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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6904). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "takign the liberty to merge this for the viewer and a new dataset being released", "<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) | 0.005004 / 0.011353 (-0.006349) | 0.003352 / 0.011008 (-0.007657) | 0.063035 / 0.038508 (0.024527) | 0.032031 / 0.023109 (0.008922) | 0.244801 / 0.275898 (-0.031097) | 0.270622 / 0.323480 (-0.052857) | 0.003110 / 0.007986 (-0.004876) | 0.002629 / 0.004328 (-0.001700) | 0.048784 / 0.004250 (0.044534) | 0.045779 / 0.037052 (0.008726) | 0.258642 / 0.258489 (0.000153) | 0.291606 / 0.293841 (-0.002235) | 0.028237 / 0.128546 (-0.100310) | 0.010184 / 0.075646 (-0.065463) | 0.202455 / 0.419271 (-0.216816) | 0.036012 / 0.043533 (-0.007521) | 0.248209 / 0.255139 (-0.006930) | 0.267315 / 0.283200 (-0.015884) | 0.019249 / 0.141683 (-0.122434) | 1.120420 / 1.452155 (-0.331735) | 1.169515 / 1.492716 (-0.323201) |\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) | 0.095193 / 0.018006 (0.077187) | 0.300544 / 0.000490 (0.300055) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 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) | 0.019001 / 0.037411 (-0.018411) | 0.061857 / 0.014526 (0.047331) | 0.073379 / 0.176557 (-0.103178) | 0.121293 / 0.737135 (-0.615843) | 0.075665 / 0.296338 (-0.220673) |\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) | 0.285153 / 0.215209 (0.069944) | 2.875527 / 2.077655 (0.797873) | 1.479851 / 1.504120 (-0.024269) | 1.360691 / 1.541195 (-0.180504) | 1.385581 / 1.468490 (-0.082909) | 0.566312 / 4.584777 (-4.018465) | 2.400202 / 3.745712 (-1.345510) | 2.719241 / 5.269862 (-2.550620) | 1.706469 / 4.565676 (-2.859208) | 0.062129 / 0.424275 (-0.362146) | 0.005291 / 0.007607 (-0.002316) | 0.334585 / 0.226044 (0.108540) | 3.293347 / 2.268929 (1.024419) | 1.790490 / 55.444624 (-53.654134) | 1.505519 / 6.876477 (-5.370958) | 1.527730 / 2.142072 (-0.614343) | 0.644554 / 4.805227 (-4.160673) | 0.119775 / 6.500664 (-6.380889) | 0.056912 / 0.075469 (-0.018557) |\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) | 0.977512 / 1.841788 (-0.864275) | 11.293883 / 8.074308 (3.219575) | 9.669439 / 10.191392 (-0.521953) | 0.129910 / 0.680424 (-0.550514) | 0.014322 / 0.534201 (-0.519879) | 0.284967 / 0.579283 (-0.294316) | 0.265355 / 0.434364 (-0.169008) | 0.321965 / 0.540337 (-0.218372) | 0.415254 / 1.386936 (-0.971682) |\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) | 0.005138 / 0.011353 (-0.006215) | 0.003321 / 0.011008 (-0.007687) | 0.049731 / 0.038508 (0.011223) | 0.032307 / 0.023109 (0.009198) | 0.266331 / 0.275898 (-0.009567) | 0.290863 / 0.323480 (-0.032617) | 0.004151 / 0.007986 (-0.003835) | 0.002684 / 0.004328 (-0.001644) | 0.048760 / 0.004250 (0.044510) | 0.042251 / 0.037052 (0.005199) | 0.280414 / 0.258489 (0.021925) | 0.305089 / 0.293841 (0.011248) | 0.029118 / 0.128546 (-0.099428) | 0.010276 / 0.075646 (-0.065370) | 0.057790 / 0.419271 (-0.361482) | 0.033290 / 0.043533 (-0.010243) | 0.267250 / 0.255139 (0.012111) | 0.285233 / 0.283200 (0.002034) | 0.018587 / 0.141683 (-0.123096) | 1.136198 / 1.452155 (-0.315957) | 1.185274 / 1.492716 (-0.307442) |\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) | 0.096355 / 0.018006 (0.078349) | 0.301827 / 0.000490 (0.301337) | 0.000216 / 0.000200 (0.000016) | 0.000052 / 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) | 0.022607 / 0.037411 (-0.014805) | 0.075724 / 0.014526 (0.061198) | 0.088197 / 0.176557 (-0.088359) | 0.127864 / 0.737135 (-0.609271) | 0.089294 / 0.296338 (-0.207044) |\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) | 0.289321 / 0.215209 (0.074112) | 2.832456 / 2.077655 (0.754802) | 1.559208 / 1.504120 (0.055088) | 1.426229 / 1.541195 (-0.114966) | 1.424564 / 1.468490 (-0.043926) | 0.557754 / 4.584777 (-4.027023) | 0.940179 / 3.745712 (-2.805533) | 2.713640 / 5.269862 (-2.556222) | 1.697583 / 4.565676 (-2.868093) | 0.062024 / 0.424275 (-0.362251) | 0.005270 / 0.007607 (-0.002337) | 0.339450 / 0.226044 (0.113406) | 3.333024 / 2.268929 (1.064096) | 1.946087 / 55.444624 (-53.498537) | 1.601057 / 6.876477 (-5.275420) | 1.599862 / 2.142072 (-0.542210) | 0.642838 / 4.805227 (-4.162390) | 0.120470 / 6.500664 (-6.380194) | 0.040815 / 0.075469 (-0.034654) |\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) | 1.012904 / 1.841788 (-0.828884) | 11.917035 / 8.074308 (3.842727) | 9.717822 / 10.191392 (-0.473570) | 0.141730 / 0.680424 (-0.538694) | 0.015750 / 0.534201 (-0.518451) | 0.284470 / 0.579283 (-0.294813) | 0.125662 / 0.434364 (-0.308702) | 0.380740 / 0.540337 (-0.159598) | 0.418119 / 1.386936 (-0.968817) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3f772468b2bbf77a7510e265f9d41e9eb77d53f \"CML watermark\")\n" ]

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[ "I think [`Dataset.to_parquet`](https://huggingface.co/docs/datasets/v1.10.2/package_reference/main_classes.html#datasets.Dataset.to_parquet) is what you're looking for.\r\n\r\nLet me know if I'm wrong ", "No, it does not save the metadata json.\r\n\r\nWe have to recode all meta json load/save\r\nwith another custome functions.\r\n\r\nsave_to_disk\r\nand load should have option with\r\n“Parquet” instead of “arrow”\r\n\r\nsince “arrow” is never user for production \r\n(only parquet).\r\n\r\nThanks !\r\n\r\n> On May 17, 2024, at 5:38, Frédéric Branchaud-Charron ***@***.***> wrote:\r\n> \r\n> \r\n> I think Dataset.to_parquet is what you're looking for.\r\n> \r\n> Let me know if I'm wrong\r\n> \r\n> —\r\n> Reply to this email directly, view it on GitHub, or unsubscribe.\r\n> You are receiving this because you authored the thread.\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6902). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005026 / 0.011353 (-0.006327) | 0.003672 / 0.011008 (-0.007336) | 0.062776 / 0.038508 (0.024268) | 0.032056 / 0.023109 (0.008947) | 0.245359 / 0.275898 (-0.030540) | 0.269371 / 0.323480 (-0.054109) | 0.004205 / 0.007986 (-0.003780) | 0.002774 / 0.004328 (-0.001555) | 0.048958 / 0.004250 (0.044708) | 0.046442 / 0.037052 (0.009390) | 0.263924 / 0.258489 (0.005434) | 0.291854 / 0.293841 (-0.001987) | 0.027299 / 0.128546 (-0.101248) | 0.010332 / 0.075646 (-0.065315) | 0.202677 / 0.419271 (-0.216595) | 0.037732 / 0.043533 (-0.005801) | 0.246028 / 0.255139 (-0.009111) | 0.272100 / 0.283200 (-0.011099) | 0.018497 / 0.141683 (-0.123186) | 1.101192 / 1.452155 (-0.350962) | 1.149683 / 1.492716 (-0.343033) |\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) | 0.097838 / 0.018006 (0.079832) | 0.305598 / 0.000490 (0.305108) | 0.000230 / 0.000200 (0.000030) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019489 / 0.037411 (-0.017922) | 0.061902 / 0.014526 (0.047376) | 0.074825 / 0.176557 (-0.101732) | 0.121664 / 0.737135 (-0.615472) | 0.076440 / 0.296338 (-0.219898) |\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) | 0.279194 / 0.215209 (0.063985) | 2.756777 / 2.077655 (0.679123) | 1.429298 / 1.504120 (-0.074822) | 1.313423 / 1.541195 (-0.227771) | 1.340466 / 1.468490 (-0.128024) | 0.556349 / 4.584777 (-4.028428) | 2.355910 / 3.745712 (-1.389802) | 2.806733 / 5.269862 (-2.463128) | 1.741903 / 4.565676 (-2.823773) | 0.061556 / 0.424275 (-0.362719) | 0.005477 / 0.007607 (-0.002130) | 0.327856 / 0.226044 (0.101812) | 3.283092 / 2.268929 (1.014164) | 1.797776 / 55.444624 (-53.646848) | 1.498683 / 6.876477 (-5.377794) | 1.518501 / 2.142072 (-0.623572) | 0.632267 / 4.805227 (-4.172960) | 0.116505 / 6.500664 (-6.384159) | 0.042446 / 0.075469 (-0.033023) |\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) | 0.982841 / 1.841788 (-0.858947) | 11.709436 / 8.074308 (3.635128) | 9.570519 / 10.191392 (-0.620873) | 0.141968 / 0.680424 (-0.538456) | 0.014299 / 0.534201 (-0.519902) | 0.285101 / 0.579283 (-0.294182) | 0.267118 / 0.434364 (-0.167246) | 0.324720 / 0.540337 (-0.215617) | 0.423626 / 1.386936 (-0.963310) |\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) | 0.005567 / 0.011353 (-0.005786) | 0.003703 / 0.011008 (-0.007306) | 0.050516 / 0.038508 (0.012008) | 0.032617 / 0.023109 (0.009508) | 0.276546 / 0.275898 (0.000648) | 0.299798 / 0.323480 (-0.023682) | 0.004282 / 0.007986 (-0.003704) | 0.002719 / 0.004328 (-0.001609) | 0.049424 / 0.004250 (0.045173) | 0.042924 / 0.037052 (0.005871) | 0.287785 / 0.258489 (0.029296) | 0.315490 / 0.293841 (0.021649) | 0.029533 / 0.128546 (-0.099013) | 0.010575 / 0.075646 (-0.065071) | 0.058210 / 0.419271 (-0.361061) | 0.033269 / 0.043533 (-0.010263) | 0.273325 / 0.255139 (0.018186) | 0.291762 / 0.283200 (0.008563) | 0.018922 / 0.141683 (-0.122761) | 1.118913 / 1.452155 (-0.333242) | 1.175554 / 1.492716 (-0.317162) |\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) | 0.099920 / 0.018006 (0.081914) | 0.317188 / 0.000490 (0.316698) | 0.000211 / 0.000200 (0.000011) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022297 / 0.037411 (-0.015114) | 0.077775 / 0.014526 (0.063249) | 0.090239 / 0.176557 (-0.086317) | 0.130498 / 0.737135 (-0.606638) | 0.092010 / 0.296338 (-0.204328) |\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) | 0.293534 / 0.215209 (0.078325) | 2.866070 / 2.077655 (0.788415) | 1.547147 / 1.504120 (0.043027) | 1.419684 / 1.541195 (-0.121510) | 1.432128 / 1.468490 (-0.036362) | 0.571365 / 4.584777 (-4.013412) | 0.968879 / 3.745712 (-2.776833) | 2.797415 / 5.269862 (-2.472446) | 1.767821 / 4.565676 (-2.797856) | 0.063281 / 0.424275 (-0.360994) | 0.005072 / 0.007607 (-0.002535) | 0.344547 / 0.226044 (0.118502) | 3.383888 / 2.268929 (1.114959) | 1.879537 / 55.444624 (-53.565087) | 1.598392 / 6.876477 (-5.278085) | 1.627788 / 2.142072 (-0.514284) | 0.641199 / 4.805227 (-4.164028) | 0.116349 / 6.500664 (-6.384315) | 0.041940 / 0.075469 (-0.033529) |\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) | 1.002494 / 1.841788 (-0.839294) | 12.310056 / 8.074308 (4.235748) | 9.819718 / 10.191392 (-0.371674) | 0.134745 / 0.680424 (-0.545679) | 0.016223 / 0.534201 (-0.517978) | 0.284791 / 0.579283 (-0.294492) | 0.124665 / 0.434364 (-0.309699) | 0.381601 / 0.540337 (-0.158737) | 0.413007 / 1.386936 (-0.973929) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6255b36be14ae22890c78749575f1f0793901f14 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6898). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "After this PR, the README file looks like:\r\n\r\n\r\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) | 0.004936 / 0.011353 (-0.006417) | 0.003591 / 0.011008 (-0.007418) | 0.062967 / 0.038508 (0.024459) | 0.031314 / 0.023109 (0.008205) | 0.248040 / 0.275898 (-0.027858) | 0.271630 / 0.323480 (-0.051850) | 0.003085 / 0.007986 (-0.004901) | 0.002605 / 0.004328 (-0.001724) | 0.049452 / 0.004250 (0.045202) | 0.044929 / 0.037052 (0.007876) | 0.264254 / 0.258489 (0.005765) | 0.287531 / 0.293841 (-0.006310) | 0.027197 / 0.128546 (-0.101349) | 0.009925 / 0.075646 (-0.065721) | 0.203165 / 0.419271 (-0.216107) | 0.035658 / 0.043533 (-0.007875) | 0.250207 / 0.255139 (-0.004932) | 0.269258 / 0.283200 (-0.013941) | 0.019975 / 0.141683 (-0.121708) | 1.093703 / 1.452155 (-0.358452) | 1.134031 / 1.492716 (-0.358685) |\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) | 0.095089 / 0.018006 (0.077082) | 0.301410 / 0.000490 (0.300920) | 0.000251 / 0.000200 (0.000051) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018453 / 0.037411 (-0.018958) | 0.061674 / 0.014526 (0.047148) | 0.073442 / 0.176557 (-0.103114) | 0.119743 / 0.737135 (-0.617392) | 0.074518 / 0.296338 (-0.221820) |\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) | 0.276351 / 0.215209 (0.061142) | 2.757670 / 2.077655 (0.680015) | 1.471199 / 1.504120 (-0.032921) | 1.363620 / 1.541195 (-0.177575) | 1.374175 / 1.468490 (-0.094315) | 0.556444 / 4.584777 (-4.028333) | 2.340637 / 3.745712 (-1.405075) | 2.728341 / 5.269862 (-2.541521) | 1.701214 / 4.565676 (-2.864463) | 0.061832 / 0.424275 (-0.362443) | 0.005287 / 0.007607 (-0.002320) | 0.331848 / 0.226044 (0.105804) | 3.334204 / 2.268929 (1.065276) | 1.791203 / 55.444624 (-53.653421) | 1.512246 / 6.876477 (-5.364231) | 1.529570 / 2.142072 (-0.612503) | 0.632193 / 4.805227 (-4.173034) | 0.116512 / 6.500664 (-6.384153) | 0.041271 / 0.075469 (-0.034198) |\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) | 0.981813 / 1.841788 (-0.859974) | 11.271398 / 8.074308 (3.197090) | 9.654613 / 10.191392 (-0.536780) | 0.140235 / 0.680424 (-0.540188) | 0.014336 / 0.534201 (-0.519865) | 0.284286 / 0.579283 (-0.294997) | 0.260265 / 0.434364 (-0.174099) | 0.321064 / 0.540337 (-0.219274) | 0.417554 / 1.386936 (-0.969382) |\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) | 0.005265 / 0.011353 (-0.006088) | 0.003237 / 0.011008 (-0.007772) | 0.049723 / 0.038508 (0.011215) | 0.031705 / 0.023109 (0.008596) | 0.255548 / 0.275898 (-0.020350) | 0.281651 / 0.323480 (-0.041829) | 0.004099 / 0.007986 (-0.003886) | 0.002739 / 0.004328 (-0.001589) | 0.049713 / 0.004250 (0.045463) | 0.041563 / 0.037052 (0.004511) | 0.269500 / 0.258489 (0.011011) | 0.293948 / 0.293841 (0.000107) | 0.029259 / 0.128546 (-0.099287) | 0.010391 / 0.075646 (-0.065255) | 0.057772 / 0.419271 (-0.361500) | 0.033125 / 0.043533 (-0.010408) | 0.258838 / 0.255139 (0.003699) | 0.278616 / 0.283200 (-0.004584) | 0.017543 / 0.141683 (-0.124139) | 1.130319 / 1.452155 (-0.321835) | 1.185976 / 1.492716 (-0.306740) |\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) | 0.094827 / 0.018006 (0.076821) | 0.296820 / 0.000490 (0.296331) | 0.000212 / 0.000200 (0.000012) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022583 / 0.037411 (-0.014828) | 0.076318 / 0.014526 (0.061792) | 0.087435 / 0.176557 (-0.089121) | 0.127351 / 0.737135 (-0.609784) | 0.089051 / 0.296338 (-0.207287) |\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) | 0.289476 / 0.215209 (0.074267) | 2.842065 / 2.077655 (0.764410) | 1.536857 / 1.504120 (0.032737) | 1.393914 / 1.541195 (-0.147281) | 1.392636 / 1.468490 (-0.075854) | 0.570299 / 4.584777 (-4.014478) | 0.982246 / 3.745712 (-2.763466) | 2.758773 / 5.269862 (-2.511088) | 1.728615 / 4.565676 (-2.837062) | 0.063944 / 0.424275 (-0.360331) | 0.005014 / 0.007607 (-0.002593) | 0.347474 / 0.226044 (0.121430) | 3.398092 / 2.268929 (1.129164) | 1.855134 / 55.444624 (-53.589491) | 1.568705 / 6.876477 (-5.307772) | 1.574201 / 2.142072 (-0.567871) | 0.649466 / 4.805227 (-4.155761) | 0.116330 / 6.500664 (-6.384334) | 0.040730 / 0.075469 (-0.034739) |\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) | 1.000675 / 1.841788 (-0.841113) | 11.899660 / 8.074308 (3.825352) | 9.913335 / 10.191392 (-0.278058) | 0.132517 / 0.680424 (-0.547907) | 0.016467 / 0.534201 (-0.517734) | 0.282221 / 0.579283 (-0.297062) | 0.125205 / 0.434364 (-0.309159) | 0.374986 / 0.540337 (-0.165351) | 0.418666 / 1.386936 (-0.968270) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2f989d01b49e3d6f98b2014d9ece3307e885b7a \"CML watermark\")\n" ]

