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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) | 0.007049 / 0.011353 (-0.004304) | 0.005034 / 0.011008 (-0.005974) | 0.097737 / 0.038508 (0.059229) | 0.033280 / 0.023109 (0.010170) | 0.301017 / 0.275898 (0.025119) | 0.336593 / 0.323480 (0.013113) | 0.005567 / 0.007986 (-0.002419) | 0.005384 / 0.004328 (0.001056) | 0.072980 / 0.004250 (0.068730) | 0.045030 / 0.037052 (0.007978) | 0.303280 / 0.258489 (0.044791) | 0.367528 / 0.293841 (0.073687) | 0.034131 / 0.128546 (-0.094415) | 0.012118 / 0.075646 (-0.063528) | 0.331677 / 0.419271 (-0.087594) | 0.049211 / 0.043533 (0.005678) | 0.297535 / 0.255139 (0.042396) | 0.318136 / 0.283200 (0.034936) | 0.101574 / 0.141683 (-0.040109) | 1.472769 / 1.452155 (0.020615) | 1.541724 / 1.492716 (0.049007) |\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) | 0.014646 / 0.018006 (-0.003360) | 0.439050 / 0.000490 (0.438560) | 0.008575 / 0.000200 (0.008375) | 0.000297 / 0.000054 (0.000242) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027591 / 0.037411 (-0.009820) | 0.111639 / 0.014526 (0.097113) | 0.117098 / 0.176557 (-0.059458) | 0.173281 / 0.737135 (-0.563855) | 0.123197 / 0.296338 (-0.173141) |\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) | 0.397507 / 0.215209 (0.182298) | 3.971457 / 2.077655 (1.893803) | 1.781158 / 1.504120 (0.277038) | 1.590419 / 1.541195 (0.049224) | 1.716374 / 1.468490 (0.247884) | 0.687150 / 4.584777 (-3.897627) | 3.691009 / 3.745712 (-0.054703) | 2.050900 / 5.269862 (-3.218961) | 1.304893 / 4.565676 (-3.260784) | 0.084507 / 0.424275 (-0.339768) | 0.012231 / 0.007607 (0.004624) | 0.493033 / 0.226044 (0.266988) | 4.929957 / 2.268929 (2.661028) | 2.209069 / 55.444624 (-53.235555) | 1.885992 / 6.876477 (-4.990485) | 2.007004 / 2.142072 (-0.135069) | 0.827265 / 4.805227 (-3.977963) | 0.168225 / 6.500664 (-6.332439) | 0.064988 / 0.075469 (-0.010481) |\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) | 1.182341 / 1.841788 (-0.659447) | 14.691983 / 8.074308 (6.617674) | 14.350720 / 10.191392 (4.159328) | 0.164307 / 0.680424 (-0.516117) | 0.017480 / 0.534201 (-0.516720) | 0.421843 / 0.579283 (-0.157441) | 0.417481 / 0.434364 (-0.016883) | 0.496587 / 0.540337 (-0.043751) | 0.581208 / 1.386936 (-0.805728) |\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) | 0.007070 / 0.011353 (-0.004283) | 0.005083 / 0.011008 (-0.005926) | 0.075009 / 0.038508 (0.036500) | 0.032343 / 0.023109 (0.009234) | 0.366788 / 0.275898 (0.090890) | 0.392273 / 0.323480 (0.068794) | 0.005512 / 0.007986 (-0.002474) | 0.003999 / 0.004328 (-0.000329) | 0.073743 / 0.004250 (0.069492) | 0.046203 / 0.037052 (0.009151) | 0.367874 / 0.258489 (0.109385) | 0.409154 / 0.293841 (0.115313) | 0.035227 / 0.128546 (-0.093319) | 0.012223 / 0.075646 (-0.063424) | 0.087149 / 0.419271 (-0.332122) | 0.045648 / 0.043533 (0.002115) | 0.362414 / 0.255139 (0.107275) | 0.379970 / 0.283200 (0.096770) | 0.100631 / 0.141683 (-0.041052) | 1.439733 / 1.452155 (-0.012422) | 1.506266 / 1.492716 (0.013550) |\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) | 0.227071 / 0.018006 (0.209065) | 0.451243 / 0.000490 (0.450753) | 0.000406 / 0.000200 (0.000206) | 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) | 0.028952 / 0.037411 (-0.008459) | 0.111934 / 0.014526 (0.097408) | 0.124080 / 0.176557 (-0.052477) | 0.174022 / 0.737135 (-0.563113) | 0.126811 / 0.296338 (-0.169527) |\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) | 0.436423 / 0.215209 (0.221214) | 4.331959 / 2.077655 (2.254304) | 2.111914 / 1.504120 (0.607794) | 1.921338 / 1.541195 (0.380143) | 1.994425 / 1.468490 (0.525935) | 0.699164 / 4.584777 (-3.885613) | 3.722143 / 3.745712 (-0.023569) | 3.516538 / 5.269862 (-1.753323) | 1.867245 / 4.565676 (-2.698431) | 0.085923 / 0.424275 (-0.338352) | 0.012059 / 0.007607 (0.004452) | 0.586147 / 0.226044 (0.360102) | 5.395823 / 2.268929 (3.126894) | 2.594430 / 55.444624 (-52.850194) | 2.275021 / 6.876477 (-4.601456) | 2.347810 / 2.142072 (0.205737) | 0.835118 / 4.805227 (-3.970109) | 0.167089 / 6.500664 (-6.333575) | 0.064893 / 0.075469 (-0.010576) |\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) | 1.291423 / 1.841788 (-0.550365) | 14.992696 / 8.074308 (6.918388) | 13.307842 / 10.191392 (3.116450) | 0.163799 / 0.680424 (-0.516625) | 0.017315 / 0.534201 (-0.516886) | 0.461319 / 0.579283 (-0.117965) | 0.430474 / 0.434364 (-0.003889) | 0.568115 / 0.540337 (0.027777) | 0.647909 / 1.386936 (-0.739027) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5161c9ecdcdde9cc99c7f212da13523d5ba6bdb \"CML watermark\")\n"
] | "2023-05-17T05:56:45" | "2023-05-17T14:26:59" | "2023-05-17T14:19:18" | MEMBER | null | false | {
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} | Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`.
Before, `None` module was returned. | {
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https://api.github.com/repos/huggingface/datasets/issues/5868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5868/comments | https://api.github.com/repos/huggingface/datasets/issues/5868/events | https://github.com/huggingface/datasets/issues/5868 | 1,711,173,098 | I_kwDODunzps5l_m3q | 5,868 | Is it possible to change a cached file and 're-cache' it instead of re-generating? | {
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"Arrow files/primitives (tables and arrays) are immutable, so re-generating them is the only option, I'm afraid.",
"> \r\n\r\nGot it, thanks for your reply"
] | "2023-05-16T03:45:42" | "2023-05-17T11:21:36" | "2023-05-17T11:21:36" | NONE | null | null | null | ### Feature request
Hi,
I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours
### Motivation
For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it.
### Your contribution
For now, I can't help, sorry. | {
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https://api.github.com/repos/huggingface/datasets/issues/5861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5861/comments | https://api.github.com/repos/huggingface/datasets/issues/5861/events | https://github.com/huggingface/datasets/pull/5861 | 1,709,807,340 | PR_kwDODunzps5Qf55q | 5,861 | Better error message when combining dataset dicts instead of datasets | {
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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) | 0.007167 / 0.011353 (-0.004185) | 0.004914 / 0.011008 (-0.006094) | 0.096858 / 0.038508 (0.058350) | 0.033468 / 0.023109 (0.010359) | 0.297276 / 0.275898 (0.021378) | 0.344289 / 0.323480 (0.020809) | 0.005703 / 0.007986 (-0.002282) | 0.003972 / 0.004328 (-0.000357) | 0.075191 / 0.004250 (0.070940) | 0.046247 / 0.037052 (0.009194) | 0.317857 / 0.258489 (0.059368) | 0.347263 / 0.293841 (0.053422) | 0.035017 / 0.128546 (-0.093529) | 0.012036 / 0.075646 (-0.063611) | 0.332522 / 0.419271 (-0.086750) | 0.050188 / 0.043533 (0.006655) | 0.296627 / 0.255139 (0.041488) | 0.319196 / 0.283200 (0.035997) | 0.101100 / 0.141683 (-0.040583) | 1.484536 / 1.452155 (0.032382) | 1.606364 / 1.492716 (0.113648) |\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) | 0.203954 / 0.018006 (0.185948) | 0.436505 / 0.000490 (0.436015) | 0.003853 / 0.000200 (0.003654) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025834 / 0.037411 (-0.011578) | 0.105759 / 0.014526 (0.091233) | 0.114289 / 0.176557 (-0.062268) | 0.174388 / 0.737135 (-0.562748) | 0.122248 / 0.296338 (-0.174090) |\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) | 0.404218 / 0.215209 (0.189009) | 4.027900 / 2.077655 (1.950245) | 1.854757 / 1.504120 (0.350637) | 1.668882 / 1.541195 (0.127687) | 1.731451 / 1.468490 (0.262961) | 0.707843 / 4.584777 (-3.876934) | 3.756386 / 3.745712 (0.010674) | 2.067751 / 5.269862 (-3.202110) | 1.313039 / 4.565676 (-3.252638) | 0.086442 / 0.424275 (-0.337833) | 0.012329 / 0.007607 (0.004722) | 0.505964 / 0.226044 (0.279919) | 5.050788 / 2.268929 (2.781860) | 2.353936 / 55.444624 (-53.090688) | 2.055560 / 6.876477 (-4.820917) | 2.162948 / 2.142072 (0.020876) | 0.850532 / 4.805227 (-3.954696) | 0.168560 / 6.500664 (-6.332104) | 0.063143 / 0.075469 (-0.012326) |\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) | 1.182723 / 1.841788 (-0.659065) | 14.779342 / 8.074308 (6.705034) | 14.461572 / 10.191392 (4.270180) | 0.163120 / 0.680424 (-0.517303) | 0.017978 / 0.534201 (-0.516223) | 0.419168 / 0.579283 (-0.160115) | 0.420955 / 0.434364 (-0.013409) | 0.509710 / 0.540337 (-0.030628) | 0.619586 / 1.386936 (-0.767350) |\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) | 0.006804 / 0.011353 (-0.004549) | 0.005136 / 0.011008 (-0.005872) | 0.074910 / 0.038508 (0.036402) | 0.032552 / 0.023109 (0.009443) | 0.374998 / 0.275898 (0.099100) | 0.399219 / 0.323480 (0.075739) | 0.005615 / 0.007986 (-0.002371) | 0.004118 / 0.004328 (-0.000210) | 0.074219 / 0.004250 (0.069969) | 0.045924 / 0.037052 (0.008871) | 0.383228 / 0.258489 (0.124739) | 0.407195 / 0.293841 (0.113354) | 0.035460 / 0.128546 (-0.093086) | 0.012460 / 0.075646 (-0.063187) | 0.087077 / 0.419271 (-0.332195) | 0.050507 / 0.043533 (0.006974) | 0.369001 / 0.255139 (0.113862) | 0.385761 / 0.283200 (0.102561) | 0.106999 / 0.141683 (-0.034684) | 1.465456 / 1.452155 (0.013302) | 1.556962 / 1.492716 (0.064246) |\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) | 0.214926 / 0.018006 (0.196920) | 0.436893 / 0.000490 (0.436403) | 0.003388 / 0.000200 (0.003188) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029919 / 0.037411 (-0.007492) | 0.110859 / 0.014526 (0.096333) | 0.120617 / 0.176557 (-0.055939) | 0.171781 / 0.737135 (-0.565355) | 0.125627 / 0.296338 (-0.170712) |\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) | 0.436024 / 0.215209 (0.220815) | 4.359167 / 2.077655 (2.281512) | 2.188399 / 1.504120 (0.684279) | 2.001196 / 1.541195 (0.460001) | 2.023710 / 1.468490 (0.555220) | 0.713799 / 4.584777 (-3.870978) | 3.832217 / 3.745712 (0.086504) | 3.269351 / 5.269862 (-2.000510) | 1.534608 / 4.565676 (-3.031068) | 0.088505 / 0.424275 (-0.335770) | 0.012345 / 0.007607 (0.004738) | 0.542446 / 0.226044 (0.316401) | 5.377757 / 2.268929 (3.108828) | 2.659837 / 55.444624 (-52.784787) | 2.272356 / 6.876477 (-4.604120) | 2.297289 / 2.142072 (0.155217) | 0.855276 / 4.805227 (-3.949952) | 0.170666 / 6.500664 (-6.329998) | 0.064549 / 0.075469 (-0.010920) |\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) | 1.255938 / 1.841788 (-0.585850) | 15.151471 / 8.074308 (7.077163) | 12.905762 / 10.191392 (2.714370) | 0.162425 / 0.680424 (-0.517999) | 0.017504 / 0.534201 (-0.516697) | 0.448671 / 0.579283 (-0.130612) | 0.422424 / 0.434364 (-0.011940) | 0.551772 / 0.540337 (0.011434) | 0.649115 / 1.386936 (-0.737821) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be73d9f192149727c5542ff257df81b03024fa39 \"CML watermark\")\n",
