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https://api.github.com/repos/huggingface/datasets/issues/7277
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PR_kwDODunzps6AyB7O
| 7,277
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Add link to video dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7277). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-11-04T10:45:12Z
| 2024-11-04T17:05:06Z
| 2024-11-04T17:05:06Z
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CONTRIBUTOR
| null | null | null |
This PR updates https://huggingface.co/docs/datasets/loading to also link to the new video loading docs.
cc @mfarre
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PR_kwDODunzps50c8Mf
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Fix check_library_imports
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7026). 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) | 0.005637 / 0.011353 (-0.005716) | 0.003967 / 0.011008 (-0.007041) | 0.064187 / 0.038508 (0.025679) | 0.031356 / 0.023109 (0.008246) | 0.239203 / 0.275898 (-0.036695) | 0.261033 / 0.323480 (-0.062447) | 0.003256 / 0.007986 (-0.004730) | 0.003416 / 0.004328 (-0.000913) | 0.049673 / 0.004250 (0.045423) | 0.047021 / 0.037052 (0.009969) | 0.252146 / 0.258489 (-0.006343) | 0.283663 / 0.293841 (-0.010178) | 0.030223 / 0.128546 (-0.098324) | 0.012342 / 0.075646 (-0.063304) | 0.213061 / 0.419271 (-0.206211) | 0.036867 / 0.043533 (-0.006665) | 0.242589 / 0.255139 (-0.012550) | 0.265584 / 0.283200 (-0.017616) | 0.019149 / 0.141683 (-0.122533) | 1.108909 / 1.452155 (-0.343246) | 1.148484 / 1.492716 (-0.344232) |\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) | 0.096815 / 0.018006 (0.078809) | 0.299633 / 0.000490 (0.299143) | 0.000212 / 0.000200 (0.000013) | 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) | 0.018947 / 0.037411 (-0.018464) | 0.061640 / 0.014526 (0.047114) | 0.074621 / 0.176557 (-0.101935) | 0.120830 / 0.737135 (-0.616305) | 0.075472 / 0.296338 (-0.220866) |\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) | 0.284626 / 0.215209 (0.069417) | 2.805299 / 2.077655 (0.727644) | 1.469879 / 1.504120 (-0.034241) | 1.355524 / 1.541195 (-0.185671) | 1.388246 / 1.468490 (-0.080244) | 0.726740 / 4.584777 (-3.858037) | 2.387461 / 3.745712 (-1.358251) | 2.834137 / 5.269862 (-2.435724) | 1.915750 / 4.565676 (-2.649927) | 0.079223 / 0.424275 (-0.345052) | 0.005489 / 0.007607 (-0.002118) | 0.335517 / 0.226044 (0.109473) | 3.299332 / 2.268929 (1.030403) | 1.817726 / 55.444624 (-53.626898) | 1.520834 / 6.876477 (-5.355642) | 1.696285 / 2.142072 (-0.445788) | 0.815147 / 4.805227 (-3.990080) | 0.136566 / 6.500664 (-6.364098) | 0.043482 / 0.075469 (-0.031987) |\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) | 0.981382 / 1.841788 (-0.860406) | 11.472890 / 8.074308 (3.398582) | 9.274181 / 10.191392 (-0.917211) | 0.133051 / 0.680424 (-0.547373) | 0.015417 / 0.534201 (-0.518784) | 0.306098 / 0.579283 (-0.273185) | 0.261424 / 0.434364 (-0.172940) | 0.338946 / 0.540337 (-0.201391) | 0.460776 / 1.386936 (-0.926160) |\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) | 0.005806 / 0.011353 (-0.005547) | 0.004274 / 0.011008 (-0.006734) | 0.050831 / 0.038508 (0.012323) | 0.033717 / 0.023109 (0.010607) | 0.280561 / 0.275898 (0.004663) | 0.302437 / 0.323480 (-0.021043) | 0.004543 / 0.007986 (-0.003442) | 0.002905 / 0.004328 (-0.001424) | 0.048897 / 0.004250 (0.044646) | 0.041089 / 0.037052 (0.004037) | 0.291439 / 0.258489 (0.032950) | 0.319762 / 0.293841 (0.025921) | 0.033178 / 0.128546 (-0.095368) | 0.012336 / 0.075646 (-0.063311) | 0.061033 / 0.419271 (-0.358238) | 0.034018 / 0.043533 (-0.009515) | 0.278514 / 0.255139 (0.023375) | 0.295648 / 0.283200 (0.012448) | 0.018621 / 0.141683 (-0.123062) | 1.160250 / 1.452155 (-0.291905) | 1.183867 / 1.492716 (-0.308850) |\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) | 0.096354 / 0.018006 (0.078348) | 0.301907 / 0.000490 (0.301417) | 0.000205 / 0.000200 (0.000006) | 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) | 0.022357 / 0.037411 (-0.015054) | 0.076218 / 0.014526 (0.061692) | 0.088172 / 0.176557 (-0.088385) | 0.128621 / 0.737135 (-0.608515) | 0.089250 / 0.296338 (-0.207089) |\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) | 0.292633 / 0.215209 (0.077424) | 2.862456 / 2.077655 (0.784801) | 1.581967 / 1.504120 (0.077847) | 1.459822 / 1.541195 (-0.081373) | 1.475896 / 1.468490 (0.007406) | 0.728550 / 4.584777 (-3.856226) | 0.958819 / 3.745712 (-2.786893) | 3.011074 / 5.269862 (-2.258788) | 1.934393 / 4.565676 (-2.631283) | 0.079831 / 0.424275 (-0.344444) | 0.005249 / 0.007607 (-0.002358) | 0.346334 / 0.226044 (0.120290) | 3.438979 / 2.268929 (1.170051) | 1.935567 / 55.444624 (-53.509057) | 1.648723 / 6.876477 (-5.227754) | 1.685489 / 2.142072 (-0.456583) | 0.800992 / 4.805227 (-4.004236) | 0.139388 / 6.500664 (-6.361276) | 0.042518 / 0.075469 (-0.032951) |\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) | 1.031715 / 1.841788 (-0.810072) | 12.486711 / 8.074308 (4.412403) | 10.430191 / 10.191392 (0.238799) | 0.146884 / 0.680424 (-0.533540) | 0.015735 / 0.534201 (-0.518466) | 0.303938 / 0.579283 (-0.275346) | 0.140374 / 0.434364 (-0.293989) | 0.338508 / 0.540337 (-0.201830) | 0.429551 / 1.386936 (-0.957385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e32336195f3ea69988148df5f129f9f59d3ab595 \"CML watermark\")\n"
] | 2024-07-04T14:18:38Z
| 2024-07-04T14:28:36Z
| 2024-07-04T14:20:02Z
|
MEMBER
| null | null | null |
move it to after the `trust_remote_code` check
Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly
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PR_kwDODunzps50bSyD
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feat: support non streamable arrow file binary format
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"requesting review - @albertvillanova @lhoestq ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7025). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq rebased the PR, It would be really helpful to have this feature into datasets, please let me know if there is anything pending on this PR, thanks. ",
"@lhoestq \r\n\r\nHave added the unit test to generate tables for both the arrow formats - file and streaming.\r\n\r\nLet me know if we have any docs changes as well. Thanks\r\n\r\n<img width=\"568\" alt=\"Screenshot 2024-07-25 at 7 04 26 PM\" src=\"https://github.com/user-attachments/assets/69fd0906-bda9-45fa-8f7e-8092e351ac29\">\r\n",
"@lhoestq any update on this thread? Thanks",
"Timely PR!\r\nCan we please look into this?",
"<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) | 0.005737 / 0.011353 (-0.005615) | 0.003894 / 0.011008 (-0.007114) | 0.067510 / 0.038508 (0.029002) | 0.033431 / 0.023109 (0.010321) | 0.262766 / 0.275898 (-0.013132) | 0.283776 / 0.323480 (-0.039704) | 0.003296 / 0.007986 (-0.004689) | 0.003577 / 0.004328 (-0.000752) | 0.052165 / 0.004250 (0.047915) | 0.047815 / 0.037052 (0.010763) | 0.263528 / 0.258489 (0.005039) | 0.292980 / 0.293841 (-0.000861) | 0.031535 / 0.128546 (-0.097011) | 0.012966 / 0.075646 (-0.062680) | 0.218827 / 0.419271 (-0.200444) | 0.039181 / 0.043533 (-0.004352) | 0.263768 / 0.255139 (0.008629) | 0.288012 / 0.283200 (0.004813) | 0.020562 / 0.141683 (-0.121121) | 1.180547 / 1.452155 (-0.271608) | 1.269283 / 1.492716 (-0.223433) |\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) | 0.098951 / 0.018006 (0.080944) | 0.318922 / 0.000490 (0.318433) | 0.000214 / 0.000200 (0.000014) | 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) | 0.021315 / 0.037411 (-0.016097) | 0.067728 / 0.014526 (0.053202) | 0.079428 / 0.176557 (-0.097129) | 0.127472 / 0.737135 (-0.609663) | 0.080455 / 0.296338 (-0.215883) |\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) | 0.308725 / 0.215209 (0.093516) | 3.043555 / 2.077655 (0.965900) | 1.587419 / 1.504120 (0.083299) | 1.444421 / 1.541195 (-0.096774) | 1.470703 / 1.468490 (0.002213) | 0.784005 / 4.584777 (-3.800772) | 2.582064 / 3.745712 (-1.163648) | 3.140269 / 5.269862 (-2.129592) | 2.031099 / 4.565676 (-2.534577) | 0.086999 / 0.424275 (-0.337277) | 0.005923 / 0.007607 (-0.001684) | 0.361333 / 0.226044 (0.135289) | 3.587173 / 2.268929 (1.318244) | 1.961448 / 55.444624 (-53.483177) | 1.649868 / 6.876477 (-5.226609) | 1.698595 / 2.142072 (-0.443478) | 0.858552 / 4.805227 (-3.946676) | 0.146001 / 6.500664 (-6.354663) | 0.046049 / 0.075469 (-0.029421) |\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) | 1.022644 / 1.841788 (-0.819144) | 12.655994 / 8.074308 (4.581686) | 10.205832 / 10.191392 (0.014440) | 0.156073 / 0.680424 (-0.524351) | 0.015550 / 0.534201 (-0.518651) | 0.327762 / 0.579283 (-0.251521) | 0.299212 / 0.434364 (-0.135152) | 0.367549 / 0.540337 (-0.172788) | 0.474499 / 1.386936 (-0.912437) |\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) | 0.005904 / 0.011353 (-0.005448) | 0.004245 / 0.011008 (-0.006763) | 0.054309 / 0.038508 (0.015801) | 0.037490 / 0.023109 (0.014381) | 0.293540 / 0.275898 (0.017642) | 0.324068 / 0.323480 (0.000588) | 0.004675 / 0.007986 (-0.003311) | 0.003091 / 0.004328 (-0.001238) | 0.052972 / 0.004250 (0.048721) | 0.045545 / 0.037052 (0.008493) | 0.301465 / 0.258489 (0.042976) | 0.342822 / 0.293841 (0.048981) | 0.033958 / 0.128546 (-0.094588) | 0.013311 / 0.075646 (-0.062336) | 0.064050 / 0.419271 (-0.355222) | 0.038127 / 0.043533 (-0.005406) | 0.297383 / 0.255139 (0.042244) | 0.312244 / 0.283200 (0.029044) | 0.019395 / 0.141683 (-0.122288) | 1.244335 / 1.452155 (-0.207820) | 1.305547 / 1.492716 (-0.187169) |\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) | 0.101847 / 0.018006 (0.083840) | 0.330827 / 0.000490 (0.330337) | 0.000211 / 0.000200 (0.000011) | 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) | 0.025734 / 0.037411 (-0.011677) | 0.085020 / 0.014526 (0.070494) | 0.096724 / 0.176557 (-0.079833) | 0.141276 / 0.737135 (-0.595859) | 0.099150 / 0.296338 (-0.197189) |\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) | 0.316058 / 0.215209 (0.100849) | 3.059459 / 2.077655 (0.981804) | 1.638394 / 1.504120 (0.134274) | 1.505313 / 1.541195 (-0.035881) | 1.526635 / 1.468490 (0.058145) | 0.777259 / 4.584777 (-3.807518) | 1.059575 / 3.745712 (-2.686137) | 2.952334 / 5.269862 (-2.317528) | 2.003894 / 4.565676 (-2.561782) | 0.084464 / 0.424275 (-0.339811) | 0.007343 / 0.007607 (-0.000265) | 0.366218 / 0.226044 (0.140174) | 3.705588 / 2.268929 (1.436660) | 2.047029 / 55.444624 (-53.397595) | 1.766970 / 6.876477 (-5.109507) | 1.883804 / 2.142072 (-0.258268) | 0.865780 / 4.805227 (-3.939447) | 0.143180 / 6.500664 (-6.357485) | 0.044943 / 0.075469 (-0.030527) |\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) | 1.141391 / 1.841788 (-0.700397) | 13.244917 / 8.074308 (5.170609) | 10.907863 / 10.191392 (0.716471) | 0.156087 / 0.680424 (-0.524337) | 0.016487 / 0.534201 (-0.517714) | 0.331377 / 0.579283 (-0.247906) | 0.148863 / 0.434364 (-0.285501) | 0.370443 / 0.540337 (-0.169895) | 0.499647 / 1.386936 (-0.887289) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce4a0c573920607bc6c814605734091b06b860e7 \"CML watermark\")\n"
] | 2024-07-04T10:11:12Z
| 2024-07-31T06:15:50Z
| 2024-07-31T06:09:31Z
|
CONTRIBUTOR
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Support Arrow files (`.arrow`) that are in non streamable binary file formats.
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Allow direct cast from binary to Audio/Image
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008337 / 0.011353 (-0.003016) | 0.005588 / 0.011008 (-0.005421) | 0.110259 / 0.038508 (0.071751) | 0.038928 / 0.023109 (0.015819) | 0.350441 / 0.275898 (0.074543) | 0.378473 / 0.323480 (0.054993) | 0.006369 / 0.007986 (-0.001616) | 0.005730 / 0.004328 (0.001401) | 0.083042 / 0.004250 (0.078792) | 0.048686 / 0.037052 (0.011634) | 0.367561 / 0.258489 (0.109072) | 0.398073 / 0.293841 (0.104232) | 0.043247 / 0.128546 (-0.085299) | 0.013862 / 0.075646 (-0.061785) | 0.386745 / 0.419271 (-0.032527) | 0.060107 / 0.043533 (0.016574) | 0.345450 / 0.255139 (0.090311) | 0.371269 / 0.283200 (0.088069) | 0.117508 / 0.141683 (-0.024175) | 1.689345 / 1.452155 (0.237191) | 1.777119 / 1.492716 (0.284402) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248248 / 0.018006 (0.230242) | 0.505200 / 0.000490 (0.504710) | 0.015354 / 0.000200 (0.015155) | 0.000794 / 0.000054 (0.000740) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030179 / 0.037411 (-0.007232) | 0.118583 / 0.014526 (0.104057) | 0.131546 / 0.176557 (-0.045010) | 0.196173 / 0.737135 (-0.540962) | 0.140532 / 0.296338 (-0.155807) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470733 / 0.215209 (0.255524) | 4.758868 / 2.077655 (2.681213) | 2.246731 / 1.504120 (0.742611) | 1.995232 / 1.541195 (0.454037) | 2.057596 / 1.468490 (0.589106) | 0.819227 / 4.584777 (-3.765550) | 4.472093 / 3.745712 (0.726381) | 2.428154 / 5.269862 (-2.841708) | 1.748023 / 4.565676 (-2.817654) | 0.101965 / 0.424275 (-0.322310) | 0.014706 / 0.007607 (0.007098) | 0.600593 / 0.226044 (0.374548) | 5.869565 / 2.268929 (3.600637) | 2.764890 / 55.444624 (-52.679735) | 2.332112 / 6.876477 (-4.544364) | 2.486190 / 2.142072 (0.344118) | 0.979123 / 4.805227 (-3.826104) | 0.199543 / 6.500664 (-6.301121) | 0.075906 / 0.075469 (0.000436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.397694 / 1.841788 (-0.444094) | 16.910500 / 8.074308 (8.836192) | 16.174131 / 10.191392 (5.982739) | 0.173975 / 0.680424 (-0.506449) | 0.021403 / 0.534201 (-0.512798) | 0.496187 / 0.579283 (-0.083096) | 0.487369 / 0.434364 (0.053005) | 0.565924 / 0.540337 (0.025587) | 0.684965 / 1.386936 (-0.701971) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008253 / 0.011353 (-0.003100) | 0.005745 / 0.011008 (-0.005263) | 0.085848 / 0.038508 (0.047340) | 0.038753 / 0.023109 (0.015644) | 0.401278 / 0.275898 (0.125379) | 0.433132 / 0.323480 (0.109652) | 0.006112 / 0.007986 (-0.001874) | 0.005973 / 0.004328 (0.001644) | 0.085339 / 0.004250 (0.081088) | 0.053297 / 0.037052 (0.016244) | 0.400265 / 0.258489 (0.141776) | 0.455155 / 0.293841 (0.161314) | 0.043116 / 0.128546 (-0.085430) | 0.013957 / 0.075646 (-0.061689) | 0.099507 / 0.419271 (-0.319764) | 0.058858 / 0.043533 (0.015325) | 0.398030 / 0.255139 (0.142891) | 0.418171 / 0.283200 (0.134971) | 0.114392 / 0.141683 (-0.027291) | 1.683102 / 1.452155 (0.230947) | 1.801427 / 1.492716 (0.308711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242271 / 0.018006 (0.224265) | 0.494920 / 0.000490 (0.494430) | 0.007328 / 0.000200 (0.007128) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034061 / 0.037411 (-0.003351) | 0.146417 / 0.014526 (0.131891) | 0.161079 / 0.176557 (-0.015477) | 0.213999 / 0.737135 (-0.523137) | 0.166704 / 0.296338 (-0.129634) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.491214 / 0.215209 (0.276005) | 4.846946 / 2.077655 (2.769291) | 2.352595 / 1.504120 (0.848475) | 2.114055 / 1.541195 (0.572860) | 2.213537 / 1.468490 (0.745047) | 0.799625 / 4.584777 (-3.785152) | 4.440519 / 3.745712 (0.694807) | 4.476103 / 5.269862 (-0.793758) | 2.249384 / 4.565676 (-2.316292) | 0.098807 / 0.424275 (-0.325468) | 0.014463 / 0.007607 (0.006856) | 0.611793 / 0.226044 (0.385748) | 6.045710 / 2.268929 (3.776782) | 2.865957 / 55.444624 (-52.578667) | 2.454052 / 6.876477 (-4.422425) | 2.606153 / 2.142072 (0.464080) | 0.969057 / 4.805227 (-3.836170) | 0.198499 / 6.500664 (-6.302166) | 0.077012 / 0.075469 (0.001543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.497020 / 1.841788 (-0.344767) | 17.834277 / 8.074308 (9.759969) | 16.413792 / 10.191392 (6.222400) | 0.201979 / 0.680424 (-0.478445) | 0.020627 / 0.534201 (-0.513574) | 0.499767 / 0.579283 (-0.079516) | 0.496982 / 0.434364 (0.062618) | 0.579554 / 0.540337 (0.039216) | 0.693287 / 1.386936 (-0.693649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a3fee942ae159ff6cfe6a23b343605e7e12f55 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007461 / 0.011353 (-0.003892) | 0.005341 / 0.011008 (-0.005668) | 0.099252 / 0.038508 (0.060744) | 0.034723 / 0.023109 (0.011614) | 0.300980 / 0.275898 (0.025082) | 0.353860 / 0.323480 (0.030380) | 0.006100 / 0.007986 (-0.001885) | 0.004149 / 0.004328 (-0.000180) | 0.074765 / 0.004250 (0.070514) | 0.052226 / 0.037052 (0.015174) | 0.305098 / 0.258489 (0.046609) | 0.357445 / 0.293841 (0.063604) | 0.036129 / 0.128546 (-0.092417) | 0.012482 / 0.075646 (-0.063165) | 0.333321 / 0.419271 (-0.085951) | 0.050489 / 0.043533 (0.006956) | 0.294728 / 0.255139 (0.039589) | 0.322722 / 0.283200 (0.039523) | 0.101226 / 0.141683 (-0.040456) | 1.436787 / 1.452155 (-0.015367) | 1.515784 / 1.492716 (0.023068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291836 / 0.018006 (0.273830) | 0.550735 / 0.000490 (0.550245) | 0.003828 / 0.000200 (0.003628) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028490 / 0.037411 (-0.008922) | 0.109543 / 0.014526 (0.095017) | 0.119451 / 0.176557 (-0.057105) | 0.176721 / 0.737135 (-0.560415) | 0.126711 / 0.296338 (-0.169628) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418863 / 0.215209 (0.203654) | 4.179167 / 2.077655 (2.101512) | 1.965126 / 1.504120 (0.461006) | 1.775544 / 1.541195 (0.234349) | 1.882667 / 1.468490 (0.414177) | 0.709201 / 4.584777 (-3.875576) | 3.754780 / 3.745712 (0.009068) | 2.175324 / 5.269862 (-3.094538) | 1.477454 / 4.565676 (-3.088223) | 0.085527 / 0.424275 (-0.338748) | 0.012685 / 0.007607 (0.005078) | 0.514276 / 0.226044 (0.288231) | 5.140518 / 2.268929 (2.871589) | 2.436011 / 55.444624 (-53.008614) | 2.114355 / 6.876477 (-4.762122) | 2.278893 / 2.142072 (0.136821) | 0.847825 / 4.805227 (-3.957402) | 0.169579 / 6.500664 (-6.331086) | 0.065306 / 0.075469 (-0.010163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190376 / 1.841788 (-0.651411) | 14.756581 / 8.074308 (6.682272) | 14.622610 / 10.191392 (4.431218) | 0.168186 / 0.680424 (-0.512238) | 0.017527 / 0.534201 (-0.516674) | 0.427808 / 0.579283 (-0.151475) | 0.437278 / 0.434364 (0.002914) | 0.509242 / 0.540337 (-0.031095) | 0.602500 / 1.386936 (-0.784436) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007331 / 0.011353 (-0.004022) | 0.005703 / 0.011008 (-0.005305) | 0.074992 / 0.038508 (0.036484) | 0.034069 / 0.023109 (0.010960) | 0.343513 / 0.275898 (0.067615) | 0.369061 / 0.323480 (0.045582) | 0.006034 / 0.007986 (-0.001951) | 0.004344 / 0.004328 (0.000016) | 0.074678 / 0.004250 (0.070428) | 0.052262 / 0.037052 (0.015210) | 0.364758 / 0.258489 (0.106269) | 0.401130 / 0.293841 (0.107289) | 0.037635 / 0.128546 (-0.090912) | 0.012599 / 0.075646 (-0.063047) | 0.086935 / 0.419271 (-0.332337) | 0.058161 / 0.043533 (0.014628) | 0.338727 / 0.255139 (0.083589) | 0.355957 / 0.283200 (0.072757) | 0.111607 / 0.141683 (-0.030076) | 1.454357 / 1.452155 (0.002202) | 1.591529 / 1.492716 (0.098813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284379 / 0.018006 (0.266373) | 0.550720 / 0.000490 (0.550230) | 0.002868 / 0.000200 (0.002668) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028876 / 0.037411 (-0.008535) | 0.110892 / 0.014526 (0.096366) | 0.122519 / 0.176557 (-0.054038) | 0.169774 / 0.737135 (-0.567361) | 0.129381 / 0.296338 (-0.166957) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429181 / 0.215209 (0.213972) | 4.251016 / 2.077655 (2.173361) | 2.056778 / 1.504120 (0.552658) | 1.860458 / 1.541195 (0.319264) | 1.958923 / 1.468490 (0.490432) | 0.712667 / 4.584777 (-3.872110) | 3.856910 / 3.745712 (0.111198) | 3.374535 / 5.269862 (-1.895327) | 1.846744 / 4.565676 (-2.718932) | 0.087238 / 0.424275 (-0.337037) | 0.012718 / 0.007607 (0.005111) | 0.524654 / 0.226044 (0.298609) | 5.209756 / 2.268929 (2.940827) | 2.494882 / 55.444624 (-52.949743) | 2.201150 / 6.876477 (-4.675327) | 2.274189 / 2.142072 (0.132117) | 0.844728 / 4.805227 (-3.960499) | 0.167467 / 6.500664 (-6.333197) | 0.064018 / 0.075469 (-0.011451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273284 / 1.841788 (-0.568503) | 15.104413 / 8.074308 (7.030105) | 15.134025 / 10.191392 (4.942633) | 0.147568 / 0.680424 (-0.532856) | 0.017429 / 0.534201 (-0.516772) | 0.422052 / 0.579283 (-0.157231) | 0.425786 / 0.434364 (-0.008578) | 0.491753 / 0.540337 (-0.048584) | 0.585091 / 1.386936 (-0.801845) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3d26e74898e0a9dc0d78490104e2e173269ef5b \"CML watermark\")\n"
] | 2023-03-15T20:02:54Z
| 2023-03-16T14:20:44Z
| 2023-03-16T14:12:55Z
|
COLLABORATOR
| null | null | null |
To address https://github.com/huggingface/datasets/discussions/5593.
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I_kwDODunzps6QkZXI
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Convert_to_parquet fails for datasets with multiple configs
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[
"Many users have encountered the same issue, which has caused inconvenience.\r\n\r\nhttps://discuss.huggingface.co/t/convert-to-parquet-fails-for-datasets-with-multiple-configs/86733\r\n",
"Thanks for reporting.\r\n\r\nI will make the code more robust.",
"I have opened an issue in the huggingface-hub repo:\r\n- https://github.com/huggingface/huggingface_hub/issues/2419\r\n\r\nI am opening a PR to avoid calling `create_branch` if the branch already exists."
] | 2024-07-23T15:09:33Z
| 2024-07-30T10:51:02Z
| 2024-07-30T10:51:02Z
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If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
```
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Features of IterableDataset set to None by remove column
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[
"Related to https://github.com/huggingface/datasets/issues/5245",
"#self-assign",
"Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\r\n\r\n_c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377",
"> Thanks @lhoestq and @alvarobartt!\n> \n> \n> \n> This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\n> \n> \n> \n> _c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377\n\nI'm almost done with at least a temporary fix to `rename_column`, `rename_columns`, and `remove_columns`, just trying to figure out how to extend it to the `map` function itself!\n\nI'll probably open the PR for review either tomorrow or Sunday hopefully! Glad I can help you and HuggingFace 🤗 ",
"Awesome - thank you so much for this PR @alvarobartt! Is much appreciated!",
"@sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs:",
"Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :)",
"Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier!",
"Hey guys, any update around this? I'm facing the same issue with a streamable dataset. ",
"Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datasets/releases/tag/2.9.0 released last week! 🤗",
"Still facing the same issue though: \r\n```\r\nfrom datasets import IterableDatasetDict, load_dataset\r\n\r\nraw_datasets = vectorized_datasets = IterableDatasetDict()\r\n\r\n\r\nraw_datasets[\"train\"] = load_dataset(\"asennoussi/private\", split=\"train\", use_auth_token=True, streaming=True)\r\nraw_datasets[\"test\"] = load_dataset(\"asennoussi/private\", split=\"test\", use_auth_token=True, streaming=True)\r\n\r\nprint(\"Original features: \", raw_datasets['train'].features.keys())\r\n\r\n...\r\n\r\ndef prepare_dataset(batch):\r\n\r\n # load and (possibly) resample audio datato 16kHz\r\n audio = batch[\"audio\"]\r\n\r\n # compute log-Mel input features from input audio array \r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n # compute input length of audio sample in seconds\r\n batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\r\n \r\n # optional pre-processing steps\r\n transcription = batch[\"sentence\"]\r\n \r\n # encode target text to label ids\r\n batch[\"labels\"] = processor.tokenizer(transcription).input_ids\r\n batch[\"labels_length\"] = len(batch[\"labels\"])\r\n return batch\r\n...\r\nvectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\nprint(\"First sample:\", next(iter(vectorized_datasets['train'])))\r\n\r\n```\r\n\r\nOutput: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\n```",
"Hmm weird, could you try to print\r\n\r\n```python\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\n```\r\n\r\nagain after iterating over the `vectorized_datasets`? In the code above, should be last line :)",
"Didn't seem to fix it: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\nProcessed features: None\r\n```",
"Actually the culprit looks to be this one: \r\n`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format(\"torch\")`\r\nWhen I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`\r\n\r\nI still get \r\n```\r\nProcessed features: None\r\n```",
"The culprit is definitely `.map` \r\nJust validated it. \r\nAny idea please? ",
"> The culprit is definitely `.map` Just validated it. Any idea please?\r\n\r\nYes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888\r\n\r\nAnyway, now you can pass the `features` as a param to `.map` as follows:\r\n\r\n```python\r\nfrom datasets import Features\r\nvectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n features=Features(\r\n {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\r\n ),\r\n).with_format(\"torch\")\r\n```\r\n\r\nAlso, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\r\n\r\nMore information about the latter at https://github.com/huggingface/datasets/pull/5287",
"@asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment above, the alternative is to provide `features` param to `.map` :hugs:",
"Got it thanks a lot! ",
"> > The culprit is definitely `.map` Just validated it. Any idea please?\n> \n> Yes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue [#3888](https://github.com/huggingface/datasets/issues/3888)\n> \n> Anyway, now you can pass the `features` as a param to `.map` as follows:\n> \n> from datasets import Features\n> vectorized_datasets = raw_datasets.map(\n> prepare_dataset,\n> features=Features(\n> {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\n> ),\n> ).with_format(\"torch\")\n> Also, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\n> \n> More information about the latter at [#5287](https://github.com/huggingface/datasets/pull/5287)\n\nI am very late to the game, but facing the same issue still after almost 2 years. Our dataset type is IterableDatasetDict and after mapping we still get the none features. We needed the feature names to be able to in later stage use interleave_datasets() to combine two datasets together, otherwise we got errors. I just want to mention here, if somebody is using a function like prepare_dataset(batch), for whatever feature you produce here, you need to only cast these features manually to your features. Just as an example:\n\n```python\nds = IterableDatasetDict()\n\nds[\"train\"] = load_dataset(str(DATA.DIR), \"default\", split=\"train\", trust_remote_code=True, streaming=True)\n\nds = ds.cast_column(\"audio_filepath\", Audio(sampling_rate=None))\n\ndef prepare_dataset(batch):\n audio = batch[\"audio_filepath\"]\n # compute log-Mel input features from input audio array\n batch[\"input_features\"] = feature_extractor(\n audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]\n ).input_features[0]\n # encode target text to label ids\n batch[\"labels\"] = tokenizer(batch[\"text\"]).input_ids\n return batch\n\nadd_new_features = Features({'input_features': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),\n 'labels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}) # features you produce in your prepare_dataset()\n\nds = ds.map(prepare_dataset) \nds = ds.cast(add_new_features)\n```"
] | 2022-11-23T10:54:59Z
| 2025-02-07T11:36:41Z
| 2022-11-28T12:53:24Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
print("Original features: ", dataset.features.keys())
# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']
dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)
# check processed features, uh-oh!
print("Processed features: ", dataset.features)
# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```
**Print Output:**
```
Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features: None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656,
-0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```
### Expected behavior
The features should be those **not** removed by the `remove_column` method, i.e. audio and text.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)
cc @polinaeterna @lhoestq
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load_dataset does not load all of the data in my input file
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[
"You should use `datasets.load_dataset` instead of `nlp.load_dataset`, as the `nlp` package is outdated.\r\n\r\nIf switching to `datasets.load_dataset` doesn't fix the issue, sharing the JSON file (feel free to replace the data with dummy data) would be nice so that we can reproduce it ourselves."
] | 2023-11-17T14:28:50Z
| 2023-11-22T17:34:58Z
| null |
NONE
| null | null |
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### Describe the bug
I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements.
### Steps to reproduce the bug
train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION)
logger.info(len(train_dataset))
logger.info(len(valid_dataset))
Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files.
### Expected behavior
I see all 127 elements in my dataset when performing len
### Environment info
Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1
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I_kwDODunzps6Alh_w
| 6,697
|
Unable to Load Dataset in Kaggle
|
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[
"FWIW, I run `load_dataset(\"llm-blender/mix-instruct\")` and it ran successfully.\r\nCan you clear your cache and try again?\r\n\r\n\r\n### Environment Info\r\n\r\n- `datasets` version: 2.17.0\r\n- Platform: Linux-6.2.6-76060206-generic-x86_64-with-glibc2.35\r\n- Python version: 3.9.13\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 1.5.3\r\n- `fsspec` version: 2023.10.0",
"It is working on the Kaggle GPU instance but gives this same error when running on the CPU instance. Still to run it on Kaggle you require to install the latest versions of datasets and transformers.",
"This error means that `fsspec>=2023.12.0` is installed, which is incompatible with the current releases (the next `datasets` release will be the first to support it). In the meantime, downgrading `fsspec` (`pip install fsspec<=2023.12.0`) should fix the issue.",
"@mariosasko Thanks I got it to work with installing that version of fsspec."
] | 2024-02-27T18:19:34Z
| 2024-02-29T17:32:42Z
| 2024-02-29T17:32:41Z
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### Describe the bug
Having installed the latest versions of transformers==4.38.1 and datasets==2.17.1 Unable to load the dataset in a kaggle notebook.
Get this Error:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[8], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("llm-blender/mix-instruct")
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1661 ignore_verifications = ignore_verifications or save_infos
1663 # Create a dataset builder
-> 1664 builder_instance = load_dataset_builder(
1665 path=path,
1666 name=name,
1667 data_dir=data_dir,
1668 data_files=data_files,
1669 cache_dir=cache_dir,
1670 features=features,
1671 download_config=download_config,
1672 download_mode=download_mode,
1673 revision=revision,
1674 use_auth_token=use_auth_token,
1675 **config_kwargs,
1676 )
1678 # Return iterable dataset in case of streaming
1679 if streaming:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1488 download_config = download_config.copy() if download_config else DownloadConfig()
1489 download_config.use_auth_token = use_auth_token
-> 1490 dataset_module = dataset_module_factory(
1491 path,
1492 revision=revision,
1493 download_config=download_config,
1494 download_mode=download_mode,
1495 data_dir=data_dir,
1496 data_files=data_files,
1497 )
1499 # Get dataset builder class from the processing script
1500 builder_cls = import_main_class(dataset_module.module_path)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1242, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1237 if isinstance(e1, FileNotFoundError):
1238 raise FileNotFoundError(
1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1241 ) from None
-> 1242 raise e1 from None
1243 else:
1244 raise FileNotFoundError(
1245 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory."
1246 )
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1230, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1215 return HubDatasetModuleFactoryWithScript(
1216 path,
1217 revision=revision,
(...)
1220 dynamic_modules_path=dynamic_modules_path,
1221 ).get_module()
1222 else:
1223 return HubDatasetModuleFactoryWithoutScript(
1224 path,
1225 revision=revision,
1226 data_dir=data_dir,
1227 data_files=data_files,
1228 download_config=download_config,
1229 download_mode=download_mode,
-> 1230 ).get_module()
1231 except Exception as e1: # noqa: all the attempts failed, before raising the error we should check if the module is already cached.
1232 try:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:846, in HubDatasetModuleFactoryWithoutScript.get_module(self)
836 token = self.download_config.use_auth_token
837 hfh_dataset_info = HfApi(config.HF_ENDPOINT).dataset_info(
838 self.name,
839 revision=self.revision,
840 token=token,
841 timeout=100.0,
842 )
843 patterns = (
844 sanitize_patterns(self.data_files)
845 if self.data_files is not None
--> 846 else get_patterns_in_dataset_repository(hfh_dataset_info)
847 )
848 data_files = DataFilesDict.from_hf_repo(
849 patterns,
850 dataset_info=hfh_dataset_info,
851 allowed_extensions=ALL_ALLOWED_EXTENSIONS,
852 )
853 infered_module_names = {
854 key: infer_module_for_data_files(data_files_list, use_auth_token=self.download_config.use_auth_token)
855 for key, data_files_list in data_files.items()
856 }
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:471, in get_patterns_in_dataset_repository(dataset_info)
469 resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info)
470 try:
--> 471 return _get_data_files_patterns(resolver)
472 except FileNotFoundError:
473 raise FileNotFoundError(
474 f"The dataset repository at '{dataset_info.id}' doesn't contain any data file."
475 ) from None
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:99, in _get_data_files_patterns(pattern_resolver)
97 try:
98 for pattern in patterns:
---> 99 data_files = pattern_resolver(pattern)
100 if len(data_files) > 0:
101 non_empty_splits.append(split)
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:303, in _resolve_single_pattern_in_dataset_repository(dataset_info, pattern, allowed_extensions)
301 data_files_ignore = FILES_TO_IGNORE
302 fs = HfFileSystem(repo_info=dataset_info)
--> 303 glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]
304 matched_paths = [
305 filepath
306 for filepath in glob_iter
307 if filepath.name not in data_files_ignore and not filepath.name.startswith(".")
308 ]
309 if allowed_extensions is not None:
File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:606, in AbstractFileSystem.glob(self, path, maxdepth, **kwargs)
602 depth = None
604 allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
--> 606 pattern = glob_translate(path + ("/" if ends_with_sep else ""))
607 pattern = re.compile(pattern)
609 out = {
610 p: info
611 for p, info in sorted(allpaths.items())
(...)
618 )
619 }
File /opt/conda/lib/python3.10/site-packages/fsspec/utils.py:734, in glob_translate(pat)
732 continue
733 elif "**" in part:
--> 734 raise ValueError(
735 "Invalid pattern: '**' can only be an entire path component"
736 )
737 if part:
738 results.extend(_translate(part, f"{not_sep}*", not_sep))
ValueError: Invalid pattern: '**' can only be an entire path component
```
```
After loading this dataset
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("llm-blender/mix-instruct")
```
### Expected behavior
The dataset should load with desired split.
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-5.15.133+-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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PR_kwDODunzps477CuP
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|
Pin rouge_score test dependency
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-22T07:18:21Z
| 2022-07-22T07:58:14Z
| 2022-07-22T07:45:18Z
|
MEMBER
| null | null | null |
Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed.
Fix #4734
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PR_kwDODunzps5mXRo8
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Minor multi gpu doc improvement
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6649). 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) | 0.005197 / 0.011353 (-0.006156) | 0.003469 / 0.011008 (-0.007539) | 0.062306 / 0.038508 (0.023798) | 0.028417 / 0.023109 (0.005308) | 0.241147 / 0.275898 (-0.034751) | 0.270910 / 0.323480 (-0.052569) | 0.003053 / 0.007986 (-0.004933) | 0.003343 / 0.004328 (-0.000985) | 0.048044 / 0.004250 (0.043794) | 0.043738 / 0.037052 (0.006686) | 0.259274 / 0.258489 (0.000785) | 0.282522 / 0.293841 (-0.011319) | 0.027807 / 0.128546 (-0.100739) | 0.010413 / 0.075646 (-0.065234) | 0.206322 / 0.419271 (-0.212950) | 0.035770 / 0.043533 (-0.007763) | 0.243465 / 0.255139 (-0.011674) | 0.261596 / 0.283200 (-0.021604) | 0.018613 / 0.141683 (-0.123070) | 1.115509 / 1.452155 (-0.336645) | 1.189403 / 1.492716 (-0.303314) |\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) | 0.086075 / 0.018006 (0.068069) | 0.296140 / 0.000490 (0.295650) | 0.000198 / 0.000200 (-0.000002) | 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) | 0.018238 / 0.037411 (-0.019173) | 0.061783 / 0.014526 (0.047257) | 0.072014 / 0.176557 (-0.104543) | 0.118746 / 0.737135 (-0.618389) | 0.073279 / 0.296338 (-0.223060) |\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) | 0.278281 / 0.215209 (0.063072) | 2.772209 / 2.077655 (0.694555) | 1.404503 / 1.504120 (-0.099617) | 1.274753 / 1.541195 (-0.266441) | 1.304394 / 1.468490 (-0.164096) | 0.556903 / 4.584777 (-4.027874) | 2.335428 / 3.745712 (-1.410284) | 2.712255 / 5.269862 (-2.557606) | 1.722252 / 4.565676 (-2.843425) | 0.061268 / 0.424275 (-0.363007) | 0.005029 / 0.007607 (-0.002578) | 0.326112 / 0.226044 (0.100067) | 3.207917 / 2.268929 (0.938988) | 1.743513 / 55.444624 (-53.701111) | 1.476418 / 6.876477 (-5.400059) | 1.489776 / 2.142072 (-0.652297) | 0.628181 / 4.805227 (-4.177046) | 0.115959 / 6.500664 (-6.384706) | 0.041854 / 0.075469 (-0.033615) |\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) | 0.969039 / 1.841788 (-0.872749) | 11.178646 / 8.074308 (3.104338) | 9.639716 / 10.191392 (-0.551676) | 0.139750 / 0.680424 (-0.540674) | 0.014230 / 0.534201 (-0.519971) | 0.285318 / 0.579283 (-0.293965) | 0.260788 / 0.434364 (-0.173576) | 0.324183 / 0.540337 (-0.216154) | 0.416326 / 1.386936 (-0.970610) |\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) | 0.005149 / 0.011353 (-0.006204) | 0.003469 / 0.011008 (-0.007539) | 0.049761 / 0.038508 (0.011253) | 0.030723 / 0.023109 (0.007614) | 0.271562 / 0.275898 (-0.004336) | 0.297843 / 0.323480 (-0.025637) | 0.004296 / 0.007986 (-0.003690) | 0.002704 / 0.004328 (-0.001624) | 0.048890 / 0.004250 (0.044640) | 0.044776 / 0.037052 (0.007723) | 0.285490 / 0.258489 (0.027001) | 0.312888 / 0.293841 (0.019047) | 0.046239 / 0.128546 (-0.082307) | 0.010238 / 0.075646 (-0.065408) | 0.057968 / 0.419271 (-0.361304) | 0.033295 / 0.043533 (-0.010238) | 0.274320 / 0.255139 (0.019181) | 0.296199 / 0.283200 (0.012999) | 0.017856 / 0.141683 (-0.123827) | 1.147532 / 1.452155 (-0.304622) | 1.211647 / 1.492716 (-0.281070) |\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) | 0.089655 / 0.018006 (0.071649) | 0.297275 / 0.000490 (0.296785) | 0.000207 / 0.000200 (0.000007) | 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) | 0.021739 / 0.037411 (-0.015672) | 0.075041 / 0.014526 (0.060515) | 0.085754 / 0.176557 (-0.090802) | 0.124512 / 0.737135 (-0.612623) | 0.086926 / 0.296338 (-0.209412) |\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) | 0.290306 / 0.215209 (0.075097) | 2.847404 / 2.077655 (0.769749) | 1.606175 / 1.504120 (0.102055) | 1.483220 / 1.541195 (-0.057974) | 1.514551 / 1.468490 (0.046061) | 0.559332 / 4.584777 (-4.025445) | 2.403089 / 3.745712 (-1.342624) | 2.715179 / 5.269862 (-2.554683) | 1.688340 / 4.565676 (-2.877337) | 0.062057 / 0.424275 (-0.362218) | 0.004955 / 0.007607 (-0.002652) | 0.338909 / 0.226044 (0.112865) | 3.356882 / 2.268929 (1.087954) | 1.942259 / 55.444624 (-53.502366) | 1.675195 / 6.876477 (-5.201282) | 1.688158 / 2.142072 (-0.453914) | 0.637270 / 4.805227 (-4.167957) | 0.114314 / 6.500664 (-6.386350) | 0.040677 / 0.075469 (-0.034792) |\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) | 1.022126 / 1.841788 (-0.819661) | 11.783359 / 8.074308 (3.709051) | 10.247652 / 10.191392 (0.056260) | 0.138188 / 0.680424 (-0.542236) | 0.014850 / 0.534201 (-0.519351) | 0.287414 / 0.579283 (-0.291869) | 0.274393 / 0.434364 (-0.159971) | 0.327255 / 0.540337 (-0.213082) | 0.416355 / 1.386936 (-0.970581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#727a952367966a98b759d54f333b1e2c28cfd4d4 \"CML watermark\")\n"
] | 2024-02-08T11:17:24Z
| 2024-02-08T11:23:35Z
| 2024-02-08T11:17:35Z
|
MEMBER
| null | null | null |
just added torch.no_grad and eval()
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I_kwDODunzps59Gm-o
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`datasets/downloads` cleanup tool
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[] | 2024-01-24T18:52:10Z
| 2024-01-24T18:55:09Z
| null |
CONTRIBUTOR
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### Feature request
Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files
e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do:
```
sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+
sudo find /data/huggingface/datasets/downloads -type d -empty -delete
```
could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space
e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO.
Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not.
Thank you
@Wauplin (requested to be tagged)
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PR_kwDODunzps46Sv_f
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Fix WMT dataset loading issue and docs update (Re-opened)
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-24T07:26:16Z
| 2022-07-08T15:39:20Z
| 2022-07-08T15:27:44Z
|
CONTRIBUTOR
| null | null | null |
This PR is a fix for #4354
Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.
