWriter` functions, I think we can implement this as follows:\r\n\r\n1. First, we would have to update the `ArrowWriter.write()` function here:\r\nhttps://github.com/huggingface/datasets/blob/fcd3c3c8e3b1d9a2f3686a496082e21f06591380/src/datasets/arrow_writer.py#L296\r\nso that it accepts an additional argument `key` which would be appended along with the example here after hashing.\r\n\r\n2. Then, we would need to create a `Hasher` class which will take the key as its input and return a hash for it (We might need to use some hash salt which can be passed to the ArrowWriter.writer() with value equal to the `split_name` for differentiating between same keys of different splits)\r\n\r\n We can use the `hashlib.md5` function for hashing which will conert each key to its byte code before hashing (depending on the data type of the key) **Thus, the `key` type will be verified here**.\r\n\r\n3. Now, we would have to edit this\r\nhttps://github.com/huggingface/datasets/blob/fcd3c3c8e3b1d9a2f3686a496082e21f06591380/src/datasets/arrow_writer.py#L257\r\n so that it iterates over each `(hash, example)` pair (sorted according to hash). We can then simply **check whether each hash is different from the previous hash** (since they will be sorted)\r\n\r\nHowever, since I'm not very familiar with how the data is being written on disk in the form of a table, I might need some guidance for Step 3. \r\nPlease let me know your thought on this. Thanks!", "Interesting !\r\nWe keep the dataset sorted in the order examples are generated by the builder (we expect the dataset builders to generate examples in deterministic order). Therefore I don't think we should shuffle the examples with the hashing. Let me know what you think.\r\nOther that that, I really like the idea of checking for keys duplicates in `write_examples_on_file` :)\r\n\r\nThis looks like a great plan ! Feel free to open a PR and ping me if you have questions or if I can help\r\n", "@lhoestq I'm glad you liked the idea!\r\nI think that since the keys will be unique and deterministic in the nature themselves, so even if we shuffle the examples according to the hash, a deterministic order would still be maintained (as the keys will always have the same hash, whenever the dataset is generated). \r\nAnd since, we are not dealing with time series data (which would require the data to be in original order), I don't think the order of examples would matter much, as long as the order is deterministic and constant for all users.\r\n\r\nI think that this is also what was originally envisioned as mentioned in the documentation here:\r\nhttps://github.com/huggingface/datasets/blob/6775661b19d2ec339784f3d84553a3996a1d86c3/src/datasets/builder.py#L973\r\n\r\nAlso, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\nLet me know your thoughts in it! I would be opening a PR soon :)", "When users load their own data, they expect the order to stay the same. I think that shuffling the data can make things inconvenient.\r\n\r\n> I think that this is also what was originally envisioned as mentioned in the documentation here:\r\n\r\nThis part was originally developed by tensorflow datasets, and tensorflow datasets indeed does the shuffling. However in this library this is probably not what we want in the general case. But if @albertvillanova and @thomwolf you have opinions on this please let us know.\r\n\r\n> Also, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\n\r\nMaybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch, but there might still be duplicates across batches. For 10 000 examples the hashes can just be stored as a python `set`.\r\n\r\nOtherwise if we want full deduplication, we need an extra tool that allows to temporarily save and query hashes that may need to use disk space rather than memory.", "Yes I think we want to keep the original order by default and only shuffle when the user ask for it (for instance by calling `dataset.shuffle()`). That’s how I had it in mind originally.", "Hey @lhoestq, I just had a more in-depth look at the original TFDS code about why the keys and hash were used in the first place.\r\n\r\nIn my opinion, the only use that the `hash(key)` serves is that it allows us to shuffle the examples in a deterministic order (as each example will always yield the same key and thus, the same hash on every system) so that the same dataset is generated for each user, irrespective of the order the examples are yielded by the dataset builder on different user systems.\r\n\r\nOtherwise, if we are not shuffling, then while yielding and writing the data, after getting the key and hashing it for an example, I can't quite see the use of the hash or the key. The hash will simply be generated for each example but not actually used anywhere?\r\n\r\n@lhoestq @thomwolf It would be great if you could explain a bit more about the usage of keys. Thanks!\r\n", "In `datasets` the keys are currently ignored.\r\nFor shuffling we don't use the keys. Instead we shuffle an array of indices. Since both the original order of the dataset and the indices shuffling are deterministic, then `dataset.shuffle` is deterministic as well.\r\nWe can use it to:\r\n1. detect duplicates\r\n2. verify that the generation order is indeed deterministic\r\n3. maybe more ?", "Thanks a lot @lhoestq. I think I understand what we need to do now. The keys can indeed be used for detecting duplicates in generated examples as well as ensuring the order.\r\n\r\n> Maybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch,\r\n\r\nI think that checking for duplicates in every batch independently would be sufficient as the probability of collisions using something like `MD5` is very low. I would be opening a draft PR soon. It would be great to have your guidance. Thanks!" ]

"2021-04-16T13:29:47Z"

"2021-05-10T17:31:21Z"

"2021-05-10T17:31:21Z"

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[ "Hi ! This sounds related to https://github.com/huggingface/datasets/issues/5406\r\n\r\nUpdating `datasets` fixes the issue ;)", "Thanks @lhoestq!\r\n\r\nI'll close this issue now." ]

"2023-03-25T18:06:15Z"

"2023-03-27T22:54:56Z"

"2023-03-27T22:54:56Z"

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"2020-12-07T14:00:41Z"

"2020-12-08T14:58:28Z"

"2020-12-08T14:42:51Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5212). All of your documentation changes will be reflected on that endpoint." ]

"2022-11-08T07:30:01Z"

"2022-11-15T06:32:27Z"

"2022-11-15T06:32:26Z"

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[ "Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?", "Ok, I redirected on our side to a new url. ⚠️ @srush: if you update the Streamlit config too to `/datasets/viewer`, let me know because I'll need to change our nginx config at the same time", "9", "‏⠀‏‏‏⠀‏‏‏⠀ ‏⠀ ", "‏⠀‏‏‏⠀‏‏‏⠀ ‏⠀ " ]

"2020-11-27T09:14:34Z"

"2021-10-31T09:12:01Z"

"2021-10-31T09:12:01Z"