"Having those different checks helps providing an appropriate error message.\r\n\r\nIf the input is a dict, we suggest to select a split. If the input lists is a mix of iterable and non-iterable, we mention that it must be one or the other.",
"<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) | 0.006559 / 0.011353 (-0.004794) | 0.004569 / 0.011008 (-0.006439) | 0.104503 / 0.038508 (0.065995) | 0.028220 / 0.023109 (0.005111) | 0.365507 / 0.275898 (0.089609) | 0.400238 / 0.323480 (0.076758) | 0.004968 / 0.007986 (-0.003017) | 0.003271 / 0.004328 (-0.001057) | 0.082804 / 0.004250 (0.078554) | 0.036299 / 0.037052 (-0.000754) | 0.361201 / 0.258489 (0.102712) | 0.410962 / 0.293841 (0.117121) | 0.030423 / 0.128546 (-0.098123) | 0.011612 / 0.075646 (-0.064034) | 0.331820 / 0.419271 (-0.087452) | 0.043822 / 0.043533 (0.000289) | 0.356242 / 0.255139 (0.101103) | 0.393035 / 0.283200 (0.109836) | 0.088426 / 0.141683 (-0.053257) | 1.484139 / 1.452155 (0.031984) | 1.566712 / 1.492716 (0.073995) |\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) | 0.195887 / 0.018006 (0.177880) | 0.402720 / 0.000490 (0.402231) | 0.003516 / 0.000200 (0.003316) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023270 / 0.037411 (-0.014141) | 0.095834 / 0.014526 (0.081308) | 0.102924 / 0.176557 (-0.073632) | 0.161397 / 0.737135 (-0.575738) | 0.105225 / 0.296338 (-0.191114) |\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) | 0.451701 / 0.215209 (0.236491) | 4.495171 / 2.077655 (2.417517) | 2.223203 / 1.504120 (0.719083) | 2.035533 / 1.541195 (0.494338) | 2.076182 / 1.468490 (0.607692) | 0.697317 / 4.584777 (-3.887460) | 3.406309 / 3.745712 (-0.339403) | 1.847179 / 5.269862 (-3.422683) | 1.158762 / 4.565676 (-3.406914) | 0.083067 / 0.424275 (-0.341208) | 0.012453 / 0.007607 (0.004846) | 0.546502 / 0.226044 (0.320458) | 5.455712 / 2.268929 (3.186784) | 2.654142 / 55.444624 (-52.790483) | 2.298722 / 6.876477 (-4.577755) | 2.383467 / 2.142072 (0.241395) | 0.805950 / 4.805227 (-3.999278) | 0.152479 / 6.500664 (-6.348185) | 0.066784 / 0.075469 (-0.008685) |\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) | 1.239129 / 1.841788 (-0.602659) | 13.603707 / 8.074308 (5.529398) | 14.062004 / 10.191392 (3.870612) | 0.130928 / 0.680424 (-0.549495) | 0.016907 / 0.534201 (-0.517294) | 0.381614 / 0.579283 (-0.197670) | 0.386770 / 0.434364 (-0.047594) | 0.455792 / 0.540337 (-0.084545) | 0.526092 / 1.386936 (-0.860844) |\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) | 0.006202 / 0.011353 (-0.005151) | 0.004478 / 0.011008 (-0.006531) | 0.076492 / 0.038508 (0.037984) | 0.026703 / 0.023109 (0.003594) | 0.355134 / 0.275898 (0.079236) | 0.391207 / 0.323480 (0.067727) | 0.004852 / 0.007986 (-0.003133) | 0.003271 / 0.004328 (-0.001057) | 0.075080 / 0.004250 (0.070830) | 0.038803 / 0.037052 (0.001750) | 0.359530 / 0.258489 (0.101041) | 0.409044 / 0.293841 (0.115203) | 0.030366 / 0.128546 (-0.098180) | 0.011544 / 0.075646 (-0.064102) | 0.084849 / 0.419271 (-0.334423) | 0.040076 / 0.043533 (-0.003457) | 0.357359 / 0.255139 (0.102220) | 0.384075 / 0.283200 (0.100875) | 0.089130 / 0.141683 (-0.052552) | 1.520400 / 1.452155 (0.068246) | 1.604403 / 1.492716 (0.111687) |\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) | 0.257127 / 0.018006 (0.239121) | 0.403691 / 0.000490 (0.403202) | 0.006894 / 0.000200 (0.006694) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024653 / 0.037411 (-0.012758) | 0.098834 / 0.014526 (0.084309) | 0.107276 / 0.176557 (-0.069281) | 0.158256 / 0.737135 (-0.578879) | 0.111339 / 0.296338 (-0.184999) |\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) | 0.445006 / 0.215209 (0.229797) | 4.452953 / 2.077655 (2.375299) | 2.168291 / 1.504120 (0.664171) | 1.969457 / 1.541195 (0.428262) | 2.003505 / 1.468490 (0.535015) | 0.695857 / 4.584777 (-3.888920) | 3.433424 / 3.745712 (-0.312288) | 2.466977 / 5.269862 (-2.802885) | 1.528167 / 4.565676 (-3.037509) | 0.082425 / 0.424275 (-0.341850) | 0.012470 / 0.007607 (0.004863) | 0.559039 / 0.226044 (0.332995) | 5.609496 / 2.268929 (3.340568) | 2.602898 / 55.444624 (-52.841726) | 2.273971 / 6.876477 (-4.602506) | 2.303370 / 2.142072 (0.161298) | 0.803875 / 4.805227 (-4.001352) | 0.151069 / 6.500664 (-6.349595) | 0.067956 / 0.075469 (-0.007513) |\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) | 1.334443 / 1.841788 (-0.507345) | 13.773252 / 8.074308 (5.698944) | 13.007042 / 10.191392 (2.815650) | 0.127939 / 0.680424 (-0.552485) | 0.016412 / 0.534201 (-0.517789) | 0.374744 / 0.579283 (-0.204539) | 0.396912 / 0.434364 (-0.037452) | 0.443197 / 0.540337 (-0.097140) | 0.528338 / 1.386936 (-0.858598) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51d9f2a3064aa89a780e3d02c6cc34000c51c4fb \"CML watermark\")\n",
"Just modified it to use only one loop. I think I managed to keep it readable as well",
"<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) | 0.007382 / 0.011353 (-0.003971) | 0.005143 / 0.011008 (-0.005865) | 0.097635 / 0.038508 (0.059127) | 0.034726 / 0.023109 (0.011616) | 0.315556 / 0.275898 (0.039658) | 0.355951 / 0.323480 (0.032472) | 0.006055 / 0.007986 (-0.001931) | 0.004264 / 0.004328 (-0.000065) | 0.073636 / 0.004250 (0.069386) | 0.050480 / 0.037052 (0.013428) | 0.316031 / 0.258489 (0.057542) | 0.363933 / 0.293841 (0.070092) | 0.035138 / 0.128546 (-0.093408) | 0.012407 / 0.075646 (-0.063239) | 0.333677 / 0.419271 (-0.085595) | 0.050586 / 0.043533 (0.007053) | 0.309507 / 0.255139 (0.054369) | 0.327043 / 0.283200 (0.043844) | 0.108975 / 0.141683 (-0.032708) | 1.447778 / 1.452155 (-0.004377) | 1.519971 / 1.492716 (0.027255) |\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) | 0.248770 / 0.018006 (0.230764) | 0.603036 / 0.000490 (0.602546) | 0.000383 / 0.000200 (0.000183) | 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) | 0.027094 / 0.037411 (-0.010317) | 0.104427 / 0.014526 (0.089901) | 0.120627 / 0.176557 (-0.055929) | 0.178790 / 0.737135 (-0.558346) | 0.124877 / 0.296338 (-0.171461) |\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) | 0.414442 / 0.215209 (0.199233) | 4.138009 / 2.077655 (2.060355) | 1.964642 / 1.504120 (0.460523) | 1.775940 / 1.541195 (0.234745) | 1.899719 / 1.468490 (0.431228) | 0.695406 / 4.584777 (-3.889371) | 3.760470 / 3.745712 (0.014758) | 3.906958 / 5.269862 (-1.362904) | 2.028164 / 4.565676 (-2.537513) | 0.086704 / 0.424275 (-0.337571) | 0.012465 / 0.007607 (0.004857) | 0.512336 / 0.226044 (0.286292) | 5.108587 / 2.268929 (2.839659) | 2.435273 / 55.444624 (-53.009352) | 2.142387 / 6.876477 (-4.734090) | 2.258234 / 2.142072 (0.116162) | 0.854035 / 4.805227 (-3.951193) | 0.170443 / 6.500664 (-6.330222) | 0.065762 / 0.075469 (-0.009707) |\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) | 1.187529 / 1.841788 (-0.654259) | 15.151164 / 8.074308 (7.076856) | 14.577545 / 10.191392 (4.386153) | 0.166973 / 0.680424 (-0.513450) | 0.017883 / 0.534201 (-0.516318) | 0.427607 / 0.579283 (-0.151676) | 0.417050 / 0.434364 (-0.017314) | 0.508116 / 0.540337 (-0.032221) | 0.590173 / 1.386936 (-0.796763) |\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) | 0.007499 / 0.011353 (-0.003854) | 0.005195 / 0.011008 (-0.005813) | 0.073600 / 0.038508 (0.035091) | 0.033574 / 0.023109 (0.010464) | 0.377506 / 0.275898 (0.101608) | 0.432752 / 0.323480 (0.109272) | 0.006042 / 0.007986 (-0.001944) | 0.006427 / 0.004328 (0.002098) | 0.071666 / 0.004250 (0.067416) | 0.053243 / 0.037052 (0.016190) | 0.363972 / 0.258489 (0.105483) | 0.454988 / 0.293841 (0.161147) | 0.035118 / 0.128546 (-0.093428) | 0.012395 / 0.075646 (-0.063251) | 0.084308 / 0.419271 (-0.334963) | 0.048589 / 0.043533 (0.005057) | 0.368036 / 0.255139 (0.112897) | 0.399414 / 0.283200 (0.116215) | 0.109043 / 0.141683 (-0.032640) | 1.462972 / 1.452155 (0.010817) | 1.574443 / 1.492716 (0.081726) |\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) | 0.215107 / 0.018006 (0.197101) | 0.550255 / 0.000490 (0.549765) | 0.004630 / 0.000200 (0.004430) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.111866 / 0.014526 (0.097340) | 0.126559 / 0.176557 (-0.049997) | 0.181443 / 0.737135 (-0.555693) | 0.130559 / 0.296338 (-0.165779) |\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) | 0.441410 / 0.215209 (0.226201) | 4.403406 / 2.077655 (2.325752) | 2.180276 / 1.504120 (0.676156) | 2.003729 / 1.541195 (0.462534) | 2.079394 / 1.468490 (0.610904) | 0.706061 / 4.584777 (-3.878716) | 3.805668 / 3.745712 (0.059956) | 3.864941 / 5.269862 (-1.404921) | 1.970468 / 4.565676 (-2.595208) | 0.086033 / 0.424275 (-0.338242) | 0.012261 / 0.007607 (0.004654) | 0.550427 / 0.226044 (0.324383) | 5.542270 / 2.268929 (3.273342) | 2.717047 / 55.444624 (-52.727577) | 2.449022 / 6.876477 (-4.427455) | 2.549567 / 2.142072 (0.407495) | 0.854981 / 4.805227 (-3.950247) | 0.169756 / 6.500664 (-6.330908) | 0.067082 / 0.075469 (-0.008387) |\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) | 1.281369 / 1.841788 (-0.560419) | 15.445090 / 8.074308 (7.370781) | 13.205652 / 10.191392 (3.014260) | 0.170070 / 0.680424 (-0.510354) | 0.017815 / 0.534201 (-0.516385) | 0.425193 / 0.579283 (-0.154090) | 0.425205 / 0.434364 (-0.009159) | 0.493561 / 0.540337 (-0.046776) | 0.588994 / 1.386936 (-0.797942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e427105fc68fce04d0f3c74efb942cbf3a65d166 \"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) | 0.006345 / 0.011353 (-0.005008) | 0.004330 / 0.011008 (-0.006678) | 0.096327 / 0.038508 (0.057819) | 0.032964 / 0.023109 (0.009855) | 0.335600 / 0.275898 (0.059702) | 0.365635 / 0.323480 (0.042155) | 0.005435 / 0.007986 (-0.002551) | 0.005005 / 0.004328 (0.000677) | 0.071107 / 0.004250 (0.066856) | 0.044363 / 0.037052 (0.007311) | 0.339988 / 0.258489 (0.081498) | 0.375575 / 0.293841 (0.081734) | 0.028343 / 0.128546 (-0.100203) | 0.008587 / 0.075646 (-0.067059) | 0.324349 / 0.419271 (-0.094922) | 0.050105 / 0.043533 (0.006573) | 0.327398 / 0.255139 (0.072259) | 0.348479 / 0.283200 (0.065279) | 0.102357 / 0.141683 (-0.039326) | 1.419905 / 1.452155 (-0.032250) | 1.534887 / 1.492716 (0.042171) |\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) | 0.212418 / 0.018006 (0.194412) | 0.433183 / 0.000490 (0.432693) | 0.000595 / 0.000200 (0.000395) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027520 / 0.037411 (-0.009891) | 0.109503 / 0.014526 (0.094977) | 0.118202 / 0.176557 (-0.058355) | 0.177236 / 0.737135 (-0.559899) | 0.123736 / 0.296338 (-0.172602) |\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) | 0.405734 / 0.215209 (0.190525) | 4.039566 / 2.077655 (1.961911) | 1.838211 / 1.504120 (0.334091) | 1.652650 / 1.541195 (0.111456) | 1.753488 / 1.468490 (0.284998) | 0.525258 / 4.584777 (-4.059519) | 3.704509 / 3.745712 (-0.041203) | 1.826794 / 5.269862 (-3.443067) | 1.236361 / 4.565676 (-3.329315) | 0.065619 / 0.424275 (-0.358656) | 0.011606 / 0.007607 (0.003999) | 0.505954 / 0.226044 (0.279910) | 5.054140 / 2.268929 (2.785211) | 2.352587 / 55.444624 (-53.092037) | 2.050601 / 6.876477 (-4.825875) | 2.097222 / 2.142072 (-0.044850) | 0.641044 / 4.805227 (-4.164183) | 0.140676 / 6.500664 (-6.359988) | 0.063217 / 0.075469 (-0.012253) |\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) | 1.177750 / 1.841788 (-0.664038) | 14.819346 / 8.074308 (6.745038) | 14.085937 / 10.191392 (3.894545) | 0.168618 / 0.680424 (-0.511806) | 0.017189 / 0.534201 (-0.517011) | 0.393415 / 0.579283 (-0.185868) | 0.422879 / 0.434364 (-0.011485) | 0.477289 / 0.540337 (-0.063048) | 0.569078 / 1.386936 (-0.817858) |\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) | 0.006502 / 0.011353 (-0.004850) | 0.004640 / 0.011008 (-0.006368) | 0.073272 / 0.038508 (0.034764) | 0.033225 / 0.023109 (0.010116) | 0.359165 / 0.275898 (0.083267) | 0.391659 / 0.323480 (0.068179) | 0.005684 / 0.007986 (-0.002302) | 0.004045 / 0.004328 (-0.000284) | 0.072880 / 0.004250 (0.068629) | 0.046260 / 0.037052 (0.009208) | 0.361772 / 0.258489 (0.103283) | 0.402905 / 0.293841 (0.109064) | 0.027732 / 0.128546 (-0.100814) | 0.008864 / 0.075646 (-0.066783) | 0.081961 / 0.419271 (-0.337310) | 0.046170 / 0.043533 (0.002637) | 0.364198 / 0.255139 (0.109059) | 0.387468 / 0.283200 (0.104269) | 0.105456 / 0.141683 (-0.036227) | 1.457176 / 1.452155 (0.005021) | 1.564899 / 1.492716 (0.072183) |\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) | 0.179129 / 0.018006 (0.161123) | 0.439699 / 0.000490 (0.439209) | 0.002882 / 0.000200 (0.002682) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029123 / 0.037411 (-0.008288) | 0.112046 / 0.014526 (0.097520) | 0.122773 / 0.176557 (-0.053784) | 0.178404 / 0.737135 (-0.558732) | 0.127904 / 0.296338 (-0.168434) |\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) | 0.440413 / 0.215209 (0.225204) | 4.407334 / 2.077655 (2.329680) | 2.112932 / 1.504120 (0.608812) | 1.911034 / 1.541195 (0.369840) | 2.057168 / 1.468490 (0.588677) | 0.525472 / 4.584777 (-4.059305) | 3.738894 / 3.745712 (-0.006818) | 1.807592 / 5.269862 (-3.462270) | 1.053837 / 4.565676 (-3.511839) | 0.066203 / 0.424275 (-0.358072) | 0.011965 / 0.007607 (0.004358) | 0.541137 / 0.226044 (0.315093) | 5.415040 / 2.268929 (3.146112) | 2.580476 / 55.444624 (-52.864148) | 2.234144 / 6.876477 (-4.642333) | 2.306014 / 2.142072 (0.163942) | 0.644221 / 4.805227 (-4.161006) | 0.142870 / 6.500664 (-6.357794) | 0.065015 / 0.075469 (-0.010454) |\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) | 1.303465 / 1.841788 (-0.538323) | 14.949683 / 8.074308 (6.875375) | 14.370871 / 10.191392 (4.179478) | 0.142714 / 0.680424 (-0.537710) | 0.017372 / 0.534201 (-0.516829) | 0.403898 / 0.579283 (-0.175385) | 0.424781 / 0.434364 (-0.009583) | 0.465984 / 0.540337 (-0.074353) | 0.570863 / 1.386936 (-0.816074) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22d1d533e8ab831b1aa1aab3e7d3c72ba42a83e8 \"CML watermark\")\n"
] | "2023-05-15T10:36:24" | "2023-05-23T10:40:13" | "2023-05-23T10:32:58" | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5860/comments | https://api.github.com/repos/huggingface/datasets/issues/5860/events | https://github.com/huggingface/datasets/pull/5860 | 1,709,727,460 | PR_kwDODunzps5QfojD | 5,860 | Minor tqdm optim | {
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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) | 0.006917 / 0.011353 (-0.004436) | 0.004803 / 0.011008 (-0.006205) | 0.097082 / 0.038508 (0.058574) | 0.035105 / 0.023109 (0.011996) | 0.325911 / 0.275898 (0.050013) | 0.371858 / 0.323480 (0.048378) | 0.006451 / 0.007986 (-0.001534) | 0.004421 / 0.004328 (0.000093) | 0.075738 / 0.004250 (0.071487) | 0.053624 / 0.037052 (0.016572) | 0.332661 / 0.258489 (0.074172) | 0.372729 / 0.293841 (0.078888) | 0.028279 / 0.128546 (-0.100267) | 0.009318 / 0.075646 (-0.066328) | 0.328505 / 0.419271 (-0.090766) | 0.066962 / 0.043533 (0.023429) | 0.316863 / 0.255139 (0.061724) | 0.344296 / 0.283200 (0.061096) | 0.120575 / 0.141683 (-0.021108) | 1.457867 / 1.452155 (0.005712) | 1.597361 / 1.492716 (0.104644) |\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) | 0.296399 / 0.018006 (0.278392) | 0.507196 / 0.000490 (0.506706) | 0.003036 / 0.000200 (0.002836) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028535 / 0.037411 (-0.008876) | 0.110566 / 0.014526 (0.096040) | 0.122078 / 0.176557 (-0.054479) | 0.182926 / 0.737135 (-0.554210) | 0.125546 / 0.296338 (-0.170792) |\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) | 0.426952 / 0.215209 (0.211742) | 4.255608 / 2.077655 (2.177953) | 2.063865 / 1.504120 (0.559745) | 1.867198 / 1.541195 (0.326004) | 2.058236 / 1.468490 (0.589746) | 0.525885 / 4.584777 (-4.058892) | 3.723607 / 3.745712 (-0.022105) | 1.919144 / 5.269862 (-3.350718) | 1.235308 / 4.565676 (-3.330368) | 0.066423 / 0.424275 (-0.357852) | 0.012045 / 0.007607 (0.004438) | 0.528432 / 0.226044 (0.302388) | 5.268723 / 2.268929 (2.999794) | 2.504071 / 55.444624 (-52.940553) | 2.137999 / 6.876477 (-4.738477) | 2.229987 / 2.142072 (0.087914) | 0.641739 / 4.805227 (-4.163488) | 0.142635 / 6.500664 (-6.358029) | 0.065649 / 0.075469 (-0.009820) |\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) | 1.182710 / 1.841788 (-0.659078) | 15.339777 / 8.074308 (7.265469) | 14.722308 / 10.191392 (4.530916) | 0.145914 / 0.680424 (-0.534510) | 0.017861 / 0.534201 (-0.516340) | 0.393092 / 0.579283 (-0.186191) | 0.431179 / 0.434364 (-0.003185) | 0.485712 / 0.540337 (-0.054625) | 0.602634 / 1.386936 (-0.784302) |\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) | 0.006792 / 0.011353 (-0.004561) | 0.005118 / 0.011008 (-0.005890) | 0.073440 / 0.038508 (0.034932) | 0.033751 / 0.023109 (0.010642) | 0.389243 / 0.275898 (0.113345) | 0.397083 / 0.323480 (0.073603) | 0.005989 / 0.007986 (-0.001997) | 0.004289 / 0.004328 (-0.000040) | 0.073228 / 0.004250 (0.068977) | 0.053490 / 0.037052 (0.016438) | 0.396070 / 0.258489 (0.137581) | 0.415134 / 0.293841 (0.121293) | 0.028649 / 0.128546 (-0.099897) | 0.009159 / 0.075646 (-0.066487) | 0.080813 / 0.419271 (-0.338458) | 0.048200 / 0.043533 (0.004667) | 0.388009 / 0.255139 (0.132870) | 0.382174 / 0.283200 (0.098975) | 0.107807 / 0.141683 (-0.033876) | 1.467276 / 1.452155 (0.015121) | 1.568091 / 1.492716 (0.075375) |\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) | 0.328030 / 0.018006 (0.310024) | 0.498058 / 0.000490 (0.497568) | 0.002513 / 0.000200 (0.002313) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029835 / 0.037411 (-0.007576) | 0.113859 / 0.014526 (0.099333) | 0.130813 / 0.176557 (-0.045743) | 0.183646 / 0.737135 (-0.553490) | 0.136561 / 0.296338 (-0.159777) |\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) | 0.438901 / 0.215209 (0.223692) | 4.376426 / 2.077655 (2.298771) | 2.220932 / 1.504120 (0.716812) | 2.043585 / 1.541195 (0.502390) | 2.161383 / 1.468490 (0.692893) | 0.523224 / 4.584777 (-4.061553) | 3.730589 / 3.745712 (-0.015123) | 1.859602 / 5.269862 (-3.410260) | 1.073415 / 4.565676 (-3.492261) | 0.066363 / 0.424275 (-0.357912) | 0.012491 / 0.007607 (0.004884) | 0.542052 / 0.226044 (0.316008) | 5.426246 / 2.268929 (3.157318) | 2.673884 / 55.444624 (-52.770740) | 2.372611 / 6.876477 (-4.503865) | 2.482216 / 2.142072 (0.340143) | 0.705669 / 4.805227 (-4.099558) | 0.141075 / 6.500664 (-6.359589) | 