Datasets:

BanUrsus

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github-issues_chapter5_section4

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[ "i tried both the latest version of `tqdm` and the version required by `autonlp` - no luck with windows 😞 \r\n\r\nit's very weird that a progress bar would trigger these kind of errors, so i'll have a look to see if it's something unique to `datasets`", "Closing since this is now fixed in #2482 " ]

"2021-06-09T17:24:40"

"2021-06-11T15:03:42"

"2021-06-11T15:03:36"

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"2020-12-09T12:18:22"

"2020-12-10T15:25:55"

"2020-12-10T15:25:54"

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[ "This is task is part of the GEM suite so will actually need a more complete dataset card. I'm taking a break for now though and will get back to it before merging :) " ]

"2020-12-01T15:05:23"

"2020-12-02T17:34:05"

"2020-12-02T17:34:05"

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[ "Gently pinging @lhoestq", "Hi ! Interresting :)\r\n\r\nCould you give more details on what kind of separators you would like to use instead ?", "In my case, I just want to use `\\n` but not `U+2028`.", "Ok I see, maybe there can be a `sep` parameter to allow users to specify what line/paragraph separator they'd like to use", "Related to:\r\n- #3729 \r\n- #3910", "Thanks for requesting this enhancement. We have recently found a somehow related issue with another dataset:\r\n- #3704\r\n\r\nLet me make a PR proposal." ]

"2022-03-02T14:50:16"

"2022-03-16T15:53:59"

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[ "I see, master is already fixed in https://github.com/huggingface/datasets/commit/9e058f098a0919efd03a136b9b9c3dec5076f626" ]

"2020-12-28T08:35:34"

"2020-12-28T08:38:07"

"2020-12-28T08:38:07"

