Split (1)

train · 5k rows

id int64 953M 3.35B	number int64 2.72k 7.75k	title stringlengths 1 290	state stringclasses 2 values	created_at timestamp[s]date 2021-07-26 12:21:17 2025-08-23 00:18:43	updated_at timestamp[s]date 2021-07-26 13:27:59 2025-08-23 12:34:39	closed_at timestamp[s]date 2021-07-26 13:27:59 2025-08-20 16:35:55 ⌀	html_url stringlengths 49 51	pull_request dict	user_login stringlengths 3 26	is_pull_request bool 2 classes	comments listlengths 0 30
1,825,133,741	6,087	fsspec dependency is set too low	closed	2023-07-27T20:08:22	2023-07-28T10:07:56	2023-07-28T10:07:03	https://github.com/huggingface/datasets/issues/6087	null	iXce	false	[ "Thanks for reporting! A PR with a fix has just been merged." ]
1,825,009,268	6,086	Support `fsspec` in `Dataset.to_<format>` methods	closed	2023-07-27T19:08:37	2024-03-07T07:22:43	2024-03-07T07:22:42	https://github.com/huggingface/datasets/issues/6086	null	mariosasko	false	[ "Hi @mariosasko unless someone's already working on it, I guess I can tackle it!", "Hi! Sure, feel free to tackle this.", "#self-assign", "I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?", "Fixed by #6096. " ]
1,824,985,188	6,085	Fix `fsspec` download	open	2023-07-27T18:54:47	2023-07-27T19:06:13	null	https://github.com/huggingface/datasets/pull/6085	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6085", "html_url": "https://github.com/huggingface/datasets/pull/6085", "diff_url": "https://github.com/huggingface/datasets/pull/6085.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6085.patch", "merged_at": null }	mariosasko	true	[ "<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) \| 0.006031 / 0.011353 (-0.005322) \| 0.003579 / 0.011008 (-0.007429) \| 0.080862 / 0.038508 (0.042354) \| 0.056660 / 0.023109 (0.033551) \| 0.388285 / 0.275898 (0.112387) \| 0.422270 / 0.323480 (0.098790) \| 0.004651 / 0.007986 (-0.003335) \| 0.002895 / 0.004328 (-0.001433) \| 0.062767 / 0.004250 (0.058517) \| 0.046491 / 0.037052 (0.009438) \| 0.389918 / 0.258489 (0.131428) \| 0.434650 / 0.293841 (0.140809) \| 0.027265 / 0.128546 (-0.101281) \| 0.007946 / 0.075646 (-0.067701) \| 0.261207 / 0.419271 (-0.158065) \| 0.045057 / 0.043533 (0.001525) \| 0.391977 / 0.255139 (0.136838) \| 0.418525 / 0.283200 (0.135326) \| 0.020705 / 0.141683 (-0.120978) \| 1.459271 / 1.452155 (0.007116) \| 1.516935 / 1.492716 (0.024218) \|\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) \| 0.174659 / 0.018006 (0.156653) \| 0.429627 / 0.000490 (0.429137) \| 0.003714 / 0.000200 (0.003514) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023255 / 0.037411 (-0.014156) \| 0.073463 / 0.014526 (0.058937) \| 0.083000 / 0.176557 (-0.093557) \| 0.146704 / 0.737135 (-0.590431) \| 0.084419 / 0.296338 (-0.211919) \|\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) \| 0.392222 / 0.215209 (0.177013) \| 3.902620 / 2.077655 (1.824966) \| 1.903056 / 1.504120 (0.398936) \| 1.753423 / 1.541195 (0.212228) \| 1.874547 / 1.468490 (0.406057) \| 0.495947 / 4.584777 (-4.088829) \| 3.084680 / 3.745712 (-0.661032) \| 4.235064 / 5.269862 (-1.034797) \| 2.626840 / 4.565676 (-1.938837) \| 0.057273 / 0.424275 (-0.367002) \| 0.006457 / 0.007607 (-0.001150) \| 0.466018 / 0.226044 (0.239974) \| 4.648264 / 2.268929 (2.379335) \| 2.520293 / 55.444624 (-52.924331) \| 2.339393 / 6.876477 (-4.537083) \| 2.538848 / 2.142072 (0.396775) \| 0.592018 / 4.805227 (-4.213210) \| 0.125041 / 6.500664 (-6.375623) \| 0.061038 / 0.075469 (-0.014431) \|\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) \| 1.244285 / 1.841788 (-0.597503) \| 18.411576 / 8.074308 (10.337268) \| 13.850100 / 10.191392 (3.658708) \| 0.131904 / 0.680424 (-0.548520) \| 0.016824 / 0.534201 (-0.517377) \| 0.328931 / 0.579283 (-0.250352) \| 0.364801 / 0.434364 (-0.069563) \| 0.376298 / 0.540337 (-0.164039) \| 0.525045 / 1.386936 (-0.861891) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006059 / 0.011353 (-0.005294) \| 0.003693 / 0.011008 (-0.007315) \| 0.062982 / 0.038508 (0.024473) \| 0.062155 / 0.023109 (0.039046) \| 0.389467 / 0.275898 (0.113568) \| 0.437046 / 0.323480 (0.113566) \| 0.004823 / 0.007986 (-0.003163) \| 0.002935 / 0.004328 (-0.001393) \| 0.062679 / 0.004250 (0.058429) \| 0.049676 / 0.037052 (0.012623) \| 0.418054 / 0.258489 (0.159565) \| 0.442467 / 0.293841 (0.148626) \| 0.027652 / 0.128546 (-0.100895) \| 0.008146 / 0.075646 (-0.067501) \| 0.069414 / 0.419271 (-0.349858) \| 0.042884 / 0.043533 (-0.000649) \| 0.387167 / 0.255139 (0.132028) \| 0.418684 / 0.283200 (0.135484) \| 0.022419 / 0.141683 (-0.119264) \| 1.460606 / 1.452155 (0.008451) \| 1.514204 / 1.492716 (0.021487) \|\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) \| 0.200523 / 0.018006 (0.182517) \| 0.415970 / 0.000490 (0.415481) \| 0.003202 / 0.000200 (0.003002) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025836 / 0.037411 (-0.011575) \| 0.078859 / 0.014526 (0.064333) \| 0.088523 / 0.176557 (-0.088034) \| 0.141572 / 0.737135 (-0.595563) \| 0.090258 / 0.296338 (-0.206080) \|\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) \| 0.416548 / 0.215209 (0.201339) \| 4.155278 / 2.077655 (2.077623) \| 2.126683 / 1.504120 (0.622563) \| 1.963762 / 1.541195 (0.422568) \| 2.029018 / 1.468490 (0.560528) \| 0.499005 / 4.584777 (-4.085772) \| 3.063503 / 3.745712 (-0.682209) \| 4.250800 / 5.269862 (-1.019061) \| 2.642634 / 4.565676 (-1.923043) \| 0.057815 / 0.424275 (-0.366460) \| 0.006784 / 0.007607 (-0.000823) \| 0.492481 / 0.226044 (0.266437) \| 4.914306 / 2.268929 (2.645377) \| 2.601582 / 55.444624 (-52.843042) \| 2.337863 / 6.876477 (-4.538614) \| 2.462854 / 2.142072 (0.320782) \| 0.593738 / 4.805227 (-4.211489) \| 0.127030 / 6.500664 (-6.373634) \| 0.064206 / 0.075469 (-0.011263) \|\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) \| 1.326919 / 1.841788 (-0.514868) \| 18.728929 / 8.074308 (10.654621) \| 13.903681 / 10.191392 (3.712289) \| 0.162670 / 0.680424 (-0.517754) \| 0.016913 / 0.534201 (-0.517288) \| 0.337504 / 0.579283 (-0.241779) \| 0.339786 / 0.434364 (-0.094577) \| 0.384955 / 0.540337 (-0.155383) \| 0.514358 / 1.386936 (-0.872578) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5c31b492c45e01c6f4593ada2b84517a75a5c7c \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6085). 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) \| 0.007610 / 0.011353 (-0.003743) \| 0.004616 / 0.011008 (-0.006392) \| 0.100330 / 0.038508 (0.061821) \| 0.084450 / 0.023109 (0.061341) \| 0.386610 / 0.275898 (0.110712) \| 0.418479 / 0.323480 (0.094999) \| 0.006085 / 0.007986 (-0.001900) \| 0.003800 / 0.004328 (-0.000529) \| 0.076248 / 0.004250 (0.071997) \| 0.065175 / 0.037052 (0.028122) \| 0.387154 / 0.258489 (0.128665) \| 0.425484 / 0.293841 (0.131643) \| 0.035946 / 0.128546 (-0.092601) \| 0.009901 / 0.075646 (-0.065745) \| 0.343015 / 0.419271 (-0.076256) \| 0.060965 / 0.043533 (0.017432) \| 0.390585 / 0.255139 (0.135446) \| 0.405873 / 0.283200 (0.122673) \| 0.026929 / 0.141683 (-0.114754) \| 1.767916 / 1.452155 (0.315761) \| 1.893431 / 1.492716 (0.400715) \|\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) \| 0.237888 / 0.018006 (0.219882) \| 0.503949 / 0.000490 (0.503459) \| 0.004769 / 0.000200 (0.004570) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031553 / 0.037411 (-0.005859) \| 0.096950 / 0.014526 (0.082424) \| 0.110374 / 0.176557 (-0.066183) \| 0.176754 / 0.737135 (-0.560381) \| 0.111703 / 0.296338 (-0.184635) \|\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) \| 0.449232 / 0.215209 (0.234023) \| 4.510247 / 2.077655 (2.432592) \| 2.188547 / 1.504120 (0.684427) \| 2.007530 / 1.541195 (0.466335) \| 2.095650 / 1.468490 (0.627160) \| 0.563262 / 4.584777 (-4.021515) \| 4.062412 / 3.745712 (0.316700) \| 6.338350 / 5.269862 (1.068489) \| 3.844669 / 4.565676 (-0.721008) \| 0.064517 / 0.424275 (-0.359758) \| 0.008536 / 0.007607 (0.000929) \| 0.553872 / 0.226044 (0.327828) \| 5.530311 / 2.268929 (3.261383) \| 2.835109 / 55.444624 (-52.609516) \| 2.493900 / 6.876477 (-4.382577) \| 2.728412 / 2.142072 (0.586340) \| 0.680161 / 4.805227 (-4.125066) \| 0.155831 / 6.500664 (-6.344833) \| 0.070359 / 0.075469 (-0.005110) \|\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) \| 1.504852 / 1.841788 (-0.336936) \| 22.806335 / 8.074308 (14.732027) \| 16.598386 / 10.191392 (6.406994) \| 0.207857 / 0.680424 (-0.472566) \| 0.021425 / 0.534201 (-0.512776) \| 0.474069 / 0.579283 (-0.105214) \| 0.472263 / 0.434364 (0.037899) \| 0.542195 / 0.540337 (0.001858) \| 0.782871 / 1.386936 (-0.604065) \|\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) \| 0.007443 / 0.011353 (-0.003910) \| 0.004465 / 0.011008 (-0.006544) \| 0.076268 / 0.038508 (0.037759) \| 0.086607 / 0.023109 (0.063498) \| 0.443295 / 0.275898 (0.167397) \| 0.472819 / 0.323480 (0.149339) \| 0.005841 / 0.007986 (-0.002144) \| 0.003727 / 0.004328 (-0.000602) \| 0.076015 / 0.004250 (0.071765) \| 0.063188 / 0.037052 (0.026136) \| 0.450555 / 0.258489 (0.192066) \| 0.478532 / 0.293841 (0.184691) \| 0.036258 / 0.128546 (-0.092288) \| 0.009869 / 0.075646 (-0.065777) \| 0.083786 / 0.419271 (-0.335486) \| 0.056546 / 0.043533 (0.013013) \| 0.449647 / 0.255139 (0.194508) \| 0.457588 / 0.283200 (0.174389) \| 0.027197 / 0.141683 (-0.114486) \| 1.769991 / 1.452155 (0.317836) \| 1.859905 / 1.492716 (0.367189) \|\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) \| 0.268637 / 0.018006 (0.250631) \| 0.492860 / 0.000490 (0.492370) \| 0.008574 / 0.000200 (0.008374) \| 0.000140 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037679 / 0.037411 (0.000268) \| 0.108258 / 0.014526 (0.093733) \| 0.117850 / 0.176557 (-0.058707) \| 0.181611 / 0.737135 (-0.555524) \| 0.120901 / 0.296338 (-0.175437) \|\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) \| 0.485780 / 0.215209 (0.270571) \| 4.851289 / 2.077655 (2.773635) \| 2.486068 / 1.504120 (0.981948) \| 2.299417 / 1.541195 (0.758222) \| 2.387093 / 1.468490 (0.918603) \| 0.568826 / 4.584777 (-4.015951) \| 4.163426 / 3.745712 (0.417713) \| 6.224964 / 5.269862 (0.955102) \| 3.255619 / 4.565676 (-1.310058) \| 0.067081 / 0.424275 (-0.357194) \| 0.009065 / 0.007607 (0.001458) \| 0.580449 / 0.226044 (0.354405) \| 5.786394 / 2.268929 (3.517465) \| 3.057780 / 55.444624 (-52.386844) \| 2.764339 / 6.876477 (-4.112138) \| 2.880718 / 2.142072 (0.738645) \| 0.681376 / 4.805227 (-4.123851) \| 0.157858 / 6.500664 (-6.342806) \| 0.072481 / 0.075469 (-0.002988) \|\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) \| 1.590704 / 1.841788 (-0.251083) \| 23.141929 / 8.074308 (15.067620) \| 17.001141 / 10.191392 (6.809749) \| 0.203790 / 0.680424 (-0.476634) \| 0.021766 / 0.534201 (-0.512435) \| 0.475309 / 0.579283 (-0.103974) \| 0.466448 / 0.434364 (0.032084) \| 0.551470 / 0.540337 (0.011132) \| 0.727876 / 1.386936 (-0.659060) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b19eea7fc5cf484e8cdf41d6ae035f94d8a671 \"CML watermark\")\n" ]
1,824,896,761	6,084	Changing pixel values of images in the Winoground dataset	open	2023-07-27T17:55:35	2023-07-27T17:55:35	null	https://github.com/huggingface/datasets/issues/6084	null	ZitengWangNYU	false	[]
1,824,832,348	6,083	set dev version	closed	2023-07-27T17:10:41	2023-07-27T17:22:05	2023-07-27T17:11:01	https://github.com/huggingface/datasets/pull/6083	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6083", "html_url": "https://github.com/huggingface/datasets/pull/6083", "diff_url": "https://github.com/huggingface/datasets/pull/6083.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6083.patch", "merged_at": "2023-07-27T17:11:01" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6083). 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) \| 0.006049 / 0.011353 (-0.005304) \| 0.003698 / 0.011008 (-0.007310) \| 0.080614 / 0.038508 (0.042106) \| 0.060955 / 0.023109 (0.037846) \| 0.337119 / 0.275898 (0.061221) \| 0.369544 / 0.323480 (0.046064) \| 0.004681 / 0.007986 (-0.003305) \| 0.002892 / 0.004328 (-0.001436) \| 0.062907 / 0.004250 (0.058657) \| 0.049235 / 0.037052 (0.012183) \| 0.338842 / 0.258489 (0.080353) \| 0.371172 / 0.293841 (0.077331) \| 0.027016 / 0.128546 (-0.101530) \| 0.007940 / 0.075646 (-0.067706) \| 0.260902 / 0.419271 (-0.158369) \| 0.044566 / 0.043533 (0.001034) \| 0.342354 / 0.255139 (0.087215) \| 0.359829 / 0.283200 (0.076629) \| 0.020801 / 0.141683 (-0.120881) \| 1.444111 / 1.452155 (-0.008044) \| 1.515595 / 1.492716 (0.022879) \|\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) \| 0.183446 / 0.018006 (0.165439) \| 0.437071 / 0.000490 (0.436581) \| 0.003124 / 0.000200 (0.002924) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023760 / 0.037411 (-0.013651) \| 0.072812 / 0.014526 (0.058286) \| 0.082790 / 0.176557 (-0.093766) \| 0.146330 / 0.737135 (-0.590805) \| 0.084469 / 0.296338 (-0.211870) \|\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) \| 0.395215 / 0.215209 (0.180006) \| 3.953023 / 2.077655 (1.875369) \| 1.914268 / 1.504120 (0.410148) \| 1.710195 / 1.541195 (0.169001) \| 1.782594 / 1.468490 (0.314104) \| 0.503651 / 4.584777 (-4.081126) \| 3.039656 / 3.745712 (-0.706056) \| 4.364691 / 5.269862 (-0.905171) \| 2.597762 / 4.565676 (-1.967915) \| 0.057384 / 0.424275 (-0.366891) \| 0.006419 / 0.007607 (-0.001188) \| 0.467214 / 0.226044 (0.241169) \| 4.661425 / 2.268929 (2.392497) \| 2.341957 / 55.444624 (-53.102667) \| 1.977598 / 6.876477 (-4.898878) \| 2.178005 / 2.142072 (0.035933) \| 0.588492 / 4.805227 (-4.216735) \| 0.124972 / 6.500664 (-6.375692) \| 0.060902 / 0.075469 (-0.014567) \|\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) \| 1.243092 / 1.841788 (-0.598695) \| 18.369971 / 8.074308 (10.295663) \| 13.939700 / 10.191392 (3.748308) \| 0.149275 / 0.680424 (-0.531149) \| 0.016873 / 0.534201 (-0.517328) \| 0.334245 / 0.579283 (-0.245038) \| 0.353832 / 0.434364 (-0.080532) \| 0.382720 / 0.540337 (-0.157617) \| 0.534634 / 1.386936 (-0.852302) \|\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) \| 0.005933 / 0.011353 (-0.005420) \| 0.003695 / 0.011008 (-0.007313) \| 0.063457 / 0.038508 (0.024949) \| 0.062347 / 0.023109 (0.039238) \| 0.412370 / 0.275898 (0.136472) \| 0.450399 / 0.323480 (0.126920) \| 0.004627 / 0.007986 (-0.003358) \| 0.002822 / 0.004328 (-0.001507) \| 0.063819 / 0.004250 (0.059569) \| 0.049154 / 0.037052 (0.012101) \| 0.428196 / 0.258489 (0.169707) \| 0.464109 / 0.293841 (0.170268) \| 0.026967 / 0.128546 (-0.101579) \| 0.007876 / 0.075646 (-0.067770) \| 0.068479 / 0.419271 (-0.350793) \| 0.041080 / 0.043533 (-0.002453) \| 0.399817 / 0.255139 (0.144678) \| 0.426900 / 0.283200 (0.143701) \| 0.019931 / 0.141683 (-0.121752) \| 1.461642 / 1.452155 (0.009487) \| 1.529314 / 1.492716 (0.036598) \|\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) \| 0.230256 / 0.018006 (0.212249) \| 0.423442 / 0.000490 (0.422952) \| 0.002492 / 0.000200 (0.002292) \| 0.000072 / 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) \| 0.025798 / 0.037411 (-0.011613) \| 0.077361 / 0.014526 (0.062836) \| 0.088454 / 0.176557 (-0.088102) \| 0.142137 / 0.737135 (-0.594998) \| 0.088213 / 0.296338 (-0.208125) \|\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) \| 0.417656 / 0.215209 (0.202447) \| 4.157095 / 2.077655 (2.079440) \| 2.132863 / 1.504120 (0.628743) \| 1.967220 / 1.541195 (0.426025) \| 2.020505 / 1.468490 (0.552015) \| 0.496835 / 4.584777 (-4.087942) \| 2.989251 / 3.745712 (-0.756462) \| 2.849315 / 5.269862 (-2.420546) \| 1.848941 / 4.565676 (-2.716736) \| 0.057307 / 0.424275 (-0.366968) \| 0.006825 / 0.007607 (-0.000782) \| 0.489103 / 0.226044 (0.263059) \| 4.904776 / 2.268929 (2.635847) \| 2.593914 / 55.444624 (-52.850710) \| 2.253384 / 6.876477 (-4.623093) \| 2.426384 / 2.142072 (0.284312) \| 0.592467 / 4.805227 (-4.212760) \| 0.126122 / 6.500664 (-6.374542) \| 0.063160 / 0.075469 (-0.012309) \|\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) \| 1.313020 / 1.841788 (-0.528768) \| 18.343984 / 8.074308 (10.269676) \| 13.763060 / 10.191392 (3.571668) \| 0.146312 / 0.680424 (-0.534111) \| 0.016980 / 0.534201 (-0.517221) \| 0.339572 / 0.579283 (-0.239711) \| 0.351310 / 0.434364 (-0.083054) \| 0.397616 / 0.540337 (-0.142721) \| 0.536879 / 1.386936 (-0.850057) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#73bed12ecda17d1573fd3bf73ed5db24d3622f86 \"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) \| 0.009979 / 0.011353 (-0.001374) \| 0.005024 / 0.011008 (-0.005984) \| 0.096566 / 0.038508 (0.058058) \| 0.081181 / 0.023109 (0.058072) \| 0.398415 / 0.275898 (0.122517) \| 0.513971 / 0.323480 (0.190491) \| 0.006716 / 0.007986 (-0.001269) \| 0.004350 / 0.004328 (0.000022) \| 0.071418 / 0.004250 (0.067168) \| 0.065002 / 0.037052 (0.027949) \| 0.424791 / 0.258489 (0.166302) \| 0.442369 / 0.293841 (0.148528) \| 0.054540 / 0.128546 (-0.074007) \| 0.014067 / 0.075646 (-0.061580) \| 0.368930 / 0.419271 (-0.050341) \| 0.082468 / 0.043533 (0.038935) \| 0.419875 / 0.255139 (0.164736) \| 0.508308 / 0.283200 (0.225108) \| 0.050411 / 0.141683 (-0.091272) \| 1.582271 / 1.452155 (0.130116) \| 1.842033 / 1.492716 (0.349317) \|\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) \| 0.290427 / 0.018006 (0.272420) \| 0.594736 / 0.000490 (0.594246) \| 0.007058 / 0.000200 (0.006858) \| 0.000149 / 0.000054 (0.000095) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027085 / 0.037411 (-0.010326) \| 0.087626 / 0.014526 (0.073101) \| 0.094299 / 0.176557 (-0.082257) \| 0.160169 / 0.737135 (-0.576966) \| 0.101474 / 0.296338 (-0.194864) \|\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) \| 0.545845 / 0.215209 (0.330636) \| 5.674389 / 2.077655 (3.596734) \| 2.489065 / 1.504120 (0.984945) \| 2.166674 / 1.541195 (0.625479) \| 2.166925 / 1.468490 (0.698434) \| 0.791244 / 4.584777 (-3.793533) \| 4.944878 / 3.745712 (1.199165) \| 4.121628 / 5.269862 (-1.148234) \| 2.701262 / 4.565676 (-1.864415) \| 0.087609 / 0.424275 (-0.336666) \| 0.006945 / 0.007607 (-0.000662) \| 0.668478 / 0.226044 (0.442434) \| 6.552813 / 2.268929 (4.283885) \| 3.164698 / 55.444624 (-52.279927) \| 2.447333 / 6.876477 (-4.429144) \| 2.608271 / 2.142072 (0.466198) \| 0.954202 / 4.805227 (-3.851025) \| 0.187730 / 6.500664 (-6.312934) \| 0.063229 / 0.075469 (-0.012240) \|\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) \| 1.461042 / 1.841788 (-0.380746) \| 21.601409 / 8.074308 (13.527101) \| 18.553604 / 10.191392 (8.362212) \| 0.234571 / 0.680424 (-0.445853) \| 0.027119 / 0.534201 (-0.507082) \| 0.423448 / 0.579283 (-0.155835) \| 0.556397 / 0.434364 (0.122033) \| 0.493958 / 0.540337 (-0.046379) \| 0.711345 / 1.386936 (-0.675591) \|\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) \| 0.008637 / 0.011353 (-0.002716) \| 0.014450 / 0.011008 (0.003442) \| 0.084135 / 0.038508 (0.045627) \| 0.080513 / 0.023109 (0.057403) \| 0.557941 / 0.275898 (0.282042) \| 0.563199 / 0.323480 (0.239719) \| 0.006475 / 0.007986 (-0.001510) \| 0.004407 / 0.004328 (0.000078) \| 0.088537 / 0.004250 (0.084287) \| 0.060871 / 0.037052 (0.023819) \| 0.593077 / 0.258489 (0.334588) \| 0.615572 / 0.293841 (0.321732) \| 0.050157 / 0.128546 (-0.078389) \| 0.014313 / 0.075646 (-0.061333) \| 0.091784 / 0.419271 (-0.327487) \| 0.065649 / 0.043533 (0.022116) \| 0.532569 / 0.255139 (0.277430) \| 0.580775 / 0.283200 (0.297575) \| 0.036434 / 0.141683 (-0.105249) \| 2.080051 / 1.452155 (0.627896) \| 1.907430 / 1.492716 (0.414713) \|\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) \| 0.297763 / 0.018006 (0.279757) \| 0.670408 / 0.000490 (0.669918) \| 0.000467 / 0.000200 (0.000267) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030297 / 0.037411 (-0.007114) \| 0.100310 / 0.014526 (0.085784) \| 0.113158 / 0.176557 (-0.063398) \| 0.149599 / 0.737135 (-0.587536) \| 0.102620 / 0.296338 (-0.193718) \|\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) \| 0.616588 / 0.215209 (0.401379) \| 6.572262 / 2.077655 (4.494608) \| 2.830748 / 1.504120 (1.326628) \| 2.478441 / 1.541195 (0.937246) \| 2.573017 / 1.468490 (1.104527) \| 0.844154 / 4.584777 (-3.740623) \| 5.161625 / 3.745712 (1.415913) \| 4.541114 / 5.269862 (-0.728748) \| 2.907804 / 4.565676 (-1.657872) \| 0.097044 / 0.424275 (-0.327231) \| 0.008692 / 0.007607 (0.001085) \| 0.806640 / 0.226044 (0.580595) \| 7.620521 / 2.268929 (5.351593) \| 3.587100 / 55.444624 (-51.857524) \| 2.901319 / 6.876477 (-3.975157) \| 3.091288 / 2.142072 (0.949215) \| 1.056109 / 4.805227 (-3.749118) \| 0.209860 / 6.500664 (-6.290804) \| 0.079575 / 0.075469 (0.004106) \|\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) \| 1.966194 / 1.841788 (0.124407) \| 28.040515 / 8.074308 (19.966207) \| 25.848647 / 10.191392 (15.657255) \| 0.255472 / 0.680424 (-0.424951) \| 0.036154 / 0.534201 (-0.498046) \| 0.515168 / 0.579283 (-0.064115) \| 0.696092 / 0.434364 (0.261728) \| 0.602712 / 0.540337 (0.062374) \| 0.781091 / 1.386936 (-0.605845) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f641aca7fbb1f21da48c087a5c10e76f4c6be35 \"CML watermark\")\n" ]