0.065339 / 0.075469 (-0.010130) |\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) | 1.316403 / 1.841788 (-0.525385) | 15.832870 / 8.074308 (7.758562) | 13.307045 / 10.191392 (3.115653) | 0.147258 / 0.680424 (-0.533166) | 0.017966 / 0.534201 (-0.516235) | 0.414396 / 0.579283 (-0.164887) | 0.431801 / 0.434364 (-0.002563) | 0.465483 / 0.540337 (-0.074855) | 0.577850 / 1.386936 (-0.809086) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c795c7e332a7c850c3e725f2034d4894b5e314f7 \"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) | 0.006368 / 0.011353 (-0.004985) | 0.004274 / 0.011008 (-0.006734) | 0.098799 / 0.038508 (0.060291) | 0.029096 / 0.023109 (0.005986) | 0.308009 / 0.275898 (0.032111) | 0.345701 / 0.323480 (0.022221) | 0.005312 / 0.007986 (-0.002674) | 0.003435 / 0.004328 (-0.000894) | 0.075912 / 0.004250 (0.071662) | 0.041993 / 0.037052 (0.004941) | 0.320075 / 0.258489 (0.061586) | 0.347506 / 0.293841 (0.053665) | 0.025456 / 0.128546 (-0.103091) | 0.008461 / 0.075646 (-0.067185) | 0.322823 / 0.419271 (-0.096448) | 0.044650 / 0.043533 (0.001117) | 0.314118 / 0.255139 (0.058979) | 0.333436 / 0.283200 (0.050237) | 0.093811 / 0.141683 (-0.047871) | 1.464464 / 1.452155 (0.012310) | 1.548098 / 1.492716 (0.055382) |\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) | 0.015905 / 0.018006 (-0.002101) | 0.427847 / 0.000490 (0.427357) | 0.007600 / 0.000200 (0.007400) | 0.000421 / 0.000054 (0.000366) |\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 (-0.012882) | 0.099907 / 0.014526 (0.085381) | 0.107282 / 0.176557 (-0.069275) | 0.168332 / 0.737135 (-0.568804) | 0.109875 / 0.296338 (-0.186464) |\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) | 0.451064 / 0.215209 (0.235855) | 4.491434 / 2.077655 (2.413779) | 2.253251 / 1.504120 (0.749131) | 2.086740 / 1.541195 (0.545545) | 2.133288 / 1.468490 (0.664798) | 0.558801 / 4.584777 (-4.025976) | 3.463525 / 3.745712 (-0.282187) | 1.747657 / 5.269862 (-3.522205) | 1.005465 / 4.565676 (-3.560211) | 0.068341 / 0.424275 (-0.355934) | 0.012521 / 0.007607 (0.004914) | 0.567002 / 0.226044 (0.340957) | 5.689529 / 2.268929 (3.420601) | 2.700562 / 55.444624 (-52.744062) | 2.384888 / 6.876477 (-4.491589) | 2.503160 / 2.142072 (0.361088) | 0.667107 / 4.805227 (-4.138120) | 0.137253 / 6.500664 (-6.363412) | 0.068300 / 0.075469 (-0.007170) |\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) | 1.202916 / 1.841788 (-0.638872) | 14.163393 / 8.074308 (6.089085) | 14.402463 / 10.191392 (4.211071) | 0.145273 / 0.680424 (-0.535151) | 0.016996 / 0.534201 (-0.517205) | 0.363520 / 0.579283 (-0.215763) | 0.421595 / 0.434364 (-0.012769) | 0.438413 / 0.540337 (-0.101925) | 0.508615 / 1.386936 (-0.878321) |\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) | 0.006419 / 0.011353 (-0.004934) | 0.004346 / 0.011008 (-0.006662) | 0.076356 / 0.038508 (0.037848) | 0.029370 / 0.023109 (0.006260) | 0.371046 / 0.275898 (0.095148) | 0.398279 / 0.323480 (0.074799) | 0.005258 / 0.007986 (-0.002728) | 0.003528 / 0.004328 (-0.000800) | 0.076787 / 0.004250 (0.072537) | 0.041575 / 0.037052 (0.004522) | 0.362319 / 0.258489 (0.103830) | 0.402134 / 0.293841 (0.108293) | 0.025633 / 0.128546 (-0.102913) | 0.008826 / 0.075646 (-0.066820) | 0.082380 / 0.419271 (-0.336892) | 0.041655 / 0.043533 (-0.001878) | 0.357583 / 0.255139 (0.102444) | 0.383486 / 0.283200 (0.100287) | 0.093682 / 0.141683 (-0.048001) | 1.488522 / 1.452155 (0.036367) | 1.576090 / 1.492716 (0.083373) |\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) | 0.185556 / 0.018006 (0.167550) | 0.431345 / 0.000490 (0.430855) | 0.002290 / 0.000200 (0.002090) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026030 / 0.037411 (-0.011382) | 0.102889 / 0.014526 (0.088364) | 0.109541 / 0.176557 (-0.067015) | 0.161050 / 0.737135 (-0.576085) | 0.113525 / 0.296338 (-0.182814) |\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) | 0.445301 / 0.215209 (0.230092) | 4.437320 / 2.077655 (2.359666) | 2.174181 / 1.504120 (0.670061) | 1.977440 / 1.541195 (0.436245) | 2.036323 / 1.468490 (0.567832) | 0.554227 / 4.584777 (-4.030550) | 3.462746 / 3.745712 (-0.282966) | 1.765257 / 5.269862 (-3.504604) | 1.014515 / 4.565676 (-3.551161) | 0.068391 / 0.424275 (-0.355884) | 0.013154 / 0.007607 (0.005546) | 0.546696 / 0.226044 (0.320652) | 5.490628 / 2.268929 (3.221699) | 2.611947 / 55.444624 (-52.832677) | 2.282659 / 6.876477 (-4.593818) | 2.333972 / 2.142072 (0.191899) | 0.663140 / 4.805227 (-4.142087) | 0.137996 / 6.500664 (-6.362668) | 0.069063 / 0.075469 (-0.006407) |\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) | 1.332147 / 1.841788 (-0.509641) | 14.781592 / 8.074308 (6.707284) | 13.399190 / 10.191392 (3.207798) | 0.139370 / 0.680424 (-0.541054) | 0.016742 / 0.534201 (-0.517459) | 0.364138 / 0.579283 (-0.215146) | 0.402479 / 0.434364 (-0.031885) | 0.427591 / 0.540337 (-0.112746) | 0.520864 / 1.386936 (-0.866072) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8279677b58b93f77995c7da67aea2a04b6a7395 \"CML watermark\")\n"
] | "2023-05-15T09:49:37" | "2023-05-17T18:46:46" | "2023-05-17T18:39:35" | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5860",
"html_url": "https://github.com/huggingface/datasets/pull/5860",
"diff_url": "https://github.com/huggingface/datasets/pull/5860.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5860.patch",
"merged_at": "2023-05-17T18:39:35"
} | Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`.
On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.",
"<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) | 0.007212 / 0.011353 (-0.004141) | 0.005125 / 0.011008 (-0.005883) | 0.098460 / 0.038508 (0.059952) | 0.034040 / 0.023109 (0.010931) | 0.320203 / 0.275898 (0.044305) | 0.357787 / 0.323480 (0.034307) | 0.006000 / 0.007986 (-0.001986) | 0.005644 / 0.004328 (0.001316) | 0.072654 / 0.004250 (0.068403) | 0.049393 / 0.037052 (0.012341) | 0.345686 / 0.258489 (0.087196) | 0.362345 / 0.293841 (0.068504) | 0.036597 / 0.128546 (-0.091949) | 0.012303 / 0.075646 (-0.063343) | 0.334374 / 0.419271 (-0.084897) | 0.062010 / 0.043533 (0.018477) | 0.312547 / 0.255139 (0.057408) | 0.336021 / 0.283200 (0.052821) | 0.112304 / 0.141683 (-0.029378) | 1.446706 / 1.452155 (-0.005449) | 1.523256 / 1.492716 (0.030540) |\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) | 0.217658 / 0.018006 (0.199652) | 0.449208 / 0.000490 (0.448718) | 0.002878 / 0.000200 (0.002679) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025735 / 0.037411 (-0.011676) | 0.105876 / 0.014526 (0.091350) | 0.114887 / 0.176557 (-0.061669) | 0.170984 / 0.737135 (-0.566152) | 0.121420 / 0.296338 (-0.174918) |\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) | 0.419670 / 0.215209 (0.204461) | 4.189453 / 2.077655 (2.111798) | 1.938236 / 1.504120 (0.434116) | 1.769747 / 1.541195 (0.228553) | 1.910919 / 1.468490 (0.442429) | 0.705046 / 4.584777 (-3.879730) | 3.783774 / 3.745712 (0.038062) | 2.096504 / 5.269862 (-3.173358) | 1.339265 / 4.565676 (-3.226412) | 0.086670 / 0.424275 (-0.337605) | 0.012243 / 0.007607 (0.004636) | 0.524701 / 0.226044 (0.298657) | 5.240689 / 2.268929 (2.971760) | 2.473622 / 55.444624 (-52.971003) | 2.170568 / 6.876477 (-4.705909) | 2.289653 / 2.142072 (0.147581) | 0.848913 / 4.805227 (-3.956314) | 0.168332 / 6.500664 (-6.332332) | 0.064926 / 0.075469 (-0.010543) |\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) | 1.193614 / 1.841788 (-0.648173) | 14.920403 / 8.074308 (6.846095) | 14.475059 / 10.191392 (4.283667) | 0.164458 / 0.680424 (-0.515966) | 0.017613 / 0.534201 (-0.516588) | 0.426311 / 0.579283 (-0.152972) | 0.431478 / 0.434364 (-0.002886) | 0.520280 / 0.540337 (-0.020057) | 0.627738 / 1.386936 (-0.759198) |\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) | 0.007458 / 0.011353 (-0.003895) | 0.005363 / 0.011008 (-0.005645) | 0.076713 / 0.038508 (0.038205) | 0.034189 / 0.023109 (0.011079) | 0.359938 / 0.275898 (0.084040) | 0.395532 / 0.323480 (0.072052) | 0.005977 / 0.007986 (-0.002008) | 0.004263 / 0.004328 (-0.000065) | 0.075971 / 0.004250 (0.071721) | 0.051924 / 0.037052 (0.014871) | 0.362818 / 0.258489 (0.104329) | 0.409897 / 0.293841 (0.116056) | 0.035494 / 0.128546 (-0.093053) | 0.012399 / 0.075646 (-0.063247) | 0.088335 / 0.419271 (-0.330937) | 0.047968 / 0.043533 (0.004435) | 0.355744 / 0.255139 (0.100606) | 0.376339 / 0.283200 (0.093139) | 0.104542 / 0.141683 (-0.037141) | 1.464826 / 1.452155 (0.012672) | 1.600665 / 1.492716 (0.107948) |\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) | 0.220841 / 0.018006 (0.202834) | 0.446444 / 0.000490 (0.445954) | 0.000392 / 0.000200 (0.000192) | 0.000057 / 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) | 0.029402 / 0.037411 (-0.008009) | 0.116511 / 0.014526 (0.101986) | 0.122959 / 0.176557 (-0.053598) | 0.171674 / 0.737135 (-0.565462) | 0.129871 / 0.296338 (-0.166468) |\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) | 0.450411 / 0.215209 (0.235202) | 4.471859 / 2.077655 (2.394205) | 2.229439 / 1.504120 (0.725319) | 2.053308 / 1.541195 (0.512114) | 2.142476 / 1.468490 (0.673986) | 0.708299 / 4.584777 (-3.876478) | 3.797830 / 3.745712 (0.052118) | 2.142509 / 5.269862 (-3.127352) | 1.333357 / 4.565676 (-3.232320) | 0.086837 / 0.424275 (-0.337439) | 0.012102 / 0.007607 (0.004495) | 0.548428 / 0.226044 (0.322384) | 5.490611 / 2.268929 (3.221682) | 2.713882 / 55.444624 (-52.730742) | 2.399638 / 6.876477 (-4.476839) | 2.481549 / 2.142072 (0.339477) | 0.839812 / 4.805227 (-3.965415) | 0.168890 / 6.500664 (-6.331774) | 0.065564 / 0.075469 (-0.009906) |\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) | 1.275507 / 1.841788 (-0.566281) | 14.896343 / 8.074308 (6.822035) | 13.159701 / 10.191392 (2.968309) | 0.172065 / 0.680424 (-0.508359) | 0.017507 / 0.534201 (-0.516694) | 0.420031 / 0.579283 (-0.159252) | 0.438835 / 0.434364 (0.004471) | 0.490597 / 0.540337 (-0.049741) | 0.583952 / 1.386936 (-0.802984) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48c9755d0ae9abe4c4d6cd8c1ce76eff849f0e5c \"CML watermark\")\n"