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[ "good catch!", "_The documentation is not available anymore as the PR was closed or merged._", "The test fail is unrelated to this PR and fixed on `main` - merging :)", "<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) | 0.008942 / 0.011353 (-0.002411) | 0.004617 / 0.011008 (-0.006391) | 0.101310 / 0.038508 (0.062802) | 0.030997 / 0.023109 (0.007888) | 0.306292 / 0.275898 (0.030394) | 0.370533 / 0.323480 (0.047053) | 0.007318 / 0.007986 (-0.000667) | 0.003473 / 0.004328 (-0.000856) | 0.078557 / 0.004250 (0.074307) | 0.036312 / 0.037052 (-0.000740) | 0.308993 / 0.258489 (0.050504) | 0.344411 / 0.293841 (0.050570) | 0.034384 / 0.128546 (-0.094162) | 0.011631 / 0.075646 (-0.064016) | 0.323948 / 0.419271 (-0.095324) | 0.041176 / 0.043533 (-0.002357) | 0.302512 / 0.255139 (0.047373) | 0.322439 / 0.283200 (0.039239) | 0.088955 / 0.141683 (-0.052728) | 1.534918 / 1.452155 (0.082763) | 1.555803 / 1.492716 (0.063087) |\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) | 0.195639 / 0.018006 (0.177633) | 0.423068 / 0.000490 (0.422579) | 0.004101 / 0.000200 (0.003901) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023691 / 0.037411 (-0.013721) | 0.100536 / 0.014526 (0.086011) | 0.108399 / 0.176557 (-0.068157) | 0.143515 / 0.737135 (-0.593620) | 0.111886 / 0.296338 (-0.184452) |\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) | 0.417519 / 0.215209 (0.202310) | 4.180463 / 2.077655 (2.102808) | 1.862511 / 1.504120 (0.358391) | 1.658724 / 1.541195 (0.117529) | 1.735847 / 1.468490 (0.267357) | 0.688257 / 4.584777 (-3.896520) | 3.447976 / 3.745712 (-0.297737) | 1.877939 / 5.269862 (-3.391922) | 1.157385 / 4.565676 (-3.408292) | 0.081418 / 0.424275 (-0.342857) | 0.012395 / 0.007607 (0.004788) | 0.518935 / 0.226044 (0.292891) | 5.220355 / 2.268929 (2.951427) | 2.308355 / 55.444624 (-53.136269) | 1.960026 / 6.876477 (-4.916450) | 2.013179 / 2.142072 (-0.128893) | 0.802850 / 4.805227 (-4.002377) | 0.146941 / 6.500664 (-6.353723) | 0.064080 / 0.075469 (-0.011389) |\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) | 1.284443 / 1.841788 (-0.557344) | 13.903755 / 8.074308 (5.829447) | 14.467101 / 10.191392 (4.275709) | 0.156813 / 0.680424 (-0.523611) | 0.028583 / 0.534201 (-0.505618) | 0.406349 / 0.579283 (-0.172934) | 0.413178 / 0.434364 (-0.021186) | 0.491283 / 0.540337 (-0.049055) | 0.571171 / 1.386936 (-0.815765) |\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) | 0.006868 / 0.011353 (-0.004484) | 0.004593 / 0.011008 (-0.006416) | 0.077574 / 0.038508 (0.039066) | 0.027703 / 0.023109 (0.004593) | 0.342096 / 0.275898 (0.066198) | 0.378500 / 0.323480 (0.055020) | 0.005785 / 0.007986 (-0.002201) | 0.003342 / 0.004328 (-0.000986) | 0.076105 / 0.004250 (0.071855) | 0.040369 / 0.037052 (0.003317) | 0.343611 / 0.258489 (0.085122) | 0.391859 / 0.293841 (0.098018) | 0.032675 / 0.128546 (-0.095871) | 0.011623 / 0.075646 (-0.064023) | 0.086623 / 0.419271 (-0.332648) | 0.051955 / 0.043533 (0.008423) | 0.343425 / 0.255139 (0.088286) | 0.368887 / 0.283200 (0.085688) | 0.097117 / 0.141683 (-0.044566) | 1.499546 / 1.452155 (0.047391) | 1.593100 / 1.492716 (0.100383) |\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) | 0.193568 / 0.018006 (0.175562) | 0.409211 / 0.000490 (0.408722) | 0.003797 / 0.000200 (0.003597) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024982 / 0.037411 (-0.012430) | 0.101367 / 0.014526 (0.086841) | 0.108546 / 0.176557 (-0.068010) | 0.144402 / 0.737135 (-0.592733) | 0.112233 / 0.296338 (-0.184105) |\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) | 0.432820 / 0.215209 (0.217611) | 4.341045 / 2.077655 (2.263391) | 2.058326 / 1.504120 (0.554207) | 1.853913 / 1.541195 (0.312718) | 1.942436 / 1.468490 (0.473946) | 0.699130 / 4.584777 (-3.885647) | 3.392879 / 3.745712 (-0.352833) | 1.908277 / 5.269862 (-3.361585) | 1.177998 / 4.565676 (-3.387678) | 0.082700 / 0.424275 (-0.341576) | 0.012505 / 0.007607 (0.004898) | 0.526286 / 0.226044 (0.300242) | 5.279599 / 2.268929 (3.010670) | 2.505771 / 55.444624 (-52.938854) | 2.158460 / 6.876477 (-4.718016) | 2.211437 / 2.142072 (0.069365) | 0.802065 / 4.805227 (-4.003163) | 0.150766 / 6.500664 (-6.349898) | 0.067639 / 0.075469 (-0.007830) |\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) | 1.286595 / 1.841788 (-0.555192) | 13.961894 / 8.074308 (5.887586) | 14.021865 / 10.191392 (3.830473) | 0.164590 / 0.680424 (-0.515834) | 0.016909 / 0.534201 (-0.517292) | 0.392215 / 0.579283 (-0.187069) | 0.408080 / 0.434364 (-0.026284) | 0.488247 / 0.540337 (-0.052090) | 0.575524 / 1.386936 (-0.811412) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#699b0293876015457bfce40f7245d346c34c7717 \"CML watermark\")\n" ]

"2023-02-20T16:16:57Z"

"2023-02-21T13:27:57Z"

"2023-02-21T13:21:07Z"

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

"2022-05-03T22:44:17Z"

"2022-05-04T16:29:32Z"

"2022-05-04T16:22:04Z"

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[ "https://huggingface.co/datasets/fgrezes/WIESP2022-NER\r\n\r\nThe error:\r\n\r\n```\r\nMessage: Couldn't find a dataset script at /src/services/worker/fgrezes/WIESP2022-NER/WIESP2022-NER.py or any data file in the same directory. Couldn't find 'fgrezes/WIESP2022-NER' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**test*', '**eval*'] in dataset repository fgrezes/WIESP2022-NER with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']\r\n```\r\n\r\nI understand the issue is not related to the dataset viewer in itself, but with the autodetection of the data files without a loading script in the datasets library. cc @lhoestq @albertvillanova @mariosasko ", "Apparently it finds `scoring-scripts/compute_seqeval.py` which matches `**eval*`, a regex that detects a test split. We should probably improve the regex because it's not supposed to catch this kind of files. It must also only check for files with supported extensions: txt, csv, png etc." ]

"2022-06-11T15:49:17Z"

"2022-07-18T13:07:33Z"

"2022-07-18T13:07:33Z"

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[]

[ "Hi,\r\n\r\nThe `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:\r\n```\r\npip uninstall bert-score\r\npip install \"bert-score<0.3.10\"\r\n```" ]

"2021-08-06T15:58:57Z"

"2021-08-09T11:16:25Z"

"2021-08-09T11:16:25Z"

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[ "Sorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!" ]

"2021-09-13T19:12:17Z"

"2021-09-14T08:40:47Z"

"2021-09-14T08:40:47Z"

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[ "Thanks for reporting, I opened a PR to fix that", "One the PR is merged the fix will be available in the next release of `datasets`.\r\n\r\nIf you don't want to wait the next release you can still load the script from the master branch with\r\n\r\n```python\r\nload_dataset(\"dane\", script_version=\"master\")\r\n```", "If you have other questions feel free to reopen :) " ]

"2021-01-05T11:54:26Z"

"2021-01-05T14:01:09Z"

"2021-01-05T14:00:13Z"

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[ "This would break a lot of existing code, so we can't really do this." ]