1,824,819,672	6,082	Release: 2.14.1	closed	2023-07-27T17:05:54	2023-07-31T06:32:16	2023-07-27T17:08:38	https://github.com/huggingface/datasets/pull/6082	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6082", "html_url": "https://github.com/huggingface/datasets/pull/6082", "diff_url": "https://github.com/huggingface/datasets/pull/6082.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6082.patch", "merged_at": "2023-07-27T17:08:38" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6082). 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) \| 0.007215 / 0.011353 (-0.004138) \| 0.004101 / 0.011008 (-0.006907) \| 0.085884 / 0.038508 (0.047376) \| 0.085375 / 0.023109 (0.062266) \| 0.351610 / 0.275898 (0.075712) \| 0.399284 / 0.323480 (0.075804) \| 0.005598 / 0.007986 (-0.002388) \| 0.003405 / 0.004328 (-0.000923) \| 0.064906 / 0.004250 (0.060656) \| 0.059000 / 0.037052 (0.021948) \| 0.354589 / 0.258489 (0.096100) \| 0.406070 / 0.293841 (0.112229) \| 0.031627 / 0.128546 (-0.096919) \| 0.008597 / 0.075646 (-0.067049) \| 0.291050 / 0.419271 (-0.128221) \| 0.054120 / 0.043533 (0.010587) \| 0.366242 / 0.255139 (0.111103) \| 0.375975 / 0.283200 (0.092776) \| 0.025608 / 0.141683 (-0.116074) \| 1.473514 / 1.452155 (0.021359) \| 1.543226 / 1.492716 (0.050510) \|\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) \| 0.198068 / 0.018006 (0.180062) \| 0.450583 / 0.000490 (0.450093) \| 0.005368 / 0.000200 (0.005168) \| 0.000102 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028323 / 0.037411 (-0.009089) \| 0.089058 / 0.014526 (0.074533) \| 0.097718 / 0.176557 (-0.078839) \| 0.154546 / 0.737135 (-0.582590) \| 0.098224 / 0.296338 (-0.198115) \|\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) \| 0.386292 / 0.215209 (0.171083) \| 3.846222 / 2.077655 (1.768567) \| 1.858695 / 1.504120 (0.354575) \| 1.685885 / 1.541195 (0.144690) \| 1.790727 / 1.468490 (0.322237) \| 0.486771 / 4.584777 (-4.098006) \| 3.658363 / 3.745712 (-0.087349) \| 5.345236 / 5.269862 (0.075374) \| 3.215942 / 4.565676 (-1.349734) \| 0.057580 / 0.424275 (-0.366695) \| 0.007382 / 0.007607 (-0.000225) \| 0.464174 / 0.226044 (0.238129) \| 4.640848 / 2.268929 (2.371920) \| 2.383152 / 55.444624 (-53.061472) \| 2.013288 / 6.876477 (-4.863188) \| 2.244142 / 2.142072 (0.102069) \| 0.585408 / 4.805227 (-4.219819) \| 0.134698 / 6.500664 (-6.365966) \| 0.060641 / 0.075469 (-0.014828) \|\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) \| 1.258414 / 1.841788 (-0.583374) \| 19.825848 / 8.074308 (11.751540) \| 14.644025 / 10.191392 (4.452633) \| 0.169198 / 0.680424 (-0.511226) \| 0.018180 / 0.534201 (-0.516021) \| 0.395100 / 0.579283 (-0.184183) \| 0.411543 / 0.434364 (-0.022821) \| 0.463364 / 0.540337 (-0.076973) \| 0.628613 / 1.386936 (-0.758323) \|\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) \| 0.006860 / 0.011353 (-0.004493) \| 0.003981 / 0.011008 (-0.007027) \| 0.065589 / 0.038508 (0.027081) \| 0.082460 / 0.023109 (0.059350) \| 0.362980 / 0.275898 (0.087082) \| 0.394837 / 0.323480 (0.071357) \| 0.005298 / 0.007986 (-0.002688) \| 0.003372 / 0.004328 (-0.000957) \| 0.064918 / 0.004250 (0.060667) \| 0.058033 / 0.037052 (0.020981) \| 0.367259 / 0.258489 (0.108770) \| 0.403122 / 0.293841 (0.109281) \| 0.031566 / 0.128546 (-0.096980) \| 0.008583 / 0.075646 (-0.067063) \| 0.071287 / 0.419271 (-0.347984) \| 0.049586 / 0.043533 (0.006053) \| 0.359252 / 0.255139 (0.104113) \| 0.378519 / 0.283200 (0.095319) \| 0.023412 / 0.141683 (-0.118271) \| 1.494522 / 1.452155 (0.042367) \| 1.559176 / 1.492716 (0.066460) \|\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) \| 0.228396 / 0.018006 (0.210390) \| 0.441865 / 0.000490 (0.441375) \| 0.000395 / 0.000200 (0.000195) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031169 / 0.037411 (-0.006242) \| 0.093427 / 0.014526 (0.078901) \| 0.100673 / 0.176557 (-0.075883) \| 0.152817 / 0.737135 (-0.584319) \| 0.102226 / 0.296338 (-0.194112) \|\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) \| 0.437032 / 0.215209 (0.221823) \| 4.376078 / 2.077655 (2.298423) \| 2.346928 / 1.504120 (0.842808) \| 2.168573 / 1.541195 (0.627378) \| 2.261024 / 1.468490 (0.792534) \| 0.497080 / 4.584777 (-4.087697) \| 3.594402 / 3.745712 (-0.151310) \| 5.090361 / 5.269862 (-0.179501) \| 3.034750 / 4.565676 (-1.530927) \| 0.058538 / 0.424275 (-0.365737) \| 0.007892 / 0.007607 (0.000285) \| 0.517643 / 0.226044 (0.291598) \| 5.173174 / 2.268929 (2.904246) \| 2.825917 / 55.444624 (-52.618708) \| 2.542593 / 6.876477 (-4.333884) \| 2.716290 / 2.142072 (0.574218) \| 0.598253 / 4.805227 (-4.206974) \| 0.135610 / 6.500664 (-6.365054) \| 0.062113 / 0.075469 (-0.013356) \|\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) \| 1.389554 / 1.841788 (-0.452233) \| 20.412868 / 8.074308 (12.338560) \| 14.539988 / 10.191392 (4.348596) \| 0.162046 / 0.680424 (-0.518378) \| 0.018508 / 0.534201 (-0.515693) \| 0.398840 / 0.579283 (-0.180443) \| 0.400902 / 0.434364 (-0.033462) \| 0.463647 / 0.540337 (-0.076691) \| 0.612921 / 1.386936 (-0.774015) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45bef1810d9341ba4cb27547d748fddb97843792 \"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) \| 0.005943 / 0.011353 (-0.005410) \| 0.003582 / 0.011008 (-0.007426) \| 0.080030 / 0.038508 (0.041522) \| 0.057458 / 0.023109 (0.034349) \| 0.390783 / 0.275898 (0.114885) \| 0.430926 / 0.323480 (0.107446) \| 0.003207 / 0.007986 (-0.004778) \| 0.003592 / 0.004328 (-0.000737) \| 0.062468 / 0.004250 (0.058217) \| 0.046739 / 0.037052 (0.009687) \| 0.394343 / 0.258489 (0.135854) \| 0.435912 / 0.293841 (0.142071) \| 0.026812 / 0.128546 (-0.101734) \| 0.007954 / 0.075646 (-0.067692) \| 0.261415 / 0.419271 (-0.157857) \| 0.044665 / 0.043533 (0.001132) \| 0.403454 / 0.255139 (0.148315) \| 0.418946 / 0.283200 (0.135747) \| 0.022247 / 0.141683 (-0.119436) \| 1.456387 / 1.452155 (0.004232) \| 1.508234 / 1.492716 (0.015518) \|\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) \| 0.182487 / 0.018006 (0.164480) \| 0.416343 / 0.000490 (0.415854) \| 0.001404 / 0.000200 (0.001204) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023643 / 0.037411 (-0.013768) \| 0.071798 / 0.014526 (0.057272) \| 0.083623 / 0.176557 (-0.092933) \| 0.146023 / 0.737135 (-0.591112) \| 0.083094 / 0.296338 (-0.213245) \|\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) \| 0.417484 / 0.215209 (0.202275) \| 4.157393 / 2.077655 (2.079738) \| 1.950438 / 1.504120 (0.446318) \| 1.766639 / 1.541195 (0.225444) \| 1.807382 / 1.468490 (0.338892) \| 0.496061 / 4.584777 (-4.088716) \| 2.975001 / 3.745712 (-0.770711) \| 3.340608 / 5.269862 (-1.929254) \| 2.236293 / 4.565676 (-2.329384) \| 0.056946 / 0.424275 (-0.367329) \| 0.006506 / 0.007607 (-0.001101) \| 0.480377 / 0.226044 (0.254332) \| 4.788525 / 2.268929 (2.519597) \| 2.430139 / 55.444624 (-53.014485) \| 2.154145 / 6.876477 (-4.722332) \| 2.321623 / 2.142072 (0.179551) \| 0.584040 / 4.805227 (-4.221188) \| 0.124508 / 6.500664 (-6.376156) \| 0.060828 / 0.075469 (-0.014641) \|\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) \| 1.201641 / 1.841788 (-0.640146) \| 18.066232 / 8.074308 (9.991924) \| 14.022304 / 10.191392 (3.830912) \| 0.146573 / 0.680424 (-0.533850) \| 0.016892 / 0.534201 (-0.517308) \| 0.333259 / 0.579283 (-0.246024) \| 0.357795 / 0.434364 (-0.076568) \| 0.391265 / 0.540337 (-0.149072) \| 0.551378 / 1.386936 (-0.835558) \|\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) \| 0.005706 / 0.011353 (-0.005647) \| 0.003448 / 0.011008 (-0.007560) \| 0.063146 / 0.038508 (0.024638) \| 0.056292 / 0.023109 (0.033183) \| 0.355533 / 0.275898 (0.079635) \| 0.394996 / 0.323480 (0.071517) \| 0.004270 / 0.007986 (-0.003716) \| 0.002790 / 0.004328 (-0.001538) \| 0.063033 / 0.004250 (0.058783) \| 0.044684 / 0.037052 (0.007631) \| 0.370621 / 0.258489 (0.112132) \| 0.401074 / 0.293841 (0.107233) \| 0.026737 / 0.128546 (-0.101809) \| 0.007872 / 0.075646 (-0.067774) \| 0.068815 / 0.419271 (-0.350457) \| 0.040976 / 0.043533 (-0.002557) \| 0.370733 / 0.255139 (0.115594) \| 0.387418 / 0.283200 (0.104218) \| 0.018854 / 0.141683 (-0.122829) \| 1.479834 / 1.452155 (0.027680) \| 1.536388 / 1.492716 (0.043672) \|\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) \| 0.222125 / 0.018006 (0.204119) \| 0.408007 / 0.000490 (0.407517) \| 0.000367 / 0.000200 (0.000167) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025100 / 0.037411 (-0.012311) \| 0.076617 / 0.014526 (0.062091) \| 0.088311 / 0.176557 (-0.088246) \| 0.143785 / 0.737135 (-0.593350) \| 0.088349 / 0.296338 (-0.207989) \|\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) \| 0.419246 / 0.215209 (0.204037) \| 4.172413 / 2.077655 (2.094759) \| 2.199355 / 1.504120 (0.695235) \| 2.025158 / 1.541195 (0.483963) \| 2.074491 / 1.468490 (0.606001) \| 0.495893 / 4.584777 (-4.088884) \| 2.998858 / 3.745712 (-0.746854) \| 2.770531 / 5.269862 (-2.499331) \| 1.817497 / 4.565676 (-2.748179) \| 0.057317 / 0.424275 (-0.366958) \| 0.006723 / 0.007607 (-0.000884) \| 0.491062 / 0.226044 (0.265017) \| 4.906155 / 2.268929 (2.637226) \| 2.654916 / 55.444624 (-52.789708) \| 2.299873 / 6.876477 (-4.576604) \| 2.451438 / 2.142072 (0.309366) \| 0.585048 / 4.805227 (-4.220179) \| 0.124778 / 6.500664 (-6.375886) \| 0.062067 / 0.075469 (-0.013402) \|\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) \| 1.298239 / 1.841788 (-0.543549) \| 18.090238 / 8.074308 (10.015930) \| 13.822568 / 10.191392 (3.631176) \| 0.130560 / 0.680424 (-0.549864) \| 0.016662 / 0.534201 (-0.517539) \| 0.333337 / 0.579283 (-0.245946) \| 0.348493 / 0.434364 (-0.085871) \| 0.386049 / 0.540337 (-0.154289) \| 0.511156 / 1.386936 (-0.875780) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) \| 0.006014 / 0.011353 (-0.005339) \| 0.003623 / 0.011008 (-0.007385) \| 0.080500 / 0.038508 (0.041992) \| 0.057713 / 0.023109 (0.034603) \| 0.325976 / 0.275898 (0.050078) \| 0.359986 / 0.323480 (0.036506) \| 0.004709 / 0.007986 (-0.003277) \| 0.002933 / 0.004328 (-0.001395) \| 0.063457 / 0.004250 (0.059207) \| 0.047514 / 0.037052 (0.010462) \| 0.331629 / 0.258489 (0.073140) \| 0.382048 / 0.293841 (0.088207) \| 0.026949 / 0.128546 (-0.101597) \| 0.008043 / 0.075646 (-0.067604) \| 0.262152 / 0.419271 (-0.157119) \| 0.045271 / 0.043533 (0.001738) \| 0.333355 / 0.255139 (0.078216) \| 0.347996 / 0.283200 (0.064796) \| 0.020814 / 0.141683 (-0.120868) \| 1.460723 / 1.452155 (0.008568) \| 1.488845 / 1.492716 (-0.003872) \|\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) \| 0.193735 / 0.018006 (0.175728) \| 0.431433 / 0.000490 (0.430943) \| 0.002494 / 0.000200 (0.002294) \| 0.000066 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023762 / 0.037411 (-0.013650) \| 0.072680 / 0.014526 (0.058154) \| 0.081687 / 0.176557 (-0.094869) \| 0.143224 / 0.737135 (-0.593911) \| 0.083083 / 0.296338 (-0.213255) \|\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) \| 0.397393 / 0.215209 (0.182184) \| 3.954643 / 2.077655 (1.876989) \| 1.950038 / 1.504120 (0.445919) \| 1.760551 / 1.541195 (0.219357) \| 1.871165 / 1.468490 (0.402675) \| 0.508645 / 4.584777 (-4.076132) \| 3.114379 / 3.745712 (-0.631333) \| 3.474554 / 5.269862 (-1.795307) \| 2.090126 / 4.565676 (-2.475551) \| 0.058008 / 0.424275 (-0.366267) \| 0.006465 / 0.007607 (-0.001142) \| 0.475009 / 0.226044 (0.248965) \| 4.767981 / 2.268929 (2.499052) \| 2.372050 / 55.444624 (-53.072574) \| 2.038094 / 6.876477 (-4.838383) \| 2.072819 / 2.142072 (-0.069253) \| 0.591913 / 4.805227 (-4.213314) \| 0.125002 / 6.500664 (-6.375662) \| 0.060055 / 0.075469 (-0.015414) \|\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) \| 1.234171 / 1.841788 (-0.607617) \| 18.121476 / 8.074308 (10.047168) \| 13.727313 / 10.191392 (3.535921) \| 0.136021 / 0.680424 (-0.544402) \| 0.016505 / 0.534201 (-0.517696) \| 0.331400 / 0.579283 (-0.247883) \| 0.346019 / 0.434364 (-0.088345) \| 0.378985 / 0.540337 (-0.161353) \| 0.522606 / 1.386936 (-0.864330) \|\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) \| 0.006035 / 0.011353 (-0.005318) \| 0.003584 / 0.011008 (-0.007425) \| 0.061953 / 0.038508 (0.023445) \| 0.059416 / 0.023109 (0.036307) \| 0.359380 / 0.275898 (0.083482) \| 0.396842 / 0.323480 (0.073363) \| 0.004716 / 0.007986 (-0.003269) \| 0.002825 / 0.004328 (-0.001504) \| 0.061697 / 0.004250 (0.057447) \| 0.049009 / 0.037052 (0.011956) \| 0.363099 / 0.258489 (0.104610) \| 0.403672 / 0.293841 (0.109831) \| 0.027722 / 0.128546 (-0.100824) \| 0.007966 / 0.075646 (-0.067680) \| 0.067455 / 0.419271 (-0.351816) \| 0.042530 / 0.043533 (-0.001003) \| 0.361257 / 0.255139 (0.106118) \| 0.388957 / 0.283200 (0.105758) \| 0.021845 / 0.141683 (-0.119838) \| 1.431989 / 1.452155 (-0.020166) \| 1.503131 / 1.492716 (0.010415) \|\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) \| 0.241493 / 0.018006 (0.223487) \| 0.429319 / 0.000490 (0.428829) \| 0.002604 / 0.000200 (0.002404) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026227 / 0.037411 (-0.011184) \| 0.077177 / 0.014526 (0.062651) \| 0.085840 / 0.176557 (-0.090717) \| 0.142280 / 0.737135 (-0.594855) \| 0.088465 / 0.296338 (-0.207873) \|\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) \| 0.434912 / 0.215209 (0.219703) \| 4.339664 / 2.077655 (2.262009) \| 2.242495 / 1.504120 (0.738375) \| 2.091353 / 1.541195 (0.550159) \| 2.161425 / 1.468490 (0.692935) \| 0.501647 / 4.584777 (-4.083130) \| 3.075326 / 3.745712 (-0.670386) \| 4.091557 / 5.269862 (-1.178304) \| 2.776425 / 4.565676 (-1.789251) \| 0.057338 / 0.424275 (-0.366937) \| 0.006767 / 0.007607 (-0.000840) \| 0.506882 / 0.226044 (0.280837) \| 5.059074 / 2.268929 (2.790146) \| 2.706665 / 55.444624 (-52.737959) \| 2.370253 / 6.876477 (-4.506224) \| 2.505421 / 2.142072 (0.363348) \| 0.590289 / 4.805227 (-4.214938) \| 0.125990 / 6.500664 (-6.374674) \| 0.062778 / 0.075469 (-0.012691) \|\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) \| 1.361287 / 1.841788 (-0.480501) \| 18.500726 / 8.074308 (10.426418) \| 13.844459 / 10.191392 (3.653067) \| 0.144416 / 0.680424 (-0.536008) \| 0.016987 / 0.534201 (-0.517214) \| 0.336237 / 0.579283 (-0.243046) \| 0.357116 / 0.434364 (-0.077248) \| 0.402062 / 0.540337 (-0.138275) \| 0.543066 / 1.386936 (-0.843870) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) \| 0.007559 / 0.011353 (-0.003794) \| 0.004379 / 0.011008 (-0.006629) \| 0.089702 / 0.038508 (0.051194) \| 0.065104 / 0.023109 (0.041995) \| 0.362016 / 0.275898 (0.086118) \| 0.376768 / 0.323480 (0.053288) \| 0.006538 / 0.007986 (-0.001447) \| 0.004167 / 0.004328 (-0.000161) \| 0.074138 / 0.004250 (0.069888) \| 0.052753 / 0.037052 (0.015701) \| 0.366367 / 0.258489 (0.107878) \| 0.389121 / 0.293841 (0.095280) \| 0.042820 / 0.128546 (-0.085727) \| 0.012560 / 0.075646 (-0.063086) \| 0.359235 / 0.419271 (-0.060037) \| 0.074250 / 0.043533 (0.030718) \| 0.384051 / 0.255139 (0.128912) \| 0.385450 / 0.283200 (0.102250) \| 0.046270 / 0.141683 (-0.095413) \| 1.593275 / 1.452155 (0.141120) \| 1.704207 / 1.492716 (0.211490) \|\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) \| 0.249390 / 0.018006 (0.231384) \| 0.614347 / 0.000490 (0.613857) \| 0.012641 / 0.000200 (0.012441) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029099 / 0.037411 (-0.008312) \| 0.090966 / 0.014526 (0.076440) \| 0.102273 / 0.176557 (-0.074284) \| 0.167564 / 0.737135 (-0.569571) \| 0.106118 / 0.296338 (-0.190220) \|\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) \| 0.536122 / 0.215209 (0.320913) \| 5.448464 / 2.077655 (3.370809) \| 2.461977 / 1.504120 (0.957857) \| 2.081506 / 1.541195 (0.540311) \| 2.091509 / 1.468490 (0.623019) \| 0.810307 / 4.584777 (-3.774470) \| 5.161304 / 3.745712 (1.415592) \| 4.525070 / 5.269862 (-0.744792) \| 2.886313 / 4.565676 (-1.679363) \| 0.093992 / 0.424275 (-0.330283) \| 0.008516 / 0.007607 (0.000909) \| 0.691978 / 0.226044 (0.465934) \| 6.834665 / 2.268929 (4.565737) \| 3.284355 / 55.444624 (-52.160270) \| 2.496803 / 6.876477 (-4.379674) \| 2.814387 / 2.142072 (0.672315) \| 0.985300 / 4.805227 (-3.819928) \| 0.210343 / 6.500664 (-6.290321) \| 0.075459 / 0.075469 (-0.000010) \|\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) \| 1.436073 / 1.841788 (-0.405714) \| 22.722401 / 8.074308 (14.648093) \| 19.988521 / 10.191392 (9.797129) \| 0.229757 / 0.680424 (-0.450667) \| 0.029672 / 0.534201 (-0.504529) \| 0.479914 / 0.579283 (-0.099369) \| 0.605106 / 0.434364 (0.170743) \| 0.511668 / 0.540337 (-0.028670) \| 0.800281 / 1.386936 (-0.586655) \|\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) \| 0.008665 / 0.011353 (-0.002688) \| 0.006009 / 0.011008 (-0.004999) \| 0.073377 / 0.038508 (0.034869) \| 0.077188 / 0.023109 (0.054079) \| 0.451422 / 0.275898 (0.175524) \| 0.484640 / 0.323480 (0.161160) \| 0.006266 / 0.007986 (-0.001719) \| 0.004129 / 0.004328 (-0.000200) \| 0.063102 / 0.004250 (0.058851) \| 0.064653 / 0.037052 (0.027601) \| 0.439521 / 0.258489 (0.181032) \| 0.458964 / 0.293841 (0.165123) \| 0.046018 / 0.128546 (-0.082528) \| 0.014109 / 0.075646 (-0.061537) \| 0.095727 / 0.419271 (-0.323544) \| 0.070133 / 0.043533 (0.026600) \| 0.440143 / 0.255139 (0.185004) \| 0.502468 / 0.283200 (0.219269) \| 0.034582 / 0.141683 (-0.107101) \| 1.656282 / 1.452155 (0.204127) \| 1.784641 / 1.492716 (0.291925) \|\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) \| 0.303111 / 0.018006 (0.285105) \| 0.599194 / 0.000490 (0.598705) \| 0.000411 / 0.000200 (0.000211) \| 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) \| 0.033061 / 0.037411 (-0.004350) \| 0.096073 / 0.014526 (0.081548) \| 0.095347 / 0.176557 (-0.081209) \| 0.161004 / 0.737135 (-0.576131) \| 0.111544 / 0.296338 (-0.184794) \|\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) \| 0.615695 / 0.215209 (0.400486) \| 5.794243 / 2.077655 (3.716588) \| 2.594720 / 1.504120 (1.090600) \| 2.566255 / 1.541195 (1.025060) \| 2.573653 / 1.468490 (1.105163) \| 0.873653 / 4.584777 (-3.711124) \| 5.353323 / 3.745712 (1.607611) \| 4.604974 / 5.269862 (-0.664887) \| 2.901282 / 4.565676 (-1.664394) \| 0.099614 / 0.424275 (-0.324661) \| 0.010368 / 0.007607 (0.002761) \| 0.775490 / 0.226044 (0.549446) \| 7.245449 / 2.268929 (4.976520) \| 3.740165 / 55.444624 (-51.704459) \| 2.986132 / 6.876477 (-3.890345) \| 3.092510 / 2.142072 (0.950438) \| 1.022461 / 4.805227 (-3.782766) \| 0.212137 / 6.500664 (-6.288527) \| 0.084534 / 0.075469 (0.009065) \|\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) \| 1.687983 / 1.841788 (-0.153805) \| 23.491808 / 8.074308 (15.417500) \| 20.722165 / 10.191392 (10.530773) \| 0.231011 / 0.680424 (-0.449413) \| 0.028309 / 0.534201 (-0.505892) \| 0.436911 / 0.579283 (-0.142372) \| 0.583126 / 0.434364 (0.148762) \| 0.559712 / 0.540337 (0.019374) \| 0.820645 / 1.386936 (-0.566291) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) \| 0.006538 / 0.011353 (-0.004815) \| 0.003952 / 0.011008 (-0.007056) \| 0.084183 / 0.038508 (0.045675) \| 0.070616 / 0.023109 (0.047507) \| 0.320491 / 0.275898 (0.044593) \| 0.352021 / 0.323480 (0.028541) \| 0.005330 / 0.007986 (-0.002656) \| 0.003400 / 0.004328 (-0.000928) \| 0.066392 / 0.004250 (0.062141) \| 0.052529 / 0.037052 (0.015477) \| 0.329581 / 0.258489 (0.071092) \| 0.374437 / 0.293841 (0.080596) \| 0.031379 / 0.128546 (-0.097167) \| 0.008576 / 0.075646 (-0.067070) \| 0.288621 / 0.419271 (-0.130650) \| 0.052748 / 0.043533 (0.009215) \| 0.319911 / 0.255139 (0.064772) \| 0.358169 / 0.283200 (0.074970) \| 0.023128 / 0.141683 (-0.118555) \| 1.479578 / 1.452155 (0.027424) \| 1.566351 / 1.492716 (0.073635) \|\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) \| 0.217616 / 0.018006 (0.199610) \| 0.471546 / 0.000490 (0.471056) \| 0.003880 / 0.000200 (0.003680) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027716 / 0.037411 (-0.009696) \| 0.081718 / 0.014526 (0.067192) \| 0.095457 / 0.176557 (-0.081100) \| 0.150746 / 0.737135 (-0.586389) \| 0.096061 / 0.296338 (-0.200277) \|\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) \| 0.406811 / 0.215209 (0.191602) \| 4.062757 / 2.077655 (1.985103) \| 2.060658 / 1.504120 (0.556538) \| 1.870944 / 1.541195 (0.329749) \| 1.908984 / 1.468490 (0.440493) \| 0.489053 / 4.584777 (-4.095724) \| 3.571038 / 3.745712 (-0.174674) \| 3.255351 / 5.269862 (-2.014511) \| 2.007078 / 4.565676 (-2.558599) \| 0.057078 / 0.424275 (-0.367197) \| 0.007240 / 0.007607 (-0.000367) \| 0.485641 / 0.226044 (0.259596) \| 4.841657 / 2.268929 (2.572729) \| 2.569676 / 55.444624 (-52.874949) \| 2.151119 / 6.876477 (-4.725357) \| 2.330337 / 2.142072 (0.188265) \| 0.581721 / 4.805227 (-4.223506) \| 0.132591 / 6.500664 (-6.368073) \| 0.060491 / 0.075469 (-0.014978) \|\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) \| 1.237699 / 1.841788 (-0.604089) \| 19.460306 / 8.074308 (11.385998) \| 14.123006 / 10.191392 (3.931614) \| 0.155669 / 0.680424 (-0.524754) \| 0.018385 / 0.534201 (-0.515816) \| 0.393330 / 0.579283 (-0.185953) \| 0.408890 / 0.434364 (-0.025474) \| 0.457348 / 0.540337 (-0.082989) \| 0.640293 / 1.386936 (-0.746643) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006582 / 0.011353 (-0.004771) \| 0.003950 / 0.011008 (-0.007059) \| 0.064636 / 0.038508 (0.026128) \| 0.077651 / 0.023109 (0.054541) \| 0.365505 / 0.275898 (0.089607) \| 0.393370 / 0.323480 (0.069890) \| 0.005466 / 0.007986 (-0.002520) \| 0.003314 / 0.004328 (-0.001014) \| 0.064960 / 0.004250 (0.060710) \| 0.057355 / 0.037052 (0.020302) \| 0.377773 / 0.258489 (0.119284) \| 0.408394 / 0.293841 (0.114553) \| 0.031698 / 0.128546 (-0.096848) \| 0.008575 / 0.075646 (-0.067071) \| 0.070390 / 0.419271 (-0.348881) \| 0.050035 / 0.043533 (0.006502) \| 0.360461 / 0.255139 (0.105323) \| 0.384862 / 0.283200 (0.101662) \| 0.025380 / 0.141683 (-0.116303) \| 1.484429 / 1.452155 (0.032275) \| 1.542944 / 1.492716 (0.050227) \|\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) \| 0.190193 / 0.018006 (0.172187) \| 0.468996 / 0.000490 (0.468506) \| 0.003012 / 0.000200 (0.002812) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031488 / 0.037411 (-0.005923) \| 0.088673 / 0.014526 (0.074147) \| 0.101886 / 0.176557 (-0.074670) \| 0.156774 / 0.737135 (-0.580361) \| 0.102818 / 0.296338 (-0.193520) \|\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) \| 0.428019 / 0.215209 (0.212810) \| 4.271369 / 2.077655 (2.193714) \| 2.271530 / 1.504120 (0.767410) \| 2.085172 / 1.541195 (0.543977) \| 2.143439 / 1.468490 (0.674949) \| 0.493468 / 4.584777 (-4.091309) \| 3.569030 / 3.745712 (-0.176683) \| 4.777962 / 5.269862 (-0.491900) \| 2.872115 / 4.565676 (-1.693562) \| 0.058200 / 0.424275 (-0.366075) \| 0.007657 / 0.007607 (0.000050) \| 0.502874 / 0.226044 (0.276830) \| 5.026721 / 2.268929 (2.757792) \| 2.734301 / 55.444624 (-52.710324) \| 2.396072 / 6.876477 (-4.480405) \| 2.574322 / 2.142072 (0.432249) \| 0.593855 / 4.805227 (-4.211373) \| 0.135134 / 6.500664 (-6.365530) \| 0.061491 / 0.075469 (-0.013978) \|\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) \| 1.320522 / 1.841788 (-0.521265) \| 19.933221 / 8.074308 (11.858912) \| 14.055921 / 10.191392 (3.864529) \| 0.149620 / 0.680424 (-0.530804) \| 0.018590 / 0.534201 (-0.515611) \| 0.399550 / 0.579283 (-0.179733) \| 0.410463 / 0.434364 (-0.023901) \| 0.469872 / 0.540337 (-0.070465) \| 0.616481 / 1.386936 (-0.770455) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n" ]