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[ "Hello, @bghira.\r\n\r\nThanks for reporting. Please note that the text originating the error is not supposed to be valid YAML: it contains the instructions to generate the actual YAML content, that should replace the instructions comment.\r\n\r\nOn the other hand, I agree that it is not nice to have that YAML error message at the top of the page: \r\n\r\n\r\nI am proposing a change to make the YAML error disappear.", "thanks albert! i looked at it for a while to figure it out. i think the `raw` view option is the correct way to look at it?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6895). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.004914 / 0.011353 (-0.006439) | 0.003621 / 0.011008 (-0.007387) | 0.062841 / 0.038508 (0.024333) | 0.031630 / 0.023109 (0.008520) | 0.247666 / 0.275898 (-0.028232) | 0.288192 / 0.323480 (-0.035288) | 0.003145 / 0.007986 (-0.004841) | 0.002655 / 0.004328 (-0.001674) | 0.049484 / 0.004250 (0.045233) | 0.046593 / 0.037052 (0.009540) | 0.271550 / 0.258489 (0.013061) | 0.293228 / 0.293841 (-0.000613) | 0.026941 / 0.128546 (-0.101606) | 0.009936 / 0.075646 (-0.065710) | 0.201741 / 0.419271 (-0.217530) | 0.035435 / 0.043533 (-0.008098) | 0.251868 / 0.255139 (-0.003271) | 0.272082 / 0.283200 (-0.011118) | 0.019731 / 0.141683 (-0.121952) | 1.125752 / 1.452155 (-0.326403) | 1.152058 / 1.492716 (-0.340659) |\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) | 0.099695 / 0.018006 (0.081689) | 0.308306 / 0.000490 (0.307816) | 0.000223 / 0.000200 (0.000023) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018616 / 0.037411 (-0.018795) | 0.061886 / 0.014526 (0.047360) | 0.074059 / 0.176557 (-0.102498) | 0.124902 / 0.737135 (-0.612234) | 0.075108 / 0.296338 (-0.221230) |\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) | 0.336707 / 0.215209 (0.121498) | 2.805197 / 2.077655 (0.727542) | 1.565826 / 1.504120 (0.061706) | 1.443708 / 1.541195 (-0.097486) | 1.341167 / 1.468490 (-0.127323) | 0.566814 / 4.584777 (-4.017963) | 2.374536 / 3.745712 (-1.371176) | 2.804921 / 5.269862 (-2.464941) | 1.739848 / 4.565676 (-2.825829) | 0.062779 / 0.424275 (-0.361496) | 0.005341 / 0.007607 (-0.002266) | 0.326482 / 0.226044 (0.100438) | 3.273460 / 2.268929 (1.004531) | 1.803656 / 55.444624 (-53.640968) | 1.502518 / 6.876477 (-5.373958) | 1.523665 / 2.142072 (-0.618407) | 0.642443 / 4.805227 (-4.162784) | 0.117820 / 6.500664 (-6.382844) | 0.042540 / 0.075469 (-0.032929) |\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) | 0.963399 / 1.841788 (-0.878388) | 11.503648 / 8.074308 (3.429340) | 9.483957 / 10.191392 (-0.707435) | 0.129118 / 0.680424 (-0.551306) | 0.014136 / 0.534201 (-0.520065) | 0.286766 / 0.579283 (-0.292517) | 0.273328 / 0.434364 (-0.161036) | 0.324075 / 0.540337 (-0.216262) | 0.420408 / 1.386936 (-0.966528) |\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) | 0.005099 / 0.011353 (-0.006254) | 0.003721 / 0.011008 (-0.007288) | 0.050614 / 0.038508 (0.012106) | 0.031882 / 0.023109 (0.008773) | 0.267619 / 0.275898 (-0.008279) | 0.291874 / 0.323480 (-0.031606) | 0.004254 / 0.007986 (-0.003731) | 0.002766 / 0.004328 (-0.001563) | 0.049291 / 0.004250 (0.045041) | 0.043302 / 0.037052 (0.006249) | 0.274891 / 0.258489 (0.016402) | 0.304977 / 0.293841 (0.011136) | 0.029088 / 0.128546 (-0.099459) | 0.010425 / 0.075646 (-0.065221) | 0.057781 / 0.419271 (-0.361491) | 0.033589 / 0.043533 (-0.009943) | 0.264293 / 0.255139 (0.009154) | 0.284861 / 0.283200 (0.001661) | 0.018025 / 0.141683 (-0.123658) | 1.124954 / 1.452155 (-0.327200) | 1.161957 / 1.492716 (-0.330759) |\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) | 0.103622 / 0.018006 (0.085615) | 0.310915 / 0.000490 (0.310425) | 0.000241 / 0.000200 (0.000041) | 0.000051 / 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) | 0.022550 / 0.037411 (-0.014862) | 0.076466 / 0.014526 (0.061940) | 0.088297 / 0.176557 (-0.088260) | 0.128659 / 0.737135 (-0.608477) | 0.091823 / 0.296338 (-0.204516) |\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) | 0.293431 / 0.215209 (0.078222) | 2.888105 / 2.077655 (0.810450) | 1.559581 / 1.504120 (0.055461) | 1.421424 / 1.541195 (-0.119771) | 1.437941 / 1.468490 (-0.030549) | 0.577544 / 4.584777 (-4.007233) | 0.968840 / 3.745712 (-2.776872) | 2.799796 / 5.269862 (-2.470066) | 1.744791 / 4.565676 (-2.820885) | 0.064159 / 0.424275 (-0.360116) | 0.005043 / 0.007607 (-0.002564) | 0.341039 / 0.226044 (0.114995) | 3.354402 / 2.268929 (1.085474) | 1.904093 / 55.444624 (-53.540532) | 1.604046 / 6.876477 (-5.272431) | 1.610384 / 2.142072 (-0.531688) | 0.658129 / 4.805227 (-4.147098) | 0.119297 / 6.500664 (-6.381367) | 0.041396 / 0.075469 (-0.034073) |\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) | 1.001109 / 1.841788 (-0.840678) | 12.081856 / 8.074308 (4.007548) | 10.090943 / 10.191392 (-0.100449) | 0.150433 / 0.680424 (-0.529991) | 0.015850 / 0.534201 (-0.518351) | 0.286590 / 0.579283 (-0.292693) | 0.131137 / 0.434364 (-0.303227) | 0.389033 / 0.540337 (-0.151304) | 0.421382 / 1.386936 (-0.965554) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22b7baed53f9f295a5dda2fe3eb0b7434bf57e89 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6893). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005388 / 0.011353 (-0.005965) | 0.003822 / 0.011008 (-0.007187) | 0.063285 / 0.038508 (0.024777) | 0.033780 / 0.023109 (0.010671) | 0.239580 / 0.275898 (-0.036318) | 0.264203 / 0.323480 (-0.059277) | 0.004207 / 0.007986 (-0.003778) | 0.002716 / 0.004328 (-0.001612) | 0.049569 / 0.004250 (0.045319) | 0.048591 / 0.037052 (0.011538) | 0.252606 / 0.258489 (-0.005884) | 0.285998 / 0.293841 (-0.007843) | 0.028650 / 0.128546 (-0.099896) | 0.010652 / 0.075646 (-0.064994) | 0.203962 / 0.419271 (-0.215310) | 0.036207 / 0.043533 (-0.007326) | 0.240374 / 0.255139 (-0.014765) | 0.263564 / 0.283200 (-0.019636) | 0.017722 / 0.141683 (-0.123961) | 1.143741 / 1.452155 (-0.308414) | 1.192452 / 1.492716 (-0.300264) |\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) | 0.141329 / 0.018006 (0.123323) | 0.320169 / 0.000490 (0.319679) | 0.000240 / 0.000200 (0.000041) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019885 / 0.037411 (-0.017526) | 0.063322 / 0.014526 (0.048796) | 0.075446 / 0.176557 (-0.101110) | 0.122619 / 0.737135 (-0.614517) | 0.077175 / 0.296338 (-0.219163) |\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) | 0.281292 / 0.215209 (0.066083) | 2.796220 / 2.077655 (0.718565) | 1.456035 / 1.504120 (-0.048085) | 1.334445 / 1.541195 (-0.206750) | 1.380223 / 1.468490 (-0.088267) | 0.575895 / 4.584777 (-4.008882) | 2.375791 / 3.745712 (-1.369921) | 2.926273 / 5.269862 (-2.343589) | 1.832586 / 4.565676 (-2.733090) | 0.064323 / 0.424275 (-0.359952) | 0.005403 / 0.007607 (-0.002204) | 0.334088 / 0.226044 (0.108043) | 3.321174 / 2.268929 (1.052246) | 1.821432 / 55.444624 (-53.623193) | 1.520181 / 6.876477 (-5.356296) | 1.582487 / 2.142072 (-0.559585) | 0.645641 / 4.805227 (-4.159586) | 0.119596 / 6.500664 (-6.381068) | 0.043144 / 0.075469 (-0.032325) |\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) | 0.985104 / 1.841788 (-0.856684) | 12.518240 / 8.074308 (4.443932) | 10.017118 / 10.191392 (-0.174274) | 0.133900 / 0.680424 (-0.546524) | 0.014591 / 0.534201 (-0.519610) | 0.288326 / 0.579283 (-0.290957) | 0.262292 / 0.434364 (-0.172072) | 0.327601 / 0.540337 (-0.212736) | 0.421525 / 1.386936 (-0.965411) |\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) | 0.005546 / 0.011353 (-0.005807) | 0.003961 / 0.011008 (-0.007047) | 0.051745 / 0.038508 (0.013237) | 0.032587 / 0.023109 (0.009478) | 0.266886 / 0.275898 (-0.009012) | 0.301327 / 0.323480 (-0.022153) | 0.004273 / 0.007986 (-0.003713) | 0.002851 / 0.004328 (-0.001477) | 0.049333 / 0.004250 (0.045082) | 0.044530 / 0.037052 (0.007478) | 0.286829 / 0.258489 (0.028340) | 0.310732 / 0.293841 (0.016892) | 0.029925 / 0.128546 (-0.098621) | 0.011270 / 0.075646 (-0.064377) | 0.059071 / 0.419271 (-0.360200) | 0.033899 / 0.043533 (-0.009633) | 0.270448 / 0.255139 (0.015309) | 0.286935 / 0.283200 (0.003735) | 0.019516 / 0.141683 (-0.122167) | 1.125815 / 1.452155 (-0.326339) | 1.179893 / 1.492716 (-0.312823) |\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) | 0.096476 / 0.018006 (0.078470) | 0.305149 / 0.000490 (0.304660) | 0.000207 / 0.000200 (0.000008) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023648 / 0.037411 (-0.013763) | 0.082847 / 0.014526 (0.068322) | 0.089210 / 0.176557 (-0.087347) | 0.130194 / 0.737135 (-0.606941) | 0.091700 / 0.296338 (-0.204639) |\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) | 0.290995 / 0.215209 (0.075786) | 2.870335 / 2.077655 (0.792680) | 1.595661 / 1.504120 (0.091541) | 1.452319 / 1.541195 (-0.088876) | 1.505647 / 1.468490 (0.037157) | 0.575856 / 4.584777 (-4.008921) | 1.005527 / 3.745712 (-2.740185) | 2.927824 / 5.269862 (-2.342038) | 1.791702 / 4.565676 (-2.773975) | 0.064804 / 0.424275 (-0.359471) | 0.005203 / 0.007607 (-0.002404) | 0.348615 / 0.226044 (0.122570) | 3.463989 / 2.268929 (1.195060) | 1.947758 / 55.444624 (-53.496866) | 1.669974 / 6.876477 (-5.206502) | 1.721663 / 2.142072 (-0.420410) | 0.650999 / 4.805227 (-4.154228) | 0.117769 / 6.500664 (-6.382895) | 0.041738 / 0.075469 (-0.033731) |\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) | 1.004140 / 1.841788 (-0.837648) | 13.035487 / 8.074308 (4.961179) | 10.318152 / 10.191392 (0.126760) | 0.143776 / 0.680424 (-0.536648) | 0.016272 / 0.534201 (-0.517929) | 0.286564 / 0.579283 (-0.292719) | 0.126579 / 0.434364 (-0.307785) | 0.397253 / 0.540337 (-0.143085) | 0.424968 / 1.386936 (-0.961968) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ddb6a283d7dfccc81a9fb12e761b819fed86c7a0 \"CML watermark\")\n", "Supersede and close: #6889" ]

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[ "Hi @DarshanDeshpande,\r\n\r\nPlease note that the default format of the method `Dataset.to_json` is [JSON-Lines](https://jsonlines.org/): it passes `orient=\"records\", lines=True` to `pandas.DataFrame.to_json`. This format is specially useful for large datasets, since unlike regular JSON files, it does not require loading all the data into memory at once, but can be done iteratively by batches.\r\n\r\nIn order to read this file using the `json` library, you should parse line by line:\r\n```python\r\nwith open(\"full_dataset.json\", \"r\") as f:\r\n data = [json.loads(line) for line in f]\r\nlen(data)\r\n```\r\nMaybe we should explain this better in our docs.", "Now we explain this better in out docs:\r\n- #6895" ]