"2023-07-19T19:53:28Z"

"2023-10-03T16:04:09Z"

"2023-10-03T16:04:09Z"

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

"2022-05-27T09:28:02Z"

"2022-05-31T15:07:21Z"

"2022-05-31T14:58:51Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think a patch release will be necessary.", "I'm also fixing https://github.com/huggingface/datasets/issues/5111 which will lalso require a patch release" ]

"2022-10-14T11:30:03Z"

"2022-10-24T06:21:09Z"

"2022-10-14T12:11:44Z"

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[ "Very cool to have this dataset, thank you for adding it :)" ]

"2020-06-29T19:11:16Z"

"2020-06-30T17:07:31Z"

"2020-06-30T17:07:31Z"

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[ "CI seems to fail due to languages incorrectly being flagged as invalid, I guess that's related to the currently-broken bcp47 validation (see #4304)", "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-05-16T14:22:28Z"

"2022-06-01T13:06:07Z"

"2022-06-01T12:57:09Z"

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[ "Hi ! From the `adlfs` docs, there are two filesystems you can use:\r\n> To use the Gen1 filesystem:\r\n> - known_implementations[‘adl’] = {‘class’: ‘adlfs.AzureDatalakeFileSystem’}\r\n> \r\n> To use the Gen2 filesystem:\r\n> - known_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n\r\nIf I'm not mistaken you're using the second one - so you should use `abfs://` instead of `adl://`, and also run this at the beginning of your script:\r\n```python\r\nfrom fsspec.registry import known_implementations\r\nknown_implementations['abfs'] = {'class': 'adlfs.AzureDatalakeFileSystem'}\r\n```\r\n\r\n", "Thank you @lhoestq . Great call.\r\nUsing the default class from `known_implementations` dict solved my problem\r\n```\r\nknown_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n```\r\nI'm closing this issue." ]

"2022-10-25T00:43:18Z"

"2022-11-17T23:37:09Z"

"2022-11-17T23:37:08Z"

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"2020-12-09T07:41:30Z"

"2020-12-10T14:28:41Z"

"2020-12-10T14:28:41Z"

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

"2022-09-06T15:01:24Z"

"2022-09-06T22:24:09Z"