1,824,486,278	6,081	Deprecate `Dataset.export`	closed	2023-07-27T14:22:18	2023-07-28T11:09:54	2023-07-28T11:01:04	https://github.com/huggingface/datasets/pull/6081	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6081", "html_url": "https://github.com/huggingface/datasets/pull/6081", "diff_url": "https://github.com/huggingface/datasets/pull/6081.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6081.patch", "merged_at": "2023-07-28T11:01:04" }	mariosasko	true	[ "_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) \| 0.006680 / 0.011353 (-0.004673) \| 0.003987 / 0.011008 (-0.007021) \| 0.084677 / 0.038508 (0.046169) \| 0.076800 / 0.023109 (0.053691) \| 0.358338 / 0.275898 (0.082440) \| 0.386573 / 0.323480 (0.063094) \| 0.005370 / 0.007986 (-0.002616) \| 0.003323 / 0.004328 (-0.001005) \| 0.064238 / 0.004250 (0.059988) \| 0.057859 / 0.037052 (0.020806) \| 0.355408 / 0.258489 (0.096919) \| 0.388302 / 0.293841 (0.094461) \| 0.030784 / 0.128546 (-0.097762) \| 0.008381 / 0.075646 (-0.067266) \| 0.287971 / 0.419271 (-0.131300) \| 0.053078 / 0.043533 (0.009545) \| 0.352719 / 0.255139 (0.097580) \| 0.370319 / 0.283200 (0.087119) \| 0.023064 / 0.141683 (-0.118619) \| 1.480661 / 1.452155 (0.028507) \| 1.555711 / 1.492716 (0.062995) \|\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) \| 0.211289 / 0.018006 (0.193283) \| 0.466957 / 0.000490 (0.466467) \| 0.003760 / 0.000200 (0.003561) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028552 / 0.037411 (-0.008859) \| 0.084469 / 0.014526 (0.069943) \| 0.096027 / 0.176557 (-0.080529) \| 0.152170 / 0.737135 (-0.584965) \| 0.096513 / 0.296338 (-0.199825) \|\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) \| 0.382940 / 0.215209 (0.167731) \| 3.841735 / 2.077655 (1.764080) \| 1.850575 / 1.504120 (0.346455) \| 1.676554 / 1.541195 (0.135360) \| 1.765241 / 1.468490 (0.296751) \| 0.482131 / 4.584777 (-4.102646) \| 3.512739 / 3.745712 (-0.232973) \| 3.977042 / 5.269862 (-1.292820) \| 2.387568 / 4.565676 (-2.178109) \| 0.056657 / 0.424275 (-0.367618) \| 0.007283 / 0.007607 (-0.000324) \| 0.468193 / 0.226044 (0.242149) \| 4.704077 / 2.268929 (2.435149) \| 2.373467 / 55.444624 (-53.071157) \| 2.002470 / 6.876477 (-4.874007) \| 2.228280 / 2.142072 (0.086208) \| 0.576908 / 4.805227 (-4.228320) \| 0.132000 / 6.500664 (-6.368664) \| 0.060544 / 0.075469 (-0.014926) \|\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) \| 1.256168 / 1.841788 (-0.585619) \| 19.965458 / 8.074308 (11.891150) \| 14.521435 / 10.191392 (4.330043) \| 0.159156 / 0.680424 (-0.521268) \| 0.018170 / 0.534201 (-0.516031) \| 0.393019 / 0.579283 (-0.186264) \| 0.415002 / 0.434364 (-0.019362) \| 0.471810 / 0.540337 (-0.068528) \| 0.658907 / 1.386936 (-0.728029) \|\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) \| 0.006836 / 0.011353 (-0.004517) \| 0.004067 / 0.011008 (-0.006942) \| 0.066242 / 0.038508 (0.027734) \| 0.078601 / 0.023109 (0.055491) \| 0.369371 / 0.275898 (0.093473) \| 0.402026 / 0.323480 (0.078546) \| 0.006097 / 0.007986 (-0.001889) \| 0.003337 / 0.004328 (-0.000991) \| 0.065854 / 0.004250 (0.061603) \| 0.057665 / 0.037052 (0.020612) \| 0.379709 / 0.258489 (0.121219) \| 0.406868 / 0.293841 (0.113027) \| 0.031946 / 0.128546 (-0.096600) \| 0.008691 / 0.075646 (-0.066955) \| 0.071430 / 0.419271 (-0.347841) \| 0.049518 / 0.043533 (0.005986) \| 0.370439 / 0.255139 (0.115300) \| 0.389235 / 0.283200 (0.106036) \| 0.023730 / 0.141683 (-0.117953) \| 1.509035 / 1.452155 (0.056880) \| 1.548890 / 1.492716 (0.056173) \|\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) \| 0.229264 / 0.018006 (0.211258) \| 0.445801 / 0.000490 (0.445312) \| 0.000363 / 0.000200 (0.000163) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032377 / 0.037411 (-0.005034) \| 0.091082 / 0.014526 (0.076556) \| 0.104816 / 0.176557 (-0.071740) \| 0.161040 / 0.737135 (-0.576095) \| 0.105165 / 0.296338 (-0.191173) \|\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) \| 0.411012 / 0.215209 (0.195803) \| 4.097256 / 2.077655 (2.019602) \| 2.088686 / 1.504120 (0.584566) \| 1.934429 / 1.541195 (0.393234) \| 2.027387 / 1.468490 (0.558896) \| 0.476262 / 4.584777 (-4.108515) \| 3.518416 / 3.745712 (-0.227296) \| 3.260919 / 5.269862 (-2.008943) \| 2.041441 / 4.565676 (-2.524235) \| 0.056302 / 0.424275 (-0.367973) \| 0.007750 / 0.007607 (0.000143) \| 0.489966 / 0.226044 (0.263922) \| 4.915844 / 2.268929 (2.646916) \| 2.617001 / 55.444624 (-52.827623) \| 2.333557 / 6.876477 (-4.542920) \| 2.484530 / 2.142072 (0.342458) \| 0.572009 / 4.805227 (-4.233219) \| 0.142557 / 6.500664 (-6.358107) \| 0.066711 / 0.075469 (-0.008758) \|\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) \| 1.359929 / 1.841788 (-0.481859) \| 20.332252 / 8.074308 (12.257943) \| 14.585842 / 10.191392 (4.394450) \| 0.170498 / 0.680424 (-0.509926) \| 0.018450 / 0.534201 (-0.515751) \| 0.395449 / 0.579283 (-0.183834) \| 0.409666 / 0.434364 (-0.024698) \| 0.467937 / 0.540337 (-0.072401) \| 0.616078 / 1.386936 (-0.770858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a888bc94dc6bce7815e3061a28e718097f4b8b9e \"CML watermark\")\n" ]
1,822,667,554	6,080	Remove README link to deprecated Colab notebook	closed	2023-07-26T15:27:49	2023-07-26T16:24:43	2023-07-26T16:14:34	https://github.com/huggingface/datasets/pull/6080	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6080", "html_url": "https://github.com/huggingface/datasets/pull/6080", "diff_url": "https://github.com/huggingface/datasets/pull/6080.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6080.patch", "merged_at": "2023-07-26T16:14:34" }	mariosasko	true	[ "_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) \| 0.006458 / 0.011353 (-0.004894) \| 0.003895 / 0.011008 (-0.007114) \| 0.084280 / 0.038508 (0.045772) \| 0.071304 / 0.023109 (0.048195) \| 0.313910 / 0.275898 (0.038012) \| 0.344070 / 0.323480 (0.020590) \| 0.005413 / 0.007986 (-0.002573) \| 0.003308 / 0.004328 (-0.001021) \| 0.064570 / 0.004250 (0.060320) \| 0.056824 / 0.037052 (0.019771) \| 0.321102 / 0.258489 (0.062613) \| 0.355834 / 0.293841 (0.061993) \| 0.031252 / 0.128546 (-0.097294) \| 0.008427 / 0.075646 (-0.067219) \| 0.287348 / 0.419271 (-0.131924) \| 0.053261 / 0.043533 (0.009728) \| 0.324892 / 0.255139 (0.069753) \| 0.335847 / 0.283200 (0.052647) \| 0.023453 / 0.141683 (-0.118230) \| 1.485456 / 1.452155 (0.033301) \| 1.531329 / 1.492716 (0.038612) \|\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) \| 0.201924 / 0.018006 (0.183918) \| 0.447188 / 0.000490 (0.446698) \| 0.005543 / 0.000200 (0.005343) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027586 / 0.037411 (-0.009825) \| 0.082412 / 0.014526 (0.067886) \| 0.094851 / 0.176557 (-0.081706) \| 0.151331 / 0.737135 (-0.585804) \| 0.094475 / 0.296338 (-0.201863) \|\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) \| 0.399004 / 0.215209 (0.183795) \| 3.974652 / 2.077655 (1.896997) \| 1.991909 / 1.504120 (0.487789) \| 1.811684 / 1.541195 (0.270489) \| 1.869774 / 1.468490 (0.401283) \| 0.487745 / 4.584777 (-4.097032) \| 3.558945 / 3.745712 (-0.186768) \| 5.530468 / 5.269862 (0.260606) \| 3.293147 / 4.565676 (-1.272529) \| 0.057531 / 0.424275 (-0.366744) \| 0.007212 / 0.007607 (-0.000395) \| 0.470325 / 0.226044 (0.244281) \| 4.701652 / 2.268929 (2.432723) \| 2.453020 / 55.444624 (-52.991605) \| 2.110152 / 6.876477 (-4.766325) \| 2.314669 / 2.142072 (0.172597) \| 0.615039 / 4.805227 (-4.190189) \| 0.133229 / 6.500664 (-6.367435) \| 0.060821 / 0.075469 (-0.014648) \|\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) \| 1.296708 / 1.841788 (-0.545079) \| 18.717251 / 8.074308 (10.642943) \| 14.325305 / 10.191392 (4.133913) \| 0.147680 / 0.680424 (-0.532744) \| 0.018312 / 0.534201 (-0.515889) \| 0.392766 / 0.579283 (-0.186517) \| 0.403319 / 0.434364 (-0.031045) \| 0.453696 / 0.540337 (-0.086641) \| 0.622564 / 1.386936 (-0.764372) \|\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) \| 0.006483 / 0.011353 (-0.004870) \| 0.004018 / 0.011008 (-0.006991) \| 0.064436 / 0.038508 (0.025928) \| 0.072365 / 0.023109 (0.049256) \| 0.387532 / 0.275898 (0.111634) \| 0.418175 / 0.323480 (0.094695) \| 0.005453 / 0.007986 (-0.002533) \| 0.003368 / 0.004328 (-0.000961) \| 0.064896 / 0.004250 (0.060645) \| 0.057018 / 0.037052 (0.019966) \| 0.406596 / 0.258489 (0.148107) \| 0.431194 / 0.293841 (0.137353) \| 0.031788 / 0.128546 (-0.096759) \| 0.008532 / 0.075646 (-0.067114) \| 0.070605 / 0.419271 (-0.348666) \| 0.053317 / 0.043533 (0.009785) \| 0.391930 / 0.255139 (0.136791) \| 0.406071 / 0.283200 (0.122872) \| 0.028652 / 0.141683 (-0.113030) \| 1.487677 / 1.452155 (0.035522) \| 1.546071 / 1.492716 (0.053355) \|\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) \| 0.220063 / 0.018006 (0.202056) \| 0.441111 / 0.000490 (0.440621) \| 0.006066 / 0.000200 (0.005867) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035179 / 0.037411 (-0.002232) \| 0.096745 / 0.014526 (0.082219) \| 0.108171 / 0.176557 (-0.068386) \| 0.164590 / 0.737135 (-0.572545) \| 0.109425 / 0.296338 (-0.186913) \|\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) \| 0.408101 / 0.215209 (0.192892) \| 4.062961 / 2.077655 (1.985306) \| 2.101849 / 1.504120 (0.597730) \| 1.935919 / 1.541195 (0.394724) \| 1.993749 / 1.468490 (0.525259) \| 0.487788 / 4.584777 (-4.096989) \| 3.533972 / 3.745712 (-0.211740) \| 3.218448 / 5.269862 (-2.051414) \| 2.002322 / 4.565676 (-2.563355) \| 0.057371 / 0.424275 (-0.366904) \| 0.007704 / 0.007607 (0.000097) \| 0.491695 / 0.226044 (0.265650) \| 4.905009 / 2.268929 (2.636080) \| 2.597879 / 55.444624 (-52.846745) \| 2.252086 / 6.876477 (-4.624391) \| 2.434439 / 2.142072 (0.292367) \| 0.583071 / 4.805227 (-4.222156) \| 0.133765 / 6.500664 (-6.366899) \| 0.061276 / 0.075469 (-0.014193) \|\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) \| 1.403111 / 1.841788 (-0.438676) \| 19.218886 / 8.074308 (11.144578) \| 13.981775 / 10.191392 (3.790383) \| 0.167784 / 0.680424 (-0.512640) \| 0.018401 / 0.534201 (-0.515800) \| 0.392038 / 0.579283 (-0.187245) \| 0.414776 / 0.434364 (-0.019587) \| 0.476221 / 0.540337 (-0.064117) \| 0.632724 / 1.386936 (-0.754212) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#420dbd92c42840d6c91ecf5d3560c6799ee0cca1 \"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) \| 0.007595 / 0.011353 (-0.003758) \| 0.004540 / 0.011008 (-0.006468) \| 0.099350 / 0.038508 (0.060842) \| 0.087062 / 0.023109 (0.063953) \| 0.415980 / 0.275898 (0.140082) \| 0.466390 / 0.323480 (0.142910) \| 0.005958 / 0.007986 (-0.002027) \| 0.003671 / 0.004328 (-0.000657) \| 0.075714 / 0.004250 (0.071463) \| 0.066062 / 0.037052 (0.029010) \| 0.426527 / 0.258489 (0.168038) \| 0.473282 / 0.293841 (0.179441) \| 0.035669 / 0.128546 (-0.092878) \| 0.009729 / 0.075646 (-0.065918) \| 0.344035 / 0.419271 (-0.075237) \| 0.061153 / 0.043533 (0.017620) \| 0.428607 / 0.255139 (0.173468) \| 0.445951 / 0.283200 (0.162752) \| 0.026373 / 0.141683 (-0.115310) \| 1.788725 / 1.452155 (0.336570) \| 1.871055 / 1.492716 (0.378339) \|\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) \| 0.230606 / 0.018006 (0.212600) \| 0.489835 / 0.000490 (0.489345) \| 0.005669 / 0.000200 (0.005469) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032197 / 0.037411 (-0.005214) \| 0.099571 / 0.014526 (0.085045) \| 0.112686 / 0.176557 (-0.063871) \| 0.179478 / 0.737135 (-0.557658) \| 0.112670 / 0.296338 (-0.183668) \|\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) \| 0.449606 / 0.215209 (0.234397) \| 4.503356 / 2.077655 (2.425701) \| 2.190480 / 1.504120 (0.686361) \| 1.986054 / 1.541195 (0.444860) \| 2.071594 / 1.468490 (0.603104) \| 0.566301 / 4.584777 (-4.018475) \| 4.088460 / 3.745712 (0.342748) \| 4.840100 / 5.269862 (-0.429761) \| 2.857697 / 4.565676 (-1.707980) \| 0.066718 / 0.424275 (-0.357557) \| 0.008642 / 0.007607 (0.001034) \| 0.539785 / 0.226044 (0.313740) \| 5.383252 / 2.268929 (3.114323) \| 2.878177 / 55.444624 (-52.566447) \| 2.374577 / 6.876477 (-4.501899) \| 2.590500 / 2.142072 (0.448428) \| 0.675196 / 4.805227 (-4.130031) \| 0.153544 / 6.500664 (-6.347120) \| 0.070958 / 0.075469 (-0.004511) \|\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) \| 1.490403 / 1.841788 (-0.351385) \| 22.085740 / 8.074308 (14.011432) \| 16.588093 / 10.191392 (6.396701) \| 0.188598 / 0.680424 (-0.491826) \| 0.021567 / 0.534201 (-0.512634) \| 0.472594 / 0.579283 (-0.106689) \| 0.472903 / 0.434364 (0.038539) \| 0.545305 / 0.540337 (0.004968) \| 0.736399 / 1.386936 (-0.650537) \|\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) \| 0.007635 / 0.011353 (-0.003718) \| 0.004731 / 0.011008 (-0.006277) \| 0.076482 / 0.038508 (0.037974) \| 0.083666 / 0.023109 (0.060557) \| 0.469596 / 0.275898 (0.193698) \| 0.493068 / 0.323480 (0.169588) \| 0.006014 / 0.007986 (-0.001971) \| 0.003902 / 0.004328 (-0.000426) \| 0.077142 / 0.004250 (0.072891) \| 0.064355 / 0.037052 (0.027303) \| 0.468859 / 0.258489 (0.210370) \| 0.504002 / 0.293841 (0.210161) \| 0.037606 / 0.128546 (-0.090940) \| 0.010141 / 0.075646 (-0.065505) \| 0.083790 / 0.419271 (-0.335482) \| 0.060923 / 0.043533 (0.017390) \| 0.464752 / 0.255139 (0.209613) \| 0.500464 / 0.283200 (0.217264) \| 0.031183 / 0.141683 (-0.110499) \| 1.779294 / 1.452155 (0.327139) \| 1.870848 / 1.492716 (0.378131) \|\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) \| 0.246567 / 0.018006 (0.228560) \| 0.477182 / 0.000490 (0.476693) \| 0.000426 / 0.000200 (0.000226) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035377 / 0.037411 (-0.002034) \| 0.106042 / 0.014526 (0.091516) \| 0.119237 / 0.176557 (-0.057320) \| 0.182145 / 0.737135 (-0.554991) \| 0.119537 / 0.296338 (-0.176801) \|\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) \| 0.491352 / 0.215209 (0.276143) \| 4.824220 / 2.077655 (2.746565) \| 2.652039 / 1.504120 (1.147919) \| 2.535310 / 1.541195 (0.994116) \| 2.620009 / 1.468490 (1.151519) \| 0.567865 / 4.584777 (-4.016912) \| 4.158795 / 3.745712 (0.413082) \| 6.042582 / 5.269862 (0.772721) \| 3.957193 / 4.565676 (-0.608484) \| 0.066647 / 0.424275 (-0.357628) \| 0.008893 / 0.007607 (0.001285) \| 0.570137 / 0.226044 (0.344093) \| 5.687126 / 2.268929 (3.418198) \| 3.137605 / 55.444624 (-52.307019) \| 2.655979 / 6.876477 (-4.220498) \| 2.893338 / 2.142072 (0.751265) \| 0.698388 / 4.805227 (-4.106840) \| 0.154897 / 6.500664 (-6.345767) \| 0.071208 / 0.075469 (-0.004261) \|\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) \| 1.619346 / 1.841788 (-0.222441) \| 22.782510 / 8.074308 (14.708202) \| 16.317395 / 10.191392 (6.126003) \| 0.197630 / 0.680424 (-0.482794) \| 0.021795 / 0.534201 (-0.512406) \| 0.466982 / 0.579283 (-0.112302) \| 0.468609 / 0.434364 (0.034245) \| 0.574380 / 0.540337 (0.034043) \| 0.759827 / 1.386936 (-0.627109) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c1c5d8268ae59a0dcaea47da825e87c3f9528b4 \"CML watermark\")\n" ]
1,822,597,471	6,079	Iterating over DataLoader based on HF datasets is stuck forever	closed	2023-07-26T14:52:37	2024-02-07T17:46:52	2023-07-30T14:09:06	https://github.com/huggingface/datasets/issues/6079	null	arindamsarkar93	false	[ "When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? ", "Thanks @mariosasko for your prompt response, here's the stack trace:\r\n\r\n```\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[12], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self)\r\n 954 # this is the shuffle buffer that we keep in memory\r\n 955 mem_buffer = []\r\n--> 956 for x in self.ex_iterable:\r\n 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n 958 i = next(indices_iterator)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self)\r\n 294 for key, pa_table in self.generate_tables_fn(kwargs_with_shuffled_shards):\r\n 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n--> 296 formatted_batch = formatter.format_batch(pa_subtable)\r\n 297 for example in _batch_to_examples(formatted_batch):\r\n 298 yield key, example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table)\r\n 446 if self.lazy:\r\n 447 return LazyBatch(pa_table, self)\r\n--> 448 batch = self.python_arrow_extractor().extract_batch(pa_table)\r\n 449 batch = self.python_features_decoder.decode_batch(batch)\r\n 450 return batch\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table)\r\n 149 def extract_batch(self, pa_table: pa.Table) -> dict:\r\n--> 150 return pa_table.to_pydict()\r\n\r\nKeyboardInterrupt: \r\n```\r\n", "Update: If i let it run, it eventually fails with:\r\n\r\n```\r\nRuntimeError Traceback (most recent call last)\r\nCell In[16], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self)\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n-> 1360 yield format_dict(example) if format_dict else example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct)\r\n 84 def recursive_tensorize(self, data_struct: dict):\r\n---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 463 mapped = [\r\n 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 463 mapped = [\r\n--> 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args)\r\n 364 # Singleton first to spare some computation\r\n 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 366 return function(data_struct)\r\n 368 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 369 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct)\r\n 80 elif isinstance(data_struct, (list, tuple)):\r\n 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])\r\n---> 82 return self._tensorize(data_struct)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value)\r\n 66 if isinstance(value, PIL.Image.Image):\r\n 67 value = np.asarray(value)\r\n---> 68 return torch.tensor(value, {default_dtype, self.torch_tensor_kwargs})\r\n\r\nRuntimeError: Could not infer dtype of decimal.Decimal\r\n```", "PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.", "I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.\r\nOn the same instance, I installed a conda env with python 3.6, and this works well.\r\n\r\nSample:\r\n\r\n```\r\ndef streaming_data_collate_fn(batch):\r\n df = pd.DataFrame.from_dict(batch)\r\n feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float))))\r\n\r\n```", "`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format(\"numpy\")`, which should make this conversion faster.", "Thanks! \r\nPython 3.10 conda-env: After replacing with_format(\"torch\") with with_format(\"numpy\"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nPython 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format(\"torch\") is the only thing that works, and executes in 1.6 seconds.\r\n\r\nI feel something else is also amiss here.", "Can you share the `datasets` and `torch` versions installed in these conda envs?\r\n\r\n> Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nHmm, that's surprising. What are your dataset's `.features`?", "Python 3.6: \r\ndatasets.__version__ 2.4.0\r\ntorch.__version__ 1.10.1+cu102\r\n\r\nPython 3.10:\r\ndatasets.__version__ 2.14.0\r\ntorch.__version__ 2.0.0\r\n\r\nAnonymized features are of the form (subset shown here):\r\n{\r\n'string_feature_i': Value(dtype='string', id=None),\r\n'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None),\r\n'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\r\n}\r\n\r\n\r\nThere is no output from .features in python 3.6 kernel BTW.", "One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.\r\n\r\n512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!", "Update: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:\r\n1. https://github.com/pytorch/pytorch/issues/102494\r\n2. https://github.com/pytorch/pytorch/issues/102269\r\n3. https://github.com/pytorch/pytorch/issues/99625\r\n\r\nOut of the suggested solutions, the one that worked in my case was:\r\n```\r\nos.environ['KMP_AFFINITY'] = \"disabled\"\r\n```\r\nIt is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.\r\n\r\n\r\n", "I'm facing a similar situation in the local VS Code. \r\n\r\nDatasets version 2.14.4\r\nTorch 2.0.1+cu118\r\n\r\nSame code runs without issues in Colab\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\nsample = next(iter(dataset[\"train\"]))\r\n```\r\n\r\nis stuck for minutes. If I interrupt, I get\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[5], line 5\r\n 1 from datasets import load_dataset\r\n 3 dataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\n----> 5 sample = next(iter(dataset[\"train\"]))\r\n 6 print(sample[\"text\"])\r\n 7 sample[\"image\"]\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353), in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255), in ArrowExamplesIterable.__iter__(self)\r\n 253 def __iter__(self):\r\n 254 formatter = PythonFormatter()\r\n--> 255 for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n 256 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n...\r\n-> 1130 return self._sslobj.read(len, buffer)\r\n 1131 else:\r\n 1132 return self._sslobj.read(len)\r\n```", "@osanseviero I assume the `self._sslobj.read(len, buffer)` line comes from the built-in `ssl` module, so this probably has something to do with your network. Please open a new issue with the full stack trace in case you haven't resolved this yet.", "Thank you reporting this and sharing the solution, I ran into this as well!", "Ran into same issue after upgrading to pytorch-2.0. Disabling KMP_AFFINITY as mentioned above worked for me. Thanks!\r\n" ]