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[ "@loicmagne, @KennethEnevoldsen", "Can you give more details on why this fix works ?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6889). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Can you give more details on why this fix works ?\r\n\r\nIn order to locate this file handle problem, I defined a print_open_files_count() function using psutil library:\r\n```python\r\ndef print_open_files_count(markstr):\r\n pid = os.getpid()\r\n p = psutil.Process(pid)\r\n open_files = p.open_files()\r\n print(f\"{markstr}_Open files count: {len(open_files)}\")\r\n\r\n\r\n```\r\n\r\nand added this function as below:\r\n```python\r\n\r\nwith open(file, \"rb\") as f:\r\n print_open_files_count('Before')\r\n...\r\n...\r\n batch_idx += 1\r\nprint_open_files_count('After')\r\n```\r\nand the console output as below when loading the 'mteb/biblenlp-corpus-mmteb' dataset :\r\n```shell\r\nBefore_Open files count: 1\r\nAfter_Open files count: 1\r\nBefore_Open files count: 2\r\nAfter_Open files count: 2\r\nBefore_Open files count: 3\r\nAfter_Open files count: 3\r\n...\r\n```\r\nwhich indicated there was a file handle leakage in the dataset loading process. So I tried to close the file handle manually using os library and found it works although the core issue has not been found temporarily", "I think it would be better to find the cause and have a cleaner fix, because while your suggested fix works for a simple case, it will lead to files that will stay open if there is an error during the dataset generation for example.\r\n\r\n\r\nBtw I was not able to reproduce locally (macbook pro m2) or on colab, so it might be something related to your environment. Also `open()` should close the file at the end of the `with` block so I don't really get how you can get this issue :/", "> Btw I was not able to reproduce locally (macbook pro m2) or on colab, so it might be something related to your environment. Also `open()` should close the file at the end of the `with` block so I don't really get how you can get this issue :/\r\n\r\nhow about setting the limitation of open files to 1024?", "I was able to reproduce on colab with\r\n\r\n```\r\n!ulimit -n 256 && python -c \"from datasets import load_dataset; load_dataset('mteb/biblenlp-corpus-mmteb')\"\r\n```\r\n\r\n(also needed to `!pip install -qq git+https://github.com/huggingface/huggingface_hub.git@less-paths-info-calls` to fix a rate limit for some reason)\r\n\r\nwhich led to me find that the issue came from the `GzipFileSystem` that wasn't closing files.\r\n\r\nto reproduce:\r\n\r\n```python\r\nimport gzip\r\nimport os\r\n\r\nimport datasets\r\nimport fsspec\r\n\r\n# os.mkdir(\"tmp\")\r\n# for i in range(300):\r\n# with gzip.open(f\"tmp/{i}.txt.gz\", \"wt\") as f:\r\n# f.write(\"yo\")\r\n\r\nfor i in range(300):\r\n with fsspec.open(f\"gzip://{i}.txt::tmp/{i}.txt.gz\", \"rb\") as f:\r\n f.read()\r\n```\r\n\r\nI opened https://github.com/huggingface/datasets/pull/6893 to fix this, can you try if it works on your side ?", "ok\n\n\n\n---- Replied Message ----\n| From | Quentin ***@***.***> |\n| Date | 05/13/2024 20:28 |\n| To | ***@***.***> |\n| Cc | ***@***.***>***@***.***> |\n| Subject | Re: [huggingface/datasets] fix bug #6877 (PR #6889) |\n\nI was able to reproduce on colab with\n\n!ulimit -n 256 && python -c \"from datasets import load_dataset; load_dataset('mteb/biblenlp-corpus-mmteb')\"\n\n\n(also needed to !pip install -qq ***@***.*** to fix a rate limit for some reason)\n\nwhich lead to me find that the issue came from the GzipFileSystem that wasn't closing files.\n\nto reproduce:\n\nimportgzipimportosimportdatasetsimportfsspec# os.mkdir(\"tmp\")# for i in range(300):# with gzip.open(f\"tmp/{i}.txt.gz\", \"wt\") as f:# f.write(\"yo\")foriinrange(300):\n withfsspec.open(f\"gzip://::tmp/{i}.txt.gz\", \"rb\") asf:\n f.read()\n\nI opened #6893 to fix this, can you try if it works on your side ?\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>", "Superseded by:\r\n- #6893" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6888). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I think webdataset splits the file name and extension using the first dot no ?\r\n\r\nhttps://github.com/webdataset/webdataset/blob/945b251a872ec0d337be8f9ea17a9c5b0d017ff3/webdataset/tariterators.py#L226\r\n\r\nlinks to this function that splits on first dot\r\n\r\n```python\r\n\r\ndef base_plus_ext(path):\r\n \"\"\"Split off all file extensions.\r\n\r\n Returns base, allext.\r\n\r\n Args:\r\n path: path with extensions\r\n\r\n Returns:\r\n path with all extensions removed\r\n \"\"\"\r\n match = re.match(r\"^((?:.*/|)[^.]+)[.]([^/]*)$\", path)\r\n if not match:\r\n return None, None\r\n return match.group(1), match.group(2)\r\n```", "So maybe the original issue is actually due to one of the files containing a dot in its file name that is not for the extension\r\n\r\n```python\r\n>>> base_plus_ext(\"15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b.png\")\r\n('15_Cohen_1-s2', '0-S0929664620300449-gr3_lrg-b.png')\r\n```", "Thanks for your review, @lhoestq.\r\n\r\nI was not aware that `webdataset` requires filenames without dots in their basenames.", "I they can have dots for the extension (that becomes the column name) but not in the key used to group files into samples" ]

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[ "Hello,\r\n\r\nI'm not sure I understand. \r\nThe return value of `ds.load_faiss_index` is None as expected.\r\n\r\nI see that loading an Index on a dataset that doesn't have an `embedding` column doesn't raise an Issue. Is that the issue?\r\n\r\nSo `ds` doesn't have an `embedding` column, but we load an index that looks for it. But this will raise an issue only when calling `ds.search`." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6885). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005232 / 0.011353 (-0.006121) | 0.003749 / 0.011008 (-0.007260) | 0.063451 / 0.038508 (0.024943) | 0.031164 / 0.023109 (0.008055) | 0.252024 / 0.275898 (-0.023874) | 0.274479 / 0.323480 (-0.049001) | 0.003238 / 0.007986 (-0.004748) | 0.002668 / 0.004328 (-0.001660) | 0.049570 / 0.004250 (0.045320) | 0.046159 / 0.037052 (0.009107) | 0.273416 / 0.258489 (0.014927) | 0.299064 / 0.293841 (0.005223) | 0.027758 / 0.128546 (-0.100788) | 0.010702 / 0.075646 (-0.064944) | 0.207244 / 0.419271 (-0.212028) | 0.036139 / 0.043533 (-0.007394) | 0.249966 / 0.255139 (-0.005173) | 0.270685 / 0.283200 (-0.012515) | 0.019938 / 0.141683 (-0.121745) | 1.133642 / 1.452155 (-0.318512) | 1.170712 / 1.492716 (-0.322004) |\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) | 0.098352 / 0.018006 (0.080346) | 0.310738 / 0.000490 (0.310248) | 0.000225 / 0.000200 (0.000025) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018151 / 0.037411 (-0.019261) | 0.061169 / 0.014526 (0.046644) | 0.073275 / 0.176557 (-0.103281) | 0.120320 / 0.737135 (-0.616815) | 0.083945 / 0.296338 (-0.212394) |\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) | 0.283285 / 0.215209 (0.068075) | 2.766129 / 2.077655 (0.688475) | 1.477831 / 1.504120 (-0.026289) | 1.363365 / 1.541195 (-0.177830) | 1.402081 / 1.468490 (-0.066409) | 0.554100 / 4.584777 (-4.030677) | 2.374885 / 3.745712 (-1.370827) | 2.866260 / 5.269862 (-2.403601) | 1.775109 / 4.565676 (-2.790567) | 0.062416 / 0.424275 (-0.361859) | 0.005490 / 0.007607 (-0.002117) | 0.379293 / 0.226044 (0.153248) | 3.330534 / 2.268929 (1.061606) | 1.881648 / 55.444624 (-53.562977) | 1.549847 / 6.876477 (-5.326629) | 1.660350 / 2.142072 (-0.481722) | 0.631013 / 4.805227 (-4.174214) | 0.116646 / 6.500664 (-6.384018) | 0.042977 / 0.075469 (-0.032492) |\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) | 0.996102 / 1.841788 (-0.845685) | 12.079143 / 8.074308 (4.004835) | 9.903568 / 10.191392 (-0.287824) | 0.141447 / 0.680424 (-0.538976) | 0.014115 / 0.534201 (-0.520086) | 0.287576 / 0.579283 (-0.291707) | 0.262951 / 0.434364 (-0.171413) | 0.325167 / 0.540337 (-0.215170) | 0.425780 / 1.386936 (-0.961156) |\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) | 0.005213 / 0.011353 (-0.006139) | 0.003686 / 0.011008 (-0.007322) | 0.049963 / 0.038508 (0.011455) | 0.030635 / 0.023109 (0.007525) | 0.263992 / 0.275898 (-0.011906) | 0.289960 / 0.323480 (-0.033520) | 0.004281 / 0.007986 (-0.003704) | 0.002709 / 0.004328 (-0.001619) | 0.049147 / 0.004250 (0.044897) | 0.041036 / 0.037052 (0.003984) | 0.277621 / 0.258489 (0.019132) | 0.305689 / 0.293841 (0.011848) | 0.029342 / 0.128546 (-0.099205) | 0.010350 / 0.075646 (-0.065296) | 0.058221 / 0.419271 (-0.361051) | 0.033774 / 0.043533 (-0.009759) | 0.266163 / 0.255139 (0.011024) | 0.286866 / 0.283200 (0.003666) | 0.018463 / 0.141683 (-0.123219) | 1.136930 / 1.452155 (-0.315225) | 1.193974 / 1.492716 (-0.298742) |\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) | 0.106787 / 0.018006 (0.088781) | 0.304229 / 0.000490 (0.303740) | 0.000209 / 0.000200 (0.000009) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022066 / 0.037411 (-0.015346) | 0.075510 / 0.014526 (0.060984) | 0.087273 / 0.176557 (-0.089284) | 0.128050 / 0.737135 (-0.609085) | 0.090492 / 0.296338 (-0.205847) |\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) | 0.299034 / 0.215209 (0.083825) | 2.899115 / 2.077655 (0.821461) | 1.625169 / 1.504120 (0.121049) | 1.456491 / 1.541195 (-0.084703) | 1.433063 / 1.468490 (-0.035427) | 0.565416 / 4.584777 (-4.019361) | 0.979298 / 3.745712 (-2.766415) | 2.748965 / 5.269862 (-2.520897) | 1.738671 / 4.565676 (-2.827005) | 0.062869 / 0.424275 (-0.361407) | 0.005001 / 0.007607 (-0.002606) | 0.348534 / 0.226044 (0.122489) | 3.437791 / 2.268929 (1.168862) | 1.896804 / 55.444624 (-53.547821) | 1.658544 / 6.876477 (-5.217933) | 1.649106 / 2.142072 (-0.492966) | 0.653791 / 4.805227 (-4.151436) | 0.125522 / 6.500664 (-6.375142) | 0.051260 / 0.075469 (-0.024209) |\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) | 1.025170 / 1.841788 (-0.816617) | 12.247968 / 8.074308 (4.173660) | 9.863777 / 10.191392 (-0.327615) | 0.140498 / 0.680424 (-0.539926) | 0.015158 / 0.534201 (-0.519043) | 0.288210 / 0.579283 (-0.291073) | 0.128207 / 0.434364 (-0.306157) | 0.398735 / 0.540337 (-0.141603) | 0.418217 / 1.386936 (-0.968719) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#871eabc7b23c27d677bc06ae2cc1ec3a2a04b10f \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6883). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Do you think this is worth making a patch release for?\r\nCC: @huggingface/datasets ", "<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) | 0.005764 / 0.011353 (-0.005589) | 0.004182 / 0.011008 (-0.006826) | 0.064520 / 0.038508 (0.026012) | 0.034260 / 0.023109 (0.011151) | 0.245677 / 0.275898 (-0.030221) | 0.277889 / 0.323480 (-0.045591) | 0.004569 / 0.007986 (-0.003417) | 0.002905 / 0.004328 (-0.001423) | 0.049346 / 0.004250 (0.045095) | 0.050529 / 0.037052 (0.013476) | 0.264718 / 0.258489 (0.006229) | 0.295705 / 0.293841 (0.001864) | 0.028144 / 0.128546 (-0.100402) | 0.011048 / 0.075646 (-0.064598) | 0.206290 / 0.419271 (-0.212982) | 0.035886 / 0.043533 (-0.007647) | 0.245038 / 0.255139 (-0.010101) | 0.269835 / 0.283200 (-0.013365) | 0.018927 / 0.141683 (-0.122756) | 1.136536 / 1.452155 (-0.315619) | 1.183256 / 1.492716 (-0.309460) |\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) | 0.115372 / 0.018006 (0.097366) | 0.315471 / 0.000490 (0.314982) | 0.000238 / 0.000200 (0.000038) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021201 / 0.037411 (-0.016210) | 0.070374 / 0.014526 (0.055848) | 0.077557 / 0.176557 (-0.099000) | 0.124713 / 0.737135 (-0.612423) | 0.078850 / 0.296338 (-0.217489) |\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) | 0.278674 / 0.215209 (0.063465) | 2.739597 / 2.077655 (0.661942) | 1.438214 / 1.504120 (-0.065906) | 1.326373 / 1.541195 (-0.214822) | 1.370961 / 1.468490 (-0.097529) | 0.569160 / 4.584777 (-4.015617) | 2.411890 / 3.745712 (-1.333822) | 2.954073 / 5.269862 (-2.315788) | 1.816883 / 4.565676 (-2.748794) | 0.063123 / 0.424275 (-0.361152) | 0.005531 / 0.007607 (-0.002076) | 0.328184 / 0.226044 (0.102140) | 3.263083 / 2.268929 (0.994155) | 1.809159 / 55.444624 (-53.635465) | 1.535257 / 6.876477 (-5.341220) | 1.583428 / 2.142072 (-0.558644) | 0.642950 / 4.805227 (-4.162277) | 0.122240 / 6.500664 (-6.378424) | 0.044596 / 0.075469 (-0.030873) |\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) | 0.999993 / 1.841788 (-0.841795) | 12.941508 / 8.074308 (4.867200) | 10.417519 / 10.191392 (0.226127) | 0.134345 / 0.680424 (-0.546079) | 0.014651 / 0.534201 (-0.519550) | 0.288660 / 0.579283 (-0.290623) | 0.274550 / 0.434364 (-0.159814) | 0.327785 / 0.540337 (-0.212553) | 0.422954 / 1.386936 (-0.963982) |\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) | 0.006051 / 0.011353 (-0.005302) | 0.003926 / 0.011008 (-0.007082) | 0.051480 / 0.038508 (0.012972) | 0.036102 / 0.023109 (0.012992) | 0.273358 / 0.275898 (-0.002540) | 0.293261 / 0.323480 (-0.030219) | 0.004562 / 0.007986 (-0.003424) | 0.002918 / 0.004328 (-0.001410) | 0.050386 / 0.004250 (0.046135) | 0.048427 / 0.037052 (0.011375) | 0.280178 / 0.258489 (0.021689) | 0.314599 / 0.293841 (0.020758) | 0.030876 / 0.128546 (-0.097670) | 0.010571 / 0.075646 (-0.065076) | 0.058555 / 0.419271 (-0.360717) | 0.034974 / 0.043533 (-0.008559) | 0.266604 / 0.255139 (0.011465) | 0.284712 / 0.283200 (0.001512) | 0.020296 / 0.141683 (-0.121387) | 1.116760 / 1.452155 (-0.335395) | 1.157794 / 1.492716 (-0.334922) |\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) | 0.103777 / 0.018006 (0.085771) | 0.314267 / 0.000490 (0.313778) | 0.000226 / 0.000200 (0.000026) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023837 / 0.037411 (-0.013574) | 0.082145 / 0.014526 (0.067619) | 0.090434 / 0.176557 (-0.086123) | 0.132096 / 0.737135 (-0.605040) | 0.092426 / 0.296338 (-0.203913) |\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) | 0.299554 / 0.215209 (0.084345) | 2.932382 / 2.077655 (0.854727) | 1.549994 / 1.504120 (0.045874) | 1.454944 / 1.541195 (-0.086251) | 1.474987 / 1.468490 (0.006497) | 0.586149 / 4.584777 (-3.998628) | 0.972118 / 3.745712 (-2.773594) | 2.991719 / 5.269862 (-2.278142) | 1.876365 / 4.565676 (-2.689311) | 0.065178 / 0.424275 (-0.359098) | 0.005114 / 0.007607 (-0.002493) | 0.353704 / 0.226044 (0.127660) | 3.500940 / 2.268929 (1.232012) | 1.965581 / 55.444624 (-53.479043) | 1.662594 / 6.876477 (-5.213883) | 1.702761 / 2.142072 (-0.439311) | 0.663879 / 4.805227 (-4.141348) | 0.120036 / 6.500664 (-6.380628) | 0.043195 / 0.075469 (-0.032274) |\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) | 0.997690 / 1.841788 (-0.844098) | 13.448914 / 8.074308 (5.374606) | 10.132469 / 10.191392 (-0.058923) | 0.148493 / 0.680424 (-0.531930) | 0.016670 / 0.534201 (-0.517531) | 0.289708 / 0.579283 (-0.289575) | 0.132938 / 0.434364 (-0.301425) | 0.411425 / 0.540337 (-0.128913) | 0.430748 / 1.386936 (-0.956188) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70e38090f070d323d452b5e746686f31b1086bd8 \"CML watermark\")\n", "maybe not super important since it was not reported by users, this can be included in the next release" ]

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[ "Changing the supplier of the proxy will solve this problem, or you can visit and follow the instructions in https://hf-mirror.com " ]