"2022-09-06T22:21:57Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005292 / 0.011353 (-0.006061) | 0.003811 / 0.011008 (-0.007197) | 0.064912 / 0.038508 (0.026404) | 0.061199 / 0.023109 (0.038090) | 0.242953 / 0.275898 (-0.032945) | 0.271789 / 0.323480 (-0.051691) | 0.003994 / 0.007986 (-0.003991) | 0.002723 / 0.004328 (-0.001606) | 0.049952 / 0.004250 (0.045701) | 0.039489 / 0.037052 (0.002437) | 0.261143 / 0.258489 (0.002654) | 0.288800 / 0.293841 (-0.005041) | 0.028130 / 0.128546 (-0.100416) | 0.010724 / 0.075646 (-0.064922) | 0.208218 / 0.419271 (-0.211054) | 0.036224 / 0.043533 (-0.007309) | 0.247189 / 0.255139 (-0.007950) | 0.274702 / 0.283200 (-0.008498) | 0.019714 / 0.141683 (-0.121969) | 1.134853 / 1.452155 (-0.317301) | 1.192655 / 1.492716 (-0.300062) |\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) | 0.096391 / 0.018006 (0.078385) | 0.303802 / 0.000490 (0.303312) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019530 / 0.037411 (-0.017881) | 0.061588 / 0.014526 (0.047062) | 0.075122 / 0.176557 (-0.101434) | 0.120980 / 0.737135 (-0.616155) | 0.075807 / 0.296338 (-0.220532) |\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) | 0.281672 / 0.215209 (0.066463) | 2.779884 / 2.077655 (0.702229) | 1.502026 / 1.504120 (-0.002094) | 1.369474 / 1.541195 (-0.171721) | 1.402694 / 1.468490 (-0.065796) | 0.559120 / 4.584777 (-4.025657) | 2.355320 / 3.745712 (-1.390393) | 2.823987 / 5.269862 (-2.445875) | 1.763888 / 4.565676 (-2.801788) | 0.061715 / 0.424275 (-0.362560) | 0.005015 / 0.007607 (-0.002592) | 0.342669 / 0.226044 (0.116625) | 3.360651 / 2.268929 (1.091722) | 1.887277 / 55.444624 (-53.557348) | 1.555613 / 6.876477 (-5.320864) | 1.614126 / 2.142072 (-0.527946) | 0.643797 / 4.805227 (-4.161430) | 0.118365 / 6.500664 (-6.382299) | 0.042596 / 0.075469 (-0.032873) |\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) | 0.951383 / 1.841788 (-0.890405) | 13.169812 / 8.074308 (5.095504) | 10.772460 / 10.191392 (0.581068) | 0.133248 / 0.680424 (-0.547176) | 0.014597 / 0.534201 (-0.519604) | 0.289758 / 0.579283 (-0.289525) | 0.266324 / 0.434364 (-0.168040) | 0.334811 / 0.540337 (-0.205526) | 0.445566 / 1.386936 (-0.941370) |\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) | 0.005668 / 0.011353 (-0.005684) | 0.003583 / 0.011008 (-0.007425) | 0.050681 / 0.038508 (0.012173) | 0.063244 / 0.023109 (0.040135) | 0.279624 / 0.275898 (0.003726) | 0.308030 / 0.323480 (-0.015450) | 0.004160 / 0.007986 (-0.003826) | 0.002633 / 0.004328 (-0.001696) | 0.048475 / 0.004250 (0.044225) | 0.043106 / 0.037052 (0.006054) | 0.283678 / 0.258489 (0.025189) | 0.309730 / 0.293841 (0.015889) | 0.030290 / 0.128546 (-0.098256) | 0.011112 / 0.075646 (-0.064534) | 0.058234 / 0.419271 (-0.361038) | 0.033553 / 0.043533 (-0.009979) | 0.279902 / 0.255139 (0.024763) | 0.298041 / 0.283200 (0.014841) | 0.019367 / 0.141683 (-0.122316) | 1.142438 / 1.452155 (-0.309717) | 1.197305 / 1.492716 (-0.295411) |\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) | 0.090875 / 0.018006 (0.072869) | 0.301174 / 0.000490 (0.300685) | 0.000216 / 0.000200 (0.000016) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021544 / 0.037411 (-0.015867) | 0.071371 / 0.014526 (0.056846) | 0.080821 / 0.176557 (-0.095736) | 0.120054 / 0.737135 (-0.617082) | 0.082611 / 0.296338 (-0.213728) |\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) | 0.293787 / 0.215209 (0.078578) | 2.862610 / 2.077655 (0.784955) | 1.597282 / 1.504120 (0.093162) | 1.485094 / 1.541195 (-0.056101) | 1.507384 / 1.468490 (0.038893) | 0.558470 / 4.584777 (-4.026307) | 2.414137 / 3.745712 (-1.331575) | 2.863342 / 5.269862 (-2.406520) | 1.776973 / 4.565676 (-2.788704) | 0.062296 / 0.424275 (-0.361979) | 0.004954 / 0.007607 (-0.002653) | 0.346037 / 0.226044 (0.119993) | 3.441864 / 2.268929 (1.172935) | 1.969842 / 55.444624 (-53.474783) | 1.714878 / 6.876477 (-5.161599) | 1.738141 / 2.142072 (-0.403931) | 0.645929 / 4.805227 (-4.159298) | 0.117332 / 6.500664 (-6.383332) | 0.041963 / 0.075469 (-0.033507) |\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) | 0.983229 / 1.841788 (-0.858559) | 13.186932 / 8.074308 (5.112624) | 11.220549 / 10.191392 (1.029157) | 0.142105 / 0.680424 (-0.538319) | 0.015210 / 0.534201 (-0.518991) | 0.290055 / 0.579283 (-0.289228) | 0.274513 / 0.434364 (-0.159851) | 0.346834 / 0.540337 (-0.193504) | 0.575897 / 1.386936 (-0.811039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d3c0694d0c47a64a3cab5d468b4d9575ad7b1d96 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6459). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005308 / 0.011353 (-0.006045) | 0.003135 / 0.011008 (-0.007873) | 0.061820 / 0.038508 (0.023312) | 0.052005 / 0.023109 (0.028895) | 0.233507 / 0.275898 (-0.042391) | 0.257790 / 0.323480 (-0.065690) | 0.002848 / 0.007986 (-0.005138) | 0.002645 / 0.004328 (-0.001683) | 0.048379 / 0.004250 (0.044128) | 0.038320 / 0.037052 (0.001268) | 0.245470 / 0.258489 (-0.013019) | 0.274854 / 0.293841 (-0.018987) | 0.027335 / 0.128546 (-0.101211) | 0.010349 / 0.075646 (-0.065297) | 0.205872 / 0.419271 (-0.213400) | 0.035896 / 0.043533 (-0.007637) | 0.241645 / 0.255139 (-0.013494) | 0.260033 / 0.283200 (-0.023167) | 0.020325 / 0.141683 (-0.121358) | 1.116768 / 1.452155 (-0.335387) | 1.188067 / 1.492716 (-0.304649) |\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) | 0.092622 / 0.018006 (0.074616) | 0.302663 / 0.000490 (0.302173) | 0.000227 / 0.000200 (0.000027) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018633 / 0.037411 (-0.018778) | 0.060117 / 0.014526 (0.045592) | 0.072713 / 0.176557 (-0.103844) | 0.119955 / 0.737135 (-0.617180) | 0.074698 / 0.296338 (-0.221640) |\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) | 0.277157 / 0.215209 (0.061948) | 2.699650 / 2.077655 (0.621995) | 1.413625 / 1.504120 (-0.090494) | 1.295900 / 1.541195 (-0.245295) | 1.306280 / 1.468490 (-0.162210) | 0.555354 / 4.584777 (-4.029423) | 2.386866 / 3.745712 (-1.358847) | 2.794069 / 5.269862 (-2.475793) | 1.736275 / 4.565676 (-2.829401) | 0.061812 / 0.424275 (-0.362464) | 0.004957 / 0.007607 (-0.002650) | 0.334533 / 0.226044 (0.108488) | 3.251096 / 2.268929 (0.982168) | 1.768193 / 55.444624 (-53.676431) | 1.473752 / 6.876477 (-5.402724) | 1.476320 / 2.142072 (-0.665753) | 0.642485 / 4.805227 (-4.162742) | 0.116986 / 6.500664 (-6.383678) | 0.042083 / 0.075469 (-0.033386) |\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) | 0.941364 / 1.841788 (-0.900424) | 11.587408 / 8.074308 (3.513100) | 10.500198 / 10.191392 (0.308806) | 0.129126 / 0.680424 (-0.551298) | 0.015206 / 0.534201 (-0.518995) | 0.286580 / 0.579283 (-0.292703) | 0.263566 / 0.434364 (-0.170798) | 0.331662 / 0.540337 (-0.208676) | 0.431423 / 1.386936 (-0.955513) |\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) | 0.005151 / 0.011353 (-0.006202) | 0.003425 / 0.011008 (-0.007583) | 0.049301 / 0.038508 (0.010793) | 0.052005 / 0.023109 (0.028895) | 0.289594 / 0.275898 (0.013696) | 0.312630 / 0.323480 (-0.010849) | 0.003988 / 0.007986 (-0.003998) | 0.002705 / 0.004328 (-0.001624) | 0.048529 / 0.004250 (0.044279) | 0.039645 / 0.037052 (0.002592) | 0.293430 / 0.258489 (0.034941) | 0.311697 / 0.293841 (0.017856) | 0.029044 / 0.128546 (-0.099502) | 0.010282 / 0.075646 (-0.065364) | 0.057641 / 0.419271 (-0.361630) | 0.032733 / 0.043533 (-0.010800) | 0.293553 / 0.255139 (0.038414) | 0.308850 / 0.283200 (0.025651) | 0.018452 / 0.141683 (-0.123231) | 1.147931 / 1.452155 (-0.304224) | 1.173093 / 1.492716 (-0.319623) |\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) | 0.100862 / 0.018006 (0.082856) | 0.309286 / 0.000490 (0.308796) | 0.000223 / 0.000200 (0.000023) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021365 / 0.037411 (-0.016046) | 0.068987 / 0.014526 (0.054461) | 0.081092 / 0.176557 (-0.095465) | 0.119852 / 0.737135 (-0.617283) | 0.082850 / 0.296338 (-0.213489) |\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) | 0.288477 / 0.215209 (0.073268) | 2.833766 / 2.077655 (0.756111) | 1.576670 / 1.504120 (0.072550) | 1.431643 / 1.541195 (-0.109552) | 1.442132 / 1.468490 (-0.026358) | 0.556079 / 4.584777 (-4.028698) | 2.465042 / 3.745712 (-1.280670) | 2.786329 / 5.269862 (-2.483532) | 1.779428 / 4.565676 (-2.786249) | 0.062278 / 0.424275 (-0.361997) | 0.004867 / 0.007607 (-0.002740) | 0.348444 / 0.226044 (0.122399) | 3.389824 / 2.268929 (1.120896) | 1.919141 / 55.444624 (-53.525484) | 1.635411 / 6.876477 (-5.241066) | 1.654869 / 2.142072 (-0.487204) | 0.634467 / 4.805227 (-4.170761) | 0.114330 / 6.500664 (-6.386334) | 0.039900 / 0.075469 (-0.035569) |\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) | 0.970851 / 1.841788 (-0.870937) | 11.951660 / 8.074308 (3.877352) | 10.571115 / 10.191392 (0.379723) | 0.131040 / 0.680424 (-0.549384) | 0.015299 / 0.534201 (-0.518902) | 0.287851 / 0.579283 (-0.291432) | 0.278366 / 0.434364 (-0.155998) | 0.326468 / 0.540337 (-0.213870) | 0.552288 / 1.386936 (-0.834648) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8214ff2a9f706427669a6c2a01ccabffa5bf0d2b \"CML watermark\")\n" ]

"2023-11-29T16:56:15Z"