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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) | 0.009691 / 0.011353 (-0.001662) | 0.006160 / 0.011008 (-0.004848) | 0.127528 / 0.038508 (0.089020) | 0.034445 / 0.023109 (0.011335) | 0.391483 / 0.275898 (0.115585) | 0.425922 / 0.323480 (0.102442) | 0.006621 / 0.007986 (-0.001365) | 0.004550 / 0.004328 (0.000221) | 0.099134 / 0.004250 (0.094884) | 0.051089 / 0.037052 (0.014037) | 0.398675 / 0.258489 (0.140186) | 0.456740 / 0.293841 (0.162899) | 0.052279 / 0.128546 (-0.076267) | 0.020878 / 0.075646 (-0.054768) | 0.414954 / 0.419271 (-0.004317) | 0.061903 / 0.043533 (0.018370) | 0.393088 / 0.255139 (0.137949) | 0.410289 / 0.283200 (0.127089) | 0.101684 / 0.141683 (-0.039998) | 1.747102 / 1.452155 (0.294947) | 1.896976 / 1.492716 (0.404260) |\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) | 0.203193 / 0.018006 (0.185187) | 0.495011 / 0.000490 (0.494521) | 0.006290 / 0.000200 (0.006090) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034840 / 0.037411 (-0.002571) | 0.122529 / 0.014526 (0.108003) | 0.133870 / 0.176557 (-0.042686) | 0.207771 / 0.737135 (-0.529364) | 0.141441 / 0.296338 (-0.154897) |\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) | 0.604190 / 0.215209 (0.388981) | 6.040295 / 2.077655 (3.962641) | 2.405703 / 1.504120 (0.901583) | 2.062767 / 1.541195 (0.521572) | 2.079313 / 1.468490 (0.610823) | 1.240107 / 4.584777 (-3.344670) | 5.316583 / 3.745712 (1.570871) | 3.104758 / 5.269862 (-2.165103) | 2.056489 / 4.565676 (-2.509187) | 0.149060 / 0.424275 (-0.275215) | 0.014467 / 0.007607 (0.006860) | 0.736882 / 0.226044 (0.510838) | 7.324142 / 2.268929 (5.055213) | 3.048752 / 55.444624 (-52.395872) | 2.385013 / 6.876477 (-4.491463) | 2.457478 / 2.142072 (0.315405) | 1.459276 / 4.805227 (-3.345951) | 0.253882 / 6.500664 (-6.246782) | 0.076756 / 0.075469 (0.001287) |\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) | 1.499166 / 1.841788 (-0.342622) | 17.294165 / 8.074308 (9.219857) | 20.385668 / 10.191392 (10.194276) | 0.254633 / 0.680424 (-0.425791) | 0.026253 / 0.534201 (-0.507948) | 0.532928 / 0.579283 (-0.046355) | 0.606095 / 0.434364 (0.171731) | 0.615025 / 0.540337 (0.074687) | 0.728651 / 1.386936 (-0.658285) |\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) | 0.009376 / 0.011353 (-0.001977) | 0.005981 / 0.011008 (-0.005027) | 0.109898 / 0.038508 (0.071390) | 0.033746 / 0.023109 (0.010637) | 0.410226 / 0.275898 (0.134328) | 0.470606 / 0.323480 (0.147126) | 0.006706 / 0.007986 (-0.001279) | 0.004482 / 0.004328 (0.000153) | 0.092280 / 0.004250 (0.088030) | 0.047988 / 0.037052 (0.010935) | 0.430628 / 0.258489 (0.172139) | 0.480668 / 0.293841 (0.186827) | 0.052099 / 0.128546 (-0.076447) | 0.018743 / 0.075646 (-0.056903) | 0.112204 / 0.419271 (-0.307068) | 0.059838 / 0.043533 (0.016305) | 0.418230 / 0.255139 (0.163091) | 0.451568 / 0.283200 (0.168368) | 0.107026 / 0.141683 (-0.034657) | 1.708111 / 1.452155 (0.255956) | 1.839268 / 1.492716 (0.346552) |\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) | 0.229558 / 0.018006 (0.211552) | 0.488099 / 0.000490 (0.487609) | 0.004643 / 0.000200 (0.004443) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030461 / 0.037411 (-0.006951) | 0.120993 / 0.014526 (0.106467) | 0.130874 / 0.176557 (-0.045682) | 0.193550 / 0.737135 (-0.543585) | 0.138164 / 0.296338 (-0.158174) |\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) | 0.635709 / 0.215209 (0.420500) | 6.225112 / 2.077655 (4.147457) | 2.639584 / 1.504120 (1.135465) | 2.254487 / 1.541195 (0.713293) | 2.280478 / 1.468490 (0.811988) | 1.205712 / 4.584777 (-3.379065) | 5.367845 / 3.745712 (1.622133) | 3.020207 / 5.269862 (-2.249655) | 2.001897 / 4.565676 (-2.563779) | 0.149582 / 0.424275 (-0.274693) | 0.014867 / 0.007607 (0.007260) | 0.759050 / 0.226044 (0.533006) | 7.692969 / 2.268929 (5.424041) | 3.274009 / 55.444624 (-52.170615) | 2.635529 / 6.876477 (-4.240948) | 2.672960 / 2.142072 (0.530888) | 1.426487 / 4.805227 (-3.378740) | 0.253368 / 6.500664 (-6.247296) | 0.078650 / 0.075469 (0.003181) |\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) | 1.620265 / 1.841788 (-0.221523) | 17.674168 / 8.074308 (9.599860) | 21.120528 / 10.191392 (10.929136) | 0.244205 / 0.680424 (-0.436218) | 0.029646 / 0.534201 (-0.504555) | 0.510948 / 0.579283 (-0.068335) | 0.586255 / 0.434364 (0.151891) | 0.589286 / 0.540337 (0.048949) | 0.736561 / 1.386936 (-0.650375) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de5fe9ae5df84c489e08dcbdc3d2d20272b312c3 \"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) | 0.007778 / 0.011353 (-0.003575) | 0.005432 / 0.011008 (-0.005577) | 0.098776 / 0.038508 (0.060268) | 0.035196 / 0.023109 (0.012087) | 0.305646 / 0.275898 (0.029748) | 0.342661 / 0.323480 (0.019181) | 0.006513 / 0.007986 (-0.001472) | 0.005897 / 0.004328 (0.001568) | 0.075797 / 0.004250 (0.071547) | 0.056060 / 0.037052 (0.019007) | 0.306645 / 0.258489 (0.048156) | 0.352447 / 0.293841 (0.058606) | 0.037304 / 0.128546 (-0.091242) | 0.012514 / 0.075646 (-0.063132) | 0.334949 / 0.419271 (-0.084323) | 0.051600 / 0.043533 (0.008067) | 0.302302 / 0.255139 (0.047163) | 0.322238 / 0.283200 (0.039038) | 0.106896 / 0.141683 (-0.034787) | 1.483163 / 1.452155 (0.031008) | 1.587483 / 1.492716 (0.094767) |\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) | 0.292318 / 0.018006 (0.274312) | 0.541541 / 0.000490 (0.541051) | 0.008342 / 0.000200 (0.008142) | 0.000339 / 0.000054 (0.000285) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028287 / 0.037411 (-0.009124) | 0.107775 / 0.014526 (0.093250) | 0.119112 / 0.176557 (-0.057445) | 0.174002 / 0.737135 (-0.563134) | 0.126531 / 0.296338 (-0.169808) |\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) | 0.401684 / 0.215209 (0.186475) | 4.024708 / 2.077655 (1.947053) | 1.812763 / 1.504120 (0.308643) | 1.629540 / 1.541195 (0.088345) | 1.731733 / 1.468490 (0.263243) | 0.711066 / 4.584777 (-3.873711) | 3.867499 / 3.745712 (0.121786) | 3.615968 / 5.269862 (-1.653893) | 1.876077 / 4.565676 (-2.689600) | 0.087003 / 0.424275 (-0.337272) | 0.012445 / 0.007607 (0.004838) | 0.499106 / 0.226044 (0.273061) | 4.975920 / 2.268929 (2.706992) | 2.279074 / 55.444624 (-53.165550) | 1.952311 / 6.876477 (-4.924166) | 2.167480 / 2.142072 (0.025408) | 0.855882 / 4.805227 (-3.949346) | 0.171378 / 6.500664 (-6.329287) | 0.066731 / 0.075469 (-0.008738) |\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) | 1.184226 / 1.841788 (-0.657561) | 15.383396 / 8.074308 (7.309088) | 15.069783 / 10.191392 (4.878391) | 0.161489 / 0.680424 (-0.518935) | 0.017763 / 0.534201 (-0.516438) | 0.427103 / 0.579283 (-0.152180) | 0.434295 / 0.434364 (-0.000069) | 0.496848 / 0.540337 (-0.043489) | 0.592572 / 1.386936 (-0.794364) |\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) | 0.008014 / 0.011353 (-0.003339) | 0.005607 / 0.011008 (-0.005401) | 0.076826 / 0.038508 (0.038318) | 0.035283 / 0.023109 (0.012174) | 0.347809 / 0.275898 (0.071911) | 0.382482 / 0.323480 (0.059003) | 0.006276 / 0.007986 (-0.001709) | 0.005978 / 0.004328 (0.001650) | 0.074938 / 0.004250 (0.070687) | 0.054323 / 0.037052 (0.017271) | 0.344027 / 0.258489 (0.085538) | 0.397623 / 0.293841 (0.103783) | 0.037851 / 0.128546 (-0.090695) | 0.012649 / 0.075646 (-0.062997) | 0.086169 / 0.419271 (-0.333103) | 0.051510 / 0.043533 (0.007977) | 0.341112 / 0.255139 (0.085973) | 0.357957 / 0.283200 (0.074757) | 0.110949 / 0.141683 (-0.030734) | 1.479573 / 1.452155 (0.027419) | 1.578572 / 1.492716 (0.085855) |\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) | 0.310678 / 0.018006 (0.292672) | 0.525504 / 0.000490 (0.525015) | 0.000447 / 0.000200 (0.000247) | 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) | 0.031262 / 0.037411 (-0.006149) | 0.113801 / 0.014526 (0.099275) | 0.124967 / 0.176557 (-0.051590) | 0.175226 / 0.737135 (-0.561909) | 0.129377 / 0.296338 (-0.166962) |\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) | 0.420672 / 0.215209 (0.205463) | 4.181337 / 2.077655 (2.103682) | 1.985524 / 1.504120 (0.481404) | 1.803468 / 1.541195 (0.262273) | 1.952915 / 1.468490 (0.484425) | 0.710928 / 4.584777 (-3.873849) | 3.886245 / 3.745712 (0.140533) | 3.737837 / 5.269862 (-1.532024) | 1.806859 / 4.565676 (-2.758818) | 0.088461 / 0.424275 (-0.335814) | 0.013125 / 0.007607 (0.005518) | 0.522410 / 0.226044 (0.296365) | 5.232591 / 2.268929 (2.963663) | 2.451188 / 55.444624 (-52.993437) | 2.127725 / 6.876477 (-4.748751) | 2.232859 / 2.142072 (0.090786) | 0.854257 / 4.805227 (-3.950970) | 0.171004 / 6.500664 (-6.329661) | 0.066724 / 0.075469 (-0.008746) |\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) | 1.257700 / 1.841788 (-0.584088) | 15.738605 / 8.074308 (7.664297) | 15.021698 / 10.191392 (4.830306) | 0.147422 / 0.680424 (-0.533002) | 0.017928 / 0.534201 (-0.516273) | 0.428121 / 0.579283 (-0.151162) | 0.432056 / 0.434364 (-0.002308) | 0.498318 / 0.540337 (-0.042020) | 0.591040 / 1.386936 (-0.795896) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ac74267032ef3608779a8c8c4361b95a83ecbcb \"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) | 0.007014 / 0.011353 (-0.004339) | 0.004792 / 0.011008 (-0.006216) | 0.099822 / 0.038508 (0.061314) | 0.029333 / 0.023109 (0.006224) | 0.306453 / 0.275898 (0.030555) | 0.344598 / 0.323480 (0.021118) | 0.005121 / 0.007986 (-0.002865) | 0.004850 / 0.004328 (0.000522) | 0.076668 / 0.004250 (0.072417) | 0.039980 / 0.037052 (0.002927) | 0.312276 / 0.258489 (0.053787) | 0.354722 / 0.293841 (0.060881) | 0.031653 / 0.128546 (-0.096893) | 0.011743 / 0.075646 (-0.063903) | 0.322998 / 0.419271 (-0.096274) | 0.042813 / 0.043533 (-0.000720) | 0.308855 / 0.255139 (0.053716) | 0.332650 / 0.283200 (0.049451) | 0.087155 / 0.141683 (-0.054528) | 1.454946 / 1.452155 (0.002791) | 1.550589 / 1.492716 (0.057873) |\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) | 0.192921 / 0.018006 (0.174914) | 0.411155 / 0.000490 (0.410666) | 0.004779 / 0.000200 (0.004579) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024462 / 0.037411 (-0.012950) | 0.100320 / 0.014526 (0.085794) | 0.105509 / 0.176557 (-0.071048) | 0.168533 / 0.737135 (-0.568602) | 0.110018 / 0.296338 (-0.186321) |\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) | 0.415025 / 0.215209 (0.199816) | 4.144583 / 2.077655 (2.066928) | 1.871627 / 1.504120 (0.367507) | 1.671638 / 1.541195 (0.130443) | 1.734458 / 1.468490 (0.265968) | 0.693435 / 4.584777 (-3.891342) | 3.487999 / 3.745712 (-0.257713) | 3.196553 / 5.269862 (-2.073308) | 1.628499 / 4.565676 (-2.937178) | 0.082999 / 0.424275 (-0.341276) | 0.012822 / 0.007607 (0.005215) | 0.514904 / 0.226044 (0.288860) | 5.157525 / 2.268929 (2.888596) | 2.313093 / 55.444624 (-53.131531) | 1.968335 / 6.876477 (-4.908142) | 2.083462 / 2.142072 (-0.058610) | 0.804485 / 4.805227 (-4.000742) | 0.152290 / 6.500664 (-6.348374) | 0.066813 / 0.075469 (-0.008656) |\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) | 1.210370 / 1.841788 (-0.631418) | 14.261779 / 8.074308 (6.187471) | 14.268121 / 10.191392 (4.076729) | 0.149216 / 0.680424 (-0.531207) | 0.016529 / 0.534201 (-0.517672) | 0.378814 / 0.579283 (-0.200469) | 0.386304 / 0.434364 (-0.048060) | 0.439653 / 0.540337 (-0.100684) | 0.523658 / 1.386936 (-0.863278) |\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) | 0.006979 / 0.011353 (-0.004374) | 0.004718 / 0.011008 (-0.006290) | 0.077023 / 0.038508 (0.038514) | 0.029080 / 0.023109 (0.005971) | 0.343145 / 0.275898 (0.067247) | 0.380633 / 0.323480 (0.057153) | 0.006057 / 0.007986 (-0.001928) | 0.003541 / 0.004328 (-0.000788) | 0.075773 / 0.004250 (0.071523) | 0.039112 / 0.037052 (0.002060) | 0.342355 / 0.258489 (0.083866) | 0.386002 / 0.293841 (0.092161) | 0.033238 / 0.128546 (-0.095308) | 0.011696 / 0.075646 (-0.063950) | 0.086178 / 0.419271 (-0.333093) | 0.045219 / 0.043533 (0.001686) | 0.360710 / 0.255139 (0.105571) | 0.367490 / 0.283200 (0.084290) | 0.093041 / 0.141683 (-0.048642) | 1.523670 / 1.452155 (0.071516) | 1.595280 / 1.492716 (0.102564) |\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) | 0.235888 / 0.018006 (0.217882) | 0.410205 / 0.000490 (0.409715) | 0.000405 / 0.000200 (0.000205) | 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) | 0.025752 / 0.037411 (-0.011659) | 0.103343 / 0.014526 (0.088818) | 0.108722 / 0.176557 (-0.067834) | 0.159241 / 0.737135 (-0.577894) | 0.113684 / 0.296338 (-0.182654) |\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) | 0.441809 / 0.215209 (0.226600) | 4.410893 / 2.077655 (2.333238) | 2.104061 / 1.504120 (0.599941) | 1.854016 / 1.541195 (0.312821) | 1.947100 / 1.468490 (0.478610) | 0.697682 / 4.584777 (-3.887095) | 3.467513 / 3.745712 (-0.278199) | 1.911603 / 5.269862 (-3.358258) | 1.187479 / 4.565676 (-3.378197) | 0.083153 / 0.424275 (-0.341122) | 0.012651 / 0.007607 (0.005044) | 0.542081 / 0.226044 (0.316036) | 5.444622 / 2.268929 (3.175693) | 2.524236 / 55.444624 (-52.920388) | 2.190463 / 6.876477 (-4.686014) | 2.265764 / 2.142072 (0.123691) | 0.810778 / 4.805227 (-3.994450) | 0.152459 / 6.500664 (-6.348205) | 0.067815 / 0.075469 (-0.007654) |\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) | 1.334388 / 1.841788 (-0.507400) | 14.640459 / 8.074308 (6.566151) | 14.714874 / 10.191392 (4.523482) | 0.153479 / 0.680424 (-0.526945) | 0.016709 / 0.534201 (-0.517492) | 0.379427 / 0.579283 (-0.199856) | 0.391602 / 0.434364 (-0.042762) | 0.438297 / 0.540337 (-0.102041) | 0.524170 / 1.386936 (-0.862766) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b277cef5cb56c0c506eda082fb69fddb839156a1 \"CML watermark\")\n" ]

"2023-03-21T14:18:09"

"2023-03-23T13:19:27"

"2023-03-23T13:12:25"