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[ "The error is caused by malformed basenames of the files within the TARs:\r\n- `15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b.png` becomes `15_Cohen_1-s2` as the grouping `__key__`, and `0-S0929664620300449-gr3_lrg-b.png` as the additional key to be added to the example\r\n- whereas the intended behavior was to use `15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b` as the grouping `__key__`, and `png` as the additional key to be added to the example\r\n\r\nTo get the expected behavior, the basenames of the files within the TARs should be fixed so that they only contain a single dot, the one separating the file extension.", "I reopen it because I think we should try to give a clearer error message with a specific error code.\r\n\r\nFor now, it's hard for the user to understand where the error comes from (not everybody knows the subtleties of the webdataset filename structure).\r\n\r\n(we can transfer it to https://github.com/huggingface/dataset-viewer if it fits better there)", "same with .jpg -> https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions\r\n\r\n```\r\nError code: DatasetGenerationError\r\nException: DatasetGenerationError\r\nMessage: An error occurred while generating the dataset\r\nTraceback: Traceback (most recent call last):\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1748, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/src/services/worker/src/worker/job_runners/config/parquet_and_info.py\", line 818, in wrapped\r\n for item in generator(*args, **kwargs):\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py\", line 109, in _generate_examples\r\n example[field_name] = {\"path\": example[\"__key__\"] + \".\" + field_name, \"bytes\": example[field_name]}\r\n KeyError: 'jpg'\r\n \r\n The above exception was the direct cause of the following exception:\r\n \r\n Traceback (most recent call last):\r\n File \"/src/services/worker/src/worker/job_runners/config/parquet_and_info.py\", line 1316, in compute_config_parquet_and_info_response\r\n parquet_operations, partial = stream_convert_to_parquet(\r\n File \"/src/services/worker/src/worker/job_runners/config/parquet_and_info.py\", line 909, in stream_convert_to_parquet\r\n builder._prepare_split(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1627, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1784, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n", "More details in the spec (https://docs.google.com/document/d/18OdLjruFNX74ILmgrdiCI9J1fQZuhzzRBCHV9URWto0/edit#heading=h.hkptaq2kct2s)\r\n\r\n> The prefix of a file is all directory components of the file plus the file name component up to the first “.” in the file name.\r\n> The last extension (i.e., the portion after the last “.”) in a file name determines the file type.\r\n\r\n> Example:\r\n\timages17/image194.left.jpg\r\n\timages17/image194.right.jpg\r\n\timages17/image194.json\r\n\timages17/image12.left.jpg\r\n\timages17/image12.json\r\n\timages17/image12.right.jpg\r\n\timages3/image1459.left.jpg\r\n> \t…\r\n> When reading this with a WebDataset library, you would get the following two dictionaries back in sequence:\r\n\r\n { “__key__”: “images17/image194”, “left.jpg”: b”...”, “right.jpg”: b”...”, “json”: b”...”}\r\n { “__key__”: “images17/image12”, “left.jpg”: b”...”, “right.jpg”: b”...”, “json”: b”...”}\r\n", "OK, the issue is different in the latter case: some files are suffixed as `.jpeg`, and others as `.jpg` :)\r\n\r\nIs it a limitation of the webdataset format, or of the datasets library @lhoestq? And could we be able to give a clearer error?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6878). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005519 / 0.011353 (-0.005834) | 0.003877 / 0.011008 (-0.007131) | 0.063989 / 0.038508 (0.025480) | 0.032348 / 0.023109 (0.009239) | 0.238288 / 0.275898 (-0.037611) | 0.265337 / 0.323480 (-0.058143) | 0.004363 / 0.007986 (-0.003623) | 0.002755 / 0.004328 (-0.001574) | 0.049836 / 0.004250 (0.045585) | 0.048456 / 0.037052 (0.011403) | 0.246526 / 0.258489 (-0.011963) | 0.280753 / 0.293841 (-0.013088) | 0.027721 / 0.128546 (-0.100825) | 0.011031 / 0.075646 (-0.064615) | 0.204168 / 0.419271 (-0.215104) | 0.036203 / 0.043533 (-0.007330) | 0.238282 / 0.255139 (-0.016857) | 0.259608 / 0.283200 (-0.023591) | 0.017781 / 0.141683 (-0.123902) | 1.147821 / 1.452155 (-0.304334) | 1.194855 / 1.492716 (-0.297861) |\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) | 0.102837 / 0.018006 (0.084831) | 0.312300 / 0.000490 (0.311811) | 0.000224 / 0.000200 (0.000024) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019410 / 0.037411 (-0.018001) | 0.065114 / 0.014526 (0.050588) | 0.076828 / 0.176557 (-0.099728) | 0.121741 / 0.737135 (-0.615394) | 0.079864 / 0.296338 (-0.216474) |\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) | 0.287773 / 0.215209 (0.072564) | 2.848936 / 2.077655 (0.771281) | 1.543819 / 1.504120 (0.039700) | 1.412708 / 1.541195 (-0.128487) | 1.454685 / 1.468490 (-0.013805) | 0.580155 / 4.584777 (-4.004622) | 2.372783 / 3.745712 (-1.372929) | 2.910514 / 5.269862 (-2.359347) | 1.813542 / 4.565676 (-2.752134) | 0.064569 / 0.424275 (-0.359706) | 0.005434 / 0.007607 (-0.002173) | 0.339309 / 0.226044 (0.113265) | 3.329972 / 2.268929 (1.061043) | 1.827597 / 55.444624 (-53.617028) | 1.592324 / 6.876477 (-5.284152) | 1.619743 / 2.142072 (-0.522329) | 0.659358 / 4.805227 (-4.145869) | 0.119887 / 6.500664 (-6.380777) | 0.043649 / 0.075469 (-0.031821) |\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) | 0.984563 / 1.841788 (-0.857225) | 12.395302 / 8.074308 (4.320994) | 9.904944 / 10.191392 (-0.286448) | 0.136141 / 0.680424 (-0.544282) | 0.014779 / 0.534201 (-0.519422) | 0.286146 / 0.579283 (-0.293137) | 0.265392 / 0.434364 (-0.168972) | 0.329484 / 0.540337 (-0.210854) | 0.425530 / 1.386936 (-0.961406) |\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) | 0.005920 / 0.011353 (-0.005433) | 0.004068 / 0.011008 (-0.006940) | 0.052281 / 0.038508 (0.013773) | 0.034907 / 0.023109 (0.011798) | 0.269551 / 0.275898 (-0.006347) | 0.292390 / 0.323480 (-0.031090) | 0.004340 / 0.007986 (-0.003646) | 0.002864 / 0.004328 (-0.001464) | 0.051466 / 0.004250 (0.047216) | 0.046410 / 0.037052 (0.009358) | 0.280103 / 0.258489 (0.021614) | 0.310616 / 0.293841 (0.016775) | 0.031044 / 0.128546 (-0.097502) | 0.011004 / 0.075646 (-0.064643) | 0.059955 / 0.419271 (-0.359316) | 0.034156 / 0.043533 (-0.009377) | 0.268113 / 0.255139 (0.012974) | 0.283569 / 0.283200 (0.000369) | 0.019758 / 0.141683 (-0.121925) | 1.155583 / 1.452155 (-0.296572) | 1.225611 / 1.492716 (-0.267106) |\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) | 0.104302 / 0.018006 (0.086295) | 0.307324 / 0.000490 (0.306834) | 0.000219 / 0.000200 (0.000019) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023672 / 0.037411 (-0.013739) | 0.081110 / 0.014526 (0.066584) | 0.091783 / 0.176557 (-0.084773) | 0.131738 / 0.737135 (-0.605397) | 0.092391 / 0.296338 (-0.203948) |\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) | 0.289341 / 0.215209 (0.074132) | 2.849894 / 2.077655 (0.772239) | 1.539679 / 1.504120 (0.035559) | 1.417975 / 1.541195 (-0.123220) | 1.473631 / 1.468490 (0.005141) | 0.583013 / 4.584777 (-4.001764) | 0.960106 / 3.745712 (-2.785606) | 2.962785 / 5.269862 (-2.307077) | 1.827539 / 4.565676 (-2.738138) | 0.063875 / 0.424275 (-0.360400) | 0.005251 / 0.007607 (-0.002356) | 0.347127 / 0.226044 (0.121082) | 3.417364 / 2.268929 (1.148435) | 1.965901 / 55.444624 (-53.478723) | 1.632337 / 6.876477 (-5.244140) | 1.683100 / 2.142072 (-0.458972) | 0.664951 / 4.805227 (-4.140277) | 0.119046 / 6.500664 (-6.381618) | 0.042828 / 0.075469 (-0.032641) |\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) | 0.999569 / 1.841788 (-0.842218) | 13.366482 / 8.074308 (5.292174) | 10.635396 / 10.191392 (0.444004) | 0.133840 / 0.680424 (-0.546584) | 0.016232 / 0.534201 (-0.517969) | 0.292764 / 0.579283 (-0.286519) | 0.128558 / 0.434364 (-0.305806) | 0.405596 / 0.540337 (-0.134741) | 0.429633 / 1.386936 (-0.957303) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4d92856bbfda0d48d07e82bb520d9434d20fae4b \"CML watermark\")\n" ]

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[ "ulimit -n 8192 can solve this problem", "> ulimit -n 8192 can solve this problem\r\n\r\nWould there be a systematic way to do this ? The data loading is part of the [MTEB](https://github.com/embeddings-benchmark/mteb) library", "> > ulimit -n 8192 can solve this problem\r\n> \r\n> Would there be a systematic way to do this ? The data loading is part of the [MTEB](https://github.com/embeddings-benchmark/mteb) library\r\n\r\n I think we could modify the _prepare_split_single function", "I fixed it with https://github.com/huggingface/datasets/pull/6893, feel free to re-open if you're still having the issue :)", "> I fixed it with #6893, feel free to re-open if you're still having the issue :)\r\n\r\nThanks a lot!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6876). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "transformers 4.40.2 was release yesterday but not sure if it contains the fix", "@lhoestq yes I knew transformers 4.40.2 was released yesterday, but I had checked that it does not contain the fix: only 2 bug fixes. That is why our CI continues failing in this PR. We will have to wait until the next minor version.", "> If we urgently need some dev feature for dataset-viewer, I would suggest pushing the feature (cherry-picked) to a dedicated branch with 2.19.1 as its starting point (without opening a PR), and install datasets from that branch.\r\n\r\nI have done so:\r\n- Created a branch from 2.19.1: https://github.com/huggingface/datasets/tree/datasets-2.19.1-hotfix\r\n- Cherry-picked the commit in this PR: https://github.com/huggingface/datasets/commit/3638183e2f7e0dce8924e46e7cc21bf6d5d7adfb\r\n- Opened a PR in dataset-viewer to update datasets to this revision: https://github.com/huggingface/dataset-viewer/pull/2783" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6875). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005191 / 0.011353 (-0.006162) | 0.003691 / 0.011008 (-0.007317) | 0.063511 / 0.038508 (0.025003) | 0.031849 / 0.023109 (0.008740) | 0.251691 / 0.275898 (-0.024207) | 0.276585 / 0.323480 (-0.046895) | 0.004080 / 0.007986 (-0.003906) | 0.002751 / 0.004328 (-0.001577) | 0.049572 / 0.004250 (0.045322) | 0.043010 / 0.037052 (0.005957) | 0.267161 / 0.258489 (0.008672) | 0.301054 / 0.293841 (0.007213) | 0.028068 / 0.128546 (-0.100479) | 0.010479 / 0.075646 (-0.065167) | 0.208458 / 0.419271 (-0.210814) | 0.035688 / 0.043533 (-0.007845) | 0.255985 / 0.255139 (0.000846) | 0.296016 / 0.283200 (0.012817) | 0.017041 / 0.141683 (-0.124642) | 1.168626 / 1.452155 (-0.283528) | 1.173419 / 1.492716 (-0.319297) |\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) | 0.092975 / 0.018006 (0.074969) | 0.302309 / 0.000490 (0.301820) | 0.000219 / 0.000200 (0.000020) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018809 / 0.037411 (-0.018602) | 0.062606 / 0.014526 (0.048080) | 0.073820 / 0.176557 (-0.102736) | 0.119451 / 0.737135 (-0.617684) | 0.075086 / 0.296338 (-0.221253) |\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) | 0.280342 / 0.215209 (0.065133) | 2.742477 / 2.077655 (0.664822) | 1.409221 / 1.504120 (-0.094899) | 1.291679 / 1.541195 (-0.249516) | 1.316628 / 1.468490 (-0.151862) | 0.554942 / 4.584777 (-4.029835) | 2.363301 / 3.745712 (-1.382411) | 2.775766 / 5.269862 (-2.494096) | 1.729123 / 4.565676 (-2.836554) | 0.061254 / 0.424275 (-0.363021) | 0.005444 / 0.007607 (-0.002163) | 0.330450 / 0.226044 (0.104406) | 3.249453 / 2.268929 (0.980524) | 1.782415 / 55.444624 (-53.662210) | 1.489778 / 6.876477 (-5.386699) | 1.521809 / 2.142072 (-0.620263) | 0.626622 / 4.805227 (-4.178605) | 0.117320 / 6.500664 (-6.383344) | 0.043110 / 0.075469 (-0.032359) |\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) | 0.981954 / 1.841788 (-0.859834) | 11.706373 / 8.074308 (3.632064) | 9.870815 / 10.191392 (-0.320577) | 0.141768 / 0.680424 (-0.538656) | 0.014455 / 0.534201 (-0.519746) | 0.287451 / 0.579283 (-0.291832) | 0.264559 / 0.434364 (-0.169805) | 0.326321 / 0.540337 (-0.214017) | 0.424084 / 1.386936 (-0.962852) |\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) | 0.005461 / 0.011353 (-0.005892) | 0.003804 / 0.011008 (-0.007204) | 0.049872 / 0.038508 (0.011364) | 0.029543 / 0.023109 (0.006433) | 0.260772 / 0.275898 (-0.015126) | 0.291571 / 0.323480 (-0.031909) | 0.004305 / 0.007986 (-0.003681) | 0.002845 / 0.004328 (-0.001484) | 0.049129 / 0.004250 (0.044879) | 0.040743 / 0.037052 (0.003690) | 0.276497 / 0.258489 (0.018008) | 0.303126 / 0.293841 (0.009285) | 0.030423 / 0.128546 (-0.098123) | 0.010660 / 0.075646 (-0.064986) | 0.058857 / 0.419271 (-0.360415) | 0.033185 / 0.043533 (-0.010348) | 0.260452 / 0.255139 (0.005313) | 0.282648 / 0.283200 (-0.000552) | 0.018025 / 0.141683 (-0.123658) | 1.147432 / 1.452155 (-0.304723) | 1.192034 / 1.492716 (-0.300683) |\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) | 0.093094 / 0.018006 (0.075088) | 0.301608 / 0.000490 (0.301119) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022071 / 0.037411 (-0.015340) | 0.075244 / 0.014526 (0.060718) | 0.087157 / 0.176557 (-0.089400) | 0.127339 / 0.737135 (-0.609797) | 0.088527 / 0.296338 (-0.207812) |\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) | 0.293033 / 0.215209 (0.077824) | 2.839842 / 2.077655 (0.762188) | 1.544730 / 1.504120 (0.040610) | 1.421727 / 1.541195 (-0.119468) | 1.446054 / 1.468490 (-0.022436) | 0.573285 / 4.584777 (-4.011492) | 0.980977 / 3.745712 (-2.764735) | 2.829034 / 5.269862 (-2.440828) | 1.800747 / 4.565676 (-2.764930) | 0.064916 / 0.424275 (-0.359360) | 0.005099 / 0.007607 (-0.002508) | 0.348054 / 0.226044 (0.122009) | 3.449111 / 2.268929 (1.180182) | 1.900115 / 55.444624 (-53.544509) | 1.620564 / 6.876477 (-5.255913) | 1.675474 / 2.142072 (-0.466598) | 0.652302 / 4.805227 (-4.152925) | 0.118438 / 6.500664 (-6.382226) | 0.041779 / 0.075469 (-0.033690) |\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) | 1.003703 / 1.841788 (-0.838085) | 12.466921 / 8.074308 (4.392613) | 9.800419 / 10.191392 (-0.390973) | 0.131567 / 0.680424 (-0.548856) | 0.015684 / 0.534201 (-0.518517) | 0.288754 / 0.579283 (-0.290530) | 0.126435 / 0.434364 (-0.307929) | 0.398608 / 0.540337 (-0.141729) | 0.427043 / 1.386936 (-0.959894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#865e9b1f2ecbe934be49a2d8d46451aba4af3485 \"CML watermark\")\n" ]