Let me know, if any additional changes are required.
Thanks
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PR_kwDODunzps48k8y4
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Require torchaudio<0.12.0 in docs
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-03T13:32:00Z
| 2022-08-03T15:07:43Z
| 2022-08-03T14:52:16Z
|
MEMBER
| null | null | null |
This PR adds to docs the requirement of torchaudio<0.12.0 to avoid RuntimeError.
Subsequent to PR:
- #4777
|
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I_kwDODunzps5iiGBQ
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Is it possible or how to define a `datasets.Sequence` that could potentially be either a dict, a str, or None?
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"Hi ! `datasets` uses Apache Arrow as backend to store the data, and it requires each column to have a fixed type. Therefore a column can't have a mix of dicts/lists/strings.\r\n\r\nThough it's possible to have one (nullable) field for each type:\r\n```python\r\nfeatures = Features({\r\n \"text_alone\": Value(\"string\"),\r\n \"text_with_idxes\": {\r\n \"text\": Value(\"string\"),\r\n \"idxes\": Value(\"int64\")\r\n }\r\n})\r\n```\r\n\r\nbut you'd have to reformat your data fiels or define a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) to apply the appropriate parsing.\r\n\r\nAlternatively we could explore supporting the Arrow [Union](https://arrow.apache.org/docs/python/generated/pyarrow.UnionType.html) type which could solve this issue, but I don't know if it's well supported in python and with the rest of the ecosystem like Parquet",
"@lhoestq Thank you! I further wonder if it's possible to use list subscripts as keys of a feature? Like\r\n```python\r\nfeatures = Features({\r\n 0: Value(\"string\"),\r\n 1: {\r\n \"text\": Value(\"string\"),\r\n \"idxes\": [Value(\"int64\")]\r\n },\r\n 2: Value(\"string\"),\r\n # ...\r\n})\r\n```",
"Column names need to be strings, so you could use \"1\", \"2\", etc. or give appropriate column names",
"@lhoestq Got it. Thank you!"
] | 2023-04-04T03:20:43Z
| 2023-04-05T14:15:18Z
| 2023-04-05T14:15:17Z
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### Feature request
Hello! Apologies if my question sounds naive:
I was wondering if it’s possible, or how one would go about defining a 'datasets.Sequence' element in datasets.Features that could potentially be either a dict, a str, or None?
Specifically, I’d like to define a feature for a list that contains 18 elements, each of which has been pre-defined as either a `dict or None` or `str or None` - as demonstrated in the slightly misaligned data provided below:
```json
[
[
{"text":"老妇人","idxes":[0,1,2]},null,{"text":"跪","idxes":[3]},null,null,null,null,{"text":"在那坑里","idxes":[4,5,6,7]},null,null,null,null,null,null,null,null,null,null],
[
{"text":"那些水","idxes":[13,14,15]},null,{"text":"舀","idxes":[11]},null,null,null,null,null,{"text":"在那坑里","idxes":[4,5,6,7]},null,{"text":"出","idxes":[12]},null,null,null,null,null,null,null],
[
{"text":"水","idxes":[38]},
null,
{"text":"舀","idxes":[40]},
"假", // note this is just a standalone string
null,null,null,{"text":"坑里","idxes":[35,36]},null,null,null,null,null,null,null,null,null,null]]
```
### Motivation
I'm currently working with a dataset of the following structure and I couldn't find a solution in the [documentation](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Features).
```json
{"qid":"3-train-1058","context":"桑桑害怕了。从玉米地里走到田埂上，他遥望着他家那幢草房子里的灯光，知道母亲没有让他回家的意思，很伤感，有点想哭。但没哭，转身朝阿恕家走去。","corefs":[[{"text":"桑桑","idxes":[0,1]},{"text":"他","idxes":[17]}]],"non_corefs":[],"outputs":[[{"text":"他","idxes":[17]},null,{"text":"走","idxes":[11]},null,null,null,null,null,{"text":"从玉米地里","idxes":[6,7,8,9,10]},{"text":"到田埂上","idxes":[12,13,14,15]},null,null,null,null,null,null,null,null],[{"text":"他","idxes":[17]},null,{"text":"走","idxes":[66]},null,null,null,null,null,null,null,{"text":"转身朝阿恕家去","idxes":[60,61,62,63,64,65,67]},null,null,null,null,null,null,null],[{"text":"灯光","idxes":[30,31]},null,null,null,null,null,null,{"text":"草房子里","idxes":[25,26,27,28]},null,null,null,null,null,null,null,null,null,null],[{"text":"他","idxes":[17]},{"text":"他家那幢草房子","idxes":[21,22,23,24,25,26,27]},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"远"],[{"text":"他","idxes":[17]},{"text":"阿恕家","idxes":[63,64,65]},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"变近"]]}
```
### Your contribution
I'm going to provide the dataset at https://huggingface.co/datasets/2030NLP/SpaCE2022 .
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I_kwDODunzps6t0jdU
| 7,449
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Cannot load data with different schemas from different parquet files
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[
"Hi ! `load_dataset` expects all the data_files to have the same schema.\n\nMaybe you can try enforcing certain `features` using:\n\n```python\nfeatures = Features({\"conversations\": {'content': Value('string'), 'role': Value('string',)}})\nds = load_dataset(..., features=features)\n```",
"Thanks! It works if I explicitly specify all nested fields of the data."
] | 2025-03-13T08:14:49Z
| 2025-03-17T07:27:48Z
| 2025-03-17T07:27:46Z
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### Describe the bug
Cannot load samples with optional fields from different files. The schema cannot be correctly derived.
### Steps to reproduce the bug
When I place two samples with an optional field `some_extra_field` within a single parquet file, it can be loaded via `load_dataset`.
```python
import pandas as pd
from datasets import load_dataset
data = [
{'conversations': {'role': 'user', 'content': 'hello'}},
{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}
]
df = pd.DataFrame(data)
df.to_parquet('data.parquet')
dataset = load_dataset('parquet', data_files='data.parquet', split='train')
print(dataset.features)
```
The schema can be derived. `some_extra_field` is set to None for the first row where it is absent.
```
{'conversations': {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None), 'some_extra_field': Value(dtype='string', id=None)}}
```
However, when I separate the samples into different files, it cannot be loaded.
```python
import pandas as pd
from datasets import load_dataset
data1 = [{'conversations': {'role': 'user', 'content': 'hello'}}]
pd.DataFrame(data1).to_parquet('data1.parquet')
data2 = [{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}]
pd.DataFrame(data2).to_parquet('data2.parquet')
dataset = load_dataset('parquet', data_files=['data1.parquet', 'data2.parquet'], split='train')
print(dataset.features)
```
Traceback:
```
Traceback (most recent call last):
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
for _, table in generator:
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<content: string, role: string, some_extra_field: string>
to
{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}
```
### Expected behavior
Correctly load data with optional fields from different parquet files.
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.10.135.bsk.4-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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docs: resolving namespace conflict, refactored variable
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006209 / 0.011353 (-0.005144) | 0.003708 / 0.011008 (-0.007300) | 0.080435 / 0.038508 (0.041926) | 0.060105 / 0.023109 (0.036995) | 0.392962 / 0.275898 (0.117064) | 0.429381 / 0.323480 (0.105902) | 0.003596 / 0.007986 (-0.004390) | 0.003849 / 0.004328 (-0.000480) | 0.062377 / 0.004250 (0.058127) | 0.048718 / 0.037052 (0.011666) | 0.400906 / 0.258489 (0.142417) | 0.440335 / 0.293841 (0.146494) | 0.027807 / 0.128546 (-0.100739) | 0.008066 / 0.075646 (-0.067580) | 0.262542 / 0.419271 (-0.156730) | 0.045513 / 0.043533 (0.001980) | 0.399608 / 0.255139 (0.144469) | 0.418007 / 0.283200 (0.134807) | 0.023475 / 0.141683 (-0.118208) | 1.476563 / 1.452155 (0.024409) | 1.528898 / 1.492716 (0.036182) |\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) | 0.223798 / 0.018006 (0.205792) | 0.430526 / 0.000490 (0.430036) | 0.009232 / 0.000200 (0.009032) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024921 / 0.037411 (-0.012490) | 0.077692 / 0.014526 (0.063166) | 0.085382 / 0.176557 (-0.091174) | 0.146220 / 0.737135 (-0.590915) | 0.086396 / 0.296338 (-0.209943) |\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) | 0.439986 / 0.215209 (0.224777) | 4.384552 / 2.077655 (2.306897) | 2.373697 / 1.504120 (0.869577) | 2.176138 / 1.541195 (0.634943) | 2.225914 / 1.468490 (0.757424) | 0.505776 / 4.584777 (-4.079001) | 3.053744 / 3.745712 (-0.691968) | 3.080443 / 5.269862 (-2.189419) | 1.904392 / 4.565676 (-2.661285) | 0.058112 / 0.424275 (-0.366163) | 0.006631 / 0.007607 (-0.000976) | 0.503409 / 0.226044 (0.277365) | 5.053375 / 2.268929 (2.784447) | 2.789963 / 55.444624 (-52.654661) | 2.452659 / 6.876477 (-4.423818) | 2.512353 / 2.142072 (0.370280) | 0.590095 / 4.805227 (-4.215132) | 0.126267 / 6.500664 (-6.374397) | 0.061246 / 0.075469 (-0.014223) |\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) | 1.249884 / 1.841788 (-0.591903) | 17.684730 / 8.074308 (9.610422) | 13.967467 / 10.191392 (3.776075) | 0.144202 / 0.680424 (-0.536222) | 0.017004 / 0.534201 (-0.517197) | 0.333634 / 0.579283 (-0.245649) | 0.387251 / 0.434364 (-0.047113) | 0.390189 / 0.540337 (-0.150148) | 0.535662 / 1.386936 (-0.851274) |\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) | 0.006379 / 0.011353 (-0.004974) | 0.003681 / 0.011008 (-0.007327) | 0.063005 / 0.038508 (0.024497) | 0.064221 / 0.023109 (0.041112) | 0.446074 / 0.275898 (0.170176) | 0.471997 / 0.323480 (0.148517) | 0.005074 / 0.007986 (-0.002911) | 0.002945 / 0.004328 (-0.001383) | 0.063305 / 0.004250 (0.059054) | 0.050608 / 0.037052 (0.013556) | 0.443260 / 0.258489 (0.184771) | 0.478497 / 0.293841 (0.184656) | 0.028980 / 0.128546 (-0.099566) | 0.008145 / 0.075646 (-0.067502) | 0.068412 / 0.419271 (-0.350859) | 0.041552 / 0.043533 (-0.001980) | 0.436649 / 0.255139 (0.181510) | 0.462397 / 0.283200 (0.179198) | 0.019929 / 0.141683 (-0.121753) | 1.530248 / 1.452155 (0.078093) | 1.611117 / 1.492716 (0.118401) |\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) | 0.232894 / 0.018006 (0.214888) | 0.421451 / 0.000490 (0.420961) | 0.003984 / 0.000200 (0.003784) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027776 / 0.037411 (-0.009635) | 0.081632 / 0.014526 (0.067106) | 0.094031 / 0.176557 (-0.082526) | 0.147930 / 0.737135 (-0.589206) | 0.094226 / 0.296338 (-0.202112) |\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) | 0.471722 / 0.215209 (0.256513) | 4.713241 / 2.077655 (2.635587) | 2.662660 / 1.504120 (1.158540) | 2.490778 / 1.541195 (0.949583) | 2.555786 / 1.468490 (1.087296) | 0.512209 / 4.584777 (-4.072568) | 3.210612 / 3.745712 (-0.535100) | 2.863346 / 5.269862 (-2.406516) | 1.884664 / 4.565676 (-2.681012) | 0.058514 / 0.424275 (-0.365761) | 0.006473 / 0.007607 (-0.001134) | 0.543279 / 0.226044 (0.317235) | 5.441485 / 2.268929 (3.172556) | 3.145398 / 55.444624 (-52.299226) | 2.749603 / 6.876477 (-4.126874) | 2.925738 / 2.142072 (0.783666) | 0.598725 / 4.805227 (-4.206502) | 0.125616 / 6.500664 (-6.375048) | 0.061314 / 0.075469 (-0.014155) |\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) | 1.384270 / 1.841788 (-0.457518) | 18.307618 / 8.074308 (10.233310) | 14.635768 / 10.191392 (4.444376) | 0.148787 / 0.680424 (-0.531637) | 0.018191 / 0.534201 (-0.516010) | 0.333166 / 0.579283 (-0.246117) | 0.405116 / 0.434364 (-0.029247) | 0.392798 / 0.540337 (-0.147540) | 0.582299 / 1.386936 (-0.804637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7004f0f2ec59832fe53af033efdca10d00377760 \"CML watermark\")\n"
] | 2023-10-18T16:10:59Z
| 2023-10-19T16:31:59Z
| 2023-10-19T16:23:07Z
|
CONTRIBUTOR
| null | null | null |
In docs of about_arrow.md, in the below example code
The variable name 'time' was being used in a way that could potentially lead to a namespace conflict with Python's built-in 'time' module. It is not a good convention and can lead to unintended variable shadowing for any user re-using the example code.
To ensure code clarity, and prevent potential naming conflicts renamed the variable 'time' to 'elapsed_time' in the example code.
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Tip for recomputing metadata
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008167 / 0.011353 (-0.003186) | 0.004404 / 0.011008 (-0.006605) | 0.100462 / 0.038508 (0.061954) | 0.028835 / 0.023109 (0.005726) | 0.326759 / 0.275898 (0.050861) | 0.355150 / 0.323480 (0.031670) | 0.007200 / 0.007986 (-0.000786) | 0.003293 / 0.004328 (-0.001035) | 0.078006 / 0.004250 (0.073756) | 0.033298 / 0.037052 (-0.003754) | 0.307119 / 0.258489 (0.048630) | 0.337689 / 0.293841 (0.043848) | 0.033016 / 0.128546 (-0.095530) | 0.011383 / 0.075646 (-0.064263) | 0.321989 / 0.419271 (-0.097283) | 0.039793 / 0.043533 (-0.003740) | 0.295388 / 0.255139 (0.040249) | 0.322694 / 0.283200 (0.039494) | 0.082989 / 0.141683 (-0.058694) | 1.496701 / 1.452155 (0.044546) | 1.548861 / 1.492716 (0.056145) |\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) | 0.176587 / 0.018006 (0.158580) | 0.397660 / 0.000490 (0.397170) | 0.001063 / 0.000200 (0.000863) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022386 / 0.037411 (-0.015025) | 0.096380 / 0.014526 (0.081854) | 0.103032 / 0.176557 (-0.073525) | 0.135050 / 0.737135 (-0.602086) | 0.105941 / 0.296338 (-0.190397) |\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) | 0.430989 / 0.215209 (0.215780) | 4.310309 / 2.077655 (2.232654) | 2.142596 / 1.504120 (0.638477) | 1.952043 / 1.541195 (0.410848) | 1.817803 / 1.468490 (0.349312) | 0.690026 / 4.584777 (-3.894751) | 3.315413 / 3.745712 (-0.430299) | 3.370336 / 5.269862 (-1.899525) | 1.668707 / 4.565676 (-2.896970) | 0.081860 / 0.424275 (-0.342415) | 0.012493 / 0.007607 (0.004886) | 0.527779 / 0.226044 (0.301735) | 5.318732 / 2.268929 (3.049804) | 2.467029 / 55.444624 (-52.977596) | 2.247171 / 6.876477 (-4.629306) | 2.270825 / 2.142072 (0.128752) | 0.802288 / 4.805227 (-4.002939) | 0.148895 / 6.500664 (-6.351770) | 0.064967 / 0.075469 (-0.010503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259304 / 1.841788 (-0.582484) | 13.662441 / 8.074308 (5.588133) | 14.074662 / 10.191392 (3.883270) | 0.152907 / 0.680424 (-0.527516) | 0.028340 / 0.534201 (-0.505861) | 0.397356 / 0.579283 (-0.181927) | 0.392600 / 0.434364 (-0.041764) | 0.467935 / 0.540337 (-0.072402) | 0.539890 / 1.386936 (-0.847046) |\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) | 0.006156 / 0.011353 (-0.005197) | 0.004371 / 0.011008 (-0.006637) | 0.076391 / 0.038508 (0.037883) | 0.026455 / 0.023109 (0.003346) | 0.339816 / 0.275898 (0.063917) | 0.370032 / 0.323480 (0.046552) | 0.004614 / 0.007986 (-0.003372) | 0.003200 / 0.004328 (-0.001129) | 0.075408 / 0.004250 (0.071157) | 0.034100 / 0.037052 (-0.002953) | 0.341232 / 0.258489 (0.082743) | 0.380290 / 0.293841 (0.086449) | 0.031021 / 0.128546 (-0.097525) | 0.011562 / 0.075646 (-0.064084) | 0.085564 / 0.419271 (-0.333708) | 0.041431 / 0.043533 (-0.002102) | 0.359570 / 0.255139 (0.104431) | 0.366919 / 0.283200 (0.083719) | 0.088242 / 0.141683 (-0.053441) | 1.460703 / 1.452155 (0.008548) | 1.534351 / 1.492716 (0.041635) |\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) | 0.225703 / 0.018006 (0.207697) | 0.395014 / 0.000490 (0.394524) | 0.000385 / 0.000200 (0.000185) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023975 / 0.037411 (-0.013436) | 0.098658 / 0.014526 (0.084132) | 0.105043 / 0.176557 (-0.071513) | 0.139988 / 0.737135 (-0.597148) | 0.106854 / 0.296338 (-0.189484) |\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) | 0.442454 / 0.215209 (0.227245) | 4.430860 / 2.077655 (2.353205) | 2.084823 / 1.504120 (0.580704) | 1.870421 / 1.541195 (0.329226) | 1.901618 / 1.468490 (0.433128) | 0.699214 / 4.584777 (-3.885563) | 3.336911 / 3.745712 (-0.408801) | 1.856479 / 5.269862 (-3.413383) | 1.166496 / 4.565676 (-3.399180) | 0.083189 / 0.424275 (-0.341086) | 0.012293 / 0.007607 (0.004686) | 0.543147 / 0.226044 (0.317102) | 5.452030 / 2.268929 (3.183101) | 2.506689 / 55.444624 (-52.937936) | 2.168186 / 6.876477 (-4.708291) | 2.172277 / 2.142072 (0.030205) | 0.813554 / 4.805227 (-3.991673) | 0.152074 / 6.500664 (-6.348590) | 0.066891 / 0.075469 (-0.008579) |\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) | 1.278635 / 1.841788 (-0.563153) | 13.690232 / 8.074308 (5.615924) | 13.403201 / 10.191392 (3.211809) | 0.128171 / 0.680424 (-0.552253) | 0.016687 / 0.534201 (-0.517514) | 0.378645 / 0.579283 (-0.200638) | 0.382922 / 0.434364 (-0.051442) | 0.467483 / 0.540337 (-0.072854) | 0.559026 / 1.386936 (-0.827910) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262d411ec0e252615a140c4e3e60e7dbd38eef1 \"CML watermark\")\n"
] | 2023-01-27T20:01:22Z
| 2023-01-30T19:22:21Z
| 2023-01-30T19:15:26Z
|
MEMBER
| null | null | null |
From this [feedback](https://discuss.huggingface.co/t/nonmatchingsplitssizeserror/30033) on the forum, thought I'd include a tip for recomputing the metadata numbers if it is your own dataset.
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PR_kwDODunzps5DnQsS
| 5,290
|
fix error where reading breaks when batch missing an assigned column feature
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5290). All of your documentation changes will be reflected on that endpoint."
] | 2022-11-24T03:53:46Z
| 2022-11-25T03:21:54Z
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NonMatchingSplitsSizesError when using data_dir
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[
"Thanks for reporting, @srehaag.\r\n\r\nWe are investigating this issue.",
"I confirm there is a bug for data-based Hub datasets when the user passes `data_dir`, which was introduced by PR:\r\n- #6714"
] | 2024-05-24T12:43:39Z
| 2024-05-31T17:10:38Z
| 2024-05-31T17:10:38Z
|
NONE
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### Describe the bug
Loading a dataset from with a data_dir argument generates a NonMatchingSplitsSizesError if there are multiple directories in the dataset.
This appears to happen because the expected split is calculated based on the data in all the directories whereas the recorded split is calculated based on the data in the directory specified using the data_dir argument.
This is recent behavior. Until the past few weeks loading using the data_dir argument worked without any issue.
### Steps to reproduce the bug
Simple test dataset available here: https://huggingface.co/datasets/srehaag/hf-bug-temp
The dataset contains two directories "data1" and "data2", each with a file called "train.parquet" with a 2 x 5 table.
from datasets import load_dataset
dataset = load_dataset("srehaag/hf-bug-temp", data_dir = "data1")
Generates:
---------------------------------------------------------------------------
NonMatchingSplitsSizesError Traceback (most recent call last)
Cell In[3], <a href='vscode-notebook-cell:?execution_count=3&line=2'>line 2</a>
<a href='vscode-notebook-cell:?execution_count=3&line=1'>1</a> from datasets import load_dataset
----> <a href='vscode-notebook-cell:?execution_count=3&line=2'>2</a> dataset = load_dataset("srehaag/hf-bug-temp", data_dir = "data1")
File ~/.python/current/lib/python3.10/site-packages/datasets/load.py:2609, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2606'>2606</a> return builder_instance.as_streaming_dataset(split=split)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2608'>2608</a> # Download and prepare data
-> <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2609'>2609</a> builder_instance.download_and_prepare(
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2610'>2610</a> download_config=download_config,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2611'>2611</a> download_mode=download_mode,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2612'>2612</a> verification_mode=verification_mode,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2613'>2613</a> num_proc=num_proc,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2614'>2614</a> storage_options=storage_options,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2615'>2615</a> )
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2617'>2617</a> # Build dataset for splits
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2618'>2618</a> keep_in_memory = (
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2619'>2619</a> keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2620'>2620</a> )
File ~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1025'>1025</a> if num_proc is not None:
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1026'>1026</a> prepare_split_kwargs["num_proc"] = num_proc
-> <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1027'>1027</a> self._download_and_prepare(
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1028'>1028</a> dl_manager=dl_manager,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1029'>1029</a> verification_mode=verification_mode,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1030'>1030</a> **prepare_split_kwargs,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1031'>1031</a> **download_and_prepare_kwargs,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1032'>1032</a> )
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1033'>1033</a> # Sync info
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1034'>1034</a> self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1140, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1137'>1137</a> dl_manager.manage_extracted_files()
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1139'>1139</a> if verification_mode == VerificationMode.BASIC_CHECKS or verification_mode == VerificationMode.ALL_CHECKS:
-> <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1140'>1140</a> verify_splits(self.info.splits, split_dict)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1142'>1142</a> # Update the info object with the splits.
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1143'>1143</a> self.info.splits = split_dict
File ~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:101, in verify_splits(expected_splits, recorded_splits)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:95'>95</a> bad_splits = [
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:96'>96</a> {"expected": expected_splits[name], "recorded": recorded_splits[name]}
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:97'>97</a> for name in expected_splits
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:98'>98</a> if expected_splits[name].num_examples != recorded_splits[name].num_examples
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:99'>99</a> ]
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:100'>100</a> if len(bad_splits) > 0:
--> <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:101'>101</a> raise NonMatchingSplitsSizesError(str(bad_splits))
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:102'>102</a> logger.info("All the splits matched successfully.")
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=212, num_examples=10, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=106, num_examples=5, shard_lengths=None, dataset_name='hf-bug-temp')}]
__________
By contrast, this loads the data from both data1/train.parquet and data2/train.parquet without any error message:
from datasets import load_dataset
dataset = load_dataset("srehaag/hf-bug-temp")
### Expected behavior
Should load the 5 x 2 table from data1/train.parquet without error message.
### Environment info
Used Codespaces to simplify environment (see details below), but bug is present across various configurations.
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-1021-azure-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.1
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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docs: ✏️ add links to the Datasets API
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"OK, thanks @lhoestq. I'll close this PR, and come back to it with @stevhliu once we work on https://github.com/huggingface/datasets-server/issues/568"
] | 2022-09-16T09:34:12Z
| 2022-09-16T13:10:14Z
| 2022-09-16T13:07:33Z
|
COLLABORATOR
| null | null | null |
I added some links to the Datasets API in the docs. See https://github.com/huggingface/datasets-server/pull/566 for a companion PR in the datasets-server. The idea is to improve the discovery of the API through the docs.
I'm a bit shy about pasting a lot of links to the API in the docs, so it's minimal for now. I'm interested in ideas to integrate the API better in these docs without being too much. cc @lhoestq @julien-c @albertvillanova @stevhliu.
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I_kwDODunzps55Zj5h
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load_dataset imageflder for aws s3 path
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| 2023-12-12T00:09:27Z
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### Feature request
I would like to load a dataset from S3 using the imagefolder option
something like
`dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) `
### Motivation
no need of data_files
### Your contribution
no experience with this
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I_kwDODunzps5lFUVL
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|
Incomplete docstring for `BuilderConfig`
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"Thanks for reporting! You are more than welcome to improve `BuilderConfig`'s docstring.\r\n\r\nThis class serves an identical purpose as `tensorflow_datasets`'s `BuilderConfig`, and its docstring is [here](https://github.com/tensorflow/datasets/blob/a95e38b5bb018312c3d3720619c2a8ef83ebf57f/tensorflow_datasets/core/dataset_builder.py#L81), so feel free to re-use parts of it."
] | 2023-05-04T12:14:34Z
| 2023-05-05T12:31:56Z
| 2023-05-05T12:31:56Z
|
CONTRIBUTOR
| null | null |
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Hi guys !
I stumbled upon this docstring while working on a project.
Some of the attributes have missing descriptions.
https://github.com/huggingface/datasets/blob/bc5fef5b6d91f009e4101684adcb374df2c170f6/src/datasets/builder.py#L104-L117
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PR_kwDODunzps557zng
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remove filecheck to enable symlinks
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7133). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The CI is failing, looks like it breaks imagefolder loading.\r\n\r\nI just checked fsspec internals and maybe instead we can detect symlink by checking `islink` and `size` to make sure it's a file\r\n```python\r\nif info[\"type\"] == \"file\" or (info.get(\"islink\") and info[\"size\"])\r\n```\r\n",
"hmm actually `size` doesn't seem to filter symlinked directories, we need another way",
"Does fsspec perhaps allow resolving symlinks? Something like https://docs.python.org/3/library/pathlib.html#pathlib.Path.resolve",
"there is `info[\"destination\"]` in case of a symlink, so maybe\r\n\r\n\r\n```python\r\nif info[\"type\"] == \"file\" or (info.get(\"islink\") and info.get(\"destination\") and os.path.isfile(info[\"destination\"]))\r\n```",
"I've added a fix which works with some temporary test files locally \r\n\r\n`(info[\"type\"] == \"file\" or (info.get(\"islink\") and os.path.isfile(os.path.realpath(filepath))))`"
] | 2024-08-30T07:36:56Z
| 2024-12-24T14:25:22Z
| 2024-12-24T14:25:22Z
|
CONTRIBUTOR
| null | null | null |
Enables streaming from local symlinks #7083
@lhoestq
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When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset?
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"Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:\r\n```python\r\ninstructions = [\r\n ReadInstruction(split_name=\"train\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"dev\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"test\", from_=0, to=5, unit='%', rounding='closest')\r\n]\r\n\r\ndataset = load_dataset('csv', data_dir=\"data/\", data_files={\"train\":\"train.tsv\", \"dev\":\"dev.tsv\", \"test\":\"test.tsv\"}, delimiter=\"\\t\", split={inst.split_name: inst for inst in instructions})\r\n```\r\n"
] | 2023-01-16T06:54:28Z
| 2023-02-24T16:19:00Z
| 2023-02-24T16:19:00Z
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### Describe the bug
I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`.
### Steps to reproduce the bug
Steps to reproduce the behaviour:
1. Import
`from datasets import load_dataset, ReadInstruction`
2. Instruction to load the dataset
```
instructions = [
ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest')
]
```
3. Load
`dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split=instructions)`
### Expected behavior
**Current behaviour**
:
**Expected behaviour**
### Environment info
``datasets==2.8.0
``
`Python==3.8.5
`
`Platform - Ubuntu 20.04.4 LTS`
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`drop_last_batch=True` for IterableDataset map function is ignored with multiprocessing DataLoader
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"My current workaround this issue is to return `None` in the second element and then filter out samples which have `None` in them.\r\n\r\n```python\r\ndef merge_samples(batch):\r\n if len(batch['a']) == 1:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [None]\r\n else:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [batch['a'][1]]\r\n return batch\r\n \r\ndef filter_fn(x):\r\n return x['d'] is not None\r\n\r\n# other code...\r\nmapped = mapped.filter(filter_fn)\r\n```",
"Hi @lhoestq , I found that this script no longer works due to this [line](https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/iterable_dataset.py#L1141), where it checks the validity before the column `a` is removed. Is this expected?"
] | 2024-01-07T02:46:50Z
| 2025-03-08T09:46:05Z
| 2024-01-11T16:10:31Z
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### Describe the bug
Scenario:
- Interleaving two iterable datasets of unequal lengths (`all_exhausted`), followed by a batch mapping with batch size 2 to effectively merge the two datasets and get a sample from each dataset in a single batch, with `drop_last_batch=True` to skip the last batch in case it doesn't have two samples.
What works:
- Using DataLoader with `num_workers=0`
What does not work:
- Using DataLoader with `num_workers=1`, errors in the last batch.
Basically, `drop_last_batch=True` is ignored when using multiple dataloading workers.
Please take a look at the minimal repro script below.
### Steps to reproduce the bug
```python
from datasets import Dataset, interleave_datasets
from torch.utils.data import DataLoader
def merge_samples(batch):
assert len(batch['a']) == 2, "Batch size must be 2"
batch['c'] = [batch['a'][0]]
batch['d'] = [batch['a'][1]]
return batch
def gen1():
for ii in range(1, 8385):
yield {"a": ii}
def gen2():
for ii in range(1, 5302):
yield {"a": ii}
if __name__ == '__main__':
dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=1024)
dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=1024)
interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
mapped = interleaved.map(merge_samples, batched=True, batch_size=2, remove_columns=interleaved.column_names,
drop_last_batch=True)
# Works
loader = DataLoader(mapped, batch_size=32, num_workers=0)
i = 0
for b in loader:
print(i, b['c'].shape, b['d'].shape)
i += 1
print("DataLoader with num_workers=0 works")
# Doesn't work
loader = DataLoader(mapped, batch_size=32, num_workers=1)
i = 0
for b in loader:
print(i, b['c'].shape, b['d'].shape)
i += 1
```
### Expected behavior
`drop_last_batch=True` should have same behaviour for `num_workers=0` and `num_workers>=1`
### Environment info
- `datasets` version: 2.16.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.2
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0
I have also tested on Linux and got the same behavior.
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PR_kwDODunzps5OreXV
| 5,772
|
Fix JSON builder when missing keys in first row
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009262 / 0.011353 (-0.002091) | 0.006157 / 0.011008 (-0.004851) | 0.125960 / 0.038508 (0.087451) | 0.036213 / 0.023109 (0.013104) | 0.399331 / 0.275898 (0.123433) | 0.453597 / 0.323480 (0.130117) | 0.006990 / 0.007986 (-0.000995) | 0.007320 / 0.004328 (0.002991) | 0.100321 / 0.004250 (0.096070) | 0.048870 / 0.037052 (0.011818) | 0.396284 / 0.258489 (0.137795) | 0.475619 / 0.293841 (0.181778) | 0.052329 / 0.128546 (-0.076217) | 0.019564 / 0.075646 (-0.056083) | 0.430942 / 0.419271 (0.011670) | 0.063224 / 0.043533 (0.019692) | 0.391717 / 0.255139 (0.136578) | 0.448342 / 0.283200 (0.165142) | 0.114055 / 0.141683 (-0.027628) | 1.793204 / 1.452155 (0.341049) | 1.895151 / 1.492716 (0.402435) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.283699 / 0.018006 (0.265693) | 0.597194 / 0.000490 (0.596704) | 0.007143 / 0.000200 (0.006944) | 0.000602 / 0.000054 (0.000548) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034761 / 0.037411 (-0.002651) | 0.124555 / 0.014526 (0.110030) | 0.149126 / 0.176557 (-0.027430) | 0.220335 / 0.737135 (-0.516801) | 0.153109 / 0.296338 (-0.143229) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.620210 / 0.215209 (0.405001) | 6.229937 / 2.077655 (4.152282) | 2.615203 / 1.504120 (1.111083) | 2.239337 / 1.541195 (0.698143) | 2.262138 / 1.468490 (0.793648) | 1.196498 / 4.584777 (-3.388279) | 5.609932 / 3.745712 (1.864220) | 3.031347 / 5.269862 (-2.238515) | 2.025530 / 4.565676 (-2.540146) | 0.139828 / 0.424275 (-0.284447) | 0.015476 / 0.007607 (0.007869) | 0.768964 / 0.226044 (0.542920) | 7.728677 / 2.268929 (5.459748) | 3.336407 / 55.444624 (-52.108217) | 2.700055 / 6.876477 (-4.176422) | 2.765223 / 2.142072 (0.623151) | 1.409073 / 4.805227 (-3.396155) | 0.246849 / 6.500664 (-6.253815) | 0.081231 / 0.075469 (0.005762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.593836 / 1.841788 (-0.247952) | 18.020525 / 8.074308 (9.946216) | 21.766822 / 10.191392 (11.575430) | 0.258615 / 0.680424 (-0.421809) | 0.026895 / 0.534201 (-0.507306) | 0.529823 / 0.579283 (-0.049460) | 0.623470 / 0.434364 (0.189106) | 0.628171 / 0.540337 (0.087833) | 0.745249 / 1.386936 (-0.641687) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008624 / 0.011353 (-0.002729) | 0.006317 / 0.011008 (-0.004691) | 0.097315 / 0.038508 (0.058807) | 0.035217 / 0.023109 (0.012108) | 0.440197 / 0.275898 (0.164299) | 0.473863 / 0.323480 (0.150383) | 0.006722 / 0.007986 (-0.001264) | 0.006444 / 0.004328 (0.002116) | 0.102056 / 0.004250 (0.097806) | 0.047142 / 0.037052 (0.010089) | 0.452476 / 0.258489 (0.193986) | 0.487619 / 0.293841 (0.193778) | 0.052456 / 0.128546 (-0.076090) | 0.018735 / 0.075646 (-0.056911) | 0.114656 / 0.419271 (-0.304616) | 0.062577 / 0.043533 (0.019044) | 0.444471 / 0.255139 (0.189332) | 0.494264 / 0.283200 (0.211065) | 0.117112 / 0.141683 (-0.024571) | 1.848965 / 1.452155 (0.396810) | 1.984008 / 1.492716 (0.491292) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290494 / 0.018006 (0.272488) | 0.588415 / 0.000490 (0.587925) | 0.000459 / 0.000200 (0.000259) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032873 / 0.037411 (-0.004538) | 0.131139 / 0.014526 (0.116614) | 0.140268 / 0.176557 (-0.036289) | 0.204561 / 0.737135 (-0.532574) | 0.147443 / 0.296338 (-0.148895) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636899 / 0.215209 (0.421690) | 6.236139 / 2.077655 (4.158484) | 2.801468 / 1.504120 (1.297348) | 2.398808 / 1.541195 (0.857613) | 2.493150 / 1.468490 (1.024659) | 1.228845 / 4.584777 (-3.355932) | 5.675874 / 3.745712 (1.930162) | 3.084939 / 5.269862 (-2.184922) | 2.061310 / 4.565676 (-2.504367) | 0.142285 / 0.424275 (-0.281990) | 0.014972 / 0.007607 (0.007365) | 0.786599 / 0.226044 (0.560555) | 7.876036 / 2.268929 (5.607107) | 3.476136 / 55.444624 (-51.968489) | 2.847922 / 6.876477 (-4.028555) | 3.040326 / 2.142072 (0.898253) | 1.448538 / 4.805227 (-3.356690) | 0.257230 / 6.500664 (-6.243434) | 0.085137 / 0.075469 (0.009668) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.668173 / 1.841788 (-0.173615) | 18.668520 / 8.074308 (10.594212) | 20.535542 / 10.191392 (10.344150) | 0.244580 / 0.680424 (-0.435844) | 0.026364 / 0.534201 (-0.507837) | 0.531753 / 0.579283 (-0.047530) | 0.616578 / 0.434364 (0.182214) | 0.618906 / 0.540337 (0.078569) | 0.738785 / 1.386936 (-0.648151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7265cafa3103d77d6d52aa897088faefcd96659 \"CML watermark\")\n"
] | 2023-04-19T14:32:57Z
| 2023-04-21T06:45:13Z
| 2023-04-21T06:35:27Z
|
MEMBER
| null | null | null |
Until now, the JSON builder only considered the keys present in the first element of the list:
- Either explicitly: by passing index 0 in `dataset[0].keys()`
- Or implicitly: `pa.Table.from_pylist(dataset)`, where "schema (default None): If not passed, will be inferred from the first row of the mapping values"
This PR fixes the bug by considering the union of the keys present in all the rows.
Fix #5726.
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[
"Hi @Hughhuh. \r\n\r\nI have formatted the issue because it was not easily readable. Additionally, the environment info is incomplete: it seems you did not run the proposed CLI command `datasets-cli env` and essential information is missing: version of `datasets`, version of `pyarrow`,...\r\n\r\nWith the information you provided, it seems an issue with the specific \"samsum\" dataset. I'm transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/samsum/discussions/5"
] | 2024-02-04T08:49:31Z
| 2024-02-06T09:26:07Z
| 2024-02-06T09:11:45Z
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### Describe the bug
unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte
### Steps to reproduce the bug
```
import sys
sys.getdefaultencoding()
'utf-8'
from datasets import load_dataset
print(f"Train dataset size: {len(dataset['train'])}")
print(f"Test dataset size: {len(dataset['test'])}")
Resolving data files: 100%
159/159 [00:00<00:00, 9909.28it/s]
Using custom data configuration samsum-0b1209637541c9e6
Downloading and preparing dataset json/samsum to C:/Users/Administrator/.cache/huggingface/datasets/json/samsum-0b1209637541c9e6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%
3/3 [00:00<00:00, 119.99it/s]
Extracting data files: 100%
3/3 [00:00<00:00, 9.54it/s]
Generating train split:
88392/0 [00:15<00:00, 86848.17 examples/s]
Generating test split:
0/0 [00:00<?, ? examples/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:132, in Json._generate_tables(self, files)
131 try:
--> 132 pa_table = paj.read_json(
133 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
134 )
135 break
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\_json.pyx:290, in pyarrow._json.read_json()
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:100, in pyarrow.lib.check_status()
ArrowInvalid: JSON parse error: Invalid value. in row 0
During handling of the above exception, another exception occurred:
UnicodeDecodeError Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1819, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1818 _time = time.time()
-> 1819 for _, table in generator:
1820 if max_shard_size is not None and writer._num_bytes > max_shard_size:
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:153, in Json._generate_tables(self, files)
152 with open(file, encoding="utf-8") as f:
--> 153 dataset = json.load(f)
154 except json.JSONDecodeError:
File ~\AppData\Local\Programs\Python\Python310\lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
276 """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
277 a JSON document) to a Python object.
278
(...)
291 kwarg; otherwise ``JSONDecoder`` is used.
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
295 parse_float=parse_float, parse_int=parse_int,
296 parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)
File ~\AppData\Local\Programs\Python\Python310\lib\codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final)
321 data = self.buffer + input
--> 322 (result, consumed) = self._buffer_decode(data, self.errors, final)
323 # keep undecoded input until the next call
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[81], line 5
1 from datasets import load_dataset
3 # Load dataset from the hub
4 #dataset = load_dataset("json",data_files="C:/Users/Administrator/Desktop/samsum/samsum/data/corpus/train.json",field="data")
----> 5 dataset = load_dataset('json',"samsum")
6 #dataset = load_dataset("samsum")
7 print(f"Train dataset size: {len(dataset['train'])}")
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py:1758, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1755 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1757 # Download and prepare data
-> 1758 builder_instance.download_and_prepare(
1759 download_config=download_config,
1760 download_mode=download_mode,
1761 ignore_verifications=ignore_verifications,
1762 try_from_hf_gcs=try_from_hf_gcs,
1763 num_proc=num_proc,
1764 )
1766 # Build dataset for splits
1767 keep_in_memory = (
1768 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1769 )
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:860, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
858 if num_proc is not None:
859 prepare_split_kwargs["num_proc"] = num_proc
--> 860 self._download_and_prepare(
861 dl_manager=dl_manager,
862 verify_infos=verify_infos,
863 **prepare_split_kwargs,
864 **download_and_prepare_kwargs,
865 )
866 # Sync info
867 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:953, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
949 split_dict.add(split_generator.split_info)
951 try:
952 # Prepare split will record examples associated to the split
--> 953 self._prepare_split(split_generator, **prepare_split_kwargs)
954 except OSError as e:
955 raise OSError(
956 "Cannot find data file. "
957 + (self.manual_download_instructions or "")
958 + "\nOriginal error:\n"
959 + str(e)
960 ) from None
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1708, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1706 gen_kwargs = split_generator.gen_kwargs
1707 job_id = 0
-> 1708 for job_id, done, content in self._prepare_split_single(
1709 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1710 ):
1711 if done:
1712 result = content
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1851, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1849 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1850 e = e.__context__
-> 1851 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1853 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
can't load dataset
### Environment info
dataset:samsum
system :win10
gpu:m40 24G
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data configuration hash suffix depends on uncanonicalized data_dir
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[
"It could even use `os.path.realpath` to resolve symlinks.",
"Indeed, it makes sense to normalize `data_dir`. Feel free to submit a PR (this can be \"fixed\" [here](https://github.com/huggingface/datasets/blob/89f775226321ba94e5bf4670a323c0fb44f5f65c/src/datasets/builder.py#L173))",
"#self-assign"
] | 2023-05-16T18:56:04Z
| 2023-06-02T15:52:05Z
| 2023-06-02T15:52:05Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
I am working with the `recipe_nlg` dataset, which requires manual download. Once it's downloaded, I've noticed that the hash in the custom data configuration is different if I add a trailing `/` to my `data_dir`. It took me a while to notice that the hashes were different, and to understand that that was the cause of my dataset being processed anew instead of the cached version being used.
### Steps to reproduce the bug
1. Follow the steps to manually download the `recipe_nlg` dataset to `/data/recipenlg`.
2. Load it using `load_dataset`, once without a trailing slash and once with one:
```python
>>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg")
Using custom data configuration default-082278caeea85765
Downloading and preparing dataset recipe_nlg/default to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74...
Dataset recipe_nlg downloaded and prepared to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.10s/it]
DatasetDict({
train: Dataset({
features: ['id', 'title', 'ingredients', 'directions', 'link', 'source', 'ner'],
num_rows: 2231142
})
})
>>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg/")
Using custom data configuration default-83e87680785d0493
Downloading and preparing dataset recipe_nlg/default to /home/user/.cache/huggingface/datasets/recipe_nlg/default-83e87680785d0493/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74...
Generating train split: 1%| | 12701/2231142 [00:04<13:15, 2790.25 examples/s
^C
```
3. Observe that the hash suffix in the custom data configuration changes due to the altered string.
### Expected behavior
I think I would expect the hash to remain constant if it actually points to the same location on disk. I would expect the use of `os.path.normpath` to canonicalize the paths.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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CI is broken: ImportError: cannot import name 'context' from 'tensorflow.python'
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[] | 2023-09-27T08:12:05Z
| 2023-09-27T08:36:40Z
| 2023-09-27T08:36:40Z
|
MEMBER
| null | null |
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Python 3.10 CI is broken for `test_py310`.
See: https://github.com/huggingface/datasets/actions/runs/6322990957/job/17169678812?pr=6262
```
FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py)
```
```
_________________________ TempSeedTest.test_tensorflow _________________________
[gw1] linux -- Python 3.10.13 /opt/hostedtoolcache/Python/3.10.13/x64/bin/python
self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow>
@require_tf
def test_tensorflow(self):
import tensorflow as tf
from tensorflow.keras import layers
model = layers.Dense(2)
def gen_random_output():
x = tf.random.uniform((1, 3))
return model(x).numpy()
> with temp_seed(42, set_tensorflow=True):
tests/test_py_utils.py:155:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/contextlib.py:135: in __enter__
return next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
seed = 42, set_pytorch = False, set_tensorflow = True
@contextmanager
def temp_seed(seed: int, set_pytorch=False, set_tensorflow=False):
"""Temporarily set the random seed. This works for python numpy, pytorch and tensorflow."""
np_state = np.random.get_state()
np.random.seed(seed)
if set_pytorch and config.TORCH_AVAILABLE:
import torch
torch_state = torch.random.get_rng_state()
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch_cuda_states = torch.cuda.get_rng_state_all()
torch.cuda.manual_seed_all(seed)
if set_tensorflow and config.TF_AVAILABLE:
import tensorflow as tf
> from tensorflow.python import context as tfpycontext
E ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/datasets/utils/py_utils.py:257: ImportError
```
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I_kwDODunzps5QxNzQ
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datasets map() handles all data at a stroke and takes long time
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[
"Hi ! Interesting question ;)\r\n\r\n> Which is better? Process in map() or in data-collator\r\n\r\nAs you said, both can be used in practice: map() if you want to preprocess before training, or a data-collator (or the equivalent `dataset.set_transform`) if you want to preprocess on-the-fly during training. Both options are great and really depend on your case.\r\n\r\nTo choose between the two, here are IMO the main caveats of each approach:\r\n- if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n- on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\n> Why huggingface advises map() function? There should be some advantages to using map()\r\n\r\nTo get the best throughput when training a model, it is often recommended to preprocess your dataset before training. Note that preprocessing may include other steps before tokenization such as data filtering, cleaning, chunking etc. which are often done before training.",
"Thanks for your clear explanation @lhoestq ! \r\n> * if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n> * on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\nI really agree with you. There should be some trade-off between processing before and during the train loop.\r\nBesides, I find `map()` function can cache the results once it has been executed. Very useful!",
"I'm closing this issue if you don't mind, feel free to reopen if needed ;)",
"@lhoestq How to preprocess on-the-fly during training？my data is about 1w hours, when I use map to preprocess, and It's not finished yet， but all disk space(2T) is full.",
"Hi ! You can do that using `set_transform`, see https://huggingface.co/docs/datasets/process#format-transform for more info :)",
"unfortunately , it not work.",
"Could you share more details ?"