1,822,501,472	6,078	resume_download with streaming=True	closed	2023-07-26T14:08:22	2023-07-28T11:05:03	2023-07-28T11:05:03	https://github.com/huggingface/datasets/issues/6078	null	NicolasMICAUX	false	[ "Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ", "Ok thank you for your answer", "I'm closing this as a duplicate of #5380" ]
1,822,486,810	6,077	Mapping gets stuck at 99%	open	2023-07-26T14:00:40	2024-07-22T12:28:06	null	https://github.com/huggingface/datasets/issues/6077	null	Laurent2916	false	[ "The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO.", "Hi @mariosasko !\r\n\r\nI agree, it's an ugly hack, but it was convenient since the resulting `mean_std` could be cached by the library. For my large dataset (which doesn't fit in RAM), I'm actually using something similar to what you suggested. I got rid of the first mapping in the above scripts and replaced it with an iterator, but the issue with the second mapping still persists.", "Have you tried to reduce `batch_size`/`writer_batch_size` in the 2nd `.map`? Also, can you interrupt the process when it gets stuck and share the error stack trace?", "I think `batch_size/writer_batch_size` is already at its lowest in the 2nd `.map` since `batched=False` implies `batch_size=1` and `len(ds) = 1000 = writer_batch_size`.\r\n\r\nHere is also a bunch of stack traces when I interrupted the process:\r\n\r\n<details>\r\n <summary>stack trace 1</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 97%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ \| 967/1000 [00:01<00:00, 534.87 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in <listcomp>\r\n [\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 2</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 988/1000 [00:20<00:00, 526.19 examples/s]Applying mean/std: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊\| 999/1000 [00:21<00:00, 9.66 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 291, in _cast_to_python_objects\r\n if config.JAX_AVAILABLE and \"jax\" in sys.modules:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 3</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ \| 989/1000 [00:01<00:00, 504.80 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 298, in _cast_to_python_objects\r\n if obj.ndim == 0:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n", "Same issue by following code:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchvision.transforms import transforms\r\n\r\npath = \"~/dataset/diffusiondb50k\" # path maybe not necessary\r\ndataset = load_dataset(\"poloclub/diffusiondb\", \"2m_first_1k\", data_dir=path)\r\n\r\ntransform = transforms.Compose([transforms.ToTensor()])\r\ndataset = dataset.map(\r\n lambda x: {\r\n 'image': transform(x['image']),\r\n 'prompt': x['prompt'],\r\n 'width': x['width'],\r\n 'height': x['height'],\r\n }, \r\n # num_proc=4,\r\n)\r\ndataset\r\n```\r\n\r\nAnd the `dataset.map()` stucks at `Map: 99% 986/1000 [00:07<00:00, 145.72 examples/s]`.\r\n\r\nAlso, there is 1 process left in `htop` with 100% CPU usage. And if I add `num_proc=4,`, there will be 4 same processes left.\r\n\r\n### Environment Info\r\n\r\n- `datasets` version: 2.15.0\r\n- Python version: 3.12.2\r\n- Platform: Linux-6.8.0-36-generic-x86_64-with-glibc2.39", "Hi @zmoki688, I've noticed since that it's pretty common for disk writes to lag behind the operations performed by the `map` operator (especially when the data is large and the operations are cheap). Since the progress bar doesn't seem to account for the writes, it speeds up to 99% but wait until all writes are done. At least that's what I think happens when monitoring my disks I/O (with `iotop` and the likes)" ]
1,822,345,597	6,076	No gzip encoding from github	closed	2023-07-26T12:46:07	2023-07-27T16:15:11	2023-07-27T16:14:40	https://github.com/huggingface/datasets/pull/6076	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6076", "html_url": "https://github.com/huggingface/datasets/pull/6076", "diff_url": "https://github.com/huggingface/datasets/pull/6076.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6076.patch", "merged_at": "2023-07-27T16:14:40" }	lhoestq	true	[ "_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) \| 0.008191 / 0.011353 (-0.003162) \| 0.004669 / 0.011008 (-0.006339) \| 0.101315 / 0.038508 (0.062807) \| 0.090235 / 0.023109 (0.067126) \| 0.381265 / 0.275898 (0.105367) \| 0.418266 / 0.323480 (0.094786) \| 0.006292 / 0.007986 (-0.001693) \| 0.003979 / 0.004328 (-0.000349) \| 0.075946 / 0.004250 (0.071696) \| 0.070678 / 0.037052 (0.033625) \| 0.378006 / 0.258489 (0.119517) \| 0.425825 / 0.293841 (0.131984) \| 0.036325 / 0.128546 (-0.092221) \| 0.009814 / 0.075646 (-0.065832) \| 0.345687 / 0.419271 (-0.073584) \| 0.063846 / 0.043533 (0.020313) \| 0.386003 / 0.255139 (0.130864) \| 0.400875 / 0.283200 (0.117675) \| 0.027806 / 0.141683 (-0.113877) \| 1.814810 / 1.452155 (0.362655) \| 1.879897 / 1.492716 (0.387180) \|\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) \| 0.218684 / 0.018006 (0.200677) \| 0.501715 / 0.000490 (0.501225) \| 0.004808 / 0.000200 (0.004608) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035494 / 0.037411 (-0.001917) \| 0.100949 / 0.014526 (0.086423) \| 0.114639 / 0.176557 (-0.061917) \| 0.188908 / 0.737135 (-0.548227) \| 0.115794 / 0.296338 (-0.180545) \|\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) \| 0.462537 / 0.215209 (0.247328) \| 4.612469 / 2.077655 (2.534814) \| 2.298065 / 1.504120 (0.793945) \| 2.088738 / 1.541195 (0.547543) \| 2.188072 / 1.468490 (0.719582) \| 0.565412 / 4.584777 (-4.019364) \| 4.180394 / 3.745712 (0.434681) \| 3.848696 / 5.269862 (-1.421165) \| 2.391381 / 4.565676 (-2.174296) \| 0.067647 / 0.424275 (-0.356628) \| 0.008847 / 0.007607 (0.001240) \| 0.553288 / 0.226044 (0.327243) \| 5.517962 / 2.268929 (3.249033) \| 2.866622 / 55.444624 (-52.578002) \| 2.439025 / 6.876477 (-4.437452) \| 2.740156 / 2.142072 (0.598084) \| 0.694796 / 4.805227 (-4.110431) \| 0.159022 / 6.500664 (-6.341642) \| 0.074471 / 0.075469 (-0.000998) \|\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) \| 1.534979 / 1.841788 (-0.306808) \| 23.297273 / 8.074308 (15.222965) \| 16.859178 / 10.191392 (6.667786) \| 0.207594 / 0.680424 (-0.472830) \| 0.021990 / 0.534201 (-0.512211) \| 0.472059 / 0.579283 (-0.107224) \| 0.497632 / 0.434364 (0.063268) \| 0.565672 / 0.540337 (0.025335) \| 0.772485 / 1.386936 (-0.614451) \|\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) \| 0.007777 / 0.011353 (-0.003576) \| 0.004679 / 0.011008 (-0.006329) \| 0.077317 / 0.038508 (0.038809) \| 0.087433 / 0.023109 (0.064324) \| 0.437389 / 0.275898 (0.161491) \| 0.479562 / 0.323480 (0.156082) \| 0.006137 / 0.007986 (-0.001849) \| 0.003938 / 0.004328 (-0.000390) \| 0.074769 / 0.004250 (0.070518) \| 0.066605 / 0.037052 (0.029553) \| 0.454865 / 0.258489 (0.196376) \| 0.485103 / 0.293841 (0.191262) \| 0.036540 / 0.128546 (-0.092006) \| 0.009983 / 0.075646 (-0.065664) \| 0.083566 / 0.419271 (-0.335706) \| 0.059527 / 0.043533 (0.015994) \| 0.449154 / 0.255139 (0.194015) \| 0.462542 / 0.283200 (0.179342) \| 0.027581 / 0.141683 (-0.114102) \| 1.776720 / 1.452155 (0.324565) \| 1.847920 / 1.492716 (0.355204) \|\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) \| 0.246792 / 0.018006 (0.228786) \| 0.494513 / 0.000490 (0.494024) \| 0.004376 / 0.000200 (0.004176) \| 0.000115 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037837 / 0.037411 (0.000426) \| 0.112752 / 0.014526 (0.098226) \| 0.121742 / 0.176557 (-0.054815) \| 0.189365 / 0.737135 (-0.547770) \| 0.124366 / 0.296338 (-0.171973) \|\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) \| 0.492890 / 0.215209 (0.277681) \| 4.920270 / 2.077655 (2.842615) \| 2.565350 / 1.504120 (1.061230) \| 2.378679 / 1.541195 (0.837484) \| 2.483794 / 1.468490 (1.015304) \| 0.579623 / 4.584777 (-4.005154) \| 4.195924 / 3.745712 (0.450212) \| 3.903382 / 5.269862 (-1.366479) \| 2.466884 / 4.565676 (-2.098793) \| 0.064145 / 0.424275 (-0.360130) \| 0.008695 / 0.007607 (0.001088) \| 0.579300 / 0.226044 (0.353256) \| 5.809064 / 2.268929 (3.540136) \| 3.145393 / 55.444624 (-52.299232) \| 2.832760 / 6.876477 (-4.043717) \| 3.020460 / 2.142072 (0.878388) \| 0.700235 / 4.805227 (-4.104992) \| 0.161262 / 6.500664 (-6.339402) \| 0.076484 / 0.075469 (0.001015) \|\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) \| 1.606504 / 1.841788 (-0.235284) \| 23.747863 / 8.074308 (15.673555) \| 17.281712 / 10.191392 (7.090320) \| 0.203874 / 0.680424 (-0.476550) \| 0.021839 / 0.534201 (-0.512362) \| 0.472365 / 0.579283 (-0.106918) \| 0.475150 / 0.434364 (0.040786) \| 0.571713 / 0.540337 (0.031376) \| 0.759210 / 1.386936 (-0.627726) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n", "> Some questions: won't this have an impact on downloading time, once we do not longer compress the payload? What is the advantage of this approach over the one with block_size: 0?\r\n\r\nSurely, but this prevents random access which is needed at multiple places in the code (eg to check the compression type).\r\nGithub isn't a good place for big files anyway so we should be fine" ]
1,822,341,398	6,075	Error loading music files using `load_dataset`	closed	2023-07-26T12:44:05	2023-07-26T13:08:08	2023-07-26T13:08:08	https://github.com/huggingface/datasets/issues/6075	null	susnato	false	[ "This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.", "I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!" ]
1,822,299,128	6,074	Misc doc improvements	closed	2023-07-26T12:20:54	2023-07-27T16:16:28	2023-07-27T16:16:02	https://github.com/huggingface/datasets/pull/6074	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6074", "html_url": "https://github.com/huggingface/datasets/pull/6074", "diff_url": "https://github.com/huggingface/datasets/pull/6074.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6074.patch", "merged_at": "2023-07-27T16:16:02" }	mariosasko	true	[ "<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) \| 0.006616 / 0.011353 (-0.004737) \| 0.003915 / 0.011008 (-0.007093) \| 0.083271 / 0.038508 (0.044763) \| 0.072595 / 0.023109 (0.049485) \| 0.307224 / 0.275898 (0.031326) \| 0.337244 / 0.323480 (0.013764) \| 0.005296 / 0.007986 (-0.002690) \| 0.003325 / 0.004328 (-0.001003) \| 0.064589 / 0.004250 (0.060339) \| 0.056369 / 0.037052 (0.019316) \| 0.310829 / 0.258489 (0.052340) \| 0.345563 / 0.293841 (0.051722) \| 0.030551 / 0.128546 (-0.097995) \| 0.008519 / 0.075646 (-0.067127) \| 0.286368 / 0.419271 (-0.132903) \| 0.052498 / 0.043533 (0.008966) \| 0.308735 / 0.255139 (0.053596) \| 0.329234 / 0.283200 (0.046034) \| 0.022588 / 0.141683 (-0.119095) \| 1.453135 / 1.452155 (0.000981) \| 1.525956 / 1.492716 (0.033239) \|\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) \| 0.199417 / 0.018006 (0.181410) \| 0.454621 / 0.000490 (0.454131) \| 0.004928 / 0.000200 (0.004728) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028436 / 0.037411 (-0.008975) \| 0.083722 / 0.014526 (0.069196) \| 0.095162 / 0.176557 (-0.081395) \| 0.153434 / 0.737135 (-0.583702) \| 0.099480 / 0.296338 (-0.196859) \|\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) \| 0.384647 / 0.215209 (0.169438) \| 3.838406 / 2.077655 (1.760751) \| 1.891267 / 1.504120 (0.387148) \| 1.751432 / 1.541195 (0.210238) \| 1.737443 / 1.468490 (0.268953) \| 0.487758 / 4.584777 (-4.097019) \| 3.635925 / 3.745712 (-0.109787) \| 5.208718 / 5.269862 (-0.061144) \| 3.029374 / 4.565676 (-1.536302) \| 0.057613 / 0.424275 (-0.366662) \| 0.007177 / 0.007607 (-0.000430) \| 0.455596 / 0.226044 (0.229552) \| 4.559969 / 2.268929 (2.291040) \| 2.325321 / 55.444624 (-53.119303) \| 2.034924 / 6.876477 (-4.841552) \| 2.163869 / 2.142072 (0.021796) \| 0.583477 / 4.805227 (-4.221750) \| 0.132870 / 6.500664 (-6.367795) \| 0.059618 / 0.075469 (-0.015851) \|\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) \| 1.263751 / 1.841788 (-0.578037) \| 19.740004 / 8.074308 (11.665696) \| 14.410980 / 10.191392 (4.219588) \| 0.170367 / 0.680424 (-0.510057) \| 0.018225 / 0.534201 (-0.515976) \| 0.390101 / 0.579283 (-0.189182) \| 0.404298 / 0.434364 (-0.030066) \| 0.455295 / 0.540337 (-0.085043) \| 0.621179 / 1.386936 (-0.765757) \|\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) \| 0.006580 / 0.011353 (-0.004773) \| 0.004078 / 0.011008 (-0.006930) \| 0.065842 / 0.038508 (0.027334) \| 0.074494 / 0.023109 (0.051385) \| 0.403644 / 0.275898 (0.127746) \| 0.430204 / 0.323480 (0.106724) \| 0.005343 / 0.007986 (-0.002643) \| 0.003366 / 0.004328 (-0.000963) \| 0.064858 / 0.004250 (0.060607) \| 0.056252 / 0.037052 (0.019200) \| 0.412556 / 0.258489 (0.154067) \| 0.434099 / 0.293841 (0.140258) \| 0.031518 / 0.128546 (-0.097028) \| 0.008543 / 0.075646 (-0.067104) \| 0.071658 / 0.419271 (-0.347613) \| 0.049962 / 0.043533 (0.006430) \| 0.398511 / 0.255139 (0.143372) \| 0.415908 / 0.283200 (0.132708) \| 0.025011 / 0.141683 (-0.116672) \| 1.492350 / 1.452155 (0.040195) \| 1.552996 / 1.492716 (0.060280) \|\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) \| 0.204971 / 0.018006 (0.186964) \| 0.439965 / 0.000490 (0.439475) \| 0.002071 / 0.000200 (0.001872) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031673 / 0.037411 (-0.005738) \| 0.087529 / 0.014526 (0.073004) \| 0.099882 / 0.176557 (-0.076675) \| 0.156994 / 0.737135 (-0.580141) \| 0.101421 / 0.296338 (-0.194918) \|\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) \| 0.407480 / 0.215209 (0.192271) \| 4.069123 / 2.077655 (1.991468) \| 2.081288 / 1.504120 (0.577169) \| 1.920367 / 1.541195 (0.379172) \| 1.981053 / 1.468490 (0.512563) \| 0.481995 / 4.584777 (-4.102782) \| 3.546486 / 3.745712 (-0.199226) \| 5.133150 / 5.269862 (-0.136712) \| 3.056444 / 4.565676 (-1.509232) \| 0.056650 / 0.424275 (-0.367625) \| 0.007746 / 0.007607 (0.000139) \| 0.490891 / 0.226044 (0.264847) \| 4.902160 / 2.268929 (2.633232) \| 2.564726 / 55.444624 (-52.879899) \| 2.234988 / 6.876477 (-4.641489) \| 2.387656 / 2.142072 (0.245583) \| 0.576315 / 4.805227 (-4.228912) \| 0.132065 / 6.500664 (-6.368599) \| 0.060728 / 0.075469 (-0.014741) \|\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) \| 1.370568 / 1.841788 (-0.471220) \| 19.883159 / 8.074308 (11.808851) \| 14.442066 / 10.191392 (4.250674) \| 0.150119 / 0.680424 (-0.530305) \| 0.018359 / 0.534201 (-0.515842) \| 0.394128 / 0.579283 (-0.185155) \| 0.411697 / 0.434364 (-0.022667) \| 0.460580 / 0.540337 (-0.079757) \| 0.608490 / 1.386936 (-0.778446) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#035d0cf842b82b14059999baa78e8d158dfbed12 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "merging now if you don't mind - this way I can make a patch release" ]
1,822,167,804	6,073	version2.3.2 load_dataset()data_files can't include .xxxx in path	closed	2023-07-26T11:09:31	2023-08-29T15:53:59	2023-08-29T15:53:59	https://github.com/huggingface/datasets/issues/6073	null	BUAAChuanWang	false	[ "Version 2.3.2 is over one year old, so please use the latest release (2.14.0) to get the expected behavior. Version 2.3.2 does not contain some fixes we made to fix resolving hidden files/directories (starting with a dot)." ]
1,822,123,560	6,072	Fix fsspec storage_options from load_dataset	closed	2023-07-26T10:44:23	2023-07-27T12:51:51	2023-07-27T12:42:57	https://github.com/huggingface/datasets/pull/6072	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6072", "html_url": "https://github.com/huggingface/datasets/pull/6072", "diff_url": "https://github.com/huggingface/datasets/pull/6072.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6072.patch", "merged_at": "2023-07-27T12:42:57" }	lhoestq	true	[ "_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) \| 0.007617 / 0.011353 (-0.003736) \| 0.004580 / 0.011008 (-0.006428) \| 0.100913 / 0.038508 (0.062405) \| 0.087703 / 0.023109 (0.064594) \| 0.424159 / 0.275898 (0.148261) \| 0.467195 / 0.323480 (0.143715) \| 0.006890 / 0.007986 (-0.001096) \| 0.003765 / 0.004328 (-0.000564) \| 0.077513 / 0.004250 (0.073262) \| 0.064889 / 0.037052 (0.027837) \| 0.422349 / 0.258489 (0.163860) \| 0.477391 / 0.293841 (0.183550) \| 0.036025 / 0.128546 (-0.092522) \| 0.009939 / 0.075646 (-0.065707) \| 0.342409 / 0.419271 (-0.076862) \| 0.061568 / 0.043533 (0.018035) \| 0.431070 / 0.255139 (0.175931) \| 0.462008 / 0.283200 (0.178809) \| 0.027480 / 0.141683 (-0.114203) \| 1.802271 / 1.452155 (0.350116) \| 1.861336 / 1.492716 (0.368620) \|\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) \| 0.255806 / 0.018006 (0.237800) \| 0.507969 / 0.000490 (0.507479) \| 0.010060 / 0.000200 (0.009860) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032286 / 0.037411 (-0.005125) \| 0.104468 / 0.014526 (0.089942) \| 0.112707 / 0.176557 (-0.063850) \| 0.181285 / 0.737135 (-0.555850) \| 0.113180 / 0.296338 (-0.183158) \|\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) \| 0.449265 / 0.215209 (0.234056) \| 4.465941 / 2.077655 (2.388287) \| 2.177889 / 1.504120 (0.673769) \| 1.969864 / 1.541195 (0.428669) \| 2.077502 / 1.468490 (0.609011) \| 0.561607 / 4.584777 (-4.023170) \| 4.281873 / 3.745712 (0.536161) \| 4.975352 / 5.269862 (-0.294510) \| 2.907121 / 4.565676 (-1.658555) \| 0.070205 / 0.424275 (-0.354070) \| 0.009164 / 0.007607 (0.001557) \| 0.581921 / 0.226044 (0.355876) \| 5.538667 / 2.268929 (3.269739) \| 2.798853 / 55.444624 (-52.645771) \| 2.314015 / 6.876477 (-4.562462) \| 2.584836 / 2.142072 (0.442763) \| 0.672333 / 4.805227 (-4.132894) \| 0.153828 / 6.500664 (-6.346836) \| 0.069757 / 0.075469 (-0.005712) \|\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) \| 1.559670 / 1.841788 (-0.282118) \| 23.994639 / 8.074308 (15.920331) \| 16.856160 / 10.191392 (6.664768) \| 0.195555 / 0.680424 (-0.484869) \| 0.021586 / 0.534201 (-0.512615) \| 0.469295 / 0.579283 (-0.109989) \| 0.481582 / 0.434364 (0.047218) \| 0.588667 / 0.540337 (0.048329) \| 0.734347 / 1.386936 (-0.652589) \|\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) \| 0.009614 / 0.011353 (-0.001739) \| 0.004616 / 0.011008 (-0.006392) \| 0.077223 / 0.038508 (0.038715) \| 0.103074 / 0.023109 (0.079965) \| 0.447834 / 0.275898 (0.171936) \| 0.524696 / 0.323480 (0.201216) \| 0.007120 / 0.007986 (-0.000866) \| 0.003890 / 0.004328 (-0.000438) \| 0.076406 / 0.004250 (0.072156) \| 0.073488 / 0.037052 (0.036436) \| 0.466221 / 0.258489 (0.207732) \| 0.532206 / 0.293841 (0.238365) \| 0.037596 / 0.128546 (-0.090950) \| 0.010029 / 0.075646 (-0.065617) \| 0.084313 / 0.419271 (-0.334959) \| 0.060088 / 0.043533 (0.016555) \| 0.437792 / 0.255139 (0.182653) \| 0.512850 / 0.283200 (0.229650) \| 0.032424 / 0.141683 (-0.109259) \| 1.762130 / 1.452155 (0.309975) \| 1.946097 / 1.492716 (0.453381) \|\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) \| 0.250774 / 0.018006 (0.232768) \| 0.506869 / 0.000490 (0.506379) \| 0.008232 / 0.000200 (0.008032) \| 0.000164 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037779 / 0.037411 (0.000368) \| 0.111933 / 0.014526 (0.097407) \| 0.122385 / 0.176557 (-0.054172) \| 0.190372 / 0.737135 (-0.546763) \| 0.122472 / 0.296338 (-0.173866) \|\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) \| 0.488502 / 0.215209 (0.273293) \| 4.878114 / 2.077655 (2.800459) \| 2.504144 / 1.504120 (1.000024) \| 2.321077 / 1.541195 (0.779883) \| 2.416797 / 1.468490 (0.948307) \| 0.583582 / 4.584777 (-4.001195) \| 4.277896 / 3.745712 (0.532184) \| 3.874780 / 5.269862 (-1.395082) \| 2.540099 / 4.565676 (-2.025577) \| 0.068734 / 0.424275 (-0.355541) \| 0.009158 / 0.007607 (0.001550) \| 0.578401 / 0.226044 (0.352357) \| 5.763354 / 2.268929 (3.494426) \| 3.167771 / 55.444624 (-52.276853) \| 2.675220 / 6.876477 (-4.201257) \| 2.920927 / 2.142072 (0.778855) \| 0.673948 / 4.805227 (-4.131280) \| 0.157908 / 6.500664 (-6.342756) \| 0.071672 / 0.075469 (-0.003797) \|\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) \| 1.635120 / 1.841788 (-0.206668) \| 24.853480 / 8.074308 (16.779172) \| 17.162978 / 10.191392 (6.971586) \| 0.209577 / 0.680424 (-0.470847) \| 0.030110 / 0.534201 (-0.504091) \| 0.546970 / 0.579283 (-0.032313) \| 0.581912 / 0.434364 (0.147548) \| 0.571460 / 0.540337 (0.031123) \| 0.823411 / 1.386936 (-0.563525) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"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) \| 0.006674 / 0.011353 (-0.004679) \| 0.004198 / 0.011008 (-0.006810) \| 0.084859 / 0.038508 (0.046351) \| 0.076065 / 0.023109 (0.052955) \| 0.316065 / 0.275898 (0.040167) \| 0.352097 / 0.323480 (0.028617) \| 0.005610 / 0.007986 (-0.002376) \| 0.003600 / 0.004328 (-0.000729) \| 0.064921 / 0.004250 (0.060671) \| 0.054493 / 0.037052 (0.017441) \| 0.318125 / 0.258489 (0.059636) \| 0.370183 / 0.293841 (0.076342) \| 0.031141 / 0.128546 (-0.097405) \| 0.008755 / 0.075646 (-0.066891) \| 0.288241 / 0.419271 (-0.131030) \| 0.052379 / 0.043533 (0.008846) \| 0.328147 / 0.255139 (0.073008) \| 0.347548 / 0.283200 (0.064348) \| 0.024393 / 0.141683 (-0.117290) \| 1.480646 / 1.452155 (0.028492) \| 1.575867 / 1.492716 (0.083151) \|\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) \| 0.268978 / 0.018006 (0.250971) \| 0.586470 / 0.000490 (0.585980) \| 0.003190 / 0.000200 (0.002990) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030595 / 0.037411 (-0.006816) \| 0.083037 / 0.014526 (0.068511) \| 0.103706 / 0.176557 (-0.072850) \| 0.164104 / 0.737135 (-0.573031) \| 0.104536 / 0.296338 (-0.191802) \|\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) \| 0.382274 / 0.215209 (0.167065) \| 3.811878 / 2.077655 (1.734223) \| 1.840098 / 1.504120 (0.335978) \| 1.670949 / 1.541195 (0.129754) \| 1.763755 / 1.468490 (0.295264) \| 0.479526 / 4.584777 (-4.105251) \| 3.544443 / 3.745712 (-0.201269) \| 3.263004 / 5.269862 (-2.006858) \| 2.092801 / 4.565676 (-2.472875) \| 0.057167 / 0.424275 (-0.367108) \| 0.007450 / 0.007607 (-0.000157) \| 0.463731 / 0.226044 (0.237686) \| 4.624630 / 2.268929 (2.355701) \| 2.327078 / 55.444624 (-53.117546) \| 1.977734 / 6.876477 (-4.898743) \| 2.237152 / 2.142072 (0.095079) \| 0.573210 / 4.805227 (-4.232018) \| 0.132095 / 6.500664 (-6.368569) \| 0.060283 / 0.075469 (-0.015186) \|\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) \| 1.243404 / 1.841788 (-0.598384) \| 20.306778 / 8.074308 (12.232470) \| 14.561660 / 10.191392 (4.370268) \| 0.170826 / 0.680424 (-0.509598) \| 0.018574 / 0.534201 (-0.515627) \| 0.392367 / 0.579283 (-0.186916) \| 0.402918 / 0.434364 (-0.031446) \| 0.476629 / 0.540337 (-0.063708) \| 0.653709 / 1.386936 (-0.733227) \|\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) \| 0.006562 / 0.011353 (-0.004791) \| 0.004092 / 0.011008 (-0.006916) \| 0.065951 / 0.038508 (0.027443) \| 0.078090 / 0.023109 (0.054981) \| 0.369679 / 0.275898 (0.093781) \| 0.411442 / 0.323480 (0.087962) \| 0.005646 / 0.007986 (-0.002339) \| 0.003537 / 0.004328 (-0.000791) \| 0.066024 / 0.004250 (0.061773) \| 0.058947 / 0.037052 (0.021895) \| 0.389219 / 0.258489 (0.130730) \| 0.414200 / 0.293841 (0.120359) \| 0.030372 / 0.128546 (-0.098174) \| 0.008631 / 0.075646 (-0.067015) \| 0.071692 / 0.419271 (-0.347580) \| 0.048035 / 0.043533 (0.004502) \| 0.376960 / 0.255139 (0.121821) \| 0.389847 / 0.283200 (0.106648) \| 0.023940 / 0.141683 (-0.117743) \| 1.487633 / 1.452155 (0.035479) \| 1.561680 / 1.492716 (0.068964) \|\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) \| 0.301467 / 0.018006 (0.283461) \| 0.544159 / 0.000490 (0.543669) \| 0.000408 / 0.000200 (0.000208) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030939 / 0.037411 (-0.006472) \| 0.087432 / 0.014526 (0.072906) \| 0.103263 / 0.176557 (-0.073293) \| 0.154551 / 0.737135 (-0.582585) \| 0.104631 / 0.296338 (-0.191707) \|\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) \| 0.422348 / 0.215209 (0.207139) \| 4.206003 / 2.077655 (2.128348) \| 2.212619 / 1.504120 (0.708499) \| 2.049616 / 1.541195 (0.508421) \| 2.139093 / 1.468490 (0.670603) \| 0.489647 / 4.584777 (-4.095130) \| 3.523291 / 3.745712 (-0.222422) \| 3.277657 / 5.269862 (-1.992205) \| 2.111353 / 4.565676 (-2.454324) \| 0.057597 / 0.424275 (-0.366679) \| 0.007675 / 0.007607 (0.000068) \| 0.493068 / 0.226044 (0.267023) \| 4.939493 / 2.268929 (2.670565) \| 2.695995 / 55.444624 (-52.748630) \| 2.374904 / 6.876477 (-4.501573) \| 2.600110 / 2.142072 (0.458038) \| 0.586306 / 4.805227 (-4.218921) \| 0.134137 / 6.500664 (-6.366527) \| 0.061897 / 0.075469 (-0.013572) \|\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) \| 1.330628 / 1.841788 (-0.511160) \| 20.557964 / 8.074308 (12.483656) \| 14.251632 / 10.191392 (4.060240) \| 0.148772 / 0.680424 (-0.531652) \| 0.018383 / 0.534201 (-0.515817) \| 0.392552 / 0.579283 (-0.186731) \| 0.403959 / 0.434364 (-0.030405) \| 0.462154 / 0.540337 (-0.078184) \| 0.608832 / 1.386936 (-0.778104) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"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) \| 0.007659 / 0.011353 (-0.003694) \| 0.004500 / 0.011008 (-0.006508) \| 0.100379 / 0.038508 (0.061871) \| 0.079731 / 0.023109 (0.056622) \| 0.381788 / 0.275898 (0.105890) \| 0.416524 / 0.323480 (0.093044) \| 0.004446 / 0.007986 (-0.003539) \| 0.003752 / 0.004328 (-0.000577) \| 0.074956 / 0.004250 (0.070706) \| 0.062885 / 0.037052 (0.025832) \| 0.383849 / 0.258489 (0.125360) \| 0.433906 / 0.293841 (0.140065) \| 0.036079 / 0.128546 (-0.092468) \| 0.009927 / 0.075646 (-0.065719) \| 0.343879 / 0.419271 (-0.075393) \| 0.061055 / 0.043533 (0.017523) \| 0.376703 / 0.255139 (0.121564) \| 0.428111 / 0.283200 (0.144911) \| 0.028667 / 0.141683 (-0.113016) \| 1.777755 / 1.452155 (0.325600) \| 1.878283 / 1.492716 (0.385567) \|\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) \| 0.220829 / 0.018006 (0.202823) \| 0.506406 / 0.000490 (0.505916) \| 0.005550 / 0.000200 (0.005350) \| 0.000123 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034928 / 0.037411 (-0.002483) \| 0.103873 / 0.014526 (0.089347) \| 0.114352 / 0.176557 (-0.062204) \| 0.188218 / 0.737135 (-0.548918) \| 0.117343 / 0.296338 (-0.178995) \|\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) \| 0.459148 / 0.215209 (0.243939) \| 4.582092 / 2.077655 (2.504437) \| 2.275603 / 1.504120 (0.771483) \| 2.058155 / 1.541195 (0.516960) \| 2.163886 / 1.468490 (0.695396) \| 0.573033 / 4.584777 (-4.011744) \| 4.414891 / 3.745712 (0.669178) \| 7.280433 / 5.269862 (2.010572) \| 4.119414 / 4.565676 (-0.446262) \| 0.067432 / 0.424275 (-0.356843) \| 0.008687 / 0.007607 (0.001080) \| 0.556029 / 0.226044 (0.329984) \| 5.557192 / 2.268929 (3.288264) \| 2.921596 / 55.444624 (-52.523028) \| 2.520249 / 6.876477 (-4.356228) \| 2.778965 / 2.142072 (0.636893) \| 0.684765 / 4.805227 (-4.120462) \| 0.159228 / 6.500664 (-6.341436) \| 0.074015 / 0.075469 (-0.001454) \|\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) \| 1.534470 / 1.841788 (-0.307318) \| 23.630693 / 8.074308 (15.556385) \| 17.058142 / 10.191392 (6.866750) \| 0.200909 / 0.680424 (-0.479515) \| 0.021637 / 0.534201 (-0.512564) \| 0.467417 / 0.579283 (-0.111866) \| 0.460456 / 0.434364 (0.026092) \| 0.541131 / 0.540337 (0.000793) \| 0.728560 / 1.386936 (-0.658376) \|\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) \| 0.007625 / 0.011353 (-0.003727) \| 0.004495 / 0.011008 (-0.006513) \| 0.076373 / 0.038508 (0.037865) \| 0.085260 / 0.023109 (0.062151) \| 0.475778 / 0.275898 (0.199880) \| 0.504604 / 0.323480 (0.181124) \| 0.006733 / 0.007986 (-0.001253) \| 0.003751 / 0.004328 (-0.000578) \| 0.074993 / 0.004250 (0.070743) \| 0.064704 / 0.037052 (0.027652) \| 0.490072 / 0.258489 (0.231583) \| 0.507560 / 0.293841 (0.213719) \| 0.036765 / 0.128546 (-0.091781) \| 0.009955 / 0.075646 (-0.065692) \| 0.082452 / 0.419271 (-0.336820) \| 0.057131 / 0.043533 (0.013598) \| 0.467664 / 0.255139 (0.212525) \| 0.482143 / 0.283200 (0.198943) \| 0.025396 / 0.141683 (-0.116287) \| 1.807587 / 1.452155 (0.355433) \| 1.853355 / 1.492716 (0.360639) \|\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) \| 0.250543 / 0.018006 (0.232537) \| 0.495685 / 0.000490 (0.495196) \| 0.000415 / 0.000200 (0.000215) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035795 / 0.037411 (-0.001616) \| 0.105954 / 0.014526 (0.091428) \| 0.120158 / 0.176557 (-0.056399) \| 0.181714 / 0.737135 (-0.555422) \| 0.121242 / 0.296338 (-0.175097) \|\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) \| 0.488241 / 0.215209 (0.273032) \| 4.866916 / 2.077655 (2.789262) \| 2.531530 / 1.504120 (1.027410) \| 2.360642 / 1.541195 (0.819448) \| 2.457320 / 1.468490 (0.988830) \| 0.571224 / 4.584777 (-4.013553) \| 4.339042 / 3.745712 (0.593330) \| 3.672812 / 5.269862 (-1.597050) \| 2.364535 / 4.565676 (-2.201142) \| 0.067004 / 0.424275 (-0.357271) \| 0.009019 / 0.007607 (0.001412) \| 0.563751 / 0.226044 (0.337707) \| 5.664917 / 2.268929 (3.395989) \| 3.043316 / 55.444624 (-52.401308) \| 2.682722 / 6.876477 (-4.193755) \| 2.863482 / 2.142072 (0.721409) \| 0.666171 / 4.805227 (-4.139056) \| 0.151862 / 6.500664 (-6.348802) \| 0.071199 / 0.075469 (-0.004271) \|\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) \| 1.601880 / 1.841788 (-0.239907) \| 23.069073 / 8.074308 (14.994765) \| 16.918377 / 10.191392 (6.726985) \| 0.173614 / 0.680424 (-0.506810) \| 0.021843 / 0.534201 (-0.512358) \| 0.470531 / 0.579283 (-0.108753) \| 0.471152 / 0.434364 (0.036788) \| 0.550968 / 0.540337 (0.010631) \| 0.718869 / 1.386936 (-0.668067) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9e6eea46fc9503765c125395e30e26c1ae2e084 \"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) \| 0.007530 / 0.011353 (-0.003823) \| 0.004151 / 0.011008 (-0.006858) \| 0.098490 / 0.038508 (0.059982) \| 0.086955 / 0.023109 (0.063846) \| 0.362133 / 0.275898 (0.086235) \| 0.391402 / 0.323480 (0.067922) \| 0.006274 / 0.007986 (-0.001712) \| 0.003711 / 0.004328 (-0.000618) \| 0.073519 / 0.004250 (0.069269) \| 0.066170 / 0.037052 (0.029118) \| 0.379057 / 0.258489 (0.120568) \| 0.398132 / 0.293841 (0.104291) \| 0.033936 / 0.128546 (-0.094610) \| 0.009977 / 0.075646 (-0.065670) \| 0.323766 / 0.419271 (-0.095505) \| 0.078615 / 0.043533 (0.035082) \| 0.352403 / 0.255139 (0.097264) \| 0.386607 / 0.283200 (0.103407) \| 0.036579 / 0.141683 (-0.105103) \| 1.691899 / 1.452155 (0.239745) \| 1.819396 / 1.492716 (0.326680) \|\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) \| 0.216888 / 0.018006 (0.198882) \| 0.465781 / 0.000490 (0.465291) \| 0.006197 / 0.000200 (0.005997) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032870 / 0.037411 (-0.004542) \| 0.096026 / 0.014526 (0.081500) \| 0.111093 / 0.176557 (-0.065464) \| 0.185982 / 0.737135 (-0.551154) \| 0.106967 / 0.296338 (-0.189371) \|\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) \| 0.441567 / 0.215209 (0.226358) \| 4.353813 / 2.077655 (2.276158) \| 2.176034 / 1.504120 (0.671914) \| 1.969631 / 1.541195 (0.428437) \| 2.048821 / 1.468490 (0.580330) \| 0.549144 / 4.584777 (-4.035633) \| 4.016166 / 3.745712 (0.270453) \| 3.764249 / 5.269862 (-1.505613) \| 2.293995 / 4.565676 (-2.271681) \| 0.065227 / 0.424275 (-0.359048) \| 0.008303 / 0.007607 (0.000695) \| 0.513783 / 0.226044 (0.287738) \| 5.247617 / 2.268929 (2.978689) \| 2.782114 / 55.444624 (-52.662510) \| 2.342776 / 6.876477 (-4.533701) \| 2.621569 / 2.142072 (0.479497) \| 0.679336 / 4.805227 (-4.125891) \| 0.152061 / 6.500664 (-6.348603) \| 0.070294 / 0.075469 (-0.005175) \|\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) \| 1.471778 / 1.841788 (-0.370010) \| 22.714904 / 8.074308 (14.640596) \| 15.607991 / 10.191392 (5.416599) \| 0.172592 / 0.680424 (-0.507832) \| 0.021799 / 0.534201 (-0.512402) \| 0.462740 / 0.579283 (-0.116543) \| 0.490885 / 0.434364 (0.056521) \| 0.552997 / 0.540337 (0.012660) \| 0.763784 / 1.386936 (-0.623152) \|\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) \| 0.007466 / 0.011353 (-0.003886) \| 0.004322 / 0.011008 (-0.006686) \| 0.074331 / 0.038508 (0.035823) \| 0.085315 / 0.023109 (0.062206) \| 0.409284 / 0.275898 (0.133386) \| 0.464584 / 0.323480 (0.141104) \| 0.005651 / 0.007986 (-0.002335) \| 0.003577 / 0.004328 (-0.000751) \| 0.070250 / 0.004250 (0.066000) \| 0.059780 / 0.037052 (0.022727) \| 0.419668 / 0.258489 (0.161179) \| 0.462984 / 0.293841 (0.169143) \| 0.034159 / 0.128546 (-0.094387) \| 0.008999 / 0.075646 (-0.066647) \| 0.076302 / 0.419271 (-0.342969) \| 0.052274 / 0.043533 (0.008741) \| 0.425938 / 0.255139 (0.170799) \| 0.430399 / 0.283200 (0.147200) \| 0.025017 / 0.141683 (-0.116666) \| 1.680697 / 1.452155 (0.228542) \| 1.774677 / 1.492716 (0.281960) \|\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) \| 0.291514 / 0.018006 (0.273508) \| 0.461175 / 0.000490 (0.460685) \| 0.023061 / 0.000200 (0.022861) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033950 / 0.037411 (-0.003462) \| 0.100032 / 0.014526 (0.085506) \| 0.118308 / 0.176557 (-0.058249) \| 0.183601 / 0.737135 (-0.553535) \| 0.116936 / 0.296338 (-0.179402) \|\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) \| 0.478779 / 0.215209 (0.263570) \| 4.709505 / 2.077655 (2.631850) \| 2.457442 / 1.504120 (0.953322) \| 2.213737 / 1.541195 (0.672542) \| 2.340642 / 1.468490 (0.872152) \| 0.567187 / 4.584777 (-4.017590) \| 3.923061 / 3.745712 (0.177349) \| 3.752989 / 5.269862 (-1.516873) \| 2.324028 / 4.565676 (-2.241649) \| 0.064471 / 0.424275 (-0.359804) \| 0.008845 / 0.007607 (0.001238) \| 0.547447 / 0.226044 (0.321402) \| 5.599435 / 2.268929 (3.330506) \| 2.980547 / 55.444624 (-52.464077) \| 2.754908 / 6.876477 (-4.121569) \| 2.832978 / 2.142072 (0.690906) \| 0.635059 / 4.805227 (-4.170168) \| 0.153478 / 6.500664 (-6.347187) \| 0.067146 / 0.075469 (-0.008323) \|\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) \| 1.555588 / 1.841788 (-0.286200) \| 22.828906 / 8.074308 (14.754597) \| 16.211008 / 10.191392 (6.019616) \| 0.168009 / 0.680424 (-0.512415) \| 0.021966 / 0.534201 (-0.512235) \| 0.464872 / 0.579283 (-0.114411) \| 0.460429 / 0.434364 (0.026065) \| 0.530498 / 0.540337 (-0.009839) \| 0.705020 / 1.386936 (-0.681916) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#deb9e703237c8310c5a6db04f54d54368e951edd \"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) \| 0.005964 / 0.011353 (-0.005389) \| 0.003644 / 0.011008 (-0.007364) \| 0.079607 / 0.038508 (0.041099) \| 0.058387 / 0.023109 (0.035278) \| 0.312226 / 0.275898 (0.036328) \| 0.349206 / 0.323480 (0.025726) \| 0.004715 / 0.007986 (-0.003271) \| 0.002869 / 0.004328 (-0.001460) \| 0.061668 / 0.004250 (0.057417) \| 0.045694 / 0.037052 (0.008642) \| 0.313516 / 0.258489 (0.055027) \| 0.357543 / 0.293841 (0.063702) \| 0.027179 / 0.128546 (-0.101367) \| 0.007961 / 0.075646 (-0.067686) \| 0.262473 / 0.419271 (-0.156798) \| 0.045588 / 0.043533 (0.002055) \| 0.313102 / 0.255139 (0.057963) \| 0.368686 / 0.283200 (0.085486) \| 0.020556 / 0.141683 (-0.121127) \| 1.447258 / 1.452155 (-0.004897) \| 1.527319 / 1.492716 (0.034602) \|\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) \| 0.199417 / 0.018006 (0.181411) \| 0.422155 / 0.000490 (0.421665) \| 0.004972 / 0.000200 (0.004772) \| 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) \| 0.023539 / 0.037411 (-0.013872) \| 0.073055 / 0.014526 (0.058529) \| 0.083631 / 0.176557 (-0.092926) \| 0.145923 / 0.737135 (-0.591212) \| 0.083820 / 0.296338 (-0.212518) \|\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) \| 0.396305 / 0.215209 (0.181096) \| 3.967065 / 2.077655 (1.889410) \| 2.101109 / 1.504120 (0.596989) \| 1.958817 / 1.541195 (0.417622) \| 2.037894 / 1.468490 (0.569404) \| 0.496955 / 4.584777 (-4.087822) \| 3.078948 / 3.745712 (-0.666764) \| 3.363655 / 5.269862 (-1.906207) \| 2.087659 / 4.565676 (-2.478018) \| 0.057171 / 0.424275 (-0.367104) \| 0.006410 / 0.007607 (-0.001197) \| 0.470535 / 0.226044 (0.244491) \| 4.715259 / 2.268929 (2.446330) \| 2.355510 / 55.444624 (-53.089114) \| 2.025270 / 6.876477 (-4.851207) \| 2.210401 / 2.142072 (0.068329) \| 0.580538 / 4.805227 (-4.224689) \| 0.125068 / 6.500664 (-6.375596) \| 0.059871 / 0.075469 (-0.015598) \|\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) \| 1.245468 / 1.841788 (-0.596320) \| 18.322042 / 8.074308 (10.247734) \| 13.609726 / 10.191392 (3.418334) \| 0.143623 / 0.680424 (-0.536801) \| 0.017068 / 0.534201 (-0.517133) \| 0.330758 / 0.579283 (-0.248525) \| 0.339946 / 0.434364 (-0.094418) \| 0.377861 / 0.540337 (-0.162476) \| 0.524593 / 1.386936 (-0.862343) \|\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) \| 0.006049 / 0.011353 (-0.005304) \| 0.003737 / 0.011008 (-0.007271) \| 0.062816 / 0.038508 (0.024308) \| 0.063768 / 0.023109 (0.040658) \| 0.362001 / 0.275898 (0.086103) \| 0.395251 / 0.323480 (0.071772) \| 0.004823 / 0.007986 (-0.003163) \| 0.002881 / 0.004328 (-0.001447) \| 0.061987 / 0.004250 (0.057737) \| 0.049950 / 0.037052 (0.012898) \| 0.362442 / 0.258489 (0.103953) \| 0.399321 / 0.293841 (0.105480) \| 0.027616 / 0.128546 (-0.100930) \| 0.007965 / 0.075646 (-0.067681) \| 0.068584 / 0.419271 (-0.350687) \| 0.044700 / 0.043533 (0.001168) \| 0.361011 / 0.255139 (0.105872) \| 0.386007 / 0.283200 (0.102807) \| 0.024621 / 0.141683 (-0.117061) \| 1.441497 / 1.452155 (-0.010657) \| 1.533145 / 1.492716 (0.040429) \|\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) \| 0.223446 / 0.018006 (0.205440) \| 0.411147 / 0.000490 (0.410657) \| 0.001821 / 0.000200 (0.001621) \| 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) \| 0.025661 / 0.037411 (-0.011751) \| 0.077838 / 0.014526 (0.063313) \| 0.086148 / 0.176557 (-0.090408) \| 0.140386 / 0.737135 (-0.596750) \| 0.088793 / 0.296338 (-0.207546) \|\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) \| 0.425209 / 0.215209 (0.210000) \| 4.250723 / 2.077655 (2.173068) \| 2.403437 / 1.504120 (0.899317) \| 2.283584 / 1.541195 (0.742390) \| 2.326870 / 1.468490 (0.858380) \| 0.504781 / 4.584777 (-4.079996) \| 3.017042 / 3.745712 (-0.728670) \| 4.643068 / 5.269862 (-0.626794) \| 2.535710 / 4.565676 (-2.029967) \| 0.058520 / 0.424275 (-0.365755) \| 0.006766 / 0.007607 (-0.000841) \| 0.500664 / 0.226044 (0.274620) \| 5.017073 / 2.268929 (2.748145) \| 2.668661 / 55.444624 (-52.775963) \| 2.335486 / 6.876477 (-4.540991) \| 2.486518 / 2.142072 (0.344445) \| 0.598795 / 4.805227 (-4.206432) \| 0.126395 / 6.500664 (-6.374269) \| 0.063154 / 0.075469 (-0.012315) \|\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) \| 1.358059 / 1.841788 (-0.483728) \| 18.615724 / 8.074308 (10.541416) \| 13.670934 / 10.191392 (3.479542) \| 0.134650 / 0.680424 (-0.545774) \| 0.016941 / 0.534201 (-0.517260) \| 0.335215 / 0.579283 (-0.244068) \| 0.356118 / 0.434364 (-0.078246) \| 0.393109 / 0.540337 (-0.147228) \| 0.534165 / 1.386936 (-0.852771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7d3b557665f34e84cd151ffe9d80b45a19fe33 \"CML watermark\")\n" ]
1,821,990,749	6,071	storage_options provided to load_dataset not fully piping through since datasets 2.14.0	closed	2023-07-26T09:37:20	2023-07-27T12:42:58	2023-07-27T12:42:58	https://github.com/huggingface/datasets/issues/6071	null	exs-avianello	false	[ "Hi ! Thanks for reporting, I opened a PR to fix this\r\n\r\nWhat filesystem are you using ?", "Hi @lhoestq ! Thank you so much 🙌 \r\n\r\nIt's a bit of a custom setup, but in practice I am using a [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) (wrapped in a `fsspec.implementations.arrow.ArrowFSWrapper` [to make it](https://arrow.apache.org/docs/python/filesystems.html#using-arrow-filesystems-with-fsspec) `fsspec` compatible). I also register it as an entrypoint with `fsspec` so that it's the one that gets automatically resolved when looking for filesystems for the `s3` protocol\r\n\r\nIn my case the `storage_option` that seemed not getting piped through was the filesystem's `endpoint_override` that I use in some tests to point at a mock S3 bucket" ]