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[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6874). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Before pandas-2.2.0, the function `ujson_loads` was named `loads`: https://github.com/pandas-dev/pandas/blob/v2.1.0/pandas/io/json/__init__.py#L5\r\n```python\r\nimport ujson_loads as loads\r\n```", "Thanks for your review, @lhoestq.\r\n\r\nThe performance gain depends on many factors, such as underlying data structures, file size...\r\n\r\nIn my benchmark, the performance gain was around 8.1%. ", "<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) | 0.005428 / 0.011353 (-0.005925) | 0.003682 / 0.011008 (-0.007326) | 0.064360 / 0.038508 (0.025852) | 0.032044 / 0.023109 (0.008934) | 0.238281 / 0.275898 (-0.037617) | 0.267542 / 0.323480 (-0.055937) | 0.003152 / 0.007986 (-0.004834) | 0.003292 / 0.004328 (-0.001037) | 0.050157 / 0.004250 (0.045906) | 0.048311 / 0.037052 (0.011259) | 0.253743 / 0.258489 (-0.004746) | 0.282729 / 0.293841 (-0.011112) | 0.027271 / 0.128546 (-0.101275) | 0.010238 / 0.075646 (-0.065408) | 0.208179 / 0.419271 (-0.211092) | 0.035607 / 0.043533 (-0.007925) | 0.246750 / 0.255139 (-0.008389) | 0.263362 / 0.283200 (-0.019837) | 0.018475 / 0.141683 (-0.123208) | 1.152978 / 1.452155 (-0.299177) | 1.158545 / 1.492716 (-0.334171) |\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) | 0.096645 / 0.018006 (0.078639) | 0.313186 / 0.000490 (0.312696) | 0.000209 / 0.000200 (0.000009) | 0.000052 / 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) | 0.018800 / 0.037411 (-0.018612) | 0.065833 / 0.014526 (0.051307) | 0.073668 / 0.176557 (-0.102888) | 0.120608 / 0.737135 (-0.616527) | 0.074936 / 0.296338 (-0.221403) |\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) | 0.281596 / 0.215209 (0.066387) | 2.814537 / 2.077655 (0.736882) | 1.482781 / 1.504120 (-0.021338) | 1.349770 / 1.541195 (-0.191424) | 1.371571 / 1.468490 (-0.096919) | 0.555068 / 4.584777 (-4.029709) | 2.369588 / 3.745712 (-1.376124) | 2.742771 / 5.269862 (-2.527091) | 1.711519 / 4.565676 (-2.854158) | 0.060921 / 0.424275 (-0.363354) | 0.005263 / 0.007607 (-0.002344) | 0.333721 / 0.226044 (0.107677) | 3.329598 / 2.268929 (1.060669) | 1.806983 / 55.444624 (-53.637641) | 1.515730 / 6.876477 (-5.360746) | 1.557622 / 2.142072 (-0.584451) | 0.619564 / 4.805227 (-4.185663) | 0.115503 / 6.500664 (-6.385161) | 0.041728 / 0.075469 (-0.033741) |\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) | 0.967300 / 1.841788 (-0.874487) | 11.295081 / 8.074308 (3.220773) | 9.535119 / 10.191392 (-0.656273) | 0.140232 / 0.680424 (-0.540192) | 0.013774 / 0.534201 (-0.520427) | 0.281847 / 0.579283 (-0.297436) | 0.260076 / 0.434364 (-0.174288) | 0.323657 / 0.540337 (-0.216681) | 0.421116 / 1.386936 (-0.965820) |\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) | 0.005276 / 0.011353 (-0.006077) | 0.003639 / 0.011008 (-0.007370) | 0.050451 / 0.038508 (0.011943) | 0.032787 / 0.023109 (0.009678) | 0.267029 / 0.275898 (-0.008869) | 0.299899 / 0.323480 (-0.023581) | 0.004177 / 0.007986 (-0.003809) | 0.002697 / 0.004328 (-0.001631) | 0.049631 / 0.004250 (0.045380) | 0.041942 / 0.037052 (0.004889) | 0.279249 / 0.258489 (0.020760) | 0.306512 / 0.293841 (0.012671) | 0.029340 / 0.128546 (-0.099207) | 0.010118 / 0.075646 (-0.065528) | 0.058243 / 0.419271 (-0.361028) | 0.033871 / 0.043533 (-0.009662) | 0.265949 / 0.255139 (0.010810) | 0.284263 / 0.283200 (0.001064) | 0.017351 / 0.141683 (-0.124332) | 1.107081 / 1.452155 (-0.345074) | 1.184946 / 1.492716 (-0.307770) |\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) | 0.095621 / 0.018006 (0.077614) | 0.304758 / 0.000490 (0.304269) | 0.000204 / 0.000200 (0.000004) | 0.000052 / 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) | 0.022444 / 0.037411 (-0.014967) | 0.075894 / 0.014526 (0.061368) | 0.089077 / 0.176557 (-0.087480) | 0.126960 / 0.737135 (-0.610176) | 0.089120 / 0.296338 (-0.207218) |\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) | 0.289885 / 0.215209 (0.074676) | 2.843219 / 2.077655 (0.765565) | 1.582704 / 1.504120 (0.078584) | 1.426551 / 1.541195 (-0.114644) | 1.431591 / 1.468490 (-0.036899) | 0.577265 / 4.584777 (-4.007512) | 0.956040 / 3.745712 (-2.789673) | 2.753517 / 5.269862 (-2.516345) | 1.732503 / 4.565676 (-2.833173) | 0.063511 / 0.424275 (-0.360764) | 0.005089 / 0.007607 (-0.002518) | 0.339205 / 0.226044 (0.113160) | 3.339148 / 2.268929 (1.070219) | 1.901543 / 55.444624 (-53.543081) | 1.618392 / 6.876477 (-5.258084) | 1.612885 / 2.142072 (-0.529188) | 0.656563 / 4.805227 (-4.148664) | 0.116740 / 6.500664 (-6.383924) | 0.040497 / 0.075469 (-0.034973) |\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) | 1.005568 / 1.841788 (-0.836219) | 11.872770 / 8.074308 (3.798462) | 9.867118 / 10.191392 (-0.324274) | 0.130193 / 0.680424 (-0.550231) | 0.022857 / 0.534201 (-0.511344) | 0.281908 / 0.579283 (-0.297375) | 0.125978 / 0.434364 (-0.308386) | 0.382604 / 0.540337 (-0.157733) | 0.415078 / 1.386936 (-0.971858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1eabcfaf87368a5cbfa0341aa2223f457508b3e9 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6873). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005301 / 0.011353 (-0.006052) | 0.003633 / 0.011008 (-0.007375) | 0.063414 / 0.038508 (0.024906) | 0.042406 / 0.023109 (0.019297) | 0.253414 / 0.275898 (-0.022484) | 0.276811 / 0.323480 (-0.046668) | 0.003148 / 0.007986 (-0.004837) | 0.002614 / 0.004328 (-0.001715) | 0.049208 / 0.004250 (0.044958) | 0.045819 / 0.037052 (0.008767) | 0.268027 / 0.258489 (0.009538) | 0.298821 / 0.293841 (0.004980) | 0.028460 / 0.128546 (-0.100086) | 0.010671 / 0.075646 (-0.064975) | 0.208602 / 0.419271 (-0.210669) | 0.036057 / 0.043533 (-0.007476) | 0.256079 / 0.255139 (0.000940) | 0.277040 / 0.283200 (-0.006160) | 0.019018 / 0.141683 (-0.122665) | 1.147070 / 1.452155 (-0.305085) | 1.175838 / 1.492716 (-0.316878) |\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) | 0.092216 / 0.018006 (0.074210) | 0.304774 / 0.000490 (0.304284) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018242 / 0.037411 (-0.019170) | 0.061088 / 0.014526 (0.046562) | 0.074517 / 0.176557 (-0.102039) | 0.120444 / 0.737135 (-0.616691) | 0.074628 / 0.296338 (-0.221710) |\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) | 0.283914 / 0.215209 (0.068705) | 2.859123 / 2.077655 (0.781469) | 1.495152 / 1.504120 (-0.008967) | 1.395514 / 1.541195 (-0.145681) | 1.454076 / 1.468490 (-0.014414) | 0.568758 / 4.584777 (-4.016019) | 2.461304 / 3.745712 (-1.284408) | 2.836192 / 5.269862 (-2.433670) | 1.815463 / 4.565676 (-2.750213) | 0.065762 / 0.424275 (-0.358513) | 0.006872 / 0.007607 (-0.000736) | 0.339304 / 0.226044 (0.113260) | 3.326544 / 2.268929 (1.057616) | 1.847970 / 55.444624 (-53.596654) | 1.572667 / 6.876477 (-5.303809) | 1.595717 / 2.142072 (-0.546355) | 0.644196 / 4.805227 (-4.161031) | 0.120320 / 6.500664 (-6.380344) | 0.043334 / 0.075469 (-0.032135) |\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) | 0.965807 / 1.841788 (-0.875981) | 11.628715 / 8.074308 (3.554406) | 9.485618 / 10.191392 (-0.705774) | 0.152387 / 0.680424 (-0.528037) | 0.013852 / 0.534201 (-0.520349) | 0.285833 / 0.579283 (-0.293450) | 0.263692 / 0.434364 (-0.170672) | 0.323086 / 0.540337 (-0.217251) | 0.418178 / 1.386936 (-0.968758) |\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) | 0.005505 / 0.011353 (-0.005848) | 0.003630 / 0.011008 (-0.007378) | 0.049780 / 0.038508 (0.011272) | 0.030469 / 0.023109 (0.007359) | 0.270052 / 0.275898 (-0.005846) | 0.294370 / 0.323480 (-0.029110) | 0.004207 / 0.007986 (-0.003779) | 0.002720 / 0.004328 (-0.001609) | 0.048952 / 0.004250 (0.044701) | 0.041006 / 0.037052 (0.003953) | 0.281585 / 0.258489 (0.023096) | 0.310600 / 0.293841 (0.016759) | 0.029457 / 0.128546 (-0.099089) | 0.010508 / 0.075646 (-0.065138) | 0.058090 / 0.419271 (-0.361181) | 0.032814 / 0.043533 (-0.010718) | 0.272755 / 0.255139 (0.017616) | 0.292154 / 0.283200 (0.008954) | 0.018312 / 0.141683 (-0.123371) | 1.177199 / 1.452155 (-0.274955) | 1.238803 / 1.492716 (-0.253913) |\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) | 0.093889 / 0.018006 (0.075883) | 0.303054 / 0.000490 (0.302564) | 0.000204 / 0.000200 (0.000004) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022556 / 0.037411 (-0.014856) | 0.075951 / 0.014526 (0.061425) | 0.086824 / 0.176557 (-0.089732) | 0.128091 / 0.737135 (-0.609044) | 0.088146 / 0.296338 (-0.208192) |\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) | 0.292563 / 0.215209 (0.077354) | 2.882656 / 2.077655 (0.805001) | 1.559814 / 1.504120 (0.055695) | 1.443760 / 1.541195 (-0.097435) | 1.460967 / 1.468490 (-0.007523) | 0.567812 / 4.584777 (-4.016965) | 0.964407 / 3.745712 (-2.781305) | 2.819782 / 5.269862 (-2.450079) | 1.733334 / 4.565676 (-2.832343) | 0.064745 / 0.424275 (-0.359530) | 0.005178 / 0.007607 (-0.002429) | 0.345322 / 0.226044 (0.119278) | 3.407204 / 2.268929 (1.138275) | 1.919337 / 55.444624 (-53.525288) | 1.643463 / 6.876477 (-5.233013) | 1.682191 / 2.142072 (-0.459881) | 0.639432 / 4.805227 (-4.165795) | 0.115659 / 6.500664 (-6.385005) | 0.041202 / 0.075469 (-0.034267) |\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) | 1.004664 / 1.841788 (-0.837123) | 12.043460 / 8.074308 (3.969152) | 9.856431 / 10.191392 (-0.334961) | 0.131351 / 0.680424 (-0.549072) | 0.015800 / 0.534201 (-0.518401) | 0.288211 / 0.579283 (-0.291072) | 0.126065 / 0.434364 (-0.308298) | 0.386494 / 0.540337 (-0.153843) | 0.424203 / 1.386936 (-0.962733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#039e275549627f22d9e04278d7cad2e80c644459 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6871). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Once merged, I think a patch release is needed.", "Once the CI is green, I am merging this PR and making a patch release, @huggingface/datasets. ", "<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) | 0.005352 / 0.011353 (-0.006001) | 0.004140 / 0.011008 (-0.006868) | 0.063844 / 0.038508 (0.025336) | 0.030712 / 0.023109 (0.007603) | 0.232790 / 0.275898 (-0.043108) | 0.262334 / 0.323480 (-0.061145) | 0.003264 / 0.007986 (-0.004721) | 0.002654 / 0.004328 (-0.001674) | 0.049775 / 0.004250 (0.045524) | 0.046803 / 0.037052 (0.009751) | 0.250667 / 0.258489 (-0.007822) | 0.283581 / 0.293841 (-0.010260) | 0.027660 / 0.128546 (-0.100886) | 0.010560 / 0.075646 (-0.065087) | 0.208676 / 0.419271 (-0.210596) | 0.035415 / 0.043533 (-0.008118) | 0.235380 / 0.255139 (-0.019759) | 0.261220 / 0.283200 (-0.021980) | 0.019551 / 0.141683 (-0.122132) | 1.140196 / 1.452155 (-0.311959) | 1.173021 / 1.492716 (-0.319696) |\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) | 0.092665 / 0.018006 (0.074659) | 0.301524 / 0.000490 (0.301034) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018485 / 0.037411 (-0.018927) | 0.061722 / 0.014526 (0.047196) | 0.074701 / 0.176557 (-0.101855) | 0.121443 / 0.737135 (-0.615692) | 0.076268 / 0.296338 (-0.220070) |\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) | 0.284143 / 0.215209 (0.068934) | 2.789979 / 2.077655 (0.712324) | 1.501156 / 1.504120 (-0.002964) | 1.379414 / 1.541195 (-0.161781) | 1.419092 / 1.468490 (-0.049398) | 0.554107 / 4.584777 (-4.030670) | 2.365659 / 3.745712 (-1.380053) | 2.763963 / 5.269862 (-2.505898) | 1.712587 / 4.565676 (-2.853090) | 0.060961 / 0.424275 (-0.363314) | 0.005301 / 0.007607 (-0.002306) | 0.346253 / 0.226044 (0.120209) | 3.351833 / 2.268929 (1.082905) | 1.831946 / 55.444624 (-53.612679) | 1.556530 / 6.876477 (-5.319947) | 1.574185 / 2.142072 (-0.567887) | 0.630396 / 4.805227 (-4.174831) | 0.116126 / 6.500664 (-6.384538) | 0.042391 / 0.075469 (-0.033078) |\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) | 0.981430 / 1.841788 (-0.860358) | 11.619671 / 8.074308 (3.545363) | 9.718227 / 10.191392 (-0.473165) | 0.130918 / 0.680424 (-0.549506) | 0.014116 / 0.534201 (-0.520085) | 0.288729 / 0.579283 (-0.290554) | 0.259183 / 0.434364 (-0.175181) | 0.323764 / 0.540337 (-0.216574) | 0.420336 / 1.386936 (-0.966600) |\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) | 0.005255 / 0.011353 (-0.006098) | 0.003664 / 0.011008 (-0.007344) | 0.051376 / 0.038508 (0.012868) | 0.030429 / 0.023109 (0.007320) | 0.263090 / 0.275898 (-0.012808) | 0.289959 / 0.323480 (-0.033521) | 0.004214 / 0.007986 (-0.003772) | 0.002782 / 0.004328 (-0.001546) | 0.049043 / 0.004250 (0.044793) | 0.041016 / 0.037052 (0.003964) | 0.275616 / 0.258489 (0.017127) | 0.303350 / 0.293841 (0.009509) | 0.029484 / 0.128546 (-0.099062) | 0.010329 / 0.075646 (-0.065317) | 0.058680 / 0.419271 (-0.360591) | 0.032818 / 0.043533 (-0.010715) | 0.263368 / 0.255139 (0.008229) | 0.286839 / 0.283200 (0.003640) | 0.018029 / 0.141683 (-0.123654) | 1.169207 / 1.452155 (-0.282948) | 1.206568 / 1.492716 (-0.286148) |\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) | 0.101394 / 0.018006 (0.083387) | 0.310414 / 0.000490 (0.309924) | 0.000213 / 0.000200 (0.000013) | 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) | 0.021662 / 0.037411 (-0.015749) | 0.075320 / 0.014526 (0.060794) | 0.086607 / 0.176557 (-0.089949) | 0.127268 / 0.737135 (-0.609867) | 0.088244 / 0.296338 (-0.208095) |\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) | 0.293591 / 0.215209 (0.078382) | 2.871845 / 2.077655 (0.794190) | 1.543624 / 1.504120 (0.039504) | 1.426698 / 1.541195 (-0.114497) | 1.445348 / 1.468490 (-0.023142) | 0.565156 / 4.584777 (-4.019621) | 0.961782 / 3.745712 (-2.783930) | 2.827904 / 5.269862 (-2.441958) | 1.747728 / 4.565676 (-2.817949) | 0.063275 / 0.424275 (-0.361000) | 0.004987 / 0.007607 (-0.002620) | 0.349652 / 0.226044 (0.123607) | 3.448635 / 2.268929 (1.179707) | 1.891734 / 55.444624 (-53.552890) | 1.624274 / 6.876477 (-5.252202) | 1.641531 / 2.142072 (-0.500541) | 0.642081 / 4.805227 (-4.163146) | 0.116136 / 6.500664 (-6.384528) | 0.040807 / 0.075469 (-0.034662) |\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) | 1.002090 / 1.841788 (-0.839697) | 12.401097 / 8.074308 (4.326788) | 9.799316 / 10.191392 (-0.392076) | 0.131770 / 0.680424 (-0.548654) | 0.016817 / 0.534201 (-0.517384) | 0.301136 / 0.579283 (-0.278147) | 0.136810 / 0.434364 (-0.297554) | 0.384740 / 0.540337 (-0.155598) | 0.423779 / 1.386936 (-0.963157) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ebd8233ad8142da73bc8b4d380e9a32046d7829 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6870). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.004997 / 0.011353 (-0.006356) | 0.003260 / 0.011008 (-0.007748) | 0.063342 / 0.038508 (0.024833) | 0.030399 / 0.023109 (0.007290) | 0.235665 / 0.275898 (-0.040233) | 0.256502 / 0.323480 (-0.066978) | 0.004113 / 0.007986 (-0.003873) | 0.002677 / 0.004328 (-0.001652) | 0.049614 / 0.004250 (0.045363) | 0.043075 / 0.037052 (0.006022) | 0.251788 / 0.258489 (-0.006701) | 0.280875 / 0.293841 (-0.012965) | 0.027479 / 0.128546 (-0.101067) | 0.010402 / 0.075646 (-0.065245) | 0.207296 / 0.419271 (-0.211975) | 0.035323 / 0.043533 (-0.008209) | 0.237719 / 0.255139 (-0.017420) | 0.259401 / 0.283200 (-0.023799) | 0.017574 / 0.141683 (-0.124109) | 1.109025 / 1.452155 (-0.343129) | 1.176264 / 1.492716 (-0.316452) |\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) | 0.098780 / 0.018006 (0.080774) | 0.304427 / 0.000490 (0.303937) | 0.000215 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018189 / 0.037411 (-0.019222) | 0.061356 / 0.014526 (0.046830) | 0.073568 / 0.176557 (-0.102988) | 0.122412 / 0.737135 (-0.614723) | 0.074428 / 0.296338 (-0.221911) |\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) | 0.284719 / 0.215209 (0.069510) | 2.805719 / 2.077655 (0.728064) | 1.474386 / 1.504120 (-0.029734) | 1.341552 / 1.541195 (-0.199642) | 1.385354 / 1.468490 (-0.083136) | 0.575694 / 4.584777 (-4.009083) | 2.435102 / 3.745712 (-1.310610) | 2.822424 / 5.269862 (-2.447437) | 1.747609 / 4.565676 (-2.818068) | 0.064461 / 0.424275 (-0.359815) | 0.005370 / 0.007607 (-0.002237) | 0.341511 / 0.226044 (0.115467) | 3.384546 / 2.268929 (1.115617) | 1.846960 / 55.444624 (-53.597665) | 1.549294 / 6.876477 (-5.327183) | 1.562997 / 2.142072 (-0.579075) | 0.651108 / 4.805227 (-4.154120) | 0.118502 / 6.500664 (-6.382162) | 0.042356 / 0.075469 (-0.033113) |\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) | 1.015542 / 1.841788 (-0.826245) | 11.504899 / 8.074308 (3.430591) | 9.660870 / 10.191392 (-0.530522) | 0.145255 / 0.680424 (-0.535169) | 0.014602 / 0.534201 (-0.519599) | 0.286148 / 0.579283 (-0.293135) | 0.268358 / 0.434364 (-0.166006) | 0.323648 / 0.540337 (-0.216689) | 0.427384 / 1.386936 (-0.959552) |\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) | 0.005671 / 0.011353 (-0.005681) | 0.004056 / 0.011008 (-0.006952) | 0.050673 / 0.038508 (0.012165) | 0.032334 / 0.023109 (0.009225) | 0.268541 / 0.275898 (-0.007357) | 0.294528 / 0.323480 (-0.028952) | 0.004592 / 0.007986 (-0.003393) | 0.002918 / 0.004328 (-0.001411) | 0.048857 / 0.004250 (0.044607) | 0.043072 / 0.037052 (0.006020) | 0.277031 / 0.258489 (0.018542) | 0.307189 / 0.293841 (0.013348) | 0.030500 / 0.128546 (-0.098046) | 0.010945 / 0.075646 (-0.064701) | 0.061067 / 0.419271 (-0.358204) | 0.060311 / 0.043533 (0.016778) | 0.268011 / 0.255139 (0.012872) | 0.290423 / 0.283200 (0.007224) | 0.019578 / 0.141683 (-0.122105) | 1.136353 / 1.452155 (-0.315802) | 1.196308 / 1.492716 (-0.296408) |\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) | 0.099429 / 0.018006 (0.081422) | 0.308350 / 0.000490 (0.307861) | 0.000221 / 0.000200 (0.000021) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022221 / 0.037411 (-0.015190) | 0.076744 / 0.014526 (0.062218) | 0.087768 / 0.176557 (-0.088788) | 0.129939 / 0.737135 (-0.607196) | 0.089763 / 0.296338 (-0.206576) |\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) | 0.299566 / 0.215209 (0.084357) | 2.916789 / 2.077655 (0.839134) | 1.555535 / 1.504120 (0.051415) | 1.432787 / 1.541195 (-0.108407) | 1.470983 / 1.468490 (0.002493) | 0.581468 / 4.584777 (-4.003309) | 0.993418 / 3.745712 (-2.752294) | 2.917487 / 5.269862 (-2.352374) | 1.799045 / 4.565676 (-2.766632) | 0.064520 / 0.424275 (-0.359755) | 0.005131 / 0.007607 (-0.002477) | 0.352277 / 0.226044 (0.126232) | 3.456564 / 2.268929 (1.187636) | 1.949195 / 55.444624 (-53.495430) | 1.627568 / 6.876477 (-5.248909) | 1.685246 / 2.142072 (-0.456826) | 0.653161 / 4.805227 (-4.152066) | 0.118308 / 6.500664 (-6.382356) | 0.042106 / 0.075469 (-0.033364) |\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) | 1.048028 / 1.841788 (-0.793759) | 12.425232 / 8.074308 (4.350924) | 10.127637 / 10.191392 (-0.063755) | 0.133095 / 0.680424 (-0.547329) | 0.015255 / 0.534201 (-0.518946) | 0.287927 / 0.579283 (-0.291357) | 0.129384 / 0.434364 (-0.304980) | 0.384828 / 0.540337 (-0.155510) | 0.427881 / 1.386936 (-0.959055) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a0bdb664436fad1d82c7988d5b413c76207f5037 \"CML watermark\")\n" ]