] | 2022-08-30T02:25:56Z
| 2023-04-06T09:43:58Z
| 2022-09-06T09:23:35Z
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**1. Background**
Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop.
The corresponding code:
```
with accelerator.main_process_first():
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on every text in dataset"
)
```
**2. The problem**
Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize.
Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization.
**3. My question**
As described above, my questions are:
* **Which is better? Process in `map()` or in `data-collator`**
* **Why huggingface advises `map()` function?** There should be some advantages to using `map()`
Thanks for your answers!
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Fix documentation card of recipe_nlg dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-12T09:49:39Z
| 2022-08-12T11:28:18Z
| 2022-08-12T11:13:40Z
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MEMBER
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Fix documentation card of recipe_nlg dataset
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.add_faiss_index and .add_elasticsearch_index returns ImportError at Google Colab
|
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[
"I can fix this.\nIt's mainly because faiss-gpu requires python<=3.10 but the default python version in colab is 3.11. We just have to downgrade the CPython version down to 3.10 and it should work fine.\n",
"I think I just had no chance to meet with faiss-cpu.\nIt could be import problem? \n_has_faiss gets its value at the beginning of datasets/search.\nI tried to call object before import faiss, so _has_faiss took False. And never updated later. ",
"Yes you can't meet the requirements because faiss-cpu runs only on\r\npython3.10 and lower but the default version for colab is python3.11 which\r\nresults in pip not being able to find wheels for faiss-cpu with python3.11.\r\n\r\nOn Mon, 17 Mar, 2025, 3:56 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>\r\n>\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> you can't meet the requirements\n\nIt is not the case (or I didn't reach this point) because the same code in notebook\n```importlib.util.find_spec(\"faiss\")```\nfinds faiss. I've mention it.\nI think the problem is in the very moment when _has_faiss takes its value and never try again. \n(or it couldn't find the path that was easily found when started from my code)",
"When you run the first cell containing pip install faiss-cpu does it\r\ninstall it?\r\n\r\nOn Mon, 17 Mar, 2025, 8:01 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>\r\n>\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> When you run the first cell containing pip install faiss-cpu does it\n> install it?\n> […](#)\n\nYes. It was installed succesfully. \nMethods of datasets library that depends on _has_faiss constant didn't start to work."
] | 2025-03-16T00:51:49Z
| 2025-03-17T15:57:19Z
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### Describe the bug
At Google Colab
```!pip install faiss-cpu``` works
```import faiss``` no error
but
```embeddings_dataset.add_faiss_index(column='embeddings')```
returns
```
[/usr/local/lib/python3.11/dist-packages/datasets/search.py](https://localhost:8080/#) in init(self, device, string_factory, metric_type, custom_index)
247 self.faiss_index = custom_index
248 if not _has_faiss:
--> 249 raise ImportError(
250 "You must install Faiss to use FaissIndex. To do so you can run conda install -c pytorch faiss-cpu or conda install -c pytorch faiss-gpu. "
251 "A community supported package is also available on pypi: pip install faiss-cpu or pip install faiss-gpu. "
```
because
```_has_faiss = importlib.util.find_spec("faiss") is not None``` at the beginning of ```datasets/search.py``` returns ```False```
when
the same code at colab notebook returns
```ModuleSpec(name='faiss', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7b7851449f50>, origin='/usr/local/lib/python3.11/dist-packages/faiss/init.py', submodule_search_locations=['/usr/local/lib/python3.11/dist-packages/faiss'])```
But
```
import datasets
datasets.search._has_faiss
```
at ```colab notebook``` also returns ```False```
The same story with ```_has_elasticsearch```
### Steps to reproduce the bug
1. Follow https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt at Google Colab
2. till ```embeddings_dataset.add_faiss_index(column='embeddings')```
3. ```embeddings_dataset.add_elasticsearch_index(column='embeddings')```
4. https://colab.research.google.com/drive/1h2cjuiClblqzbNQgrcoLYOC8zBqTLLcv#scrollTo=3ddzRp72auOF
### Expected behavior
I've only started Tutorial and don't know exactly. But something tells me that ```embeddings_dataset.add_faiss_index(column='embeddings')```
should work without ```Import Error```
### Environment info
Google Colab notebook with default config
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6344). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008237 / 0.011353 (-0.003116) | 0.004658 / 0.011008 (-0.006351) | 0.105902 / 0.038508 (0.067394) | 0.082690 / 0.023109 (0.059581) | 0.471745 / 0.275898 (0.195847) | 0.464772 / 0.323480 (0.141292) | 0.006373 / 0.007986 (-0.001613) | 0.003823 / 0.004328 (-0.000505) | 0.077721 / 0.004250 (0.073471) | 0.068371 / 0.037052 (0.031318) | 0.457004 / 0.258489 (0.198515) | 0.500989 / 0.293841 (0.207148) | 0.036688 / 0.128546 (-0.091858) | 0.010004 / 0.075646 (-0.065643) | 0.363398 / 0.419271 (-0.055874) | 0.065354 / 0.043533 (0.021821) | 0.440326 / 0.255139 (0.185187) | 0.475314 / 0.283200 (0.192115) | 0.029024 / 0.141683 (-0.112659) | 1.851005 / 1.452155 (0.398851) | 1.939997 / 1.492716 (0.447281) |\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) | 0.269739 / 0.018006 (0.251732) | 0.510411 / 0.000490 (0.509922) | 0.013423 / 0.000200 (0.013223) | 0.000513 / 0.000054 (0.000458) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032912 / 0.037411 (-0.004499) | 0.097497 / 0.014526 (0.082971) | 0.111945 / 0.176557 (-0.064612) | 0.179264 / 0.737135 (-0.557871) | 0.111901 / 0.296338 (-0.184437) |\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) | 0.480994 / 0.215209 (0.265785) | 4.800969 / 2.077655 (2.723314) | 2.467390 / 1.504120 (0.963270) | 2.283219 / 1.541195 (0.742024) | 2.407735 / 1.468490 (0.939245) | 0.573862 / 4.584777 (-4.010915) | 4.213394 / 3.745712 (0.467682) | 4.120092 / 5.269862 (-1.149770) | 2.479549 / 4.565676 (-2.086128) | 0.077204 / 0.424275 (-0.347071) | 0.009165 / 0.007607 (0.001558) | 0.583887 / 0.226044 (0.357842) | 5.760759 / 2.268929 (3.491830) | 3.089220 / 55.444624 (-52.355404) | 2.652330 / 6.876477 (-4.224146) | 2.746255 / 2.142072 (0.604182) | 0.689010 / 4.805227 (-4.116217) | 0.158042 / 6.500664 (-6.342622) | 0.072789 / 0.075469 (-0.002680) |\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) | 1.658877 / 1.841788 (-0.182911) | 22.928756 / 8.074308 (14.854448) | 17.231823 / 10.191392 (7.040431) | 0.201475 / 0.680424 (-0.478949) | 0.025533 / 0.534201 (-0.508668) | 0.467023 / 0.579283 (-0.112260) | 0.470779 / 0.434364 (0.036415) | 0.643192 / 0.540337 (0.102855) | 0.822006 / 1.386936 (-0.564930) |\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) | 0.008096 / 0.011353 (-0.003257) | 0.004708 / 0.011008 (-0.006300) | 0.076607 / 0.038508 (0.038099) | 0.086278 / 0.023109 (0.063168) | 0.478027 / 0.275898 (0.202129) | 0.533121 / 0.323480 (0.209641) | 0.006331 / 0.007986 (-0.001654) | 0.004005 / 0.004328 (-0.000324) | 0.076018 / 0.004250 (0.071767) | 0.067240 / 0.037052 (0.030188) | 0.484882 / 0.258489 (0.226393) | 0.536924 / 0.293841 (0.243083) | 0.045064 / 0.128546 (-0.083482) | 0.010071 / 0.075646 (-0.065575) | 0.084319 / 0.419271 (-0.334953) | 0.066267 / 0.043533 (0.022734) | 0.479283 / 0.255139 (0.224144) | 0.507832 / 0.283200 (0.224633) | 0.026436 / 0.141683 (-0.115247) | 1.820043 / 1.452155 (0.367889) | 1.954663 / 1.492716 (0.461947) |\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) | 0.292672 / 0.018006 (0.274666) | 0.495523 / 0.000490 (0.495033) | 0.020836 / 0.000200 (0.020636) | 0.000143 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038326 / 0.037411 (0.000915) | 0.114629 / 0.014526 (0.100103) | 0.126036 / 0.176557 (-0.050521) | 0.191498 / 0.737135 (-0.545638) | 0.128763 / 0.296338 (-0.167575) |\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) | 0.507657 / 0.215209 (0.292448) | 5.062056 / 2.077655 (2.984401) | 2.765895 / 1.504120 (1.261775) | 2.590335 / 1.541195 (1.049141) | 2.790912 / 1.468490 (1.322422) | 0.582819 / 4.584777 (-4.001958) | 4.350034 / 3.745712 (0.604322) | 3.899466 / 5.269862 (-1.370396) | 2.499655 / 4.565676 (-2.066021) | 0.068909 / 0.424275 (-0.355366) | 0.008633 / 0.007607 (0.001026) | 0.593597 / 0.226044 (0.367553) | 5.934398 / 2.268929 (3.665470) | 3.358549 / 55.444624 (-52.086075) | 3.145686 / 6.876477 (-3.730791) | 3.232153 / 2.142072 (1.090080) | 0.753039 / 4.805227 (-4.052188) | 0.164043 / 6.500664 (-6.336621) | 0.072084 / 0.075469 (-0.003385) |\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) | 1.632702 / 1.841788 (-0.209086) | 23.411084 / 8.074308 (15.336776) | 17.035726 / 10.191392 (6.844334) | 0.223460 / 0.680424 (-0.456964) | 0.023723 / 0.534201 (-0.510478) | 0.474160 / 0.579283 (-0.105124) | 0.538638 / 0.434364 (0.104274) | 0.595591 / 0.540337 (0.055254) | 0.803324 / 1.386936 (-0.583612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84855c8ddc8d3e33b516f04b687e01d498d0906e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008300 / 0.011353 (-0.003053) | 0.004667 / 0.011008 (-0.006341) | 0.101028 / 0.038508 (0.062520) | 0.100269 / 0.023109 (0.077160) | 0.418651 / 0.275898 (0.142752) | 0.459061 / 0.323480 (0.135581) | 0.006786 / 0.007986 (-0.001199) | 0.003926 / 0.004328 (-0.000403) | 0.076682 / 0.004250 (0.072432) | 0.066173 / 0.037052 (0.029120) | 0.430644 / 0.258489 (0.172155) | 0.466244 / 0.293841 (0.172403) | 0.040601 / 0.128546 (-0.087946) | 0.009856 / 0.075646 (-0.065790) | 0.351467 / 0.419271 (-0.067805) | 0.068727 / 0.043533 (0.025194) | 0.419527 / 0.255139 (0.164388) | 0.431245 / 0.283200 (0.148045) | 0.028933 / 0.141683 (-0.112750) | 1.749540 / 1.452155 (0.297386) | 1.829076 / 1.492716 (0.336360) |\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) | 0.282248 / 0.018006 (0.264242) | 0.587293 / 0.000490 (0.586803) | 0.014497 / 0.000200 (0.014297) | 0.000383 / 0.000054 (0.000329) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031861 / 0.037411 (-0.005550) | 0.097395 / 0.014526 (0.082869) | 0.113610 / 0.176557 (-0.062946) | 0.181208 / 0.737135 (-0.555927) | 0.115340 / 0.296338 (-0.180999) |\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) | 0.459746 / 0.215209 (0.244537) | 4.582387 / 2.077655 (2.504733) | 2.247968 / 1.504120 (0.743848) | 2.032340 / 1.541195 (0.491145) | 2.151766 / 1.468490 (0.683276) | 0.567664 / 4.584777 (-4.017113) | 4.491732 / 3.745712 (0.746020) | 4.000651 / 5.269862 (-1.269211) | 2.429113 / 4.565676 (-2.136564) | 0.067052 / 0.424275 (-0.357223) | 0.009095 / 0.007607 (0.001488) | 0.546461 / 0.226044 (0.320417) | 5.473524 / 2.268929 (3.204595) | 2.902091 / 55.444624 (-52.542533) | 2.517510 / 6.876477 (-4.358966) | 2.572537 / 2.142072 (0.430464) | 0.683499 / 4.805227 (-4.121728) | 0.154863 / 6.500664 (-6.345801) | 0.071298 / 0.075469 (-0.004171) |\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) | 1.625236 / 1.841788 (-0.216552) | 23.531541 / 8.074308 (15.457233) | 16.762514 / 10.191392 (6.571122) | 0.215922 / 0.680424 (-0.464502) | 0.021928 / 0.534201 (-0.512273) | 0.466055 / 0.579283 (-0.113228) | 0.553036 / 0.434364 (0.118672) | 0.590063 / 0.540337 (0.049725) | 0.789959 / 1.386936 (-0.596977) |\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) | 0.008240 / 0.011353 (-0.003113) | 0.004151 / 0.011008 (-0.006858) | 0.077988 / 0.038508 (0.039479) | 0.092865 / 0.023109 (0.069756) | 0.468238 / 0.275898 (0.192340) | 0.512882 / 0.323480 (0.189402) | 0.006632 / 0.007986 (-0.001354) | 0.003879 / 0.004328 (-0.000450) | 0.076238 / 0.004250 (0.071988) | 0.069372 / 0.037052 (0.032319) | 0.481040 / 0.258489 (0.222550) | 0.526332 / 0.293841 (0.232491) | 0.036768 / 0.128546 (-0.091778) | 0.009891 / 0.075646 (-0.065756) | 0.084426 / 0.419271 (-0.334846) | 0.062382 / 0.043533 (0.018849) | 0.480667 / 0.255139 (0.225528) | 0.509001 / 0.283200 (0.225802) | 0.029215 / 0.141683 (-0.112468) | 1.776075 / 1.452155 (0.323920) | 1.948558 / 1.492716 (0.455841) |\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) | 0.257879 / 0.018006 (0.239873) | 0.471038 / 0.000490 (0.470548) | 0.009273 / 0.000200 (0.009073) | 0.000208 / 0.000054 (0.000154) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039249 / 0.037411 (0.001838) | 0.133281 / 0.014526 (0.118755) | 0.138261 / 0.176557 (-0.038296) | 0.191051 / 0.737135 (-0.546084) | 0.134493 / 0.296338 (-0.161845) |\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) | 0.507165 / 0.215209 (0.291955) | 5.081018 / 2.077655 (3.003364) | 2.747633 / 1.504120 (1.243513) | 2.558265 / 1.541195 (1.017070) | 2.710839 / 1.468490 (1.242348) | 0.579913 / 4.584777 (-4.004864) | 4.843657 / 3.745712 (1.097945) | 3.942503 / 5.269862 (-1.327358) | 2.529641 / 4.565676 (-2.036036) | 0.068826 / 0.424275 (-0.355449) | 0.008847 / 0.007607 (0.001240) | 0.605332 / 0.226044 (0.379287) | 6.039574 / 2.268929 (3.770646) | 3.437291 / 55.444624 (-52.007333) | 3.086631 / 6.876477 (-3.789846) | 3.189340 / 2.142072 (1.047267) | 0.702650 / 4.805227 (-4.102578) | 0.157403 / 6.500664 (-6.343261) | 0.074637 / 0.075469 (-0.000832) |\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) | 1.816532 / 1.841788 (-0.025256) | 24.526675 / 8.074308 (16.452367) | 17.371691 / 10.191392 (7.180299) | 0.236044 / 0.680424 (-0.444380) | 0.024759 / 0.534201 (-0.509442) | 0.530578 / 0.579283 (-0.048705) | 0.527424 / 0.434364 (0.093060) | 0.620267 / 0.540337 (0.079929) | 0.791159 / 1.386936 (-0.595777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78cfce823b98b6cce79a9297fe6fa9e8f80a869c \"CML watermark\")\n"
] | 2023-10-23T15:13:28Z
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PR_kwDODunzps5unSPA
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Release 2.19.1
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dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working
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[
"Seems like it's a problem with the dataset, since in the [README](https://huggingface.co/datasets/Hyperspace-Technologies/scp-wiki-text/blob/main/README.md) the validation is not specified. Try cloning the dataset, removing the README (or validation split), and loading it locally/ ",
"@VarunNSrivastava thanks brother, working beautiful now\r\n\r\n```\r\nC:\\_Work\\_datasets>py dataset.py\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████| 3/3 [00:00<?, ?it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 599.90it/s]\r\nGenerating train split: 314294 examples [00:00, 1293222.03 examples/s]\r\nGenerating validation split: 120 examples [00:00, 59053.91 examples/s]\r\nGenerating test split: 34922 examples [00:00, 1343275.84 examples/s]\r\n```"
] | 2023-11-11T04:09:07Z
| 2023-11-20T17:45:20Z
| 2023-11-20T17:45:20Z
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### Describe the bug
```
(datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py
Downloading readme: 100%|███████████████████████████████████| 360/360 [00:00<00:00, 2.16MB/s]
Downloading data: 100%|█████████████████████████████████| 65.1M/65.1M [00:19<00:00, 3.38MB/s]
Downloading data: 100%|█████████████████████████████████| 6.35k/6.35k [00:00<00:00, 20.7kB/s]
Downloading data: 100%|█████████████████████████████████| 7.29M/7.29M [00:01<00:00, 3.99MB/s]
Downloading data files: 100%|██████████████████████████████████| 3/3 [00:21<00:00, 7.14s/it]
Extracting data files: 100%|█████████████████████████████████| 3/3 [00:00<00:00, 1624.23it/s]
Generating train split: 100%|█████████████| 314294/314294 [00:00<00:00, 668186.58 examples/s]
Generating validation split: 120 examples [00:00, 100422.28 examples/s]
Generating test split: 100%|████████████████| 34922/34922 [00:00<00:00, 754683.41 examples/s]
Traceback (most recent call last):
File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module>
dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text")
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset
builder_instance.download_and_prepare(
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits
raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits)))
datasets.utils.info_utils.UnexpectedSplits: {'validation'}
```
### Steps to reproduce the bug
Name:
`dataset.py`
Code:
```
from datasets import load_dataset
dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text")
```
### Expected behavior
Run without errors
### Environment info
```
name: datasets
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=1_gnu
- bzip2=1.0.8=h7b6447c_0
- ca-certificates=2023.08.22=h06a4308_0
- ld_impl_linux-64=2.38=h1181459_1
- libffi=3.4.4=h6a678d5_0
- libgcc-ng=11.2.0=h1234567_1
- libgomp=11.2.0=h1234567_1
- libstdcxx-ng=11.2.0=h1234567_1
- libuuid=1.41.5=h5eee18b_0
- ncurses=6.4=h6a678d5_0
- openssl=3.0.12=h7f8727e_0
- python=3.10.13=h955ad1f_0
- readline=8.2=h5eee18b_0
- setuptools=68.0.0=py310h06a4308_0
- sqlite=3.41.2=h5eee18b_0
- tk=8.6.12=h1ccaba5_0
- wheel=0.41.2=py310h06a4308_0
- xz=5.4.2=h5eee18b_0
- zlib=1.2.13=h5eee18b_0
- pip:
- aiohttp==3.8.6
- aiosignal==1.3.1
- async-timeout==4.0.3
- attrs==23.1.0
- certifi==2023.7.22
- charset-normalizer==3.3.2
- click==8.1.7
- datasets==2.14.6
- dill==0.3.7
- filelock==3.13.1
- frozenlist==1.4.0
- fsspec==2023.10.0
- huggingface-hub==0.19.0
- idna==3.4
- multidict==6.0.4
- multiprocess==0.70.15
- numpy==1.26.1
- openai==0.27.8
- packaging==23.2
- pandas==2.1.3
- pip==23.3.1
- platformdirs==4.0.0
- pyarrow==14.0.1
- python-dateutil==2.8.2
- pytz==2023.3.post1
- pyyaml==6.0.1
- requests==2.31.0
- six==1.16.0
- tomli==2.0.1
- tqdm==4.66.1
- typer==0.9.0
- typing-extensions==4.8.0
- tzdata==2023.3
- urllib3==2.0.7
- xxhash==3.4.1
- yarl==1.9.2
prefix: /home/mruserbox/miniconda3/envs/datasets
```
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Multi process map did not load cache file correctly
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[
"The inconsistency may be caused by the usage of \"update_fingerprint\" and setting \"trust_remote_code\" to \"True.\"\r\nWhen the tokenizer employs \"trust_remote_code,\" the behavior of the map function varies with each code execution. Even if the remote code of the tokenizer remains the same, the result of \"asher.hexdigest()\" is found to be inconsistent each time.\r\nThis may result in different processes executing multiple maps\r\n\r\n\r\n\r\n",
"The issue may be related to problems previously discussed in GitHub issues [#3847](https://github.com/huggingface/datasets/issues/3847) and [#6318](https://github.com/huggingface/datasets/pull/6318). \r\nThis arises from the fact that tokenizer.tokens_trie._tokens is an unordered set, leading to varying hash results:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\nConsequently, this results in different outcomes each time for:\r\n`new_fingerprint = update_fingerprint(datasets._fingerprint, transform, kwargs_for_fingerprint)`\r\n\r\nTo address this issue, it's essential to make `Trie._tokens` a deterministic set while ensuring a consistent order after the final update of `_tokens`.\r\n",
"We now sort `set` and `dict` items to make their hashes deterministic (install from `main` with `pip install git+https://github.com/huggingface/datasets` to test this). Consequently, this should also make the `tokenizer.tokens_trie`'s hash deterministic. Feel free to re-open the issue if this is not the case."
] | 2023-11-01T06:36:54Z
| 2023-11-30T16:04:46Z
| 2023-11-30T16:04:45Z
|
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### Describe the bug
When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process.
Code is modified from [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484)
### Steps to reproduce the bug
```
block_size = data_args.block_size
IGNORE_INDEX = -100
Ignore_Input = False
def tokenize_function(examples):
sources = []
targets = []
for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']):
source = instruction + inputs
target = f"{output}{tokenizer.eos_token}"
sources.append(source)
targets.append(target)
tokenized_sources = tokenizer(sources, return_attention_mask=False)
tokenized_targets = tokenizer(targets, return_attention_mask=False,
add_special_tokens=False
)
all_input_ids = []
all_labels = []
for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']):
if len(s) > block_size and Ignore_Input == False:
# print(s)
continue
input_ids = torch.LongTensor(s + t)[:block_size]
if Ignore_Input:
labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size]
else:
labels = input_ids
assert len(input_ids) == len(labels)
all_input_ids.append(input_ids)
all_labels.append(labels)
results = {
'input_ids': all_input_ids,
'labels': all_labels,
}
return results
with training_args.main_process_first(desc="dataset map tokenization ", local=False):
# print('local_rank',training_args.local_rank)
if not data_args.streaming:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset ",
)
else:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=column_names,
desc="Running tokenizer on dataset "
)
```
### Expected behavior
This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting
### Environment info
transformers == 4.34.1
datasets == 2.14.5
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Add filter desc
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008477 / 0.011353 (-0.002875) | 0.004565 / 0.011008 (-0.006443) | 0.101640 / 0.038508 (0.063132) | 0.029581 / 0.023109 (0.006472) | 0.296524 / 0.275898 (0.020625) | 0.363175 / 0.323480 (0.039695) | 0.006961 / 0.007986 (-0.001024) | 0.003365 / 0.004328 (-0.000963) | 0.079689 / 0.004250 (0.075439) | 0.034881 / 0.037052 (-0.002171) | 0.310979 / 0.258489 (0.052489) | 0.348663 / 0.293841 (0.054822) | 0.034549 / 0.128546 (-0.093997) | 0.011463 / 0.075646 (-0.064184) | 0.326218 / 0.419271 (-0.093053) | 0.041393 / 0.043533 (-0.002140) | 0.297604 / 0.255139 (0.042465) | 0.335751 / 0.283200 (0.052551) | 0.086521 / 0.141683 (-0.055162) | 1.478906 / 1.452155 (0.026752) | 1.512777 / 1.492716 (0.020060) |\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) | 0.008767 / 0.018006 (-0.009239) | 0.397386 / 0.000490 (0.396897) | 0.003136 / 0.000200 (0.002936) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022804 / 0.037411 (-0.014608) | 0.097591 / 0.014526 (0.083066) | 0.103189 / 0.176557 (-0.073368) | 0.138165 / 0.737135 (-0.598970) | 0.107464 / 0.296338 (-0.188874) |\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) | 0.428956 / 0.215209 (0.213747) | 4.269656 / 2.077655 (2.192001) | 2.154418 / 1.504120 (0.650298) | 1.914176 / 1.541195 (0.372982) | 1.818452 / 1.468490 (0.349962) | 0.701381 / 4.584777 (-3.883396) | 3.425190 / 3.745712 (-0.320522) | 1.862545 / 5.269862 (-3.407316) | 1.166271 / 4.565676 (-3.399405) | 0.083678 / 0.424275 (-0.340597) | 0.012254 / 0.007607 (0.004647) | 0.535710 / 0.226044 (0.309665) | 5.342528 / 2.268929 (3.073600) | 2.627135 / 55.444624 (-52.817489) | 2.308313 / 6.876477 (-4.568164) | 2.325568 / 2.142072 (0.183496) | 0.818318 / 4.805227 (-3.986909) | 0.149812 / 6.500664 (-6.350853) | 0.064559 / 0.075469 (-0.010910) |\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) | 1.253611 / 1.841788 (-0.588176) | 13.646763 / 8.074308 (5.572455) | 14.387630 / 10.191392 (4.196238) | 0.159937 / 0.680424 (-0.520487) | 0.029123 / 0.534201 (-0.505078) | 0.400909 / 0.579283 (-0.178374) | 0.422830 / 0.434364 (-0.011534) | 0.488205 / 0.540337 (-0.052133) | 0.577982 / 1.386936 (-0.808954) |\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) | 0.006430 / 0.011353 (-0.004923) | 0.004433 / 0.011008 (-0.006576) | 0.077459 / 0.038508 (0.038951) | 0.026949 / 0.023109 (0.003840) | 0.350276 / 0.275898 (0.074378) | 0.376189 / 0.323480 (0.052709) | 0.004945 / 0.007986 (-0.003041) | 0.003280 / 0.004328 (-0.001048) | 0.076465 / 0.004250 (0.072215) | 0.037510 / 0.037052 (0.000457) | 0.350410 / 0.258489 (0.091921) | 0.386778 / 0.293841 (0.092937) | 0.031933 / 0.128546 (-0.096613) | 0.011691 / 0.075646 (-0.063956) | 0.086519 / 0.419271 (-0.332753) | 0.042490 / 0.043533 (-0.001043) | 0.355930 / 0.255139 (0.100791) | 0.366500 / 0.283200 (0.083301) | 0.089542 / 0.141683 (-0.052141) | 1.492859 / 1.452155 (0.040704) | 1.548626 / 1.492716 (0.055910) |\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) | 0.220123 / 0.018006 (0.202117) | 0.396970 / 0.000490 (0.396480) | 0.000398 / 0.000200 (0.000198) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024831 / 0.037411 (-0.012580) | 0.099681 / 0.014526 (0.085156) | 0.108922 / 0.176557 (-0.067635) | 0.143004 / 0.737135 (-0.594131) | 0.109671 / 0.296338 (-0.186667) |\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) | 0.444237 / 0.215209 (0.229028) | 4.430330 / 2.077655 (2.352675) | 2.235003 / 1.504120 (0.730883) | 2.010499 / 1.541195 (0.469305) | 2.030585 / 1.468490 (0.562095) | 0.701938 / 4.584777 (-3.882839) | 3.334569 / 3.745712 (-0.411144) | 1.861680 / 5.269862 (-3.408181) | 1.166072 / 4.565676 (-3.399604) | 0.083870 / 0.424275 (-0.340405) | 0.012615 / 0.007607 (0.005008) | 0.548789 / 0.226044 (0.322744) | 5.488064 / 2.268929 (3.219136) | 2.614926 / 55.444624 (-52.829698) | 2.246455 / 6.876477 (-4.630022) | 2.277439 / 2.142072 (0.135367) | 0.808449 / 4.805227 (-3.996778) | 0.152434 / 6.500664 (-6.348230) | 0.066709 / 0.075469 (-0.008760) |\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) | 1.316880 / 1.841788 (-0.524908) | 13.965269 / 8.074308 (5.890961) | 13.660187 / 10.191392 (3.468795) | 0.157801 / 0.680424 (-0.522623) | 0.016580 / 0.534201 (-0.517621) | 0.382834 / 0.579283 (-0.196449) | 0.394717 / 0.434364 (-0.039647) | 0.465138 / 0.540337 (-0.075200) | 0.552399 / 1.386936 (-0.834537) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa06927a62e2983e2f0e8b7ba8262070c1543d78 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009341 / 0.011353 (-0.002012) | 0.005303 / 0.011008 (-0.005705) | 0.099287 / 0.038508 (0.060779) | 0.035587 / 0.023109 (0.012478) | 0.295146 / 0.275898 (0.019248) | 0.370470 / 0.323480 (0.046990) | 0.008910 / 0.007986 (0.000925) | 0.004358 / 0.004328 (0.000029) | 0.076298 / 0.004250 (0.072047) | 0.047187 / 0.037052 (0.010135) | 0.309025 / 0.258489 (0.050536) | 0.346659 / 0.293841 (0.052818) | 0.038378 / 0.128546 (-0.090168) | 0.012475 / 0.075646 (-0.063172) | 0.334370 / 0.419271 (-0.084901) | 0.048391 / 0.043533 (0.004858) | 0.298613 / 0.255139 (0.043474) | 0.317329 / 0.283200 (0.034130) | 0.108748 / 0.141683 (-0.032934) | 1.450454 / 1.452155 (-0.001701) | 1.519883 / 1.492716 (0.027167) |\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) | 0.011513 / 0.018006 (-0.006494) | 0.498941 / 0.000490 (0.498451) | 0.005098 / 0.000200 (0.004898) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030523 / 0.037411 (-0.006888) | 0.105478 / 0.014526 (0.090952) | 0.121101 / 0.176557 (-0.055456) | 0.159951 / 0.737135 (-0.577184) | 0.126766 / 0.296338 (-0.169572) |\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) | 0.399101 / 0.215209 (0.183892) | 3.997069 / 2.077655 (1.919414) | 1.851592 / 1.504120 (0.347472) | 1.695708 / 1.541195 (0.154513) | 1.759504 / 1.468490 (0.291014) | 0.708241 / 4.584777 (-3.876536) | 3.786724 / 3.745712 (0.041012) | 3.523731 / 5.269862 (-1.746131) | 1.899474 / 4.565676 (-2.666203) | 0.086680 / 0.424275 (-0.337595) | 0.012232 / 0.007607 (0.004625) | 0.508507 / 0.226044 (0.282462) | 5.086320 / 2.268929 (2.817391) | 2.234906 / 55.444624 (-53.209718) | 1.911090 / 6.876477 (-4.965386) | 1.989232 / 2.142072 (-0.152841) | 0.863660 / 4.805227 (-3.941567) | 0.169334 / 6.500664 (-6.331330) | 0.063273 / 0.075469 (-0.012196) |\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) | 1.237590 / 1.841788 (-0.604198) | 15.417631 / 8.074308 (7.343323) | 15.235308 / 10.191392 (5.043916) | 0.209431 / 0.680424 (-0.470993) | 0.029214 / 0.534201 (-0.504987) | 0.444767 / 0.579283 (-0.134516) | 0.447776 / 0.434364 (0.013413) | 0.538440 / 0.540337 (-0.001897) | 0.635760 / 1.386936 (-0.751176) |\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) | 0.007758 / 0.011353 (-0.003594) | 0.005539 / 0.011008 (-0.005469) | 0.077011 / 0.038508 (0.038503) | 0.034305 / 0.023109 (0.011196) | 0.363352 / 0.275898 (0.087454) | 0.411882 / 0.323480 (0.088403) | 0.006286 / 0.007986 (-0.001700) | 0.004378 / 0.004328 (0.000050) | 0.075504 / 0.004250 (0.071253) | 0.052728 / 0.037052 (0.015675) | 0.370122 / 0.258489 (0.111633) | 0.421910 / 0.293841 (0.128069) | 0.038444 / 0.128546 (-0.090102) | 0.012602 / 0.075646 (-0.063045) | 0.088540 / 0.419271 (-0.330731) | 0.060321 / 0.043533 (0.016788) | 0.350502 / 0.255139 (0.095363) | 0.393211 / 0.283200 (0.110011) | 0.113057 / 0.141683 (-0.028626) | 1.453275 / 1.452155 (0.001120) | 1.541033 / 1.492716 (0.048317) |\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) | 0.333603 / 0.018006 (0.315597) | 0.510548 / 0.000490 (0.510058) | 0.003573 / 0.000200 (0.003373) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032783 / 0.037411 (-0.004628) | 0.111943 / 0.014526 (0.097418) | 0.127154 / 0.176557 (-0.049403) | 0.171716 / 0.737135 (-0.565420) | 0.132441 / 0.296338 (-0.163898) |\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) | 0.439110 / 0.215209 (0.223901) | 4.440874 / 2.077655 (2.363220) | 2.145850 / 1.504120 (0.641730) | 1.909566 / 1.541195 (0.368371) | 2.032199 / 1.468490 (0.563709) | 0.711295 / 4.584777 (-3.873482) | 3.845729 / 3.745712 (0.100017) | 3.583555 / 5.269862 (-1.686307) | 1.836856 / 4.565676 (-2.728820) | 0.085966 / 0.424275 (-0.338309) | 0.012479 / 0.007607 (0.004872) | 0.545379 / 0.226044 (0.319334) | 5.425724 / 2.268929 (3.156796) | 2.648304 / 55.444624 (-52.796321) | 2.286369 / 6.876477 (-4.590108) | 2.367714 / 2.142072 (0.225642) | 0.831035 / 4.805227 (-3.974192) | 0.167603 / 6.500664 (-6.333061) | 0.064721 / 0.075469 (-0.010748) |\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) | 1.244495 / 1.841788 (-0.597292) | 15.304267 / 8.074308 (7.229958) | 13.912185 / 10.191392 (3.720793) | 0.156459 / 0.680424 (-0.523965) | 0.019181 / 0.534201 (-0.515019) | 0.425940 / 0.579283 (-0.153343) | 0.427956 / 0.434364 (-0.006408) | 0.529126 / 0.540337 (-0.011212) | 0.628360 / 1.386936 (-0.758576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da31f6ee02af29d92ee5541e4a3fc388c3d9abfc \"CML watermark\")\n"
] | 2023-02-21T14:04:42Z
| 2023-02-21T14:19:54Z
| 2023-02-21T14:12:39Z
|
MEMBER
| null | null | null |
Otherwise it would show a `Map` progress bar, since it uses `map` under the hood
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I_kwDODunzps6R4B2E
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|
Disable warning when using with_format format on tensors
|
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### Feature request
If we write this code:
```python
"""Get data and define datasets."""
from enum import StrEnum
from datasets import load_dataset
from torch.utils.data import DataLoader
from torchvision import transforms
class Split(StrEnum):
"""Describes what type of split to use in the dataloader"""
TRAIN = "train"
TEST = "test"
VAL = "validation"
class ImageNetDataLoader(DataLoader):
"""Create an ImageNetDataloader"""
_preprocess_transform = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
]
)
def __init__(self, batch_size: int = 4, split: Split = Split.TRAIN):
dataset = (
load_dataset(
"imagenet-1k",
split=split,
trust_remote_code=True,
streaming=True,
)
.with_format("torch")
.map(self._preprocess)
)
super().__init__(dataset=dataset, batch_size=batch_size)
def _preprocess(self, data):
if data["image"].shape[0] < 3:
data["image"] = data["image"].repeat(3, 1, 1)
data["image"] = self._preprocess_transform(data["image"].float())
return data
if __name__ == "__main__":
dataloader = ImageNetDataLoader(batch_size=2)
for batch in dataloader:
print(batch["image"])
break
```
This will trigger an user warning :
```bash
datasets\formatting\torch_formatter.py:85: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```
### Motivation
This happens because the the way the formatted tensor is returned in `TorchFormatter._tensorize`.
This function handle values of different types, according to some tests it seems that possible value types are `int`, `numpy.ndarray` and `torch.Tensor`.
In particular this warning is triggered when the value type is `torch.Tensor`, because is not the suggested Pytorch way of doing it:
- https://stackoverflow.com/questions/55266154/pytorch-preferred-way-to-copy-a-tensor
- https://discuss.pytorch.org/t/it-is-recommended-to-use-source-tensor-clone-detach-or-sourcetensor-clone-detach-requires-grad-true/101218#:~:text=The%20warning%20points%20to%20wrapping%20a%20tensor%20in%20torch.tensor%2C%20which%20is%20not%20recommended.%0AInstead%20of%20torch.tensor(outputs)%20use%20outputs.clone().detach()%20or%20the%20same%20with%20.requires_grad_(True)%2C%20if%20necessary.
### Your contribution
A solution that I found to be working is to change the current way of doing it:
```python
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```
To:
```python
if (isinstance(value, torch.Tensor)):
tensor = value.clone().detach()
if self.torch_tensor_kwargs.get('requires_grad', False):
tensor.requires_grad_()
return tensor
else:
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```
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I_kwDODunzps5jAAGn
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Fallback JSON Dataset loading does not load all values when features specified manually
|
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[
"Thanks for reporting, @myluki2000.\r\n\r\nI am working on a fix."
] | 2023-04-10T15:22:14Z
| 2023-04-21T06:35:28Z
| 2023-04-21T06:35:28Z
|
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### Describe the bug
The fallback JSON dataset loader located here:
https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L130-L153
does not load the values of features correctly when features are specified manually and not all features have a value in the first entry of the dataset. I'm pretty sure this is not supposed to be expected bahavior?
To fix this you'd have to change this line:
https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L140
To pass a schema to pyarrow which has the same structure as the features argument passed to the load_dataset() method.
### Steps to reproduce the bug
Consider a dataset JSON like this:
```
[
{
"instruction": "Do stuff",
"output": "Answer stuff"
},
{
"instruction": "Do stuff2",
"input": "Additional Input2",
"output": "Answer stuff2"
}
]
```
Using this code to load the dataset:
```
from datasets import load_dataset, Features, Value
features = {
"instruction": Value("string"),
"input": Value("string"),
"output": Value("string")
}
features = Features(features)
ds = load_dataset("json", data_files="./ds.json", features=features)
for row in ds["train"]:
print(row)
```
we get a dataset that looks like this:
| **Instruction** | **Input** | **Output** |
|-----------------|--------------------|-----------------|
| "Do stuff" | None | "Answer Stuff" |
| "Do stuff2" | None | "Answer Stuff2" |
### Expected behavior
The input column should contain values other than None for dataset entries that have the "input" attribute set:
| **Instruction** | **Input** | **Output** |
|-----------------|--------------------|-----------------|
| "Do stuff" | None | "Answer Stuff" |
| "Do stuff2" | "Additional Input2" | "Answer Stuff2" |
### Environment info
Python 3.10.10
Datasets 2.11.0
Windows 10
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Allow for variation in metadata file names as per issue #7123
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| 2024-11-08T00:44:47Z
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Allow metadata files to have an identifying preface. Specifically, it will recognize files with `-metadata.csv` or `_metadata.csv` as metadata files for the purposes of the dataset viewer functionality.
Resolves #7123.
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I_kwDODunzps56Ml90
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Bug get_metadata_patterns arg error
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CONTRIBUTOR
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metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)
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Dataset uses excessive memory when loading files
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"small update: I converted the jsons to parquet and it now works well with 32 proc and the same node. \nI still think this needs to be understood, since json is a very popular and easy-to-use format. ",
"Hi ! The JSON loader loads full files in memory, unless they are JSON Lines. In this case it iterates on the JSON Lines in a memory efficient manner.\n\nI know there is an `ijson` package that works similarly but for general JSON files, maybe it can help and remove the need to load full JSON files in memory",
"Hi, i understand that json files are probably loaded into memory to read them but aren't they released when we write all the file content into arrow or something? ",
"Yes correct, the JSON data is only in memory during the conversion to Arrow. Then, the data is memory mapped from you disk",
"so the json files are all loaded into memory before converting to arrow? or do they convert 1 json at a time and then they are realeased?\nI don't understand how 200GB worth of jsons fill a 378GB node's memory.",
"Each process converts one JSON file at at time, So the total memory usage is num_proc * json_file_size * overhead, where overhead can be around 2 or 3 for the conversion.\n\nSo it's indeed surprising that you run out of memory. Is the dataset available somewhere ? or a subset maybe ?",
"This is a tokenized dataset I created for training a speech-language model with a few features (so it is not private but not easily available). I can send/upload a shard or two and you can copy them however many times you want so you can debug. this should give you something comparable to what I have, but will be easier than creating it yourself. so if you want that, let me know :)",
"Maybe you can measure the memory usage when loading 1 file with num_proc=1 ? This should already be helpful.\n\nMemory usage for tokenized data can be bigger than just text, for example the tokens type can be inferred as int64 and the lists offsets are int32",
"OK, I will try to do this in the near future. I am a little swamped at the moment. do you have a preferred tool?\n\nalso My data is just list of ints, there is no offsets",
"> so the json files are all loaded into memory before converting to arrow? or do they convert 1 json at a time and then they are realeased? I don't understand how 200GB worth of jsons fill a 378GB node's memory.\n\nHello! Is your query solved? I have the same confusion and would like to ask you for advice",
"no, the issue is still present. I converted the json files to parquet, but json seems to have a problem.\n\nUnfortunately i didn't have the time to try and profile the memory usage for 1 file. So if you want to do that, it will be great! "
] | 2025-04-13T21:09:49Z
| 2025-04-26T15:33:13Z
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### Describe the bug
Hi
I am having an issue when loading a dataset.
I have about 200 json files each about 1GB (total about 215GB). each row has a few features which are a list of ints.
I am trying to load the dataset using `load_dataset`.
The dataset is about 1.5M samples
I use `num_proc=32` and a node with 378GB of memory.
About a third of the way there I get an OOM.
I also saw an old bug with a similar issue, which says to set `writer_batch_size`. I tried to lower it to 10, but it still crashed.
I also tried to lower the `num_proc` to 16 and even 8, but still the same issue.
### Steps to reproduce the bug
`dataset = load_dataset("json", data_dir=data_config.train_path, num_proc=data_config.num_proc, writer_batch_size=50)["train"]`
### Expected behavior
Loading a dataset with more than 100GB to spare should not cause an OOM error.
maybe i am missing something but I would love some help.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-6.6.20-aufs-1-x86_64-with-glibc2.36
- Python version: 3.11.2
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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Support `converters` in `CsvBuilder`
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] | 2022-10-03T14:23:21Z
| 2022-10-04T11:19:28Z
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COLLABORATOR
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Add the `converters` param to `CsvBuilder`, to help in situations like [this one](https://discuss.huggingface.co/t/typeerror-in-load-dataset-related-to-a-sequence-of-strings/23545).