1,820,836,330	6,070	Fix Quickstart notebook link	closed	2023-07-25T17:48:37	2023-07-25T18:19:01	2023-07-25T18:10:16	https://github.com/huggingface/datasets/pull/6070	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6070", "html_url": "https://github.com/huggingface/datasets/pull/6070", "diff_url": "https://github.com/huggingface/datasets/pull/6070.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6070.patch", "merged_at": "2023-07-25T18:10:16" }	mariosasko	true	[ "_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) \| 0.008473 / 0.011353 (-0.002880) \| 0.004734 / 0.011008 (-0.006274) \| 0.103895 / 0.038508 (0.065387) \| 0.071838 / 0.023109 (0.048729) \| 0.379949 / 0.275898 (0.104051) \| 0.397375 / 0.323480 (0.073895) \| 0.006695 / 0.007986 (-0.001290) \| 0.004536 / 0.004328 (0.000207) \| 0.076151 / 0.004250 (0.071901) \| 0.058690 / 0.037052 (0.021638) \| 0.379937 / 0.258489 (0.121448) \| 0.411833 / 0.293841 (0.117992) \| 0.046805 / 0.128546 (-0.081741) \| 0.013689 / 0.075646 (-0.061958) \| 0.327896 / 0.419271 (-0.091375) \| 0.063873 / 0.043533 (0.020340) \| 0.378451 / 0.255139 (0.123312) \| 0.398725 / 0.283200 (0.115525) \| 0.034961 / 0.141683 (-0.106722) \| 1.604999 / 1.452155 (0.152845) \| 1.748370 / 1.492716 (0.255654) \|\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) \| 0.224634 / 0.018006 (0.206628) \| 0.548468 / 0.000490 (0.547979) \| 0.005049 / 0.000200 (0.004849) \| 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) \| 0.028144 / 0.037411 (-0.009267) \| 0.092184 / 0.014526 (0.077659) \| 0.102987 / 0.176557 (-0.073570) \| 0.176987 / 0.737135 (-0.560149) \| 0.103093 / 0.296338 (-0.193246) \|\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) \| 0.578410 / 0.215209 (0.363201) \| 5.664781 / 2.077655 (3.587126) \| 2.487763 / 1.504120 (0.983643) \| 2.254213 / 1.541195 (0.713018) \| 2.239693 / 1.468490 (0.771202) \| 0.810380 / 4.584777 (-3.774397) \| 5.036540 / 3.745712 (1.290828) \| 7.064695 / 5.269862 (1.794834) \| 4.215101 / 4.565676 (-0.350575) \| 0.089792 / 0.424275 (-0.334483) \| 0.008487 / 0.007607 (0.000879) \| 0.692292 / 0.226044 (0.466248) \| 6.780226 / 2.268929 (4.511297) \| 3.245510 / 55.444624 (-52.199114) \| 2.575984 / 6.876477 (-4.300493) \| 2.747546 / 2.142072 (0.605473) \| 0.956604 / 4.805227 (-3.848623) \| 0.198937 / 6.500664 (-6.301727) \| 0.070849 / 0.075469 (-0.004620) \|\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) \| 1.536469 / 1.841788 (-0.305319) \| 21.750583 / 8.074308 (13.676275) \| 20.559532 / 10.191392 (10.368140) \| 0.241244 / 0.680424 (-0.439180) \| 0.030078 / 0.534201 (-0.504123) \| 0.462204 / 0.579283 (-0.117079) \| 0.600103 / 0.434364 (0.165739) \| 0.535074 / 0.540337 (-0.005264) \| 0.764427 / 1.386936 (-0.622509) \|\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) \| 0.009712 / 0.011353 (-0.001641) \| 0.005036 / 0.011008 (-0.005972) \| 0.073683 / 0.038508 (0.035175) \| 0.078684 / 0.023109 (0.055574) \| 0.445096 / 0.275898 (0.169198) \| 0.496233 / 0.323480 (0.172754) \| 0.006231 / 0.007986 (-0.001755) \| 0.004720 / 0.004328 (0.000392) \| 0.076444 / 0.004250 (0.072194) \| 0.060932 / 0.037052 (0.023880) \| 0.505727 / 0.258489 (0.247238) \| 0.498702 / 0.293841 (0.204861) \| 0.047115 / 0.128546 (-0.081431) \| 0.014028 / 0.075646 (-0.061618) \| 0.099292 / 0.419271 (-0.319980) \| 0.061571 / 0.043533 (0.018038) \| 0.468435 / 0.255139 (0.213296) \| 0.481747 / 0.283200 (0.198547) \| 0.033962 / 0.141683 (-0.107721) \| 1.665397 / 1.452155 (0.213242) \| 1.830488 / 1.492716 (0.337772) \|\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) \| 0.268217 / 0.018006 (0.250211) \| 0.555123 / 0.000490 (0.554633) \| 0.000451 / 0.000200 (0.000251) \| 0.000156 / 0.000054 (0.000101) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034262 / 0.037411 (-0.003150) \| 0.107807 / 0.014526 (0.093281) \| 0.115631 / 0.176557 (-0.060926) \| 0.175914 / 0.737135 (-0.561221) \| 0.118775 / 0.296338 (-0.177564) \|\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) \| 0.583260 / 0.215209 (0.368051) \| 5.934976 / 2.077655 (3.857321) \| 2.752304 / 1.504120 (1.248184) \| 2.382746 / 1.541195 (0.841551) \| 2.389402 / 1.468490 (0.920912) \| 0.794213 / 4.584777 (-3.790564) \| 5.215269 / 3.745712 (1.469557) \| 7.083595 / 5.269862 (1.813733) \| 3.776136 / 4.565676 (-0.789540) \| 0.091141 / 0.424275 (-0.333135) \| 0.008803 / 0.007607 (0.001196) \| 0.726510 / 0.226044 (0.500465) \| 6.926860 / 2.268929 (4.657931) \| 3.475612 / 55.444624 (-51.969012) \| 2.730237 / 6.876477 (-4.146240) \| 2.879145 / 2.142072 (0.737073) \| 0.959956 / 4.805227 (-3.845271) \| 0.189812 / 6.500664 (-6.310852) \| 0.071624 / 0.075469 (-0.003845) \|\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) \| 1.748184 / 1.841788 (-0.093603) \| 23.764520 / 8.074308 (15.690212) \| 19.502461 / 10.191392 (9.311069) \| 0.233987 / 0.680424 (-0.446437) \| 0.028116 / 0.534201 (-0.506085) \| 0.478838 / 0.579283 (-0.100445) \| 0.560952 / 0.434364 (0.126588) \| 0.529902 / 0.540337 (-0.010435) \| 0.735095 / 1.386936 (-0.651841) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dda3e389212f44117a40b44bb0cdf358cfd9f71e \"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) \| 0.006735 / 0.011353 (-0.004618) \| 0.004131 / 0.011008 (-0.006878) \| 0.085619 / 0.038508 (0.047111) \| 0.076973 / 0.023109 (0.053864) \| 0.315175 / 0.275898 (0.039277) \| 0.354703 / 0.323480 (0.031223) \| 0.005409 / 0.007986 (-0.002577) \| 0.003438 / 0.004328 (-0.000891) \| 0.064773 / 0.004250 (0.060523) \| 0.056117 / 0.037052 (0.019064) \| 0.313825 / 0.258489 (0.055336) \| 0.354654 / 0.293841 (0.060813) \| 0.031384 / 0.128546 (-0.097163) \| 0.008537 / 0.075646 (-0.067109) \| 0.288528 / 0.419271 (-0.130744) \| 0.053036 / 0.043533 (0.009504) \| 0.312213 / 0.255139 (0.057074) \| 0.335952 / 0.283200 (0.052752) \| 0.023165 / 0.141683 (-0.118518) \| 1.497559 / 1.452155 (0.045404) \| 1.561949 / 1.492716 (0.069233) \|\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) \| 0.212558 / 0.018006 (0.194552) \| 0.456555 / 0.000490 (0.456065) \| 0.000334 / 0.000200 (0.000134) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028571 / 0.037411 (-0.008840) \| 0.085154 / 0.014526 (0.070628) \| 0.095961 / 0.176557 (-0.080596) \| 0.153041 / 0.737135 (-0.584094) \| 0.099234 / 0.296338 (-0.197105) \|\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) \| 0.381796 / 0.215209 (0.166587) \| 3.806948 / 2.077655 (1.729294) \| 1.829597 / 1.504120 (0.325477) \| 1.659065 / 1.541195 (0.117870) \| 1.738524 / 1.468490 (0.270034) \| 0.483379 / 4.584777 (-4.101398) \| 3.540648 / 3.745712 (-0.205064) \| 3.269188 / 5.269862 (-2.000673) \| 2.042113 / 4.565676 (-2.523564) \| 0.056905 / 0.424275 (-0.367370) \| 0.007235 / 0.007607 (-0.000373) \| 0.460581 / 0.226044 (0.234537) \| 4.597451 / 2.268929 (2.328522) \| 2.334284 / 55.444624 (-53.110340) \| 1.960026 / 6.876477 (-4.916450) \| 2.172118 / 2.142072 (0.030045) \| 0.576758 / 4.805227 (-4.228470) \| 0.131196 / 6.500664 (-6.369468) \| 0.060053 / 0.075469 (-0.015417) \|\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) \| 1.289466 / 1.841788 (-0.552322) \| 19.713059 / 8.074308 (11.638750) \| 14.292390 / 10.191392 (4.100998) \| 0.146199 / 0.680424 (-0.534225) \| 0.018123 / 0.534201 (-0.516078) \| 0.392492 / 0.579283 (-0.186791) \| 0.416544 / 0.434364 (-0.017820) \| 0.457166 / 0.540337 (-0.083171) \| 0.645490 / 1.386936 (-0.741446) \|\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) \| 0.006508 / 0.011353 (-0.004845) \| 0.004010 / 0.011008 (-0.006998) \| 0.065201 / 0.038508 (0.026693) \| 0.076322 / 0.023109 (0.053213) \| 0.364198 / 0.275898 (0.088300) \| 0.398251 / 0.323480 (0.074771) \| 0.005328 / 0.007986 (-0.002658) \| 0.003298 / 0.004328 (-0.001031) \| 0.064378 / 0.004250 (0.060128) \| 0.056053 / 0.037052 (0.019000) \| 0.365431 / 0.258489 (0.106942) \| 0.402777 / 0.293841 (0.108936) \| 0.031014 / 0.128546 (-0.097532) \| 0.008507 / 0.075646 (-0.067140) \| 0.071471 / 0.419271 (-0.347801) \| 0.048300 / 0.043533 (0.004768) \| 0.359700 / 0.255139 (0.104561) \| 0.382244 / 0.283200 (0.099044) \| 0.023783 / 0.141683 (-0.117900) \| 1.517518 / 1.452155 (0.065363) \| 1.569732 / 1.492716 (0.077015) \|\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) \| 0.257447 / 0.018006 (0.239440) \| 0.452598 / 0.000490 (0.452109) \| 0.015187 / 0.000200 (0.014987) \| 0.000164 / 0.000054 (0.000109) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030958 / 0.037411 (-0.006454) \| 0.090066 / 0.014526 (0.075540) \| 0.101120 / 0.176557 (-0.075437) \| 0.154295 / 0.737135 (-0.582840) \| 0.103582 / 0.296338 (-0.192756) \|\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) \| 0.415945 / 0.215209 (0.200736) \| 4.146464 / 2.077655 (2.068809) \| 2.121414 / 1.504120 (0.617294) \| 1.956885 / 1.541195 (0.415690) \| 2.047955 / 1.468490 (0.579465) \| 0.486334 / 4.584777 (-4.098443) \| 3.506263 / 3.745712 (-0.239449) \| 4.942274 / 5.269862 (-0.327587) \| 2.907836 / 4.565676 (-1.657841) \| 0.057344 / 0.424275 (-0.366931) \| 0.007813 / 0.007607 (0.000206) \| 0.497888 / 0.226044 (0.271844) \| 4.978017 / 2.268929 (2.709089) \| 2.600447 / 55.444624 (-52.844177) \| 2.335050 / 6.876477 (-4.541427) \| 2.480373 / 2.142072 (0.338301) \| 0.597954 / 4.805227 (-4.207274) \| 0.134794 / 6.500664 (-6.365870) \| 0.062605 / 0.075469 (-0.012864) \|\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) \| 1.344390 / 1.841788 (-0.497398) \| 20.020067 / 8.074308 (11.945759) \| 14.344626 / 10.191392 (4.153234) \| 0.172101 / 0.680424 (-0.508322) \| 0.018549 / 0.534201 (-0.515652) \| 0.393589 / 0.579283 (-0.185694) \| 0.438401 / 0.434364 (0.004037) \| 0.463800 / 0.540337 (-0.076537) \| 0.618269 / 1.386936 (-0.768667) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0177910b32712f28d147879395e511207e39958 \"CML watermark\")\n" ]
1,820,831,535	6,069	KeyError: dataset has no key "image"	closed	2023-07-25T17:45:50	2024-09-06T08:16:16	2023-07-27T12:42:17	https://github.com/huggingface/datasets/issues/6069	null	etetteh	false	[ "You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.\r\n\r\n\r\n", "This is the piece of code I am running:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef resize(examples):\r\n examples[\"pixel_values\"] = [image.convert(\"RGB\").resize((300, 300)) for image in examples[\"image\"]]\r\n return examples\r\n\r\ndef preprocess_train(example_batch):\r\n print(f\"Example batch: \\n{example_batch}\")\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\nimage_dataset = image_dataset.map(resize, remove_columns=[\"image\"], batched=True)\r\n\r\nimage_dataset[\"train\"].set_transform(preprocess_train)\r\nimage_dataset[\"validation\"].set_transform(preprocess_val)\r\n```\r\n\r\nWhen I print ds.column_names I get the following\r\n`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`\r\n\r\nThe `print(f\"Example batch: \\n{example_batch}\")` in the `preprocess_train` function outputs only labels without images:\r\n```\r\nExample batch: \r\n{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}\r\n```\r\n\r\nThe weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.", "The `remove_columns=[\"image\"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.", "The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.\r\n\r\nP.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed", "All the `image_dataset.column_names` after the `map` call should also be present in `preprocess_train `/`preprocess_val` unless (input) `columns` in `set_transform` are specified.\r\n\r\nIf that's not the case, we need a full reproducer (not snippets) with the environment info.", "I have resolved the error after including a collate function as indicated in the Quick Start session of the Datasets docs.:\r\n\r\nHere is what I did:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef preprocess_train(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef collate_fn(examples):\r\n images = []\r\n labels = []\r\n for example in examples:\r\n images.append((example[\"pixel_values\"]))\r\n labels.append(example[\"label\"])\r\n\r\n pixel_values = torch.stack(images)\r\n labels = torch.tensor(labels)\r\n return {\"pixel_values\": pixel_values, \"label\": labels}\r\n\r\ntrain_dataset = image_dataset[\"train\"].with_transform(preprocess_train)\r\nval_dataset = image_dataset[\"validation\"].with_transform(preprocess_val)\r\n\r\nimage_datasets = {\r\n \"train\": train_dataset,\r\n \"val\": val_dataset\r\n}\r\n\r\nsamplers = {\r\n \"train\": data.RandomSampler(train_dataset),\r\n \"val\": data.SequentialSampler(val_dataset),\r\n}\r\n\r\ndataloaders = {\r\n x: data.DataLoader(\r\n image_datasets[x],\r\n collate_fn=collate_fn,\r\n batch_size=batch_size,\r\n sampler=samplers[x],\r\n num_workers=args.num_workers,\r\n worker_init_fn=utils.set_seed_for_worker,\r\n generator=g,\r\n pin_memory=True,\r\n )\r\n for x in [\"train\", \"val\"]\r\n}\r\n\r\ntrain_loader, val_loader = dataloaders[\"train\"], dataloaders[\"val\"]\r\n```\r\nEverything runs fine without any bug now. ", "are you using hf Trainer? hf trainer will remove columns not used in model.forward. set `remove_unused_columns=False` might works" ]
1,820,106,952	6,068	fix tqdm lock deletion	closed	2023-07-25T11:17:25	2023-07-25T15:29:39	2023-07-25T15:17:50	https://github.com/huggingface/datasets/pull/6068	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6068", "html_url": "https://github.com/huggingface/datasets/pull/6068", "diff_url": "https://github.com/huggingface/datasets/pull/6068.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6068.patch", "merged_at": "2023-07-25T15:17:50" }	lhoestq	true	[ "_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) \| 0.006573 / 0.011353 (-0.004780) \| 0.004014 / 0.011008 (-0.006994) \| 0.084999 / 0.038508 (0.046491) \| 0.074965 / 0.023109 (0.051855) \| 0.313294 / 0.275898 (0.037396) \| 0.349678 / 0.323480 (0.026198) \| 0.005401 / 0.007986 (-0.002585) \| 0.003401 / 0.004328 (-0.000927) \| 0.065363 / 0.004250 (0.061112) \| 0.057159 / 0.037052 (0.020107) \| 0.313260 / 0.258489 (0.054771) \| 0.354654 / 0.293841 (0.060813) \| 0.030895 / 0.128546 (-0.097651) \| 0.008605 / 0.075646 (-0.067042) \| 0.289190 / 0.419271 (-0.130081) \| 0.052474 / 0.043533 (0.008942) \| 0.316193 / 0.255139 (0.061054) \| 0.339966 / 0.283200 (0.056767) \| 0.024112 / 0.141683 (-0.117571) \| 1.515606 / 1.452155 (0.063452) \| 1.571428 / 1.492716 (0.078711) \|\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) \| 0.203284 / 0.018006 (0.185278) \| 0.452720 / 0.000490 (0.452230) \| 0.003891 / 0.000200 (0.003691) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028992 / 0.037411 (-0.008419) \| 0.083170 / 0.014526 (0.068644) \| 0.097739 / 0.176557 (-0.078817) \| 0.153401 / 0.737135 (-0.583734) \| 0.098628 / 0.296338 (-0.197711) \|\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) \| 0.390190 / 0.215209 (0.174981) \| 3.901272 / 2.077655 (1.823617) \| 1.887194 / 1.504120 (0.383074) \| 1.723696 / 1.541195 (0.182501) \| 1.800537 / 1.468490 (0.332047) \| 0.481758 / 4.584777 (-4.103019) \| 3.605098 / 3.745712 (-0.140614) \| 3.304482 / 5.269862 (-1.965380) \| 2.053515 / 4.565676 (-2.512161) \| 0.056997 / 0.424275 (-0.367278) \| 0.007347 / 0.007607 (-0.000260) \| 0.461367 / 0.226044 (0.235323) \| 4.606152 / 2.268929 (2.337223) \| 2.470048 / 55.444624 (-52.974576) \| 2.060019 / 6.876477 (-4.816458) \| 2.320507 / 2.142072 (0.178435) \| 0.575050 / 4.805227 (-4.230178) \| 0.133030 / 6.500664 (-6.367634) \| 0.061508 / 0.075469 (-0.013962) \|\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) \| 1.275430 / 1.841788 (-0.566357) \| 19.725453 / 8.074308 (11.651145) \| 14.396360 / 10.191392 (4.204968) \| 0.157980 / 0.680424 (-0.522443) \| 0.018516 / 0.534201 (-0.515685) \| 0.394717 / 0.579283 (-0.184566) \| 0.404948 / 0.434364 (-0.029415) \| 0.474001 / 0.540337 (-0.066336) \| 0.668463 / 1.386936 (-0.718474) \|\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) \| 0.006697 / 0.011353 (-0.004656) \| 0.004206 / 0.011008 (-0.006802) \| 0.065458 / 0.038508 (0.026950) \| 0.075845 / 0.023109 (0.052735) \| 0.365051 / 0.275898 (0.089153) \| 0.400919 / 0.323480 (0.077439) \| 0.005347 / 0.007986 (-0.002638) \| 0.003386 / 0.004328 (-0.000943) \| 0.065398 / 0.004250 (0.061148) \| 0.057320 / 0.037052 (0.020268) \| 0.379161 / 0.258489 (0.120672) \| 0.406892 / 0.293841 (0.113051) \| 0.031986 / 0.128546 (-0.096560) \| 0.008674 / 0.075646 (-0.066972) \| 0.071723 / 0.419271 (-0.347549) \| 0.049897 / 0.043533 (0.006364) \| 0.372034 / 0.255139 (0.116895) \| 0.394293 / 0.283200 (0.111094) \| 0.023681 / 0.141683 (-0.118002) \| 1.479793 / 1.452155 (0.027639) \| 1.553105 / 1.492716 (0.060389) \|\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) \| 0.233660 / 0.018006 (0.215654) \| 0.454412 / 0.000490 (0.453923) \| 0.004473 / 0.000200 (0.004273) \| 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) \| 0.031115 / 0.037411 (-0.006296) \| 0.090541 / 0.014526 (0.076015) \| 0.104363 / 0.176557 (-0.072193) \| 0.161022 / 0.737135 (-0.576114) \| 0.105114 / 0.296338 (-0.191225) \|\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) \| 0.427668 / 0.215209 (0.212459) \| 4.263145 / 2.077655 (2.185490) \| 2.247043 / 1.504120 (0.742923) \| 2.082554 / 1.541195 (0.541360) \| 2.170505 / 1.468490 (0.702015) \| 0.491802 / 4.584777 (-4.092975) \| 3.587295 / 3.745712 (-0.158417) \| 3.344697 / 5.269862 (-1.925165) \| 2.060529 / 4.565676 (-2.505148) \| 0.057829 / 0.424275 (-0.366446) \| 0.007780 / 0.007607 (0.000173) \| 0.503374 / 0.226044 (0.277330) \| 5.034742 / 2.268929 (2.765814) \| 2.701957 / 55.444624 (-52.742667) \| 2.479002 / 6.876477 (-4.397474) \| 2.622055 / 2.142072 (0.479982) \| 0.591363 / 4.805227 (-4.213864) \| 0.133834 / 6.500664 (-6.366830) \| 0.062276 / 0.075469 (-0.013193) \|\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) \| 1.338788 / 1.841788 (-0.503000) \| 20.333599 / 8.074308 (12.259291) \| 14.783196 / 10.191392 (4.591804) \| 0.168695 / 0.680424 (-0.511729) \| 0.018478 / 0.534201 (-0.515723) \| 0.397398 / 0.579283 (-0.181885) \| 0.409900 / 0.434364 (-0.024464) \| 0.475315 / 0.540337 (-0.065023) \| 0.644267 / 1.386936 (-0.742669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb0b324e0bae4c93bb5509b2f0731bc346adb21b \"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) \| 0.007315 / 0.011353 (-0.004038) \| 0.004294 / 0.011008 (-0.006714) \| 0.100300 / 0.038508 (0.061792) \| 0.077780 / 0.023109 (0.054670) \| 0.353728 / 0.275898 (0.077830) \| 0.400538 / 0.323480 (0.077058) \| 0.005807 / 0.007986 (-0.002178) \| 0.003649 / 0.004328 (-0.000680) \| 0.077548 / 0.004250 (0.073297) \| 0.058834 / 0.037052 (0.021781) \| 0.352064 / 0.258489 (0.093574) \| 0.399951 / 0.293841 (0.106110) \| 0.036472 / 0.128546 (-0.092074) \| 0.008653 / 0.075646 (-0.066994) \| 0.323089 / 0.419271 (-0.096182) \| 0.075127 / 0.043533 (0.031594) \| 0.334412 / 0.255139 (0.079273) \| 0.375718 / 0.283200 (0.092519) \| 0.027915 / 0.141683 (-0.113768) \| 1.698795 / 1.452155 (0.246640) \| 1.781447 / 1.492716 (0.288730) \|\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) \| 0.216111 / 0.018006 (0.198104) \| 0.507706 / 0.000490 (0.507216) \| 0.000851 / 0.000200 (0.000651) \| 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) \| 0.030451 / 0.037411 (-0.006960) \| 0.087488 / 0.014526 (0.072962) \| 0.105094 / 0.176557 (-0.071462) \| 0.168130 / 0.737135 (-0.569006) \| 0.106791 / 0.296338 (-0.189547) \|\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) \| 0.426291 / 0.215209 (0.211082) \| 4.281046 / 2.077655 (2.203391) \| 2.162268 / 1.504120 (0.658148) \| 1.909503 / 1.541195 (0.368309) \| 1.943165 / 1.468490 (0.474675) \| 0.516667 / 4.584777 (-4.068110) \| 4.113218 / 3.745712 (0.367506) \| 5.931372 / 5.269862 (0.661510) \| 3.563521 / 4.565676 (-1.002155) \| 0.062415 / 0.424275 (-0.361860) \| 0.007577 / 0.007607 (-0.000030) \| 0.534588 / 0.226044 (0.308543) \| 5.183490 / 2.268929 (2.914561) \| 2.790662 / 55.444624 (-52.653962) \| 2.258630 / 6.876477 (-4.617846) \| 2.499930 / 2.142072 (0.357857) \| 0.606154 / 4.805227 (-4.199073) \| 0.136093 / 6.500664 (-6.364571) \| 0.061151 / 0.075469 (-0.014318) \|\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) \| 1.398392 / 1.841788 (-0.443396) \| 21.482150 / 8.074308 (13.407842) \| 15.477336 / 10.191392 (5.285944) \| 0.192878 / 0.680424 (-0.487546) \| 0.021764 / 0.534201 (-0.512437) \| 0.437149 / 0.579283 (-0.142134) \| 0.439976 / 0.434364 (0.005612) \| 0.514498 / 0.540337 (-0.025840) \| 0.762642 / 1.386936 (-0.624294) \|\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) \| 0.007504 / 0.011353 (-0.003849) \| 0.004526 / 0.011008 (-0.006482) \| 0.071008 / 0.038508 (0.032500) \| 0.078305 / 0.023109 (0.055195) \| 0.436160 / 0.275898 (0.160262) \| 0.439048 / 0.323480 (0.115568) \| 0.006061 / 0.007986 (-0.001925) \| 0.003681 / 0.004328 (-0.000648) \| 0.069445 / 0.004250 (0.065195) \| 0.059258 / 0.037052 (0.022206) \| 0.437745 / 0.258489 (0.179256) \| 0.464247 / 0.293841 (0.170406) \| 0.033286 / 0.128546 (-0.095260) \| 0.009846 / 0.075646 (-0.065800) \| 0.076330 / 0.419271 (-0.342941) \| 0.051919 / 0.043533 (0.008386) \| 0.432817 / 0.255139 (0.177678) \| 0.426295 / 0.283200 (0.143095) \| 0.029818 / 0.141683 (-0.111865) \| 1.747640 / 1.452155 (0.295485) \| 1.726653 / 1.492716 (0.233937) \|\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) \| 0.251253 / 0.018006 (0.233247) \| 0.483394 / 0.000490 (0.482904) \| 0.003992 / 0.000200 (0.003793) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032180 / 0.037411 (-0.005231) \| 0.095425 / 0.014526 (0.080900) \| 0.105908 / 0.176557 (-0.070648) \| 0.164732 / 0.737135 (-0.572403) \| 0.115903 / 0.296338 (-0.180435) \|\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) \| 0.469467 / 0.215209 (0.254258) \| 4.633239 / 2.077655 (2.555584) \| 2.517557 / 1.504120 (1.013437) \| 2.352726 / 1.541195 (0.811531) \| 2.314618 / 1.468490 (0.846128) \| 0.548446 / 4.584777 (-4.036331) \| 3.908797 / 3.745712 (0.163085) \| 3.525941 / 5.269862 (-1.743921) \| 2.178858 / 4.565676 (-2.386819) \| 0.057614 / 0.424275 (-0.366661) \| 0.008604 / 0.007607 (0.000997) \| 0.554756 / 0.226044 (0.328711) \| 5.325635 / 2.268929 (3.056706) \| 3.014266 / 55.444624 (-52.430359) \| 2.844165 / 6.876477 (-4.032312) \| 2.903019 / 2.142072 (0.760947) \| 0.617750 / 4.805227 (-4.187478) \| 0.144259 / 6.500664 (-6.356405) \| 0.065944 / 0.075469 (-0.009525) \|\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) \| 1.504625 / 1.841788 (-0.337163) \| 22.400787 / 8.074308 (14.326479) \| 15.223702 / 10.191392 (5.032310) \| 0.213357 / 0.680424 (-0.467067) \| 0.019310 / 0.534201 (-0.514891) \| 0.456596 / 0.579283 (-0.122687) \| 0.473811 / 0.434364 (0.039447) \| 0.517800 / 0.540337 (-0.022537) \| 0.792468 / 1.386936 (-0.594468) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#03750f4a4c664125c7de910be004710b181dd354 \"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) \| 0.007420 / 0.011353 (-0.003933) \| 0.004502 / 0.011008 (-0.006506) \| 0.097882 / 0.038508 (0.059374) \| 0.079084 / 0.023109 (0.055975) \| 0.361797 / 0.275898 (0.085899) \| 0.416563 / 0.323480 (0.093083) \| 0.006106 / 0.007986 (-0.001879) \| 0.003803 / 0.004328 (-0.000526) \| 0.074669 / 0.004250 (0.070418) \| 0.062168 / 0.037052 (0.025116) \| 0.378844 / 0.258489 (0.120355) \| 0.426601 / 0.293841 (0.132760) \| 0.035619 / 0.128546 (-0.092927) \| 0.009686 / 0.075646 (-0.065960) \| 0.336481 / 0.419271 (-0.082790) \| 0.065553 / 0.043533 (0.022021) \| 0.362501 / 0.255139 (0.107362) \| 0.399752 / 0.283200 (0.116552) \| 0.028685 / 0.141683 (-0.112998) \| 1.683495 / 1.452155 (0.231340) \| 1.786105 / 1.492716 (0.293388) \|\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) \| 0.220792 / 0.018006 (0.202786) \| 0.501936 / 0.000490 (0.501447) \| 0.000389 / 0.000200 (0.000189) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032180 / 0.037411 (-0.005232) \| 0.093079 / 0.014526 (0.078553) \| 0.107967 / 0.176557 (-0.068589) \| 0.171747 / 0.737135 (-0.565389) \| 0.107920 / 0.296338 (-0.188418) \|\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) \| 0.444431 / 0.215209 (0.229222) \| 4.454934 / 2.077655 (2.377279) \| 2.140265 / 1.504120 (0.636145) \| 1.960126 / 1.541195 (0.418931) \| 2.049649 / 1.468490 (0.581158) \| 0.557861 / 4.584777 (-4.026916) \| 4.046240 / 3.745712 (0.300528) \| 4.513748 / 5.269862 (-0.756114) \| 2.593643 / 4.565676 (-1.972034) \| 0.066795 / 0.424275 (-0.357480) \| 0.008302 / 0.007607 (0.000694) \| 0.535643 / 0.226044 (0.309599) \| 5.299429 / 2.268929 (3.030500) \| 2.656019 / 55.444624 (-52.788606) \| 2.281214 / 6.876477 (-4.595263) \| 2.302910 / 2.142072 (0.160837) \| 0.661696 / 4.805227 (-4.143532) \| 0.149787 / 6.500664 (-6.350877) \| 0.069609 / 0.075469 (-0.005860) \|\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) \| 1.509842 / 1.841788 (-0.331946) \| 21.717504 / 8.074308 (13.643196) \| 15.825102 / 10.191392 (5.633710) \| 0.168115 / 0.680424 (-0.512309) \| 0.021637 / 0.534201 (-0.512564) \| 0.454270 / 0.579283 (-0.125013) \| 0.458531 / 0.434364 (0.024167) \| 0.523052 / 0.540337 (-0.017285) \| 0.711219 / 1.386936 (-0.675717) \|\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) \| 0.007189 / 0.011353 (-0.004164) \| 0.004437 / 0.011008 (-0.006571) \| 0.075111 / 0.038508 (0.036603) \| 0.079245 / 0.023109 (0.056136) \| 0.423169 / 0.275898 (0.147270) \| 0.455007 / 0.323480 (0.131527) \| 0.006076 / 0.007986 (-0.001909) \| 0.003819 / 0.004328 (-0.000509) \| 0.074976 / 0.004250 (0.070726) \| 0.062127 / 0.037052 (0.025075) \| 0.456809 / 0.258489 (0.198320) \| 0.474707 / 0.293841 (0.180867) \| 0.036221 / 0.128546 (-0.092325) \| 0.009428 / 0.075646 (-0.066218) \| 0.082842 / 0.419271 (-0.336429) \| 0.057086 / 0.043533 (0.013553) \| 0.436121 / 0.255139 (0.180982) \| 0.453934 / 0.283200 (0.170734) \| 0.026045 / 0.141683 (-0.115638) \| 1.789782 / 1.452155 (0.337627) \| 1.820934 / 1.492716 (0.328218) \|\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) \| 0.230790 / 0.018006 (0.212784) \| 0.497987 / 0.000490 (0.497497) \| 0.002775 / 0.000200 (0.002575) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034418 / 0.037411 (-0.002994) \| 0.105567 / 0.014526 (0.091041) \| 0.113134 / 0.176557 (-0.063423) \| 0.173742 / 0.737135 (-0.563394) \| 0.115936 / 0.296338 (-0.180403) \|\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) \| 0.502259 / 0.215209 (0.287050) \| 4.969877 / 2.077655 (2.892222) \| 2.684860 / 1.504120 (1.180740) \| 2.484386 / 1.541195 (0.943192) \| 2.543061 / 1.468490 (1.074571) \| 0.545733 / 4.584777 (-4.039044) \| 4.029660 / 3.745712 (0.283948) \| 5.927883 / 5.269862 (0.658021) \| 3.528372 / 4.565676 (-1.037305) \| 0.065957 / 0.424275 (-0.358318) \| 0.008933 / 0.007607 (0.001326) \| 0.601630 / 0.226044 (0.375585) \| 5.825872 / 2.268929 (3.556944) \| 3.230721 / 55.444624 (-52.213904) \| 2.891308 / 6.876477 (-3.985169) \| 3.054994 / 2.142072 (0.912922) \| 0.665480 / 4.805227 (-4.139747) \| 0.154815 / 6.500664 (-6.345849) \| 0.072997 / 0.075469 (-0.002472) \|\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) \| 1.549892 / 1.841788 (-0.291896) \| 22.337484 / 8.074308 (14.263176) \| 16.308286 / 10.191392 (6.116894) \| 0.189594 / 0.680424 (-0.490830) \| 0.021844 / 0.534201 (-0.512357) \| 0.456958 / 0.579283 (-0.122325) \| 0.459957 / 0.434364 (0.025593) \| 0.529014 / 0.540337 (-0.011323) \| 0.700359 / 1.386936 (-0.686577) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e4df86b5fb0bc164433ce615af641ec3ba437e \"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) \| 0.009050 / 0.011353 (-0.002303) \| 0.004968 / 0.011008 (-0.006040) \| 0.114315 / 0.038508 (0.075807) \| 0.084475 / 0.023109 (0.061366) \| 0.426325 / 0.275898 (0.150427) \| 0.457870 / 0.323480 (0.134390) \| 0.007076 / 0.007986 (-0.000910) \| 0.004635 / 0.004328 (0.000307) \| 0.082950 / 0.004250 (0.078700) \| 0.065414 / 0.037052 (0.028361) \| 0.441936 / 0.258489 (0.183447) \| 0.476983 / 0.293841 (0.183142) \| 0.048575 / 0.128546 (-0.079972) \| 0.013929 / 0.075646 (-0.061717) \| 0.377498 / 0.419271 (-0.041774) \| 0.081503 / 0.043533 (0.037970) \| 0.426706 / 0.255139 (0.171567) \| 0.460374 / 0.283200 (0.177175) \| 0.046052 / 0.141683 (-0.095631) \| 1.894896 / 1.452155 (0.442741) \| 1.998639 / 1.492716 (0.505923) \|\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) \| 0.313267 / 0.018006 (0.295261) \| 0.607501 / 0.000490 (0.607012) \| 0.003369 / 0.000200 (0.003169) \| 0.000102 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032266 / 0.037411 (-0.005145) \| 0.120138 / 0.014526 (0.105613) \| 0.115044 / 0.176557 (-0.061513) \| 0.181374 / 0.737135 (-0.555761) \| 0.114681 / 0.296338 (-0.181657) \|\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) \| 0.648039 / 0.215209 (0.432830) \| 6.005048 / 2.077655 (3.927394) \| 2.674524 / 1.504120 (1.170404) \| 2.284831 / 1.541195 (0.743637) \| 2.360150 / 1.468490 (0.891660) \| 0.888021 / 4.584777 (-3.696756) \| 5.419840 / 3.745712 (1.674128) \| 4.825816 / 5.269862 (-0.444046) \| 3.140876 / 4.565676 (-1.424801) \| 0.099511 / 0.424275 (-0.324764) \| 0.009176 / 0.007607 (0.001569) \| 0.735646 / 0.226044 (0.509602) \| 7.224026 / 2.268929 (4.955097) \| 3.551146 / 55.444624 (-51.893478) \| 2.844374 / 6.876477 (-4.032103) \| 3.145307 / 2.142072 (1.003235) \| 1.077636 / 4.805227 (-3.727591) \| 0.217754 / 6.500664 (-6.282910) \| 0.081755 / 0.075469 (0.006286) \|\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) \| 1.670956 / 1.841788 (-0.170831) \| 25.524961 / 8.074308 (17.450653) \| 23.061596 / 10.191392 (12.870204) \| 0.247524 / 0.680424 (-0.432899) \| 0.031712 / 0.534201 (-0.502489) \| 0.513049 / 0.579283 (-0.066234) \| 0.614568 / 0.434364 (0.180204) \| 0.574669 / 0.540337 (0.034331) \| 0.816621 / 1.386936 (-0.570315) \|\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) \| 0.009384 / 0.011353 (-0.001969) \| 0.004959 / 0.011008 (-0.006049) \| 0.084782 / 0.038508 (0.046274) \| 0.098086 / 0.023109 (0.074977) \| 0.544395 / 0.275898 (0.268497) \| 0.585157 / 0.323480 (0.261677) \| 0.006507 / 0.007986 (-0.001479) \| 0.004151 / 0.004328 (-0.000178) \| 0.088596 / 0.004250 (0.084345) \| 0.069149 / 0.037052 (0.032097) \| 0.533109 / 0.258489 (0.274620) \| 0.604117 / 0.293841 (0.310276) \| 0.047685 / 0.128546 (-0.080861) \| 0.013651 / 0.075646 (-0.061996) \| 0.096566 / 0.419271 (-0.322705) \| 0.062022 / 0.043533 (0.018489) \| 0.561897 / 0.255139 (0.306758) \| 0.617636 / 0.283200 (0.334436) \| 0.034636 / 0.141683 (-0.107047) \| 1.854667 / 1.452155 (0.402512) \| 1.908923 / 1.492716 (0.416207) \|\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) \| 0.260633 / 0.018006 (0.242627) \| 0.622268 / 0.000490 (0.621778) \| 0.002116 / 0.000200 (0.001916) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035161 / 0.037411 (-0.002250) \| 0.103707 / 0.014526 (0.089181) \| 0.115467 / 0.176557 (-0.061090) \| 0.180077 / 0.737135 (-0.557059) \| 0.118871 / 0.296338 (-0.177467) \|\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) \| 0.628481 / 0.215209 (0.413271) \| 6.304929 / 2.077655 (4.227275) \| 3.027775 / 1.504120 (1.523655) \| 2.753880 / 1.541195 (1.212686) \| 2.820442 / 1.468490 (1.351952) \| 0.851103 / 4.584777 (-3.733674) \| 5.427383 / 3.745712 (1.681670) \| 7.434310 / 5.269862 (2.164449) \| 4.418790 / 4.565676 (-0.146887) \| 0.101733 / 0.424275 (-0.322542) \| 0.009701 / 0.007607 (0.002094) \| 0.763033 / 0.226044 (0.536989) \| 7.497927 / 2.268929 (5.228998) \| 3.735335 / 55.444624 (-51.709290) \| 3.149200 / 6.876477 (-3.727277) \| 3.306214 / 2.142072 (1.164141) \| 1.085440 / 4.805227 (-3.719787) \| 0.207562 / 6.500664 (-6.293102) \| 0.078091 / 0.075469 (0.002622) \|\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) \| 1.820097 / 1.841788 (-0.021691) \| 25.525539 / 8.074308 (17.451231) \| 21.874219 / 10.191392 (11.682827) \| 0.228391 / 0.680424 (-0.452033) \| 0.029584 / 0.534201 (-0.504617) \| 0.511546 / 0.579283 (-0.067737) \| 0.602719 / 0.434364 (0.168355) \| 0.581874 / 0.540337 (0.041537) \| 0.802861 / 1.386936 (-0.584075) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6063ea2069c8b5641b983ba2c1d39b60afe7c00a \"CML watermark\")\n" ]
1,819,919,025	6,067	fix tqdm lock	closed	2023-07-25T09:32:16	2023-07-25T10:02:43	2023-07-25T09:54:12	https://github.com/huggingface/datasets/pull/6067	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6067", "html_url": "https://github.com/huggingface/datasets/pull/6067", "diff_url": "https://github.com/huggingface/datasets/pull/6067.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6067.patch", "merged_at": "2023-07-25T09:54:12" }	lhoestq	true	[ "_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) \| 0.006578 / 0.011353 (-0.004775) \| 0.003953 / 0.011008 (-0.007055) \| 0.084417 / 0.038508 (0.045908) \| 0.076729 / 0.023109 (0.053620) \| 0.315369 / 0.275898 (0.039471) \| 0.347012 / 0.323480 (0.023533) \| 0.005299 / 0.007986 (-0.002686) \| 0.003321 / 0.004328 (-0.001007) \| 0.063954 / 0.004250 (0.059704) \| 0.055810 / 0.037052 (0.018758) \| 0.317651 / 0.258489 (0.059162) \| 0.352603 / 0.293841 (0.058762) \| 0.031355 / 0.128546 (-0.097192) \| 0.008493 / 0.075646 (-0.067153) \| 0.287295 / 0.419271 (-0.131977) \| 0.052716 / 0.043533 (0.009183) \| 0.316410 / 0.255139 (0.061271) \| 0.328893 / 0.283200 (0.045693) \| 0.024005 / 0.141683 (-0.117678) \| 1.520333 / 1.452155 (0.068178) \| 1.601268 / 1.492716 (0.108552) \|\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) \| 0.205144 / 0.018006 (0.187138) \| 0.459160 / 0.000490 (0.458670) \| 0.000321 / 0.000200 (0.000121) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027503 / 0.037411 (-0.009908) \| 0.081476 / 0.014526 (0.066950) \| 0.096759 / 0.176557 (-0.079798) \| 0.157888 / 0.737135 (-0.579247) \| 0.094592 / 0.296338 (-0.201746) \|\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) \| 0.384762 / 0.215209 (0.169553) \| 3.843503 / 2.077655 (1.765849) \| 1.921685 / 1.504120 (0.417565) \| 1.752441 / 1.541195 (0.211246) \| 1.822105 / 1.468490 (0.353615) \| 0.480243 / 4.584777 (-4.104534) \| 3.577220 / 3.745712 (-0.168492) \| 5.047560 / 5.269862 (-0.222302) \| 2.988008 / 4.565676 (-1.577669) \| 0.056430 / 0.424275 (-0.367845) \| 0.007180 / 0.007607 (-0.000427) \| 0.458113 / 0.226044 (0.232069) \| 4.584096 / 2.268929 (2.315168) \| 2.395307 / 55.444624 (-53.049317) \| 2.080530 / 6.876477 (-4.795947) \| 2.239000 / 2.142072 (0.096927) \| 0.575822 / 4.805227 (-4.229405) \| 0.133303 / 6.500664 (-6.367361) \| 0.059449 / 0.075469 (-0.016020) \|\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) \| 1.256496 / 1.841788 (-0.585291) \| 19.651614 / 8.074308 (11.577306) \| 14.232480 / 10.191392 (4.041088) \| 0.146461 / 0.680424 (-0.533963) \| 0.018632 / 0.534201 (-0.515569) \| 0.399844 / 0.579283 (-0.179439) \| 0.411225 / 0.434364 (-0.023139) \| 0.458203 / 0.540337 (-0.082135) \| 0.669916 / 1.386936 (-0.717020) \|\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) \| 0.006463 / 0.011353 (-0.004890) \| 0.003898 / 0.011008 (-0.007110) \| 0.064037 / 0.038508 (0.025529) \| 0.071982 / 0.023109 (0.048873) \| 0.361936 / 0.275898 (0.086038) \| 0.393165 / 0.323480 (0.069685) \| 0.005207 / 0.007986 (-0.002779) \| 0.003231 / 0.004328 (-0.001098) \| 0.064318 / 0.004250 (0.060068) \| 0.055776 / 0.037052 (0.018724) \| 0.383087 / 0.258489 (0.124598) \| 0.402428 / 0.293841 (0.108587) \| 0.031587 / 0.128546 (-0.096959) \| 0.008527 / 0.075646 (-0.067119) \| 0.070495 / 0.419271 (-0.348777) \| 0.048806 / 0.043533 (0.005273) \| 0.369932 / 0.255139 (0.114793) \| 0.385268 / 0.283200 (0.102068) \| 0.023183 / 0.141683 (-0.118500) \| 1.491175 / 1.452155 (0.039020) \| 1.534191 / 1.492716 (0.041475) \|\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) \| 0.224526 / 0.018006 (0.206520) \| 0.445460 / 0.000490 (0.444970) \| 0.003612 / 0.000200 (0.003412) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029829 / 0.037411 (-0.007583) \| 0.087951 / 0.014526 (0.073425) \| 0.100069 / 0.176557 (-0.076487) \| 0.154944 / 0.737135 (-0.582192) \| 0.101271 / 0.296338 (-0.195067) \|\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) \| 0.412385 / 0.215209 (0.197175) \| 4.108038 / 2.077655 (2.030384) \| 2.163578 / 1.504120 (0.659459) \| 2.031934 / 1.541195 (0.490740) \| 2.155857 / 1.468490 (0.687367) \| 0.481132 / 4.584777 (-4.103645) \| 3.620868 / 3.745712 (-0.124844) \| 5.222175 / 5.269862 (-0.047687) \| 3.115637 / 4.565676 (-1.450039) \| 0.056480 / 0.424275 (-0.367795) \| 0.007761 / 0.007607 (0.000154) \| 0.483553 / 0.226044 (0.257509) \| 4.830087 / 2.268929 (2.561159) \| 2.629919 / 55.444624 (-52.814705) \| 2.327551 / 6.876477 (-4.548926) \| 2.539934 / 2.142072 (0.397861) \| 0.587963 / 4.805227 (-4.217265) \| 0.131085 / 6.500664 (-6.369579) \| 0.060807 / 0.075469 (-0.014662) \|\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) \| 1.350003 / 1.841788 (-0.491785) \| 19.491713 / 8.074308 (11.417405) \| 14.030429 / 10.191392 (3.839037) \| 0.174762 / 0.680424 (-0.505662) \| 0.018523 / 0.534201 (-0.515678) \| 0.394946 / 0.579283 (-0.184337) \| 0.407652 / 0.434364 (-0.026712) \| 0.465806 / 0.540337 (-0.074531) \| 0.605417 / 1.386936 (-0.781519) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc85979df3a39657079fdf0844c7e64547507f1a \"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) \| 0.006235 / 0.011353 (-0.005118) \| 0.003675 / 0.011008 (-0.007333) \| 0.080680 / 0.038508 (0.042171) \| 0.064378 / 0.023109 (0.041268) \| 0.394312 / 0.275898 (0.118414) \| 0.428143 / 0.323480 (0.104663) \| 0.004794 / 0.007986 (-0.003191) \| 0.002899 / 0.004328 (-0.001429) \| 0.062592 / 0.004250 (0.058342) \| 0.050957 / 0.037052 (0.013904) \| 0.396831 / 0.258489 (0.138342) \| 0.438280 / 0.293841 (0.144439) \| 0.027743 / 0.128546 (-0.100804) \| 0.008068 / 0.075646 (-0.067578) \| 0.262541 / 0.419271 (-0.156730) \| 0.060837 / 0.043533 (0.017304) \| 0.397941 / 0.255139 (0.142802) \| 0.417012 / 0.283200 (0.133813) \| 0.030153 / 0.141683 (-0.111530) \| 1.477115 / 1.452155 (0.024960) \| 1.516642 / 1.492716 (0.023926) \|\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) \| 0.178032 / 0.018006 (0.160026) \| 0.445775 / 0.000490 (0.445286) \| 0.004275 / 0.000200 (0.004075) \| 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) \| 0.025025 / 0.037411 (-0.012386) \| 0.074113 / 0.014526 (0.059587) \| 0.083814 / 0.176557 (-0.092743) \| 0.148860 / 0.737135 (-0.588275) \| 0.085408 / 0.296338 (-0.210931) \|\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) \| 0.393714 / 0.215209 (0.178505) \| 3.936589 / 2.077655 (1.858934) \| 1.910501 / 1.504120 (0.406381) \| 1.729670 / 1.541195 (0.188475) \| 1.777647 / 1.468490 (0.309156) \| 0.499532 / 4.584777 (-4.085245) \| 3.002385 / 3.745712 (-0.743327) \| 2.906916 / 5.269862 (-2.362945) \| 1.883321 / 4.565676 (-2.682356) \| 0.057546 / 0.424275 (-0.366730) \| 0.006492 / 0.007607 (-0.001115) \| 0.463605 / 0.226044 (0.237560) \| 4.620215 / 2.268929 (2.351287) \| 2.399021 / 55.444624 (-53.045603) \| 2.182962 / 6.876477 (-4.693514) \| 2.357344 / 2.142072 (0.215272) \| 0.583946 / 4.805227 (-4.221282) \| 0.124644 / 6.500664 (-6.376021) \| 0.060831 / 0.075469 (-0.014638) \|\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) \| 1.276412 / 1.841788 (-0.565375) \| 18.462522 / 8.074308 (10.388214) \| 13.877375 / 10.191392 (3.685983) \| 0.150584 / 0.680424 (-0.529840) \| 0.016675 / 0.534201 (-0.517526) \| 0.331711 / 0.579283 (-0.247573) \| 0.366659 / 0.434364 (-0.067705) \| 0.396400 / 0.540337 (-0.143938) \| 0.555418 / 1.386936 (-0.831518) \|\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) \| 0.005995 / 0.011353 (-0.005358) \| 0.003610 / 0.011008 (-0.007399) \| 0.061802 / 0.038508 (0.023294) \| 0.059265 / 0.023109 (0.036156) \| 0.392628 / 0.275898 (0.116730) \| 0.413143 / 0.323480 (0.089663) \| 0.004687 / 0.007986 (-0.003299) \| 0.002843 / 0.004328 (-0.001486) \| 0.061932 / 0.004250 (0.057682) \| 0.049466 / 0.037052 (0.012413) \| 0.402718 / 0.258489 (0.144229) \| 0.415039 / 0.293841 (0.121198) \| 0.027352 / 0.128546 (-0.101194) \| 0.007965 / 0.075646 (-0.067682) \| 0.067456 / 0.419271 (-0.351815) \| 0.042336 / 0.043533 (-0.001196) \| 0.405543 / 0.255139 (0.150404) \| 0.403209 / 0.283200 (0.120010) \| 0.021459 / 0.141683 (-0.120224) \| 1.442861 / 1.452155 (-0.009293) \| 1.491213 / 1.492716 (-0.001503) \|\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) \| 0.248225 / 0.018006 (0.230219) \| 0.434174 / 0.000490 (0.433684) \| 0.001973 / 0.000200 (0.001773) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025475 / 0.037411 (-0.011936) \| 0.077865 / 0.014526 (0.063339) \| 0.086980 / 0.176557 (-0.089577) \| 0.143682 / 0.737135 (-0.593453) \| 0.088634 / 0.296338 (-0.207705) \|\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) \| 0.417591 / 0.215209 (0.202382) \| 4.168700 / 2.077655 (2.091045) \| 2.335743 / 1.504120 (0.831623) \| 2.208174 / 1.541195 (0.666980) \| 2.256658 / 1.468490 (0.788168) \| 0.503164 / 4.584777 (-4.081613) \| 3.026667 / 3.745712 (-0.719045) \| 4.496675 / 5.269862 (-0.773187) \| 2.741049 / 4.565676 (-1.824628) \| 0.057781 / 0.424275 (-0.366494) \| 0.006810 / 0.007607 (-0.000797) \| 0.490803 / 0.226044 (0.264759) \| 4.914369 / 2.268929 (2.645441) \| 2.594250 / 55.444624 (-52.850375) \| 2.274552 / 6.876477 (-4.601925) \| 2.397529 / 2.142072 (0.255456) \| 0.593008 / 4.805227 (-4.212220) \| 0.126194 / 6.500664 (-6.374470) \| 0.062261 / 0.075469 (-0.013208) \|\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) \| 1.357561 / 1.841788 (-0.484227) \| 18.622995 / 8.074308 (10.548687) \| 14.142569 / 10.191392 (3.951177) \| 0.146527 / 0.680424 (-0.533897) \| 0.016863 / 0.534201 (-0.517338) \| 0.336219 / 0.579283 (-0.243064) \| 0.348650 / 0.434364 (-0.085714) \| 0.385958 / 0.540337 (-0.154380) \| 0.517958 / 1.386936 (-0.868978) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3da7a5a7d0d0415476ecebb0458e7c60df24445 \"CML watermark\")\n" ]
1,819,717,542	6,066	AttributeError: '_tqdm_cls' object has no attribute '_lock'	closed	2023-07-25T07:24:36	2023-07-26T10:56:25	2023-07-26T10:56:24	https://github.com/huggingface/datasets/issues/6066	null	codingl2k1	false	[ "Hi ! I opened https://github.com/huggingface/datasets/pull/6067 to add the missing `_lock`\r\n\r\nWe'll do a patch release soon, but feel free to install `datasets` from source in the meantime", "I have tested the latest main, it does not work.\r\n\r\nI add more logs to reproduce this issue, it looks like a multi threading bug:\r\n\r\n```python\r\n@contextmanager\r\ndef ensure_lock(tqdm_class, lock_name=\"\"):\r\n \"\"\"get (create if necessary) and then restore `tqdm_class`'s lock\"\"\"\r\n import os\r\n import threading\r\n print(os.getpid(), threading.get_ident(), \"ensure_lock\", tqdm_class, lock_name)\r\n old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock\r\n lock = old_lock or tqdm_class.get_lock() # maybe create a new lock\r\n lock = getattr(lock, lock_name, lock) # maybe subtype\r\n tqdm_class.set_lock(lock)\r\n print(os.getpid(), threading.get_ident(), \"set_lock\")\r\n yield lock\r\n if old_lock is None:\r\n print(os.getpid(), threading.get_ident(), \"del tqdm_class\")\r\n del tqdm_class._lock\r\n else:\r\n tqdm_class.set_lock(old_lock)\r\n```\r\noutput\r\n```\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11568967680 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11568967680 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11568967680 del tqdm_class\r\n```\r\n\r\nThread `11638370304` del the _lock from tqdm_class first, then thread `11568967680` del _lock failed.", "Maybe it is a bug of tqdm? I think simply use `try ... except AttributeError ...` wraps `del tqdm_class._lock` should work.", "Yes it looks like a bug on their end indeed, do you want to open a PR on tqdm ?\r\n\r\nLet me see if I can find a workaround in the meantime", "I opened https://github.com/huggingface/datasets/pull/6068 if you want to try it out", "> I opened #6068 if you want to try it out\r\n\r\nThis fix works! Thanks.", "Awesome ! closing this then :)\r\nWe'll do a patch release today or tomorrow" ]
1,819,334,932	6,065	Add column type guessing from map return function	closed	2023-07-25T00:34:17	2023-07-26T15:13:45	2023-07-26T15:13:44	https://github.com/huggingface/datasets/pull/6065	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6065", "html_url": "https://github.com/huggingface/datasets/pull/6065", "diff_url": "https://github.com/huggingface/datasets/pull/6065.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6065.patch", "merged_at": null }	piercefreeman	true	[ "Thanks for working on this. However, having thought about this issue a bit more, supporting this doesn't seem like a good idea - it's better to be explicit than implicit, according to the Zen of Python 🙂. Also, I don't think many users would use this, so this raises the question of whether this is something we want to maintain.\r\n\r\ncc @lhoestq for the 2nd opinion", "@mariosasko I was going to quote the Zen of Python in the other direction :) To me, this actually is much more explicit than the current behavior of guessing pyarrow types based on the raw dictionary return values. Explicit typehinting is increasingly the de facto way to deal with this dynamic type serialization - plus it feels like a clearer fit to me than separating out the mapper function from the feature column definition in the call to the actual `.map()`. Another benefit is providing typehinting support for clients that use mypy or other static typecheckers to detect return mismatches.\r\n\r\nBut will leave it to you and @lhoestq to see if it's something you'd like in core versus a support package.", "I meant that explicitly specifying the target features (the `features` param) is cleaner (easier to track) than relying on type hints.", "Passing features= to `map()` is richer and more explicit. Also I don't think users would guess that such API exist.\r\n\r\nOther libraries like dask also infer the type from the output or requires the typing to be specified using the `meta` argument", "Point about discoverability is a fair one, would certainly need some docs around it. All good! Will close this out and keep in our extension utilities." ]