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[ "Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?", "Well I just sharded the original commonVoice dataset and pushed a small chunk of it in a private rep\n\nWhat did go wrong?\n\nHolen Sie sich Outlook für iOS<https://aka.ms/o0ukef>\n________________________________\nVon: Quentin Lhoest ***@***.***>\nGesendet: Tuesday, November 22, 2022 3:03:40 PM\nAn: huggingface/datasets ***@***.***>\nCc: capsabogdan ***@***.***>; Author ***@***.***>\nBetreff: Re: [huggingface/datasets] Bug in downloading common_voice data and snall chunk of it to one's own hub (Issue #5276)\n\n\nSounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5276#issuecomment-1323727434>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ALSIFOAPAL2V4TBJTSPMAULWJTHDZANCNFSM6AAAAAASHQJ63U>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n", "It should be all good then !\r\nCould you share a link to your repository for me to investigate what went wrong ?", "https://huggingface.co/datasets/DTU54DL/common-voice-test16k\n\nAm Di., 22. Nov. 2022 um 16:43 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> It should be all good then !\n> Could you share a link to your repository for me to investigate what went\n> wrong ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1323876682>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOEUJRZWXAM7DYA5VJDWJTS3NANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "I see ! This is a bug with MP3 files.\r\n\r\nWhen we store audio data in parquet, we store the bytes and the file name. From the file name extension we know if it's a WAV, an MP3 or else. But here it looks like the paths are all None.\r\n\r\nIt looks like it comes from here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/audio.py#L212\r\n\r\nCc @polinaeterna maybe we should simply put the file name instead of None values ?", "@lhoestq I remember we wanted to avoid storing redundant data but maybe it's not that crucial indeed to store one more string value. \r\nOr we can store paths only for mp3s, considering that for other formats we don't have such a problem with reading from bytes without format specified. ", "It doesn't cost much to always store the file name IMO", "thanks for the help!\n\ncan I do anything on my side? we are doing a DL project and we need the\ndata really quick.\n\nthanks\nbogdan\n\n> Message ID: ***@***.***>\n>\n", "I opened a pull requests here: https://github.com/huggingface/datasets/pull/5285, we'll do a new release soon with this fix.\r\n\r\nOtherwise if you're really in a hurry you can install `datasets` from this PR", "[image: image.png]\n\n> Message ID: ***@***.***>\n>\n", "any idea on what's going wrong here?\n\nthanks\n\nAm So., 27. Nov. 2022 um 13:53 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> [image: image.png]\n>\n>> Message ID: ***@***.***>\n>>\n>\n", "hi @capsabogdan! \r\ncould you please share more specifically what problem do you have now?", "I have attached this screenshot above . can u pls help? So can not pip from pull request\r\n\r\n\r\n", "The pull request has been merged on `main`.\r\nYou can install `datasets` from `main` using\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```", "I've tried to load this dataset DTU54DL/common-voice-test16k, but am\ngetting the same error.\n\nSo the bug fix will fix only if I upload a new dataset, or also loading\npreviously uploaded datasets?\n\nthanks\n\nAm Mo., 28. Nov. 2022 um 19:51 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> The pull request has been merged on main.\n> You can install datasets from main using\n>\n> pip install git+https://github.com/huggingface/datasets.git\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1329587334>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOCNYYIGHM2EX3ZIO6DWKT5MXANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "> So the bug fix will fix only if I upload a new dataset, or also loading\r\npreviously uploaded datasets?\r\n\r\nYou have to reupload the dataset, sorry for the inconvenience", "thank you so much for the help! works like a charm!\n\nAm Di., 29. Nov. 2022 um 12:15 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> So the bug fix will fix only if I upload a new dataset, or also loading\n> previously uploaded datasets?\n>\n> You have to reupload the dataset, sorry for the inconvenience\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1330468393>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOBKEFZO57BAKY4IGW3WKXQUZANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n" ]

"2022-11-22T08:17:53"

"2023-07-21T14:33:10"

"2023-07-21T14:33:10"

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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) | 0.007108 / 0.011353 (-0.004245) | 0.004446 / 0.011008 (-0.006562) | 0.084013 / 0.038508 (0.045505) | 0.084271 / 0.023109 (0.061162) | 0.324496 / 0.275898 (0.048598) | 0.347783 / 0.323480 (0.024303) | 0.004382 / 0.007986 (-0.003604) | 0.005200 / 0.004328 (0.000872) | 0.065117 / 0.004250 (0.060866) | 0.063368 / 0.037052 (0.026316) | 0.328731 / 0.258489 (0.070242) | 0.356676 / 0.293841 (0.062835) | 0.031155 / 0.128546 (-0.097392) | 0.008672 / 0.075646 (-0.066975) | 0.287573 / 0.419271 (-0.131698) | 0.053692 / 0.043533 (0.010160) | 0.308796 / 0.255139 (0.053657) | 0.330521 / 0.283200 (0.047321) | 0.025010 / 0.141683 (-0.116672) | 1.498968 / 1.452155 (0.046813) | 1.552096 / 1.492716 (0.059380) |\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) | 0.263580 / 0.018006 (0.245574) | 0.559765 / 0.000490 (0.559275) | 0.003450 / 0.000200 (0.003250) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029403 / 0.037411 (-0.008008) | 0.088154 / 0.014526 (0.073628) | 0.100372 / 0.176557 (-0.076185) | 0.157777 / 0.737135 (-0.579359) | 0.102273 / 0.296338 (-0.194066) |\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) | 0.387027 / 0.215209 (0.171818) | 3.854260 / 2.077655 (1.776605) | 1.875159 / 1.504120 (0.371039) | 1.703734 / 1.541195 (0.162539) | 1.814305 / 1.468490 (0.345815) | 0.482524 / 4.584777 (-4.102253) | 3.463602 / 3.745712 (-0.282110) | 4.004766 / 5.269862 (-1.265095) | 2.406751 / 4.565676 (-2.158925) | 0.057069 / 0.424275 (-0.367206) | 0.007448 / 0.007607 (-0.000159) | 0.465801 / 0.226044 (0.239757) | 4.636700 / 2.268929 (2.367771) | 2.329475 / 55.444624 (-53.115150) | 1.998330 / 6.876477 (-4.878146) | 2.264617 / 2.142072 (0.122544) | 0.577998 / 4.805227 (-4.227230) | 0.130846 / 6.500664 (-6.369818) | 0.059713 / 0.075469 (-0.015756) |\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) | 1.275931 / 1.841788 (-0.565857) | 20.396288 / 8.074308 (12.321980) | 13.875242 / 10.191392 (3.683850) | 0.164367 / 0.680424 (-0.516057) | 0.018573 / 0.534201 (-0.515628) | 0.397516 / 0.579283 (-0.181767) | 0.398977 / 0.434364 (-0.035387) | 0.462386 / 0.540337 (-0.077951) | 0.610129 / 1.386936 (-0.776807) |\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) | 0.006912 / 0.011353 (-0.004441) | 0.004212 / 0.011008 (-0.006797) | 0.065707 / 0.038508 (0.027199) | 0.090435 / 0.023109 (0.067325) | 0.380539 / 0.275898 (0.104641) | 0.412692 / 0.323480 (0.089212) | 0.005545 / 0.007986 (-0.002441) | 0.003657 / 0.004328 (-0.000672) | 0.065380 / 0.004250 (0.061130) | 0.062901 / 0.037052 (0.025848) | 0.385931 / 0.258489 (0.127442) | 0.416272 / 0.293841 (0.122431) | 0.031974 / 0.128546 (-0.096572) | 0.008783 / 0.075646 (-0.066863) | 0.071424 / 0.419271 (-0.347847) | 0.049454 / 0.043533 (0.005921) | 0.374231 / 0.255139 (0.119092) | 0.386530 / 0.283200 (0.103331) | 0.025404 / 0.141683 (-0.116279) | 1.469869 / 1.452155 (0.017715) | 1.548629 / 1.492716 (0.055913) |\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) | 0.218413 / 0.018006 (0.200406) | 0.573863 / 0.000490 (0.573373) | 0.004156 / 0.000200 (0.003956) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032610 / 0.037411 (-0.004801) | 0.088270 / 0.014526 (0.073744) | 0.106821 / 0.176557 (-0.069735) | 0.164498 / 0.737135 (-0.572638) | 0.106881 / 0.296338 (-0.189457) |\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) | 0.433730 / 0.215209 (0.218520) | 4.323902 / 2.077655 (2.246247) | 2.308607 / 1.504120 (0.804487) | 2.138888 / 1.541195 (0.597693) | 2.246760 / 1.468490 (0.778269) | 0.486863 / 4.584777 (-4.097914) | 3.561826 / 3.745712 (-0.183886) | 5.592685 / 5.269862 (0.322824) | 3.318560 / 4.565676 (-1.247116) | 0.057348 / 0.424275 (-0.366927) | 0.007434 / 0.007607 (-0.000174) | 0.506767 / 0.226044 (0.280723) | 5.083097 / 2.268929 (2.814168) | 2.780618 / 55.444624 (-52.664006) | 2.456924 / 6.876477 (-4.419553) | 2.564184 / 2.142072 (0.422112) | 0.580693 / 4.805227 (-4.224534) | 0.134471 / 6.500664 (-6.366194) | 0.062883 / 0.075469 (-0.012586) |\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) | 1.346618 / 1.841788 (-0.495169) | 20.547998 / 8.074308 (12.473690) | 14.404159 / 10.191392 (4.212767) | 0.176612 / 0.680424 (-0.503812) | 0.018372 / 0.534201 (-0.515829) | 0.395636 / 0.579283 (-0.183647) | 0.410661 / 0.434364 (-0.023703) | 0.468782 / 0.540337 (-0.071555) | 0.637476 / 1.386936 (-0.749460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0172d4dac0ca823e8bd293cfd4d28e78d92efe42 \"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) | 0.009896 / 0.011353 (-0.001457) | 0.004658 / 0.011008 (-0.006351) | 0.101185 / 0.038508 (0.062677) | 0.075480 / 0.023109 (0.052371) | 0.410620 / 0.275898 (0.134722) | 0.470639 / 0.323480 (0.147159) | 0.007042 / 0.007986 (-0.000943) | 0.003909 / 0.004328 (-0.000419) | 0.079676 / 0.004250 (0.075425) | 0.066921 / 0.037052 (0.029869) | 0.423624 / 0.258489 (0.165135) | 0.473008 / 0.293841 (0.179167) | 0.048492 / 0.128546 (-0.080054) | 0.012833 / 0.075646 (-0.062813) | 0.335286 / 0.419271 (-0.083985) | 0.083506 / 0.043533 (0.039973) | 0.401918 / 0.255139 (0.146779) | 0.467975 / 0.283200 (0.184775) | 0.050025 / 0.141683 (-0.091658) | 1.679392 / 1.452155 (0.227237) | 1.852812 / 1.492716 (0.360095) |\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) | 0.248067 / 0.018006 (0.230061) | 0.584818 / 0.000490 (0.584328) | 0.021558 / 0.000200 (0.021358) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028572 / 0.037411 (-0.008839) | 0.097212 / 0.014526 (0.082686) | 0.121675 / 0.176557 (-0.054881) | 0.186597 / 0.737135 (-0.550538) | 0.122285 / 0.296338 (-0.174053) |\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) | 0.586279 / 0.215209 (0.371070) | 5.634402 / 2.077655 (3.556747) | 2.560648 / 1.504120 (1.056528) | 2.288796 / 1.541195 (0.747601) | 2.402580 / 1.468490 (0.934090) | 0.801453 / 4.584777 (-3.783324) | 5.036654 / 3.745712 (1.290942) | 8.319972 / 5.269862 (3.050110) | 4.665620 / 4.565676 (0.099944) | 0.107292 / 0.424275 (-0.316983) | 0.009206 / 0.007607 (0.001599) | 0.766505 / 0.226044 (0.540461) | 7.333784 / 2.268929 (5.064856) | 3.601875 / 55.444624 (-51.842749) | 2.886388 / 6.876477 (-3.990089) | 3.231797 / 2.142072 (1.089725) | 1.179509 / 4.805227 (-3.625718) | 0.224656 / 6.500664 (-6.276008) | 0.084749 / 0.075469 (0.009280) |\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) | 1.772345 / 1.841788 (-0.069443) | 24.138788 / 8.074308 (16.064480) | 20.712416 / 10.191392 (10.521024) | 0.254655 / 0.680424 (-0.425769) | 0.028858 / 0.534201 (-0.505343) | 0.499314 / 0.579283 (-0.079969) | 0.605797 / 0.434364 (0.171433) | 0.567628 / 0.540337 (0.027290) | 0.752288 / 1.386936 (-0.634648) |\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) | 0.010134 / 0.011353 (-0.001219) | 0.004630 / 0.011008 (-0.006378) | 0.082282 / 0.038508 (0.043774) | 0.081722 / 0.023109 (0.058613) | 0.465018 / 0.275898 (0.189120) | 0.516392 / 0.323480 (0.192912) | 0.006618 / 0.007986 (-0.001368) | 0.004310 / 0.004328 (-0.000018) | 0.078990 / 0.004250 (0.074739) | 0.077729 / 0.037052 (0.040677) | 0.464892 / 0.258489 (0.206403) | 0.510551 / 0.293841 (0.216710) | 0.050750 / 0.128546 (-0.077796) | 0.014402 / 0.075646 (-0.061244) | 0.092587 / 0.419271 (-0.326685) | 0.074769 / 0.043533 (0.031237) | 0.468591 / 0.255139 (0.213452) | 0.508138 / 0.283200 (0.224938) | 0.047774 / 0.141683 (-0.093909) | 1.798354 / 1.452155 (0.346199) | 1.851431 / 1.492716 (0.358714) |\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) | 0.282528 / 0.018006 (0.264522) | 0.588286 / 0.000490 (0.587797) | 0.004892 / 0.000200 (0.004692) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037048 / 0.037411 (-0.000364) | 0.101513 / 0.014526 (0.086987) | 0.133238 / 0.176557 (-0.043319) | 0.234799 / 0.737135 (-0.502336) | 0.120636 / 0.296338 (-0.175703) |\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) | 0.615377 / 0.215209 (0.400168) | 6.225717 / 2.077655 (4.148062) | 2.974137 / 1.504120 (1.470018) | 2.642168 / 1.541195 (1.100973) | 2.706051 / 1.468490 (1.237561) | 0.837171 / 4.584777 (-3.747606) | 5.143368 / 3.745712 (1.397656) | 4.560241 / 5.269862 (-0.709621) | 2.838375 / 4.565676 (-1.727301) | 0.092505 / 0.424275 (-0.331770) | 0.008962 / 0.007607 (0.001355) | 0.726361 / 0.226044 (0.500317) | 7.323998 / 2.268929 (5.055070) | 3.650531 / 55.444624 (-51.794094) | 2.960886 / 6.876477 (-3.915591) | 3.003889 / 2.142072 (0.861816) | 0.979264 / 4.805227 (-3.825963) | 0.204531 / 6.500664 (-6.296133) | 0.078285 / 0.075469 (0.002816) |\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) | 1.774225 / 1.841788 (-0.067563) | 26.399536 / 8.074308 (18.325228) | 22.312890 / 10.191392 (12.121498) | 0.244651 / 0.680424 (-0.435773) | 0.026950 / 0.534201 (-0.507251) | 0.493037 / 0.579283 (-0.086246) | 0.620399 / 0.434364 (0.186036) | 0.748985 / 0.540337 (0.208648) | 0.799766 / 1.386936 (-0.587170) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49ac2864177ec4fb34c43b59a6e49de1f21f973 \"CML watermark\")\n" ]