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006113 / 0.011353 (-0.005239) | 0.004195 / 0.011008 (-0.006813) | 0.098103 / 0.038508 (0.059595) | 0.027970 / 0.023109 (0.004860) | 0.300992 / 0.275898 (0.025094) | 0.335402 / 0.323480 (0.011922) | 0.005079 / 0.007986 (-0.002906) | 0.003516 / 0.004328 (-0.000813) | 0.077311 / 0.004250 (0.073061) | 0.037863 / 0.037052 (0.000810) | 0.302638 / 0.258489 (0.044149) | 0.346554 / 0.293841 (0.052713) | 0.025218 / 0.128546 (-0.103328) | 0.008630 / 0.075646 (-0.067017) | 0.319748 / 0.419271 (-0.099523) | 0.049182 / 0.043533 (0.005650) | 0.306233 / 0.255139 (0.051094) | 0.331040 / 0.283200 (0.047840) | 0.089203 / 0.141683 (-0.052480) | 1.496104 / 1.452155 (0.043949) | 1.567878 / 1.492716 (0.075162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215774 / 0.018006 (0.197768) | 0.436810 / 0.000490 (0.436320) | 0.000307 / 0.000200 (0.000107) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024102 / 0.037411 (-0.013310) | 0.095459 / 0.014526 (0.080933) | 0.106564 / 0.176557 (-0.069992) | 0.169894 / 0.737135 (-0.567241) | 0.109152 / 0.296338 (-0.187186) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429066 / 0.215209 (0.213857) | 4.297385 / 2.077655 (2.219730) | 2.054854 / 1.504120 (0.550734) | 1.846844 / 1.541195 (0.305649) | 1.840807 / 1.468490 (0.372317) | 0.553193 / 4.584777 (-4.031584) | 3.366788 / 3.745712 (-0.378924) | 1.727337 / 5.269862 (-3.542525) | 0.994357 / 4.565676 (-3.571319) | 0.067790 / 0.424275 (-0.356485) | 0.012002 / 0.007607 (0.004395) | 0.533335 / 0.226044 (0.307291) | 5.341341 / 2.268929 (3.072412) | 2.543581 / 55.444624 (-52.901043) | 2.220374 / 6.876477 (-4.656103) | 2.321656 / 2.142072 (0.179583) | 0.654408 / 4.805227 (-4.150819) | 0.134693 / 6.500664 (-6.365971) | 0.066926 / 0.075469 (-0.008544) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209463 / 1.841788 (-0.632325) | 13.568221 / 8.074308 (5.493913) | 13.965418 / 10.191392 (3.774026) | 0.145049 / 0.680424 (-0.535375) | 0.016936 / 0.534201 (-0.517265) | 0.371587 / 0.579283 (-0.207696) | 0.386363 / 0.434364 (-0.048001) | 0.437137 / 0.540337 (-0.103201) | 0.514779 / 1.386936 (-0.872157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006245 / 0.011353 (-0.005108) | 0.004232 / 0.011008 (-0.006776) | 0.075682 / 0.038508 (0.037174) | 0.027858 / 0.023109 (0.004749) | 0.425325 / 0.275898 (0.149427) | 0.466732 / 0.323480 (0.143253) | 0.005240 / 0.007986 (-0.002745) | 0.003506 / 0.004328 (-0.000823) | 0.075294 / 0.004250 (0.071044) | 0.041677 / 0.037052 (0.004624) | 0.426552 / 0.258489 (0.168063) | 0.469452 / 0.293841 (0.175611) | 0.025443 / 0.128546 (-0.103104) | 0.008526 / 0.075646 (-0.067120) | 0.082190 / 0.419271 (-0.337081) | 0.040906 / 0.043533 (-0.002626) | 0.428406 / 0.255139 (0.173267) | 0.446795 / 0.283200 (0.163595) | 0.093837 / 0.141683 (-0.047846) | 1.518639 / 1.452155 (0.066484) | 1.620214 / 1.492716 (0.127498) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223259 / 0.018006 (0.205253) | 0.425077 / 0.000490 (0.424588) | 0.001980 / 0.000200 (0.001780) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025813 / 0.037411 (-0.011599) | 0.103062 / 0.014526 (0.088536) | 0.108958 / 0.176557 (-0.067598) | 0.161591 / 0.737135 (-0.575544) | 0.112130 / 0.296338 (-0.184209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472843 / 0.215209 (0.257634) | 4.713281 / 2.077655 (2.635626) | 2.458216 / 1.504120 (0.954096) | 2.272467 / 1.541195 (0.731273) | 2.324456 / 1.468490 (0.855965) | 0.554686 / 4.584777 (-4.030091) | 3.445079 / 3.745712 (-0.300634) | 3.451896 / 5.269862 (-1.817966) | 1.431065 / 4.565676 (-3.134612) | 0.067868 / 0.424275 (-0.356407) | 0.012093 / 0.007607 (0.004486) | 0.573571 / 0.226044 (0.347526) | 5.820452 / 2.268929 (3.551523) | 2.934858 / 55.444624 (-52.509767) | 2.602719 / 6.876477 (-4.273758) | 2.645999 / 2.142072 (0.503927) | 0.660688 / 4.805227 (-4.144540) | 0.137490 / 6.500664 (-6.363174) | 0.068311 / 0.075469 (-0.007158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.321709 / 1.841788 (-0.520079) | 14.592346 / 8.074308 (6.518038) | 14.520748 / 10.191392 (4.329356) | 0.132689 / 0.680424 (-0.547735) | 0.016422 / 0.534201 (-0.517779) | 0.370071 / 0.579283 (-0.209212) | 0.397091 / 0.434364 (-0.037273) | 0.431979 / 0.540337 (-0.108358) | 0.509965 / 1.386936 (-0.876971) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006182 / 0.011353 (-0.005171) | 0.004153 / 0.011008 (-0.006855) | 0.095715 / 0.038508 (0.057207) | 0.032457 / 0.023109 (0.009347) | 0.314961 / 0.275898 (0.039063) | 0.353696 / 0.323480 (0.030216) | 0.005256 / 0.007986 (-0.002729) | 0.004870 / 0.004328 (0.000541) | 0.072442 / 0.004250 (0.068192) | 0.046102 / 0.037052 (0.009050) | 0.324410 / 0.258489 (0.065921) | 0.366861 / 0.293841 (0.073020) | 0.027088 / 0.128546 (-0.101458) | 0.008572 / 0.075646 (-0.067075) | 0.325988 / 0.419271 (-0.093284) | 0.049494 / 0.043533 (0.005961) | 0.311221 / 0.255139 (0.056082) | 0.359720 / 0.283200 (0.076521) | 0.095101 / 0.141683 (-0.046581) | 1.472821 / 1.452155 (0.020667) | 1.516157 / 1.492716 (0.023441) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210456 / 0.018006 (0.192450) | 0.439440 / 0.000490 (0.438950) | 0.003764 / 0.000200 (0.003564) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024076 / 0.037411 (-0.013335) | 0.104886 / 0.014526 (0.090360) | 0.114164 / 0.176557 (-0.062393) | 0.167289 / 0.737135 (-0.569847) | 0.116457 / 0.296338 (-0.179882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400039 / 0.215209 (0.184830) | 3.973243 / 2.077655 (1.895588) | 1.801991 / 1.504120 (0.297871) | 1.592017 / 1.541195 (0.050822) | 1.612564 / 1.468490 (0.144074) | 0.527475 / 4.584777 (-4.057302) | 3.676246 / 3.745712 (-0.069466) | 1.806423 / 5.269862 (-3.463438) | 1.176921 / 4.565676 (-3.388756) | 0.065902 / 0.424275 (-0.358373) | 0.012245 / 0.007607 (0.004638) | 0.490883 / 0.226044 (0.264838) | 4.905270 / 2.268929 (2.636341) | 2.218694 / 55.444624 (-53.225930) | 1.903074 / 6.876477 (-4.973403) | 1.979505 / 2.142072 (-0.162567) | 0.644415 / 4.805227 (-4.160812) | 0.142433 / 6.500664 (-6.358231) | 0.063564 / 0.075469 (-0.011905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193756 / 1.841788 (-0.648032) | 14.673103 / 8.074308 (6.598795) | 13.410951 / 10.191392 (3.219559) | 0.159175 / 0.680424 (-0.521249) | 0.017076 / 0.534201 (-0.517125) | 0.388880 / 0.579283 (-0.190403) | 0.409974 / 0.434364 (-0.024390) | 0.454494 / 0.540337 (-0.085844) | 0.556873 / 1.386936 (-0.830063) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006107 / 0.011353 (-0.005246) | 0.004433 / 0.011008 (-0.006575) | 0.073892 / 0.038508 (0.035384) | 0.032386 / 0.023109 (0.009277) | 0.370339 / 0.275898 (0.094441) | 0.388996 / 0.323480 (0.065516) | 0.005438 / 0.007986 (-0.002548) | 0.003875 / 0.004328 (-0.000454) | 0.073867 / 0.004250 (0.069617) | 0.048350 / 0.037052 (0.011298) | 0.380328 / 0.258489 (0.121839) | 0.411373 / 0.293841 (0.117532) | 0.028183 / 0.128546 (-0.100363) | 0.008924 / 0.075646 (-0.066723) | 0.082484 / 0.419271 (-0.336787) | 0.047321 / 0.043533 (0.003788) | 0.371702 / 0.255139 (0.116563) | 0.380535 / 0.283200 (0.097335) | 0.100772 / 0.141683 (-0.040911) | 1.475038 / 1.452155 (0.022883) | 1.564293 / 1.492716 (0.071577) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214589 / 0.018006 (0.196583) | 0.437193 / 0.000490 (0.436703) | 0.003676 / 0.000200 (0.003476) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027991 / 0.037411 (-0.009421) | 0.111154 / 0.014526 (0.096628) | 0.120365 / 0.176557 (-0.056191) | 0.173601 / 0.737135 (-0.563535) | 0.126244 / 0.296338 (-0.170094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442848 / 0.215209 (0.227639) | 4.398336 / 2.077655 (2.320681) | 2.217058 / 1.504120 (0.712938) | 2.011155 / 1.541195 (0.469960) | 2.123086 / 1.468490 (0.654596) | 0.525857 / 4.584777 (-4.058920) | 3.730191 / 3.745712 (-0.015521) | 3.517680 / 5.269862 (-1.752181) | 1.557940 / 4.565676 (-3.007736) | 0.066309 / 0.424275 (-0.357967) | 0.011788 / 0.007607 (0.004181) | 0.548506 / 0.226044 (0.322462) | 5.483615 / 2.268929 (3.214687) | 2.663784 / 55.444624 (-52.780840) | 2.325744 / 6.876477 (-4.550732) | 2.344179 / 2.142072 (0.202106) | 0.644217 / 4.805227 (-4.161010) | 0.141546 / 6.500664 (-6.359118) | 0.063730 / 0.075469 (-0.011739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296032 / 1.841788 (-0.545756) | 14.903729 / 8.074308 (6.829421) | 14.505409 / 10.191392 (4.314017) | 0.170478 / 0.680424 (-0.509946) | 0.017876 / 0.534201 (-0.516325) | 0.401047 / 0.579283 (-0.178236) | 0.417855 / 0.434364 (-0.016509) | 0.472138 / 0.540337 (-0.068200) | 0.570859 / 1.386936 (-0.816077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008495 / 0.011353 (-0.002858) | 0.005322 / 0.011008 (-0.005686) | 0.125471 / 0.038508 (0.086962) | 0.034604 / 0.023109 (0.011495) | 0.419831 / 0.275898 (0.143933) | 0.415707 / 0.323480 (0.092227) | 0.007471 / 0.007986 (-0.000515) | 0.005441 / 0.004328 (0.001112) | 0.095412 / 0.004250 (0.091162) | 0.053865 / 0.037052 (0.016812) | 0.375257 / 0.258489 (0.116768) | 0.438114 / 0.293841 (0.144273) | 0.046183 / 0.128546 (-0.082363) | 0.013663 / 0.075646 (-0.061984) | 0.438317 / 0.419271 (0.019045) | 0.065665 / 0.043533 (0.022133) | 0.387640 / 0.255139 (0.132501) | 0.431350 / 0.283200 (0.148150) | 0.112841 / 0.141683 (-0.028842) | 1.778639 / 1.452155 (0.326484) | 1.891948 / 1.492716 (0.399232) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284371 / 0.018006 (0.266365) | 0.598247 / 0.000490 (0.597758) | 0.013674 / 0.000200 (0.013474) | 0.000483 / 0.000054 (0.000428) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032437 / 0.037411 (-0.004974) | 0.120547 / 0.014526 (0.106021) | 0.129845 / 0.176557 (-0.046711) | 0.203455 / 0.737135 (-0.533680) | 0.140039 / 0.296338 (-0.156300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596549 / 0.215209 (0.381340) | 6.138766 / 2.077655 (4.061111) | 2.515506 / 1.504120 (1.011386) | 2.124472 / 1.541195 (0.583277) | 2.160812 / 1.468490 (0.692322) | 0.898965 / 4.584777 (-3.685812) | 5.588152 / 3.745712 (1.842440) | 2.717580 / 5.269862 (-2.552282) | 1.683641 / 4.565676 (-2.882036) | 0.108045 / 0.424275 (-0.316230) | 0.014089 / 0.007607 (0.006481) | 0.749567 / 0.226044 (0.523523) | 7.518051 / 2.268929 (5.249123) | 3.198238 / 55.444624 (-52.246386) | 2.575156 / 6.876477 (-4.301321) | 2.725818 / 2.142072 (0.583745) | 1.149338 / 4.805227 (-3.655889) | 0.220443 / 6.500664 (-6.280221) | 0.081452 / 0.075469 (0.005983) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624462 / 1.841788 (-0.217325) | 18.204963 / 8.074308 (10.130655) | 21.379169 / 10.191392 (11.187777) | 0.248520 / 0.680424 (-0.431903) | 0.030121 / 0.534201 (-0.504080) | 0.499542 / 0.579283 (-0.079741) | 0.599783 / 0.434364 (0.165419) | 0.597642 / 0.540337 (0.057305) | 0.681948 / 1.386936 (-0.704988) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008431 / 0.011353 (-0.002921) | 0.006143 / 0.011008 (-0.004865) | 0.107531 / 0.038508 (0.069023) | 0.036308 / 0.023109 (0.013199) | 0.480555 / 0.275898 (0.204657) | 0.556407 / 0.323480 (0.232927) | 0.007614 / 0.007986 (-0.000372) | 0.004749 / 0.004328 (0.000421) | 0.105734 / 0.004250 (0.101484) | 0.051619 / 0.037052 (0.014567) | 0.514821 / 0.258489 (0.256332) | 0.562143 / 0.293841 (0.268302) | 0.042957 / 0.128546 (-0.085589) | 0.015142 / 0.075646 (-0.060505) | 0.143161 / 0.419271 (-0.276111) | 0.061910 / 0.043533 (0.018377) | 0.496923 / 0.255139 (0.241784) | 0.556302 / 0.283200 (0.273102) | 0.136700 / 0.141683 (-0.004983) | 1.886184 / 1.452155 (0.434029) | 2.004087 / 1.492716 (0.511371) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235530 / 0.018006 (0.217523) | 0.600796 / 0.000490 (0.600306) | 0.009074 / 0.000200 (0.008874) | 0.000203 / 0.000054 (0.000149) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036345 / 0.037411 (-0.001066) | 0.126112 / 0.014526 (0.111586) | 0.143369 / 0.176557 (-0.033188) | 0.211381 / 0.737135 (-0.525755) | 0.151095 / 0.296338 (-0.145243) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.695022 / 0.215209 (0.479813) | 6.685981 / 2.077655 (4.608326) | 3.104521 / 1.504120 (1.600401) | 2.758323 / 1.541195 (1.217128) | 2.706286 / 1.468490 (1.237796) | 0.941182 / 4.584777 (-3.643595) | 5.715839 / 3.745712 (1.970127) | 5.089636 / 5.269862 (-0.180226) | 2.594739 / 4.565676 (-1.970937) | 0.112621 / 0.424275 (-0.311655) | 0.014001 / 0.007607 (0.006394) | 0.812990 / 0.226044 (0.586945) | 8.060890 / 2.268929 (5.791961) | 3.832506 / 55.444624 (-51.612119) | 3.148051 / 6.876477 (-3.728425) | 3.110096 / 2.142072 (0.968023) | 1.105050 / 4.805227 (-3.700178) | 0.219835 / 6.500664 (-6.280829) | 0.078600 / 0.075469 (0.003131) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.707551 / 1.841788 (-0.134237) | 19.238194 / 8.074308 (11.163885) | 22.167076 / 10.191392 (11.975684) | 0.233458 / 0.680424 (-0.446966) | 0.025131 / 0.534201 (-0.509070) | 0.525241 / 0.579283 (-0.054042) | 0.649666 / 0.434364 (0.215303) | 0.602941 / 0.540337 (0.062603) | 0.718472 / 1.386936 (-0.668464) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n"
] | 2023-05-30T14:59:48Z
| 2023-05-30T18:03:10Z
| 2023-05-30T17:53:29Z
|
COLLABORATOR
| null | null | null |
Fix #5906
|
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PR_kwDODunzps5n23Jz
| 6,694
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__add__ for Dataset, IterableDataset
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"Hi! You can find a reason why we are against this feature in https://github.com/huggingface/datasets/issues/3449. \r\n\r\n> It's too cumbersome to write this command every time we perform a dataset merging operation\r\n\r\nExplicit is better than implicit, so this isn't a good enough reason. \r\n\r\nThanks for the effort nonetheless :)!"
] | 2024-02-26T01:46:55Z
| 2024-02-29T16:52:58Z
| null |
NONE
| null | null | null |
It's too cumbersome to write this command every time we perform a dataset merging operation. ```pythonfrom datasets import concatenate_datasets``` We have added a simple `__add__` magic method to each class using `concatenate_datasets.`
```python
from datasets import load_dataset
bookcorpus = load_dataset("bookcorpus", split="train")
wiki = load_dataset("wikimedia/wikipedia", "20231101.ab", split="train")
wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"]) # only keep the 'text' column
bookcorpus + wiki
#Dataset({
# features: ['text'],
# num_rows: 74004228
#})
#Dataset({
# features: ['text'],
# num_rows: 6152
#})
#Dataset({
# features: ['text'],
# num_rows: 74010380
#})
```
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I_kwDODunzps6DOH0k
| 6,748
|
Strange slicing behavior
|
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"As explained in the [docs](https://huggingface.co/docs/datasets/v2.18.0/en/access#slicing), slicing a `Dataset` returns a dictionary that maps its column names to their values. So, `len(dataset[:300])=2` is expected, assuming your dataset has 2 columns (the returned dict has 2 keys, but each value in the dict has 300 items).\r\n` "
] | 2024-03-22T01:49:13Z
| 2024-03-22T16:43:57Z
| null |
NONE
| null | null |
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### Describe the bug
I have loaded a dataset, and then slice first 300 samples using `:` ops, however, the resulting dataset is not expected, as the output below:
```bash
len(dataset)=1050324
len(dataset[:300])=2
len(dataset[0:300])=2
len(dataset.select(range(300)))=300
```
### Steps to reproduce the bug
load a dataset then:
```bash
dataset = load_from_disk(args.train_data_dir)
print(f"{len(dataset)=}", flush=True)
print(f"{len(dataset[:300])=}", flush=True)
print(f"{len(dataset[0:300])=}", flush=True)
print(f"{len(dataset.select(range(300)))=}", flush=True)
```
### Expected behavior
```bash
len(dataset)=1050324
len(dataset[:300])=300
len(dataset[0:300])=300
len(dataset.select(range(300)))=300
```
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- `huggingface_hub` version: 0.20.2
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0
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PR_kwDODunzps6OsxR1
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release: 3.4.0
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7453). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-14T16:30:45Z
| 2025-03-14T16:38:10Z
| 2025-03-14T16:38:08Z
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MEMBER
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I_kwDODunzps5t97z4
| 6,137
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(`from_spark()`) Unable to connect HDFS in pyspark YARN setting
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### Describe the bug
related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613
---
Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script.
Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process.
These are some peculiarities in my environment that might caused this issue.
* **Cluster requires kerberos authentication**
* But I think the error message implies that's not the problem in this case
* **The user that runs the worker process is different from that built the docker image**
* To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone
* **Pyspark-part of my code has no problem interacting with HDFS.**
* Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker)
* The problem occurs only when it runs as cluster's worker runtime
Hope I could get some help. Thanks.
```bash
2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
### Steps to reproduce the bug
Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path.
### Expected behavior
Work as described in document
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
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Improve features resolution in streaming
|
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Just took your comment into account @mariosasko , let me know if it's good for you now :)"
] | 2022-07-28T17:28:11Z
| 2022-09-09T17:17:39Z
| 2022-09-09T17:15:30Z
|
MEMBER
| null | null | null |
`IterableDataset._resolve_features` was returning the features sorted alphabetically by column name, which is not consistent with non-streaming. I changed this and used the order of columns from the data themselves. It was causing some inconsistencies in the dataset viewer as well.
I also fixed `interleave_datasets` that was not filling missing columns with None, because it was not using the columns from `IterableDataset._resolve_features`
cc @severo
|
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I_kwDODunzps6A3JA0
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Push samples to dataset on hub without having the dataset locally
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[
"Hi ! For now I would recommend creating a new Parquet file using `dataset_new.to_parquet()` and upload it to HF using `huggingface_hub` every time you get a new batch of data. You can name the Parquet files `0000.parquet`, `0001.parquet`, etc.\r\n\r\nThough maybe make sure to not upload one file per sample since that would be inefficient. You can buffer your data and upload when you have enough new samples for example",
"This is excellent, thanks!"
] | 2024-02-29T19:17:12Z
| 2024-03-08T21:08:38Z
| 2024-03-08T21:08:38Z
|
NONE
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### Feature request
Say I have the following code:
```
from datasets import Dataset
import pandas as pd
new_data = {
"column_1": ["value1", "value2"],
"column_2": ["value3", "value4"],
}
df_new = pd.DataFrame(new_data)
dataset_new = Dataset.from_pandas(df_new)
# add these samples to a remote dataset
```
It would be great to have a way to push dataset_new to a remote dataset that respects the same schema. This way one would not have to do the following:
```
from datasets import load_dataset
dataset = load_dataset('username/dataset_name', use_auth_token='your_hf_token_here')
updated_dataset = dataset['train'].concatenate(dataset_new)
updated_dataset.push_to_hub('username/dataset_name', use_auth_token='your_hf_token_here')
```
### Motivation
No need to download the dataset.
### Your contribution
Maybe this feature already exists, didnt see it though. I do not have the expertise to do this.
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IterableDataset Arrow formatting
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007593 / 0.011353 (-0.003760) | 0.005554 / 0.011008 (-0.005454) | 0.097663 / 0.038508 (0.059155) | 0.034915 / 0.023109 (0.011806) | 0.303116 / 0.275898 (0.027218) | 0.342376 / 0.323480 (0.018897) | 0.006044 / 0.007986 (-0.001942) | 0.004239 / 0.004328 (-0.000090) | 0.074561 / 0.004250 (0.070310) | 0.049109 / 0.037052 (0.012057) | 0.311302 / 0.258489 (0.052813) | 0.360717 / 0.293841 (0.066876) | 0.035119 / 0.128546 (-0.093428) | 0.012465 / 0.075646 (-0.063181) | 0.333648 / 0.419271 (-0.085624) | 0.051294 / 0.043533 (0.007762) | 0.297298 / 0.255139 (0.042159) | 0.321957 / 0.283200 (0.038757) | 0.108206 / 0.141683 (-0.033477) | 1.425023 / 1.452155 (-0.027132) | 1.526395 / 1.492716 (0.033678) |\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) | 0.300694 / 0.018006 (0.282688) | 0.515141 / 0.000490 (0.514651) | 0.003965 / 0.000200 (0.003765) | 0.000260 / 0.000054 (0.000206) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029428 / 0.037411 (-0.007983) | 0.107634 / 0.014526 (0.093108) | 0.123662 / 0.176557 (-0.052895) | 0.182886 / 0.737135 (-0.554249) | 0.128361 / 0.296338 (-0.167977) |\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) | 0.398809 / 0.215209 (0.183600) | 3.984428 / 2.077655 (1.906773) | 1.795337 / 1.504120 (0.291217) | 1.609235 / 1.541195 (0.068040) | 1.724825 / 1.468490 (0.256335) | 0.698413 / 4.584777 (-3.886364) | 3.857479 / 3.745712 (0.111767) | 2.135203 / 5.269862 (-3.134659) | 1.348458 / 4.565676 (-3.217218) | 0.086445 / 0.424275 (-0.337830) | 0.012717 / 0.007607 (0.005110) | 0.498713 / 0.226044 (0.272668) | 4.988685 / 2.268929 (2.719757) | 2.284764 / 55.444624 (-53.159860) | 1.961162 / 6.876477 (-4.915315) | 2.147514 / 2.142072 (0.005441) | 0.850334 / 4.805227 (-3.954894) | 0.171664 / 6.500664 (-6.329000) | 0.065526 / 0.075469 (-0.009943) |\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) | 1.204398 / 1.841788 (-0.637390) | 15.625790 / 8.074308 (7.551482) | 14.614980 / 10.191392 (4.423588) | 0.167135 / 0.680424 (-0.513289) | 0.017631 / 0.534201 (-0.516570) | 0.427337 / 0.579283 (-0.151946) | 0.439203 / 0.434364 (0.004839) | 0.499670 / 0.540337 (-0.040668) | 0.587577 / 1.386936 (-0.799359) |\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) | 0.007866 / 0.011353 (-0.003486) | 0.005798 / 0.011008 (-0.005210) | 0.075803 / 0.038508 (0.037295) | 0.035773 / 0.023109 (0.012664) | 0.361965 / 0.275898 (0.086067) | 0.402780 / 0.323480 (0.079300) | 0.006521 / 0.007986 (-0.001465) | 0.004613 / 0.004328 (0.000284) | 0.075196 / 0.004250 (0.070946) | 0.055324 / 0.037052 (0.018272) | 0.363468 / 0.258489 (0.104979) | 0.410344 / 0.293841 (0.116503) | 0.036324 / 0.128546 (-0.092222) | 0.012891 / 0.075646 (-0.062755) | 0.086991 / 0.419271 (-0.332280) | 0.048082 / 0.043533 (0.004549) | 0.357238 / 0.255139 (0.102099) | 0.377065 / 0.283200 (0.093865) | 0.118586 / 0.141683 (-0.023097) | 1.463161 / 1.452155 (0.011007) | 1.582686 / 1.492716 (0.089969) |\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) | 0.267916 / 0.018006 (0.249909) | 0.540862 / 0.000490 (0.540373) | 0.003148 / 0.000200 (0.002948) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032290 / 0.037411 (-0.005122) | 0.115468 / 0.014526 (0.100943) | 0.125743 / 0.176557 (-0.050814) | 0.177469 / 0.737135 (-0.559667) | 0.133579 / 0.296338 (-0.162759) |\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) | 0.446727 / 0.215209 (0.231518) | 4.467938 / 2.077655 (2.390284) | 2.330171 / 1.504120 (0.826052) | 2.165624 / 1.541195 (0.624429) | 2.298063 / 1.468490 (0.829573) | 0.702241 / 4.584777 (-3.882536) | 3.845302 / 3.745712 (0.099590) | 2.169278 / 5.269862 (-3.100584) | 1.401392 / 4.565676 (-3.164285) | 0.086672 / 0.424275 (-0.337603) | 0.012355 / 0.007607 (0.004748) | 0.543639 / 0.226044 (0.317595) | 5.425876 / 2.268929 (3.156947) | 2.781794 / 55.444624 (-52.662831) | 2.503724 / 6.876477 (-4.372752) | 2.622580 / 2.142072 (0.480507) | 0.847143 / 4.805227 (-3.958084) | 0.171721 / 6.500664 (-6.328943) | 0.067894 / 0.075469 (-0.007575) |\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) | 1.292194 / 1.841788 (-0.549594) | 15.497311 / 8.074308 (7.423003) | 15.002463 / 10.191392 (4.811071) | 0.152244 / 0.680424 (-0.528180) | 0.018085 / 0.534201 (-0.516116) | 0.445787 / 0.579283 (-0.133496) | 0.448960 / 0.434364 (0.014596) | 0.515319 / 0.540337 (-0.025019) | 0.623840 / 1.386936 (-0.763096) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8417a41547ce0c939bd342398be621f5ce3e340 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006938 / 0.011353 (-0.004415) | 0.005100 / 0.011008 (-0.005909) | 0.096525 / 0.038508 (0.058017) | 0.033764 / 0.023109 (0.010655) | 0.301107 / 0.275898 (0.025209) | 0.333140 / 0.323480 (0.009660) | 0.005719 / 0.007986 (-0.002266) | 0.005192 / 0.004328 (0.000864) | 0.073685 / 0.004250 (0.069434) | 0.048149 / 0.037052 (0.011096) | 0.299244 / 0.258489 (0.040754) | 0.347518 / 0.293841 (0.053677) | 0.034810 / 0.128546 (-0.093736) | 0.012284 / 0.075646 (-0.063363) | 0.333600 / 0.419271 (-0.085672) | 0.050750 / 0.043533 (0.007217) | 0.299782 / 0.255139 (0.044643) | 0.322712 / 0.283200 (0.039512) | 0.105659 / 0.141683 (-0.036024) | 1.457536 / 1.452155 (0.005381) | 1.571604 / 1.492716 (0.078887) |\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) | 0.207190 / 0.018006 (0.189184) | 0.439230 / 0.000490 (0.438740) | 0.006403 / 0.000200 (0.006203) | 0.000282 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027424 / 0.037411 (-0.009987) | 0.107180 / 0.014526 (0.092655) | 0.118356 / 0.176557 (-0.058201) | 0.175557 / 0.737135 (-0.561579) | 0.125671 / 0.296338 (-0.170668) |\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) | 0.411249 / 0.215209 (0.196039) | 4.094494 / 2.077655 (2.016839) | 1.946843 / 1.504120 (0.442723) | 1.766503 / 1.541195 (0.225308) | 1.831406 / 1.468490 (0.362916) | 0.704637 / 4.584777 (-3.880140) | 3.819204 / 3.745712 (0.073492) | 3.412598 / 5.269862 (-1.857263) | 1.796385 / 4.565676 (-2.769291) | 0.084591 / 0.424275 (-0.339684) | 0.012568 / 0.007607 (0.004961) | 0.506372 / 0.226044 (0.280327) | 5.049461 / 2.268929 (2.780532) | 2.409860 / 55.444624 (-53.034765) | 2.064514 / 6.876477 (-4.811963) | 2.192808 / 2.142072 (0.050735) | 0.833773 / 4.805227 (-3.971455) | 0.167948 / 6.500664 (-6.332716) | 0.064617 / 0.075469 (-0.010852) |\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) | 1.174739 / 1.841788 (-0.667048) | 14.605634 / 8.074308 (6.531326) | 14.321043 / 10.191392 (4.129651) | 0.145892 / 0.680424 (-0.534532) | 0.017413 / 0.534201 (-0.516788) | 0.444940 / 0.579283 (-0.134343) | 0.430792 / 0.434364 (-0.003572) | 0.539699 / 0.540337 (-0.000638) | 0.640279 / 1.386936 (-0.746657) |\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) | 0.007159 / 0.011353 (-0.004194) | 0.005313 / 0.011008 (-0.005695) | 0.073630 / 0.038508 (0.035122) | 0.033459 / 0.023109 (0.010350) | 0.356959 / 0.275898 (0.081061) | 0.385918 / 0.323480 (0.062438) | 0.005714 / 0.007986 (-0.002272) | 0.004074 / 0.004328 (-0.000254) | 0.073278 / 0.004250 (0.069028) | 0.047193 / 0.037052 (0.010140) | 0.360300 / 0.258489 (0.101811) | 0.398052 / 0.293841 (0.104212) | 0.035670 / 0.128546 (-0.092876) | 0.012499 / 0.075646 (-0.063147) | 0.086677 / 0.419271 (-0.332595) | 0.046534 / 0.043533 (0.003001) | 0.370029 / 0.255139 (0.114890) | 0.376040 / 0.283200 (0.092841) | 0.105184 / 0.141683 (-0.036499) | 1.419779 / 1.452155 (-0.032375) | 1.538925 / 1.492716 (0.046209) |\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) | 0.220465 / 0.018006 (0.202459) | 0.438836 / 0.000490 (0.438346) | 0.000428 / 0.000200 (0.000228) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029114 / 0.037411 (-0.008298) | 0.111871 / 0.014526 (0.097345) | 0.124367 / 0.176557 (-0.052189) | 0.173737 / 0.737135 (-0.563398) | 0.128435 / 0.296338 (-0.167904) |\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) | 0.440706 / 0.215209 (0.225497) | 4.414826 / 2.077655 (2.337171) | 2.128899 / 1.504120 (0.624780) | 1.929551 / 1.541195 (0.388357) | 2.013130 / 1.468490 (0.544640) | 0.708566 / 4.584777 (-3.876211) | 3.846459 / 3.745712 (0.100747) | 2.158829 / 5.269862 (-3.111032) | 1.339454 / 4.565676 (-3.226223) | 0.086345 / 0.424275 (-0.337930) | 0.012085 / 0.007607 (0.004478) | 0.546360 / 0.226044 (0.320316) | 5.461612 / 2.268929 (3.192683) | 2.657388 / 55.444624 (-52.787237) | 2.298403 / 6.876477 (-4.578074) | 2.344572 / 2.142072 (0.202499) | 0.844276 / 4.805227 (-3.960951) | 0.170225 / 6.500664 (-6.330439) | 0.064684 / 0.075469 (-0.010785) |\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) | 1.265114 / 1.841788 (-0.576674) | 15.058156 / 8.074308 (6.983848) | 14.485182 / 10.191392 (4.293790) | 0.165960 / 0.680424 (-0.514464) | 0.017481 / 0.534201 (-0.516719) | 0.425141 / 0.579283 (-0.154142) | 0.434883 / 0.434364 (0.000519) | 0.506701 / 0.540337 (-0.033637) | 0.613240 / 1.386936 (-0.773697) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f019dffffb214b44b30dd9ac56fdea12259e148 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007651 / 0.011353 (-0.003702) | 0.005503 / 0.011008 (-0.005505) | 0.098751 / 0.038508 (0.060243) | 0.036822 / 0.023109 (0.013713) | 0.340754 / 0.275898 (0.064856) | 0.387247 / 0.323480 (0.063767) | 0.006513 / 0.007986 (-0.001473) | 0.006135 / 0.004328 (0.001807) | 0.073656 / 0.004250 (0.069406) | 0.055508 / 0.037052 (0.018456) | 0.352493 / 0.258489 (0.094004) | 0.408003 / 0.293841 (0.114162) | 0.036346 / 0.128546 (-0.092201) | 0.012562 / 0.075646 (-0.063085) | 0.335111 / 0.419271 (-0.084160) | 0.051928 / 0.043533 (0.008395) | 0.339405 / 0.255139 (0.084266) | 0.366840 / 0.283200 (0.083640) | 0.114353 / 0.141683 (-0.027330) | 1.449062 / 1.452155 (-0.003092) | 1.567310 / 1.492716 (0.074594) |\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) | 0.262975 / 0.018006 (0.244968) | 0.570302 / 0.000490 (0.569813) | 0.003419 / 0.000200 (0.003219) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027363 / 0.037411 (-0.010049) | 0.109033 / 0.014526 (0.094507) | 0.119048 / 0.176557 (-0.057509) | 0.175891 / 0.737135 (-0.561244) | 0.124577 / 0.296338 (-0.171762) |\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) | 0.397988 / 0.215209 (0.182779) | 3.993210 / 2.077655 (1.915555) | 1.809275 / 1.504120 (0.305155) | 1.614664 / 1.541195 (0.073469) | 1.723650 / 1.468490 (0.255159) | 0.698484 / 4.584777 (-3.886293) | 3.914135 / 3.745712 (0.168423) | 2.142622 / 5.269862 (-3.127239) | 1.360215 / 4.565676 (-3.205461) | 0.086340 / 0.424275 (-0.337935) | 0.012836 / 0.007607 (0.005229) | 0.500728 / 0.226044 (0.274684) | 5.006744 / 2.268929 (2.737815) | 2.350668 / 55.444624 (-53.093956) | 1.979816 / 6.876477 (-4.896660) | 2.190159 / 2.142072 (0.048087) | 0.854063 / 4.805227 (-3.951164) | 0.170203 / 6.500664 (-6.330461) | 0.066903 / 0.075469 (-0.008566) |\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) | 1.184012 / 1.841788 (-0.657775) | 15.407350 / 8.074308 (7.333042) | 14.758180 / 10.191392 (4.566788) | 0.169280 / 0.680424 (-0.511144) | 0.017419 / 0.534201 (-0.516781) | 0.434359 / 0.579283 (-0.144925) | 0.442515 / 0.434364 (0.008151) | 0.503132 / 0.540337 (-0.037205) | 0.602589 / 1.386936 (-0.784347) |\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) | 0.008022 / 0.011353 (-0.003331) | 0.005473 / 0.011008 (-0.005535) | 0.076106 / 0.038508 (0.037598) | 0.037065 / 0.023109 (0.013956) | 0.380039 / 0.275898 (0.104141) | 0.394205 / 0.323480 (0.070725) | 0.006447 / 0.007986 (-0.001539) | 0.006011 / 0.004328 (0.001682) | 0.075236 / 0.004250 (0.070985) | 0.054425 / 0.037052 (0.017372) | 0.381707 / 0.258489 (0.123218) | 0.411237 / 0.293841 (0.117396) | 0.037222 / 0.128546 (-0.091324) | 0.012627 / 0.075646 (-0.063020) | 0.086733 / 0.419271 (-0.332538) | 0.053857 / 0.043533 (0.010324) | 0.373374 / 0.255139 (0.118235) | 0.381680 / 0.283200 (0.098480) | 0.121962 / 0.141683 (-0.019721) | 1.430804 / 1.452155 (-0.021351) | 1.562517 / 1.492716 (0.069801) |\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) | 0.262034 / 0.018006 (0.244028) | 0.563497 / 0.000490 (0.563007) | 0.002726 / 0.000200 (0.002526) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031071 / 0.037411 (-0.006341) | 0.111983 / 0.014526 (0.097457) | 0.126634 / 0.176557 (-0.049923) | 0.177511 / 0.737135 (-0.559625) | 0.132599 / 0.296338 (-0.163739) |\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) | 0.436148 / 0.215209 (0.220939) | 4.344850 / 2.077655 (2.267195) | 2.105877 / 1.504120 (0.601757) | 1.920934 / 1.541195 (0.379739) | 2.072930 / 1.468490 (0.604440) | 0.701793 / 4.584777 (-3.882984) | 3.841621 / 3.745712 (0.095909) | 3.602550 / 5.269862 (-1.667311) | 1.775999 / 4.565676 (-2.789677) | 0.086024 / 0.424275 (-0.338251) | 0.012275 / 0.007607 (0.004668) | 0.532815 / 0.226044 (0.306770) | 5.336273 / 2.268929 (3.067344) | 2.638842 / 55.444624 (-52.805782) | 2.301842 / 6.876477 (-4.574635) | 2.407448 / 2.142072 (0.265376) | 0.855836 / 4.805227 (-3.949392) | 0.170348 / 6.500664 (-6.330317) | 0.066926 / 0.075469 (-0.008543) |\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) | 1.291515 / 1.841788 (-0.550272) | 15.869825 / 8.074308 (7.795517) | 15.068227 / 10.191392 (4.876835) | 0.156953 / 0.680424 (-0.523471) | 0.017761 / 0.534201 (-0.516440) | 0.429515 / 0.579283 (-0.149768) | 0.432758 / 0.434364 (-0.001605) | 0.500080 / 0.540337 (-0.040258) | 0.601451 / 1.386936 (-0.785485) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00b148b09da2074fcaba0538a23c7f46d28d387c \"CML watermark\")\n",
"Will need to take https://github.com/huggingface/datasets/pull/5810 into account if it gets merged before this one",
"<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) | 0.006914 / 0.011353 (-0.004439) | 0.004727 / 0.011008 (-0.006281) | 0.098880 / 0.038508 (0.060372) | 0.036663 / 0.023109 (0.013554) | 0.317575 / 0.275898 (0.041677) | 0.360301 / 0.323480 (0.036821) | 0.006084 / 0.007986 (-0.001901) | 0.004118 / 0.004328 (-0.000210) | 0.074330 / 0.004250 (0.070079) | 0.042422 / 0.037052 (0.005369) | 0.335625 / 0.258489 (0.077136) | 0.366616 / 0.293841 (0.072775) | 0.028523 / 0.128546 (-0.100023) | 0.008883 / 0.075646 (-0.066763) | 0.332475 / 0.419271 (-0.086797) | 0.051746 / 0.043533 (0.008214) | 0.324952 / 0.255139 (0.069813) | 0.339660 / 0.283200 (0.056460) | 0.103714 / 0.141683 (-0.037969) | 1.472130 / 1.452155 (0.019976) | 1.516548 / 1.492716 (0.023831) |\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) | 0.229538 / 0.018006 (0.211532) | 0.449077 / 0.000490 (0.448588) | 0.003707 / 0.000200 (0.003507) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027897 / 0.037411 (-0.009514) | 0.115452 / 0.014526 (0.100926) | 0.118830 / 0.176557 (-0.057726) | 0.176228 / 0.737135 (-0.560907) | 0.125966 / 0.296338 (-0.170372) |\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) | 0.436947 / 0.215209 (0.221738) | 4.355687 / 2.077655 (2.278033) | 2.195857 / 1.504120 (0.691737) | 2.028133 / 1.541195 (0.486938) | 2.119872 / 1.468490 (0.651382) | 0.524256 / 4.584777 (-4.060521) | 3.864064 / 3.745712 (0.118352) | 3.446181 / 5.269862 (-1.823680) | 1.610307 / 4.565676 (-2.955370) | 0.065981 / 0.424275 (-0.358294) | 0.012172 / 0.007607 (0.004565) | 0.545341 / 0.226044 (0.319297) | 5.451728 / 2.268929 (3.182800) | 2.690734 / 55.444624 (-52.753890) | 2.368203 / 6.876477 (-4.508274) | 2.549533 / 2.142072 (0.407460) | 0.651296 / 4.805227 (-4.153931) | 0.143697 / 6.500664 (-6.356968) | 0.065170 / 0.075469 (-0.010299) |\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) | 1.198898 / 1.841788 (-0.642890) | 15.349348 / 8.074308 (7.275040) | 15.314467 / 10.191392 (5.123075) | 0.177219 / 0.680424 (-0.503205) | 0.018223 / 0.534201 (-0.515978) | 0.396209 / 0.579283 (-0.183074) | 0.427810 / 0.434364 (-0.006554) | 0.475107 / 0.540337 (-0.065230) | 0.561224 / 1.386936 (-0.825712) |\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) | 0.007024 / 0.011353 (-0.004329) | 0.004851 / 0.011008 (-0.006157) | 0.075031 / 0.038508 (0.036523) | 0.036411 / 0.023109 (0.013302) | 0.375999 / 0.275898 (0.100101) | 0.433033 / 0.323480 (0.109553) | 0.006089 / 0.007986 (-0.001897) | 0.005638 / 0.004328 (0.001309) | 0.072599 / 0.004250 (0.068348) | 0.048489 / 0.037052 (0.011436) | 0.381807 / 0.258489 (0.123318) | 0.441531 / 0.293841 (0.147691) | 0.029044 / 0.128546 (-0.099503) | 0.009052 / 0.075646 (-0.066595) | 0.080086 / 0.419271 (-0.339186) | 0.046919 / 0.043533 (0.003386) | 0.360399 / 0.255139 (0.105260) | 0.405445 / 0.283200 (0.122245) | 0.108815 / 0.141683 (-0.032868) | 1.415168 / 1.452155 (-0.036987) | 1.511756 / 1.492716 (0.019040) |\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) | 0.210287 / 0.018006 (0.192281) | 0.445139 / 0.000490 (0.444650) | 0.000386 / 0.000200 (0.000186) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030457 / 0.037411 (-0.006954) | 0.117225 / 0.014526 (0.102699) | 0.122833 / 0.176557 (-0.053724) | 0.170441 / 0.737135 (-0.566694) | 0.131589 / 0.296338 (-0.164750) |\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) | 0.446541 / 0.215209 (0.231332) | 4.471214 / 2.077655 (2.393560) | 2.145894 / 1.504120 (0.641774) | 1.958113 / 1.541195 (0.416919) | 2.069623 / 1.468490 (0.601132) | 0.527562 / 4.584777 (-4.057215) | 3.838285 / 3.745712 (0.092573) | 1.884780 / 5.269862 (-3.385081) | 1.088124 / 4.565676 (-3.477553) | 0.066099 / 0.424275 (-0.358176) | 0.011973 / 0.007607 (0.004366) | 0.540369 / 0.226044 (0.314325) | 5.403554 / 2.268929 (3.134626) | 2.749920 / 55.444624 (-52.694704) | 2.543169 / 6.876477 (-4.333308) | 2.403116 / 2.142072 (0.261043) | 0.638723 / 4.805227 (-4.166505) | 0.142232 / 6.500664 (-6.358432) | 0.065551 / 0.075469 (-0.009918) |\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) | 1.298307 / 1.841788 (-0.543481) | 15.986177 / 8.074308 (7.911869) | 15.530453 / 10.191392 (5.339061) | 0.160138 / 0.680424 (-0.520286) | 0.017988 / 0.534201 (-0.516213) | 0.397857 / 0.579283 (-0.181427) | 0.435071 / 0.434364 (0.000707) | 0.480096 / 0.540337 (-0.060241) | 0.589139 / 1.386936 (-0.797797) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5bd9c974e08e059ce36dc0843256747016e843c5 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006976 / 0.011353 (-0.004377) | 0.005068 / 0.011008 (-0.005940) | 0.098178 / 0.038508 (0.059670) | 0.035167 / 0.023109 (0.012057) | 0.324093 / 0.275898 (0.048195) | 0.350749 / 0.323480 (0.027269) | 0.006128 / 0.007986 (-0.001858) | 0.004361 / 0.004328 (0.000033) | 0.075412 / 0.004250 (0.071161) | 0.052083 / 0.037052 (0.015031) | 0.326726 / 0.258489 (0.068237) | 0.371450 / 0.293841 (0.077609) | 0.028522 / 0.128546 (-0.100025) | 0.009210 / 0.075646 (-0.066436) | 0.329296 / 0.419271 (-0.089976) | 0.051182 / 0.043533 (0.007649) | 0.319863 / 0.255139 (0.064724) | 0.329140 / 0.283200 (0.045941) | 0.111653 / 0.141683 (-0.030030) | 1.464205 / 1.452155 (0.012050) | 1.555779 / 1.492716 (0.063062) |\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) | 0.282372 / 0.018006 (0.264366) | 0.569227 / 0.000490 (0.568737) | 0.005289 / 0.000200 (0.005089) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029875 / 0.037411 (-0.007537) | 0.111889 / 0.014526 (0.097364) | 0.125678 / 0.176557 (-0.050878) | 0.184695 / 0.737135 (-0.552441) | 0.129737 / 0.296338 (-0.166602) |\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) | 0.417404 / 0.215209 (0.202195) | 4.172367 / 2.077655 (2.094712) | 2.008088 / 1.504120 (0.503968) | 1.813182 / 1.541195 (0.271988) | 1.882727 / 1.468490 (0.414237) | 0.525764 / 4.584777 (-4.059013) | 3.815202 / 3.745712 (0.069490) | 1.884197 / 5.269862 (-3.385664) | 1.073779 / 4.565676 (-3.491897) | 0.066125 / 0.424275 (-0.358150) | 0.012473 / 0.007607 (0.004866) | 0.522197 / 0.226044 (0.296153) | 5.218486 / 2.268929 (2.949557) | 2.413846 / 55.444624 (-53.030779) | 2.093298 / 6.876477 (-4.783179) | 2.320583 / 2.142072 (0.178511) | 0.648832 / 4.805227 (-4.156395) | 0.146168 / 6.500664 (-6.354496) | 0.065869 / 0.075469 (-0.009600) |\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) | 1.181859 / 1.841788 (-0.659929) | 15.369517 / 8.074308 (7.295209) | 14.896270 / 10.191392 (4.704878) | 0.146793 / 0.680424 (-0.533630) | 0.017960 / 0.534201 (-0.516241) | 0.421801 / 0.579283 (-0.157482) | 0.438357 / 0.434364 (0.003993) | 0.524554 / 0.540337 (-0.015783) | 0.621041 / 1.386936 (-0.765895) |\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) | 0.007104 / 0.011353 (-0.004249) | 0.004895 / 0.011008 (-0.006113) | 0.075641 / 0.038508 (0.037133) | 0.034821 / 0.023109 (0.011712) | 0.363875 / 0.275898 (0.087977) | 0.403042 / 0.323480 (0.079562) | 0.006747 / 0.007986 (-0.001238) | 0.005793 / 0.004328 (0.001465) | 0.074709 / 0.004250 (0.070458) | 0.058801 / 0.037052 (0.021749) | 0.366900 / 0.258489 (0.108411) | 0.414442 / 0.293841 (0.120601) | 0.029099 / 0.128546 (-0.099448) | 0.009394 / 0.075646 (-0.066253) | 0.082612 / 0.419271 (-0.336659) | 0.049076 / 0.043533 (0.005543) | 0.358828 / 0.255139 (0.103689) | 0.378261 / 0.283200 (0.095061) | 0.122147 / 0.141683 (-0.019535) | 1.454155 / 1.452155 (0.002000) | 1.572437 / 1.492716 (0.079720) |\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) | 0.293133 / 0.018006 (0.275127) | 0.536785 / 0.000490 (0.536295) | 0.000457 / 0.000200 (0.000257) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031046 / 0.037411 (-0.006366) | 0.113929 / 0.014526 (0.099403) | 0.126222 / 0.176557 (-0.050335) | 0.173992 / 0.737135 (-0.563143) | 0.129635 / 0.296338 (-0.166704) |\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) | 0.441984 / 0.215209 (0.226775) | 4.406002 / 2.077655 (2.328348) | 2.173912 / 1.504120 (0.669792) | 2.000507 / 1.541195 (0.459312) | 2.172766 / 1.468490 (0.704276) | 0.524530 / 4.584777 (-4.060247) | 3.758827 / 3.745712 (0.013115) | 1.886701 / 5.269862 (-3.383160) | 1.073601 / 4.565676 (-3.492075) | 0.066137 / 0.424275 (-0.358139) | 0.011926 / 0.007607 (0.004319) | 0.541103 / 0.226044 (0.315059) | 5.404162 / 2.268929 (3.135233) | 2.634271 / 55.444624 (-52.810354) | 2.366156 / 6.876477 (-4.510321) | 2.566877 / 2.142072 (0.424804) | 0.639088 / 4.805227 (-4.166139) | 0.141810 / 6.500664 (-6.358854) | 0.065446 / 0.075469 (-0.010023) |\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) | 1.288173 / 1.841788 (-0.553614) | 15.897051 / 8.074308 (7.822743) | 15.243404 / 10.191392 (5.052012) | 0.162380 / 0.680424 (-0.518043) | 0.017716 / 0.534201 (-0.516485) | 0.396400 / 0.579283 (-0.182883) | 0.420479 / 0.434364 (-0.013885) | 0.476238 / 0.540337 (-0.064099) | 0.583039 / 1.386936 (-0.803897) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bd373f69f12e926f4e2a489c14df36c38ce07bcc \"CML watermark\")\n",