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[ "I guess the issue is caused by the customization of BuilderConfig that you use from the repo [https://github.com/BeyonderXX/InstructUIE](https://github.com/BeyonderXX/InstructUIE/blob/master/src/uie_dataset.py). You should report to them.\r\n\r\nI see you already opened an issue in their repo:\r\n- https://github.com/BeyonderXX/InstructUIE/issues/40" ]

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[ "Thanks! Feel free to ping me for examples. May not respond immediately because we're all busy but would like to help.", "Hi @natolambert, could you please give some examples of JSON files to benchmark?\r\n\r\nPlease note that this JSON file (https://huggingface.co/datasets/allenai/reward-bench-results/blob/main/eval-set-scores/Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback.json) is not in \"records\" orient; instead it has the following structure:\r\n```json\r\n{\r\n \"chat_template\": \"tulu\",\r\n \"id\": [30, 34, 35,...],\r\n \"model\": \"Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback\",\r\n \"model_type\": \"Seq. Classifier\",\r\n \"results\": [1, 1, 1, ...],\r\n \"scores_chosen\": [4.421875, 1.8916015625, 3.8515625,...],\r\n \"scores_rejected\": [-2.416015625, -1.47265625, -0.9912109375,...],\r\n \"subset\": [\"alpacaeval-easy\", \"alpacaeval-easy\", \"alpacaeval-easy\",...]\r\n \"text_chosen\": [\"<s>[INST] How do I detail a...\",...],\r\n \"text_rejected\": [\"<s>[INST] How do I detail a...\",...]\r\n}\r\n```\r\n\r\nNote that \"records\" orient should be a list (not a dict) with each row as one item of the list:\r\n```json\r\n[\r\n {\"chat_template\": \"tulu\", \"id\": 30,... },\r\n {\"chat_template\": \"tulu\", \"id\": 34,... },\r\n ...\r\n]\r\n```", "We use a mix (which is a mess), here's an example with the records orient\r\nhttps://huggingface.co/datasets/allenai/reward-bench-results/blob/main/best-of-n/alpaca_eval/tulu-13b/OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5.json\r\n\r\nThere are more in that folder, ~40mb maybe?", "@albertvillanova here's a snippet so you don't need to click\r\n```\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 0\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.076171875\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 1\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.87890625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 2\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.287109375\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 3\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 1.6337890625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 4\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 5.27734375\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 5\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.0625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 6\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 2.29296875\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 7\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 6.77734375\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 8\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 3.853515625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 9\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 4.86328125\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 10\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 2.890625\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 11\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 4.70703125\r\n}\r\n{\r\n \"config\": \"top_p=0.9;temp=1.0\",\r\n \"dataset_details\": \"helpful_base\",\r\n \"id\": [\r\n 0,\r\n 12\r\n ],\r\n \"model\": \"allenai/tulu-2-dpo-13b\",\r\n \"scores\": 4.45703125\r\n}\r\n```", "Thanks again for your feedback, @natolambert.\r\n\r\nHowever, strictly speaking, the last file is not in JSON format but in kind of JSON-Lines like format (although not properly either because there are multiple newline characters within each object). Not even pandas can read that file format.\r\n\r\nAnyway, for JSON-Lines, I would expect that `datasets` and `pandas` have the same performance for JSON Lines files, as both use `pyarrow` under the hood...\r\n\r\nA proper JSON file in records orient should be a list (a JSON array): the first character should be `[`.\r\n\r\nAnyway, I am generating a JSON file from your JSON-Lines file to test performance." ]

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[ "Potentially related:\r\n* #6864\r\n* #6850\r\n* #6848\r\n* #6819", "Hi @jerome-white, thnaks for reporting.\r\n\r\nHowever, I cannot reproduce your issue:\r\n```python\r\n>>> from datasets import get_dataset_config_names\r\n\r\n>>> get_dataset_config_names(\"open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5\")\r\n['harness_arc_challenge_25',\r\n 'harness_gsm8k_5',\r\n 'harness_hellaswag_10',\r\n 'harness_hendrycksTest_5',\r\n 'harness_hendrycksTest_abstract_algebra_5',\r\n 'harness_hendrycksTest_anatomy_5',\r\n 'harness_hendrycksTest_astronomy_5',\r\n 'harness_hendrycksTest_business_ethics_5',\r\n 'harness_hendrycksTest_clinical_knowledge_5',\r\n 'harness_hendrycksTest_college_biology_5',\r\n 'harness_hendrycksTest_college_chemistry_5',\r\n 'harness_hendrycksTest_college_computer_science_5',\r\n 'harness_hendrycksTest_college_mathematics_5',\r\n 'harness_hendrycksTest_college_medicine_5',\r\n 'harness_hendrycksTest_college_physics_5',\r\n 'harness_hendrycksTest_computer_security_5',\r\n 'harness_hendrycksTest_conceptual_physics_5',\r\n 'harness_hendrycksTest_econometrics_5',\r\n 'harness_hendrycksTest_electrical_engineering_5',\r\n 'harness_hendrycksTest_elementary_mathematics_5',\r\n 'harness_hendrycksTest_formal_logic_5',\r\n 'harness_hendrycksTest_global_facts_5',\r\n 'harness_hendrycksTest_high_school_biology_5',\r\n 'harness_hendrycksTest_high_school_chemistry_5',\r\n 'harness_hendrycksTest_high_school_computer_science_5',\r\n 'harness_hendrycksTest_high_school_european_history_5',\r\n 'harness_hendrycksTest_high_school_geography_5',\r\n 'harness_hendrycksTest_high_school_government_and_politics_5',\r\n 'harness_hendrycksTest_high_school_macroeconomics_5',\r\n 'harness_hendrycksTest_high_school_mathematics_5',\r\n 'harness_hendrycksTest_high_school_microeconomics_5',\r\n 'harness_hendrycksTest_high_school_physics_5',\r\n 'harness_hendrycksTest_high_school_psychology_5',\r\n 'harness_hendrycksTest_high_school_statistics_5',\r\n 'harness_hendrycksTest_high_school_us_history_5',\r\n 'harness_hendrycksTest_high_school_world_history_5',\r\n 'harness_hendrycksTest_human_aging_5',\r\n 'harness_hendrycksTest_human_sexuality_5',\r\n 'harness_hendrycksTest_international_law_5',\r\n 'harness_hendrycksTest_jurisprudence_5',\r\n 'harness_hendrycksTest_logical_fallacies_5',\r\n 'harness_hendrycksTest_machine_learning_5',\r\n 'harness_hendrycksTest_management_5',\r\n 'harness_hendrycksTest_marketing_5',\r\n 'harness_hendrycksTest_medical_genetics_5',\r\n 'harness_hendrycksTest_miscellaneous_5',\r\n 'harness_hendrycksTest_moral_disputes_5',\r\n 'harness_hendrycksTest_moral_scenarios_5',\r\n 'harness_hendrycksTest_nutrition_5',\r\n 'harness_hendrycksTest_philosophy_5',\r\n 'harness_hendrycksTest_prehistory_5',\r\n 'harness_hendrycksTest_professional_accounting_5',\r\n 'harness_hendrycksTest_professional_law_5',\r\n 'harness_hendrycksTest_professional_medicine_5',\r\n 'harness_hendrycksTest_professional_psychology_5',\r\n 'harness_hendrycksTest_public_relations_5',\r\n 'harness_hendrycksTest_security_studies_5',\r\n 'harness_hendrycksTest_sociology_5',\r\n 'harness_hendrycksTest_us_foreign_policy_5',\r\n 'harness_hendrycksTest_virology_5',\r\n 'harness_hendrycksTest_world_religions_5',\r\n 'harness_truthfulqa_mc_0',\r\n 'harness_winogrande_5',\r\n 'results']\r\n```\r\n\r\nMaybe it was just a temporary issue...", "> Maybe it was just a temporary issue...\r\n\r\nPerhaps. I've changed my workflow to use the hub's `HfFileSystem`, so for now this is no longer a blocker for me. I'll reopen the issue if that changes." ]