"2023-07-12T15:46:12"

"2023-07-12T16:29:26"

"2023-07-12T16:18:04"

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[ "Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?", "Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)" ]

"2021-10-22T08:02:50"

"2021-10-28T05:44:49"

"2021-10-28T05:44:49"

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

"2022-03-25T10:37:18"

"2022-03-25T15:01:02"

"2022-03-25T14:56:10"

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[]

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[ "Hi,\r\n\r\nthanks for the report, but this is a duplicate of #2052. ", "@mariosasko \r\nThank you for your quick response! Following #2052, I've fixed the problem." ]

"2021-03-28T08:30:18"

"2021-03-28T12:29:25"

"2021-03-28T12:29:25"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "100% agree that the checksum verification is overkill and not super useful. But I think this PR would also disable the check on num_examples no ?\r\n \r\nAs a user I would like to know if the dataset I'm loading changed significantly.\r\nAnd I also think it can be useful to make sure the metadata are up to date.\r\n\r\nWhat do you think ?\r\n\r\nWe could have a default `ignore_verifications=\"ignore_checksums\"`", "> We could have a default `ignore_verifications=\"ignore_checksums\"`\r\n\r\nAccepting multiple types (booleans and strings) at the same time is not the best design. Maybe we could define an enum for this parameter?", "Yes an enum sounds good !", "so we can have three verification levels, - smth like \"ignore_all\" (to skip both checksums and all other info like num_examples verification), \"ignore_checksums\" (to skip only checksums verification), and \"verify_all\" (to perform all verification)?\r\nand deprecate `ignore_verifications` param.\r\n\r\n@mariosasko if you're not going to work on this PR in the coming days, I can take over it if you want (this PR will help me with [this issue](https://github.com/huggingface/datasets/issues/5315), not super urgent though).", "Okay, I propose deprecating `ignore_verifications` in favor of `verification_mode` (`load_dataset` already has `download_mode`; some other projects use this name for verification control). `verification_mode` would accept the following enum (or strings in the same manner as `download_mode` does):\r\n\r\n```python\r\nclass VerificationMode(enum.Enum):\r\n FULL = \"full\" # runs all verification checks \r\n BASIC = \"basic\" # default, runs only the cheap ones (skips the checksum check)\r\n NONE = \"none\" # skips all the checks\r\n```\r\n\r\nWDTY?", "(copy paste from my message on slack)\r\n\r\nWhat do you think of a config variable in config.py to switch from one verification mode to another ? This way we don’t deprecate anything\r\n\r\nMany users are familiar with ignore_verifications=True, it might be overkill to deprecate it", "@lhoestq So we have \"basic\" verification mode in `config.py` and continue to have `False` as a default \r\nvalue for `ignore_verifications`? That way running all verifications including checksums would not be possible without switching the config var, right? \r\n\r\nI like having a `VerificationMode` enum because it's aligned with `DownloadMode` and sounds more natural to me (`ignore_verifications` feels a bit semantically reverted but this is probably just my feeling) and it's flexible (no need to worry about `config.py`, I'm not sure that users even know it exists, wdyt?).\r\n\r\nThe usage point seems also valid to me, but cases when users are stuck with NonMatchingX errors also happen from time to time and to figure out what's wrong is non-trivial here. \r\n\r\nAs a note aside - I suggest to add instructions to the NonMatchingX error message (how to use `ignore_verifications` / `verification_mode`), this would save users who don't know about this param a lot of time.", "Ok I see. I'm fine with the new parameter then (even though I had a small pref for the config variable) :)", "I like the idea of an enum and the `verification_mode` parameter. \r\n\r\nIn relation with the config parameter, we could additionally add a `DEFAULT_VERIFICATION_MODE`, maybe only if users require it. Note that until now there wasn't any config parameter for a default `ignore_verifications` value: I guess people are explicitly passing `ignore_verifications=True`...\r\n\r\nAs a note aside, I like the suggestion by @polinaeterna: we could give actionable messages when verifying checksums. This could be done in other PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012891 / 0.011353 (0.001538) | 0.006474 / 0.011008 (-0.004535) | 0.144038 / 0.038508 (0.105530) | 0.036151 / 0.023109 (0.013042) | 0.404366 / 0.275898 (0.128468) | 0.479988 / 0.323480 (0.156508) | 0.010219 / 0.007986 (0.002233) | 0.005319 / 0.004328 (0.000990) | 0.099705 / 0.004250 (0.095455) | 0.046639 / 0.037052 (0.009586) | 0.398997 / 0.258489 (0.140508) | 0.478431 / 0.293841 (0.184590) | 0.069125 / 0.128546 (-0.059421) | 0.019603 / 0.075646 (-0.056043) | 0.400829 / 0.419271 (-0.018443) | 0.066549 / 0.043533 (0.023016) | 0.398343 / 0.255139 (0.143204) | 0.417928 / 0.283200 (0.134728) | 0.121124 / 0.141683 (-0.020559) | 1.751513 / 1.452155 (0.299358) | 1.821239 / 1.492716 (0.328523) |\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) | 0.251603 / 0.018006 (0.233597) | 0.579916 / 0.000490 (0.579427) | 0.003257 / 0.000200 (0.003058) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031502 / 0.037411 (-0.005909) | 0.134688 / 0.014526 (0.120162) | 0.152306 / 0.176557 (-0.024251) | 0.198943 / 0.737135 (-0.538192) | 0.142551 / 0.296338 (-0.153788) |\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) | 0.634672 / 0.215209 (0.419463) | 6.370215 / 2.077655 (4.292561) | 2.548123 / 1.504120 (1.044003) | 2.184263 / 1.541195 (0.643069) | 2.239026 / 1.468490 (0.770536) | 1.233340 / 4.584777 (-3.351437) | 5.791824 / 3.745712 (2.046112) | 5.093032 / 5.269862 (-0.176830) | 2.849833 / 4.565676 (-1.715844) | 0.143787 / 0.424275 (-0.280488) | 0.015279 / 0.007607 (0.007672) | 0.757984 / 0.226044 (0.531939) | 7.883604 / 2.268929 (5.614675) | 3.321591 / 55.444624 (-52.123033) | 2.671777 / 6.876477 (-4.204700) | 2.685215 / 2.142072 (0.543142) | 1.546709 / 4.805227 (-3.258519) | 0.247186 / 6.500664 (-6.253478) | 0.085117 / 0.075469 (0.009648) |\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) | 1.679809 / 1.841788 (-0.161979) | 18.528893 / 8.074308 (10.454585) | 23.168590 / 10.191392 (12.977198) | 0.277618 / 0.680424 (-0.402806) | 0.045109 / 0.534201 (-0.489092) | 0.568873 / 0.579283 (-0.010410) | 0.695017 / 0.434364 (0.260653) | 0.671024 / 0.540337 (0.130687) | 0.823817 / 1.386936 (-0.563119) |\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) | 0.009809 / 0.011353 (-0.001544) | 0.006890 / 0.011008 (-0.004118) | 0.099211 / 0.038508 (0.060703) | 0.035387 / 0.023109 (0.012278) | 0.507603 / 0.275898 (0.231705) | 0.535553 / 0.323480 (0.212073) | 0.007346 / 0.007986 (-0.000640) | 0.007559 / 0.004328 (0.003231) | 0.099132 / 0.004250 (0.094882) | 0.048048 / 0.037052 (0.010996) | 0.518096 / 0.258489 (0.259607) | 0.561134 / 0.293841 (0.267294) | 0.057580 / 0.128546 (-0.070966) | 0.023665 / 0.075646 (-0.051982) | 0.138409 / 0.419271 (-0.280862) | 0.061989 / 0.043533 (0.018456) | 0.510568 / 0.255139 (0.255429) | 0.552722 / 0.283200 (0.269522) | 0.115990 / 0.141683 (-0.025693) | 1.884900 / 1.452155 (0.432745) | 1.990604 / 1.492716 (0.497888) |\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) | 0.280638 / 0.018006 (0.262632) | 0.592837 / 0.000490 (0.592347) | 0.000465 / 0.000200 (0.000265) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030253 / 0.037411 (-0.007158) | 0.141580 / 0.014526 (0.127054) | 0.135114 / 0.176557 (-0.041443) | 0.190003 / 0.737135 (-0.547133) | 0.160230 / 0.296338 (-0.136109) |\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) | 0.699762 / 0.215209 (0.484553) | 6.632344 / 2.077655 (4.554689) | 2.718803 / 1.504120 (1.214683) | 2.485294 / 1.541195 (0.944099) | 2.579889 / 1.468490 (1.111399) | 1.268795 / 4.584777 (-3.315982) | 5.777745 / 3.745712 (2.032033) | 3.232551 / 5.269862 (-2.037311) | 2.127699 / 4.565676 (-2.437977) | 0.146570 / 0.424275 (-0.277705) | 0.015971 / 0.007607 (0.008364) | 0.803181 / 0.226044 (0.577137) | 8.377192 / 2.268929 (6.108264) | 3.551242 / 55.444624 (-51.893382) | 2.865228 / 6.876477 (-4.011249) | 2.774869 / 2.142072 (0.632797) | 1.553856 / 4.805227 (-3.251371) | 0.264510 / 6.500664 (-6.236154) | 0.087918 / 0.075469 (0.012449) |\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) | 1.653396 / 1.841788 (-0.188391) | 18.703863 / 8.074308 (10.629555) | 22.067331 / 10.191392 (11.875939) | 0.257424 / 0.680424 (-0.422999) | 0.026448 / 0.534201 (-0.507753) | 0.550100 / 0.579283 (-0.029183) | 0.647296 / 0.434364 (0.212932) | 0.657476 / 0.540337 (0.117138) | 0.781119 / 1.386936 (-0.605817) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c4a9cb95f8742a2850f11d59abbef71d6c1f60c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008889 / 0.011353 (-0.002464) | 0.004563 / 0.011008 (-0.006445) | 0.101627 / 0.038508 (0.063118) | 0.030526 / 0.023109 (0.007417) | 0.297175 / 0.275898 (0.021277) | 0.368454 / 0.323480 (0.044974) | 0.007246 / 0.007986 (-0.000740) | 0.003565 / 0.004328 (-0.000763) | 0.078644 / 0.004250 (0.074394) | 0.038616 / 0.037052 (0.001564) | 0.310521 / 0.258489 (0.052032) | 0.348014 / 0.293841 (0.054173) | 0.033463 / 0.128546 (-0.095083) | 0.011544 / 0.075646 (-0.064102) | 0.323281 / 0.419271 (-0.095990) | 0.040187 / 0.043533 (-0.003346) | 0.298015 / 0.255139 (0.042876) | 0.326392 / 0.283200 (0.043193) | 0.088730 / 0.141683 (-0.052952) | 1.503387 / 1.452155 (0.051233) | 1.548704 / 1.492716 (0.055988) |\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) | 0.185983 / 0.018006 (0.167977) | 0.451889 / 0.000490 (0.451400) | 0.001433 / 0.000200 (0.001233) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023396 / 0.037411 (-0.014015) | 0.118236 / 0.014526 (0.103710) | 0.124594 / 0.176557 (-0.051962) | 0.159089 / 0.737135 (-0.578047) | 0.129369 / 0.296338 (-0.166969) |\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) | 0.423161 / 0.215209 (0.207952) | 4.228211 / 2.077655 (2.150556) | 1.853862 / 1.504120 (0.349742) | 1.649471 / 1.541195 (0.108276) | 1.708631 / 1.468490 (0.240141) | 0.697456 / 4.584777 (-3.887321) | 3.473244 / 3.745712 (-0.272468) | 1.942586 / 5.269862 (-3.327275) | 1.291592 / 4.565676 (-3.274084) | 0.082758 / 0.424275 (-0.341517) | 0.012256 / 0.007607 (0.004649) | 0.528355 / 0.226044 (0.302311) | 5.277620 / 2.268929 (3.008691) | 2.299604 / 55.444624 (-53.145020) | 1.954940 / 6.876477 (-4.921537) | 2.055543 / 2.142072 (-0.086529) | 0.814723 / 4.805227 (-3.990505) | 0.149937 / 6.500664 (-6.350727) | 0.064529 / 0.075469 (-0.010941) |\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) | 1.266240 / 1.841788 (-0.575547) | 14.144016 / 8.074308 (6.069708) | 14.331733 / 10.191392 (4.140340) | 0.138963 / 0.680424 (-0.541461) | 0.029034 / 0.534201 (-0.505167) | 0.397325 / 0.579283 (-0.181958) | 0.405293 / 0.434364 (-0.029071) | 0.480745 / 0.540337 (-0.059592) | 0.573386 / 1.386936 (-0.813550) |\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) | 0.007214 / 0.011353 (-0.004139) | 0.004569 / 0.011008 (-0.006439) | 0.078718 / 0.038508 (0.040209) | 0.031104 / 0.023109 (0.007995) | 0.342562 / 0.275898 (0.066664) | 0.387802 / 0.323480 (0.064322) | 0.005378 / 0.007986 (-0.002608) | 0.003414 / 0.004328 (-0.000915) | 0.077249 / 0.004250 (0.072999) | 0.044337 / 0.037052 (0.007285) | 0.341397 / 0.258489 (0.082907) | 0.385536 / 0.293841 (0.091695) | 0.033257 / 0.128546 (-0.095289) | 0.011825 / 0.075646 (-0.063821) | 0.086723 / 0.419271 (-0.332549) | 0.045951 / 0.043533 (0.002418) | 0.340914 / 0.255139 (0.085775) | 0.367126 / 0.283200 (0.083926) | 0.096326 / 0.141683 (-0.045357) | 1.608612 / 1.452155 (0.156458) | 1.687251 / 1.492716 (0.194534) |\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) | 0.227595 / 0.018006 (0.209589) | 0.418502 / 0.000490 (0.418013) | 0.000392 / 0.000200 (0.000192) | 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) | 0.026232 / 0.037411 (-0.011179) | 0.101020 / 0.014526 (0.086494) | 0.110017 / 0.176557 (-0.066539) | 0.153497 / 0.737135 (-0.583639) | 0.110602 / 0.296338 (-0.185737) |\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) | 0.433789 / 0.215209 (0.218579) | 4.329350 / 2.077655 (2.251696) | 2.052136 / 1.504120 (0.548016) | 1.848457 / 1.541195 (0.307262) | 1.936791 / 1.468490 (0.468301) | 0.700609 / 4.584777 (-3.884168) | 3.391983 / 3.745712 (-0.353729) | 1.903220 / 5.269862 (-3.366642) | 1.179463 / 4.565676 (-3.386213) | 0.084025 / 0.424275 (-0.340250) | 0.012743 / 0.007607 (0.005136) | 0.536816 / 0.226044 (0.310772) | 5.420230 / 2.268929 (3.151302) | 2.507438 / 55.444624 (-52.937187) | 2.178907 / 6.876477 (-4.697570) | 2.228586 / 2.142072 (0.086514) | 0.812527 / 4.805227 (-3.992701) | 0.153382 / 6.500664 (-6.347282) | 0.069932 / 0.075469 (-0.005537) |\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) | 1.256861 / 1.841788 (-0.584927) | 14.309236 / 8.074308 (6.234928) | 13.740323 / 10.191392 (3.548931) | 0.142698 / 0.680424 (-0.537726) | 0.016998 / 0.534201 (-0.517203) | 0.385489 / 0.579283 (-0.193794) | 0.391515 / 0.434364 (-0.042849) | 0.472704 / 0.540337 (-0.067633) | 0.565042 / 1.386936 (-0.821894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b0713ddf2e2e7129d9ccda791d265684c96675c \"CML watermark\")\n", "This is ready for review. \r\n\r\nIf `verification_mode` is None, it defaults to `VerificationMode.BASIC` instead of `VerificationMode.NONE`, so maybe we should find a better name for the latter to avoid confusion.\r\n\r\nPS: `ignore_verifications` is still present in the `test`/`run_beam` commands for simplicity. Let me know if you think these commands should support all three modes.", "> I would also prefer to change the name for the NONE verification mode, but don't have really good ideas in mind. maybe smth like SKIP_ALL ?\r\n\r\nI decided to go with the following names:\r\n* `no_checks` (previously `none`)\r\n* `basic_checks` (previously `basic`)\r\n* `all_checks` (previously `full`)\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008900 / 0.011353 (-0.002453) | 0.004492 / 0.011008 (-0.006516) | 0.100957 / 0.038508 (0.062449) | 0.030145 / 0.023109 (0.007036) | 0.302531 / 0.275898 (0.026633) | 0.344072 / 0.323480 (0.020592) | 0.007032 / 0.007986 (-0.000953) | 0.004150 / 0.004328 (-0.000178) | 0.078272 / 0.004250 (0.074021) | 0.034142 / 0.037052 (-0.002910) | 0.310798 / 0.258489 (0.052308) | 0.350077 / 0.293841 (0.056236) | 0.034497 / 0.128546 (-0.094050) | 0.011417 / 0.075646 (-0.064230) | 0.323427 / 0.419271 (-0.095844) | 0.045664 / 0.043533 (0.002132) | 0.304688 / 0.255139 (0.049549) | 0.336591 / 0.283200 (0.053391) | 0.086116 / 0.141683 (-0.055567) | 1.519278 / 1.452155 (0.067123) | 1.576728 / 1.492716 (0.084011) |\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) | 0.242482 / 0.018006 (0.224476) | 0.403548 / 0.000490 (0.403058) | 0.001217 / 0.000200 (0.001017) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023466 / 0.037411 (-0.013945) | 0.095220 / 0.014526 (0.080694) | 0.104119 / 0.176557 (-0.072438) | 0.141107 / 0.737135 (-0.596029) | 0.107236 / 0.296338 (-0.189102) |\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) | 0.416290 / 0.215209 (0.201081) | 4.159068 / 2.077655 (2.081413) | 1.846014 / 1.504120 (0.341894) | 1.634789 / 1.541195 (0.093594) | 1.724687 / 1.468490 (0.256196) | 0.696887 / 4.584777 (-3.887890) | 3.313861 / 3.745712 (-0.431851) | 1.907239 / 5.269862 (-3.362622) | 1.266815 / 4.565676 (-3.298861) | 0.081660 / 0.424275 (-0.342615) | 0.012290 / 0.007607 (0.004683) | 0.522866 / 0.226044 (0.296822) | 5.237356 / 2.268929 (2.968428) | 2.294645 / 55.444624 (-53.149979) | 1.946407 / 6.876477 (-4.930069) | 1.995441 / 2.142072 (-0.146632) | 0.808340 / 4.805227 (-3.996887) | 0.149670 / 6.500664 (-6.350994) | 0.065162 / 0.075469 (-0.010307) |\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) | 1.219476 / 1.841788 (-0.622312) | 13.868709 / 8.074308 (5.794401) | 14.115783 / 10.191392 (3.924391) | 0.149403 / 0.680424 (-0.531021) | 0.028514 / 0.534201 (-0.505686) | 0.398194 / 0.579283 (-0.181089) | 0.410898 / 0.434364 (-0.023466) | 0.485763 / 0.540337 (-0.054574) | 0.574924 / 1.386936 (-0.812012) |\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) | 0.006906 / 0.011353 (-0.004447) | 0.004446 / 0.011008 (-0.006562) | 0.075936 / 0.038508 (0.037428) | 0.027693 / 0.023109 (0.004584) | 0.339505 / 0.275898 (0.063607) | 0.383315 / 0.323480 (0.059835) | 0.005138 / 0.007986 (-0.002847) | 0.004636 / 0.004328 (0.000308) | 0.074829 / 0.004250 (0.070578) | 0.040327 / 0.037052 (0.003274) | 0.340516 / 0.258489 (0.082027) | 0.388569 / 0.293841 (0.094729) | 0.031562 / 0.128546 (-0.096984) | 0.011585 / 0.075646 (-0.064061) | 0.084753 / 0.419271 (-0.334518) | 0.041310 / 0.043533 (-0.002223) | 0.338272 / 0.255139 (0.083133) | 0.367243 / 0.283200 (0.084043) | 0.092653 / 0.141683 (-0.049029) | 1.515973 / 1.452155 (0.063818) | 1.582869 / 1.492716 (0.090152) |\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) | 0.229366 / 0.018006 (0.211360) | 0.414404 / 0.000490 (0.413914) | 0.002922 / 0.000200 (0.002723) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026391 / 0.037411 (-0.011020) | 0.106754 / 0.014526 (0.092228) | 0.110718 / 0.176557 (-0.065839) | 0.145786 / 0.737135 (-0.591350) | 0.113180 / 0.296338 (-0.183159) |\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) | 0.446340 / 0.215209 (0.231131) | 4.499756 / 2.077655 (2.422101) | 2.071485 / 1.504120 (0.567365) | 1.873223 / 1.541195 (0.332029) | 1.931562 / 1.468490 (0.463071) | 0.699270 / 4.584777 (-3.885507) | 3.452383 / 3.745712 (-0.293329) | 2.970630 / 5.269862 (-2.299232) | 1.300859 / 4.565676 (-3.264817) | 0.083971 / 0.424275 (-0.340304) | 0.012489 / 0.007607 (0.004882) | 0.544190 / 0.226044 (0.318146) | 5.460097 / 2.268929 (3.191169) | 2.700244 / 55.444624 (-52.744380) | 2.396694 / 6.876477 (-4.479783) | 2.376334 / 2.142072 (0.234262) | 0.812845 / 4.805227 (-3.992382) | 0.154441 / 6.500664 (-6.346223) | 0.069510 / 0.075469 (-0.005959) |\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) | 1.278836 / 1.841788 (-0.562952) | 14.153158 / 8.074308 (6.078850) | 13.821290 / 10.191392 (3.629898) | 0.160464 / 0.680424 (-0.519960) | 0.016742 / 0.534201 (-0.517459) | 