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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) | 0.007565 / 0.011353 (-0.003788) | 0.005361 / 0.011008 (-0.005647) | 0.098963 / 0.038508 (0.060455) | 0.034271 / 0.023109 (0.011162) | 0.323421 / 0.275898 (0.047523) | 0.348495 / 0.323480 (0.025015) | 0.006244 / 0.007986 (-0.001741) | 0.004215 / 0.004328 (-0.000113) | 0.073614 / 0.004250 (0.069364) | 0.049334 / 0.037052 (0.012282) | 0.315277 / 0.258489 (0.056788) | 0.354325 / 0.293841 (0.060484) | 0.035001 / 0.128546 (-0.093545) | 0.012149 / 0.075646 (-0.063497) | 0.335614 / 0.419271 (-0.083657) | 0.050532 / 0.043533 (0.006999) | 0.308500 / 0.255139 (0.053361) | 0.324620 / 0.283200 (0.041421) | 0.110241 / 0.141683 (-0.031442) | 1.443923 / 1.452155 (-0.008232) | 1.559289 / 1.492716 (0.066573) |\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) | 0.207629 / 0.018006 (0.189622) | 0.433251 / 0.000490 (0.432762) | 0.003021 / 0.000200 (0.002821) | 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) | 0.028312 / 0.037411 (-0.009100) | 0.111829 / 0.014526 (0.097303) | 0.127099 / 0.176557 (-0.049458) | 0.184702 / 0.737135 (-0.552433) | 0.125062 / 0.296338 (-0.171277) |\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) | 0.399451 / 0.215209 (0.184242) | 3.966528 / 2.077655 (1.888874) | 1.826004 / 1.504120 (0.321884) | 1.669547 / 1.541195 (0.128353) | 1.751584 / 1.468490 (0.283094) | 0.688308 / 4.584777 (-3.896469) | 3.813275 / 3.745712 (0.067562) | 3.181554 / 5.269862 (-2.088307) | 1.750566 / 4.565676 (-2.815111) | 0.085038 / 0.424275 (-0.339237) | 0.011992 / 0.007607 (0.004385) | 0.502374 / 0.226044 (0.276330) | 4.970614 / 2.268929 (2.701686) | 2.309617 / 55.444624 (-53.135007) | 2.012427 / 6.876477 (-4.864050) | 2.156348 / 2.142072 (0.014276) | 0.834415 / 4.805227 (-3.970812) | 0.167912 / 6.500664 (-6.332752) | 0.065711 / 0.075469 (-0.009758) |\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) | 1.223132 / 1.841788 (-0.618656) | 15.126753 / 8.074308 (7.052445) | 14.829184 / 10.191392 (4.637792) | 0.142582 / 0.680424 (-0.537842) | 0.017483 / 0.534201 (-0.516718) | 0.429768 / 0.579283 (-0.149516) | 0.422745 / 0.434364 (-0.011619) | 0.508813 / 0.540337 (-0.031525) | 0.618716 / 1.386936 (-0.768220) |\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) | 0.007749 / 0.011353 (-0.003604) | 0.005433 / 0.011008 (-0.005576) | 0.076223 / 0.038508 (0.037715) | 0.036334 / 0.023109 (0.013225) | 0.375339 / 0.275898 (0.099441) | 0.413674 / 0.323480 (0.090194) | 0.006207 / 0.007986 (-0.001778) | 0.004085 / 0.004328 (-0.000244) | 0.076154 / 0.004250 (0.071904) | 0.050324 / 0.037052 (0.013271) | 0.382919 / 0.258489 (0.124429) | 0.442508 / 0.293841 (0.148667) | 0.035951 / 0.128546 (-0.092595) | 0.012067 / 0.075646 (-0.063580) | 0.087649 / 0.419271 (-0.331623) | 0.048786 / 0.043533 (0.005253) | 0.373541 / 0.255139 (0.118402) | 0.400437 / 0.283200 (0.117237) | 0.102622 / 0.141683 (-0.039061) | 1.472443 / 1.452155 (0.020288) | 1.580178 / 1.492716 (0.087462) |\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) | 0.222105 / 0.018006 (0.204098) | 0.445465 / 0.000490 (0.444975) | 0.003671 / 0.000200 (0.003471) | 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) | 0.030808 / 0.037411 (-0.006603) | 0.116687 / 0.014526 (0.102161) | 0.124972 / 0.176557 (-0.051584) | 0.175621 / 0.737135 (-0.561514) | 0.129029 / 0.296338 (-0.167310) |\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) | 0.434627 / 0.215209 (0.219418) | 4.330268 / 2.077655 (2.252613) | 2.140266 / 1.504120 (0.636146) | 1.960705 / 1.541195 (0.419510) | 2.035949 / 1.468490 (0.567459) | 0.696830 / 4.584777 (-3.887947) | 3.790468 / 3.745712 (0.044756) | 3.194112 / 5.269862 (-2.075750) | 1.577728 / 4.565676 (-2.987948) | 0.085445 / 0.424275 (-0.338830) | 0.012207 / 0.007607 (0.004600) | 0.555199 / 0.226044 (0.329154) | 5.551539 / 2.268929 (3.282610) | 2.630917 / 55.444624 (-52.813707) | 2.383362 / 6.876477 (-4.493114) | 2.476301 / 2.142072 (0.334229) | 0.845773 / 4.805227 (-3.959455) | 0.169229 / 6.500664 (-6.331435) | 0.066064 / 0.075469 (-0.009405) |\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) | 1.277543 / 1.841788 (-0.564245) | 15.775637 / 8.074308 (7.701329) | 13.528588 / 10.191392 (3.337196) | 0.167428 / 0.680424 (-0.512996) | 0.017581 / 0.534201 (-0.516620) | 0.454472 / 0.579283 (-0.124811) | 0.427987 / 0.434364 (-0.006377) | 0.551512 / 0.540337 (0.011175) | 0.650811 / 1.386936 (-0.736125) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#96a6f5f526cc90330df597ae0097274742d5b84f \"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) | 0.009800 / 0.011353 (-0.001552) | 0.006443 / 0.011008 (-0.004565) | 0.144137 / 0.038508 (0.105629) | 0.037493 / 0.023109 (0.014383) | 0.482306 / 0.275898 (0.206408) | 0.467625 / 0.323480 (0.144145) | 0.006812 / 0.007986 (-0.001174) | 0.004810 / 0.004328 (0.000481) | 0.109047 / 0.004250 (0.104796) | 0.047169 / 0.037052 (0.010116) | 0.451253 / 0.258489 (0.192764) | 0.511339 / 0.293841 (0.217498) | 0.055583 / 0.128546 (-0.072963) | 0.021810 / 0.075646 (-0.053836) | 0.426522 / 0.419271 (0.007250) | 0.070282 / 0.043533 (0.026749) | 0.469631 / 0.255139 (0.214492) | 0.484951 / 0.283200 (0.201751) | 0.117370 / 0.141683 (-0.024313) | 1.809917 / 1.452155 (0.357763) | 1.882659 / 1.492716 (0.389943) |\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) | 0.223843 / 0.018006 (0.205837) | 0.549216 / 0.000490 (0.548726) | 0.007120 / 0.000200 (0.006920) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033057 / 0.037411 (-0.004354) | 0.128242 / 0.014526 (0.113716) | 0.140906 / 0.176557 (-0.035650) | 0.213122 / 0.737135 (-0.524013) | 0.148115 / 0.296338 (-0.148224) |\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) | 0.638712 / 0.215209 (0.423503) | 6.383684 / 2.077655 (4.306029) | 2.477020 / 1.504120 (0.972900) | 2.129190 / 1.541195 (0.587996) | 2.230503 / 1.468490 (0.762013) | 1.367167 / 4.584777 (-3.217610) | 5.570586 / 3.745712 (1.824873) | 5.462857 / 5.269862 (0.192996) | 2.990604 / 4.565676 (-1.575073) | 0.146543 / 0.424275 (-0.277732) | 0.016060 / 0.007607 (0.008453) | 0.812691 / 0.226044 (0.586646) | 7.928041 / 2.268929 (5.659112) | 3.329494 / 55.444624 (-52.115130) | 2.523452 / 6.876477 (-4.353025) | 2.672374 / 2.142072 (0.530302) | 1.598554 / 4.805227 (-3.206673) | 0.284727 / 6.500664 (-6.215937) | 0.080359 / 0.075469 (0.004889) |\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) | 1.501112 / 1.841788 (-0.340675) | 17.553644 / 8.074308 (9.479335) | 22.704062 / 10.191392 (12.512670) | 0.225575 / 0.680424 (-0.454849) | 0.026531 / 0.534201 (-0.507670) | 0.520129 / 0.579283 (-0.059154) | 0.626220 / 0.434364 (0.191856) | 0.631740 / 0.540337 (0.091403) | 0.750611 / 1.386936 (-0.636325) |\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) | 0.009866 / 0.011353 (-0.001487) | 0.005733 / 0.011008 (-0.005275) | 0.111529 / 0.038508 (0.073021) | 0.042001 / 0.023109 (0.018891) | 0.458578 / 0.275898 (0.182680) | 0.507796 / 0.323480 (0.184316) | 0.006547 / 0.007986 (-0.001438) | 0.005611 / 0.004328 (0.001282) | 0.115321 / 0.004250 (0.111070) | 0.048741 / 0.037052 (0.011689) | 0.447611 / 0.258489 (0.189122) | 0.531830 / 0.293841 (0.237989) | 0.052176 / 0.128546 (-0.076370) | 0.022431 / 0.075646 (-0.053216) | 0.120709 / 0.419271 (-0.298562) | 0.067301 / 0.043533 (0.023769) | 0.460577 / 0.255139 (0.205438) | 0.497805 / 0.283200 (0.214605) | 0.121830 / 0.141683 (-0.019853) | 1.876436 / 1.452155 (0.424281) | 1.983491 / 1.492716 (0.490775) |\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) | 0.230982 / 0.018006 (0.212976) | 0.540643 / 0.000490 (0.540153) | 0.004646 / 0.000200 (0.004446) | 0.000131 / 0.000054 (0.000077) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034230 / 0.037411 (-0.003181) | 0.136454 / 0.014526 (0.121928) | 0.143370 / 0.176557 (-0.033187) | 0.206752 / 0.737135 (-0.530384) | 0.148722 / 0.296338 (-0.147617) |\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) | 0.704667 / 0.215209 (0.489458) | 7.112079 / 2.077655 (5.034424) | 3.083916 / 1.504120 (1.579797) | 2.606388 / 1.541195 (1.065193) | 2.738505 / 1.468490 (1.270015) | 1.314897 / 4.584777 (-3.269880) | 5.764442 / 3.745712 (2.018729) | 3.491890 / 5.269862 (-1.777972) | 2.299983 / 4.565676 (-2.265693) | 0.169655 / 0.424275 (-0.254620) | 0.015251 / 0.007607 (0.007643) | 0.977230 / 0.226044 (0.751186) | 9.697773 / 2.268929 (7.428844) | 3.826928 / 55.444624 (-51.617697) | 3.108238 / 6.876477 (-3.768239) | 3.103242 / 2.142072 (0.961169) | 1.586645 / 4.805227 (-3.218582) | 0.287181 / 6.500664 (-6.213483) | 0.107332 / 0.075469 (0.031863) |\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) | 1.712710 / 1.841788 (-0.129077) | 19.169403 / 8.074308 (11.095095) | 21.777301 / 10.191392 (11.585909) | 0.216918 / 0.680424 (-0.463506) | 0.026551 / 0.534201 (-0.507650) | 0.570383 / 0.579283 (-0.008900) | 0.643885 / 0.434364 (0.209521) | 0.673906 / 0.540337 (0.133568) | 0.824573 / 1.386936 (-0.562363) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ead18b6921c9576a3078d2fb685c38f1e1a4b8a \"CML watermark\")\n"
] | "2023-05-12T12:01:01" | "2023-05-12T13:48:47" | "2023-05-12T13:39:06" | CONTRIBUTOR | null | false | {
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} | The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently).
Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845). | {
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] | null | [] | "2023-05-11T20:52:13" | "2023-05-23T10:32:59" | "2023-05-23T10:32:59" | NONE | null | null | null | ### System Info
standard env
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful-
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3
[41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %%
----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted")
File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy)
[122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]:
[123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122) if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)):
--> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123) raise ValueError(
[125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124) f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects."
[126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125) )
[127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
[128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127) raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")
ValueError: Unable to interleave a with a . Expected a list of Dataset objects or a list of IterableDataset objects.
```
### Expected behavior
the error message should hopefully be more clear | {
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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) | 0.007592 / 0.011353 (-0.003761) | 0.005223 / 0.011008 (-0.005786) | 0.110218 / 0.038508 (0.071710) | 0.027644 / 0.023109 (0.004534) | 0.335063 / 0.275898 (0.059165) | 0.347102 / 0.323480 (0.023623) | 0.005107 / 0.007986 (-0.002878) | 0.003932 / 0.004328 (-0.000396) | 0.086095 / 0.004250 (0.081845) | 0.034735 / 0.037052 (-0.002317) | 0.329029 / 0.258489 (0.070540) | 0.370282 / 0.293841 (0.076441) | 0.043040 / 0.128546 (-0.085507) | 0.019626 / 0.075646 (-0.056021) | 0.336452 / 0.419271 (-0.082819) | 0.070365 / 0.043533 (0.026832) | 0.326881 / 0.255139 (0.071742) | 0.354984 / 0.283200 (0.071785) | 0.102605 / 0.141683 (-0.039077) | 1.459161 / 1.452155 (0.007007) | 1.453599 / 1.492716 (-0.039117) |\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) | 0.201021 / 0.018006 (0.183015) | 0.456415 / 0.000490 (0.455926) | 0.012349 / 0.000200 (0.012149) | 0.000115 / 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) | 0.025199 / 0.037411 (-0.012213) | 0.098536 / 0.014526 (0.084010) | 0.107528 / 0.176557 (-0.069028) | 0.160492 / 0.737135 (-0.576643) | 0.108660 / 0.296338 (-0.187679) |\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) | 0.527020 / 0.215209 (0.311811) | 5.357635 / 2.077655 (3.279980) | 2.062930 / 1.504120 (0.558811) | 1.783009 / 1.541195 (0.241815) | 1.840225 / 1.468490 (0.371735) | 1.074278 / 4.584777 (-3.510499) | 4.710533 / 3.745712 (0.964821) | 2.611202 / 5.269862 (-2.658660) | 1.885487 / 4.565676 (-2.680189) | 0.123201 / 0.424275 (-0.301074) | 0.013880 / 0.007607 (0.006273) | 0.636511 / 0.226044 (0.410467) | 6.516075 / 2.268929 (4.247146) | 2.710138 / 55.444624 (-52.734486) | 2.046606 / 6.876477 (-4.829871) | 2.085907 / 2.142072 (-0.056166) | 1.199489 / 4.805227 (-3.605738) | 0.211668 / 6.500664 (-6.288996) | 0.075436 / 0.075469 (-0.000033) |\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) | 1.219771 / 1.841788 (-0.622016) | 14.276215 / 8.074308 (6.201907) | 16.611529 / 10.191392 (6.420137) | 0.221091 / 0.680424 (-0.459333) | 0.024922 / 0.534201 (-0.509279) | 0.431906 / 0.579283 (-0.147377) | 0.518863 / 0.434364 (0.084499) | 0.515366 / 0.540337 (-0.024971) | 0.640411 / 1.386936 (-0.746525) |\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) | 0.007955 / 0.011353 (-0.003398) | 0.004813 / 0.011008 (-0.006196) | 0.076508 / 0.038508 (0.038000) | 0.028137 / 0.023109 (0.005028) | 0.349609 / 0.275898 (0.073711) | 0.403588 / 0.323480 (0.080109) | 0.005456 / 0.007986 (-0.002530) | 0.005677 / 0.004328 (0.001349) | 0.076882 / 0.004250 (0.072632) | 0.039832 / 0.037052 (0.002779) | 0.351930 / 0.258489 (0.093440) | 0.390492 / 0.293841 (0.096651) | 0.045199 / 0.128546 (-0.083347) | 0.023945 / 0.075646 (-0.051701) | 0.091140 / 0.419271 (-0.328132) | 0.057728 / 0.043533 (0.014195) | 0.370663 / 0.255139 (0.115524) | 0.380649 / 0.283200 (0.097449) | 0.097017 / 0.141683 (-0.044666) | 1.362248 / 1.452155 (-0.089907) | 1.445699 / 1.492716 (-0.047018) |\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) | 0.204207 / 0.018006 (0.186201) | 0.474471 / 0.000490 (0.473981) | 0.012187 / 0.000200 (0.011987) | 0.000151 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023123 / 0.037411 (-0.014288) | 0.097547 / 0.014526 (0.083021) | 0.113877 / 0.176557 (-0.062679) | 0.158307 / 0.737135 (-0.578828) | 0.113876 / 0.296338 (-0.182462) |\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) | 0.519920 / 0.215209 (0.304711) | 5.384371 / 2.077655 (3.306716) | 2.263276 / 1.504120 (0.759156) | 1.960604 / 1.541195 (0.419409) | 2.022864 / 1.468490 (0.554374) | 1.015430 / 4.584777 (-3.569347) | 4.774426 / 3.745712 (1.028714) | 4.549598 / 5.269862 (-0.720264) | 2.412638 / 4.565676 (-2.153039) | 0.117983 / 0.424275 (-0.306292) | 0.013340 / 0.007607 (0.005733) | 0.639826 / 0.226044 (0.413782) | 6.491622 / 2.268929 (4.222693) | 2.946892 / 55.444624 (-52.497732) | 2.376393 / 6.876477 (-4.500084) | 2.285592 / 2.142072 (0.143519) | 1.185049 / 4.805227 (-3.620178) | 0.204127 / 6.500664 (-6.296537) | 0.070285 / 0.075469 (-0.005184) |\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) | 1.439736 / 1.841788 (-0.402052) | 14.852087 / 8.074308 (6.777779) | 15.675742 / 10.191392 (5.484350) | 0.206577 / 0.680424 (-0.473846) | 0.031688 / 0.534201 (-0.502513) | 0.471003 / 0.579283 (-0.108280) | 0.505449 / 0.434364 (0.071085) | 0.506114 / 0.540337 (-0.034224) | 0.583752 / 1.386936 (-0.803184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6fcff8a031db39cb31079bc1fa62ded6e35218c \"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) | 0.012965 / 0.011353 (0.001612) | 0.006660 / 0.011008 (-0.004348) | 0.126060 / 0.038508 (0.087551) | 0.041154 / 0.023109 (0.018045) | 0.413428 / 0.275898 (0.137530) | 0.429035 / 0.323480 (0.105555) | 0.006680 / 0.007986 (-0.001305) | 0.005063 / 0.004328 (0.000734) | 0.092161 / 0.004250 (0.087911) | 0.056092 / 0.037052 (0.019039) | 0.421460 / 0.258489 (0.162971) | 0.450291 / 0.293841 (0.156450) | 0.050820 / 0.128546 (-0.077726) | 0.021392 / 0.075646 (-0.054255) | 0.426915 / 0.419271 (0.007643) | 0.064908 / 0.043533 (0.021375) | 0.406769 / 0.255139 (0.151630) | 0.434344 / 0.283200 (0.151144) | 0.127967 / 0.141683 (-0.013716) | 1.922414 / 1.452155 (0.470260) | 1.940717 / 1.492716 (0.448000) |\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) | 0.288024 / 0.018006 (0.270017) | 0.615859 / 0.000490 (0.615369) | 0.007095 / 0.000200 (0.006895) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028182 / 0.037411 (-0.009230) | 0.126277 / 0.014526 (0.111752) | 0.131687 / 0.176557 (-0.044870) | 0.206191 / 0.737135 (-0.530944) | 0.141799 / 0.296338 (-0.154539) |\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) | 0.631580 / 0.215209 (0.416371) | 6.141942 / 2.077655 (4.064287) | 2.476721 / 1.504120 (0.972602) | 2.128850 / 1.541195 (0.587655) | 2.236468 / 1.468490 (0.767978) | 1.188665 / 4.584777 (-3.396112) | 5.481179 / 3.745712 (1.735467) | 3.120333 / 5.269862 (-2.149529) | 2.365889 / 4.565676 (-2.199787) | 0.145081 / 0.424275 (-0.279194) | 0.015866 / 0.007607 (0.008259) | 0.795650 / 0.226044 (0.569605) | 7.595289 / 2.268929 (5.326361) | 3.174418 / 55.444624 (-52.270207) | 2.905207 / 6.876477 (-3.971270) | 2.428263 / 2.142072 (0.286191) | 1.408900 / 4.805227 (-3.396328) | 0.265485 / 6.500664 (-6.235179) | 0.083882 / 0.075469 (0.008413) |\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) | 1.517025 / 1.841788 (-0.324762) | 18.110288 / 8.074308 (10.035980) | 20.810003 / 10.191392 (10.618611) | 0.210380 / 0.680424 (-0.470044) | 0.030180 / 0.534201 (-0.504021) | 0.523453 / 0.579283 (-0.055830) | 0.603896 / 0.434364 (0.169532) | 0.622554 / 0.540337 (0.082216) | 0.737973 / 1.386936 (-0.648963) |\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) | 0.009795 / 0.011353 (-0.001558) | 0.006269 / 0.011008 (-0.004739) | 0.099938 / 0.038508 (0.061430) | 0.035162 / 0.023109 (0.012052) | 0.506353 / 0.275898 (0.230455) | 0.527804 / 0.323480 (0.204324) | 0.007211 / 0.007986 (-0.000775) | 0.005498 / 0.004328 (0.001169) | 0.098325 / 0.004250 (0.094075) | 0.054513 / 0.037052 (0.017461) | 0.525764 / 0.258489 (0.267274) | 0.576699 / 0.293841 (0.282858) | 0.052800 / 0.128546 (-0.075747) | 0.021192 / 0.075646 (-0.054454) | 0.117676 / 0.419271 (-0.301596) | 0.055415 / 0.043533 (0.011882) | 0.516746 / 0.255139 (0.261607) | 0.528417 / 0.283200 (0.245217) | 0.116947 / 0.141683 (-0.024735) | 1.757864 / 1.452155 (0.305709) | 2.043632 / 1.492716 (0.550916) |\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) | 0.284018 / 0.018006 (0.266011) | 0.595086 / 0.000490 (0.594596) | 0.001945 / 0.000200 (0.001745) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032255 / 0.037411 (-0.005157) | 0.128201 / 0.014526 (0.113676) | 0.139189 / 0.176557 (-0.037367) | 0.199750 / 0.737135 (-0.537385) | 0.149406 / 0.296338 (-0.146933) |\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) | 0.652184 / 0.215209 (0.436975) | 6.453319 / 2.077655 (4.375664) | 2.831566 / 1.504120 (1.327446) | 2.453064 / 1.541195 (0.911869) | 2.622056 / 1.468490 (1.153566) | 1.191279 / 4.584777 (-3.393498) | 5.504720 / 3.745712 (1.759007) | 5.916900 / 5.269862 (0.647038) | 2.974400 / 4.565676 (-1.591277) | 0.142851 / 0.424275 (-0.281424) | 0.015241 / 0.007607 (0.007634) | 0.917537 / 0.226044 (0.691493) | 8.277645 / 2.268929 (6.008717) | 3.700495 / 55.444624 (-51.744130) | 3.047127 / 6.876477 (-3.829350) | 3.093216 / 2.142072 (0.951143) | 1.413529 / 4.805227 (-3.391698) | 0.259395 / 6.500664 (-6.241270) | 0.083144 / 0.075469 (0.007675) |\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) | 1.632240 / 1.841788 (-0.209548) | 18.687403 / 8.074308 (10.613095) | 20.134091 / 10.191392 (9.942699) | 0.238792 / 0.680424 (-0.441632) | 0.027645 / 0.534201 (-0.506556) | 0.518200 / 0.579283 (-0.061083) | 0.613535 / 0.434364 (0.179171) | 0.631414 / 0.540337 (0.091076) | 0.724658 / 1.386936 (-0.662278) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac7caa5e195ad76c7e8ef98914813383f4f668cf \"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) | 0.006228 / 0.011353 (-0.005125) | 0.004517 / 0.011008 (-0.006492) | 0.097998 / 0.038508 (0.059490) | 0.027903 / 0.023109 (0.004793) | 0.309789 / 0.275898 (0.033891) | 0.332784 / 0.323480 (0.009304) | 0.004757 / 0.007986 (-0.003228) | 0.003348 / 0.004328 (-0.000981) | 0.075193 / 0.004250 (0.070942) | 0.037382 / 0.037052 (0.000330) | 0.306929 / 0.258489 (0.048440) | 0.347304 / 0.293841 (0.053463) | 0.030235 / 0.128546 (-0.098312) | 0.011516 / 0.075646 (-0.064131) | 0.322249 / 0.419271 (-0.097023) | 0.044125 / 0.043533 (0.000592) | 0.303874 / 0.255139 (0.048735) | 0.326808 / 0.283200 (0.043608) | 0.088137 / 0.141683 (-0.053546) | 1.521426 / 1.452155 (0.069272) | 1.573823 / 1.492716 (0.081107) |\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) | 0.203204 / 0.018006 (0.185197) | 0.402247 / 0.000490 (0.401757) | 0.003146 / 0.000200 (0.002946) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022955 / 0.037411 (-0.014456) | 0.096059 / 0.014526 (0.081533) | 0.105552 / 0.176557 (-0.071004) | 0.167459 / 0.737135 (-0.569676) | 0.106723 / 0.296338 (-0.189615) |\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) | 0.454626 / 0.215209 (0.239417) | 4.556346 / 2.077655 (2.478691) | 2.220349 / 1.504120 (0.716229) | 2.011820 / 1.541195 (0.470625) | 2.048149 / 1.468490 (0.579659) | 0.697583 / 4.584777 (-3.887194) | 3.428394 / 3.745712 (-0.317318) | 1.863872 / 5.269862 (-3.405989) | 1.159691 / 4.565676 (-3.405985) | 0.082598 / 0.424275 (-0.341677) | 0.012202 / 0.007607 (0.004594) | 0.555617 / 0.226044 (0.329572) | 5.545481 / 2.268929 (3.276553) | 2.650850 / 55.444624 (-52.793775) | 2.305864 / 6.876477 (-4.570613) | 2.392252 / 2.142072 (0.250179) | 0.808512 / 4.805227 (-3.996716) | 0.152086 / 6.500664 (-6.348578) | 0.066440 / 0.075469 (-0.009029) |\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) | 1.211789 / 1.841788 (-0.629999) | 13.515546 / 8.074308 (5.441238) | 13.859870 / 10.191392 (3.668478) | 0.150335 / 0.680424 (-0.530088) | 0.016578 / 0.534201 (-0.517623) | 0.379145 / 0.579283 (-0.200138) | 0.393735 / 0.434364 (-0.040628) | 0.460219 / 0.540337 (-0.080118) | 0.555896 / 1.386936 (-0.831040) |\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) | 0.006402 / 0.011353 (-0.004950) | 0.004558 / 0.011008 (-0.006450) | 0.077332 / 0.038508 (0.038824) | 0.027955 / 0.023109 (0.004846) | 0.407877 / 0.275898 (0.131979) | 0.432552 / 0.323480 (0.109072) | 0.004850 / 0.007986 (-0.003135) | 0.003329 / 0.004328 (-0.000999) | 0.075767 / 0.004250 (0.071517) | 0.035940 / 0.037052 (-0.001112) | 0.419544 / 0.258489 (0.161055) | 0.454672 / 0.293841 (0.160831) | 0.030461 / 0.128546 (-0.098085) | 0.011536 / 0.075646 (-0.064111) | 0.085774 / 0.419271 (-0.333498) | 0.039408 / 0.043533 (-0.004125) | 0.389909 / 0.255139 (0.134770) | 0.403287 / 0.283200 (0.120088) | 0.088385 / 0.141683 (-0.053298) | 1.596840 / 1.452155 (0.144686) | 1.659296 / 1.492716 (0.166580) |\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) | 0.216349 / 0.018006 (0.198342) | 0.394969 / 0.000490 (0.394479) | 0.000408 / 0.000200 (0.000208) | 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) | 0.024346 / 0.037411 (-0.013066) | 0.099609 / 0.014526 (0.085084) | 0.106779 / 0.176557 (-0.069778) | 0.156889 / 0.737135 (-0.580247) | 0.110625 / 0.296338 (-0.185714) |\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) | 0.443809 / 0.215209 (0.228600) | 4.450524 / 2.077655 (2.372870) | 2.151694 / 1.504120 (0.647574) | 1.952521 / 1.541195 (0.411326) | 1.963320 / 1.468490 (0.494830) | 0.709291 / 4.584777 (-3.875486) | 3.415708 / 3.745712 (-0.330005) | 1.850498 / 5.269862 (-3.419363) | 1.164355 / 4.565676 (-3.401321) | 0.084977 / 0.424275 (-0.339298) | 0.013284 / 0.007607 (0.005677) | 0.555103 / 0.226044 (0.329059) | 5.583587 / 2.268929 (3.314658) | 2.608754 / 55.444624 (-52.835870) | 2.264079 / 6.876477 (-4.612398) | 2.272455 / 2.142072 (0.130382) | 0.820849 / 4.805227 (-3.984379) | 0.155063 / 6.500664 (-6.345601) | 0.069709 / 0.075469 (-0.005760) |\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) | 1.293285 / 1.841788 (-0.548503) | 14.181867 / 8.074308 (6.107559) | 13.021280 / 10.191392 (2.829888) | 0.130101 / 0.680424 (-0.550323) | 0.016461 / 0.534201 (-0.517740) | 0.383651 / 0.579283 (-0.195632) | 0.387353 / 0.434364 (-0.047011) | 0.443351 / 0.540337 (-0.096986) | 0.529448 / 1.386936 (-0.857488) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05145d50b5bb1b7b42b76516cd6492d4868c46ba \"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) | 0.007513 / 0.011353 (-0.003840) | 0.005328 / 0.011008 (-0.005680) | 0.096937 / 0.038508 (0.058429) | 0.036230 / 0.023109 (0.013121) | 0.325808 / 0.275898 (0.049910) | 0.363601 / 0.323480 (0.040121) | 0.006130 / 0.007986 (-0.001855) | 0.004352 / 0.004328 (0.000023) | 0.073543 / 0.004250 (0.069293) | 0.054114 / 0.037052 (0.017062) | 0.328952 / 0.258489 (0.070463) | 0.366943 / 0.293841 (0.073102) | 0.035768 / 0.128546 (-0.092778) | 0.012505 / 0.075646 (-0.063142) | 0.332260 / 0.419271 (-0.087012) | 0.066673 / 0.043533 (0.023140) | 0.323866 / 0.255139 (0.068727) | 0.341311 / 0.283200 (0.058112) | 0.129898 / 0.141683 (-0.011785) | 1.456890 / 1.452155 (0.004735) | 1.546933 / 1.492716 (0.054217) |\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) | 0.299236 / 0.018006 (0.281229) | 0.496134 / 0.000490 (0.495645) | 0.004233 / 0.000200 (0.004033) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028089 / 0.037411 (-0.009322) | 0.104723 / 0.014526 (0.090197) | 0.121032 / 0.176557 (-0.055525) | 0.179916 / 0.737135 (-0.557220) | 0.126628 / 0.296338 (-0.169711) |\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) | 0.403497 / 0.215209 (0.188288) | 4.052481 / 2.077655 (1.974827) | 1.804419 / 1.504120 (0.300299) | 1.619833 / 1.541195 (0.078638) | 1.732438 / 1.468490 (0.263948) | 0.702474 / 4.584777 (-3.882303) | 3.808973 / 3.745712 (0.063261) | 3.682764 / 5.269862 (-1.587098) | 1.919184 / 4.565676 (-2.646493) | 0.086638 / 0.424275 (-0.337637) | 0.012265 / 0.007607 (0.004658) | 0.501273 / 0.226044 (0.275229) | 5.010918 / 2.268929 (2.741989) | 2.278114 / 55.444624 (-53.166510) | 1.942266 / 6.876477 (-4.934211) | 2.101982 / 2.142072 (-0.040091) | 0.847622 / 4.805227 (-3.957606) | 0.172973 / 6.500664 (-6.327691) | 0.066884 / 0.075469 (-0.008586) |\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) | 1.187609 / 1.841788 (-0.654179) | 15.089485 / 8.074308 (7.015177) | 14.787398 / 10.191392 (4.596006) | 0.168254 / 0.680424 (-0.512170) | 0.018266 / 0.534201 (-0.515935) | 0.423204 / 0.579283 (-0.156079) | 0.435238 / 0.434364 (0.000874) | 0.512473 / 0.540337 (-0.027864) | 0.618091 / 1.386936 (-0.768845) |\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) | 0.007249 / 0.011353 (-0.004104) | 0.005297 / 0.011008 (-0.005711) | 0.076428 / 0.038508 (0.037920) | 0.033565 / 0.023109 (0.010456) | 0.373756 / 0.275898 (0.097858) | 0.407405 / 0.323480 (0.083925) | 0.006100 / 0.007986 (-0.001886) | 0.006482 / 0.004328 (0.002153) | 0.075884 / 0.004250 (0.071633) | 0.055338 / 0.037052 (0.018286) | 0.378721 / 0.258489 (0.120232) | 0.427065 / 0.293841 (0.133224) | 0.036285 / 0.128546 (-0.092261) | 0.012460 / 0.075646 (-0.063186) | 0.087641 / 0.419271 (-0.331630) | 0.048199 / 0.043533 (0.004666) | 0.386785 / 0.255139 (0.131646) | 0.386702 / 0.283200 (0.103503) | 0.110087 / 0.141683 (-0.031596) | 1.511204 / 1.452155 (0.059050) | 1.585671 / 1.492716 (0.092954) |\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) | 0.313558 / 0.018006 (0.295552) | 0.496991 / 0.000490 (0.496501) | 0.001492 / 0.000200 (0.001292) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031814 / 0.037411 (-0.005597) | 0.113486 / 0.014526 (0.098960) | 0.125208 / 0.176557 (-0.051348) | 0.174469 / 0.737135 (-0.562666) | 0.131095 / 0.296338 (-0.165244) |\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) | 0.439282 / 0.215209 (0.224073) | 4.362286 / 2.077655 (2.284631) | 2.153271 / 1.504120 (0.649151) | 1.990482 / 1.541195 (0.449288) | 2.103322 / 1.468490 (0.634831) | 0.692522 / 4.584777 (-3.892254) | 3.861931 / 3.745712 (0.116219) | 3.686294 / 5.269862 (-1.583567) | 1.734525 / 4.565676 (-2.831152) | 0.085057 / 0.424275 (-0.339218) | 0.012116 / 0.007607 (0.004509) | 0.547996 / 0.226044 (0.321952) | 5.513835 / 2.268929 (3.244906) | 2.723829 / 55.444624 (-52.720795) | 2.404715 / 6.876477 (-4.471761) | 2.514768 / 2.142072 (0.372696) | 0.834972 / 4.805227 (-3.970255) | 0.168261 / 6.500664 (-6.332403) | 0.066464 / 0.075469 (-0.009005) |\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) | 1.259923 / 1.841788 (-0.581865) | 15.646277 / 8.074308 (7.571969) | 13.097598 / 10.191392 (2.906206) | 0.187991 / 0.680424 (-0.492433) | 0.017358 / 0.534201 (-0.516843) | 0.427979 / 0.579283 (-0.151304) | 0.425747 / 0.434364 (-0.008617) | 0.501907 / 0.540337 (-0.038431) | 0.595106 / 1.386936 (-0.791830) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db56f7f0d2f0b99af4da17d388c205152504c7d9 \"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) | 0.009378 / 0.011353 (-0.001975) | 0.006434 / 0.011008 (-0.004574) | 0.120603 / 0.038508 (0.082095) | 0.042929 / 0.023109 (0.019820) | 0.366853 / 0.275898 (0.090955) | 0.436795 / 0.323480 (0.113315) | 0.007730 / 0.007986 (-0.000256) | 0.004842 / 0.004328 (0.000513) | 0.091058 / 0.004250 (0.086808) | 0.058256 / 0.037052 (0.021203) | 0.378692 / 0.258489 (0.120203) | 0.467384 / 0.293841 (0.173543) | 0.042948 / 0.128546 (-0.085598) | 0.015172 / 0.075646 (-0.060475) | 0.409225 / 0.419271 (-0.010046) | 0.083672 / 0.043533 (0.040140) | 0.390088 / 0.255139 (0.134949) | 0.406965 / 0.283200 (0.123765) | 0.142132 / 0.141683 (0.000449) | 1.765737 / 1.452155 (0.313582) | 1.895419 / 1.492716 (0.402703) |\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) | 0.244052 / 0.018006 (0.226046) | 0.553383 / 0.000490 (0.552893) | 0.006798 / 0.000200 (0.006598) | 0.000227 / 0.000054 (0.000173) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032032 / 0.037411 (-0.005380) | 0.129990 / 0.014526 (0.115464) | 0.140338 / 0.176557 (-0.036219) | 0.212155 / 0.737135 (-0.524980) | 0.147395 / 0.296338 (-0.148943) |\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) | 0.478760 / 