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[ "Hi @vinodrajendran001, thanks for reporting.\r\n\r\nIndeed the dataset no longer exists on the Hub. The URL https://huggingface.co/datasets/rewardsignal/reddit_writing_prompts gives 404 Not Found error." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6861). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005029 / 0.011353 (-0.006324) | 0.003217 / 0.011008 (-0.007791) | 0.062747 / 0.038508 (0.024239) | 0.030086 / 0.023109 (0.006976) | 0.251548 / 0.275898 (-0.024350) | 0.273215 / 0.323480 (-0.050265) | 0.003197 / 0.007986 (-0.004789) | 0.002706 / 0.004328 (-0.001623) | 0.049013 / 0.004250 (0.044763) | 0.044160 / 0.037052 (0.007107) | 0.266556 / 0.258489 (0.008067) | 0.291854 / 0.293841 (-0.001987) | 0.027463 / 0.128546 (-0.101083) | 0.010331 / 0.075646 (-0.065315) | 0.207195 / 0.419271 (-0.212077) | 0.035416 / 0.043533 (-0.008116) | 0.253180 / 0.255139 (-0.001959) | 0.274663 / 0.283200 (-0.008536) | 0.019132 / 0.141683 (-0.122551) | 1.174875 / 1.452155 (-0.277279) | 1.166828 / 1.492716 (-0.325888) |\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) | 0.092240 / 0.018006 (0.074234) | 0.299385 / 0.000490 (0.298895) | 0.000222 / 0.000200 (0.000022) | 0.000050 / 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) | 0.017994 / 0.037411 (-0.019417) | 0.066868 / 0.014526 (0.052342) | 0.074616 / 0.176557 (-0.101941) | 0.120632 / 0.737135 (-0.616503) | 0.074595 / 0.296338 (-0.221743) |\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) | 0.279008 / 0.215209 (0.063798) | 2.777927 / 2.077655 (0.700273) | 1.529495 / 1.504120 (0.025376) | 1.391528 / 1.541195 (-0.149666) | 1.420149 / 1.468490 (-0.048341) | 0.567526 / 4.584777 (-4.017251) | 2.400467 / 3.745712 (-1.345245) | 2.735778 / 5.269862 (-2.534083) | 1.718224 / 4.565676 (-2.847452) | 0.063009 / 0.424275 (-0.361266) | 0.005339 / 0.007607 (-0.002268) | 0.340130 / 0.226044 (0.114086) | 3.352796 / 2.268929 (1.083868) | 1.887427 / 55.444624 (-53.557198) | 1.598804 / 6.876477 (-5.277672) | 1.601566 / 2.142072 (-0.540506) | 0.640684 / 4.805227 (-4.164543) | 0.116694 / 6.500664 (-6.383970) | 0.041206 / 0.075469 (-0.034263) |\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) | 0.969163 / 1.841788 (-0.872625) | 11.475685 / 8.074308 (3.401377) | 9.397987 / 10.191392 (-0.793405) | 0.140131 / 0.680424 (-0.540293) | 0.014544 / 0.534201 (-0.519657) | 0.288122 / 0.579283 (-0.291161) | 0.262631 / 0.434364 (-0.171733) | 0.323565 / 0.540337 (-0.216773) | 0.421775 / 1.386936 (-0.965161) |\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) | 0.005059 / 0.011353 (-0.006294) | 0.003185 / 0.011008 (-0.007824) | 0.050132 / 0.038508 (0.011624) | 0.030872 / 0.023109 (0.007763) | 0.257822 / 0.275898 (-0.018076) | 0.281645 / 0.323480 (-0.041835) | 0.004129 / 0.007986 (-0.003857) | 0.002703 / 0.004328 (-0.001625) | 0.049695 / 0.004250 (0.045445) | 0.040452 / 0.037052 (0.003400) | 0.278701 / 0.258489 (0.020212) | 0.297726 / 0.293841 (0.003885) | 0.028829 / 0.128546 (-0.099717) | 0.010011 / 0.075646 (-0.065636) | 0.058569 / 0.419271 (-0.360703) | 0.032564 / 0.043533 (-0.010969) | 0.259944 / 0.255139 (0.004805) | 0.279954 / 0.283200 (-0.003245) | 0.017804 / 0.141683 (-0.123879) | 1.147748 / 1.452155 (-0.304406) | 1.188390 / 1.492716 (-0.304327) |\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) | 0.091252 / 0.018006 (0.073246) | 0.308462 / 0.000490 (0.307972) | 0.000217 / 0.000200 (0.000017) | 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) | 0.022216 / 0.037411 (-0.015195) | 0.075547 / 0.014526 (0.061021) | 0.086085 / 0.176557 (-0.090471) | 0.128326 / 0.737135 (-0.608809) | 0.087253 / 0.296338 (-0.209085) |\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) | 0.301886 / 0.215209 (0.086677) | 2.940181 / 2.077655 (0.862527) | 1.663247 / 1.504120 (0.159127) | 1.545711 / 1.541195 (0.004517) | 1.542904 / 1.468490 (0.074414) | 0.556951 / 4.584777 (-4.027826) | 0.941925 / 3.745712 (-2.803788) | 2.740733 / 5.269862 (-2.529128) | 1.722801 / 4.565676 (-2.842875) | 0.060156 / 0.424275 (-0.364120) | 0.005008 / 0.007607 (-0.002599) | 0.348988 / 0.226044 (0.122944) | 3.454972 / 2.268929 (1.186044) | 2.015828 / 55.444624 (-53.428796) | 1.737828 / 6.876477 (-5.138649) | 1.747451 / 2.142072 (-0.394622) | 0.626865 / 4.805227 (-4.178362) | 0.114565 / 6.500664 (-6.386099) | 0.040562 / 0.075469 (-0.034907) |\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) | 0.997070 / 1.841788 (-0.844718) | 11.748577 / 8.074308 (3.674269) | 9.591721 / 10.191392 (-0.599671) | 0.131613 / 0.680424 (-0.548811) | 0.016560 / 0.534201 (-0.517641) | 0.288938 / 0.579283 (-0.290345) | 0.122196 / 0.434364 (-0.312168) | 0.380217 / 0.540337 (-0.160121) | 0.429886 / 1.386936 (-0.957050) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ae4314c34dae6a5339c11f7d1a2cbdfb76144d7 \"CML watermark\")\n" ]

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[ "I think this needs to be fixed on transformers.\r\n\r\nCC: @Wauplin ", "See:\r\n- https://github.com/huggingface/transformers/issues/30618", "Opened https://github.com/huggingface/transformers/pull/30620" ]

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[ "I downloaded the jsonl file and extract it manually. \r\nThe issue seems to be related to pyarrow.json \r\n\r\n\r\n\r\npython3 -q -X faulthandler -c \"from datasets import load_dataset; load_dataset('json', data_files='/Users/scampion/Downloads/1998-09.jsonl')\"\r\nGenerating train split: 0 examples [00:00, ? examples/s]Fatal Python error: Segmentation fault\r\n\r\nThread 0x00007000000c1000 (most recent call first):\r\n <no Python frame>\r\n\r\nThread 0x00007000024df000 (most recent call first):\r\n File \"/usr/local/Cellar/python@3.11/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 331 in wait\r\n File \"/usr/local/Cellar/python@3.11/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 629 in wait\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/tqdm/_monitor.py\", line 60 in run\r\n File \"/usr/local/Cellar/python@3.11/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 1045 in _bootstrap_inner\r\n File \"/usr/local/Cellar/python@3.11/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py\", line 1002 in _bootstrap\r\n\r\nThread 0x00007ff845c66640 (most recent call first):\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 122 in _generate_tables\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1995 in _prepare_split_single\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1882 in _prepare_split\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1122 in _download_and_prepare\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/builder.py\", line 1027 in download_and_prepare\r\n File \"/Users/scampion/src/test/venv_test/lib/python3.11/site-packages/datasets/load.py\", line 2609 in load_dataset\r\n File \"<string>\", line 1 in <module>\r\n\r\nExtension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, pyarrow.lib, pyarrow._hdfsio, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, charset_normalizer.md, yaml._yaml, pyarrow._parquet, pyarrow._fs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, aiohttp._helpers, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json (total: 72)\r\n[1] 56678 segmentation fault python3 -q -X faulthandler -c\r\n/usr/local/Cellar/python@3.11/3.11.7/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown\r\n warnings.warn('resource_tracker: There appear to be %d '\r\n(venv_test)", "The error comes from data where one line contains \"null\"" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6857). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005050 / 0.011353 (-0.006303) | 0.003400 / 0.011008 (-0.007609) | 0.063488 / 0.038508 (0.024980) | 0.029112 / 0.023109 (0.006002) | 0.245872 / 0.275898 (-0.030026) | 0.270682 / 0.323480 (-0.052798) | 0.003145 / 0.007986 (-0.004841) | 0.002671 / 0.004328 (-0.001658) | 0.048862 / 0.004250 (0.044612) | 0.044330 / 0.037052 (0.007278) | 0.269066 / 0.258489 (0.010577) | 0.294806 / 0.293841 (0.000965) | 0.027717 / 0.128546 (-0.100829) | 0.010189 / 0.075646 (-0.065458) | 0.206853 / 0.419271 (-0.212419) | 0.035655 / 0.043533 (-0.007877) | 0.254554 / 0.255139 (-0.000585) | 0.275104 / 0.283200 (-0.008095) | 0.018786 / 0.141683 (-0.122897) | 1.147165 / 1.452155 (-0.304989) | 1.202755 / 1.492716 (-0.289961) |\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) | 0.094693 / 0.018006 (0.076687) | 0.303049 / 0.000490 (0.302559) | 0.000217 / 0.000200 (0.000017) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018375 / 0.037411 (-0.019036) | 0.061080 / 0.014526 (0.046554) | 0.082140 / 0.176557 (-0.094416) | 0.119962 / 0.737135 (-0.617173) | 0.074596 / 0.296338 (-0.221743) |\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) | 0.278483 / 0.215209 (0.063274) | 2.757734 / 2.077655 (0.680079) | 1.431875 / 1.504120 (-0.072245) | 1.320315 / 1.541195 (-0.220879) | 1.319433 / 1.468490 (-0.149058) | 0.566134 / 4.584777 (-4.018643) | 2.407416 / 3.745712 (-1.338296) | 2.765087 / 5.269862 (-2.504775) | 1.727335 / 4.565676 (-2.838341) | 0.065267 / 0.424275 (-0.359008) | 0.005466 / 0.007607 (-0.002141) | 0.336667 / 0.226044 (0.110622) | 3.311721 / 2.268929 (1.042792) | 1.768960 / 55.444624 (-53.675664) | 1.510854 / 6.876477 (-5.365623) | 1.499345 / 2.142072 (-0.642728) | 0.649205 / 4.805227 (-4.156022) | 0.118920 / 6.500664 (-6.381744) | 0.041570 / 0.075469 (-0.033899) |\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) | 0.976127 / 1.841788 (-0.865660) | 11.646120 / 8.074308 (3.571812) | 9.710204 / 10.191392 (-0.481188) | 0.129081 / 0.680424 (-0.551342) | 0.013874 / 0.534201 (-0.520327) | 0.287044 / 0.579283 (-0.292239) | 0.268684 / 0.434364 (-0.165680) | 0.328465 / 0.540337 (-0.211872) | 0.420433 / 1.386936 (-0.966503) |\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) | 0.005380 / 0.011353 (-0.005973) | 0.003582 / 0.011008 (-0.007427) | 0.049539 / 0.038508 (0.011031) | 0.032363 / 0.023109 (0.009253) | 0.277697 / 0.275898 (0.001799) | 0.303861 / 0.323480 (-0.019618) | 0.004226 / 0.007986 (-0.003759) | 0.002749 / 0.004328 (-0.001579) | 0.049404 / 0.004250 (0.045153) | 0.040602 / 0.037052 (0.003550) | 0.292995 / 0.258489 (0.034506) | 0.317958 / 0.293841 (0.024117) | 0.030052 / 0.128546 (-0.098494) | 0.010179 / 0.075646 (-0.065467) | 0.058600 / 0.419271 (-0.360672) | 0.033202 / 0.043533 (-0.010331) | 0.282474 / 0.255139 (0.027335) | 0.299330 / 0.283200 (0.016130) | 0.017612 / 0.141683 (-0.124071) | 1.160199 / 1.452155 (-0.291955) | 1.193248 / 1.492716 (-0.299468) |\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) | 0.093450 / 0.018006 (0.075443) | 0.311391 / 0.000490 (0.310901) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 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) | 0.022045 / 0.037411 (-0.015366) | 0.075238 / 0.014526 (0.060712) | 0.086648 / 0.176557 (-0.089908) | 0.128595 / 0.737135 (-0.608540) | 0.088785 / 0.296338 (-0.207553) |\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) | 0.283928 / 0.215209 (0.068719) | 2.780663 / 2.077655 (0.703008) | 1.517870 / 1.504120 (0.013751) | 1.402606 / 1.541195 (-0.138588) | 1.408382 / 1.468490 (-0.060108) | 0.579216 / 4.584777 (-4.005560) | 0.979349 / 3.745712 (-2.766363) | 2.847551 / 5.269862 (-2.422311) | 1.774713 / 4.565676 (-2.790963) | 0.064635 / 0.424275 (-0.359640) | 0.005038 / 0.007607 (-0.002569) | 0.341763 / 0.226044 (0.115719) | 3.351240 / 2.268929 (1.082311) | 1.871082 / 55.444624 (-53.573542) | 1.592683 / 6.876477 (-5.283794) | 1.619814 / 2.142072 (-0.522259) | 0.661628 / 4.805227 (-4.143599) | 0.118287 / 6.500664 (-6.382377) | 0.041289 / 0.075469 (-0.034180) |\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) | 1.010075 / 1.841788 (-0.831712) | 11.949132 / 8.074308 (3.874824) | 10.004906 / 10.191392 (-0.186486) | 0.138622 / 0.680424 (-0.541802) | 0.015134 / 0.534201 (-0.519067) | 0.286300 / 0.579283 (-0.292984) | 0.125163 / 0.434364 (-0.309201) | 0.378641 / 0.540337 (-0.161696) | 0.422805 / 1.386936 (-0.964131) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#282379fbd58df2b5065b70330750688acb4eb461 \"CML watermark\")\n" ]

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[ "After investigation, I have found that when a local file is uploaded to the Hub, the new line character is no longer transformed to \"\\n\": on Windows machine now it is kept as \"\\r\\n\".\r\n\r\nAny idea why this changed?\r\nCC: @lhoestq " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6855). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The CI errors were unrelated. I am merging main once they were fixed:\r\n- #6857", "The new CI tests failing are also unrelated to this PR.\r\n\r\nThey are caused the the release of huggingface_hub-0.23.0, which now raises a FutureWarning for resume_download. See:\r\n- #6860", "I have merged main once the CI was fixed:\r\n- #6861", "This PR is ready for review @huggingface/datasets.", "<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) | 0.005015 / 0.011353 (-0.006338) | 0.003576 / 0.011008 (-0.007432) | 0.063797 / 0.038508 (0.025289) | 0.030198 / 0.023109 (0.007089) | 0.237408 / 0.275898 (-0.038490) | 0.266534 / 0.323480 (-0.056946) | 0.003133 / 0.007986 (-0.004852) | 0.002639 / 0.004328 (-0.001689) | 0.049051 / 0.004250 (0.044801) | 0.044650 / 0.037052 (0.007597) | 0.253239 / 0.258489 (-0.005250) | 0.288301 / 0.293841 (-0.005540) | 0.027459 / 0.128546 (-0.101087) | 0.010457 / 0.075646 (-0.065189) | 0.207209 / 0.419271 (-0.212063) | 0.035537 / 0.043533 (-0.007996) | 0.240914 / 0.255139 (-0.014225) | 0.266817 / 0.283200 (-0.016383) | 0.019133 / 0.141683 (-0.122550) | 1.113268 / 1.452155 (-0.338887) | 1.183576 / 1.492716 (-0.309140) |\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) | 0.091218 / 0.018006 (0.073212) | 0.301690 / 0.000490 (0.301200) | 0.000234 / 0.000200 (0.000034) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018489 / 0.037411 (-0.018922) | 0.061379 / 0.014526 (0.046853) | 0.072854 / 0.176557 (-0.103703) | 0.120470 / 0.737135 (-0.616665) | 0.074206 / 0.296338 (-0.222133) |\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) | 0.281725 / 0.215209 (0.066516) | 2.805469 / 2.077655 (0.727814) | 1.478755 / 1.504120 (-0.025365) | 1.361718 / 1.541195 (-0.179477) | 1.381460 / 1.468490 (-0.087030) | 0.570758 / 4.584777 (-4.014019) | 2.434707 / 3.745712 (-1.311005) | 2.853322 / 5.269862 (-2.416539) | 1.785684 / 4.565676 (-2.779992) | 0.063551 / 0.424275 (-0.360724) | 0.005322 / 0.007607 (-0.002285) | 0.330938 / 0.226044 (0.104894) | 3.247414 / 2.268929 (0.978486) | 1.821401 / 55.444624 (-53.623223) | 1.554258 / 6.876477 (-5.322219) | 1.589263 / 2.142072 (-0.552809) | 0.651232 / 4.805227 (-4.153995) | 0.117903 / 6.500664 (-6.382761) | 0.041948 / 0.075469 (-0.033522) |\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) | 1.000386 / 1.841788 (-0.841402) | 11.645406 / 8.074308 (3.571098) | 9.567803 / 10.191392 (-0.623589) | 0.142869 / 0.680424 (-0.537555) | 0.014250 / 0.534201 (-0.519951) | 0.287054 / 0.579283 (-0.292229) | 0.268849 / 0.434364 (-0.165515) | 0.323307 / 0.540337 (-0.217031) | 0.418965 / 1.386936 (-0.967971) |\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) | 0.005216 / 0.011353 (-0.006137) | 0.003714 / 0.011008 (-0.007294) | 0.049544 / 0.038508 (0.011036) | 0.030897 / 0.023109 (0.007788) | 0.262478 / 0.275898 (-0.013420) | 0.289693 / 0.323480 (-0.033787) | 0.004226 / 0.007986 (-0.003760) | 0.002811 / 0.004328 (-0.001518) | 0.048256 / 0.004250 (0.044006) | 0.040974 / 0.037052 (0.003922) | 0.279431 / 0.258489 (0.020942) | 0.306538 / 0.293841 (0.012697) | 0.029493 / 0.128546 (-0.099054) | 0.010550 / 0.075646 (-0.065097) | 0.057826 / 0.419271 (-0.361445) | 0.033045 / 0.043533 (-0.010488) | 0.264820 / 0.255139 (0.009681) | 0.282362 / 0.283200 (-0.000838) | 0.018387 / 0.141683 (-0.123296) | 1.167956 / 1.452155 (-0.284199) | 1.247261 / 1.492716 (-0.245455) |\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) | 0.091962 / 0.018006 (0.073956) | 0.300725 / 0.000490 (0.300236) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021835 / 0.037411 (-0.015576) | 0.076954 / 0.014526 (0.062428) | 0.087224 / 0.176557 (-0.089332) | 0.127529 / 0.737135 (-0.609606) | 0.089651 / 0.296338 (-0.206688) |\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) | 0.290878 / 0.215209 (0.075669) | 2.845647 / 2.077655 (0.767992) | 1.550515 / 1.504120 (0.046395) | 1.422251 / 1.541195 (-0.118944) | 1.425366 / 1.468490 (-0.043124) | 0.559228 / 4.584777 (-4.025549) | 0.970661 / 3.745712 (-2.775051) | 2.755494 / 5.269862 (-2.514367) | 1.724285 / 4.565676 (-2.841391) | 0.062981 / 0.424275 (-0.361294) | 0.006644 / 0.007607 (-0.000963) | 0.344315 / 0.226044 (0.118270) | 3.383452 / 2.268929 (1.114524) | 1.914809 / 55.444624 (-53.529815) | 1.626189 / 6.876477 (-5.250288) | 1.614631 / 2.142072 (-0.527441) | 0.636415 / 4.805227 (-4.168812) | 0.115318 / 6.500664 (-6.385346) | 0.040337 / 0.075469 (-0.035132) |\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) | 1.006257 / 1.841788 (-0.835531) | 12.152942 / 8.074308 (4.078634) | 9.744413 / 10.191392 (-0.446979) | 0.139431 / 0.680424 (-0.540993) | 0.015601 / 0.534201 (-0.518600) | 0.287069 / 0.579283 (-0.292214) | 0.125020 / 0.434364 (-0.309344) | 0.380366 / 0.540337 (-0.159971) | 0.423486 / 1.386936 (-0.963450) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bf8a46cc7b096d5c547ea3794f6a4b6c31ea762 \"CML watermark\")\n" ]