"2023-12-13T13:54:48Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008499 / 0.011353 (-0.002854) | 0.006155 / 0.011008 (-0.004853) | 0.124032 / 0.038508 (0.085524) | 0.037337 / 0.023109 (0.014228) | 0.389274 / 0.275898 (0.113376) | 0.427736 / 0.323480 (0.104257) | 0.006929 / 0.007986 (-0.001057) | 0.005017 / 0.004328 (0.000689) | 0.096356 / 0.004250 (0.092105) | 0.055694 / 0.037052 (0.018642) | 0.391417 / 0.258489 (0.132928) | 0.448098 / 0.293841 (0.154257) | 0.042442 / 0.128546 (-0.086105) | 0.013456 / 0.075646 (-0.062190) | 0.423502 / 0.419271 (0.004230) | 0.062919 / 0.043533 (0.019386) | 0.384317 / 0.255139 (0.129178) | 0.410851 / 0.283200 (0.127652) | 0.112807 / 0.141683 (-0.028875) | 1.746050 / 1.452155 (0.293895) | 1.977974 / 1.492716 (0.485257) |\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) | 0.306382 / 0.018006 (0.288375) | 0.620310 / 0.000490 (0.619820) | 0.009309 / 0.000200 (0.009109) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026900 / 0.037411 (-0.010511) | 0.140125 / 0.014526 (0.125599) | 0.136295 / 0.176557 (-0.040261) | 0.207721 / 0.737135 (-0.529414) | 0.146328 / 0.296338 (-0.150011) |\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) | 0.616712 / 0.215209 (0.401503) | 6.237820 / 2.077655 (4.160166) | 2.503809 / 1.504120 (0.999689) | 2.129739 / 1.541195 (0.588544) | 2.160768 / 1.468490 (0.692277) | 0.971273 / 4.584777 (-3.613504) | 5.687161 / 3.745712 (1.941449) | 2.738148 / 5.269862 (-2.531713) | 1.692695 / 4.565676 (-2.872981) | 0.113701 / 0.424275 (-0.310574) | 0.014809 / 0.007607 (0.007202) | 0.774795 / 0.226044 (0.548750) | 7.660012 / 2.268929 (5.391083) | 3.253036 / 55.444624 (-52.191588) | 2.607498 / 6.876477 (-4.268979) | 2.681678 / 2.142072 (0.539606) | 1.095275 / 4.805227 (-3.709952) | 0.239078 / 6.500664 (-6.261586) | 0.081034 / 0.075469 (0.005565) |\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) | 1.574547 / 1.841788 (-0.267240) | 18.323566 / 8.074308 (10.249258) | 19.274482 / 10.191392 (9.083090) | 0.210275 / 0.680424 (-0.470149) | 0.031843 / 0.534201 (-0.502358) | 0.514843 / 0.579283 (-0.064440) | 0.633782 / 0.434364 (0.199418) | 0.588569 / 0.540337 (0.048232) | 0.721401 / 1.386936 (-0.665535) |\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) | 0.008866 / 0.011353 (-0.002487) | 0.006460 / 0.011008 (-0.004548) | 0.121337 / 0.038508 (0.082829) | 0.033896 / 0.023109 (0.010786) | 0.455702 / 0.275898 (0.179804) | 0.509685 / 0.323480 (0.186205) | 0.007650 / 0.007986 (-0.000336) | 0.005578 / 0.004328 (0.001250) | 0.098505 / 0.004250 (0.094255) | 0.056122 / 0.037052 (0.019069) | 0.478483 / 0.258489 (0.219994) | 0.560008 / 0.293841 (0.266167) | 0.044926 / 0.128546 (-0.083620) | 0.014562 / 0.075646 (-0.061085) | 0.115027 / 0.419271 (-0.304244) | 0.066494 / 0.043533 (0.022961) | 0.463434 / 0.255139 (0.208296) | 0.513856 / 0.283200 (0.230656) | 0.126436 / 0.141683 (-0.015247) | 1.874729 / 1.452155 (0.422575) | 1.925080 / 1.492716 (0.432364) |\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) | 0.012672 / 0.018006 (-0.005334) | 0.615797 / 0.000490 (0.615307) | 0.001606 / 0.000200 (0.001406) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031104 / 0.037411 (-0.006307) | 0.130107 / 0.014526 (0.115581) | 0.140587 / 0.176557 (-0.035970) | 0.205081 / 0.737135 (-0.532054) | 0.144068 / 0.296338 (-0.152270) |\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) | 0.646549 / 0.215209 (0.431340) | 6.403962 / 2.077655 (4.326307) | 2.812594 / 1.504120 (1.308474) | 2.478480 / 1.541195 (0.937285) | 2.552385 / 1.468490 (1.083895) | 0.991987 / 4.584777 (-3.592790) | 5.777917 / 3.745712 (2.032205) | 5.697830 / 5.269862 (0.427969) | 2.370583 / 4.565676 (-2.195094) | 0.109905 / 0.424275 (-0.314370) | 0.013801 / 0.007607 (0.006193) | 0.799932 / 0.226044 (0.573888) | 8.155672 / 2.268929 (5.886743) | 3.711662 / 55.444624 (-51.732963) | 3.042164 / 6.876477 (-3.834312) | 3.073549 / 2.142072 (0.931477) | 1.137515 / 4.805227 (-3.667712) | 0.231266 / 6.500664 (-6.269398) | 0.080893 / 0.075469 (0.005424) |\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) | 1.669210 / 1.841788 (-0.172577) | 18.747144 / 8.074308 (10.672836) | 21.084589 / 10.191392 (10.893197) | 0.241379 / 0.680424 (-0.439045) | 0.029473 / 0.534201 (-0.504728) | 0.524605 / 0.579283 (-0.054678) | 0.622852 / 0.434364 (0.188488) | 0.604941 / 0.540337 (0.064604) | 0.715978 / 1.386936 (-0.670958) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<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) | 0.005973 / 0.011353 (-0.005380) | 0.004389 / 0.011008 (-0.006620) | 0.096076 / 0.038508 (0.057568) | 0.031569 / 0.023109 (0.008460) | 0.328300 / 0.275898 (0.052402) | 0.359356 / 0.323480 (0.035876) | 0.005378 / 0.007986 (-0.002607) | 0.003703 / 0.004328 (-0.000625) | 0.075251 / 0.004250 (0.071000) | 0.042340 / 0.037052 (0.005287) | 0.346103 / 0.258489 (0.087614) | 0.379896 / 0.293841 (0.086055) | 0.027493 / 0.128546 (-0.101053) | 0.009033 / 0.075646 (-0.066613) | 0.327829 / 0.419271 (-0.091442) | 0.064074 / 0.043533 (0.020541) | 0.337703 / 0.255139 (0.082564) | 0.355335 / 0.283200 (0.072136) | 0.101179 / 0.141683 (-0.040504) | 1.471738 / 1.452155 (0.019584) | 1.539031 / 1.492716 (0.046315) |\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) | 0.194097 / 0.018006 (0.176091) | 0.434190 / 0.000490 (0.433701) | 0.005730 / 0.000200 (0.005530) | 0.000088 / 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) | 0.025634 / 0.037411 (-0.011778) | 0.105080 / 0.014526 (0.090555) | 0.116508 / 0.176557 (-0.060049) | 0.173867 / 0.737135 (-0.563269) | 0.117749 / 0.296338 (-0.178590) |\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) | 0.401566 / 0.215209 (0.186357) | 4.003558 / 2.077655 (1.925903) | 1.802756 / 1.504120 (0.298636) | 1.604222 / 1.541195 (0.063027) | 1.656617 / 1.468490 (0.188127) | 0.523385 / 4.584777 (-4.061392) | 3.744292 / 3.745712 (-0.001420) | 1.794295 / 5.269862 (-3.475567) | 1.044690 / 4.565676 (-3.520987) | 0.064992 / 0.424275 (-0.359284) | 0.011542 / 0.007607 (0.003935) | 0.507830 / 0.226044 (0.281785) | 5.061574 / 2.268929 (2.792645) | 2.252896 / 55.444624 (-53.191729) | 1.912551 / 6.876477 (-4.963926) | 2.073510 / 2.142072 (-0.068562) | 0.642148 / 4.805227 (-4.163079) | 0.140151 / 6.500664 (-6.360513) | 0.062623 / 0.075469 (-0.012846) |\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) | 1.180367 / 1.841788 (-0.661421) | 14.263475 / 8.074308 (6.189167) | 12.917251 / 10.191392 (2.725859) | 0.143815 / 0.680424 (-0.536608) | 0.017286 / 0.534201 (-0.516915) | 0.388411 / 0.579283 (-0.190872) | 0.430512 / 0.434364 (-0.003851) | 0.466595 / 0.540337 (-0.073742) | 0.564545 / 1.386936 (-0.822391) |\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) | 0.006059 / 0.011353 (-0.005294) | 0.004419 / 0.011008 (-0.006590) | 0.074206 / 0.038508 (0.035697) | 0.031180 / 0.023109 (0.008071) | 0.380031 / 0.275898 (0.104133) | 0.410373 / 0.323480 (0.086893) | 0.005397 / 0.007986 (-0.002589) | 0.003952 / 0.004328 (-0.000376) | 0.074426 / 0.004250 (0.070176) | 0.046256 / 0.037052 (0.009203) | 0.385543 / 0.258489 (0.127054) | 0.430724 / 0.293841 (0.136883) | 0.028052 / 0.128546 (-0.100494) | 0.008810 / 0.075646 (-0.066836) | 0.080749 / 0.419271 (-0.338522) | 0.046746 / 0.043533 (0.003214) | 0.380325 / 0.255139 (0.125186) | 0.398901 / 0.283200 (0.115701) | 0.099607 / 0.141683 (-0.042076) | 1.433343 / 1.452155 (-0.018812) | 1.520447 / 1.492716 (0.027730) |\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) | 0.202232 / 0.018006 (0.184225) | 0.431342 / 0.000490 (0.430852) | 0.001020 / 0.000200 (0.000820) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028762 / 0.037411 (-0.008649) | 0.111777 / 0.014526 (0.097251) | 0.119283 / 0.176557 (-0.057273) | 0.168151 / 0.737135 (-0.568985) | 0.126093 / 0.296338 (-0.170245) |\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) | 0.442689 / 0.215209 (0.227480) | 4.369202 / 2.077655 (2.291547) | 2.167703 / 1.504120 (0.663583) | 1.960580 / 1.541195 (0.419385) | 2.001459 / 1.468490 (0.532969) | 0.527169 / 4.584777 (-4.057608) | 3.738987 / 3.745712 (-0.006726) | 1.819002 / 5.269862 (-3.450860) | 1.082786 / 4.565676 (-3.482891) | 0.066209 / 0.424275 (-0.358066) | 0.011549 / 0.007607 (0.003942) | 0.545959 / 0.226044 (0.319915) | 5.466655 / 2.268929 (3.197727) | 2.671448 / 55.444624 (-52.773176) | 2.340968 / 6.876477 (-4.535509) | 2.358805 / 2.142072 (0.216733) | 0.649456 / 4.805227 (-4.155771) | 0.142009 / 6.500664 (-6.358655) | 0.064199 / 0.075469 (-0.011270) |\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) | 1.259819 / 1.841788 (-0.581969) | 14.456988 / 8.074308 (6.382680) | 14.478982 / 10.191392 (4.287590) | 0.163156 / 0.680424 (-0.517268) | 0.017090 / 0.534201 (-0.517111) | 0.391339 / 0.579283 (-0.187944) | 0.422021 / 0.434364 (-0.012343) | 0.465340 / 0.540337 (-0.074997) | 0.564517 / 1.386936 (-0.822419) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]