"I fixed the docstring and type hint",
"<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) | 0.006310 / 0.011353 (-0.005043) | 0.004297 / 0.011008 (-0.006711) | 0.098288 / 0.038508 (0.059780) | 0.029295 / 0.023109 (0.006185) | 0.386804 / 0.275898 (0.110906) | 0.425717 / 0.323480 (0.102237) | 0.005516 / 0.007986 (-0.002470) | 0.005058 / 0.004328 (0.000730) | 0.074318 / 0.004250 (0.070068) | 0.040609 / 0.037052 (0.003557) | 0.388159 / 0.258489 (0.129670) | 0.428683 / 0.293841 (0.134842) | 0.026207 / 0.128546 (-0.102340) | 0.008655 / 0.075646 (-0.066991) | 0.321601 / 0.419271 (-0.097671) | 0.055329 / 0.043533 (0.011796) | 0.390452 / 0.255139 (0.135313) | 0.409084 / 0.283200 (0.125884) | 0.099555 / 0.141683 (-0.042128) | 1.484289 / 1.452155 (0.032134) | 1.549892 / 1.492716 (0.057176) |\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) | 0.219466 / 0.018006 (0.201460) | 0.437288 / 0.000490 (0.436798) | 0.003556 / 0.000200 (0.003356) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023876 / 0.037411 (-0.013535) | 0.100205 / 0.014526 (0.085679) | 0.106365 / 0.176557 (-0.070191) | 0.164353 / 0.737135 (-0.572782) | 0.109987 / 0.296338 (-0.186352) |\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) | 0.418819 / 0.215209 (0.203610) | 4.168558 / 2.077655 (2.090903) | 1.862883 / 1.504120 (0.358764) | 1.673308 / 1.541195 (0.132114) | 1.742338 / 1.468490 (0.273848) | 0.550113 / 4.584777 (-4.034664) | 3.492085 / 3.745712 (-0.253627) | 1.734579 / 5.269862 (-3.535283) | 1.006876 / 4.565676 (-3.558801) | 0.068014 / 0.424275 (-0.356261) | 0.012242 / 0.007607 (0.004634) | 0.520633 / 0.226044 (0.294588) | 5.214095 / 2.268929 (2.945167) | 2.319282 / 55.444624 (-53.125343) | 1.979521 / 6.876477 (-4.896956) | 2.099595 / 2.142072 (-0.042477) | 0.659306 / 4.805227 (-4.145921) | 0.135282 / 6.500664 (-6.365382) | 0.067417 / 0.075469 (-0.008052) |\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) | 1.232099 / 1.841788 (-0.609689) | 13.967219 / 8.074308 (5.892910) | 14.347105 / 10.191392 (4.155713) | 0.146360 / 0.680424 (-0.534063) | 0.017021 / 0.534201 (-0.517180) | 0.363254 / 0.579283 (-0.216030) | 0.404391 / 0.434364 (-0.029973) | 0.428670 / 0.540337 (-0.111668) | 0.514942 / 1.386936 (-0.871994) |\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) | 0.006360 / 0.011353 (-0.004993) | 0.004160 / 0.011008 (-0.006848) | 0.074856 / 0.038508 (0.036347) | 0.028624 / 0.023109 (0.005515) | 0.355624 / 0.275898 (0.079726) | 0.403678 / 0.323480 (0.080198) | 0.005253 / 0.007986 (-0.002732) | 0.004808 / 0.004328 (0.000480) | 0.074215 / 0.004250 (0.069964) | 0.040641 / 0.037052 (0.003588) | 0.358473 / 0.258489 (0.099984) | 0.414442 / 0.293841 (0.120601) | 0.025595 / 0.128546 (-0.102951) | 0.008506 / 0.075646 (-0.067140) | 0.081547 / 0.419271 (-0.337725) | 0.039719 / 0.043533 (-0.003814) | 0.355420 / 0.255139 (0.100281) | 0.380953 / 0.283200 (0.097753) | 0.100064 / 0.141683 (-0.041618) | 1.459639 / 1.452155 (0.007484) | 1.557288 / 1.492716 (0.064572) |\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) | 0.232837 / 0.018006 (0.214831) | 0.424788 / 0.000490 (0.424298) | 0.000397 / 0.000200 (0.000197) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026156 / 0.037411 (-0.011256) | 0.103633 / 0.014526 (0.089107) | 0.109633 / 0.176557 (-0.066923) | 0.159407 / 0.737135 (-0.577728) | 0.113874 / 0.296338 (-0.182465) |\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) | 0.471980 / 0.215209 (0.256771) | 4.724424 / 2.077655 (2.646769) | 2.459950 / 1.504120 (0.955830) | 2.280926 / 1.541195 (0.739731) | 2.368478 / 1.468490 (0.899987) | 0.552809 / 4.584777 (-4.031968) | 3.461985 / 3.745712 (-0.283728) | 1.757060 / 5.269862 (-3.512802) | 1.009599 / 4.565676 (-3.556077) | 0.068407 / 0.424275 (-0.355868) | 0.012341 / 0.007607 (0.004734) | 0.576287 / 0.226044 (0.350242) | 5.767331 / 2.268929 (3.498402) | 2.965743 / 55.444624 (-52.478882) | 2.644935 / 6.876477 (-4.231542) | 2.699663 / 2.142072 (0.557591) | 0.656005 / 4.805227 (-4.149222) | 0.136315 / 6.500664 (-6.364349) | 0.068355 / 0.075469 (-0.007114) |\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) | 1.308301 / 1.841788 (-0.533486) | 14.587268 / 8.074308 (6.512960) | 14.385670 / 10.191392 (4.194278) | 0.148154 / 0.680424 (-0.532270) | 0.016798 / 0.534201 (-0.517402) | 0.360761 / 0.579283 (-0.218523) | 0.392566 / 0.434364 (-0.041798) | 0.431604 / 0.540337 (-0.108734) | 0.528463 / 1.386936 (-0.858473) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2778e1ab255545cb2171379fd2276c85768a2ad \"CML watermark\")\n",
"let me know if it sounds good for you now @albertvillanova :)",
"<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) | 0.008414 / 0.011353 (-0.002939) | 0.005320 / 0.011008 (-0.005688) | 0.115585 / 0.038508 (0.077077) | 0.040815 / 0.023109 (0.017706) | 0.363453 / 0.275898 (0.087555) | 0.385954 / 0.323480 (0.062474) | 0.006463 / 0.007986 (-0.001523) | 0.005571 / 0.004328 (0.001242) | 0.084831 / 0.004250 (0.080581) | 0.050294 / 0.037052 (0.013242) | 0.375684 / 0.258489 (0.117195) | 0.394672 / 0.293841 (0.100831) | 0.033618 / 0.128546 (-0.094928) | 0.010451 / 0.075646 (-0.065195) | 0.388937 / 0.419271 (-0.030334) | 0.059974 / 0.043533 (0.016441) | 0.360437 / 0.255139 (0.105298) | 0.375149 / 0.283200 (0.091950) | 0.118397 / 0.141683 (-0.023286) | 1.726759 / 1.452155 (0.274604) | 1.811928 / 1.492716 (0.319212) |\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) | 0.239186 / 0.018006 (0.221180) | 0.483728 / 0.000490 (0.483238) | 0.003285 / 0.000200 (0.003085) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030514 / 0.037411 (-0.006898) | 0.127111 / 0.014526 (0.112585) | 0.136185 / 0.176557 (-0.040371) | 0.204541 / 0.737135 (-0.532594) | 0.143228 / 0.296338 (-0.153111) |\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) | 0.465840 / 0.215209 (0.250631) | 4.611160 / 2.077655 (2.533506) | 2.119307 / 1.504120 (0.615187) | 1.882463 / 1.541195 (0.341268) | 1.946067 / 1.468490 (0.477577) | 0.602352 / 4.584777 (-3.982425) | 4.576313 / 3.745712 (0.830601) | 2.112860 / 5.269862 (-3.157001) | 1.224388 / 4.565676 (-3.341289) | 0.073808 / 0.424275 (-0.350467) | 0.013157 / 0.007607 (0.005550) | 0.592208 / 0.226044 (0.366163) | 5.948971 / 2.268929 (3.680042) | 2.690144 / 55.444624 (-52.754480) | 2.236489 / 6.876477 (-4.639987) | 2.423617 / 2.142072 (0.281545) | 0.752053 / 4.805227 (-4.053175) | 0.168185 / 6.500664 (-6.332480) | 0.075454 / 0.075469 (-0.000015) |\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) | 1.407432 / 1.841788 (-0.434356) | 17.054545 / 8.074308 (8.980236) | 15.661362 / 10.191392 (5.469970) | 0.175027 / 0.680424 (-0.505397) | 0.020262 / 0.534201 (-0.513939) | 0.479052 / 0.579283 (-0.100231) | 0.509829 / 0.434364 (0.075465) | 0.601935 / 0.540337 (0.061598) | 0.726754 / 1.386936 (-0.660182) |\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) | 0.007698 / 0.011353 (-0.003655) | 0.005267 / 0.011008 (-0.005741) | 0.085832 / 0.038508 (0.047324) | 0.041974 / 0.023109 (0.018865) | 0.418966 / 0.275898 (0.143068) | 0.466314 / 0.323480 (0.142834) | 0.006580 / 0.007986 (-0.001406) | 0.007063 / 0.004328 (0.002735) | 0.087120 / 0.004250 (0.082870) | 0.054908 / 0.037052 (0.017856) | 0.423813 / 0.258489 (0.165323) | 0.489878 / 0.293841 (0.196037) | 0.032823 / 0.128546 (-0.095723) | 0.010471 / 0.075646 (-0.065175) | 0.095839 / 0.419271 (-0.323432) | 0.056421 / 0.043533 (0.012888) | 0.420526 / 0.255139 (0.165387) | 0.447975 / 0.283200 (0.164775) | 0.126604 / 0.141683 (-0.015079) | 1.723097 / 1.452155 (0.270942) | 1.819539 / 1.492716 (0.326822) |\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) | 0.279604 / 0.018006 (0.261598) | 0.496129 / 0.000490 (0.495639) | 0.005419 / 0.000200 (0.005219) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035069 / 0.037411 (-0.002343) | 0.133064 / 0.014526 (0.118538) | 0.145404 / 0.176557 (-0.031152) | 0.205237 / 0.737135 (-0.531898) | 0.150684 / 0.296338 (-0.145654) |\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) | 0.513596 / 0.215209 (0.298387) | 5.104861 / 2.077655 (3.027206) | 2.487908 / 1.504120 (0.983788) | 2.271383 / 1.541195 (0.730188) | 2.421043 / 1.468490 (0.952553) | 0.625204 / 4.584777 (-3.959573) | 4.555389 / 3.745712 (0.809677) | 4.181518 / 5.269862 (-1.088344) | 1.676059 / 4.565676 (-2.889617) | 0.078786 / 0.424275 (-0.345489) | 0.014186 / 0.007607 (0.006579) | 0.638360 / 0.226044 (0.412315) | 6.367915 / 2.268929 (4.098986) | 3.095175 / 55.444624 (-52.349449) | 2.706707 / 6.876477 (-4.169769) | 2.735907 / 2.142072 (0.593835) | 0.756323 / 4.805227 (-4.048905) | 0.164783 / 6.500664 (-6.335881) | 0.076291 / 0.075469 (0.000822) |\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) | 1.667058 / 1.841788 (-0.174730) | 18.687459 / 8.074308 (10.613151) | 17.111596 / 10.191392 (6.920204) | 0.167218 / 0.680424 (-0.513206) | 0.020995 / 0.534201 (-0.513206) | 0.463985 / 0.579283 (-0.115298) | 0.502705 / 0.434364 (0.068341) | 0.562877 / 0.540337 (0.022540) | 0.682249 / 1.386936 (-0.704687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#028822a5d657f6c1251f61b56a701c4d7d2ab0a7 \"CML watermark\")\n",
"> Maybe we should fix all the tests in test_iterable_dataset.py that contain .with_format(\"torch\")?\r\n\r\nthey're updated in https://github.com/huggingface/datasets/pull/5852",
"<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) | 0.005931 / 0.011353 (-0.005421) | 0.004004 / 0.011008 (-0.007004) | 0.098632 / 0.038508 (0.060124) | 0.027820 / 0.023109 (0.004711) | 0.302944 / 0.275898 (0.027046) | 0.332684 / 0.323480 (0.009204) | 0.005529 / 0.007986 (-0.002457) | 0.004814 / 0.004328 (0.000485) | 0.074477 / 0.004250 (0.070227) | 0.034875 / 0.037052 (-0.002178) | 0.304542 / 0.258489 (0.046053) | 0.342853 / 0.293841 (0.049012) | 0.025263 / 0.128546 (-0.103283) | 0.008558 / 0.075646 (-0.067089) | 0.322522 / 0.419271 (-0.096750) | 0.043980 / 0.043533 (0.000447) | 0.306618 / 0.255139 (0.051479) | 0.331692 / 0.283200 (0.048492) | 0.087434 / 0.141683 (-0.054248) | 1.464686 / 1.452155 (0.012531) | 1.575038 / 1.492716 (0.082322) |\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) | 0.221920 / 0.018006 (0.203914) | 0.417108 / 0.000490 (0.416619) | 0.004625 / 0.000200 (0.004425) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023493 / 0.037411 (-0.013918) | 0.096684 / 0.014526 (0.082158) | 0.102035 / 0.176557 (-0.074522) | 0.166609 / 0.737135 (-0.570526) | 0.107456 / 0.296338 (-0.188883) |\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) | 0.418713 / 0.215209 (0.203504) | 4.156913 / 2.077655 (2.079258) | 1.869064 / 1.504120 (0.364944) | 1.666219 / 1.541195 (0.125024) | 1.676491 / 1.468490 (0.208001) | 0.553843 / 4.584777 (-4.030934) | 3.380471 / 3.745712 (-0.365241) | 2.970370 / 5.269862 (-2.299491) | 1.421597 / 4.565676 (-3.144080) | 0.068019 / 0.424275 (-0.356256) | 0.012995 / 0.007607 (0.005387) | 0.519410 / 0.226044 (0.293365) | 5.198251 / 2.268929 (2.929323) | 2.352969 / 55.444624 (-53.091655) | 2.008981 / 6.876477 (-4.867496) | 2.066519 / 2.142072 (-0.075553) | 0.658982 / 4.805227 (-4.146245) | 0.134341 / 6.500664 (-6.366323) | 0.065893 / 0.075469 (-0.009576) |\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) | 1.207509 / 1.841788 (-0.634279) | 13.863838 / 8.074308 (5.789530) | 13.363359 / 10.191392 (3.171967) | 0.129076 / 0.680424 (-0.551348) | 0.016818 / 0.534201 (-0.517383) | 0.357956 / 0.579283 (-0.221327) | 0.386174 / 0.434364 (-0.048189) | 0.418663 / 0.540337 (-0.121674) | 0.498708 / 1.386936 (-0.888228) |\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) | 0.006132 / 0.011353 (-0.005220) | 0.004335 / 0.011008 (-0.006673) | 0.078517 / 0.038508 (0.040009) | 0.027685 / 0.023109 (0.004576) | 0.357956 / 0.275898 (0.082058) | 0.392397 / 0.323480 (0.068918) | 0.005364 / 0.007986 (-0.002622) | 0.004922 / 0.004328 (0.000593) | 0.078061 / 0.004250 (0.073810) | 0.038889 / 0.037052 (0.001837) | 0.360952 / 0.258489 (0.102463) | 0.402790 / 0.293841 (0.108949) | 0.025542 / 0.128546 (-0.103004) | 0.008718 / 0.075646 (-0.066929) | 0.085799 / 0.419271 (-0.333472) | 0.044256 / 0.043533 (0.000723) | 0.358366 / 0.255139 (0.103227) | 0.393500 / 0.283200 (0.110300) | 0.096382 / 0.141683 (-0.045301) | 1.530889 / 1.452155 (0.078735) | 1.621007 / 1.492716 (0.128291) |\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) | 0.180572 / 0.018006 (0.162566) | 0.429478 / 0.000490 (0.428988) | 0.002966 / 0.000200 (0.002766) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024530 / 0.037411 (-0.012881) | 0.101401 / 0.014526 (0.086875) | 0.108208 / 0.176557 (-0.068349) | 0.159582 / 0.737135 (-0.577554) | 0.111170 / 0.296338 (-0.185168) |\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) | 0.465768 / 0.215209 (0.250559) | 4.706311 / 2.077655 (2.628656) | 2.437756 / 1.504120 (0.933636) | 2.245694 / 1.541195 (0.704499) | 2.282637 / 1.468490 (0.814147) | 0.552752 / 4.584777 (-4.032025) | 3.432992 / 3.745712 (-0.312720) | 1.800054 / 5.269862 (-3.469808) | 1.037852 / 4.565676 (-3.527824) | 0.068240 / 0.424275 (-0.356035) | 0.012433 / 0.007607 (0.004826) | 0.574867 / 0.226044 (0.348822) | 5.707623 / 2.268929 (3.438695) | 2.909746 / 55.444624 (-52.534878) | 2.585423 / 6.876477 (-4.291054) | 2.636801 / 2.142072 (0.494729) | 0.686593 / 4.805227 (-4.118634) | 0.136633 / 6.500664 (-6.364031) | 0.068598 / 0.075469 (-0.006871) |\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) | 1.286628 / 1.841788 (-0.555159) | 14.333258 / 8.074308 (6.258949) | 14.355793 / 10.191392 (4.164401) | 0.133459 / 0.680424 (-0.546965) | 0.017090 / 0.534201 (-0.517111) | 0.358852 / 0.579283 (-0.220431) | 0.399929 / 0.434364 (-0.034435) | 0.422838 / 0.540337 (-0.117500) | 0.515199 / 1.386936 (-0.871737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7437d0f676da8634b5655a227cb8c3508c7372a2 \"CML watermark\")\n"
] | 2023-05-04T17:23:43Z
| 2023-05-31T09:43:26Z
| 2023-05-31T09:36:18Z
|
MEMBER
| null | null | null |
Adding an optional `.iter_arrow` to examples iterable. This allows to use Arrow formatting in map/filter.
This will also be useful for torch formatting, since we can reuse the TorchFormatter that converts Arrow data to torch tensors
Related to https://github.com/huggingface/datasets/issues/5793 and https://github.com/huggingface/datasets/issues/3444
Required for https://github.com/huggingface/datasets/pull/5852
### Example:
Speed x10 in map
```python
from datasets import Dataset
import pyarrow.compute as pc
import time
ds = Dataset.from_dict({"a": range(100_000)})
ids = ds.to_iterable_dataset()
ids = ids.map(lambda x: {"a": [a + 10 for a in x["a"]]}, batched=True)
_start = time.time()
print(f"Python ({sum(1 for _ in ids)} items):\t{(time.time() - _start) * 1000:.1f}ms")
# Python (100000 items): 695.7ms
ids = ds.to_iterable_dataset().with_format("arrow")
ids = ids.map(lambda t: t.set_column(0, "a", pc.add(t[0], 10)), batched=True)
ids = ids.with_format(None)
_start = time.time()
print(f"Arrow ({sum(1 for _ in ids)} items):\t{(time.time() - _start) * 1000:.1f}ms)")
# Arrow (100000 items): 81.0ms)
```
### Implementation details
I added an optional `iter_arrow` method to examples iterable. If an example iterable has this method, then it can be used to iterate on the examples by batch of arrow tables.
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PR_kwDODunzps5Fiss1
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Support for writing arrow files directly with BeamWriter
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5364). All of your documentation changes will be reflected on that endpoint.",
"Deleting `BeamPipeline` and `upload_local_to_remote` would break the existing Beam scripts, so I reverted this change.\r\n\r\nFrom what I understand, we need these components in our scripts for the pattern:\r\n```python\r\nif not pipeline.is_local():\r\n dl_manager.ship_files_with_pipeline()\r\n```\r\n\r\nI plan to address this in a subsequent PR by (implicitly) downloading the files directly to the remote storage of the non-local runners.",
"I got `AttributeError: 'Pipeline' object has no attribute 'is_local'` when running\r\n```python\r\nload_dataset(\"wikipedia\", language=\"af\", date=\"20230101\", beam_runner=\"DirectRunner\")\r\n```\r\n```python\r\n~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)\r\n 965 # Use dictionary since testing mock always returns the same result.\r\n 966 downloaded_files = dl_manager.download({\"xml\": xml_urls})\r\n--> 967 if not pipeline.is_local():\r\n 968 downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)\r\n 969 \r\n\r\nAttributeError: 'Pipeline' object has no attribute 'is_local'\r\n```",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010649 / 0.011353 (-0.000704) | 0.006116 / 0.011008 (-0.004892) | 0.115568 / 0.038508 (0.077060) | 0.041704 / 0.023109 (0.018595) | 0.360459 / 0.275898 (0.084561) | 0.425679 / 0.323480 (0.102200) | 0.008992 / 0.007986 (0.001006) | 0.006321 / 0.004328 (0.001993) | 0.090223 / 0.004250 (0.085973) | 0.049877 / 0.037052 (0.012824) | 0.382447 / 0.258489 (0.123958) | 0.406567 / 0.293841 (0.112726) | 0.045138 / 0.128546 (-0.083409) | 0.014203 / 0.075646 (-0.061444) | 0.388897 / 0.419271 (-0.030375) | 0.057176 / 0.043533 (0.013644) | 0.358729 / 0.255139 (0.103590) | 0.386086 / 0.283200 (0.102887) | 0.119221 / 0.141683 (-0.022462) | 1.731574 / 1.452155 (0.279419) | 1.744103 / 1.492716 (0.251386) |\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) | 0.230380 / 0.018006 (0.212373) | 0.493690 / 0.000490 (0.493201) | 0.005150 / 0.000200 (0.004950) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030771 / 0.037411 (-0.006641) | 0.123196 / 0.014526 (0.108671) | 0.134097 / 0.176557 (-0.042459) | 0.190442 / 0.737135 (-0.546693) | 0.138416 / 0.296338 (-0.157923) |\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) | 0.469763 / 0.215209 (0.254554) | 4.682847 / 2.077655 (2.605192) | 2.076717 / 1.504120 (0.572597) | 1.843721 / 1.541195 (0.302527) | 1.923486 / 1.468490 (0.454996) | 0.817680 / 4.584777 (-3.767097) | 4.482409 / 3.745712 (0.736697) | 3.898695 / 5.269862 (-1.371167) | 2.078291 / 4.565676 (-2.487386) | 0.100285 / 0.424275 (-0.323990) | 0.014761 / 0.007607 (0.007154) | 0.611261 / 0.226044 (0.385217) | 5.926919 / 2.268929 (3.657990) | 2.685080 / 55.444624 (-52.759544) | 2.232179 / 6.876477 (-4.644298) | 2.305576 / 2.142072 (0.163504) | 0.993729 / 4.805227 (-3.811498) | 0.194491 / 6.500664 (-6.306173) | 0.074176 / 0.075469 (-0.001293) |\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) | 1.388592 / 1.841788 (-0.453196) | 17.146945 / 8.074308 (9.072636) | 15.989570 / 10.191392 (5.798178) | 0.200147 / 0.680424 (-0.480277) | 0.034009 / 0.534201 (-0.500192) | 0.517531 / 0.579283 (-0.061753) | 0.533966 / 0.434364 (0.099602) | 0.637024 / 0.540337 (0.096687) | 0.749166 / 1.386936 (-0.637770) |\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) | 0.008240 / 0.011353 (-0.003113) | 0.006139 / 0.011008 (-0.004869) | 0.112258 / 0.038508 (0.073750) | 0.039001 / 0.023109 (0.015891) | 0.449467 / 0.275898 (0.173569) | 0.483422 / 0.323480 (0.159942) | 0.006176 / 0.007986 (-0.001810) | 0.006340 / 0.004328 (0.002012) | 0.083105 / 0.004250 (0.078855) | 0.047002 / 0.037052 (0.009950) | 0.458564 / 0.258489 (0.200075) | 0.513704 / 0.293841 (0.219863) | 0.041359 / 0.128546 (-0.087188) | 0.014515 / 0.075646 (-0.061131) | 0.392599 / 0.419271 (-0.026673) | 0.055222 / 0.043533 (0.011690) | 0.446956 / 0.255139 (0.191817) | 0.469194 / 0.283200 (0.185994) | 0.118212 / 0.141683 (-0.023471) | 1.682647 / 1.452155 (0.230492) | 1.780076 / 1.492716 (0.287360) |\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) | 0.259124 / 0.018006 (0.241117) | 0.507559 / 0.000490 (0.507069) | 0.001080 / 0.000200 (0.000880) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031969 / 0.037411 (-0.005442) | 0.126997 / 0.014526 (0.112471) | 0.139593 / 0.176557 (-0.036963) | 0.182735 / 0.737135 (-0.554400) | 0.145871 / 0.296338 (-0.150468) |\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) | 0.530894 / 0.215209 (0.315685) | 5.284979 / 2.077655 (3.207324) | 2.592886 / 1.504120 (1.088766) | 2.407202 / 1.541195 (0.866007) | 2.434079 / 1.468490 (0.965589) | 0.829382 / 4.584777 (-3.755395) | 4.481710 / 3.745712 (0.735998) | 3.912280 / 5.269862 (-1.357581) | 1.962291 / 4.565676 (-2.603386) | 0.101840 / 0.424275 (-0.322435) | 0.014528 / 0.007607 (0.006921) | 0.639956 / 0.226044 (0.413911) | 6.414685 / 2.268929 (4.145756) | 3.240290 / 55.444624 (-52.204334) | 2.795208 / 6.876477 (-4.081269) | 2.912122 / 2.142072 (0.770050) | 0.992188 / 4.805227 (-3.813039) | 0.200701 / 6.500664 (-6.299964) | 0.074235 / 0.075469 (-0.001234) |\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) | 1.455075 / 1.841788 (-0.386712) | 17.186669 / 8.074308 (9.112361) | 15.404357 / 10.191392 (5.212965) | 0.168267 / 0.680424 (-0.512157) | 0.020774 / 0.534201 (-0.513427) | 0.502603 / 0.579283 (-0.076680) | 0.506500 / 0.434364 (0.072136) | 0.624245 / 0.540337 (0.083907) | 0.735529 / 1.386936 (-0.651407) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"I think we could close this PR.",
"indeed, since Beam stuff are deprecated"
] | 2022-12-15T12:38:05Z
| 2024-01-11T14:52:33Z
| 2024-01-11T14:45:15Z
|
COLLABORATOR
| null | null | null |
Make it possible to write Arrow files directly with `BeamWriter` rather than converting from Parquet to Arrow, which is sub-optimal, especially for big datasets for which Beam is primarily used.
|
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I_kwDODunzps6erqEj
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Why return_tensors='pt' doesn't work？
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[
"Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format(\"torch\")`",
"> Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format(\"torch\")`\r\n\r\nIt does work! Thanks for your suggestion!"
] | 2024-11-15T15:01:23Z
| 2024-11-18T13:47:08Z
| null |
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### Describe the bug
I tried to add input_ids to dataset with map(), and I used the return_tensors='pt', but why I got the callback with the type of List？
### Steps to reproduce the bug
### Expected behavior
Sorry for this silly question, I'm noob on using this tool. But I think it should return a tensor value as I have used the protocol？
When I tokenize only one sentence using tokenized_input=tokenizer(input, return_tensors='pt' )，it does return in tensor type. Why doesn't it work in map()？
### Environment info
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
gradio>=4.0.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0
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I_kwDODunzps5ulRt_
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DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
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[
"Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.",
"@mariosasko Ok. What exactly does it mean to provide a reproducer",
"To provide a code that reproduces the issue :)",
"@mariosasko I complete the above code, is it enough?",
"@mariosasko That's all the code, I'm using locally stored data",
"Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?",
"@mariosasko I didn't add any parameters for catch. Nor did any cache configuration change.",
"@mariosasko And I changed the data file, but executing load_dataset is always the previous result. I had to change something in images.py to use the new results. Using 'cleanup_cache_files' is invalid! Help me.",
"@mariosasko I added a comprehensive error message. Check that _column_requires_decoding is being passed where it shouldn't be. DatasetInfo.__init__() Whether this parameter is required",
"I can see the issue now... \r\n\r\nYou can fix it by returning a `DatasetInfo` object in the `_info` method as follows:\r\n```python\r\n def _info(self):\r\n if self.config.name == \"similar_pairs\":\r\n features = datasets.Features(\r\n {\r\n \"image1\": datasets.features.Image(),\r\n \"prompt1\": datasets.Value(\"string\"),\r\n \"image2\": datasets.features.Image(),\r\n \"prompt2\": datasets.Value(\"string\"),\r\n \"similarity\": datasets.Value(\"float32\"),\r\n }\r\n )\r\n elif self.config.name == \"image_prompt_pairs\":\r\n features = datasets.Features(\r\n {\"image\": datasets.features.Image(), \"prompt\": datasets.Value(\"string\")}\r\n )\r\n return datasets.DatasetInfo(features=features)\r\n```",
"@mariosasko Oh, that's the problem. Thank you very much. Returned the wrong object and it actually works? I've been training with it for a long time",
"@mariosasko The original code can still see progress. emmm, I can't see how many examples is generated so far, so I don't know if we should wait",
"The original issue has been addressed, so I'm closing it. \r\n\r\nPlease open a new issue if you encounter more errors."
] | 2023-08-17T15:48:11Z
| 2023-09-27T17:36:14Z
| 2023-09-27T17:36:14Z
|
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### Describe the bug
When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[3], line 1
----> 1 dataset = load_dataset(
2 "/home/aihao/workspace/DeepLearningContent/datasets/manga",
3 data_dir="/home/aihao/workspace/DeepLearningContent/datasets/manga",
4 split="train",
5 )
File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2142 # Build dataset for splits
2143 keep_in_memory = (
2144 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2145 )
-> 2146 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2147 # Rename and cast features to match task schema
2148 if task is not None:
2149 # To avoid issuing the same warning twice
File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1187 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1189 # Create a dataset for each of the given splits
-> 1190 datasets = map_nested(
1191 partial(
1192 self._build_single_dataset,
...
File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379), in DatasetInfo.copy(self)
378 def copy(self) -> "DatasetInfo":
--> 379 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
TypeError: DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
```
### Steps to reproduce the bug
/home/aihao/workspace/DeepLearningContent/datasets/images/images.py
```python
from logging import config
import datasets
import os
from PIL import Image
import csv
import json
class ImagesConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ImagesConfig, self).__init__(**kwargs)
class Images(datasets.GeneratorBasedBuilder):
def _split_generators(self, dl_manager: datasets.DownloadManager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split": datasets.Split.TRAIN},
)
]
BUILDER_CONFIGS = [
ImagesConfig(
name="similar_pairs",
description="simliar pair dataset,item is a pair of similar images",
),
ImagesConfig(
name="image_prompt_pairs",
description="image prompt pairs",
),
]
def _info(self):
if self.config.name == "similar_pairs":
return datasets.Features(
{
"image1": datasets.features.Image(),
"image2": datasets.features.Image(),
"similarity": datasets.Value("float32"),
}
)
elif self.config.name == "image_prompt_pairs":
return datasets.Features(
{"image": datasets.features.Image(), "prompt": datasets.Value("string")}
)
def _generate_examples(self, split):
data_path = os.path.join(self.config.data_dir, "data")
if self.config.name == "similar_pairs":
prompts = {}
with open(os.path.join(data_path ,"prompts.json"), "r") as f:
prompts = json.load(f)
with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f:
reader = csv.reader(f)
for row in reader:
image1_path, image2_path, similarity = row
yield image1_path + ":" + image2_path + ":", {
"image1": Image.open(image1_path),
"prompt1": prompts[image1_path],
"image2": Image.open(image2_path),
"prompt2": prompts[image2_path],
"similarity": float(similarity),
}
```
Code that indicates an error:
```python
from datasets import load_dataset
import json
import csv
import ast
import torch
data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images"
dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs")
```
### Expected behavior
The first execution gives an error, but it works fine
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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Drop redundant None guard.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6596). 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) | 0.004768 / 0.011353 (-0.006585) | 0.003084 / 0.011008 (-0.007924) | 0.062775 / 0.038508 (0.024267) | 0.029909 / 0.023109 (0.006800) | 0.242905 / 0.275898 (-0.032993) | 0.265609 / 0.323480 (-0.057871) | 0.003856 / 0.007986 (-0.004130) | 0.002610 / 0.004328 (-0.001718) | 0.048631 / 0.004250 (0.044381) | 0.040464 / 0.037052 (0.003412) | 0.256023 / 0.258489 (-0.002467) | 0.285914 / 0.293841 (-0.007927) | 0.027305 / 0.128546 (-0.101241) | 0.010345 / 0.075646 (-0.065301) | 0.206264 / 0.419271 (-0.213008) | 0.035290 / 0.043533 (-0.008243) | 0.247785 / 0.255139 (-0.007353) | 0.267053 / 0.283200 (-0.016147) | 0.017910 / 0.141683 (-0.123773) | 1.166096 / 1.452155 (-0.286059) | 1.210717 / 1.492716 (-0.281999) |\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) | 0.095759 / 0.018006 (0.077753) | 0.311030 / 0.000490 (0.310540) | 0.000234 / 0.000200 (0.000034) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017828 / 0.037411 (-0.019583) | 0.060123 / 0.014526 (0.045597) | 0.071947 / 0.176557 (-0.104610) | 0.119353 / 0.737135 (-0.617782) | 0.073529 / 0.296338 (-0.222809) |\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) | 0.282737 / 0.215209 (0.067528) | 2.761914 / 2.077655 (0.684260) | 1.480310 / 1.504120 (-0.023810) | 1.329977 / 1.541195 (-0.211218) | 1.332686 / 1.468490 (-0.135804) | 0.566309 / 4.584777 (-4.018468) | 2.361838 / 3.745712 (-1.383874) | 2.775613 / 5.269862 (-2.494249) | 1.744985 / 4.565676 (-2.820692) | 0.063038 / 0.424275 (-0.361237) | 0.004969 / 0.007607 (-0.002638) | 0.335543 / 0.226044 (0.109499) | 3.293779 / 2.268929 (1.024851) | 1.816093 / 55.444624 (-53.628532) | 1.562658 / 6.876477 (-5.313819) | 1.544888 / 2.142072 (-0.597185) | 0.641762 / 4.805227 (-4.163465) | 0.117904 / 6.500664 (-6.382760) | 0.042534 / 0.075469 (-0.032935) |\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) | 0.935577 / 1.841788 (-0.906211) | 11.565833 / 8.074308 (3.491525) | 10.314723 / 10.191392 (0.123331) | 0.138912 / 0.680424 (-0.541512) | 0.013968 / 0.534201 (-0.520233) | 0.296270 / 0.579283 (-0.283013) | 0.266106 / 0.434364 (-0.168258) | 0.334729 / 0.540337 (-0.205609) | 0.443191 / 1.386936 (-0.943745) |\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) | 0.004865 / 0.011353 (-0.006488) | 0.003523 / 0.011008 (-0.007485) | 0.049303 / 0.038508 (0.010795) | 0.029252 / 0.023109 (0.006143) | 0.271288 / 0.275898 (-0.004610) | 0.290529 / 0.323480 (-0.032951) | 0.003982 / 0.007986 (-0.004004) | 0.002740 / 0.004328 (-0.001589) | 0.048513 / 0.004250 (0.044262) | 0.044473 / 0.037052 (0.007420) | 0.282072 / 0.258489 (0.023583) | 0.311321 / 0.293841 (0.017480) | 0.028825 / 0.128546 (-0.099721) | 0.010311 / 0.075646 (-0.065335) | 0.057071 / 0.419271 (-0.362200) | 0.052629 / 0.043533 (0.009097) | 0.273134 / 0.255139 (0.017995) | 0.290989 / 0.283200 (0.007789) | 0.018074 / 0.141683 (-0.123609) | 1.171724 / 1.452155 (-0.280431) | 1.236178 / 1.492716 (-0.256538) |\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) | 0.097099 / 0.018006 (0.079093) | 0.309788 / 0.000490 (0.309298) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021703 / 0.037411 (-0.015708) | 0.076104 / 0.014526 (0.061578) | 0.088202 / 0.176557 (-0.088355) | 0.127351 / 0.737135 (-0.609784) | 0.089754 / 0.296338 (-0.206585) |\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) | 0.294574 / 0.215209 (0.079365) | 2.851581 / 2.077655 (0.773926) | 1.599117 / 1.504120 (0.094997) | 1.476183 / 1.541195 (-0.065012) | 1.512309 / 1.468490 (0.043819) | 0.559785 / 4.584777 (-4.024992) | 2.453287 / 3.745712 (-1.292425) | 2.660101 / 5.269862 (-2.609760) | 1.743043 / 4.565676 (-2.822633) | 0.063450 / 0.424275 (-0.360825) | 0.005019 / 0.007607 (-0.002589) | 0.351507 / 0.226044 (0.125462) | 3.431587 / 2.268929 (1.162658) | 1.943349 / 55.444624 (-53.501275) | 1.658706 / 6.876477 (-5.217771) | 1.780042 / 2.142072 (-0.362030) | 0.641364 / 4.805227 (-4.163863) | 0.118052 / 6.500664 (-6.382612) | 0.040961 / 0.075469 (-0.034508) |\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) | 0.974219 / 1.841788 (-0.867568) | 12.257824 / 8.074308 (4.183516) | 10.821225 / 10.191392 (0.629833) | 0.139399 / 0.680424 (-0.541025) | 0.015277 / 0.534201 (-0.518924) | 0.286975 / 0.579283 (-0.292309) | 0.283419 / 0.434364 (-0.150945) | 0.324299 / 0.540337 (-0.216039) | 0.424538 / 1.386936 (-0.962398) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2ba3f30bae5ff75ac48f0e653240b924d7982d5 \"CML watermark\")\n"
] | 2024-01-16T06:31:54Z
| 2024-01-16T17:16:16Z
| 2024-01-16T17:05:52Z
|
CONTRIBUTOR
| null | null | null |
`xxx if xxx is not None else None` is no-op.
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fix: Image Feature in Datasets Library Fails to Handle bytearray Objects from Spark DataFrames (#7517)
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"@lhoestq let me know if you prefer to change the spark iterator so it outputs `bytes`"
] | 2025-04-15T21:23:58Z
| 2025-04-16T06:57:22Z
| null |
NONE
| null | null | null |
## Task
Support bytes-like objects (bytes and bytearray) in Features classes
### Description
The `Features` classes only accept `bytes` objects for binary data, but not `bytearray`. This leads to errors when using `IterableDataset.from_spark()` with Spark DataFrames as they contain `bytearray` objects, even though both `bytes` and `bytearray` are valid [*bytes-like objects* in Python](https://docs.python.org/3/glossary.html#term-bytes-like-object).
### Changes
- Updated `Features` classes to accept both `bytes` and `bytearray` types for binary data fields.
### Reasoning
- `bytes` and `bytearray` serve the same purpose for binary data, with the only difference being mutability.
- Modifying the Spark iterator to convert `bytearray` to `bytes` would be a workaround, not a true fix. I think the correct solution is to accept all bytes-like objects as input.
- This approach is more robust and future-proof since Python 3.12+ provides a [standard way to check for buffer protocol](https://docs.python.org/3/c-api/buffer.html#bufferobjects).
### Testing
- Added tests to cover `bytearray` inputs for image features.