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[ "Version 2.18 works without problem.", "@Namangarg110 @mohsen-goodarzi The bug appears because the number of urls is less than 16 and the algorithm is meant to work on the previously created mode for a single url as stated on line 314: https://github.com/huggingface/datasets/blob/1bf8a46cc7b096d5c547ea3794f6a4b6c31ea762/src/datasets/download/download_manager.py#L314\r\n\r\nIn addition, previously `map_nested` function was supported without batching and it is meant to be the default performance. \r\n\r\nOne of the shortest walk-arounds would be changing the part of the manager with the current setting:\r\n```\r\n if len(url_or_urls) >= 16:\r\n download_func = partial(self._download_batched, download_config=download_config)\r\n else:\r\n download_func = partial(self._download_single, download_config=download_config)\r\n\r\n start_time = datetime.now()\r\n with stack_multiprocessing_download_progress_bars():\r\n downloaded_path_or_paths = map_nested(\r\n download_func,\r\n url_or_urls,\r\n map_tuple=True,\r\n num_proc=download_config.num_proc,\r\n desc=\"Downloading data files\",\r\n batched=True if len(url_or_urls) >= 16 else False,\r\n batch_size=-1,\r\n )\r\n```\r\n\r\nI would suggest to consider other datasets for similar issues and make a pull-request. ", "Thanks for reporting @Namangarg110 and thanks for the investigation @MilanaShhanukova.\r\n\r\nApparently, there is an issue with the download functionality.\r\nI am proposing a fix." ]

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[ "Same issue here." ]

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[ "This should help fixing this issue: https://github.com/huggingface/datasets/pull/6832", "I'm having a similar issue when using splices:\r\n<img width=\"947\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/28941213/2153faac-e1fe-4b6d-a79b-30b2699407e8\">\r\n<img width=\"823\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/28941213/80919eca-eb6c-407d-8070-52642fdcee54\">\r\n<img width=\"914\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/28941213/5219c201-e22e-4536-acc3-a922677785ff\">\r\n\r\n\r\nIt seems to be downloading, loading, and generating splits using the entire dataset." ]

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[ "In every iteration you load the full \"random_input\" column in memory, only then to access it's i-th element.\r\n\r\nYou can try using this instead\r\n\r\na,b=dataset[i]['random_input'],dataset[i]['random_output']" ]

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[ "I encountered this same issue when loading a customized dataset for ORPO training, in which there were three columns and two of them were lists. \r\nI debugged and found that it might be caused by the type-infer mechanism and because in some chunks one of the columns is always an empty list ([]), it was regarded as ```list<item: null>```, however in some other chunk it was ```list<item: string>```. This triggered a TypeError running the function ```table_cast()```.\r\n\r\nI temporarily fixed this by re-dumping the file into a regular JSON format instead of lines of JSON dict. I didn't dig deeper for the lack of knowledge and programming ability but I do hope some developer of this repo will find and fix it." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6844). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@Wauplin This PR is indeed not needed as explained in https://github.com/huggingface/datasets/issues/6843#issuecomment-2079630389. \r\n\r\nSo, I'm closing it." ]

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[ "Thanks for reporting! I've opened a PR with a fix.", "Thanks, @mariosasko! Related question (although I guess this is a feature request): could we have some kind of exponential back-off for these retries? Here's my reasoning:\r\n- If a one-time accidental error happens, you should retry immediately and will succeed immediately.\r\n- If the Hub has a small outage on the order of minutes, you don't want to retry on the order of hours. \r\n- If the Hub has a prologned outage of several hours, we don't want to keep retrying on the order of minutes.\r\n\r\nThere actually already exists an implementation for (clipped) exponential backoff in the HuggingFace suite ([here](https://github.com/huggingface/huggingface_hub/blob/61b156a4f2e5fe1a492ed8712b26803e2122bde0/src/huggingface_hub/utils/_http.py#L306)), but I don't think it is used here.\r\n\r\nThe requirements are basically that you have an initial minimum waiting time and a maximum waiting time, and with each retry, the waiting time is doubled. We don't want to overload your servers with needless retries, especially when they're down :sweat_smile:", "Oh, I've just remembered that we added retries to the `HfFileSystem` in `huggingface_hub` 0.21.0 (see [this](https://github.com/huggingface/huggingface_hub/blob/61b156a4f2e5fe1a492ed8712b26803e2122bde0/src/huggingface_hub/hf_file_system.py#L703)), so I'll close the linked PR as we don't want to retry the retries :).\r\n\r\nI agree with the exponential backoff suggestion, so I'll open another PR.", "@mariosasko The call you linked indeed points to the implementation I linked in my previous comment, yes, but it has no configurability. Arguably, you want to have this hidden backoff under the hood that catches small network disturbances on the time scale of seconds -- perhaps even with hardcoded limits as is the case currently -- but you also still want to have a separate backoff on top of that with the configurability as suggested by @lhoestq in [the comment I linked](https://github.com/huggingface/datasets/issues/6172#issuecomment-1794876229).\r\n\r\nMy particular use-case is that I'm streaming a dataset while training on a university cluster with a very long scheduling queue. This means that when the backoff runs out of retries (which happens in under 30 seconds with the call you linked), I lose my spot on the cluster and have to queue for a whole day or more. Ideally, I should be able to specify that I want to retry for 2 to 3 hours but with more and more time between requests, so that I can smooth over hours-long outages without a setback of days.", "I also have my runs crash a surprising amount due to the dataloader crashing because of the hub, some way to address this would be nice." ]

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[ "Hi! I've opened a [PR](https://huggingface.co/datasets/GEM/wiki_auto_asset_turk/discussions/5) with a fix. While waiting for it to be merged, you can load the dataset from the PR branch with `datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")`", "Thanks Mario. Still getting the same issue though with the suggested fix\r\n\r\n#cat gem_sari.py\r\nimport datasets\r\nprint (datasets.__version__)\r\ndataset =datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")\r\n\r\nEnd up with \r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1767, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1565, in _prepare_split\r\n split_info = self.info.splits[split_generator.name]\r\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/splits.py\", line 532, in __getitem__\r\n instructions = make_file_instructions(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_reader.py\", line 121, in make_file_instructions\r\n info.name: filenames_for_dataset_split(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/naming.py\", line 72, in filenames_for_dataset_split\r\n prefix = os.path.join(path, prefix)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"<frozen posixpath>\", line 76, in join\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType", "Hmm, that's weird. Maybe try deleting the cache with `!rm -rf ~/.cache/huggingface/datasets` and then re-download.", "Tried that a couple of time. It does download the data fresh but end up with same error. Is there a way to see if its using the right version ?", "You can check the version with `python -c \"import datasets; print(datasets.__version__)\"`", "the datasets version is 2.18. \r\n\r\nI wanted to see if the command datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\") is using the right revision (refs/pr/5). \r\n\r\n\r\n\r\n\r\n\r\n " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6839). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005311 / 0.011353 (-0.006042) | 0.003691 / 0.011008 (-0.007317) | 0.063714 / 0.038508 (0.025206) | 0.030875 / 0.023109 (0.007766) | 0.251210 / 0.275898 (-0.024688) | 0.280539 / 0.323480 (-0.042941) | 0.004262 / 0.007986 (-0.003724) | 0.002723 / 0.004328 (-0.001606) | 0.049487 / 0.004250 (0.045237) | 0.045655 / 0.037052 (0.008603) | 0.264399 / 0.258489 (0.005910) | 0.306613 / 0.293841 (0.012772) | 0.028513 / 0.128546 (-0.100033) | 0.010726 / 0.075646 (-0.064921) | 0.210601 / 0.419271 (-0.208670) | 0.036918 / 0.043533 (-0.006614) | 0.257872 / 0.255139 (0.002733) | 0.278951 / 0.283200 (-0.004249) | 0.017900 / 0.141683 (-0.123783) | 1.096749 / 1.452155 (-0.355406) | 1.152603 / 1.492716 (-0.340113) |\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) | 0.095193 / 0.018006 (0.077187) | 0.303919 / 0.000490 (0.303429) | 0.000226 / 0.000200 (0.000026) | 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) | 0.018558 / 0.037411 (-0.018853) | 0.061106 / 0.014526 (0.046580) | 0.076233 / 0.176557 (-0.100323) | 0.122402 / 0.737135 (-0.614734) | 0.075579 / 0.296338 (-0.220760) |\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) | 0.283586 / 0.215209 (0.068377) | 2.766179 / 2.077655 (0.688524) | 1.481069 / 1.504120 (-0.023051) | 1.355004 / 1.541195 (-0.186191) | 1.392940 / 1.468490 (-0.075550) | 0.578878 / 4.584777 (-4.005899) | 2.432890 / 3.745712 (-1.312822) | 2.837912 / 5.269862 (-2.431949) | 1.762803 / 4.565676 (-2.802873) | 0.063339 / 0.424275 (-0.360937) | 0.005392 / 0.007607 (-0.002215) | 0.340271 / 0.226044 (0.114227) | 3.388371 / 2.268929 (1.119443) | 1.862622 / 55.444624 (-53.582002) | 1.543209 / 6.876477 (-5.333268) | 1.569858 / 2.142072 (-0.572215) | 0.651487 / 4.805227 (-4.153740) | 0.119048 / 6.500664 (-6.381616) | 0.042309 / 0.075469 (-0.033160) |\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) | 0.991161 / 1.841788 (-0.850627) | 11.778857 / 8.074308 (3.704549) | 9.586019 / 10.191392 (-0.605373) | 0.148093 / 0.680424 (-0.532331) | 0.014301 / 0.534201 (-0.519900) | 0.287983 / 0.579283 (-0.291301) | 0.266070 / 0.434364 (-0.168293) | 0.328261 / 0.540337 (-0.212076) | 0.417908 / 1.386936 (-0.969028) |\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) | 0.005252 / 0.011353 (-0.006100) | 0.003740 / 0.011008 (-0.007268) | 0.049622 / 0.038508 (0.011114) | 0.030040 / 0.023109 (0.006931) | 0.262224 / 0.275898 (-0.013674) | 0.312216 / 0.323480 (-0.011264) | 0.004213 / 0.007986 (-0.003773) | 0.002737 / 0.004328 (-0.001592) | 0.049159 / 0.004250 (0.044908) | 0.041060 / 0.037052 (0.004008) | 0.275826 / 0.258489 (0.017337) | 0.301879 / 0.293841 (0.008038) | 0.029364 / 0.128546 (-0.099182) | 0.010453 / 0.075646 (-0.065193) | 0.058095 / 0.419271 (-0.361176) | 0.032898 / 0.043533 (-0.010635) | 0.263876 / 0.255139 (0.008737) | 0.281686 / 0.283200 (-0.001514) | 0.018711 / 0.141683 (-0.122971) | 1.126056 / 1.452155 (-0.326098) | 1.185125 / 1.492716 (-0.307591) |\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) | 0.094153 / 0.018006 (0.076147) | 0.300719 / 0.000490 (0.300229) | 0.000207 / 0.000200 (0.000007) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022610 / 0.037411 (-0.014801) | 0.075502 / 0.014526 (0.060977) | 0.088858 / 0.176557 (-0.087699) | 0.129421 / 0.737135 (-0.607714) | 0.089331 / 0.296338 (-0.207007) |\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) | 0.291595 / 0.215209 (0.076386) | 2.864377 / 2.077655 (0.786722) | 1.543387 / 1.504120 (0.039267) | 1.404273 / 1.541195 (-0.136922) | 1.421964 / 1.468490 (-0.046526) | 0.579275 / 4.584777 (-4.005502) | 0.979212 / 3.745712 (-2.766500) | 2.822043 / 5.269862 (-2.447818) | 1.745015 / 4.565676 (-2.820661) | 0.064626 / 0.424275 (-0.359649) | 0.005006 / 0.007607 (-0.002601) | 0.345509 / 0.226044 (0.119464) | 3.410369 / 2.268929 (1.141440) | 1.875930 / 55.444624 (-53.568694) | 1.600841 / 6.876477 (-5.275636) | 1.611818 / 2.142072 (-0.530254) | 0.662277 / 4.805227 (-4.142950) | 0.117861 / 6.500664 (-6.382803) | 0.041061 / 0.075469 (-0.034408) |\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) | 1.007834 / 1.841788 (-0.833954) | 12.345653 / 8.074308 (4.271345) | 9.775237 / 10.191392 (-0.416155) | 0.135166 / 0.680424 (-0.545258) | 0.016799 / 0.534201 (-0.517402) | 0.289235 / 0.579283 (-0.290048) | 0.126196 / 0.434364 (-0.308168) | 0.382905 / 0.540337 (-0.157432) | 0.435248 / 1.386936 (-0.951688) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22bf5388748611a9255d8e17218d36d2f799f182 \"CML watermark\")\n" ]

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[ "There are 2 workarounds, tho:\r\n1. Download datasets from web and just load them locally\r\n2. Use metadata directly (temporal solution, since metadata can change)\r\n```\r\nimport datasets\r\nfrom datasets.data_files import DataFilesDict, DataFilesList\r\n\r\ndata_files_list = DataFilesList(\r\n [\r\n \"hf://datasets/allenai/c4@1588ec454efa1a09f29cd18ddd04fe05fc8653a2/en/c4-train.00000-of-01024.json.gz\"\r\n ],\r\n [(\"allenai/c4\", \"1588ec454efa1a09f29cd18ddd04fe05fc8653a2\")],\r\n)\r\ndata_files = DataFilesDict({\"train\": data_files_list})\r\nc4_dataset = datasets.load_dataset(\r\n path=\"allenai/c4\",\r\n data_files=data_files,\r\n split=\"train\",\r\n cache_dir=\"/datesets/cache\",\r\n download_mode=\"reuse_cache_if_exists\",\r\n token=False,\r\n)\r\n```\r\nSecond solution also shows where to find the bug. I suggest that the hashing functions should always use only original parameter `data_files`, and not the one they get after connecting to the server and creating `DataFilesDict`", "Hi! You need to set the `HF_DATASETS_OFFLINE` env variable to `1` to load cached datasets offline, as explained in the docs [here](https://huggingface.co/docs/datasets/v2.19.0/en/loading#offline).", "Just tested. It doesn't work, because of the exact problem I described above: hash of dataset config is different.\r\nThe only error difference is the reason why it cannot connect to HuggingFace (now it's 'offline mode is enabled')\r\n\r\n" ]

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