0.379840 / 0.579283 (-0.199443) | 0.391903 / 0.434364 (-0.042461) | 0.461646 / 0.540337 (-0.078691) | 0.550691 / 1.386936 (-0.836245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aeb637daab938d51b8b15ad4d175d06817e99512 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009858 / 0.011353 (-0.001495) | 0.005383 / 0.011008 (-0.005625) | 0.100527 / 0.038508 (0.062019) | 0.037176 / 0.023109 (0.014067) | 0.295204 / 0.275898 (0.019306) | 0.364511 / 0.323480 (0.041031) | 0.008486 / 0.007986 (0.000500) | 0.004273 / 0.004328 (-0.000055) | 0.076538 / 0.004250 (0.072288) | 0.046250 / 0.037052 (0.009197) | 0.307102 / 0.258489 (0.048613) | 0.339313 / 0.293841 (0.045472) | 0.040783 / 0.128546 (-0.087763) | 0.012323 / 0.075646 (-0.063323) | 0.336216 / 0.419271 (-0.083055) | 0.050480 / 0.043533 (0.006947) | 0.293689 / 0.255139 (0.038550) | 0.315034 / 0.283200 (0.031834) | 0.113775 / 0.141683 (-0.027908) | 1.438738 / 1.452155 (-0.013416) | 1.499874 / 1.492716 (0.007157) |\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) | 0.202392 / 0.018006 (0.184386) | 0.442784 / 0.000490 (0.442295) | 0.003004 / 0.000200 (0.002804) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027792 / 0.037411 (-0.009620) | 0.110886 / 0.014526 (0.096360) | 0.121041 / 0.176557 (-0.055515) | 0.166803 / 0.737135 (-0.570333) | 0.127617 / 0.296338 (-0.168722) |\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) | 0.409762 / 0.215209 (0.194553) | 4.073297 / 2.077655 (1.995643) | 1.836375 / 1.504120 (0.332255) | 1.651507 / 1.541195 (0.110312) | 1.734134 / 1.468490 (0.265644) | 0.690900 / 4.584777 (-3.893877) | 3.812045 / 3.745712 (0.066333) | 2.101378 / 5.269862 (-3.168483) | 1.438242 / 4.565676 (-3.127434) | 0.083256 / 0.424275 (-0.341020) | 0.012436 / 0.007607 (0.004829) | 0.501702 / 0.226044 (0.275658) | 5.007679 / 2.268929 (2.738751) | 2.315158 / 55.444624 (-53.129466) | 2.003934 / 6.876477 (-4.872543) | 2.154658 / 2.142072 (0.012586) | 0.831749 / 4.805227 (-3.973478) | 0.165058 / 6.500664 (-6.335606) | 0.062166 / 0.075469 (-0.013303) |\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) | 1.212435 / 1.841788 (-0.629353) | 15.022673 / 8.074308 (6.948365) | 14.649631 / 10.191392 (4.458239) | 0.172121 / 0.680424 (-0.508303) | 0.028791 / 0.534201 (-0.505410) | 0.440290 / 0.579283 (-0.138993) | 0.437359 / 0.434364 (0.002995) | 0.543603 / 0.540337 (0.003265) | 0.643241 / 1.386936 (-0.743695) |\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) | 0.007572 / 0.011353 (-0.003781) | 0.005207 / 0.011008 (-0.005801) | 0.074427 / 0.038508 (0.035919) | 0.033384 / 0.023109 (0.010275) | 0.334538 / 0.275898 (0.058640) | 0.371556 / 0.323480 (0.048076) | 0.006453 / 0.007986 (-0.001532) | 0.004010 / 0.004328 (-0.000319) | 0.073488 / 0.004250 (0.069238) | 0.048082 / 0.037052 (0.011030) | 0.337325 / 0.258489 (0.078836) | 0.395143 / 0.293841 (0.101302) | 0.036714 / 0.128546 (-0.091832) | 0.012089 / 0.075646 (-0.063557) | 0.086008 / 0.419271 (-0.333263) | 0.049277 / 0.043533 (0.005744) | 0.333848 / 0.255139 (0.078709) | 0.354003 / 0.283200 (0.070803) | 0.105012 / 0.141683 (-0.036671) | 1.450769 / 1.452155 (-0.001386) | 1.554538 / 1.492716 (0.061821) |\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) | 0.208407 / 0.018006 (0.190400) | 0.438778 / 0.000490 (0.438288) | 0.000399 / 0.000200 (0.000199) | 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) | 0.030180 / 0.037411 (-0.007232) | 0.115432 / 0.014526 (0.100906) | 0.126106 / 0.176557 (-0.050451) | 0.167508 / 0.737135 (-0.569627) | 0.130566 / 0.296338 (-0.165772) |\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) | 0.421408 / 0.215209 (0.206198) | 4.208492 / 2.077655 (2.130838) | 2.024177 / 1.504120 (0.520057) | 1.834356 / 1.541195 (0.293161) | 1.923234 / 1.468490 (0.454744) | 0.699548 / 4.584777 (-3.885229) | 3.933775 / 3.745712 (0.188063) | 2.124526 / 5.269862 (-3.145336) | 1.360934 / 4.565676 (-3.204742) | 0.086568 / 0.424275 (-0.337707) | 0.012351 / 0.007607 (0.004744) | 0.517431 / 0.226044 (0.291387) | 5.175428 / 2.268929 (2.906499) | 2.471031 / 55.444624 (-52.973593) | 2.131529 / 6.876477 (-4.744948) | 2.202512 / 2.142072 (0.060440) | 0.849364 / 4.805227 (-3.955863) | 0.171505 / 6.500664 (-6.329159) | 0.065864 / 0.075469 (-0.009605) |\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) | 1.270054 / 1.841788 (-0.571734) | 15.254502 / 8.074308 (7.180194) | 13.874969 / 10.191392 (3.683577) | 0.144131 / 0.680424 (-0.536293) | 0.017743 / 0.534201 (-0.516458) | 0.421990 / 0.579283 (-0.157293) | 0.423924 / 0.434364 (-0.010439) | 0.522560 / 0.540337 (-0.017778) | 0.626159 / 1.386936 (-0.760777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05bd726a575a3c1c337022424fa7d226f1a2ebee \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008643 / 0.011353 (-0.002710) | 0.004479 / 0.011008 (-0.006529) | 0.102372 / 0.038508 (0.063864) | 0.029703 / 0.023109 (0.006594) | 0.301479 / 0.275898 (0.025581) | 0.370970 / 0.323480 (0.047490) | 0.007044 / 0.007986 (-0.000942) | 0.004868 / 0.004328 (0.000540) | 0.079568 / 0.004250 (0.075318) | 0.035344 / 0.037052 (-0.001708) | 0.308091 / 0.258489 (0.049602) | 0.353812 / 0.293841 (0.059971) | 0.033406 / 0.128546 (-0.095140) | 0.011476 / 0.075646 (-0.064170) | 0.324343 / 0.419271 (-0.094929) | 0.040293 / 0.043533 (-0.003240) | 0.300007 / 0.255139 (0.044868) | 0.334410 / 0.283200 (0.051210) | 0.086553 / 0.141683 (-0.055130) | 1.463814 / 1.452155 (0.011659) | 1.501580 / 1.492716 (0.008864) |\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) | 0.198032 / 0.018006 (0.180025) | 0.409970 / 0.000490 (0.409480) | 0.001075 / 0.000200 (0.000875) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022941 / 0.037411 (-0.014471) | 0.097320 / 0.014526 (0.082794) | 0.106445 / 0.176557 (-0.070111) | 0.139073 / 0.737135 (-0.598063) | 0.108408 / 0.296338 (-0.187930) |\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) | 0.419315 / 0.215209 (0.204106) | 4.199273 / 2.077655 (2.121618) | 1.877689 / 1.504120 (0.373569) | 1.670442 / 1.541195 (0.129247) | 1.735034 / 1.468490 (0.266544) | 0.694691 / 4.584777 (-3.890086) | 3.323644 / 3.745712 (-0.422069) | 2.884349 / 5.269862 (-2.385513) | 1.518882 / 4.565676 (-3.046794) | 0.082390 / 0.424275 (-0.341886) | 0.012884 / 0.007607 (0.005277) | 0.525103 / 0.226044 (0.299058) | 5.277297 / 2.268929 (3.008369) | 2.328639 / 55.444624 (-53.115985) | 1.983210 / 6.876477 (-4.893267) | 2.037985 / 2.142072 (-0.104088) | 0.809520 / 4.805227 (-3.995707) | 0.150150 / 6.500664 (-6.350514) | 0.065578 / 0.075469 (-0.009891) |\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) | 1.221971 / 1.841788 (-0.619817) | 13.692361 / 8.074308 (5.618052) | 13.874582 / 10.191392 (3.683190) | 0.138182 / 0.680424 (-0.542242) | 0.028618 / 0.534201 (-0.505583) | 0.395104 / 0.579283 (-0.184179) | 0.397169 / 0.434364 (-0.037195) | 0.457509 / 0.540337 (-0.082829) | 0.537275 / 1.386936 (-0.849661) |\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) | 0.006835 / 0.011353 (-0.004518) | 0.004585 / 0.011008 (-0.006423) | 0.076877 / 0.038508 (0.038369) | 0.027305 / 0.023109 (0.004196) | 0.349085 / 0.275898 (0.073187) | 0.401416 / 0.323480 (0.077936) | 0.004912 / 0.007986 (-0.003074) | 0.003315 / 0.004328 (-0.001014) | 0.075676 / 0.004250 (0.071425) | 0.038960 / 0.037052 (0.001907) | 0.346196 / 0.258489 (0.087707) | 0.403185 / 0.293841 (0.109344) | 0.032054 / 0.128546 (-0.096493) | 0.011742 / 0.075646 (-0.063905) | 0.086631 / 0.419271 (-0.332640) | 0.041633 / 0.043533 (-0.001900) | 0.343519 / 0.255139 (0.088380) | 0.385413 / 0.283200 (0.102213) | 0.091430 / 0.141683 (-0.050253) | 1.478886 / 1.452155 (0.026731) | 1.546873 / 1.492716 (0.054156) |\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) | 0.167882 / 0.018006 (0.149876) | 0.396464 / 0.000490 (0.395974) | 0.003629 / 0.000200 (0.003429) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024829 / 0.037411 (-0.012583) | 0.099607 / 0.014526 (0.085081) | 0.106187 / 0.176557 (-0.070370) | 0.142379 / 0.737135 (-0.594756) | 0.109307 / 0.296338 (-0.187032) |\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) | 0.442276 / 0.215209 (0.227067) | 4.427099 / 2.077655 (2.349444) | 2.093407 / 1.504120 (0.589287) | 1.880973 / 1.541195 (0.339778) | 1.915592 / 1.468490 (0.447102) | 0.708196 / 4.584777 (-3.876581) | 3.417649 / 3.745712 (-0.328063) | 2.859953 / 5.269862 (-2.409909) | 1.528380 / 4.565676 (-3.037297) | 0.084054 / 0.424275 (-0.340221) | 0.012585 / 0.007607 (0.004978) | 0.537614 / 0.226044 (0.311569) | 5.409915 / 2.268929 (3.140987) | 2.555853 / 55.444624 (-52.888771) | 2.195075 / 6.876477 (-4.681402) | 2.232775 / 2.142072 (0.090703) | 0.814994 / 4.805227 (-3.990233) | 0.152882 / 6.500664 (-6.347782) | 0.067467 / 0.075469 (-0.008002) |\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) | 1.306007 / 1.841788 (-0.535780) | 13.923981 / 8.074308 (5.849673) | 13.385881 / 10.191392 (3.194489) | 0.150712 / 0.680424 (-0.529712) | 0.016731 / 0.534201 (-0.517470) | 0.376557 / 0.579283 (-0.202726) | 0.379396 / 0.434364 (-0.054968) | 0.456251 / 0.540337 (-0.084087) | 0.545731 / 1.386936 (-0.841205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc637d107ef3e3b9948691379312a8099b6476aa \"CML watermark\")\n" ]