### Related Issues
- Fixes: #7517
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PR_kwDODunzps6PABJa
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Minor fix for metadata files in extension counter
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-17T21:57:11Z
| 2025-03-18T15:21:43Z
| 2025-03-18T15:21:41Z
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MEMBER
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I_kwDODunzps6B2uek
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Issue Downloading Certain Datasets After Setting Custom `HF_ENDPOINT`
|
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"Through debugging, I found a potential solution is to modify the code in the error handling module of `huggingface_hub`: https://github.com/huggingface/huggingface_hub/commit/56d6c798c44e83d2a3167e74c022737d8fcbe822 ",
"@Wauplin ",
"Thanks for investigating and reporting the bug @padeoe! I've opened a PR in `huggingface_hub` with your suggested fix! :) https://github.com/huggingface/huggingface_hub/pull/2119"
] | 2024-03-11T09:06:38Z
| 2024-03-15T14:52:07Z
| 2024-03-15T14:52:07Z
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### Describe the bug
This bug is triggered under the following conditions:
- datasets repo ids without organization names trigger errors, such as `bookcorpus`, `gsm8k`, `wikipedia`, rather than in the form of `A/B`.
- If `HF_ENDPOINT` is set and the hostname is not in the form of `(hub-ci.)?huggingface.co`.
- This issue occurs with `datasets>2.15.0` or `huggingface-hub>0.19.4`. For example, using the latest versions: `datasets==2.18.0` and `huggingface-hub==0.21.4`,
### Steps to reproduce the bug
the issue can be reproduced with the following code:
1. install specific datasets and huggingface_hub.
```bash
pip install datasets==2.18.0
pip install huggingface_hub==0.21.4
```
2. execute python code.
```Python
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
from datasets import load_dataset
bookcorpus = load_dataset('bookcorpus', split='train')
```
console output:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory
raise e1 from None
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1830, in dataset_module_factory
with fs.open(f"datasets/{path}/{filename}", "r", encoding="utf-8") as f:
File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1295, in open
self.open(
File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1307, in open
f = self._open(
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 228, in _open
return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 615, in __init__
self.resolved_path = fs.resolve_path(path, revision=revision)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 180, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 117, in _repo_and_revision_exist
self._api.repo_info(repo_id, revision=revision, repo_type=repo_type)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2413, in repo_info
return method(
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2286, in dataset_info
hf_raise_for_status(r)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 362, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 401 Client Error: Unauthorized for url: https://hf-mirror.com/api/datasets/bookcorpus/bookcorpus.py (Request ID: Root=1-65ee8659-5ab10eec5960c63e71f2bb58;b00bdbea-fd6e-4a74-8fe0-bc4682ae090e)
```
### Expected behavior
The dataset was downloaded correctly without any errors.
### Environment info
datasets==2.18.0
huggingface-hub==0.21.4
|
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Fix import in `ClassLabel` docstring example
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-27T11:35:29Z
| 2022-09-27T14:03:24Z
| 2022-09-27T12:27:50Z
|
MEMBER
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This PR addresses a super-simple fix: adding a missing `import` to the `ClassLabel` docstring example, as it was formatted as `from datasets Features`, so it's been fixed to `from datasets import Features`.
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PR_kwDODunzps6EvC5Q
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Release: 3.2.0
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7317). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-12-10T16:53:20Z
| 2024-12-10T16:56:58Z
| 2024-12-10T16:56:56Z
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I_kwDODunzps5oNtbE
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Sequence of array not supported for most dtype
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[
"Related, `float16` is the only dtype not supported by `Array2D` (probably by every `ArrayND`):\r\n\r\n```python\r\nfrom datasets import Array2D, Features, Dataset\r\n\r\nimport numpy as np\r\n\r\nfor dtype in [\r\n \"bool\", # ok\r\n \"int8\", # ok\r\n \"int16\", # ok\r\n \"int32\", # ok\r\n \"int64\", # ok\r\n \"uint8\", # ok\r\n \"uint16\", # ok\r\n \"uint32\", # ok\r\n \"uint64\", # ok\r\n \"float16\", # failed\r\n \"float32\", # ok\r\n \"float64\", # ok\r\n]:\r\n features = Features({\"foo\": Array2D(dtype=dtype, shape=(3, 4))})\r\n array = np.zeros((3, 4), dtype=dtype)\r\n try:\r\n dataset = Dataset.from_dict({\"foo\": [array]}, features=features)\r\n except Exception as e:\r\n print(f\"Failed for dtype={dtype}\")\r\n```",
"Here's something I can't explain:\r\n\r\nWhen an array is encoded in the `from_dict` method, the numpy array is converted to a list (thus losing the original dtype, which is transfromed to the nearest builtin Python type)\r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/features/features.py#L524-L525\r\n\r\nHowever, later on, this same data is written to memory, and it seems authorized that the data is an array (or in this case, a list of arrays). \r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/arrow_writer.py#L185-L186\r\n\r\nSo the question is: why convert it to a Python list? This seems to be quite expensive both in terms of write time (all data is copied) and memory (e.g., an int8 is converted to an int64).\r\n\r\nFinally, if I try to remove this step, it solves all the previous problems, and it seems to me that it doesn't break anything (the CI passes without problem).",
"Arrow only support 1d numpy arrays, so we convert multidim arrays to lists of 1s arrays (and keep the dtype).\r\n\r\nThough you noticed that it's concerting to lists and lose the dtype. If it's the case then it's a bug.",
"Ok the conversion to list shouldn't be there indeed ! Could you open a PR to remove it ?"
] | 2023-06-08T18:18:07Z
| 2023-06-14T15:03:34Z
| 2023-06-14T15:03:34Z
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### Describe the bug
Create a dataset composed of sequence of array fails for most dtypes (see code below).
### Steps to reproduce the bug
```python
from datasets import Sequence, Array2D, Features, Dataset
import numpy as np
for dtype in [
"bool", # ok
"int8", # failed
"int16", # failed
"int32", # failed
"int64", # ok
"uint8", # failed
"uint16", # failed
"uint32", # failed
"uint64", # failed
"float16", # failed
"float32", # failed
"float64", # ok
]:
features = Features({"foo": Sequence(Array2D(dtype=dtype, shape=(2, 2)))})
sequence = [
[[1.0, 2.0], [3.0, 4.0]],
[[5.0, 6.0], [7.0, 8.0]],
]
array = np.array(sequence, dtype=dtype)
try:
dataset = Dataset.from_dict({"foo": [array]}, features=features)
except Exception as e:
print(f"Failed for dtype={dtype}")
```
Traceback for `dtype="int8"`:
```
Traceback (most recent call last):
File "/home/qgallouedec/datasets/a.py", line 29, in <module>
raise e
File "/home/qgallouedec/datasets/a.py", line 26, in <module>
dataset = Dataset.from_dict({"foo": [array]}, features=features)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 899, in from_dict
pa_table = InMemoryTable.from_pydict(mapping=mapping)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 799, in from_pydict
return cls(pa.Table.from_pydict(*args, **kwargs))
File "pyarrow/table.pxi", line 3725, in pyarrow.lib.Table.from_pydict
File "pyarrow/table.pxi", line 5254, in pyarrow.lib._from_pydict
File "pyarrow/array.pxi", line 350, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 236, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 204, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2091, in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2139, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast
return pa_type.wrap_array(array)
File "pyarrow/types.pxi", line 879, in pyarrow.lib.BaseExtensionType.wrap_array
TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: int8>>, got list<item: list<item: int64>>
```
### Expected behavior
Not to fail.
### Environment info
- Python 3.10.6
- datasets: master branch
- Numpy: 1.23.4
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Use pyarrow Tensor dtype
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"Hi ! We're using the Arrow format for the datasets, and PyArrow tensors are not part of the Arrow format AFAIK:\r\n\r\n> There is no direct support in the arrow columnar format to store Tensors as column values.\r\n\r\nsource: https://github.com/apache/arrow/issues/4802#issuecomment-508494694",
"@wesm @rok its been around three years. any updates, regarding dataset arrow tensor support? 🙏 I know you must be very busy, would appreciate to learn what is the state of art. I saw the PR is still open [#8510](https://github.com/apache/arrow/pull/8510)",
"Hey @franz101 & @lhoestq!\r\nThere is a plan and a PR to create an [ExtensionArray of Tensors](https://github.com/apache/arrow/pull/8510) of equal sizes as well as a plan to do the same for Tensors of different sizes [ARROW-8714](https://issues.apache.org/jira/browse/ARROW-8714).",
"The work stalled a little because it was not clear where TensorArray would live. However Arrow community recently agreed to make a [well-known-extension-type document](https://lists.apache.org/thread/sxd5fhc42hb6svs79t3fd79gkqj83pfh) and I would like https://github.com/apache/arrow/pull/8510 to land there and add an implementation to C++/Python + another language. Is that something you would find beneficial to you?",
"that is a great update, thank you.\r\nit looks like this feature would benefit datasets implementation of [ArrayExtensionArray](https://github.com/huggingface/datasets/blob/9f2ff14673cac1f1ad56d80221a793f5938b68c7/src/datasets/features/features.py#L585-L641). Is that correct @eladsegal @lhoestq?\r\n\r\n",
"TensorArray sounds great ! Looking forward to it :)\r\n\r\nWe've had our own ExtensionArray for fixed shape tensors for a while now, hoping to see something more standardized by the arrow community.\r\n\r\nAlso super interested in the extension array for tensors of different sizes cc @mariosasko ",
"[FixedShapeTensor ExtensionType](https://github.com/apache/arrow/pull/8510) was merged and will be in Arrow 12.0.0 (release is planned mid April).\r\n",
"@rok Thanks for keeping us updated! I think it's best to introduce a new feature type that would use this extension type under the hood. I'll create an issue to discuss the design with the community in the coming days.\r\n\r\nAlso, is there a tentative time frame for the variable-shape Tensor extension type?",
"@mariosasko please tag me in the discussion, perhaps I can contribute.\r\n\r\nAs for the [variable shape tensor array](https://github.com/apache/arrow/issues/24868) - I'd be interested in working on it but didn't see much interest in community yet. Are you saying `huggingface/datasets` could use it?",
"pyarrow 12 is out 🎉, will have a look if I can work on it for the ExtensionArray",
"I think these two issues need to be fixed first on the Arrow side before adding the tensor feature type here: https://github.com/apache/arrow/issues/35573 and https://github.com/apache/arrow/issues/35599.\r\n\r\n@rok We've had a couple of requests for supporting variable-shape tensors on the forum/GH, but I did not manage to find the concrete issues using the search. TF/TFDS (and PyTorch with the `nested_tensor` API) support them, so it makes sense for us to do the same eventually (the Ray project has an [extension](https://github.com/ray-project/ray/blob/42a8d1489b37243f203120899a23d919dc85bf2a/python/ray/air/util/tensor_extensions/arrow.py#L634) type to support this case)",
"> @rok We've had a couple of requests for supporting variable-shape tensors on the forum/GH, but I did not manage to find the concrete issues using the search. TF/TFDS (and PyTorch with the `nested_tensor` API) support them, so it makes sense for us to do the same eventually (the Ray project has an [extension](https://github.com/ray-project/ray/blob/42a8d1489b37243f203120899a23d919dc85bf2a/python/ray/air/util/tensor_extensions/arrow.py#L634) type to support this case)\r\n\r\nThat does make sense indeed. We should probably also be careful about memory layout to enable zero-copy interface to TF/PyTorch.",
"So there is no way we can use [pyarrow.Tensor](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html#pyarrow.Tensor) ?",
"Not with with the Arrow format, and therefore not in `datasets`. But they released a new [FixedShapeTensorArray](https://arrow.apache.org/docs/python/extending_types.html#fixed-size-tensor) to store tensors in Arrow format. We plan to support this in `datasets` at one point !",
"There is also an open issue to enable the conversion of `pyarrow.Tensor` to `pyarrow.FixedShapeTensorType`: https://github.com/apache/arrow/issues/35068. This way one could indirectly use `pyarrow.Tensor` in Arrow format.",
"We started a [mailing list discussion](https://lists.apache.org/thread/qc9qho0fg5ph1dns4hjq56hp4tj7rk1k) about potential `VariableShapeTensor` extension array, please check it out and give feedback. For more details here's also a PR https://github.com/apache/arrow/pull/37166.",
"Kindly ask what's the recent progress?"
] | 2022-11-20T15:18:41Z
| 2024-11-11T03:03:17Z
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### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary
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Raise error for incorrect JSON serialization
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"PTAL @lhoestq @albertvillanova ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7273). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-11-01T11:54:35Z
| 2024-11-18T11:25:01Z
| 2024-11-18T11:25:01Z
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CONTRIBUTOR
| null | null | null |
Raise error when `lines = False` and `batch_size < Dataset.num_rows` in `Dataset.to_json()`.
Issue: #7037
Related PRs:
#7039 #7181
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Document the HF_DATASETS_CACHE environment variable in the datasets cache documentation
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This pull request updates the Datasets documentation to include the `HF_DATASETS_CACHE` environment variable. While the current documentation only mentions `HF_HOME` for overriding the default cache directory, `HF_DATASETS_CACHE` is also a supported and useful option for specifying a custom cache location for datasets stored in Arrow format.
This addition is based on the discussion in (https://github.com/huggingface/datasets/issues/7457), where users noted the absence of this variable in the documentation despite its functionality. The update adds a new section to `cache.mdx` that explains how to use `HF_DATASETS_CACHE` with an example.
This change aims to improve clarity and help users better manage their cache directories when working in shared environments or with limited local storage.
Closes #7457.
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[
"Hi ! This issue comes from the fact that `map()` with `num_proc>1` shards the dataset in multiple chunks to be processed (one per process) and merges them. The DatasetInfos of each chunk are then merged together, but for some fields like `dataset_name` it's not been implemented and default to None.\r\n\r\nThe DatasetInfo merge is defined here, in case you'd like to contribute an improvement: \r\n\r\nhttps://github.com/huggingface/datasets/blob/d2e0034122a788015c0834a72e6c6279e7ecbac5/src/datasets/info.py#L269-L270",
"#self-assign"
] | 2024-01-12T13:39:19Z
| 2024-01-12T14:08:24Z
| null |
CONTRIBUTOR
| null | null |
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|
### Describe the bug
Hello and thanks for developing this package!
When I process a Dataset with the map function using multiple processors some set attributes of the DatasetInfo get lost and are None in the resulting Dataset.
### Steps to reproduce the bug
```python
from datasets import Dataset, DatasetInfo
def run_map(num_proc):
dataset = Dataset.from_dict(
{"col1": [0, 1], "col2": [3, 4]},
info=DatasetInfo(
dataset_name="my_dataset",
),
)
ds = dataset.map(lambda x: x, num_proc=num_proc)
print(ds.info.dataset_name)
run_map(1)
run_map(2)
```
This puts out:
```bash
Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s]
my_dataset
Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s]
None
```
### Expected behavior
I expect the DatasetInfo to be kept as it was and there should be no difference in the output of running map with num_proc=1 and num_proc=2.
Expected output:
```bash
Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s]
my_dataset
Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s]
my_dataset
```
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.17
- Python version: 3.8.18
- `huggingface_hub` version: 0.20.2
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.9.2
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PR_kwDODunzps5INgD9
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Support fsspec 2023.1.0 in CI
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008227 / 0.011353 (-0.003126) | 0.004496 / 0.011008 (-0.006512) | 0.099319 / 0.038508 (0.060811) | 0.029929 / 0.023109 (0.006820) | 0.296686 / 0.275898 (0.020788) | 0.355372 / 0.323480 (0.031892) | 0.006864 / 0.007986 (-0.001122) | 0.003458 / 0.004328 (-0.000871) | 0.077234 / 0.004250 (0.072983) | 0.037072 / 0.037052 (0.000020) | 0.311675 / 0.258489 (0.053186) | 0.338965 / 0.293841 (0.045124) | 0.033562 / 0.128546 (-0.094985) | 0.011399 / 0.075646 (-0.064248) | 0.322406 / 0.419271 (-0.096865) | 0.043034 / 0.043533 (-0.000499) | 0.298083 / 0.255139 (0.042944) | 0.323661 / 0.283200 (0.040462) | 0.089380 / 0.141683 (-0.052303) | 1.479363 / 1.452155 (0.027208) | 1.518337 / 1.492716 (0.025620) |\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) | 0.177822 / 0.018006 (0.159816) | 0.400806 / 0.000490 (0.400317) | 0.002121 / 0.000200 (0.001921) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021986 / 0.037411 (-0.015426) | 0.096749 / 0.014526 (0.082223) | 0.101443 / 0.176557 (-0.075113) | 0.137519 / 0.737135 (-0.599616) | 0.105558 / 0.296338 (-0.190780) |\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) | 0.418983 / 0.215209 (0.203774) | 4.189579 / 2.077655 (2.111924) | 1.877831 / 1.504120 (0.373711) | 1.666213 / 1.541195 (0.125019) | 1.680735 / 1.468490 (0.212245) | 0.693033 / 4.584777 (-3.891744) | 3.420553 / 3.745712 (-0.325160) | 1.819647 / 5.269862 (-3.450214) | 1.144934 / 4.565676 (-3.420743) | 0.082209 / 0.424275 (-0.342066) | 0.012433 / 0.007607 (0.004826) | 0.526781 / 0.226044 (0.300737) | 5.273689 / 2.268929 (3.004760) | 2.323468 / 55.444624 (-53.121156) | 1.960508 / 6.876477 (-4.915969) | 2.035338 / 2.142072 (-0.106735) | 0.812789 / 4.805227 (-3.992438) | 0.148429 / 6.500664 (-6.352235) | 0.064727 / 0.075469 (-0.010742) |\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) | 1.253218 / 1.841788 (-0.588569) | 13.303426 / 8.074308 (5.229118) | 13.651074 / 10.191392 (3.459682) | 0.135178 / 0.680424 (-0.545246) | 0.028483 / 0.534201 (-0.505717) | 0.393284 / 0.579283 (-0.185999) | 0.401957 / 0.434364 (-0.032407) | 0.457136 / 0.540337 (-0.083201) | 0.535835 / 1.386936 (-0.851101) |\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) | 0.006335 / 0.011353 (-0.005017) | 0.004454 / 0.011008 (-0.006554) | 0.097565 / 0.038508 (0.059057) | 0.026917 / 0.023109 (0.003808) | 0.350779 / 0.275898 (0.074881) | 0.391979 / 0.323480 (0.068499) | 0.004648 / 0.007986 (-0.003337) | 0.003204 / 0.004328 (-0.001124) | 0.076987 / 0.004250 (0.072737) | 0.035257 / 0.037052 (-0.001796) | 0.347193 / 0.258489 (0.088704) | 0.391462 / 0.293841 (0.097621) | 0.031244 / 0.128546 (-0.097302) | 0.011460 / 0.075646 (-0.064186) | 0.321606 / 0.419271 (-0.097665) | 0.041218 / 0.043533 (-0.002315) | 0.341884 / 0.255139 (0.086745) | 0.374920 / 0.283200 (0.091720) | 0.086383 / 0.141683 (-0.055300) | 1.501750 / 1.452155 (0.049595) | 1.565060 / 1.492716 (0.072344) |\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) | 0.165447 / 0.018006 (0.147441) | 0.401885 / 0.000490 (0.401395) | 0.000975 / 0.000200 (0.000775) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024494 / 0.037411 (-0.012917) | 0.097334 / 0.014526 (0.082808) | 0.105324 / 0.176557 (-0.071232) | 0.142430 / 0.737135 (-0.594705) | 0.107249 / 0.296338 (-0.189089) |\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) | 0.441632 / 0.215209 (0.226423) | 4.407729 / 2.077655 (2.330074) | 2.078167 / 1.504120 (0.574047) | 1.864210 / 1.541195 (0.323015) | 1.885948 / 1.468490 (0.417458) | 0.693974 / 4.584777 (-3.890803) | 3.386837 / 3.745712 (-0.358875) | 1.840291 / 5.269862 (-3.429571) | 1.150524 / 4.565676 (-3.415153) | 0.082240 / 0.424275 (-0.342035) | 0.012488 / 0.007607 (0.004881) | 0.537589 / 0.226044 (0.311545) | 5.404007 / 2.268929 (3.135078) | 2.537467 / 55.444624 (-52.907157) | 2.190775 / 6.876477 (-4.685702) | 2.224746 / 2.142072 (0.082674) | 0.799524 / 4.805227 (-4.005703) | 0.150639 / 6.500664 (-6.350025) | 0.066473 / 0.075469 (-0.008997) |\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) | 1.258559 / 1.841788 (-0.583228) | 13.773583 / 8.074308 (5.699275) | 13.964322 / 10.191392 (3.772930) | 0.156295 / 0.680424 (-0.524129) | 0.016824 / 0.534201 (-0.517377) | 0.377476 / 0.579283 (-0.201807) | 0.390163 / 0.434364 (-0.044201) | 0.442541 / 0.540337 (-0.097796) | 0.529404 / 1.386936 (-0.857532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f500a5c554b213aafe87293bd593920567742c3 \"CML watermark\")\n"
] | 2023-01-20T12:53:17Z
| 2023-01-20T13:32:50Z
| 2023-01-20T13:26:03Z
|
MEMBER
| null | null | null |
Support fsspec 2023.1.0 in CI.
In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`:
- from `ReadOnlyRegistry`, with an attribute called `target`
- to `MappingProxyType`, without that attribute
Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute.
Fix #5448.
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PR_kwDODunzps5ia6Os
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Cache backward compatibility with 2.15.0
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6514). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"it's hard to tell if this works as expected without a test but i guess it's not trivial to implement such a test.\r\n\r\ni tried to reproduce locally (with this branch merged into the lazy-resolve-and-cache-reload) and it didn't work. \r\nI run:\r\n```\r\n ds = load_dataset(\"polinaeterna/audiofolder_two_configs_in_metadata\", \"v2\", data_files=\"v2/train/*\") \r\n```\r\nand i got this in the cache:\r\n```\r\nv2-374bfde4f55442bc/\r\n└── 0.0.0\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5 # - from this pr\r\n │ ├── audiofolder_two_configs_in_metadata-train.arrow\r\n │ └── dataset_info.json\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5_builder.lock\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5.incomplete_info.lock\r\n ├── 7896925d64deea5d # from 2.15.0\r\n │ ├── audiofolder_two_configs_in_metadata-train.arrow\r\n │ └── dataset_info.json\r\n ├── 7896925d64deea5d_builder.lock\r\n └── 7896925d64deea5d.incomplete_info.lock\r\n```\r\nso the first hash (the top-level dir v2-374bfde4f55442bc) matches but the second (after version) doesn't.\r\nmaybe i did something wrong though.\r\n\r\nalso i'm not sure if this is worth too much effort, maybe nobody notices if their datasets will be generated again :D idk",
"I just pushed a fix, it should work just fine now :)",
"<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) | 0.004798 / 0.011353 (-0.006555) | 0.003203 / 0.011008 (-0.007805) | 0.062247 / 0.038508 (0.023738) | 0.029906 / 0.023109 (0.006797) | 0.259370 / 0.275898 (-0.016528) | 0.276084 / 0.323480 (-0.047396) | 0.002910 / 0.007986 (-0.005076) | 0.002364 / 0.004328 (-0.001964) | 0.048080 / 0.004250 (0.043830) | 0.041168 / 0.037052 (0.004116) | 0.259833 / 0.258489 (0.001343) | 0.289882 / 0.293841 (-0.003959) | 0.026790 / 0.128546 (-0.101756) | 0.010336 / 0.075646 (-0.065311) | 0.209628 / 0.419271 (-0.209643) | 0.035080 / 0.043533 (-0.008452) | 0.256278 / 0.255139 (0.001139) | 0.279502 / 0.283200 (-0.003697) | 0.019755 / 0.141683 (-0.121928) | 1.121552 / 1.452155 (-0.330602) | 1.174360 / 1.492716 (-0.318356) |\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) | 0.093510 / 0.018006 (0.075504) | 0.302065 / 0.000490 (0.301575) | 0.000214 / 0.000200 (0.000014) | 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) | 0.017652 / 0.037411 (-0.019759) | 0.060512 / 0.014526 (0.045986) | 0.072441 / 0.176557 (-0.104115) | 0.118058 / 0.737135 (-0.619078) | 0.072657 / 0.296338 (-0.223682) |\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) | 0.283949 / 0.215209 (0.068740) | 2.803275 / 2.077655 (0.725620) | 1.527353 / 1.504120 (0.023233) | 1.408176 / 1.541195 (-0.133019) | 1.375335 / 1.468490 (-0.093155) | 0.546426 / 4.584777 (-4.038351) | 2.402210 / 3.745712 (-1.343502) | 2.765879 / 5.269862 (-2.503982) | 1.703722 / 4.565676 (-2.861955) | 0.062669 / 0.424275 (-0.361606) | 0.005006 / 0.007607 (-0.002601) | 0.337941 / 0.226044 (0.111897) | 3.385494 / 2.268929 (1.116566) | 1.817360 / 55.444624 (-53.627264) | 1.548594 / 6.876477 (-5.327883) | 1.548610 / 2.142072 (-0.593463) | 0.630188 / 4.805227 (-4.175040) | 0.117079 / 6.500664 (-6.383585) | 0.042077 / 0.075469 (-0.033392) |\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) | 0.941606 / 1.841788 (-0.900182) | 11.226277 / 8.074308 (3.151969) | 10.118005 / 10.191392 (-0.073387) | 0.130408 / 0.680424 (-0.550015) | 0.014419 / 0.534201 (-0.519782) | 0.284812 / 0.579283 (-0.294471) | 0.266951 / 0.434364 (-0.167413) | 0.322251 / 0.540337 (-0.218087) | 0.415014 / 1.386936 (-0.971922) |\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) | 0.005192 / 0.011353 (-0.006161) | 0.003028 / 0.011008 (-0.007980) | 0.048322 / 0.038508 (0.009814) | 0.030550 / 0.023109 (0.007441) | 0.264360 / 0.275898 (-0.011538) | 0.289544 / 0.323480 (-0.033936) | 0.004053 / 0.007986 (-0.003933) | 0.002480 / 0.004328 (-0.001848) | 0.048215 / 0.004250 (0.043964) | 0.044208 / 0.037052 (0.007156) | 0.263943 / 0.258489 (0.005454) | 0.297648 / 0.293841 (0.003807) | 0.029315 / 0.128546 (-0.099231) | 0.010533 / 0.075646 (-0.065114) | 0.057021 / 0.419271 (-0.362251) | 0.053751 / 0.043533 (0.010218) | 0.265153 / 0.255139 (0.010014) | 0.284988 / 0.283200 (0.001788) | 0.018459 / 0.141683 (-0.123224) | 1.225657 / 1.452155 (-0.226498) | 1.195737 / 1.492716 (-0.296979) |\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) | 0.093030 / 0.018006 (0.075024) | 0.301022 / 0.000490 (0.300533) | 0.000228 / 0.000200 (0.000028) | 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) | 0.022073 / 0.037411 (-0.015339) | 0.075912 / 0.014526 (0.061386) | 0.087628 / 0.176557 (-0.088929) | 0.125607 / 0.737135 (-0.611529) | 0.088568 / 0.296338 (-0.207770) |\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) | 0.303482 / 0.215209 (0.088273) | 2.965987 / 2.077655 (0.888333) | 1.615273 / 1.504120 (0.111153) | 1.482851 / 1.541195 (-0.058344) | 1.562627 / 1.468490 (0.094137) | 0.563626 / 4.584777 (-4.021151) | 2.448741 / 3.745712 (-1.296971) | 2.761006 / 5.269862 (-2.508855) | 1.711242 / 4.565676 (-2.854434) | 0.064593 / 0.424275 (-0.359682) | 0.005044 / 0.007607 (-0.002563) | 0.354131 / 0.226044 (0.128087) | 3.511698 / 2.268929 (1.242770) | 1.951087 / 55.444624 (-53.493538) | 1.682171 / 6.876477 (-5.194305) | 1.666330 / 2.142072 (-0.475742) | 0.654880 / 4.805227 (-4.150347) | 0.118544 / 6.500664 (-6.382120) | 0.040753 / 0.075469 (-0.034717) |\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) | 0.967771 / 1.841788 (-0.874017) | 12.017277 / 8.074308 (3.942969) | 10.624947 / 10.191392 (0.433555) | 0.128834 / 0.680424 (-0.551590) | 0.015739 / 0.534201 (-0.518462) | 0.285906 / 0.579283 (-0.293377) | 0.273659 / 0.434364 (-0.160705) | 0.324044 / 0.540337 (-0.216293) | 0.419469 / 1.386936 (-0.967467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2afbf785f8d0551cdd65a81c5c3228e469613724 \"CML watermark\")\n"
] | 2023-12-19T23:52:25Z
| 2023-12-21T21:14:11Z
| 2023-12-21T21:07:55Z
|
MEMBER
| null | null | null |
...for datasets without scripts
It takes into account the changes in cache from
- https://github.com/huggingface/datasets/pull/6493: switch to `config/version/commit_sha` schema
- https://github.com/huggingface/datasets/pull/6454: fix `DataFilesDict` keys ordering when hashing
requires https://github.com/huggingface/datasets/pull/6493 to be merged
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I_kwDODunzps5VWupr
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Allow passing a subset of output features to Dataset.map
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[] | 2022-11-01T20:07:20Z
| 2022-11-01T20:07:34Z
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CONTRIBUTOR
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### Feature request
Currently, map does one of two things to the features (if I'm not mistaken):
* when you do not pass features, types are assumed to be equal to the input if they can be cast, and inferred otherwise
* when you pass a full specification of features, output features are set to this
However, sometimes you want to just pass some of the output types, particularly when the first of these modes makes an incorrect type. This currently crashes.
### Motivation
To give a little background: this problem appears in converting labels to ids, where the labels happen to be floats rather than strings
Consider the following use of map to convert from float to int
```python
data = Dataset.from_dict({'y':[1.0,2.0,3.0]})
mapped = data.map(lambda r: {'y': int(r['y'])})
mapped['y'] # is floats, not ints
```
The result is a float again, since after the mapping operation it forces the old datatypes back on the data.
Passing `features=Features({"y": Value(dtype="int64")})` to map works in principle, but then extending it a little to e.g.
```python
def format_data(r):
return {**tokenizer(r["text"]), "y": int(r["y"])}
data = Dataset.from_dict({"y": [1.0, 2.0, 3.0], "text": ["one", "two", "three"]})
mapped = data.map(
format_data,
features=Features({'y': Value(dtype="int64")}),
remove_columns=["text"],
)
```
Results in a crash in dataset internals, as it expects either all or no output features to be specified.
Of course one can pass a full feature specification, but this becomes tokenizer specific and very awkward.
### Your contribution
I've looked at `write_batch` and particularly `col_type = features[col] if features else None`, but checking for `col in features` here makes it fail elsewhere, but the structure makes it hard to understand how and why. I do not think I would have the time myself to get to the bottom of this anytime soon.
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PR_kwDODunzps50D3gi
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Support fsspec 2024.6.1
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7017). 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) | 0.005520 / 0.011353 (-0.005832) | 0.004216 / 0.011008 (-0.006792) | 0.063465 / 0.038508 (0.024957) | 0.032116 / 0.023109 (0.009007) | 0.242486 / 0.275898 (-0.033412) | 0.262554 / 0.323480 (-0.060925) | 0.004218 / 0.007986 (-0.003768) | 0.003264 / 0.004328 (-0.001064) | 0.050306 / 0.004250 (0.046056) | 0.044995 / 0.037052 (0.007942) | 0.257797 / 0.258489 (-0.000693) | 0.284595 / 0.293841 (-0.009246) | 0.030623 / 0.128546 (-0.097924) | 0.012245 / 0.075646 (-0.063401) | 0.205496 / 0.419271 (-0.213775) | 0.039327 / 0.043533 (-0.004206) | 0.246834 / 0.255139 (-0.008305) | 0.269296 / 0.283200 (-0.013903) | 0.017714 / 0.141683 (-0.123969) | 1.127246 / 1.452155 (-0.324909) | 1.172147 / 1.492716 (-0.320569) |\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) | 0.137621 / 0.018006 (0.119615) | 0.299843 / 0.000490 (0.299353) | 0.000248 / 0.000200 (0.000048) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018968 / 0.037411 (-0.018443) | 0.062636 / 0.014526 (0.048111) | 0.074098 / 0.176557 (-0.102459) | 0.121139 / 0.737135 (-0.615996) | 0.075121 / 0.296338 (-0.221217) |\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) | 0.289907 / 0.215209 (0.074698) | 2.872250 / 2.077655 (0.794595) | 1.508635 / 1.504120 (0.004515) | 1.345356 / 1.541195 (-0.195839) | 1.361858 / 1.468490 (-0.106632) | 0.738961 / 4.584777 (-3.845816) | 2.414616 / 3.745712 (-1.331097) | 2.843464 / 5.269862 (-2.426398) | 1.953716 / 4.565676 (-2.611961) | 0.079063 / 0.424275 (-0.345212) | 0.005498 / 0.007607 (-0.002109) | 0.346211 / 0.226044 (0.120166) | 3.446294 / 2.268929 (1.177366) | 1.857191 / 55.444624 (-53.587433) | 1.536924 / 6.876477 (-5.339553) | 1.655782 / 2.142072 (-0.486290) | 0.800508 / 4.805227 (-4.004719) | 0.136116 / 6.500664 (-6.364548) | 0.042648 / 0.075469 (-0.032821) |\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) | 0.964286 / 1.841788 (-0.877501) | 11.574645 / 8.074308 (3.500336) | 9.351631 / 10.191392 (-0.839761) | 0.139693 / 0.680424 (-0.540731) | 0.014368 / 0.534201 (-0.519833) | 0.303953 / 0.579283 (-0.275330) | 0.263302 / 0.434364 (-0.171062) | 0.342436 / 0.540337 (-0.197901) | 0.457195 / 1.386936 (-0.929741) |\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) | 0.005526 / 0.011353 (-0.005827) | 0.003959 / 0.011008 (-0.007050) | 0.049979 / 0.038508 (0.011471) | 0.032695 / 0.023109 (0.009586) | 0.269461 / 0.275898 (-0.006437) | 0.296622 / 0.323480 (-0.026858) | 0.004410 / 0.007986 (-0.003576) | 0.002708 / 0.004328 (-0.001621) | 0.048413 / 0.004250 (0.044163) | 0.040567 / 0.037052 (0.003515) | 0.278854 / 0.258489 (0.020364) | 0.318839 / 0.293841 (0.024998) | 0.031228 / 0.128546 (-0.097318) | 0.012411 / 0.075646 (-0.063236) | 0.060077 / 0.419271 (-0.359194) | 0.033072 / 0.043533 (-0.010461) | 0.275281 / 0.255139 (0.020142) | 0.292588 / 0.283200 (0.009388) | 0.018218 / 0.141683 (-0.123465) | 1.124877 / 1.452155 (-0.327278) | 1.164880 / 1.492716 (-0.327836) |\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) | 0.095098 / 0.018006 (0.077092) | 0.298341 / 0.000490 (0.297851) | 0.000225 / 0.000200 (0.000025) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022502 / 0.037411 (-0.014909) | 0.076650 / 0.014526 (0.062124) | 0.088851 / 0.176557 (-0.087705) | 0.128261 / 0.737135 (-0.608875) | 0.089305 / 0.296338 (-0.207033) |\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) | 0.298704 / 0.215209 (0.083495) | 2.917605 / 2.077655 (0.839951) | 1.568964 / 1.504120 (0.064844) | 1.437668 / 1.541195 (-0.103527) | 1.458787 / 1.468490 (-0.009704) | 0.732347 / 4.584777 (-3.852430) | 0.960834 / 3.745712 (-2.784878) | 2.947899 / 5.269862 (-2.321963) | 1.885576 / 4.565676 (-2.680100) | 0.079093 / 0.424275 (-0.345182) | 0.005199 / 0.007607 (-0.002408) | 0.353754 / 0.226044 (0.127710) | 3.495197 / 2.268929 (1.226268) | 1.936840 / 55.444624 (-53.507785) | 1.622797 / 6.876477 (-5.253680) | 1.627132 / 2.142072 (-0.514940) | 0.804007 / 4.805227 (-4.001221) | 0.135990 / 6.500664 (-6.364674) | 0.041606 / 0.075469 (-0.033863) |\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) | 1.004860 / 1.841788 (-0.836928) | 12.027573 / 8.074308 (3.953265) | 10.478055 / 10.191392 (0.286663) | 0.143946 / 0.680424 (-0.536477) | 0.015538 / 0.534201 (-0.518663) | 0.302592 / 0.579283 (-0.276691) | 0.123177 / 0.434364 (-0.311187) | 0.340752 / 0.540337 (-0.199585) | 0.436536 / 1.386936 (-0.950400) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#100361d7ccae451a34c6bd9e48dee55d6a3c6006 \"CML watermark\")\n"
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Support fsspec 2024.6.1.
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PR_kwDODunzps5r3kWg
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Make Image cast storage faster
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6786). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Hi ! Thanks for diving into this, this conversion to python lists is indeed quite slow.\r\n\r\nArray2DExtensionType and Array3DExtensionType currently rely on pyarrow lists, but we will soon modify them to use FixedShapeTensorArray instead which is more efficient (e.g. doesn't need to store an offset for each value). So ideally it would be cool to speed this code up without using those extension types or it will be blocking to improve Array2DExtensionType and Array3DExtensionType.\r\n\r\nIf I understand correctly you just need the logic from ArrayExtensionArray.to_numpy ? If so feel free to make a separate function and ArrayExtensionArray.to_numpy can call it",
"Hey! I didn't have time to look into this but I just stumbled upon another problem. \r\nWhile my fix kind of made it usable I now pre-embedded the images and even as Array3D they are really slow to load. \r\nDon't think this can be resolved with using ArrayExtensionArray.to_numpy.\r\n\r\nI think actually making the Array3DExtensionType faster would probably resolve both issues as you mentioned.\r\nIs there an update on using FixedShapeTensorArray?\r\nI'd gladly help implementing/testing it if there is some outline how to do it.",
"No one is working on this atm afaik (and actually we don't have any ETA unfortunately).\r\n\r\nTo do this change I think we need to:\r\n- update the `_ArrayXD` parent class of all the `Array2D`, `Array3D` types to use `pa.fixed_shape_tensor` type\r\n ```diff\r\n - pa_type = globals()[self.__class__.__name__ + \"ExtensionType\"](self.shape, self.dtype)\r\n + pa_type = pa.fixed_shape_tensor(self.shape, string_to_arrow(self.dtype))\r\n ```\r\n- remove the old extension type `_ArrayXDExtensionType` and extension array `ArrayExtensionArray`\r\n- probably update some functions in `features.py` that were using those types and use the new ones instead",
"Thanks, I have looked into this and have a working solution at least for my specific case.\r\nBut I had quite a few issues along the way that are not solved nicely.\r\nIt follows your suggestion though internally it is then just a fixed_shape_tensor as there is no ExtensionType anymore.\r\n\r\nHopefully, I can create a separate PR with these changes soon.",
"Nice, thanks @Modexus ! ",
"I have run into some issues, notably I don't think `FixedShapeTensorArray` is completely supported by `pandas `and `polars`.\r\nWell it seems to work for `pandas `but one loses the actual shape of the extension.\r\n`Polars `just throws an error and this cannot be changed with `schema_overrides` as they are applied after.\r\n\r\nI have tried to somehow cast the `FixedShapeTensorArray` to something else like a nested FixedSizeLists, however I have not found a clean solution to do that.\r\nIf somebody has a clean solution to cast it to something such that the shape survives the roundtrip to `pandas`/`polars `and back, it may be possible.\r\n",
"Can we start using FixedShapeTensor or FixedSizeList even if pandas/polars don't support them fully yet ?\r\n\r\nWe would still get the benefit of optimized conversion to numpy"
] | 2024-04-05T17:00:46Z
| 2024-10-01T09:09:14Z
| null |
CONTRIBUTOR
| null | null | null |
PR for issue #6782.
Makes `cast_storage` of the `Image` class faster by removing the slow call to `.pylist`.
Instead directly convert each `ListArray` item to either `Array2DExtensionType` or `Array3DExtensionType`.
This also preserves the `dtype` removing the warning if the array is already `uint8`.
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Download only split data
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"Hi @capsabogdan! Unfortunately, it's hard to implement because quite often datasets data is being hosted in a single archive for all splits :( So we have to download the whole archive to split it into splits. This is the case for CommonVoice too. \r\n\r\nHowever, for cases when data is distributed in separate archives ащк different splits I suppose it can (and will) be implemented someday. \r\n\r\n\r\nBtw for quick check of the dataset you can use [streaming](https://huggingface.co/docs/datasets/stream):\r\n```python\r\ncv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)\r\ncv = iter(cv)\r\nprint(next(cv))\r\n\r\n>> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',\r\n>> 'path': None,\r\n>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',\r\n>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,\r\n>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),\r\n>> ...}\r\n\r\n```",
"thank you for the answer but am not sure if this will not be helpful, as we\nneed maybe just 10% of the datasets for some experiment\n\ncan we get just a portion of the dataset with stream?\n\n\nis there really no solution? :(\n\nAm Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n***@***.***>:\n\n> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n> hard to implement because quite often datasets data is being hosted in a\n> single archive for all splits :( So we have to download the whole archive\n> to split it into splits. This is the case for CommonVoice too.\n>\n> However, for cases when data is distributed in separate archives in\n> different splits I suppose it can be implemented someday.\n>\n> Btw for quick check of the dataset you can use streaming\n> <https://huggingface.co/docs/datasets/stream>:\n>\n> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n",
"maybe it would be nice if you guys ould do some sort of shard before\nloading the dataset, so users can download just chunks of data :)\n\nI think this would be very helpful\n\nAm Di., 15. Nov. 2022 um 19:24 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> thank you for the answer but am not sure if this will not be helpful, as\n> we need maybe just 10% of the datasets for some experiment\n>\n> can we get just a portion of the dataset with stream?\n>\n>\n> is there really no solution? :(\n>\n> Am Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n> ***@***.***>:\n>\n>> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n>> hard to implement because quite often datasets data is being hosted in a\n>> single archive for all splits :( So we have to download the whole archive\n>> to split it into splits. This is the case for CommonVoice too.\n>>\n>> However, for cases when data is distributed in separate archives in\n>> different splits I suppose it can be implemented someday.\n>>\n>> Btw for quick check of the dataset you can use streaming\n>> <https://huggingface.co/docs/datasets/stream>:\n>>\n>> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n>> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>>\n>> —\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n>> .\n>> You are receiving this because you were mentioned.Message ID:\n>> ***@***.***>\n>>\n>\n",
"+1 on this feature request - I am running into the same problem, where I only need the test set for a dataset that has a huge training set",
"Hey, I'm also interested in that as a feature. I'm having the same problem with Common Voice 13.0. The dataset is super big but I only want the test data to benchmark multilingual models, but I don't have much Terabytes to store all the dataset...",
"Consider this approach: Download and save individual audio files by streaming each split, then compile a CSV file that contains the file names and corresponding text.\r\n\r\n```python3\r\nimport os\r\nimport shutil\r\nfrom pathlib import Path\r\n\r\nimport datasets\r\nimport pandas as pd\r\nimport soundfile\r\nfrom datasets import Dataset, concatenate_datasets, load_dataset\r\n\r\n\r\ndataset = load_dataset(\"librispeech_asr\", 'clean', split=\"train.100\", streaming=True)\r\ndataset = iter(dataset)\r\n\r\ndownload_path = os.path.join(os.getcwd(), 'librispeech', 'clips')\r\ncsv_name = os.path.join(os.getcwd(), 'librispeech', 'clean_train_100.csv')\r\n\r\nrows = []\r\nfor i, row in enumerate(dataset):\r\n print(i)\r\n path = os.path.join(download_path, row['audio']['path'])\r\n soundfile.write(path, row['audio']['array'], row['audio']['sampling_rate'])\r\n\r\n del row['audio']\r\n rows.append(row)\r\n\r\ndf = pd.DataFrame(rows)\r\ndf.to_csv(csv_name, index=False, header=True)\r\n```",
"Faced this issue as well so wrote a short script that pulls a hub dataset, creates a small sample of it, and pushes the sample data to the hub as a new dataset.\n\n```python\ndef create_sample_dataset(full_dataset_name, sample_count=100, username=\"my-username\", cache_dir=\"./dataset\"):\n # Create a directory to save the sampled dataset\n os.makedirs(cache_dir, exist_ok=True)\n\n # Get the dataset name\n dataset_name = full_dataset_name.split(\"/\")[-1]\n dataset_name_sample = f\"{dataset_name}-sample-{sample_count}\"\n\n # Load the dataset\n dataset = datasets.load_dataset(full_dataset_name, cache_dir=cache_dir)\n\n # Sample 100 rows from the training split (or modify for other splits)\n train_sample = dataset[\"train\"].shuffle(seed=42).select(range(sample_count))\n test_sample = dataset[\"test\"].shuffle(seed=42).select(range(sample_count))\n\n # Push to hub\n train_sample.push_to_hub(dataset_name_sample, split=\"train\")\n print(\"INFO: Train split pushed to the hub successfully\")\n\n test_sample.push_to_hub(dataset_name_sample, split=\"test\")\n print(\"INFO: Test split pushed to the hub successfully\")\n```\n\nOnce sampled / pushed you have a smaller version of your dataset in the hub to pull from.\n\nThe [full gist is here](https://gist.github.com/neonwatty/2205277501928f5945726e9e90950ae9)."