"2023-06-07T16:09:39Z"

"2023-06-09T10:16:58Z"

"2023-06-09T09:53:16Z"

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[ "@lhoestq I have refactorized the logic. Instead of the previous hierarchy call (local temp file opening -> remote call -> use again temp local file logic but from within the remote caller scope), now it is flattened. Schematically:\r\n```python\r\nwith src.open() as src_file, dst.open() as dst_file:\r\n src_file.fetch(dst_file)\r\n```\r\n\r\nI have created `RemotePath` (analogue to Path) with method `.open()` that returns `FtpFile`/`HttpFile` (analogue to file-like).\r\n\r\nNow I am going to implement `RemotePath.exists()` method (analogue to the Path's method) to check if remote resource is accessible, using `Ftp/Http.head()`.", "Quick update on this one:\r\nwe discussed offline with @albertvillanova on this PR and I think using `fsspec` can help a lot, since it already implements many parts of the abstraction we need to have nice download tools for both http and ftp (and others !)" ]

"2021-02-15T17:36:24Z"

"2022-07-06T15:19:47Z"

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"2022-04-21T19:19:26Z"

"2022-05-03T11:29:05Z"

"2022-04-22T06:12:25Z"

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[ "Hi ! The ClassLabel feature type encodes the labels as integers.\r\nThe integer corresponds to the index of the label name in the `names` list of the ClassLabel.\r\nHere that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).\r\n\r\nYou can get the label names back by using `a.features['label'].int2str(i)`.\r\n", "Hi! You can also easily reorder the label with the [`Dataset.align_labels_with_mapping`](https://huggingface.co/docs/datasets/master/en/process#align) method." ]

"2021-04-11T10:03:56Z"

"2022-06-01T16:23:08Z"

"2022-06-01T16:21:10Z"

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[ "Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)", "No, problem ^^ It might just be a temporary fix :)", "Fixed." ]

"2020-04-15T14:14:54Z"

"2020-04-29T09:23:22Z"

"2020-04-29T09:23:22Z"

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[ "Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n \"train\": \"train-v1.1.json\", # you can use a URL or a relative path from the python script to your file in the repository\r\n \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n ]\r\n```", "Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't", "Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly 🚀 \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro", "Great ! We'll take a look at the viewer to fix it", "@lhoestq I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n datasets = load_dataset(\r\n os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n f\"xfun.{data_args.lang}\",\r\n additional_langs=data_args.additional_langs,\r\n keep_in_memory=True,\r\n )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n downloaded_file = dl_manager.download_and_extract(\"https://guillaumejaume.github.io/FUNSD/dataset.zip\")\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/training_data/\"}\r\n ),\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/testing_data/\"}\r\n ),\r\n ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 652, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 975, in _prepare_split\r\n generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```", "Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset/testing_data\")` instead of `f\"downloaded_file}/dataset/testing_data/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example)", "Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can. We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have \"filepaths\".)\r\n", "Fixed in the viewer: https://huggingface.co/datasets/pietrolesci/ag_news" ]

"2021-11-19T15:20:52Z"

"2021-12-22T10:57:56Z"

"2021-12-22T10:57:56Z"

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[ "I replaced all the \"we\" and applied your suggestion", "Merging this for now, we can continue improving this card in other PRs :)" ]