"2022-11-25T18:39:09"

"2023-02-13T16:50:42"

"2023-02-13T16:43:47"

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[]

[]

[ "Thanks @brainshawn for this update" ]

"2020-07-03T11:23:19"

"2020-07-03T12:33:50"

"2020-07-03T12:33:50"

{ "diff_url": "https://github.com/huggingface/datasets/pull/340.diff", "html_url": "https://github.com/huggingface/datasets/pull/340", "merged_at": "2020-07-03T12:33:50Z", "patch_url": "https://github.com/huggingface/datasets/pull/340.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/340" }

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[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

[]

[ "I don't think we need completion on the datasets-cli, since we're mainly developing huggingface-cli", "I see." ]

"2022-11-19T13:48:59"

"2022-11-21T15:06:15"

"2022-11-21T15:06:14"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6220). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005950 / 0.011353 (-0.005403) | 0.003578 / 0.011008 (-0.007431) | 0.079327 / 0.038508 (0.040819) | 0.057862 / 0.023109 (0.034752) | 0.317288 / 0.275898 (0.041390) | 0.358210 / 0.323480 (0.034730) | 0.004685 / 0.007986 (-0.003301) | 0.002879 / 0.004328 (-0.001450) | 0.062355 / 0.004250 (0.058105) | 0.045093 / 0.037052 (0.008041) | 0.322520 / 0.258489 (0.064031) | 0.367114 / 0.293841 (0.073273) | 0.027233 / 0.128546 (-0.101313) | 0.007941 / 0.075646 (-0.067705) | 0.260511 / 0.419271 (-0.158761) | 0.044355 / 0.043533 (0.000822) | 0.332993 / 0.255139 (0.077854) | 0.351363 / 0.283200 (0.068163) | 0.020784 / 0.141683 (-0.120899) | 1.429044 / 1.452155 (-0.023111) | 1.489355 / 1.492716 (-0.003362) |\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) | 0.180903 / 0.018006 (0.162897) | 0.421566 / 0.000490 (0.421077) | 0.003259 / 0.000200 (0.003059) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023765 / 0.037411 (-0.013646) | 0.072815 / 0.014526 (0.058289) | 0.084592 / 0.176557 (-0.091965) | 0.143556 / 0.737135 (-0.593579) | 0.083591 / 0.296338 (-0.212748) |\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) | 0.401896 / 0.215209 (0.186687) | 4.006344 / 2.077655 (1.928689) | 2.092280 / 1.504120 (0.588160) | 1.937828 / 1.541195 (0.396633) | 2.026901 / 1.468490 (0.558411) | 0.499999 / 4.584777 (-4.084778) | 3.008715 / 3.745712 (-0.736997) | 2.789735 / 5.269862 (-2.480127) | 1.827319 / 4.565676 (-2.738358) | 0.057413 / 0.424275 (-0.366862) | 0.006716 / 0.007607 (-0.000891) | 0.473061 / 0.226044 (0.247016) | 4.733256 / 2.268929 (2.464327) | 2.403922 / 55.444624 (-53.040702) | 2.017466 / 6.876477 (-4.859011) | 2.209710 / 2.142072 (0.067638) | 0.590813 / 4.805227 (-4.214414) | 0.124760 / 6.500664 (-6.375904) | 0.060976 / 0.075469 (-0.014494) |\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) | 1.229172 / 1.841788 (-0.612616) | 17.924644 / 8.074308 (9.850336) | 13.697347 / 10.191392 (3.505955) | 0.128258 / 0.680424 (-0.552166) | 0.016780 / 0.534201 (-0.517421) | 0.329301 / 0.579283 (-0.249982) | 0.344527 / 0.434364 (-0.089837) | 0.379482 / 0.540337 (-0.160855) | 0.513851 / 1.386936 (-0.873085) |\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) | 0.005962 / 0.011353 (-0.005391) | 0.003613 / 0.011008 (-0.007396) | 0.062428 / 0.038508 (0.023920) | 0.058151 / 0.023109 (0.035042) | 0.452926 / 0.275898 (0.177027) | 0.489740 / 0.323480 (0.166260) | 0.006137 / 0.007986 (-0.001848) | 0.002890 / 0.004328 (-0.001438) | 0.062880 / 0.004250 (0.058629) | 0.046175 / 0.037052 (0.009123) | 0.452416 / 0.258489 (0.193927) | 0.486047 / 0.293841 (0.192206) | 0.028517 / 0.128546 (-0.100029) | 0.008102 / 0.075646 (-0.067544) | 0.068251 / 0.419271 (-0.351020) | 0.040569 / 0.043533 (-0.002964) | 0.461306 / 0.255139 (0.206167) | 0.477675 / 0.283200 (0.194475) | 0.020944 / 0.141683 (-0.120739) | 1.414300 / 1.452155 (-0.037855) | 1.502108 / 1.492716 (0.009391) |\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) | 0.217786 / 0.018006 (0.199780) | 0.410757 / 0.000490 (0.410267) | 0.002981 / 0.000200 (0.002781) | 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) | 0.026846 / 0.037411 (-0.010565) | 0.080098 / 0.014526 (0.065572) | 0.090591 / 0.176557 (-0.085965) | 0.144674 / 0.737135 (-0.592461) | 0.091287 / 0.296338 (-0.205052) |\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) | 0.458224 / 0.215209 (0.243015) | 4.590541 / 2.077655 (2.512886) | 2.511251 / 1.504120 (1.007131) | 2.329165 / 1.541195 (0.787970) | 2.379187 / 1.468490 (0.910696) | 0.507485 / 4.584777 (-4.077292) | 3.135011 / 3.745712 (-0.610701) | 2.805913 / 5.269862 (-2.463948) | 1.851382 / 4.565676 (-2.714295) | 0.057981 / 0.424275 (-0.366294) | 0.006557 / 0.007607 (-0.001050) | 0.532496 / 0.226044 (0.306452) | 5.348802 / 2.268929 (3.079874) | 2.993379 / 55.444624 (-52.451245) | 2.636372 / 6.876477 (-4.240104) | 2.753219 / 2.142072 (0.611147) | 0.591989 / 4.805227 (-4.213238) | 0.126691 / 6.500664 (-6.373973) | 0.062359 / 0.075469 (-0.013110) |\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) | 1.345498 / 1.841788 (-0.496290) | 18.335767 / 8.074308 (10.261458) | 15.115449 / 10.191392 (4.924057) | 0.147382 / 0.680424 (-0.533041) | 0.017729 / 0.534201 (-0.516472) | 0.334337 / 0.579283 (-0.244946) | 0.359035 / 0.434364 (-0.075329) | 0.386319 / 0.540337 (-0.154019) | 0.536378 / 1.386936 (-0.850558) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b028fd83d74e7701e7b8f2d87e740a989505a7 \"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) | 0.009136 / 0.011353 (-0.002216) | 0.005567 / 0.011008 (-0.005442) | 0.120320 / 0.038508 (0.081812) | 0.078082 / 0.023109 (0.054973) | 0.405579 / 0.275898 (0.129681) | 0.459714 / 0.323480 (0.136234) | 0.006327 / 0.007986 (-0.001659) | 0.007187 / 0.004328 (0.002859) | 0.084373 / 0.004250 (0.080122) | 0.059727 / 0.037052 (0.022675) | 0.418918 / 0.258489 (0.160429) | 0.486767 / 0.293841 (0.192927) | 0.047715 / 0.128546 (-0.080831) | 0.014417 / 0.075646 (-0.061229) | 0.379847 / 0.419271 (-0.039425) | 0.067472 / 0.043533 (0.023939) | 0.419304 / 0.255139 (0.164166) | 0.466260 / 0.283200 (0.183060) | 0.036872 / 0.141683 (-0.104811) | 1.876273 / 1.452155 (0.424119) | 2.043856 / 1.492716 (0.551140) |\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) | 0.296266 / 0.018006 (0.278260) | 0.601843 / 0.000490 (0.601354) | 0.005663 / 0.000200 (0.005463) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033272 / 0.037411 (-0.004139) | 0.098839 / 0.014526 (0.084313) | 0.124658 / 0.176557 (-0.051899) | 0.190226 / 0.737135 (-0.546909) | 0.119288 / 0.296338 (-0.177051) |\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) | 0.600878 / 0.215209 (0.385668) | 6.011749 / 2.077655 (3.934095) | 2.611809 / 1.504120 (1.107689) | 2.314985 / 1.541195 (0.773790) | 2.398988 / 1.468490 (0.930498) | 0.835577 / 4.584777 (-3.749200) | 5.482848 / 3.745712 (1.737136) | 4.965393 / 5.269862 (-0.304469) | 3.082420 / 4.565676 (-1.483256) | 0.098048 / 0.424275 (-0.326227) | 0.009148 / 0.007607 (0.001541) | 0.725721 / 0.226044 (0.499676) | 7.297429 / 2.268929 (5.028501) | 3.558050 / 55.444624 (-51.886575) | 2.815884 / 6.876477 (-4.060593) | 3.094103 / 2.142072 (0.952031) | 1.023617 / 4.805227 (-3.781610) | 0.222453 / 6.500664 (-6.278211) | 0.081707 / 0.075469 (0.006238) |\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) | 1.788327 / 1.841788 (-0.053461) | 25.285829 / 8.074308 (17.211521) | 21.878811 / 10.191392 (11.687419) | 0.215494 / 0.680424 (-0.464930) | 0.032050 / 0.534201 (-0.502151) | 0.505210 / 0.579283 (-0.074073) | 0.623545 / 0.434364 (0.189181) | 0.583342 / 0.540337 (0.043005) | 0.826497 / 1.386936 (-0.560439) |\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) | 0.009640 / 0.011353 (-0.001713) | 0.005479 / 0.011008 (-0.005529) | 0.088940 / 0.038508 (0.050432) | 0.084186 / 0.023109 (0.061077) | 0.552290 / 0.275898 (0.276392) | 0.583296 / 0.323480 (0.259816) | 0.006999 / 0.007986 (-0.000987) | 0.004597 / 0.004328 (0.000269) | 0.089407 / 0.004250 (0.085157) | 0.067210 / 0.037052 (0.030157) | 0.554968 / 0.258489 (0.296479) | 0.595635 / 0.293841 (0.301794) | 0.052245 / 0.128546 (-0.076301) | 0.015914 / 0.075646 (-0.059733) | 0.097037 / 0.419271 (-0.322235) | 0.063954 / 0.043533 (0.020421) | 0.533752 / 0.255139 (0.278614) | 0.573789 / 0.283200 (0.290589) | 0.036526 / 0.141683 (-0.105157) | 1.867713 / 1.452155 (0.415558) | 1.996901 / 1.492716 (0.504185) |\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) | 0.414967 / 0.018006 (0.396961) | 0.632367 / 0.000490 (0.631877) | 0.064061 / 0.000200 (0.063861) | 0.000565 / 0.000054 (0.000510) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035953 / 0.037411 (-0.001458) | 0.112603 / 0.014526 (0.098077) | 0.126227 / 0.176557 (-0.050330) | 0.196881 / 0.737135 (-0.540255) | 0.127635 / 0.296338 (-0.168704) |\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) | 0.674735 / 0.215209 (0.459526) | 6.614578 / 2.077655 (4.536923) | 3.208198 / 1.504120 (1.704078) | 2.870412 / 1.541195 (1.329217) | 2.979358 / 1.468490 (1.510868) | 0.872589 / 4.584777 (-3.712187) | 5.501771 / 3.745712 (1.756059) | 4.865191 / 5.269862 (-0.404671) | 3.075281 / 4.565676 (-1.490396) | 0.098048 / 0.424275 (-0.326227) | 0.009121 / 0.007607 (0.001514) | 0.801639 / 0.226044 (0.575595) | 8.062040 / 2.268929 (5.793111) | 3.996693 / 55.444624 (-51.447931) | 3.343770 / 6.876477 (-3.532706) | 3.555977 / 2.142072 (1.413904) | 1.035050 / 4.805227 (-3.770177) | 0.227552 / 6.500664 (-6.273112) | 0.097733 / 0.075469 (0.022264) |\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) | 1.897210 / 1.841788 (0.055422) | 25.762459 / 8.074308 (17.688151) | 22.771290 / 10.191392 (12.579898) | 0.252650 / 0.680424 (-0.427773) | 0.032534 / 0.534201 (-0.501667) | 0.521047 / 0.579283 (-0.058236) | 0.620850 / 0.434364 (0.186486) | 0.612750 / 0.540337 (0.072413) | 0.837486 / 1.386936 (-0.549451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f522e5bdd73c45f7ba0a03f2ecd4e7de7351f2e \"CML watermark\")\n" ]

"2023-09-06T15:40:33"

"2023-09-06T15:52:33"

"2023-09-06T15:41:13"

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[]

[ "@LysandreJik not so sure who to @\r\nCould you help?", "Hi @hyusterr, I believe it is @wlhgtc from https://github.com/huggingface/transformers/pull/9887" ]

"2021-12-09T17:54:35"

"2021-12-22T11:21:33"

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[]

[]

[]

"2020-10-02T12:08:04"

"2020-10-02T12:12:02"

"2020-10-02T12:12:01"

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