] | 2022-11-15T10:15:54Z
| 2025-02-25T14:47:03Z
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### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
cache_dir="cache/path...",
use_auth_token=True,
download_config=DownloadConfig(delete_extracted='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')
)
### Motivation
efficiency improvement
### Your contribution
n/a
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I_kwDODunzps6Rw6Ad
| 7,086
|
load_dataset ignores cached datasets and tries to hit HF Hub, resulting in API rate limit errors
|
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### Describe the bug
I have been running lm-eval-harness a lot which has results in an API rate limit. This seems strange, since all of the data should be cached locally. I have in fact verified this.
### Steps to reproduce the bug
1. Be Me
2. Run `load_dataset("TAUR-Lab/MuSR")`
3. Hit rate limit error
4. Dataset is in .cache/huggingface/datasets
5. ???
### Expected behavior
We should not run into API rate limits if we have cached the dataset
### Environment info
datasets 2.16.0
python 3.10.4
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I_kwDODunzps52nn-B
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Cannot import datasets on google colab (python 3.10.12)
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[
"You are most likely using an outdated version of `datasets` in the notebook, which can be verified with the `!datasets-cli env` command. You can run `!pip install -U datasets` to update the installation.",
"okay, it works! thank you so much! 😄 "
] | 2023-11-13T08:14:43Z
| 2023-11-16T05:04:22Z
| 2023-11-16T05:04:21Z
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### Describe the bug
I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12)
I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements.
Please can anyone help me solve this problem. Thank you 😅
### Steps to reproduce the bug
Error:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from datasets import load_dataset
2
3 # Print all the available datasets
4 from huggingface_hub import list_datasets
5 print([dataset.id for dataset in list_datasets()])
6 frames
[/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated)
59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it
60 # from the wrapped function when updating __dict__
---> 61 wrapper.__wrapped__ = wrapped
62 # Return the wrapper so this can be used as a decorator via partial()
63 return wrapper
AttributeError: readonly attribute
```
### Expected behavior
Run success on Google Colab (free)
### Environment info
Windows 11 x64, Google Colab free
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I_kwDODunzps5TaDwS
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Bug loading `sentence-transformers/parallel-sentences`
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[
"tagging @nreimers ",
"The dataset is sadly not really compatible to be loaded with `load_dataset`. So far it is better to git clone it and to use the files directly.\r\n\r\nA data loading script would be needed to be added to this dataset. But this was too much overhead / not really intuitive how to create it.",
"Since the dataset is a bunch of TSVs we should not need a dataset script I think.\r\n\r\nBy default it tries to load all the TSVs at once, which fails here because they don't all have the same columns (pd.read_csv uses the first line as header by default). But those files have no header ! So, to properly load any TSV file in this repo, one has to pass `names=[...]` for pd.read_csv to know which column names to use.\r\n\r\nTo fix this situation, we can either do\r\n1. replace the TSVs by TSV with column names\r\n2. OR specify the pd.read_csv kwargs as YAML in the dataset card - and `datasets` would use that by default\r\n\r\nWDTY ?",
"There are more issues in the dataset.\r\nTo load OpenSubtitles I have to provide this (see `skiprows`):\r\n\r\n```python\r\ndf_os = pd.read_csv(\r\n \"./parallel-sentences/OpenSubtitles/OpenSubtitles-en-de-train.tsv.gz\", \r\n sep=\"\\t\", \r\n quoting=csv.QUOTE_NONE,\r\n header=None,\r\n names=[\"en\", \"de\"],\r\n skiprows=[540344, 9151700, 10040173, 10040199, 11314673, 11338258, 11869223, 12159297, 12251078, 12303334],\r\n)\r\n```",
"What's wrong with those lines exactly ?\r\nMaybe passing `error_bad_lines=False` (and maybe `warn_bad_lines=True`) can be helpful",
"> What's wrong with those lines exactly ? \r\n\r\nStuff like this: `ParserError: Error tokenizing data. C error: Expected 2 fields in line 540345, saw 3`\r\n\r\n",
"> Maybe passing error_bad_lines=False (and maybe warn_bad_lines=True) can be helpful\r\n\r\nYes. That would hide the issue but not solve it.",
"@nreimers WDYT about the two options mentioned above ?"
] | 2022-10-06T10:47:51Z
| 2022-10-11T10:00:48Z
| null |
CONTRIBUTOR
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## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("sentence-transformers/parallel-sentences")
```
raises this:
```
/home/phmay/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:697: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
/home/phmay/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:697: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [4], line 1
----> 1 dataset = load_dataset("sentence-transformers/parallel-sentences", split="train")
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/load.py:1693, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1692 # Download and prepare data
-> 1693 builder_instance.download_and_prepare(
1694 download_config=download_config,
1695 download_mode=download_mode,
1696 ignore_verifications=ignore_verifications,
1697 try_from_hf_gcs=try_from_hf_gcs,
1698 use_auth_token=use_auth_token,
1699 )
1701 # Build dataset for splits
1702 keep_in_memory = (
1703 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1704 )
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:807, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)
801 if not downloaded_from_gcs:
802 prepare_split_kwargs = {
803 "file_format": file_format,
804 "max_shard_size": max_shard_size,
805 **download_and_prepare_kwargs,
806 }
--> 807 self._download_and_prepare(
808 dl_manager=dl_manager,
809 verify_infos=verify_infos,
810 **prepare_split_kwargs,
811 **download_and_prepare_kwargs,
812 )
813 # Sync info
814 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:898, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
894 split_dict.add(split_generator.split_info)
896 try:
897 # Prepare split will record examples associated to the split
--> 898 self._prepare_split(split_generator, **prepare_split_kwargs)
899 except OSError as e:
900 raise OSError(
901 "Cannot find data file. "
902 + (self.manual_download_instructions or "")
903 + "\nOriginal error:\n"
904 + str(e)
905 ) from None
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:1513, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)
1506 shard_id += 1
1507 writer = writer_class(
1508 features=writer._features,
1509 path=fpath.replace("SSSSS", f"{shard_id:05d}"),
1510 storage_options=self._fs.storage_options,
1511 embed_local_files=embed_local_files,
1512 )
-> 1513 writer.write_table(table)
1514 finally:
1515 num_shards = shard_id + 1
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/arrow_writer.py:540, in ArrowWriter.write_table(self, pa_table, writer_batch_size)
538 if self.pa_writer is None:
539 self._build_writer(inferred_schema=pa_table.schema)
--> 540 pa_table = table_cast(pa_table, self._schema)
541 if self.embed_local_files:
542 pa_table = embed_table_storage(pa_table)
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/table.py:2044, in table_cast(table, schema)
2032 """Improved version of pa.Table.cast.
2033
2034 It supports casting to feature types stored in the schema metadata.
(...)
2041 table (:obj:`pyarrow.Table`): the casted table
2042 """
2043 if table.schema != schema:
-> 2044 return cast_table_to_schema(table, schema)
2045 elif table.schema.metadata != schema.metadata:
2046 return table.replace_schema_metadata(schema.metadata)
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/table.py:2005, in cast_table_to_schema(table, schema)
2003 features = Features.from_arrow_schema(schema)
2004 if sorted(table.column_names) != sorted(features):
-> 2005 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2006 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
2007 return pa.Table.from_arrays(arrays, schema=schema)
ValueError: Couldn't cast
Action taken on Parliament's resolutions: see Minutes: string
Následný postup na základě usnesení Parlamentu: viz zápis: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 742
to
{'Membership of Parliament: see Minutes': Value(dtype='string', id=None), 'Състав на Парламента: вж. протоколи': Value(dtype='string', id=None)}
because column names don't match
```
## Expected results
no error
## Actual results
error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: Python 3.9.13
- PyArrow version: pyarrow 9.0.0
- transformers 4.22.2
- datasets 2.5.2
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| 1,956,917,893
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PR_kwDODunzps5dhGpW
| 6,340
|
Release 2.14.5
|
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6340). All of your documentation changes will be reflected on that endpoint."
] | 2023-10-23T11:10:22Z
| 2023-10-23T14:20:46Z
| 2023-10-23T11:12:40Z
|
MEMBER
| null | null | null |
(wrong release number - I was continuing the 2.14 branch but 2.14.5 was released from `main`)
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| 2,467,788,212
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PR_kwDODunzps54dAhE
| 7,104
|
remove more script docs
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7104). 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) | 0.005343 / 0.011353 (-0.006010) | 0.003562 / 0.011008 (-0.007447) | 0.062785 / 0.038508 (0.024277) | 0.031459 / 0.023109 (0.008349) | 0.246497 / 0.275898 (-0.029401) | 0.268258 / 0.323480 (-0.055222) | 0.003201 / 0.007986 (-0.004785) | 0.004153 / 0.004328 (-0.000175) | 0.049003 / 0.004250 (0.044753) | 0.042780 / 0.037052 (0.005728) | 0.263857 / 0.258489 (0.005368) | 0.278578 / 0.293841 (-0.015263) | 0.030357 / 0.128546 (-0.098190) | 0.012341 / 0.075646 (-0.063305) | 0.206010 / 0.419271 (-0.213262) | 0.036244 / 0.043533 (-0.007289) | 0.245799 / 0.255139 (-0.009340) | 0.265467 / 0.283200 (-0.017733) | 0.019473 / 0.141683 (-0.122210) | 1.147913 / 1.452155 (-0.304242) | 1.209968 / 1.492716 (-0.282749) |\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) | 0.099393 / 0.018006 (0.081387) | 0.300898 / 0.000490 (0.300408) | 0.000258 / 0.000200 (0.000058) | 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) | 0.018888 / 0.037411 (-0.018523) | 0.062452 / 0.014526 (0.047926) | 0.073799 / 0.176557 (-0.102757) | 0.121297 / 0.737135 (-0.615839) | 0.074855 / 0.296338 (-0.221484) |\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) | 0.283969 / 0.215209 (0.068760) | 2.808820 / 2.077655 (0.731165) | 1.446106 / 1.504120 (-0.058014) | 1.321622 / 1.541195 (-0.219573) | 1.348317 / 1.468490 (-0.120173) | 0.738369 / 4.584777 (-3.846408) | 2.349825 / 3.745712 (-1.395887) | 2.913964 / 5.269862 (-2.355897) | 1.870585 / 4.565676 (-2.695092) | 0.080141 / 0.424275 (-0.344134) | 0.005174 / 0.007607 (-0.002433) | 0.335977 / 0.226044 (0.109933) | 3.356267 / 2.268929 (1.087338) | 1.811149 / 55.444624 (-53.633475) | 1.510685 / 6.876477 (-5.365792) | 1.524960 / 2.142072 (-0.617112) | 0.803900 / 4.805227 (-4.001328) | 0.138294 / 6.500664 (-6.362370) | 0.042241 / 0.075469 (-0.033229) |\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) | 0.975597 / 1.841788 (-0.866191) | 11.395109 / 8.074308 (3.320801) | 9.837724 / 10.191392 (-0.353668) | 0.141474 / 0.680424 (-0.538950) | 0.015075 / 0.534201 (-0.519126) | 0.304285 / 0.579283 (-0.274998) | 0.267845 / 0.434364 (-0.166519) | 0.342808 / 0.540337 (-0.197529) | 0.434299 / 1.386936 (-0.952637) |\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) | 0.005612 / 0.011353 (-0.005741) | 0.003808 / 0.011008 (-0.007201) | 0.050533 / 0.038508 (0.012024) | 0.032635 / 0.023109 (0.009526) | 0.265522 / 0.275898 (-0.010376) | 0.289763 / 0.323480 (-0.033716) | 0.004395 / 0.007986 (-0.003590) | 0.002868 / 0.004328 (-0.001460) | 0.048443 / 0.004250 (0.044193) | 0.040047 / 0.037052 (0.002995) | 0.279013 / 0.258489 (0.020524) | 0.314499 / 0.293841 (0.020658) | 0.032321 / 0.128546 (-0.096225) | 0.011902 / 0.075646 (-0.063744) | 0.059827 / 0.419271 (-0.359445) | 0.034388 / 0.043533 (-0.009145) | 0.270660 / 0.255139 (0.015521) | 0.290776 / 0.283200 (0.007576) | 0.017875 / 0.141683 (-0.123808) | 1.188085 / 1.452155 (-0.264070) | 1.221384 / 1.492716 (-0.271332) |\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) | 0.095619 / 0.018006 (0.077613) | 0.305331 / 0.000490 (0.304841) | 0.000217 / 0.000200 (0.000018) | 0.000049 / 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) | 0.022481 / 0.037411 (-0.014930) | 0.076957 / 0.014526 (0.062431) | 0.087830 / 0.176557 (-0.088726) | 0.128290 / 0.737135 (-0.608845) | 0.090565 / 0.296338 (-0.205774) |\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) | 0.291861 / 0.215209 (0.076652) | 2.869776 / 2.077655 (0.792121) | 1.575114 / 1.504120 (0.070994) | 1.449873 / 1.541195 (-0.091322) | 1.450333 / 1.468490 (-0.018158) | 0.723319 / 4.584777 (-3.861458) | 0.972603 / 3.745712 (-2.773109) | 2.940909 / 5.269862 (-2.328953) | 1.889664 / 4.565676 (-2.676012) | 0.078654 / 0.424275 (-0.345621) | 0.005197 / 0.007607 (-0.002410) | 0.344380 / 0.226044 (0.118336) | 3.387509 / 2.268929 (1.118580) | 1.981590 / 55.444624 (-53.463034) | 1.643214 / 6.876477 (-5.233263) | 1.640435 / 2.142072 (-0.501638) | 0.802037 / 4.805227 (-4.003191) | 0.133016 / 6.500664 (-6.367648) | 0.040861 / 0.075469 (-0.034608) |\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) | 1.026372 / 1.841788 (-0.815416) | 11.959931 / 8.074308 (3.885623) | 10.122523 / 10.191392 (-0.068869) | 0.144443 / 0.680424 (-0.535981) | 0.015629 / 0.534201 (-0.518572) | 0.304802 / 0.579283 (-0.274481) | 0.120538 / 0.434364 (-0.313826) | 0.343394 / 0.540337 (-0.196943) | 0.437544 / 1.386936 (-0.949392) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84832c07f614e5f51a762166b2fa9ac27e988173 \"CML watermark\")\n"
] | 2024-08-15T10:13:26Z
| 2024-08-15T10:24:13Z
| 2024-08-15T10:18:25Z
|
MEMBER
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I_kwDODunzps5tjV06
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Reinforcement Learning and Robotics are not task categories in HF datasets metadata
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[] | 2023-08-05T23:59:42Z
| 2023-08-18T12:28:42Z
| 2023-08-18T12:28:42Z
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### Describe the bug
In https://huggingface.co/models there are task categories for RL and robotics but none in https://huggingface.co/datasets
Our lab is currently moving our datasets over to hugging face and would like to be able to add those 2 tags
Moreover we see some older datasets that do have that tag, but we can't seem to add it ourselves.
### Steps to reproduce the bug
1. Create a new dataset on Hugging face
2. Try to type reinforcemement-learning or robotics into the tasks categories, it does not allow you to commit
### Expected behavior
Expected to be able to add RL and robotics as task categories as some previous datasets have these tags
### Environment info
N/A
|
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PR_kwDODunzps5nYUIf
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Improve error message for gated datasets on load
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[
"Thank you ! Should we also add the link to the dataset page ?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6684). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"> Thank you ! Should we also add the link to the dataset page ?\r\n\r\nGood idea! Done in https://github.com/huggingface/datasets/pull/6684/commits/4ab55210dca1815b6c2f23901598bfb29fc92a47",
"Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n\r\nApparently your new message is also shown for datasets that don't exist, which is maybe not ideal",
"Ah let me take a look!",
"> Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n> \r\n> Apparently your new message is also shown for datasets that don't exist, which is maybe not ideal\r\n\r\nFixed by reverting the error message root + added a small clarifying part",
"<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) | 0.005634 / 0.011353 (-0.005719) | 0.003786 / 0.011008 (-0.007222) | 0.064245 / 0.038508 (0.025737) | 0.031228 / 0.023109 (0.008119) | 0.248162 / 0.275898 (-0.027736) | 0.273454 / 0.323480 (-0.050026) | 0.003176 / 0.007986 (-0.004809) | 0.002814 / 0.004328 (-0.001515) | 0.049234 / 0.004250 (0.044984) | 0.046075 / 0.037052 (0.009023) | 0.262410 / 0.258489 (0.003921) | 0.290597 / 0.293841 (-0.003244) | 0.028545 / 0.128546 (-0.100001) | 0.010881 / 0.075646 (-0.064766) | 0.212098 / 0.419271 (-0.207173) | 0.036406 / 0.043533 (-0.007127) | 0.244571 / 0.255139 (-0.010568) | 0.269537 / 0.283200 (-0.013663) | 0.019574 / 0.141683 (-0.122109) | 1.120369 / 1.452155 (-0.331785) | 1.170188 / 1.492716 (-0.322529) |\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) | 0.108088 / 0.018006 (0.090082) | 0.299836 / 0.000490 (0.299346) | 0.000204 / 0.000200 (0.000004) | 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) | 0.020881 / 0.037411 (-0.016531) | 0.065290 / 0.014526 (0.050764) | 0.074283 / 0.176557 (-0.102274) | 0.122189 / 0.737135 (-0.614947) | 0.077772 / 0.296338 (-0.218566) |\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) | 0.278329 / 0.215209 (0.063120) | 2.709885 / 2.077655 (0.632230) | 1.428824 / 1.504120 (-0.075296) | 1.314338 / 1.541195 (-0.226857) | 1.349445 / 1.468490 (-0.119045) | 0.571863 / 4.584777 (-4.012914) | 2.358306 / 3.745712 (-1.387407) | 2.873498 / 5.269862 (-2.396364) | 1.779897 / 4.565676 (-2.785779) | 0.062828 / 0.424275 (-0.361447) | 0.005416 / 0.007607 (-0.002191) | 0.337645 / 0.226044 (0.111601) | 3.328868 / 2.268929 (1.059940) | 1.793387 / 55.444624 (-53.651238) | 1.539201 / 6.876477 (-5.337276) | 1.589552 / 2.142072 (-0.552520) | 0.645454 / 4.805227 (-4.159773) | 0.116966 / 6.500664 (-6.383698) | 0.043339 / 0.075469 (-0.032130) |\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) | 0.995743 / 1.841788 (-0.846045) | 12.096551 / 8.074308 (4.022243) | 10.214299 / 10.191392 (0.022907) | 0.133025 / 0.680424 (-0.547399) | 0.014393 / 0.534201 (-0.519808) | 0.289018 / 0.579283 (-0.290266) | 0.267879 / 0.434364 (-0.166485) | 0.324362 / 0.540337 (-0.215976) | 0.425596 / 1.386936 (-0.961340) |\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) | 0.005739 / 0.011353 (-0.005614) | 0.003992 / 0.011008 (-0.007017) | 0.051362 / 0.038508 (0.012854) | 0.031707 / 0.023109 (0.008598) | 0.274807 / 0.275898 (-0.001091) | 0.298897 / 0.323480 (-0.024583) | 0.004363 / 0.007986 (-0.003622) | 0.002862 / 0.004328 (-0.001466) | 0.050462 / 0.004250 (0.046212) | 0.048158 / 0.037052 (0.011106) | 0.282759 / 0.258489 (0.024270) | 0.317766 / 0.293841 (0.023926) | 0.060245 / 0.128546 (-0.068301) | 0.011279 / 0.075646 (-0.064367) | 0.061175 / 0.419271 (-0.358097) | 0.035876 / 0.043533 (-0.007656) | 0.273963 / 0.255139 (0.018824) | 0.288788 / 0.283200 (0.005589) | 0.019690 / 0.141683 (-0.121992) | 1.167074 / 1.452155 (-0.285080) | 1.206344 / 1.492716 (-0.286372) |\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) | 0.091211 / 0.018006 (0.073205) | 0.299295 / 0.000490 (0.298805) | 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) | 0.022718 / 0.037411 (-0.014693) | 0.079483 / 0.014526 (0.064957) | 0.087437 / 0.176557 (-0.089120) | 0.126977 / 0.737135 (-0.610159) | 0.089678 / 0.296338 (-0.206660) |\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) | 0.294719 / 0.215209 (0.079510) | 2.864505 / 2.077655 (0.786851) | 1.583993 / 1.504120 (0.079873) | 1.455079 / 1.541195 (-0.086115) | 1.504080 / 1.468490 (0.035590) | 0.569040 / 4.584777 (-4.015737) | 2.423472 / 3.745712 (-1.322240) | 2.742848 / 5.269862 (-2.527014) | 1.785244 / 4.565676 (-2.780432) | 0.062655 / 0.424275 (-0.361620) | 0.005027 / 0.007607 (-0.002580) | 0.343863 / 0.226044 (0.117818) | 3.376286 / 2.268929 (1.107358) | 1.933846 / 55.444624 (-53.510779) | 1.667316 / 6.876477 (-5.209161) | 1.815621 / 2.142072 (-0.326451) | 0.639378 / 4.805227 (-4.165850) | 0.116514 / 6.500664 (-6.384150) | 0.042191 / 0.075469 (-0.033279) |\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) | 1.007103 / 1.841788 (-0.834685) | 12.791193 / 8.074308 (4.716885) | 10.870575 / 10.191392 (0.679183) | 0.131040 / 0.680424 (-0.549384) | 0.016510 / 0.534201 (-0.517691) | 0.288372 / 0.579283 (-0.290911) | 0.275574 / 0.434364 (-0.158790) | 0.327801 / 0.540337 (-0.212536) | 0.415942 / 1.386936 (-0.970994) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b775390900132834e5edf487f5cbbf1299af1d88 \"CML watermark\")\n"
] | 2024-02-20T10:51:27Z
| 2024-02-20T15:40:52Z
| 2024-02-20T15:33:56Z
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MEMBER
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Internal Slack discussion: https://huggingface.slack.com/archives/C02V51Q3800/p1708424971135029
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PR_kwDODunzps5mwgKP
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|
fix: show correct package name to install biopython
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6662). 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) | 0.005135 / 0.011353 (-0.006218) | 0.003666 / 0.011008 (-0.007342) | 0.062660 / 0.038508 (0.024152) | 0.028656 / 0.023109 (0.005546) | 0.249601 / 0.275898 (-0.026297) | 0.265745 / 0.323480 (-0.057735) | 0.002935 / 0.007986 (-0.005051) | 0.002606 / 0.004328 (-0.001723) | 0.048774 / 0.004250 (0.044523) | 0.043643 / 0.037052 (0.006591) | 0.263114 / 0.258489 (0.004625) | 0.284596 / 0.293841 (-0.009245) | 0.027818 / 0.128546 (-0.100728) | 0.010726 / 0.075646 (-0.064921) | 0.205900 / 0.419271 (-0.213371) | 0.035646 / 0.043533 (-0.007887) | 0.245599 / 0.255139 (-0.009540) | 0.267706 / 0.283200 (-0.015493) | 0.018441 / 0.141683 (-0.123242) | 1.143365 / 1.452155 (-0.308790) | 1.191823 / 1.492716 (-0.300893) |\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) | 0.089703 / 0.018006 (0.071696) | 0.298073 / 0.000490 (0.297583) | 0.000209 / 0.000200 (0.000009) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018068 / 0.037411 (-0.019343) | 0.061416 / 0.014526 (0.046890) | 0.075989 / 0.176557 (-0.100567) | 0.120765 / 0.737135 (-0.616370) | 0.075476 / 0.296338 (-0.220863) |\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) | 0.284043 / 0.215209 (0.068834) | 2.770282 / 2.077655 (0.692627) | 1.473040 / 1.504120 (-0.031080) | 1.349064 / 1.541195 (-0.192131) | 1.362783 / 1.468490 (-0.105708) | 0.560765 / 4.584777 (-4.024012) | 2.357731 / 3.745712 (-1.387981) | 2.745771 / 5.269862 (-2.524090) | 1.726764 / 4.565676 (-2.838913) | 0.061212 / 0.424275 (-0.363063) | 0.004902 / 0.007607 (-0.002705) | 0.336963 / 0.226044 (0.110919) | 3.324519 / 2.268929 (1.055591) | 1.825826 / 55.444624 (-53.618798) | 1.548811 / 6.876477 (-5.327666) | 1.570618 / 2.142072 (-0.571454) | 0.642411 / 4.805227 (-4.162816) | 0.116068 / 6.500664 (-6.384596) | 0.042433 / 0.075469 (-0.033036) |\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) | 0.988402 / 1.841788 (-0.853386) | 11.509601 / 8.074308 (3.435293) | 9.555338 / 10.191392 (-0.636054) | 0.138728 / 0.680424 (-0.541696) | 0.014107 / 0.534201 (-0.520094) | 0.285465 / 0.579283 (-0.293818) | 0.263086 / 0.434364 (-0.171278) | 0.327469 / 0.540337 (-0.212869) | 0.444799 / 1.386936 (-0.942137) |\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) | 0.005359 / 0.011353 (-0.005993) | 0.003605 / 0.011008 (-0.007403) | 0.049734 / 0.038508 (0.011226) | 0.029792 / 0.023109 (0.006683) | 0.276384 / 0.275898 (0.000486) | 0.297915 / 0.323480 (-0.025564) | 0.004949 / 0.007986 (-0.003036) | 0.002713 / 0.004328 (-0.001616) | 0.049499 / 0.004250 (0.045249) | 0.044969 / 0.037052 (0.007917) | 0.284558 / 0.258489 (0.026069) | 0.315170 / 0.293841 (0.021329) | 0.029457 / 0.128546 (-0.099089) | 0.010573 / 0.075646 (-0.065073) | 0.058191 / 0.419271 (-0.361080) | 0.051461 / 0.043533 (0.007928) | 0.270744 / 0.255139 (0.015605) | 0.291664 / 0.283200 (0.008465) | 0.018607 / 0.141683 (-0.123076) | 1.158799 / 1.452155 (-0.293355) | 1.210509 / 1.492716 (-0.282208) |\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) | 0.090277 / 0.018006 (0.072270) | 0.298748 / 0.000490 (0.298258) | 0.000228 / 0.000200 (0.000028) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021850 / 0.037411 (-0.015561) | 0.075433 / 0.014526 (0.060907) | 0.087171 / 0.176557 (-0.089386) | 0.125828 / 0.737135 (-0.611308) | 0.090343 / 0.296338 (-0.205996) |\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) | 0.297267 / 0.215209 (0.082058) | 2.865234 / 2.077655 (0.787579) | 1.595024 / 1.504120 (0.090904) | 1.476100 / 1.541195 (-0.065094) | 1.494896 / 1.468490 (0.026406) | 0.569086 / 4.584777 (-4.015691) | 2.401976 / 3.745712 (-1.343736) | 2.676091 / 5.269862 (-2.593771) | 1.742087 / 4.565676 (-2.823590) | 0.065161 / 0.424275 (-0.359114) | 0.005006 / 0.007607 (-0.002602) | 0.342302 / 0.226044 (0.116257) | 3.450571 / 2.268929 (1.181643) | 1.928754 / 55.444624 (-53.515871) | 1.672823 / 6.876477 (-5.203653) | 1.798830 / 2.142072 (-0.343243) | 0.648730 / 4.805227 (-4.156498) | 0.116433 / 6.500664 (-6.384231) | 0.040683 / 0.075469 (-0.034786) |\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) | 1.006158 / 1.841788 (-0.835630) | 12.200093 / 8.074308 (4.125785) | 10.180691 / 10.191392 (-0.010701) | 0.146620 / 0.680424 (-0.533804) | 0.015621 / 0.534201 (-0.518580) | 0.287956 / 0.579283 (-0.291327) | 0.277231 / 0.434364 (-0.157133) | 0.323815 / 0.540337 (-0.216522) | 0.429655 / 1.386936 (-0.957281) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#273e16f9a21d6eaba1fd40fbdf0c05e66642c5a7 \"CML watermark\")\n"
] | 2024-02-13T14:15:04Z
| 2024-03-01T17:49:48Z
| 2024-03-01T17:43:39Z
|
CONTRIBUTOR
| null | null | null |
When you try to download a dataset that uses [biopython](https://github.com/biopython/biopython), like `load_dataset("InstaDeepAI/multi_species_genomes")`, you get the error:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("InstaDeepAI/multi_species_genomes")
/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py:1454: FutureWarning: The repository for InstaDeepAI/multi_species_genomes contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/InstaDeepAI/multi_species_genomes
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
warnings.warn(
Downloading builder script: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.51k/7.51k [00:00<00:00, 7.67MB/s]
Downloading readme: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.2k/17.2k [00:00<00:00, 11.0MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2548, in load_dataset
builder_instance = load_dataset_builder(
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2220, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1871, in dataset_module_factory
raise e1 from None
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1844, in dataset_module_factory
).get_module()
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1466, in get_module
local_imports = _download_additional_modules(
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 346, in _download_additional_modules
raise ImportError(
ImportError: To be able to use InstaDeepAI/multi_species_genomes, you need to install the following dependency: Bio.
Please install it using 'pip install Bio' for instance.
>>>
```
`Bio` comes from the `biopython` package that can be installed with `pip install biopython`, not with `pip install Bio` as suggested.
This PR adds special logic to show the correct package name in the error message of ` _download_additional_modules`, similarly as is done for `sklearn` / `scikit-learn` already.
There are more packages where importable module name differs from the PyPI package name, so this could be made more generic, like:
```
# Mapping of importable module names to their PyPI package names
package_map = {
"sklearn": "scikit-learn",
"Bio": "biopython",
"PIL": "Pillow",
"bs4": "beautifulsoup4"
}
for module_name, pypi_name in package_map.items():
if module_name in needs_to_be_installed.keys():
needs_to_be_installed[module_name] = pypi_name
```
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https://github.com/huggingface/datasets/pull/6696
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PR_kwDODunzps5n6ipH
| 6,696
|
Make JSON builder support an array of strings
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6696). 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) | 0.005057 / 0.011353 (-0.006296) | 0.003665 / 0.011008 (-0.007343) | 0.063217 / 0.038508 (0.024709) | 0.028789 / 0.023109 (0.005679) | 0.233597 / 0.275898 (-0.042301) | 0.254792 / 0.323480 (-0.068687) | 0.003065 / 0.007986 (-0.004921) | 0.002686 / 0.004328 (-0.001642) | 0.050182 / 0.004250 (0.045932) | 0.042204 / 0.037052 (0.005151) | 0.254262 / 0.258489 (-0.004227) | 0.277099 / 0.293841 (-0.016742) | 0.027564 / 0.128546 (-0.100982) | 0.010768 / 0.075646 (-0.064878) | 0.207302 / 0.419271 (-0.211969) | 0.035737 / 0.043533 (-0.007796) | 0.242388 / 0.255139 (-0.012751) | 0.259833 / 0.283200 (-0.023367) | 0.019833 / 0.141683 (-0.121850) | 1.135928 / 1.452155 (-0.316227) | 1.162851 / 1.492716 (-0.329865) |\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) | 0.089209 / 0.018006 (0.071202) | 0.300493 / 0.000490 (0.300003) | 0.000216 / 0.000200 (0.000016) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017968 / 0.037411 (-0.019444) | 0.061773 / 0.014526 (0.047247) | 0.073835 / 0.176557 (-0.102722) | 0.118592 / 0.737135 (-0.618544) | 0.073606 / 0.296338 (-0.222732) |\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) | 0.287858 / 0.215209 (0.072649) | 2.822917 / 2.077655 (0.745262) | 1.485259 / 1.504120 (-0.018861) | 1.355922 / 1.541195 (-0.185273) | 1.364008 / 1.468490 (-0.104482) | 0.557713 / 4.584777 (-4.027064) | 2.378972 / 3.745712 (-1.366741) | 2.737218 / 5.269862 (-2.532643) | 1.718317 / 4.565676 (-2.847359) | 0.062362 / 0.424275 (-0.361913) | 0.004992 / 0.007607 (-0.002615) | 0.350765 / 0.226044 (0.124721) | 3.387579 / 2.268929 (1.118650) | 1.860408 / 55.444624 (-53.584216) | 1.569355 / 6.876477 (-5.307122) | 1.593013 / 2.142072 (-0.549059) | 0.639325 / 4.805227 (-4.165902) | 0.121769 / 6.500664 (-6.378895) | 0.042148 / 0.075469 (-0.033322) |\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) | 0.990594 / 1.841788 (-0.851194) | 11.460904 / 8.074308 (3.386596) | 9.438691 / 10.191392 (-0.752701) | 0.141884 / 0.680424 (-0.538540) | 0.013725 / 0.534201 (-0.520476) | 0.288847 / 0.579283 (-0.290436) | 0.278815 / 0.434364 (-0.155549) | 0.337108 / 0.540337 (-0.203229) | 0.441659 / 1.386936 (-0.945277) |\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) | 0.003734 / 0.011008 (-0.007274) | 0.049365 / 0.038508 (0.010857) | 0.030483 / 0.023109 (0.007373) | 0.275085 / 0.275898 (-0.000813) | 0.296004 / 0.323480 (-0.027475) | 0.004964 / 0.007986 (-0.003022) | 0.002542 / 0.004328 (-0.001787) | 0.048734 / 0.004250 (0.044483) | 0.044098 / 0.037052 (0.007046) | 0.292517 / 0.258489 (0.034028) | 0.319992 / 0.293841 (0.026151) | 0.029552 / 0.128546 (-0.098994) | 0.010669 / 0.075646 (-0.064977) | 0.058887 / 0.419271 (-0.360385) | 0.051163 / 0.043533 (0.007630) | 0.277266 / 0.255139 (0.022127) | 0.295347 / 0.283200 (0.012147) | 0.018403 / 0.141683 (-0.123280) | 1.151979 / 1.452155 (-0.300176) | 1.204583 / 1.492716 (-0.288134) |\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) | 0.091157 / 0.018006 (0.073151) | 0.300109 / 0.000490 (0.299619) | 0.000211 / 0.000200 (0.000011) | 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) | 0.021521 / 0.037411 (-0.015890) | 0.074954 / 0.014526 (0.060428) | 0.087010 / 0.176557 (-0.089546) | 0.125853 / 0.737135 (-0.611282) | 0.087877 / 0.296338 (-0.208461) |\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) | 0.297890 / 0.215209 (0.082681) | 2.912159 / 2.077655 (0.834504) | 1.619311 / 1.504120 (0.115192) | 1.501726 / 1.541195 (-0.039468) | 1.494143 / 1.468490 (0.025652) | 0.566744 / 4.584777 (-4.018033) | 2.497594 / 3.745712 (-1.248118) | 2.631403 / 5.269862 (-2.638459) | 1.727896 / 4.565676 (-2.837780) | 0.065937 / 0.424275 (-0.358339) | 0.005023 / 0.007607 (-0.002585) | 0.345747 / 0.226044 (0.119702) | 3.417615 / 2.268929 (1.148686) | 1.949970 / 55.444624 (-53.494654) | 1.680019 / 6.876477 (-5.196457) | 1.789879 / 2.142072 (-0.352193) | 0.648053 / 4.805227 (-4.157174) | 0.117408 / 6.500664 (-6.383256) | 0.040681 / 0.075469 (-0.034788) |\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) | 1.012535 / 1.841788 (-0.829252) | 11.935819 / 8.074308 (3.861511) | 10.241452 / 10.191392 (0.050060) | 0.130956 / 0.680424 (-0.549468) | 0.015396 / 0.534201 (-0.518805) | 0.289166 / 0.579283 (-0.290117) | 0.274149 / 0.434364 (-0.160215) | 0.325844 / 0.540337 (-0.214493) | 0.424919 / 1.386936 (-0.962017) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb834d9c63ab8cb14725ae8e4fc2da8672892a6d \"CML watermark\")\n"
] | 2024-02-26T13:18:31Z
| 2024-02-28T06:45:23Z
| 2024-02-28T06:39:12Z
|
MEMBER
| null | null | null |
Support JSON file with an array of strings.
Fix #6695.
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I_kwDODunzps56kBqT
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IndexError: Invalid key: 47682 is out of bounds for size 0 while using PEFT
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[
"@sabman @pvl @kashif @vigsterkr ",
"This is surely the same issue as https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/25 that comes from the `transformers` `Trainer`. You should add `remove_unused_columns=False` to `TrainingArguments`\r\n\r\nAlso check your logs: the `Trainer` should log the length of your dataset before training starts and it surely showed length=0.",
"the same error \r\nIndexError: Invalid key: 22330 is out of bounds for size 0"
] | 2023-12-26T10:14:33Z
| 2024-02-05T08:42:31Z
| null |
NONE
| null | null |
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### Describe the bug
I am trying to fine-tune the t5 model on the paraphrasing task. While running the same code without-
model = get_peft_model(model, config)
the model trains without any issues. However, using the model returned from get_peft_model raises the following error due to datasets-
IndexError: Invalid key: 47682 is out of bounds for size 0.
I had raised this in https://github.com/huggingface/peft/issues/1299#issue-2056173386 and they suggested that I raise it here.
Here is the complete error-
IndexError Traceback (most recent call last)
in <cell line: 1>()
----> 1 trainer.train()
11 frames
[/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1553 hf_hub_utils.enable_progress_bars()
1554 else:
-> 1555 return inner_training_loop(
1556 args=args,
1557 resume_from_checkpoint=resume_from_checkpoint,
[/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
1836
1837 step = -1
-> 1838 for step, inputs in enumerate(epoch_iterator):
1839 total_batched_samples += 1
1840 if rng_to_sync:
[/usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py](https://localhost:8080/#) in iter(self)
446 # We iterate one batch ahead to check when we are at the end
447 try:
--> 448 current_batch = next(dataloader_iter)
449 except StopIteration:
450 yield
[/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in next(self)
628 # TODO(https://github.com/pytorch/pytorch/issues/76750)
629 self._reset() # type: ignore[call-arg]
--> 630 data = self._next_data()
631 self._num_yielded += 1
632 if self._dataset_kind == _DatasetKind.Iterable and \
[/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in _next_data(self)
672 def _next_data(self):
673 index = self._next_index() # may raise StopIteration
--> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
675 if self._pin_memory:
676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
[/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py](https://localhost:8080/#) in fetch(self, possibly_batched_index)
47 if self.auto_collation:
48 if hasattr(self.dataset, "getitems") and self.dataset.getitems:
---> 49 data = self.dataset.getitems(possibly_batched_index)
50 else:
51 data = [self.dataset[idx] for idx in possibly_batched_index]
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in getitems(self, keys)
2802 def getitems(self, keys: List) -> List:
2803 """Can be used to get a batch using a list of integers indices."""
-> 2804 batch = self.getitem(keys)
2805 n_examples = len(batch[next(iter(batch))])
2806 return [{col: array[i] for col, array in batch.items()} for i in range(n_examples)]
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in getitem(self, key)
2798 def getitem(self, key): # noqa: F811
2799 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2800 return self._getitem(key)
2801
2802 def getitems(self, keys: List) -> List:
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _getitem(self, key, **kwargs)
2782 format_kwargs = format_kwargs if format_kwargs is not None else {}
2783 formatter = get_formatter(format_type, features=self._info.features, **format_kwargs)
-> 2784 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
2785 formatted_output = format_table(
2786 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in query_table(table, key, indices)
581 else:
582 size = indices.num_rows if indices is not None else table.num_rows
--> 583 _check_valid_index_key(key, size)
584 # Query the main table
585 if indices is None:
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size)
534 elif isinstance(key, Iterable):
535 if len(key) > 0:
--> 536 _check_valid_index_key(int(max(key)), size=size)
537 _check_valid_index_key(int(min(key)), size=size)
538 else:
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size)
524 if isinstance(key, int):
525 if (key < 0 and key + size < 0) or (key >= size):
--> 526 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
527 return
528 elif isinstance(key, slice):
IndexError: Invalid key: 47682 is out of bounds for size 0
### Steps to reproduce the bug
device = "cuda:0" if torch.cuda.is_available() else "cpu"
#defining model name for tokenizer and model loading
model_name= "t5-small"
#loading the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
def preprocess_function(data, tokenizer):
inputs = [f"Paraphrase this sentence: {doc}" for doc in data["text"]]
model_inputs = tokenizer(inputs, max_length=150, truncation=True)
labels = [ast.literal_eval(i)[0] for i in data['paraphrases']]
labels = tokenizer(labels, max_length=150, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
train_dataset = load_dataset("humarin/chatgpt-paraphrases", split="train").shuffle(seed=42).select(range(50000))
val_dataset = load_dataset("humarin/chatgpt-paraphrases", split="train").shuffle(seed=42).select(range(50000,55000))
tokenized_train = train_dataset.map(lambda batch: preprocess_function(batch, tokenizer), batched=True)
tokenized_val = val_dataset.map(lambda batch: preprocess_function(batch, tokenizer), batched=True)
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
config = LoraConfig(
r=16, #attention heads
lora_alpha=32, #alpha scaling
lora_dropout=0.05,
bias="none",
task_type="Seq2Seq"
)
#loading the model
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
model = get_peft_model(model, config)
print_trainable_parameters(model)
#loading the data collator
data_collator = DataCollatorForSeq2Seq(
tokenizer=tokenizer,
model=model,
label_pad_token_id=-100,
padding="longest"
)
#defining the training arguments
training_args = Seq2SeqTrainingArguments(
output_dir=os.getcwd(),
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
weight_decay=1e-3,
save_total_limit=3,
load_best_model_at_end=True,
num_train_epochs=1,
predict_with_generate=True
)
def compute_metric_with_extra(tokenizer):
def compute_metrics(eval_preds):
metric = evaluate.load('rouge')
preds, labels = eval_preds
# decode preds and labels
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# rougeLSum expects newline after each sentence
decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
return result
return compute_metrics
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_val,
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics= compute_metric_with_extra(tokenizer)
)
trainer.train()
### Expected behavior
I would want the trainer to train normally as it was before I used-
model = get_peft_model(model, config)
### Environment info
datasets version- 2.16.0
peft version- 0.7.1
transformers version- 4.35.2
accelerate version- 0.25.0
python- 3.10.12
enviroment- google colab
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Bug: Type Mismatch in Dataset Mapping
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"By the way, following code is working. This show the inconsistentcy.\r\n```python\r\nfrom datasets import Dataset\r\n\r\n# Original data\r\ndata = {\r\n 'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],\r\n 'label': [0, 1, 0, 1, 1, 0]\r\n}\r\n\r\n# Creating a Dataset object\r\ndataset = Dataset.from_dict(data)\r\n\r\n# Mapping function to convert label to string\r\ndef add_one(example):\r\n example['label'] += 1\r\n return example\r\n\r\n# Applying the mapping function\r\ndataset = dataset.map(add_one)\r\n\r\n# Iterating over the dataset to show results\r\nfor item in dataset:\r\n print(item)\r\n print(type(item['label']))\r\n```",
"Hello, thanks for submitting an issue.\r\n\r\nFWIU, the issue is that `datasets` tries to limit casting [ref](https://github.com/huggingface/datasets/blob/ca58154bba185c1916ca5eea4e33b27258642044/src/datasets/arrow_writer.py#L526) and as such will try to convert your strings back to int to preserve the `Features`. \r\n\r\nA quick solution would be to use `dataset.cast` or to supply `features` when calling `dataset.map`.\r\n\r\n\r\n```python\r\n# using Dataset.cast\r\ndataset = dataset.cast_column('label', Value('string'))\r\n\r\n# Alternative, supply features\r\ndataset = dataset.map(add_one, features=Features({**dataset.features, 'label': Value('string')}))\r\n```",
"LGTM! Thanks for the review.\r\n\r\nJust to clarify, is this intended behavior, or is it something that might be addressed in a future update?\r\nI'll leave this issue open until it's fixed if this is not the intended behavior."
] | 2024-09-03T16:37:01Z
| 2024-09-05T14:09:05Z
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# Issue: Type Mismatch in Dataset Mapping
## Description
There is an issue with the `map` function in the `datasets` library where the mapped output does not reflect the expected type change. After applying a mapping function to convert an integer label to a string, the resulting type remains an integer instead of a string.