0.215209 (0.263551) | 4.751335 / 2.077655 (2.673680) | 2.164755 / 1.504120 (0.660635) | 1.944288 / 1.541195 (0.403094) | 2.077657 / 1.468490 (0.609167) | 0.818519 / 4.584777 (-3.766258) | 4.689013 / 3.745712 (0.943301) | 2.484079 / 5.269862 (-2.785782) | 1.788632 / 4.565676 (-2.777044) | 0.100484 / 0.424275 (-0.323791) | 0.013838 / 0.007607 (0.006231) | 0.589650 / 0.226044 (0.363605) | 5.859461 / 2.268929 (3.590533) | 2.670025 / 55.444624 (-52.774599) | 2.688709 / 6.876477 (-4.187768) | 2.408060 / 2.142072 (0.265988) | 0.972107 / 4.805227 (-3.833120) | 0.194425 / 6.500664 (-6.306239) | 0.076077 / 0.075469 (0.000608) |\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) | 1.430150 / 1.841788 (-0.411638) | 17.710507 / 8.074308 (9.636199) | 16.210789 / 10.191392 (6.019397) | 0.163940 / 0.680424 (-0.516484) | 0.020295 / 0.534201 (-0.513906) | 0.472596 / 0.579283 (-0.106687) | 0.483107 / 0.434364 (0.048743) | 0.585269 / 0.540337 (0.044931) | 0.705526 / 1.386936 (-0.681410) |\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) | 0.008864 / 0.011353 (-0.002489) | 0.006095 / 0.011008 (-0.004913) | 0.088702 / 0.038508 (0.050194) | 0.041596 / 0.023109 (0.018486) | 0.453515 / 0.275898 (0.177617) | 0.476217 / 0.323480 (0.152737) | 0.007574 / 0.007986 (-0.000412) | 0.004727 / 0.004328 (0.000398) | 0.087271 / 0.004250 (0.083021) | 0.059631 / 0.037052 (0.022578) | 0.449379 / 0.258489 (0.190890) | 0.494436 / 0.293841 (0.200595) | 0.043448 / 0.128546 (-0.085098) | 0.014580 / 0.075646 (-0.061067) | 0.103836 / 0.419271 (-0.315435) | 0.057537 / 0.043533 (0.014004) | 0.449359 / 0.255139 (0.194220) | 0.447577 / 0.283200 (0.164377) | 0.123600 / 0.141683 (-0.018083) | 1.748448 / 1.452155 (0.296294) | 1.902116 / 1.492716 (0.409399) |\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) | 0.237214 / 0.018006 (0.219207) | 0.497648 / 0.000490 (0.497158) | 0.003519 / 0.000200 (0.003319) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034477 / 0.037411 (-0.002934) | 0.132627 / 0.014526 (0.118101) | 0.139721 / 0.176557 (-0.036836) | 0.195705 / 0.737135 (-0.541430) | 0.150762 / 0.296338 (-0.145577) |\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) | 0.521306 / 0.215209 (0.306097) | 5.184982 / 2.077655 (3.107328) | 2.503979 / 1.504120 (0.999859) | 2.301054 / 1.541195 (0.759860) | 2.352713 / 1.468490 (0.884222) | 0.819804 / 4.584777 (-3.764973) | 4.584011 / 3.745712 (0.838299) | 2.497311 / 5.269862 (-2.772550) | 1.561262 / 4.565676 (-3.004414) | 0.101814 / 0.424275 (-0.322461) | 0.014078 / 0.007607 (0.006471) | 0.666564 / 0.226044 (0.440520) | 6.616379 / 2.268929 (4.347450) | 3.263892 / 55.444624 (-52.180732) | 2.891774 / 6.876477 (-3.984703) | 2.945260 / 2.142072 (0.803188) | 1.014379 / 4.805227 (-3.790848) | 0.201762 / 6.500664 (-6.298902) | 0.078012 / 0.075469 (0.002543) |\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) | 1.567808 / 1.841788 (-0.273980) | 19.096552 / 8.074308 (11.022244) | 15.522285 / 10.191392 (5.330893) | 0.226568 / 0.680424 (-0.453856) | 0.021078 / 0.534201 (-0.513123) | 0.501686 / 0.579283 (-0.077597) | 0.517575 / 0.434364 (0.083211) | 0.589685 / 0.540337 (0.049348) | 0.705053 / 1.386936 (-0.681883) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db56f7f0d2f0b99af4da17d388c205152504c7d9 \"CML watermark\")\n"
] | "2023-05-11T17:29:57" | "2023-05-15T07:39:13" | "2023-05-12T15:14:48" | CONTRIBUTOR | null | false | {
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} | Adds the `date_format` param introduced in Pandas 2.0 to the CSV reader and improves its type hints. | {
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"Hi ! You can try to use the [Image](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Image) type which [decodes images on-the-fly](https://huggingface.co/docs/datasets/v2.12.0/en/about_dataset_features#image-feature) into pytorch tensors :)\r\n\r\n```python\r\nds = Dataset.from_dict({\"tensor\":a}).with_format(\"torch\")\r\n%time sum(1 for _ in ds)\r\n# CPU times: user 5.04 s, sys: 96.5 ms, total: 5.14 s\r\n# Wall time: 5.14 s\r\n# 10000\r\n```\r\n\r\n```python\r\nfeatures = Features({\"tensor\": Image()})\r\nds = Dataset.from_dict({\"tensor\":a}, features=features).with_format(\"torch\")\r\n%time sum(1 for _ in ds)\r\n# CPU times: user 1.86 s, sys: 49 ms, total: 1.91 s\r\n# Wall time: 1.9 s\r\n# 10000\r\n```\r\n\r\n-> Speed x2.7\r\n\r\nAnd if you want to keep using arrays of integers, consider using the [Array2D](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Array2D) or [Array3D](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Array3D) types which are even faster (since it doesn't decode images):\r\n\r\n```python\r\nfeatures = Features({\"tensor\": Array2D(shape=(100, 224), dtype=\"float32\")})\r\nds = Dataset.from_dict({\"tensor\":a}, features=features).with_format(\"torch\")\r\n%time sum(1 for _ in ds)\r\n# CPU times: user 828 ms, sys: 68.4 ms, total: 896 ms\r\n# Wall time: 897 ms\r\n# 10000\r\n```\r\n\r\n-> Speed x5.7\r\n\r\nBatching also speeds up a lot\r\n\r\n```python\r\nfrom torch.utils.data import DataLoader\r\ndl = DataLoader(ds, batch_size=100)\r\n%time sum(1 for _ in dl)\r\n# CPU times: user 564 ms, sys: 83.5 ms, total: 648 ms\r\n# Wall time: 579 ms\r\n# 100\r\n```\r\n\r\n-> Speed x8.9\r\n\r\n```python\r\n%time sum(1 for _ in ds.iter(batch_size=100))\r\n# CPU times: user 119 ms, sys: 96.8 ms, total: 215 ms\r\n# Wall time: 117 ms\r\n# 100\r\n```\r\n\r\n-> Speed x46",
"Anyway, regarding the speed difference between numpy and pytorch, I think the issue is that we first convert numpy sub-arrays to pytorch and then consolidate into one tensor, while we should to the opposite. Indeed converting a numpy array to pytorch has a fix cost that seems to cause a slow down. The current pipeline is\r\n\r\n```\r\narrow -> nested numpy arrays -> lists of torch tensors -> one torch tensor\r\n```\r\n\r\nand we should do\r\n\r\n```\r\narrow -> nested numpy arrays -> one numpy array -> one torch tensor\r\n```",
"I have a similar issue: iterating over a dataset takes 5s without applying any transform, but takes ~30s after applying a transform.\r\nHere is the minimum code to reproduce the problem\r\n\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset, DatasetDict, load_dataset, Array3D, Image, Features\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport torchvision \r\nfrom torchvision.transforms import ToTensor, Normalize\r\n\r\n\r\n#################################\r\n# Without transform\r\n#################################\r\n \r\ntrain_dataset = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\n\r\ntrain_dataset.set_format(type=\"numpy\", columns=[\"img\", \"fine_label\"])\r\n\r\ntrain_loader= DataLoader(\r\n train_dataset,\r\n batch_size=100,\r\n pin_memory=False,\r\n shuffle=True,\r\n num_workers=8,\r\n)\r\n\r\nfor batch in tqdm(train_loader, desc=\"Loading data, no transform\"):\r\n pass\r\n\r\n\r\n#################################\r\n# With transform\r\n#################################\r\n\r\ntransform_func = torchvision.transforms.Compose([\r\n ToTensor(), \r\n Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] \r\n)\r\n \r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"img\": transform_func(x[\"img\"])},\r\n)\r\n\r\ntrain_dataset.set_format(type=\"numpy\", columns=[\"img\", \"fine_label\"])\r\n\r\n\r\ntrain_loader= DataLoader(\r\n train_dataset,\r\n batch_size=100,\r\n pin_memory=False,\r\n shuffle=True,\r\n num_workers=8,\r\n)\r\n\r\n\r\nfor batch in tqdm(train_loader, desc=\"Loading data after transform\"):\r\n pass \r\n```\r\n\r\nI have also tried converting the Image column to an Array3D\r\n```python\r\nimg_shape = train_dataset[0][\"img\"].shape\r\n\r\nfeatures = train_dataset.features.copy()\r\nfeatures[\"x\"] = Array3D(shape=img_shape, dtype=\"float32\")\r\n\r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"x\": np.array(x[\"img\"], dtype=np.uint8)},\r\n features=features,\r\n)\r\ntrain_dataset.cast_column(\"x\", Array3D(shape=img_shape, dtype=\"float32\"))\r\ntrain_dataset.set_format(type=\"numpy\", columns=[\"x\", \"fine_label\"])\r\n```\r\nbut to no avail. Any clue?",
"Thanks! I convert my dataset feature to Array3D and this speed became awesome!"
] | "2023-05-11T08:04:09" | "2023-05-15T15:38:13" | "2023-05-15T15:38:13" | NONE | null | null | null | ### Describe the bug
I am attempting to iterate through an image dataset, but I am encountering a significant slowdown in the iteration speed. In order to investigate this issue, I conducted the following experiment:
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
I noticed that the dataset in numpy format performs significantly faster than the one in torch format. My hypothesis is that the dataset undergoes a transformation process of torch->python->numpy(torch) in the background, which might be causing the slowdown. Is there any way to expedite the process by bypassing such transformations?
Furthermore, if I increase the size of a to an image shape, like:
```python
a=torch.randn(3,224,224)
```
the iteration speed becomes absurdly slow, around 100 iterations per second, whereas the speed with numpy format is approximately 250 iterations per second. This level of speed would be unacceptable for large image datasets, as it could take several hours just to iterate through a single epoch.
### Steps to reproduce the bug
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
### Expected behavior
iteration faster
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.10
- Python version: 3.8.16
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5840/comments | https://api.github.com/repos/huggingface/datasets/issues/5840/events | https://github.com/huggingface/datasets/issues/5840 | 1,705,212,085 | I_kwDODunzps5lo3i1 | 5,840 | load model error. | {
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"Please report this in the `transformers` repo, as it's not related to `datasets`"
] | "2023-05-11T07:12:38" | "2023-05-12T13:44:07" | "2023-05-12T13:44:06" | NONE | null | null | null | ### Describe the bug
I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.
my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/
### Steps to reproduce the bug
γγγ
### Expected behavior
γγγ
### Environment info
γγγ | {
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