"2021-04-16T18:37:37Z"

"2021-04-21T09:33:09Z"

"2021-04-21T09:33:08Z"

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[ "Hi @RylanSchaeffer, thanks for reporting.\r\n\r\nI'm sorry, but I was not able to reproduce your problem.\r\n\r\nNormally, the reason for this type of error is that, during your download of the data files, this was not fully complete.\r\n\r\nCould you please try to load the dataset again but forcing its redownload? Please use:\r\n```python\r\ndataset = load_dataset(\"openwebtext\", download_mode=\"FORCE_REDOWNLOAD\")\r\n```\r\n\r\nLet me know if the problem persists.", "I close this issue for the moment. Feel free to re-open it again if the problem persists." ]

"2021-10-11T17:05:27Z"

"2021-10-28T05:52:21Z"

"2021-10-28T05:52:21Z"

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"2020-12-08T11:40:06Z"

"2020-12-11T17:42:20Z"

"2020-12-11T17:42:20Z"

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[ "Could you add this function to the documentation please ?\r\n\r\nYou can add it in `main_classes.rst`, and maybe add a `Tip` section in the `map` section in the `process.rst`" ]

"2021-10-05T15:18:49Z"

"2021-10-18T15:00:21Z"

"2021-10-18T14:59:30Z"

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[ "The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1", "TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 \r\n- [x] \"conll2000\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"crime_and_punish\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"dart\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"iwslt2017\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [ ] \"mc4\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"the_pile\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"timit_asr\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card", "The bulk edit is finished." ]

"2022-11-17T15:13:15Z"

"2023-02-16T09:49:05Z"

"2022-11-19T06:51:37Z"

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"2020-12-03T16:06:01Z"

"2020-12-04T13:46:05Z"

"2020-12-04T13:46:05Z"

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

"2022-07-29T07:01:56Z"

"2022-07-29T11:16:49Z"

"2022-07-29T11:03:58Z"

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[ "Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https://huggingface.co/docs/datasets/master/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.", "You can use a local metric script just by providing its path instead of the usual shortcut name ", "#self-assign I have starting working on this issue to enhance the metric API." ]

"2022-01-20T13:35:36Z"

"2022-03-09T17:18:20Z"

"2022-03-09T17:18:20Z"

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"2023-06-05T14:46:04Z"

"2023-06-19T17:22:43Z"

"2023-06-19T17:22:43Z"

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[ "Upd. the above code works if we replace `25` with `1`, but the result value at key \"hr\" is not a tensor but a list of lists of lists of uint8.\r\n\r\nAdding `train_data.set_format(\"torch\")` after map fixes this, but the original issue remains\r\n\r\n", "As a workaround, one can replace\r\n`return {\"hr\": torch.stack([crop_transf(tensor) for _ in range(25)])}`\r\nwith\r\n`return {f\"hr_crop_{i}\": crop_transf(tensor) for i in range(25)}`\r\nand then choose appropriate crop randomly in further processing, but I still don't understand why the original approach doesn't work(\r\n" ]

"2023-03-21T09:33:27Z"

"2023-03-21T10:32:07Z"

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[ "Maybe we should only keep one citation ?\r\ncc @thomwolf @yjernite ", "For info:\r\n- The one automatically generated by Zenodo is version-specific, and a new one will be generated after each release.\r\n- Zenodo has also generated a project-specific DOI (they call it *Concept DOI* as opposed to *Version DOI*), but currently this only redirects to the DOI page of the latest version.\r\n- All the information automatically generated by Zenodo can be corrected/customized if necessary.\r\n - If we decide to correct/update metadata, take into account that there are the following fields (among others): Authors, Contributors, Title, Description, Keywords, Additional Notes, License,...\r\n\r\nAccording to Zenodo: https://help.zenodo.org/#versioning\r\n> **Which DOI should I use in citations?**\r\n> \r\n> You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility. By default, Zenodo uses the specific version to generate citations.\r\n> \r\n> You can use the Concept DOI representing all versions in citations when it is desirable to cite an evolving research artifact, without being specific about the version.", "Thanks for the details ! As zenodo says we should probably just show the versioned DOI. And we can remove the old citation.", "I have removed the old citation.\r\n\r\nWhat about the new one? Should we customize it? I have fixed some author names (replaced nickname with first and family names). Note that the list of authors is created automatically by Zenodo from this list: https://github.com/huggingface/datasets/graphs/contributors\r\nI do not know if this default automatic list of authors is what we want to show in the citation..." ]

"2021-05-31T10:05:42Z"

"2021-06-08T07:53:22Z"

"2021-06-08T07:53:22Z"

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"2021-10-19T14:08:11Z"

"2021-10-19T14:26:22Z"

"2021-10-19T14:26:21Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the reply @lhoestq !\r\n\r\nI have sucessed on `datasets-cli test ./datasets/super_glue --name record --save_infos`,\r\nBut as you can see, the check ran into `FAILED tests/test_dataset_cards.py::test_changed_dataset_card[super_glue] - V...`.\r\nHow can we solve it?", "That would be neat! Let me implement it." ]

"2022-06-12T11:56:28Z"

"2022-08-19T23:23:02Z"

"2022-08-19T13:23:48Z"

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"2021-07-12T05:10:10Z"

"2021-07-12T14:08:34Z"

"2021-07-12T08:40:07Z"

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[ "I agree this fix the problem for the CI where the files are always created in a new and clean temporary directory.\r\n\r\nHowever, in a general setting of a succession of fast distributed operation, the files could already exist from previous metrics runs but one process may still finish before another has even started in which case it would mix results from separate operations.\r\n\r\nI feel like the most robust way to solve this is to setup a rendez-vous on the first time we write on files and where each process will test and only finish its operation when it cannot acquire a lock on all the other processes (meaning they all have started).\r\n\r\nWhat do you think?", "What do you think of this @thomwolf ? I check all the locks before finalizing", "Ok on my side @lhoestq (cannot add you as a reviewer)", "The test doesn't pass if I add:\r\n```python\r\n import time\r\n if self.process_id == 1:\r\n time.sleep(0.5)\r\n```\r\nright before `self.add_batch` in `Metric.compute`.\r\n\r\nI'm investigating why it doesn't work in that case", "It looks like the process 1 runs `_check_all_processes_locks` correctly and then finishes and releases its lock before process 0 even managed to to run `_check_all_processes_locks` correctly.", "Strange!", "I changed the way the rendez-vous is done @thomwolf , let me know what you think.\r\nThe idea is that the master process has an additional lock `rendez_vous_lock` to tell every other process to wait for everyone to be ready before starting to write" ]

"2020-09-02T12:58:50Z"

"2020-09-09T09:13:23Z"

"2020-09-09T09:13:22Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.", "Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub." ]

"2022-10-03T13:13:22Z"

"2022-10-04T13:49:00Z"

"2022-10-04T13:48:59Z"

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[ "Looks like I added my comments while you were editing - sorry about that" ]

"2022-03-03T15:03:35Z"

"2022-03-03T16:37:44Z"

"2022-03-03T16:37:43Z"

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[ "Hi @leondz, thanks for reporting.\r\n\r\nIndeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.\r\n\r\nIn particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`.\r\n\r\nLet me know if you need some help: I could push a commit to your repo with the fix.", "Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)" ]