## Reproduction Code
Below is a Python script that demonstrates the problem:
```python
from datasets import Dataset
# Original data
data = {
'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],
'label': [0, 1, 0, 1, 1, 0]
}
# Creating a Dataset object
dataset = Dataset.from_dict(data)
# Mapping function to convert label to string
def add_one(example):
example['label'] = str(example['label'])
return example
# Applying the mapping function
dataset = dataset.map(add_one)
# Iterating over the dataset to show results
for item in dataset:
print(item)
print(type(item['label']))
```
## Expected Output
After applying the mapping function, the expected output should have the `label` field as strings:
```plaintext
{'text': 'Hello', 'label': '0'}
<class 'str'>
{'text': 'world', 'label': '1'}
<class 'str'>
{'text': 'this', 'label': '0'}
<class 'str'>
{'text': 'is', 'label': '1'}
<class 'str'>
{'text': 'a', 'label': '1'}
<class 'str'>
{'text': 'test', 'label': '0'}
<class 'str'>
```
## Actual Output
The actual output still shows the `label` field values as integers:
```plaintext
{'text': 'Hello', 'label': 0}
<class 'int'>
{'text': 'world', 'label': 1}
<class 'int'>
{'text': 'this', 'label': 0}
<class 'int'>
{'text': 'is', 'label': 1}
<class 'int'>
{'text': 'a', 'label': 1}
<class 'int'>
{'text': 'test', 'label': 0}
<class 'int'>
```
## Why necessary
In the case of Image process we often need to convert PIL to tensor with same column name.
Thank for every dev who review this issue. 🤗
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Fix Colab notebook link
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011196 / 0.011353 (-0.000157) | 0.006039 / 0.011008 (-0.004969) | 0.122497 / 0.038508 (0.083989) | 0.043884 / 0.023109 (0.020774) | 0.372982 / 0.275898 (0.097084) | 0.444229 / 0.323480 (0.120749) | 0.009489 / 0.007986 (0.001503) | 0.004612 / 0.004328 (0.000284) | 0.093921 / 0.004250 (0.089670) | 0.052698 / 0.037052 (0.015646) | 0.372327 / 0.258489 (0.113838) | 0.426586 / 0.293841 (0.132745) | 0.046755 / 0.128546 (-0.081792) | 0.014848 / 0.075646 (-0.060799) | 0.410474 / 0.419271 (-0.008798) | 0.058206 / 0.043533 (0.014674) | 0.367051 / 0.255139 (0.111912) | 0.389950 / 0.283200 (0.106750) | 0.120857 / 0.141683 (-0.020826) | 1.795195 / 1.452155 (0.343040) | 1.823938 / 1.492716 (0.331222) |\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) | 0.215199 / 0.018006 (0.197192) | 0.482420 / 0.000490 (0.481930) | 0.001834 / 0.000200 (0.001634) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034483 / 0.037411 (-0.002928) | 0.135503 / 0.014526 (0.120977) | 0.149991 / 0.176557 (-0.026565) | 0.198482 / 0.737135 (-0.538653) | 0.153556 / 0.296338 (-0.142783) |\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) | 0.504492 / 0.215209 (0.289283) | 4.950949 / 2.077655 (2.873294) | 2.251186 / 1.504120 (0.747067) | 2.049195 / 1.541195 (0.508000) | 2.123325 / 1.468490 (0.654835) | 0.865651 / 4.584777 (-3.719126) | 4.652297 / 3.745712 (0.906585) | 4.417260 / 5.269862 (-0.852602) | 2.362390 / 4.565676 (-2.203287) | 0.098845 / 0.424275 (-0.325430) | 0.014675 / 0.007607 (0.007068) | 0.608048 / 0.226044 (0.382003) | 6.063863 / 2.268929 (3.794935) | 2.753041 / 55.444624 (-52.691583) | 2.340961 / 6.876477 (-4.535516) | 2.511934 / 2.142072 (0.369862) | 0.989297 / 4.805227 (-3.815930) | 0.195770 / 6.500664 (-6.304894) | 0.076027 / 0.075469 (0.000558) |\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) | 1.479617 / 1.841788 (-0.362170) | 18.917860 / 8.074308 (10.843552) | 18.219594 / 10.191392 (8.028202) | 0.218494 / 0.680424 (-0.461930) | 0.037207 / 0.534201 (-0.496994) | 0.571543 / 0.579283 (-0.007741) | 0.527884 / 0.434364 (0.093520) | 0.658661 / 0.540337 (0.118324) | 0.755449 / 1.386936 (-0.631487) |\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) | 0.008762 / 0.011353 (-0.002591) | 0.006019 / 0.011008 (-0.004989) | 0.118756 / 0.038508 (0.080248) | 0.039584 / 0.023109 (0.016474) | 0.400127 / 0.275898 (0.124229) | 0.468114 / 0.323480 (0.144634) | 0.006771 / 0.007986 (-0.001215) | 0.004689 / 0.004328 (0.000360) | 0.087274 / 0.004250 (0.083023) | 0.055548 / 0.037052 (0.018496) | 0.419901 / 0.258489 (0.161412) | 0.459516 / 0.293841 (0.165675) | 0.044197 / 0.128546 (-0.084349) | 0.014162 / 0.075646 (-0.061484) | 0.409634 / 0.419271 (-0.009638) | 0.058668 / 0.043533 (0.015135) | 0.404758 / 0.255139 (0.149619) | 0.431562 / 0.283200 (0.148363) | 0.122361 / 0.141683 (-0.019322) | 1.726597 / 1.452155 (0.274442) | 1.798977 / 1.492716 (0.306260) |\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) | 0.250831 / 0.018006 (0.232825) | 0.489811 / 0.000490 (0.489321) | 0.000490 / 0.000200 (0.000290) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035666 / 0.037411 (-0.001745) | 0.134899 / 0.014526 (0.120374) | 0.153156 / 0.176557 (-0.023401) | 0.202409 / 0.737135 (-0.534726) | 0.157350 / 0.296338 (-0.138989) |\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) | 0.522464 / 0.215209 (0.307254) | 5.204449 / 2.077655 (3.126794) | 2.617410 / 1.504120 (1.113290) | 2.406246 / 1.541195 (0.865052) | 2.494487 / 1.468490 (1.025997) | 0.834923 / 4.584777 (-3.749854) | 4.794186 / 3.745712 (1.048474) | 2.617939 / 5.269862 (-2.651922) | 1.648310 / 4.565676 (-2.917367) | 0.109785 / 0.424275 (-0.314490) | 0.015217 / 0.007607 (0.007610) | 0.682970 / 0.226044 (0.456926) | 6.853894 / 2.268929 (4.584966) | 3.277150 / 55.444624 (-52.167475) | 2.832502 / 6.876477 (-4.043975) | 2.984874 / 2.142072 (0.842802) | 1.005307 / 4.805227 (-3.799921) | 0.200623 / 6.500664 (-6.300041) | 0.076852 / 0.075469 (0.001383) |\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) | 1.556656 / 1.841788 (-0.285131) | 19.088978 / 8.074308 (11.014669) | 16.946406 / 10.191392 (6.755014) | 0.204419 / 0.680424 (-0.476004) | 0.021456 / 0.534201 (-0.512745) | 0.523603 / 0.579283 (-0.055680) | 0.530067 / 0.434364 (0.095703) | 0.604058 / 0.540337 (0.063721) | 0.731531 / 1.386936 (-0.655405) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2022-12-28T11:44:53Z
| 2023-01-03T15:36:14Z
| 2023-01-03T15:27:31Z
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MEMBER
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Fix notebook link to open in Colab.
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PR_kwDODunzps49qF-w
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Fix documentation card of covid_qa_castorini dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4877). All of your documentation changes will be reflected on that endpoint."
] | 2022-08-23T16:52:33Z
| 2022-08-23T18:05:01Z
| 2022-08-23T18:05:00Z
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MEMBER
| null | null | null |
Fix documentation card of covid_qa_castorini dataset.
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Added cache dirs to load and file_utils
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"hi ! the `hf_hub_download` cache_dir is a different cache directory than the one for `datasets`.\r\n\r\n`hf_hub_download` uses the `huggingface_hub` cache which is located in by default in `~/.cache/huggingface/hub`, while `datasets` uses a different cache for Arrow files and map() results `~/.cache/huggingface/datasets`",
"Is there a way to change the default cache directory for both of these on calling load_dataset? Currently, cache_dir makes dealing with where I want files to go a bit confusing as the documentation doesn't mention it only relocates.../datasets and not .../hub.",
"You can set `HF_HOME` which is the common parent directory for those two caches. Or individually `HF_DATASETS_CACHE` and `HF_HUB_CACHE`",
"Got it. Can this be added to the documentation for load_dataset and related functions to avoid confusion with cache_dir?"
] | 2025-04-04T22:36:04Z
| 2025-04-15T13:19:15Z
| null |
NONE
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When adding "cache_dir" to datasets.load_dataset, the cache_dir gets lost in the function calls, changing the cache dir to the default path. This fixes a few of these instances.
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Tutorial for creating a dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012018 / 0.011353 (0.000665) | 0.006204 / 0.011008 (-0.004804) | 0.134119 / 0.038508 (0.095611) | 0.038436 / 0.023109 (0.015327) | 0.381397 / 0.275898 (0.105499) | 0.456362 / 0.323480 (0.132882) | 0.009826 / 0.007986 (0.001840) | 0.004746 / 0.004328 (0.000417) | 0.103755 / 0.004250 (0.099505) | 0.043867 / 0.037052 (0.006815) | 0.395322 / 0.258489 (0.136833) | 0.475812 / 0.293841 (0.181971) | 0.057865 / 0.128546 (-0.070682) | 0.019919 / 0.075646 (-0.055727) | 0.465343 / 0.419271 (0.046072) | 0.061574 / 0.043533 (0.018041) | 0.371668 / 0.255139 (0.116529) | 0.400375 / 0.283200 (0.117176) | 0.106539 / 0.141683 (-0.035144) | 1.822931 / 1.452155 (0.370776) | 1.875535 / 1.492716 (0.382819) |\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) | 0.013583 / 0.018006 (-0.004423) | 0.535515 / 0.000490 (0.535025) | 0.007920 / 0.000200 (0.007720) | 0.000305 / 0.000054 (0.000250) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030204 / 0.037411 (-0.007207) | 0.131671 / 0.014526 (0.117145) | 0.143977 / 0.176557 (-0.032579) | 0.175498 / 0.737135 (-0.561637) | 0.166134 / 0.296338 (-0.130204) |\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) | 0.630995 / 0.215209 (0.415786) | 6.152275 / 2.077655 (4.074620) | 2.519887 / 1.504120 (1.015767) | 2.110926 / 1.541195 (0.569732) | 2.207555 / 1.468490 (0.739064) | 1.296197 / 4.584777 (-3.288580) | 5.510619 / 3.745712 (1.764906) | 3.167468 / 5.269862 (-2.102394) | 2.043924 / 4.565676 (-2.521753) | 0.144772 / 0.424275 (-0.279503) | 0.014456 / 0.007607 (0.006848) | 0.783629 / 0.226044 (0.557585) | 7.836962 / 2.268929 (5.568033) | 3.248593 / 55.444624 (-52.196032) | 2.577092 / 6.876477 (-4.299385) | 2.671918 / 2.142072 (0.529846) | 1.471586 / 4.805227 (-3.333641) | 0.251391 / 6.500664 (-6.249273) | 0.091947 / 0.075469 (0.016478) |\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) | 1.594839 / 1.841788 (-0.246949) | 18.250630 / 8.074308 (10.176322) | 23.948781 / 10.191392 (13.757389) | 0.275505 / 0.680424 (-0.404919) | 0.045202 / 0.534201 (-0.488999) | 0.545552 / 0.579283 (-0.033731) | 0.639352 / 0.434364 (0.204989) | 0.666345 / 0.540337 (0.126008) | 0.795614 / 1.386936 (-0.591322) |\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) | 0.011234 / 0.011353 (-0.000119) | 0.005983 / 0.011008 (-0.005025) | 0.109144 / 0.038508 (0.070636) | 0.036070 / 0.023109 (0.012961) | 0.429313 / 0.275898 (0.153415) | 0.490615 / 0.323480 (0.167135) | 0.007448 / 0.007986 (-0.000538) | 0.004424 / 0.004328 (0.000095) | 0.097100 / 0.004250 (0.092850) | 0.049719 / 0.037052 (0.012667) | 0.412719 / 0.258489 (0.154230) | 0.485717 / 0.293841 (0.191876) | 0.061168 / 0.128546 (-0.067378) | 0.021510 / 0.075646 (-0.054136) | 0.116598 / 0.419271 (-0.302673) | 0.066116 / 0.043533 (0.022583) | 0.426212 / 0.255139 (0.171073) | 0.448368 / 0.283200 (0.165168) | 0.116003 / 0.141683 (-0.025680) | 1.799329 / 1.452155 (0.347175) | 1.967256 / 1.492716 (0.474540) |\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) | 0.214893 / 0.018006 (0.196887) | 0.497843 / 0.000490 (0.497354) | 0.000464 / 0.000200 (0.000264) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031758 / 0.037411 (-0.005653) | 0.131182 / 0.014526 (0.116656) | 0.141251 / 0.176557 (-0.035305) | 0.186526 / 0.737135 (-0.550609) | 0.142975 / 0.296338 (-0.153363) |\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) | 0.662094 / 0.215209 (0.446885) | 6.664841 / 2.077655 (4.587186) | 2.690613 / 1.504120 (1.186493) | 2.305399 / 1.541195 (0.764205) | 2.383697 / 1.468490 (0.915207) | 1.280692 / 4.584777 (-3.304085) | 5.629215 / 3.745712 (1.883503) | 5.007083 / 5.269862 (-0.262778) | 2.482163 / 4.565676 (-2.083513) | 0.147662 / 0.424275 (-0.276613) | 0.017770 / 0.007607 (0.010163) | 0.818380 / 0.226044 (0.592335) | 8.006521 / 2.268929 (5.737592) | 3.472262 / 55.444624 (-51.972363) | 2.709550 / 6.876477 (-4.166926) | 2.775138 / 2.142072 (0.633066) | 1.570545 / 4.805227 (-3.234683) | 0.266323 / 6.500664 (-6.234341) | 0.090591 / 0.075469 (0.015122) |\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) | 1.657927 / 1.841788 (-0.183861) | 18.448981 / 8.074308 (10.374673) | 20.336909 / 10.191392 (10.145517) | 0.230322 / 0.680424 (-0.450102) | 0.025972 / 0.534201 (-0.508229) | 0.561361 / 0.579283 (-0.017922) | 0.623758 / 0.434364 (0.189394) | 0.664120 / 0.540337 (0.123783) | 0.763144 / 1.386936 (-0.623792) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29de6179766418c937fb33b0cc8803ec24a39e9e \"CML watermark\")\n"
] | 2023-02-16T22:09:35Z
| 2023-02-17T18:50:46Z
| 2023-02-17T18:41:28Z
|
MEMBER
| null | null | null |
A tutorial for creating datasets based on the folder-based builders and `from_dict` and `from_generator` methods. I've also mentioned loading scripts as a next step, but I think we should keep the tutorial focused on the low-code methods. Let me know what you think! 🙂
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Dataset Viewer issue for super_glue
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[
"Thanks for reporting @wzsxxa.\r\n\r\nHowever the \"super_glue\" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue"
] | 2022-08-16T01:34:56Z
| 2022-08-22T10:08:01Z
| 2022-08-22T10:07:45Z
|
NONE
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### Link
https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_
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Fix CI benchmarks by temporarily pinning Docker image version
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008519 / 0.011353 (-0.002834) | 0.004451 / 0.011008 (-0.006558) | 0.102401 / 0.038508 (0.063893) | 0.029779 / 0.023109 (0.006669) | 0.302654 / 0.275898 (0.026756) | 0.366002 / 0.323480 (0.042522) | 0.007044 / 0.007986 (-0.000942) | 0.003350 / 0.004328 (-0.000978) | 0.078213 / 0.004250 (0.073963) | 0.035208 / 0.037052 (-0.001844) | 0.312980 / 0.258489 (0.054491) | 0.344217 / 0.293841 (0.050376) | 0.033089 / 0.128546 (-0.095457) | 0.011443 / 0.075646 (-0.064203) | 0.353143 / 0.419271 (-0.066128) | 0.040851 / 0.043533 (-0.002682) | 0.304501 / 0.255139 (0.049362) | 0.329118 / 0.283200 (0.045918) | 0.087399 / 0.141683 (-0.054284) | 1.500200 / 1.452155 (0.048046) | 1.536176 / 1.492716 (0.043459) |\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) | 0.209626 / 0.018006 (0.191619) | 0.425551 / 0.000490 (0.425061) | 0.001168 / 0.000200 (0.000968) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023664 / 0.037411 (-0.013748) | 0.096792 / 0.014526 (0.082266) | 0.105652 / 0.176557 (-0.070905) | 0.140796 / 0.737135 (-0.596340) | 0.109319 / 0.296338 (-0.187019) |\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) | 0.414802 / 0.215209 (0.199593) | 4.152619 / 2.077655 (2.074964) | 1.814403 / 1.504120 (0.310283) | 1.611392 / 1.541195 (0.070198) | 1.667350 / 1.468490 (0.198860) | 0.691855 / 4.584777 (-3.892922) | 3.406584 / 3.745712 (-0.339128) | 1.940332 / 5.269862 (-3.329530) | 1.279061 / 4.565676 (-3.286615) | 0.082938 / 0.424275 (-0.341337) | 0.012388 / 0.007607 (0.004781) | 0.521738 / 0.226044 (0.295693) | 5.233764 / 2.268929 (2.964835) | 2.306573 / 55.444624 (-53.138051) | 1.954631 / 6.876477 (-4.921845) | 2.048315 / 2.142072 (-0.093757) | 0.816921 / 4.805227 (-3.988306) | 0.150983 / 6.500664 (-6.349681) | 0.066628 / 0.075469 (-0.008842) |\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) | 1.235939 / 1.841788 (-0.605849) | 14.047114 / 8.074308 (5.972806) | 14.149842 / 10.191392 (3.958450) | 0.152836 / 0.680424 (-0.527588) | 0.028837 / 0.534201 (-0.505364) | 0.396232 / 0.579283 (-0.183051) | 0.409950 / 0.434364 (-0.024414) | 0.460296 / 0.540337 (-0.080041) | 0.556787 / 1.386936 (-0.830149) |\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) | 0.006582 / 0.011353 (-0.004771) | 0.004491 / 0.011008 (-0.006518) | 0.100093 / 0.038508 (0.061585) | 0.026826 / 0.023109 (0.003717) | 0.413971 / 0.275898 (0.138073) | 0.445625 / 0.323480 (0.122145) | 0.004892 / 0.007986 (-0.003094) | 0.003295 / 0.004328 (-0.001034) | 0.077879 / 0.004250 (0.073628) | 0.039177 / 0.037052 (0.002125) | 0.353299 / 0.258489 (0.094810) | 0.406566 / 0.293841 (0.112725) | 0.031633 / 0.128546 (-0.096913) | 0.011517 / 0.075646 (-0.064130) | 0.320939 / 0.419271 (-0.098332) | 0.041487 / 0.043533 (-0.002046) | 0.353735 / 0.255139 (0.098596) | 0.434786 / 0.283200 (0.151586) | 0.087722 / 0.141683 (-0.053961) | 1.515134 / 1.452155 (0.062979) | 1.588908 / 1.492716 (0.096191) |\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) | 0.225312 / 0.018006 (0.207305) | 0.398324 / 0.000490 (0.397834) | 0.000453 / 0.000200 (0.000253) | 0.000064 / 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) | 0.024645 / 0.037411 (-0.012766) | 0.099399 / 0.014526 (0.084873) | 0.107006 / 0.176557 (-0.069550) | 0.145090 / 0.737135 (-0.592045) | 0.110046 / 0.296338 (-0.186292) |\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) | 0.450573 / 0.215209 (0.235364) | 4.498030 / 2.077655 (2.420375) | 2.193164 / 1.504120 (0.689044) | 1.940103 / 1.541195 (0.398908) | 1.957137 / 1.468490 (0.488647) | 0.697599 / 4.584777 (-3.887178) | 3.465146 / 3.745712 (-0.280566) | 1.918209 / 5.269862 (-3.351653) | 1.183921 / 4.565676 (-3.381756) | 0.082540 / 0.424275 (-0.341735) | 0.012495 / 0.007607 (0.004888) | 0.549702 / 0.226044 (0.323658) | 5.526841 / 2.268929 (3.257912) | 2.658611 / 55.444624 (-52.786014) | 2.259542 / 6.876477 (-4.616935) | 2.310139 / 2.142072 (0.168066) | 0.810550 / 4.805227 (-3.994677) | 0.152369 / 6.500664 (-6.348295) | 0.066295 / 0.075469 (-0.009174) |\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) | 1.289240 / 1.841788 (-0.552547) | 14.032143 / 8.074308 (5.957834) | 13.973492 / 10.191392 (3.782100) | 0.140082 / 0.680424 (-0.540342) | 0.017113 / 0.534201 (-0.517088) | 0.386534 / 0.579283 (-0.192749) | 0.393723 / 0.434364 (-0.040641) | 0.448891 / 0.540337 (-0.091446) | 0.533085 / 1.386936 (-0.853851) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n"
] | 2023-01-17T07:15:31Z
| 2023-01-17T08:58:22Z
| 2023-01-17T08:51:17Z
|
MEMBER
| null | null | null |
This PR fixes CI benchmarks, by temporarily pinning Docker image version, instead of "latest" tag.
It also updates deprecated `cml-send-comment` command and using `cml comment create` instead.
Fix #5431.
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Dict feature is non-nullable while nested dict feature is
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[
"It looks like a bug, by default every feature should be nullable.",
"I've linked a PR with a fix :)",
"@mariosasko awesome thank you!"
] | 2024-03-18T14:31:47Z
| 2024-03-20T10:24:15Z
| 2024-03-19T20:05:20Z
|
CONTRIBUTOR
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When i try to create a `Dataset` object with None values inside a dict column, like this:
```python
from datasets import Dataset, Features, Value
Dataset.from_dict(
{
"dict": [{"a": 0, "b": 0}, None],
}, features=Features(
{"dict": {"a": Value("int16"), "b": Value("int16")}}
)
)
```
i get `ValueError: Got None but expected a dictionary instead`.
At the same time, having None in _nested_ dict feature works, for example, this doesn't throw any errors:
```python
from datasets import Dataset, Features, Value, Sequence
dataset = Dataset.from_dict(
{
"list_dict": [[{"a": 0, "b": 0}], None],
"sequence_dict": [[{"a": 0, "b": 0}], None],
}, features=Features({
"list_dict": [{"a": Value("int16"), "b": Value("int16")}],
"sequence_dict": Sequence({"a": Value("int16"), "b": Value("int16")}),
})
)
```
Other types of features also seem to be nullable (but I haven't checked all of them).
Version of `datasets` is the latest atm (2.18.0)
Is this an expected behavior or a bug?
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I_kwDODunzps6c3993
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Accessing audio dataset value throws Format not recognised error
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"Hi ! can you try if this works ?\r\n\r\n```python\r\nimport soundfile as sf\r\n\r\nwith open('C:\\\\Users\\\\Nawaz-Server\\\\.cache\\\\huggingface\\\\hub\\\\datasets--fawazahmed0--bug-audio\\\\snapshots\\\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\\\data\\\\Ghamadi\\\\037136.mp3', 'rb') as f:\r\n print(sf.read(f))\r\n```",
"@lhoestq Same error, here is the output:\r\n\r\n```bash\r\n(mypy) C:\\Users\\Nawaz-Server\\Documents\\ml>python myest.py\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Nawaz-Server\\Documents\\ml\\myest.py\", line 5, in <module>\r\n print(sf.read(f))\r\n ^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 285, in read\r\n with SoundFile(file, 'r', samplerate, channels,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 658, in __init__\r\n self._file = self._open(file, mode_int, closefd)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 1216, in _open\r\n raise LibsndfileError(err, prefix=\"Error opening {0!r}: \".format(self.name))\r\nsoundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\\\Users\\\\Nawaz-Server\\\\.cache\\\\huggingface\\\\hub\\\\datasets--fawazahmed0--bug-audio\\\\snapshots\\\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\\\data\\\\Ghamadi\\\\037136.mp3'>: Format not recognised.\r\n\r\n```",
"upstream bug: https://github.com/bastibe/python-soundfile/issues/439"
] | 2024-11-04T05:59:13Z
| 2024-11-09T18:51:52Z
| null |
NONE
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### Describe the bug
Accessing audio dataset value throws `Format not recognised error`
### Steps to reproduce the bug
**code:**
```py
from datasets import load_dataset
dataset = load_dataset("fawazahmed0/bug-audio")
for data in dataset["train"]:
print(data)
```
**output:**
```bash
(mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py
[C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed!
{'audio': {'path': 'C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037135.mp3', 'array': array([ 0.00000000e+00, -2.86519935e-22, -2.56504911e-21, ...,
-1.94239747e-02, -2.42924765e-02, -2.99104657e-02]), 'sampling_rate': 22050}, 'reciter': 'Ghamadi', 'transcription': 'الا عجوز ا في الغبرين', 'line': 3923, 'chapter': 37, 'verse': 135, 'text': 'إِلَّا عَجُوزࣰ ا فِي ٱلۡغَٰبِرِينَ'}
Traceback (most recent call last):
File "C:\Users\Nawaz-Server\Documents\ml\myest.py", line 5, in <module>
for data in dataset["train"]:
~~~~~~~^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\arrow_dataset.py", line 2372, in __iter__
formatted_output = format_table(
^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 403, in __call__
return self.format_row(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 444, in format_row
row = self.python_features_decoder.decode_row(row)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 222, in decode_row
return self.features.decode_example(row) if self.features else row
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 2042, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\audio.py", line 184, in decode_example
array, sampling_rate = sf.read(f)
^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 285, in read
with SoundFile(file, 'r', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'>: Format not recognised.
```
### Expected behavior
Everything should work fine, as loading the problematic audio file directly with soundfile package works fine
**code:**
```
import soundfile as sf
print(sf.read('C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'))
```
**output:**
```bash
(mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py
[C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed!
(array([ 0.00000000e+00, -8.43723821e-22, -2.45370628e-22, ...,
-7.71464454e-03, -6.90496899e-03, -8.63333419e-03]), 22050)
```
### Environment info
- `datasets` version: 3.0.2
- Platform: Windows-11-10.0.22621-SP0
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.10.0
- soundfile: 0.12.1
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I_kwDODunzps5nCSpv
| 5,908
|
Unbearably slow sorting on big mapped datasets
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"Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well",
"@lhoestq \r\n\r\n> contiguous=True (which should be the default imo)\r\n\r\nFor `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (when we add `IterableDataset.shard`)",
"Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nIf the dataset is made of one shard it's indeed not possible to shard it contiguously though",
"> Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nBut sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? ",
"Yes indeed !",
"I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.\n\nThere are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I saw some issue like that somewhere (about the mapped datasets in general), but can't find it now.\n\nMaybe indices should be flattened before any additional processing, then."
] | 2023-05-27T11:08:32Z
| 2023-06-13T17:45:10Z
| null |
CONTRIBUTOR
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### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lines at all, with flattening takes about a minute).
### Steps to reproduce the bug
```Python
from datasets import load_dataset
import time
dataset = load_dataset("xnli", "en", split="train")
dataset = dataset.shard(10, 0)
print(len(dataset))
t = time.time()
# dataset = dataset.flatten_indices() # uncomment this line and it's fast
dataset = dataset.sort("label", reverse=True, load_from_cache_file=False)
print(f"finished in {time.time() - t:.4f} seconds")
```
### Expected behavior
Expect sorting to take the same or less time than flattening and then sorting.
### Environment info
- `datasets` version: 2.12.1.dev0 (same with 2.12.0 too)
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.10.10
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
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I_kwDODunzps5kHaUJ
| 5,783
|
Offset overflow while doing regex on a text column
|
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[
"Hi! This looks like an Arrow bug, but it can be avoided by reducing the `writer_batch_size`.\r\n\r\n(`ds = ds.map(get_text_caption, writer_batch_size=100)` in Colab runs without issues)\r\n",
"@mariosasko I ran into this problem with load_dataset. What should I do",
"@AisingioroHao0 You can also pass the `writer_batch_size` parameter to `load_dataset`, e.g., `load_dataset(\"mnist\", writer_batch_size=100)`",
"@mariosasko How do I determine the optimal size of write_batch_size? My training is sometimes fast and sometimes slow. Is it because write_batch_size is too small? Each batch of the current dataloader should be the same size. I preprocessed the dataset using map",
"@aihao2000 It's unlikely `writer_batch_size` is the problem. You can use the following code to profile the training loop (e.g., on a subset of data) and find slow parts:\r\n```python\r\nimport cProfile, pstats\r\n\r\nwith cProfile.Profile() as profiler:\r\n ... # training loop code\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\n",
"@nishanthcgit ok，thanks.Recently I found dataset.with_transform to be faster and more stable with multiple processes",
"@mariosasko Is the larger the num_proc of load_dataset within the number of cpu cores, the better? Then the num_proc of data_loader is the number of cpu cores/number of training processes"
] | 2023-04-22T19:12:03Z
| 2023-09-22T06:44:07Z
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NONE
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### Describe the bug
`ArrowInvalid: offset overflow while concatenating arrays`
Same error as [here](https://github.com/huggingface/datasets/issues/615)
### Steps to reproduce the bug
Steps to reproduce: (dataset is a few GB big so try in colab maybe)
```
import datasets
import re
ds = datasets.load_dataset('nishanthc/dnd_map_dataset_v0.1', split = 'train')
def get_text_caption(example):
regex_pattern = r'\s\d+x\d+|,\sLQ|,\sgrid|\.\w+$'
example['text_caption'] = re.sub(regex_pattern, '', example['picture_text'])
return example
ds = ds.map(get_text_caption)
```
I am trying to apply a regex to remove certain patterns from a text column. Not sure why this error is showing up.
### Expected behavior
Dataset should have a new column with processed text
### Environment info
Datasets version - 2.11.0
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PR_kwDODunzps5m67g0
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Revert the changes in `arrow_writer.py` from #6636
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6664). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"> Hi! We can't revert this as the \"reverted\" implementation has quadratic time complexity. Instead, let's fix it:\r\n\r\nI agree, but it's the implementation we have had so far. Why don't we:\r\n1. Release a hotfix ASAP (since would be doing a revert, we know it works as before) so people can continue using this library fine since AFAIU right now mostly writing examples for people is broken.\r\n2. Then, focus on still applying the performance improvement and release again",
"The fix is straightforward, so one patch release (after this PR is merged) is enough.\r\n\r\nBtw, let's also add a test to `tests/test_arrow_writer.py` to avoid this issue in the future.",
"> Btw, let's also add a test to tests/test_arrow_writer.py to avoid this issue in the future.\r\n\r\nWould you mind adding such test, as you're more familiar with the codebase?",
"<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) | 0.005083 / 0.011353 (-0.006270) | 0.003697 / 0.011008 (-0.007311) | 0.063302 / 0.038508 (0.024794) | 0.028866 / 0.023109 (0.005757) | 0.249987 / 0.275898 (-0.025911) | 0.270803 / 0.323480 (-0.052677) | 0.004096 / 0.007986 (-0.003890) | 0.002752 / 0.004328 (-0.001577) | 0.049156 / 0.004250 (0.044906) | 0.042936 / 0.037052 (0.005884) | 0.266907 / 0.258489 (0.008418) | 0.291462 / 0.293841 (-0.002379) | 0.027703 / 0.128546 (-0.100844) | 0.011006 / 0.075646 (-0.064641) | 0.206238 / 0.419271 (-0.213033) | 0.035446 / 0.043533 (-0.008087) | 0.248923 / 0.255139 (-0.006216) | 0.264141 / 0.283200 (-0.019058) | 0.017545 / 0.141683 (-0.124138) | 1.157145 / 1.452155 (-0.295009) | 1.199007 / 1.492716 (-0.293710) |\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) | 0.092741 / 0.018006 (0.074734) | 0.299057 / 0.000490 (0.298567) | 0.000211 / 0.000200 (0.000011) | 0.000049 / 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) | 0.017936 / 0.037411 (-0.019475) | 0.061552 / 0.014526 (0.047026) | 0.072938 / 0.176557 (-0.103618) | 0.118192 / 0.737135 (-0.618944) | 0.074589 / 0.296338 (-0.221750) |\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) | 0.287186 / 0.215209 (0.071977) | 2.795694 / 2.077655 (0.718039) | 1.474386 / 1.504120 (-0.029734) | 1.359065 / 1.541195 (-0.182130) | 1.375295 / 1.468490 (-0.093196) | 0.569448 / 4.584777 (-4.015329) | 2.374428 / 3.745712 (-1.371284) | 2.770198 / 5.269862 (-2.499663) | 1.716346 / 4.565676 (-2.849330) | 0.063173 / 0.424275 (-0.361102) | 0.005031 / 0.007607 (-0.002576) | 0.333197 / 0.226044 (0.107153) | 3.271739 / 2.268929 (1.002811) | 1.826406 / 55.444624 (-53.618218) | 1.554537 / 6.876477 (-5.321939) | 1.565927 / 2.142072 (-0.576146) | 0.649796 / 4.805227 (-4.155431) | 0.118371 / 6.500664 (-6.382293) | 0.042536 / 0.075469 (-0.032933) |\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) | 0.969882 / 1.841788 (-0.871906) | 11.638201 / 8.074308 (3.563893) | 9.759370 / 10.191392 (-0.432022) | 0.128069 / 0.680424 (-0.552355) | 0.013493 / 0.534201 (-0.520708) | 0.287324 / 0.579283 (-0.291959) | 0.267542 / 0.434364 (-0.166821) | 0.320072 / 0.540337 (-0.220265) | 0.421132 / 1.386936 (-0.965804) |\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) | 0.005679 / 0.011353 (-0.005674) | 0.003746 / 0.011008 (-0.007262) | 0.050149 / 0.038508 (0.011641) | 0.034382 / 0.023109 (0.011273) | 0.289802 / 0.275898 (0.013904) | 0.314993 / 0.323480 (-0.008487) | 0.004488 / 0.007986 (-0.003498) | 0.002786 / 0.004328 (-0.001542) | 0.047987 / 0.004250 (0.043737) | 0.046589 / 0.037052 (0.009537) | 0.301420 / 0.258489 (0.042931) | 0.335384 / 0.293841 (0.041543) | 0.050701 / 0.128546 (-0.077845) | 0.010987 / 0.075646 (-0.064660) | 0.058292 / 0.419271 (-0.360979) | 0.033973 / 0.043533 (-0.009560) | 0.288923 / 0.255139 (0.033784) | 0.306263 / 0.283200 (0.023064) | 0.018856 / 0.141683 (-0.122827) | 1.160721 / 1.452155 (-0.291433) | 1.208151 / 1.492716 (-0.284565) |\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) | 0.092633 / 0.018006 (0.074626) | 0.300353 / 0.000490 (0.299864) | 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) | 0.022257 / 0.037411 (-0.015154) | 0.075417 / 0.014526 (0.060892) | 0.087289 / 0.176557 (-0.089268) | 0.125416 / 0.737135 (-0.611720) | 0.088751 / 0.296338 (-0.207588) |\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) | 0.286477 / 0.215209 (0.071268) | 2.801931 / 2.077655 (0.724277) | 1.553034 / 1.504120 (0.048914) | 1.426152 / 1.541195 (-0.115043) | 1.443824 / 1.468490 (-0.024666) | 0.563298 / 4.584777 (-4.021479) | 2.428968 / 3.745712 (-1.316744) | 2.685964 / 5.269862 (-2.583897) | 1.752304 / 4.565676 (-2.813372) | 0.064174 / 0.424275 (-0.360101) | 0.005079 / 0.007607 (-0.002528) | 0.344899 / 0.226044 (0.118855) | 3.372528 / 2.268929 (1.103600) | 1.900723 / 55.444624 (-53.543901) | 1.623721 / 6.876477 (-5.252756) | 1.781009 / 2.142072 (-0.361064) | 0.655229 / 4.805227 (-4.149998) | 0.116050 / 6.500664 (-6.384614) | 0.040374 / 0.075469 (-0.035095) |\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) | 1.004714 / 1.841788 (-0.837074) | 12.108179 / 8.074308 (4.033871) | 10.233447 / 10.191392 (0.042055) | 0.141438 / 0.680424 (-0.538986) | 0.015387 / 0.534201 (-0.518814) | 0.288068 / 0.579283 (-0.291216) | 0.277025 / 0.434364 (-0.157339) | 0.331714 / 0.540337 (-0.208623) | 0.424209 / 1.386936 (-0.962727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bdebf1922663c30744efb8869c86b28f102b84dd \"CML watermark\")\n"
] | 2024-02-15T01:47:33Z
| 2024-02-16T14:02:39Z
| 2024-02-16T02:31:11Z
|
CONTRIBUTOR
| null | null | null |
#6636 broke `write_examples_on_file` and `write_batch` from the class `ArrowWriter`. I'm undoing these changes. See #6663.
Note the current implementation doesn't keep the order of the columns and the schema, thus setting a wrong schema for each column.
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Cannot use both set_format and set_transform
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"Currently, it's not possible to chain `set_format`/`set_transform` calls (plus, this is a breaking change if we decide to implement it), so I see two possible solutions:\r\n* using `set_format`/`set_transform` for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n* implementing and registering a custom formatter (the relevant code is [here](https://github.com/huggingface/datasets/tree/main/src/datasets/formatting))\r\n\r\nBtw, your example requires a single `set_format` call:\r\n```python\r\nds.set_format(\"torch\", columns=[\"image\"], output_all_columns=True, dtype=torch.double)\r\n```",
"Hey Mario,\r\nThanks, for getting back to me. the toDouble was just an example my real life case requires many more transforms.\r\n\r\nWhat do you mean by:\r\n> using set_format/set_transform for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n\r\nHow would that go, I thought you can't chain them?\r\n\r\nAs for the custom formatter, is it possible to reference an existing formatter, in my case `torch_formatter` inside of my custom formatter?\r\n\r\nmaybe I can inherit from it and just call `super.recursive_tensorize()`?",
"> How would that go, I thought you can't chain them?\r\n\r\nYes, they cannot be chained. This is what I meant:\r\n```python\r\nds.set_transform(first_transform)\r\n# calling the 2nd transform on each accessed batch\r\nsecond_transform(ds[2:3])\r\n```\r\n\r\n> As for the custom formatter, is it possible to reference an existing formatter, in my case torch_formatter inside of my custom formatter?\r\n>\r\n>maybe I can inherit from it and just call super.recursive_tensorize()?\r\n\r\nYes, subclassing makes the most sense.",
"Great, thank you for the details.",
"https://github.com/huggingface/datasets/issues/6012"
] | 2023-05-27T19:22:23Z
| 2023-07-09T21:40:54Z
| 2023-06-16T14:41:24Z
|
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### Describe the bug
I need to process some data using the set_transform method but I also need the data to be formatted for pytorch before processing it.
I don't see anywhere in the documentation something that says that both methods cannot be used at the same time.
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset("mnist", split="train")
ds.set_format(type="torch")
def transform(entry):
return entry["image"].double()
ds.set_transform(transform)
print(ds[0])
```
### Expected behavior
It should print the pytorch tensor image as a double, but it errors because "entry" in the transform function doesn't receive a pytorch tensor to begin with, it receives a PIL Image -> entry.double() errors because entry isn't a pytorch tensor.
### Environment info
Latest versions.
### Note:
It would be at least handy to have access to a function that can do the dataset.set_format in the set_transform function.
Something like:
```
from datasets import load_dataset, do_format
ds = load_dataset("mnist", split="train")
def transform(entry):
entry = do_format(entry, type="torch")
return entry["image"].double()
ds.set_transform(transform)
print(ds[0])
```
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Cache only changed columns?
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[
"so I guess a workaround to this is to simply remove all columns except the ones to cache and then add them back with `concatenate_datasets(..., axis=1)`.",
"yes this is the right workaround. We're keeping the cache like this to make it easier for people to delete intermediate cache files"
] | 2024-09-05T12:56:47Z
| 2024-09-20T13:27:20Z
| null |
CONTRIBUTOR
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### Feature request
Cache only the actual changes to the dataset i.e. changed columns.
### Motivation
I realized that caching actually saves the complete dataset again.
This is especially problematic for image datasets if one wants to only change another column e.g. some metadata and then has to save 5 TB again.
### Your contribution
Is this even viable in the current architecture of the package?
I quickly looked into it and it seems it would require significant changes.
I would spend some time looking into this but maybe somebody could help with the feasibility and some plan to implement before spending too much time on it?
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PR_kwDODunzps5uUZNT
| 6,855
|
Fix dataset name for community Hub script-datasets
|
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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"
] | 2024-05-02T07:05:44Z
| 2024-05-03T15:58:00Z
| 2024-05-03T15:51:57Z
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MEMBER
| null | null | null |
Fix dataset name for community Hub script-datasets by passing explicit dataset_name to HubDatasetModuleFactoryWithScript.
Fix #6854.
CC: @Wauplin
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I_kwDODunzps56nViN
| 6,537
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Adding support for netCDF (*.nc) files
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"Related to #3113 ",
"Conceptually, we can use xarray to load the netCDF file, then xarray -> pandas -> pyarrow.",
"I'd still need to verify that such a conversion would be lossless, especially for multi-dimensional data."
] | 2023-12-27T09:27:29Z
| 2023-12-27T20:46:53Z
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### Feature request
netCDF (*.nc) is a file format for storing multidimensional scientific data, which is used by packages like `xarray` (labelled multi-dimensional arrays in Python). It would be nice to have native support for netCDF in `datasets`.
### Motivation
When uploading *.nc files onto Huggingface Hub through the `datasets` API, I would like to be able to preview the dataset without converting it to another format.
### Your contribution
I can submit a PR, provided I have the time.
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I_kwDODunzps5ujYXq
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Why not use self._epoch as seed to shuffle in distributed training with IterableDataset
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"@lhoestq ",
"`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`).\r\n\r\nTo fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`",
"Thanks for the reply"
] | 2023-08-17T10:58:20Z
| 2023-08-17T14:33:15Z
| 2023-08-17T14:33:14Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping.
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177
My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes.
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801
If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator?
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206
### Steps to reproduce the bug
As mentioned above.
### Expected behavior
As mentioned above.
### Environment info
Not related
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I_kwDODunzps5bjcXt
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Support latest Docker image in CI benchmarks
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[
"Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.",
"Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.",
"Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!\r\n\r\nWe are reviewing your PR to unpin the container image."
] | 2023-01-17T09:06:08Z
| 2023-01-18T06:29:08Z
| 2023-01-18T06:29:08Z
|
MEMBER
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Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432
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I_kwDODunzps5lBf9a
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Setting `num_proc` errors when `.map` returns additional items.
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[
"Hi ! Unfortunately I couldn't reproduce on my side locally and with datasets 2.11 and python 3.10.11 on colab.\r\nWhat version of `multiprocess` are you using ?",
"I've got `multiprocess` version `0.70.14`.\r\n\r\nI've done some more testing and the error only occurs in PyCharm's Python Console. It seems to be [this PyCharm bug](https://youtrack.jetbrains.com/issue/PY-51922/Multiprocessing-bug.-Can-only-run-in-debugger.), I'll close this.",
"For other users facing this, my workaround is to conditionally set `num_proc` so I can work interactively in the PyCharm Python Console while developing, then when I'm ready to run on the whole dataset, run it as a script and use multiprocessing.\r\n\r\n```py\r\nmapped_ds = ds.map(\r\n my_map_function,\r\n batched=True,\r\n remove_columns=ds.column_names,\r\n num_proc=1 if \"PYCHARM_HOSTED\" in os.environ else 8,\r\n)\r\n```"
] | 2023-05-03T21:46:53Z
| 2023-05-04T21:14:21Z
| 2023-05-04T20:22:25Z
|
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### Describe the bug
I'm using a map function that returns more rows than are passed in.
If I try to use `num_proc` I get:
```
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 563, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3097, in map
for rank, done, content in iflatmap_unordered(
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1372, in iflatmap_unordered
yield queue.get(timeout=0.05)
File "<string>", line 2, in get
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/managers.py", line 818, in _callmethod
kind, result = conn.recv()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 258, in recv
buf = self._recv_bytes()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 422, in _recv_bytes
buf = self._recv(4)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 391, in _recv
raise EOFError
EOFError
```
### Steps to reproduce the bug
This is copied from the [Datasets docs](https://huggingface.co/docs/datasets/v2.12.0/en/process#batch-processing), with `num_proc` added, and will error.
```py
import datasets
dataset = ... # any old dataset
def chunk_examples(examples):
chunks = []
for sentence in examples["text"]:
chunks += [sentence[i : i + 50] for i in range(0, len(sentence), 50)]
return {"chunks": chunks}
chunked_dataset = dataset.map(
chunk_examples,
batched=True,
remove_columns=dataset.column_names,
num_proc=2, # Remove and it works
)
```
### Expected behavior
Should work fine. On a related note, multi-processing also fails if there is a Meta class anywhere in scope (and there are plenty in the standard library). This is the fault of `dill` and is a long standing issue.
Have you considered using Loky for multiprocessing? I've found that the built-in `datasets` multi-processing breaks more than it works so have written my own function using `loky`, for reference:
```py
import datasets
import loky
def fast_loop(dataset: datasets.Dataset, func, num_proc=None):
if num_proc is None:
import os
num_proc = len(os.sched_getaffinity(0))
shards = [
dataset.shard(num_shards=num_proc, index=i, contiguous=True)
for i in range(num_proc)
]
executor = loky.get_reusable_executor(max_workers=num_proc)
results = executor.map(func, shards)
return datasets.combine.concatenate_datasets(list(results))
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.1
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