"2022-05-06T12:03:27Z"

"2022-05-09T08:25:58Z"

"2022-05-09T08:25:58Z"

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[ "The current version of Wiki-Auto dataset contains a filtered version of the aligned dataset. The commit adds unfiltered versions of the data that can be useful the GEM task participants." ]

"2021-01-12T05:26:04Z"

"2021-01-12T18:14:53Z"

"2021-01-12T17:35:57Z"

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"2020-12-02T12:33:54Z"

"2020-12-02T13:45:40Z"

"2020-12-02T13:26:06Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI failures are about missing content in the dataset cards or bad tags, and this is unrelated to this PR. Merging :)" ]

"2022-07-19T11:52:54Z"

"2022-07-19T12:54:34Z"

"2022-07-19T12:41:16Z"

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"2020-12-05T10:38:02Z"

"2020-12-05T17:05:45Z"

"2020-12-05T17:05:45Z"

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[ "Oups too late, sorry" ]

"2020-10-02T09:35:46Z"

"2020-10-02T09:55:14Z"

"2020-10-02T09:52:18Z"

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[ "Now, I can't find any reference to set_progress_bar_enabled in the code.\r\n\r\nI think it have been deleted", "Hi @virilo,\r\n\r\nPlease note that since `datasets` version 2.0.0, we have aligned with `transformers` the management of the progress bar (among other things):\r\n- #3897\r\n\r\nNow, you should update your code to use `datasets.logging.disable_progress_bar`.\r\n\r\nYou have more info in our docs: [Logging methods](https://huggingface.co/docs/datasets/package_reference/logging_methods)", "One important thing for beginner like me is: from datasets.utils.logging import disable_progress_bar\r\nDo not forget the 'utils' or you will waste a long time like me...." ]

"2022-04-02T12:40:10Z"

"2022-09-17T02:18:03Z"

"2022-04-04T06:44:34Z"

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"2021-01-29T14:47:35Z"

"2021-01-29T16:53:32Z"

"2021-01-29T16:53:32Z"

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[ "Hi! I agree `skip` can be inefficient to use in the current state.\r\n\r\nTo make it fast, we could use \"statistics\" stored in Parquet metadata and read only the chunks needed to form a dataset. \r\n\r\nAnd thanks to the \"datasets-server\" project, which aims to store the Parquet versions of the Hub datasets (only the smaller datasets are covered currently), this solution can also be applied to datasets stored in formats other than Parquet. (cc @severo)", "@mariosasko do the current parquet files created by the datasets-server already have the required \"statistics\"? If not, please open an issue on https://github.com/huggingface/datasets-server with some details to make sure we implement it.", "Yes, nothing has to be changed on the datasets-server side. What I mean by \"statistics\" is that we can use the \"row_group\" metadata embedded in a Parquet file (by default) to fetch the requested rows more efficiently.", "Glad to see the feature could be of interest. \r\n\r\nI'm sure there are many possible ways to implement this feature. I don't know enough about the datasets-server, but I guess that it is not instantaneous, in the sense that user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all), which could be cumbersome. Having optionally that information in the `dataset_infos.json` file would make it easier for users to control the skip process a bit.", "re: statistics:\r\n\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.FileMetaData.html\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.RowGroupMetaData.html\r\n\r\n```python\r\n>>> import pyarrow.parquet as pq\r\n>>> import hffs\r\n>>> fs = hffs.HfFileSystem(\"glue\", repo_type=\"dataset\", revision=\"refs/convert/parquet\")\r\n>>> metadata = pq.read_metadata(\"ax/glue-test.parquet\", filesystem=fs)\r\n>>> metadata\r\n<pyarrow._parquet.FileMetaData object at 0x7f4537cec400>\r\n created_by: parquet-cpp-arrow version 7.0.0\r\n num_columns: 4\r\n num_rows: 1104\r\n num_row_groups: 2\r\n format_version: 1.0\r\n serialized_size: 2902\r\n>>> metadata.row_group(0)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f45564bcbd0>\r\n num_columns: 4\r\n num_rows: 1000\r\n total_byte_size: 164474\r\n>>> metadata.row_group(1)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f455005c400>\r\n num_columns: 4\r\n num_rows: 104\r\n total_byte_size: 13064\r\n```", "> user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all)\r\n\r\nprivate datasets are not supported yet (https://github.com/huggingface/datasets-server/issues/39)", "@versae `Dataset.push_to_hub` writes shards in Parquet, so this solution would also work for such datasets (immediately after the push). ", "@mariosasko that is right. However, there are still a good amount of datasets for which the shards are created manually. In our very specific case, we create medium-sized datasets (rarely over 100-200GB) of both text and audio, we prepare the shards by hand and then upload then. It would be great to have immediate access to this download skipping feature for them too.", "From looking at Arrow's source, it seems Parquet stores metadata at the end, which means one needs to iterate over a Parquet file's data before accessing its metadata. We could mimic Dask to address this \"limitation\" and write metadata in a `_metadata`/`_common_metadata` file in `to_parquet`/`push_to_hub`, which we could then use to optimize reads (if present). Plus, it's handy that PyArrow can also parse these metadata files.", "So if Parquet metadata needs to be in its own file anyway, why not implement this skipping feature by storing the example counts per shard in `dataset_infos.json`? That would allow:\r\n- Support both private and public datasets\r\n- Immediate access to the feature upon uploading of shards\r\n- Use any dataset, not only those uploaded using `.push_to_hub()`\r\n\r\nA proper Parquet metadata file could still be created and \"overwrite\" the `dataset_infos.json` info in the datasets-server." ]

"2022-12-20T11:25:23Z"

"2023-03-08T10:47:12Z"

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"2020-12-24T08:02:31Z"

"2020-12-28T17:23:39Z"

"2020-12-28T17:23:39Z"

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[ "As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.", "> I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps://github.com/huggingface/datasets/pull/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.", "@mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https://github.com/huggingface/datasets/runs/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================", "Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).", "> I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n", "Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio/image features (like label or text, for example) using `.map`\r\n- decoding should be enabled in a `.map` only if the `.map` function accesses the audio/image feature (implemented using `LazyDict`)", "For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.", "Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.", "Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?" ]

"2021-12-22T15:26:32Z"

"2021-12-27T14:13:21Z"

"2021-12-23T15:21:57Z"

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[ "I guess,test failing due to connection error?", "We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?", "If I'm not wrong this function returns None if no main class was found.\r\nI think it makes things less clear not to have a return at the end of the function.\r\nI guess we can have one return in the for loop instead of the break statement, AND one return at the end to explicitly return None.\r\nWhat do you think ?" ]

"2020-06-22T01:40:55Z"

"2020-06-23T17:57:58Z"

"2020-06-23T09:37:02Z"

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[ "Hi ! Can you try updating `fsspec` ? The minimum version is `2021.05.0`", "hey @lhoestq. I'm using `fsspec-2021.11.1` but still getting that error.", "Maybe this discussion can help:\r\n\r\nhttps://github.com/fsspec/filesystem_spec/issues/597#issuecomment-958646964", "Thanks @lhoestq. Downgrading `fsspec and s3fs` to `2021.10` fixed this issue!" ]

"2021-12-07T17:57:59Z"

"2021-12-17T07:00:35Z"

"2021-12-17T07:00:35Z"

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