1,818,703,725	6,064	set dev version	closed	2023-07-24T15:56:00	2023-07-24T16:05:19	2023-07-24T15:56:10	https://github.com/huggingface/datasets/pull/6064	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6064", "html_url": "https://github.com/huggingface/datasets/pull/6064", "diff_url": "https://github.com/huggingface/datasets/pull/6064.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6064.patch", "merged_at": "2023-07-24T15:56:10" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6064). 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) \| 0.006704 / 0.011353 (-0.004649) \| 0.004208 / 0.011008 (-0.006800) \| 0.085895 / 0.038508 (0.047387) \| 0.079303 / 0.023109 (0.056193) \| 0.353430 / 0.275898 (0.077532) \| 0.390814 / 0.323480 (0.067334) \| 0.006565 / 0.007986 (-0.001420) \| 0.003588 / 0.004328 (-0.000740) \| 0.065249 / 0.004250 (0.060999) \| 0.059772 / 0.037052 (0.022720) \| 0.356315 / 0.258489 (0.097826) \| 0.404812 / 0.293841 (0.110971) \| 0.031127 / 0.128546 (-0.097419) \| 0.008656 / 0.075646 (-0.066991) \| 0.288734 / 0.419271 (-0.130537) \| 0.053157 / 0.043533 (0.009625) \| 0.354651 / 0.255139 (0.099512) \| 0.370590 / 0.283200 (0.087391) \| 0.024944 / 0.141683 (-0.116738) \| 1.472393 / 1.452155 (0.020238) \| 1.548946 / 1.492716 (0.056229) \|\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) \| 0.223430 / 0.018006 (0.205424) \| 0.567359 / 0.000490 (0.566870) \| 0.006744 / 0.000200 (0.006544) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030174 / 0.037411 (-0.007237) \| 0.084865 / 0.014526 (0.070339) \| 0.098986 / 0.176557 (-0.077571) \| 0.161458 / 0.737135 (-0.575678) \| 0.099198 / 0.296338 (-0.197141) \|\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) \| 0.404324 / 0.215209 (0.189115) \| 4.043744 / 2.077655 (1.966090) \| 1.972834 / 1.504120 (0.468714) \| 1.801634 / 1.541195 (0.260439) \| 1.891198 / 1.468490 (0.422708) \| 0.488511 / 4.584777 (-4.096266) \| 3.566890 / 3.745712 (-0.178823) \| 3.369415 / 5.269862 (-1.900447) \| 2.054995 / 4.565676 (-2.510682) \| 0.057225 / 0.424275 (-0.367050) \| 0.007360 / 0.007607 (-0.000247) \| 0.471813 / 0.226044 (0.245769) \| 4.734397 / 2.268929 (2.465468) \| 2.526585 / 55.444624 (-52.918039) \| 2.230535 / 6.876477 (-4.645942) \| 2.434403 / 2.142072 (0.292330) \| 0.630090 / 4.805227 (-4.175137) \| 0.138544 / 6.500664 (-6.362120) \| 0.060099 / 0.075469 (-0.015370) \|\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) \| 1.260951 / 1.841788 (-0.580837) \| 20.051513 / 8.074308 (11.977204) \| 14.675938 / 10.191392 (4.484546) \| 0.169535 / 0.680424 (-0.510889) \| 0.018574 / 0.534201 (-0.515627) \| 0.394255 / 0.579283 (-0.185028) \| 0.412713 / 0.434364 (-0.021651) \| 0.475891 / 0.540337 (-0.064446) \| 0.658223 / 1.386936 (-0.728713) \|\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) \| 0.006969 / 0.011353 (-0.004384) \| 0.004417 / 0.011008 (-0.006591) \| 0.064399 / 0.038508 (0.025891) \| 0.082928 / 0.023109 (0.059819) \| 0.402285 / 0.275898 (0.126387) \| 0.440032 / 0.323480 (0.116552) \| 0.005896 / 0.007986 (-0.002090) \| 0.003580 / 0.004328 (-0.000749) \| 0.065340 / 0.004250 (0.061090) \| 0.060363 / 0.037052 (0.023311) \| 0.417413 / 0.258489 (0.158924) \| 0.448527 / 0.293841 (0.154686) \| 0.032238 / 0.128546 (-0.096308) \| 0.008820 / 0.075646 (-0.066826) \| 0.071516 / 0.419271 (-0.347755) \| 0.050614 / 0.043533 (0.007081) \| 0.406565 / 0.255139 (0.151426) \| 0.422527 / 0.283200 (0.139328) \| 0.025866 / 0.141683 (-0.115817) \| 1.512256 / 1.452155 (0.060101) \| 1.568433 / 1.492716 (0.075717) \|\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) \| 0.266521 / 0.018006 (0.248515) \| 0.564524 / 0.000490 (0.564034) \| 0.005236 / 0.000200 (0.005036) \| 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) \| 0.031998 / 0.037411 (-0.005413) \| 0.090754 / 0.014526 (0.076229) \| 0.105954 / 0.176557 (-0.070602) \| 0.164506 / 0.737135 (-0.572629) \| 0.108792 / 0.296338 (-0.187546) \|\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) \| 0.422044 / 0.215209 (0.206835) \| 4.204449 / 2.077655 (2.126795) \| 2.232060 / 1.504120 (0.727940) \| 2.060389 / 1.541195 (0.519194) \| 2.152723 / 1.468490 (0.684233) \| 0.488456 / 4.584777 (-4.096321) \| 3.591102 / 3.745712 (-0.154611) \| 5.250401 / 5.269862 (-0.019461) \| 3.060259 / 4.565676 (-1.505417) \| 0.057558 / 0.424275 (-0.366717) \| 0.007881 / 0.007607 (0.000274) \| 0.508631 / 0.226044 (0.282587) \| 5.064857 / 2.268929 (2.795928) \| 2.719068 / 55.444624 (-52.725556) \| 2.389992 / 6.876477 (-4.486485) \| 2.595073 / 2.142072 (0.453000) \| 0.590179 / 4.805227 (-4.215048) \| 0.136149 / 6.500664 (-6.364515) \| 0.062546 / 0.075469 (-0.012923) \|\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) \| 1.369252 / 1.841788 (-0.472535) \| 20.637580 / 8.074308 (12.563272) \| 14.217129 / 10.191392 (4.025737) \| 0.195464 / 0.680424 (-0.484960) \| 0.018452 / 0.534201 (-0.515749) \| 0.397044 / 0.579283 (-0.182239) \| 0.401127 / 0.434364 (-0.033237) \| 0.465033 / 0.540337 (-0.075305) \| 0.613484 / 1.386936 (-0.773452) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9f1651128e50e7887f5e8eaaf6b55fe4cd84fdc \"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) \| 0.006793 / 0.011353 (-0.004559) \| 0.004374 / 0.011008 (-0.006635) \| 0.084958 / 0.038508 (0.046450) \| 0.080440 / 0.023109 (0.057331) \| 0.317951 / 0.275898 (0.042053) \| 0.376133 / 0.323480 (0.052653) \| 0.005775 / 0.007986 (-0.002211) \| 0.003644 / 0.004328 (-0.000684) \| 0.064823 / 0.004250 (0.060573) \| 0.059442 / 0.037052 (0.022390) \| 0.319636 / 0.258489 (0.061147) \| 0.389668 / 0.293841 (0.095827) \| 0.031181 / 0.128546 (-0.097365) \| 0.008725 / 0.075646 (-0.066921) \| 0.288514 / 0.419271 (-0.130757) \| 0.053466 / 0.043533 (0.009933) \| 0.323131 / 0.255139 (0.067992) \| 0.345276 / 0.283200 (0.062076) \| 0.025046 / 0.141683 (-0.116637) \| 1.491659 / 1.452155 (0.039504) \| 1.562105 / 1.492716 (0.069389) \|\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) \| 0.286325 / 0.018006 (0.268319) \| 0.578021 / 0.000490 (0.577531) \| 0.007240 / 0.000200 (0.007040) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030163 / 0.037411 (-0.007248) \| 0.082100 / 0.014526 (0.067574) \| 0.098331 / 0.176557 (-0.078225) \| 0.160517 / 0.737135 (-0.576618) \| 0.098479 / 0.296338 (-0.197859) \|\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) \| 0.401782 / 0.215209 (0.186573) \| 4.006330 / 2.077655 (1.928675) \| 2.033841 / 1.504120 (0.529721) \| 1.853248 / 1.541195 (0.312053) \| 1.980046 / 1.468490 (0.511556) \| 0.480636 / 4.584777 (-4.104141) \| 3.684482 / 3.745712 (-0.061231) \| 5.601940 / 5.269862 (0.332079) \| 3.369683 / 4.565676 (-1.195993) \| 0.057105 / 0.424275 (-0.367170) \| 0.007462 / 0.007607 (-0.000145) \| 0.474860 / 0.226044 (0.248815) \| 4.749624 / 2.268929 (2.480695) \| 2.492084 / 55.444624 (-52.952540) \| 2.157985 / 6.876477 (-4.718491) \| 2.420997 / 2.142072 (0.278925) \| 0.574718 / 4.805227 (-4.230509) \| 0.134672 / 6.500664 (-6.365992) \| 0.061677 / 0.075469 (-0.013792) \|\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) \| 1.284151 / 1.841788 (-0.557637) \| 20.186823 / 8.074308 (12.112515) \| 14.247024 / 10.191392 (4.055632) \| 0.171606 / 0.680424 (-0.508818) \| 0.018619 / 0.534201 (-0.515582) \| 0.394156 / 0.579283 (-0.185127) \| 0.424684 / 0.434364 (-0.009679) \| 0.476056 / 0.540337 (-0.064281) \| 0.668751 / 1.386936 (-0.718185) \|\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) \| 0.006807 / 0.011353 (-0.004546) \| 0.004142 / 0.011008 (-0.006867) \| 0.065503 / 0.038508 (0.026995) \| 0.083232 / 0.023109 (0.060122) \| 0.378278 / 0.275898 (0.102380) \| 0.410191 / 0.323480 (0.086711) \| 0.005660 / 0.007986 (-0.002326) \| 0.003486 / 0.004328 (-0.000842) \| 0.066109 / 0.004250 (0.061859) \| 0.059654 / 0.037052 (0.022601) \| 0.375965 / 0.258489 (0.117476) \| 0.420046 / 0.293841 (0.126205) \| 0.031587 / 0.128546 (-0.096959) \| 0.008693 / 0.075646 (-0.066953) \| 0.071121 / 0.419271 (-0.348151) \| 0.049468 / 0.043533 (0.005935) \| 0.373785 / 0.255139 (0.118646) \| 0.395577 / 0.283200 (0.112377) \| 0.024138 / 0.141683 (-0.117545) \| 1.465451 / 1.452155 (0.013297) \| 1.547565 / 1.492716 (0.054849) \|\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) \| 0.325241 / 0.018006 (0.307234) \| 0.532415 / 0.000490 (0.531925) \| 0.004755 / 0.000200 (0.004555) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033472 / 0.037411 (-0.003939) \| 0.090574 / 0.014526 (0.076048) \| 0.106712 / 0.176557 (-0.069845) \| 0.164353 / 0.737135 (-0.572783) \| 0.109344 / 0.296338 (-0.186994) \|\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) \| 0.420161 / 0.215209 (0.204952) \| 4.192334 / 2.077655 (2.114679) \| 2.178181 / 1.504120 (0.674061) \| 2.017405 / 1.541195 (0.476211) \| 2.182783 / 1.468490 (0.714293) \| 0.484037 / 4.584777 (-4.100740) \| 3.641911 / 3.745712 (-0.103801) \| 5.543874 / 5.269862 (0.274013) \| 3.440084 / 4.565676 (-1.125593) \| 0.056662 / 0.424275 (-0.367614) \| 0.007773 / 0.007607 (0.000166) \| 0.498357 / 0.226044 (0.272313) \| 4.951315 / 2.268929 (2.682386) \| 2.656732 / 55.444624 (-52.787892) \| 2.370566 / 6.876477 (-4.505910) \| 2.682289 / 2.142072 (0.540217) \| 0.598479 / 4.805227 (-4.206749) \| 0.151546 / 6.500664 (-6.349118) \| 0.063278 / 0.075469 (-0.012191) \|\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) \| 1.385897 / 1.841788 (-0.455891) \| 20.961851 / 8.074308 (12.887543) \| 14.465688 / 10.191392 (4.274296) \| 0.166156 / 0.680424 (-0.514268) \| 0.018848 / 0.534201 (-0.515353) \| 0.401712 / 0.579283 (-0.177571) \| 0.416674 / 0.434364 (-0.017690) \| 0.471834 / 0.540337 (-0.068503) \| 0.622463 / 1.386936 (-0.764473) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e3ab9bc6ae8cc42f7e7d01afbd2637d51c3faf6 \"CML watermark\")\n" ]
1,818,679,485	6,063	Release: 2.14.0	closed	2023-07-24T15:41:19	2023-07-24T16:05:16	2023-07-24T15:47:51	https://github.com/huggingface/datasets/pull/6063	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6063", "html_url": "https://github.com/huggingface/datasets/pull/6063", "diff_url": "https://github.com/huggingface/datasets/pull/6063.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6063.patch", "merged_at": "2023-07-24T15:47:51" }	lhoestq	true	[ "_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) \| 0.007703 / 0.011353 (-0.003650) \| 0.004699 / 0.011008 (-0.006309) \| 0.090195 / 0.038508 (0.051687) \| 0.119165 / 0.023109 (0.096056) \| 0.361435 / 0.275898 (0.085537) \| 0.404429 / 0.323480 (0.080949) \| 0.006172 / 0.007986 (-0.001814) \| 0.003932 / 0.004328 (-0.000397) \| 0.068384 / 0.004250 (0.064133) \| 0.066730 / 0.037052 (0.029678) \| 0.360978 / 0.258489 (0.102489) \| 0.401301 / 0.293841 (0.107460) \| 0.032836 / 0.128546 (-0.095710) \| 0.010821 / 0.075646 (-0.064825) \| 0.294526 / 0.419271 (-0.124745) \| 0.068751 / 0.043533 (0.025218) \| 0.368427 / 0.255139 (0.113288) \| 0.376969 / 0.283200 (0.093770) \| 0.040538 / 0.141683 (-0.101145) \| 1.509966 / 1.452155 (0.057811) \| 1.564885 / 1.492716 (0.072169) \|\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) \| 0.292243 / 0.018006 (0.274237) \| 0.662067 / 0.000490 (0.661577) \| 0.004966 / 0.000200 (0.004766) \| 0.000103 / 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) \| 0.029050 / 0.037411 (-0.008361) \| 0.099880 / 0.014526 (0.085354) \| 0.109277 / 0.176557 (-0.067280) \| 0.167877 / 0.737135 (-0.569258) \| 0.110770 / 0.296338 (-0.185569) \|\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) \| 0.395742 / 0.215209 (0.180533) \| 3.944152 / 2.077655 (1.866498) \| 1.875295 / 1.504120 (0.371175) \| 1.705088 / 1.541195 (0.163893) \| 1.884443 / 1.468490 (0.415953) \| 0.497243 / 4.584777 (-4.087534) \| 3.749287 / 3.745712 (0.003575) \| 4.418826 / 5.269862 (-0.851035) \| 2.481149 / 4.565676 (-2.084528) \| 0.058260 / 0.424275 (-0.366015) \| 0.007744 / 0.007607 (0.000137) \| 0.472531 / 0.226044 (0.246486) \| 4.716022 / 2.268929 (2.447094) \| 2.480446 / 55.444624 (-52.964179) \| 2.163098 / 6.876477 (-4.713379) \| 2.217555 / 2.142072 (0.075482) \| 0.601965 / 4.805227 (-4.203262) \| 0.139364 / 6.500664 (-6.361301) \| 0.067097 / 0.075469 (-0.008372) \|\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) \| 1.330537 / 1.841788 (-0.511251) \| 22.176270 / 8.074308 (14.101962) \| 16.224981 / 10.191392 (6.033589) \| 0.173708 / 0.680424 (-0.506715) \| 0.019402 / 0.534201 (-0.514799) \| 0.401994 / 0.579283 (-0.177289) \| 0.432597 / 0.434364 (-0.001767) \| 0.489933 / 0.540337 (-0.050404) \| 0.672334 / 1.386936 (-0.714602) \|\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) \| 0.008622 / 0.011353 (-0.002731) \| 0.004609 / 0.011008 (-0.006399) \| 0.067791 / 0.038508 (0.029283) \| 0.112770 / 0.023109 (0.089661) \| 0.380939 / 0.275898 (0.105041) \| 0.416940 / 0.323480 (0.093460) \| 0.006170 / 0.007986 (-0.001815) \| 0.003876 / 0.004328 (-0.000452) \| 0.066227 / 0.004250 (0.061976) \| 0.073132 / 0.037052 (0.036080) \| 0.390120 / 0.258489 (0.131631) \| 0.420893 / 0.293841 (0.127052) \| 0.033235 / 0.128546 (-0.095311) \| 0.009659 / 0.075646 (-0.065987) \| 0.072668 / 0.419271 (-0.346604) \| 0.051333 / 0.043533 (0.007801) \| 0.393828 / 0.255139 (0.138689) \| 0.412376 / 0.283200 (0.129176) \| 0.027760 / 0.141683 (-0.113923) \| 1.494369 / 1.452155 (0.042214) \| 1.592862 / 1.492716 (0.100145) \|\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) \| 0.345376 / 0.018006 (0.327369) \| 0.609399 / 0.000490 (0.608909) \| 0.000546 / 0.000200 (0.000346) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035601 / 0.037411 (-0.001810) \| 0.106527 / 0.014526 (0.092001) \| 0.114388 / 0.176557 (-0.062168) \| 0.175607 / 0.737135 (-0.561529) \| 0.113009 / 0.296338 (-0.183329) \|\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) \| 0.417237 / 0.215209 (0.202028) \| 4.136329 / 2.077655 (2.058675) \| 2.147134 / 1.504120 (0.643014) \| 2.009501 / 1.541195 (0.468306) \| 2.139499 / 1.468490 (0.671009) \| 0.491593 / 4.584777 (-4.093184) \| 3.766734 / 3.745712 (0.021022) \| 5.652446 / 5.269862 (0.382585) \| 3.021654 / 4.565676 (-1.544022) \| 0.058458 / 0.424275 (-0.365817) \| 0.008271 / 0.007607 (0.000664) \| 0.488229 / 0.226044 (0.262184) \| 4.861343 / 2.268929 (2.592415) \| 2.694142 / 55.444624 (-52.750482) \| 2.489130 / 6.876477 (-4.387346) \| 2.679376 / 2.142072 (0.537304) \| 0.589959 / 4.805227 (-4.215268) \| 0.137939 / 6.500664 (-6.362725) \| 0.066833 / 0.075469 (-0.008636) \|\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) \| 1.444871 / 1.841788 (-0.396916) \| 22.874961 / 8.074308 (14.800653) \| 15.842130 / 10.191392 (5.650738) \| 0.175529 / 0.680424 (-0.504895) \| 0.019024 / 0.534201 (-0.515177) \| 0.406551 / 0.579283 (-0.172732) \| 0.430335 / 0.434364 (-0.004029) \| 0.475750 / 0.540337 (-0.064587) \| 0.624836 / 1.386936 (-0.762100) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dabbb7467f49fd22ae1a43cc577eb43008d63ee8 \"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) \| 0.006068 / 0.011353 (-0.005285) \| 0.003694 / 0.011008 (-0.007315) \| 0.080321 / 0.038508 (0.041813) \| 0.061738 / 0.023109 (0.038629) \| 0.329675 / 0.275898 (0.053777) \| 0.364008 / 0.323480 (0.040528) \| 0.004722 / 0.007986 (-0.003263) \| 0.002857 / 0.004328 (-0.001471) \| 0.062447 / 0.004250 (0.058197) \| 0.047006 / 0.037052 (0.009953) \| 0.335730 / 0.258489 (0.077241) \| 0.373047 / 0.293841 (0.079206) \| 0.027273 / 0.128546 (-0.101274) \| 0.007979 / 0.075646 (-0.067667) \| 0.262693 / 0.419271 (-0.156579) \| 0.045416 / 0.043533 (0.001883) \| 0.340774 / 0.255139 (0.085635) \| 0.359667 / 0.283200 (0.076468) \| 0.020848 / 0.141683 (-0.120835) \| 1.450110 / 1.452155 (-0.002045) \| 1.489511 / 1.492716 (-0.003206) \|\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) \| 0.185090 / 0.018006 (0.167084) \| 0.429823 / 0.000490 (0.429334) \| 0.000703 / 0.000200 (0.000503) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024398 / 0.037411 (-0.013013) \| 0.072983 / 0.014526 (0.058457) \| 0.084012 / 0.176557 (-0.092544) \| 0.146160 / 0.737135 (-0.590975) \| 0.084068 / 0.296338 (-0.212270) \|\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) \| 0.432204 / 0.215209 (0.216995) \| 4.320593 / 2.077655 (2.242939) \| 2.261260 / 1.504120 (0.757140) \| 2.087148 / 1.541195 (0.545954) \| 2.144520 / 1.468490 (0.676029) \| 0.501477 / 4.584777 (-4.083300) \| 3.119557 / 3.745712 (-0.626156) \| 3.572527 / 5.269862 (-1.697335) \| 2.208836 / 4.565676 (-2.356840) \| 0.057232 / 0.424275 (-0.367043) \| 0.006494 / 0.007607 (-0.001113) \| 0.508135 / 0.226044 (0.282091) \| 5.090416 / 2.268929 (2.821488) \| 2.739800 / 55.444624 (-52.704824) \| 2.416105 / 6.876477 (-4.460372) \| 2.616037 / 2.142072 (0.473965) \| 0.583730 / 4.805227 (-4.221497) \| 0.124312 / 6.500664 (-6.376352) \| 0.060760 / 0.075469 (-0.014709) \|\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) \| 1.256097 / 1.841788 (-0.585691) \| 18.326073 / 8.074308 (10.251765) \| 13.859173 / 10.191392 (3.667781) \| 0.143639 / 0.680424 (-0.536785) \| 0.016649 / 0.534201 (-0.517552) \| 0.331671 / 0.579283 (-0.247612) \| 0.365370 / 0.434364 (-0.068994) \| 0.392753 / 0.540337 (-0.147584) \| 0.549302 / 1.386936 (-0.837634) \|\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) \| 0.006054 / 0.011353 (-0.005299) \| 0.003641 / 0.011008 (-0.007367) \| 0.063109 / 0.038508 (0.024601) \| 0.060482 / 0.023109 (0.037372) \| 0.404047 / 0.275898 (0.128149) \| 0.425436 / 0.323480 (0.101956) \| 0.004603 / 0.007986 (-0.003382) \| 0.002905 / 0.004328 (-0.001423) \| 0.063207 / 0.004250 (0.058956) \| 0.048248 / 0.037052 (0.011196) \| 0.404325 / 0.258489 (0.145836) \| 0.432652 / 0.293841 (0.138811) \| 0.027630 / 0.128546 (-0.100916) \| 0.008062 / 0.075646 (-0.067584) \| 0.068367 / 0.419271 (-0.350905) \| 0.042169 / 0.043533 (-0.001364) \| 0.384903 / 0.255139 (0.129764) \| 0.418617 / 0.283200 (0.135417) \| 0.020767 / 0.141683 (-0.120915) \| 1.463606 / 1.452155 (0.011451) \| 1.512081 / 1.492716 (0.019365) \|\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) \| 0.229601 / 0.018006 (0.211594) \| 0.417878 / 0.000490 (0.417388) \| 0.000373 / 0.000200 (0.000173) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026354 / 0.037411 (-0.011057) \| 0.078100 / 0.014526 (0.063574) \| 0.087122 / 0.176557 (-0.089434) \| 0.140017 / 0.737135 (-0.597118) \| 0.089923 / 0.296338 (-0.206415) \|\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) \| 0.422405 / 0.215209 (0.207196) \| 4.237383 / 2.077655 (2.159728) \| 2.161104 / 1.504120 (0.656984) \| 1.982337 / 1.541195 (0.441142) \| 2.050216 / 1.468490 (0.581726) \| 0.499281 / 4.584777 (-4.085496) \| 2.996953 / 3.745712 (-0.748759) \| 5.027069 / 5.269862 (-0.242792) \| 2.804703 / 4.565676 (-1.760974) \| 0.057707 / 0.424275 (-0.366568) \| 0.006809 / 0.007607 (-0.000798) \| 0.495196 / 0.226044 (0.269152) \| 4.946593 / 2.268929 (2.677665) \| 2.598965 / 55.444624 (-52.845660) \| 2.349871 / 6.876477 (-4.526606) \| 2.451665 / 2.142072 (0.309593) \| 0.592314 / 4.805227 (-4.212913) \| 0.125685 / 6.500664 (-6.374979) \| 0.063252 / 0.075469 (-0.012217) \|\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) \| 1.325422 / 1.841788 (-0.516366) \| 18.521059 / 8.074308 (10.446751) \| 14.046757 / 10.191392 (3.855365) \| 0.133009 / 0.680424 (-0.547415) \| 0.017097 / 0.534201 (-0.517104) \| 0.339804 / 0.579283 (-0.239479) \| 0.345464 / 0.434364 (-0.088900) \| 0.387623 / 0.540337 (-0.152714) \| 0.519880 / 1.386936 (-0.867056) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"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) \| 0.008671 / 0.011353 (-0.002682) \| 0.004681 / 0.011008 (-0.006327) \| 0.107517 / 0.038508 (0.069008) \| 0.078846 / 0.023109 (0.055737) \| 0.449745 / 0.275898 (0.173847) \| 0.504075 / 0.323480 (0.180596) \| 0.005837 / 0.007986 (-0.002148) \| 0.004031 / 0.004328 (-0.000297) \| 0.092021 / 0.004250 (0.087771) \| 0.065954 / 0.037052 (0.028902) \| 0.442082 / 0.258489 (0.183593) \| 0.529349 / 0.293841 (0.235508) \| 0.052527 / 0.128546 (-0.076019) \| 0.013854 / 0.075646 (-0.061792) \| 0.367315 / 0.419271 (-0.051956) \| 0.068731 / 0.043533 (0.025199) \| 0.494733 / 0.255139 (0.239594) \| 0.472801 / 0.283200 (0.189601) \| 0.036791 / 0.141683 (-0.104892) \| 1.877648 / 1.452155 (0.425493) \| 1.928399 / 1.492716 (0.435683) \|\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) \| 0.231910 / 0.018006 (0.213904) \| 0.553464 / 0.000490 (0.552974) \| 0.011915 / 0.000200 (0.011715) \| 0.000378 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028232 / 0.037411 (-0.009179) \| 0.091441 / 0.014526 (0.076916) \| 0.110394 / 0.176557 (-0.066162) \| 0.187638 / 0.737135 (-0.549497) \| 0.111810 / 0.296338 (-0.184529) \|\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) \| 0.599987 / 0.215209 (0.384778) \| 6.008709 / 2.077655 (3.931054) \| 2.518769 / 1.504120 (1.014650) \| 2.197029 / 1.541195 (0.655834) \| 2.217165 / 1.468490 (0.748675) \| 0.894939 / 4.584777 (-3.689837) \| 5.001217 / 3.745712 (1.255505) \| 4.636482 / 5.269862 (-0.633379) \| 3.237613 / 4.565676 (-1.328063) \| 0.104227 / 0.424275 (-0.320048) \| 0.008504 / 0.007607 (0.000897) \| 0.750190 / 0.226044 (0.524145) \| 7.514571 / 2.268929 (5.245642) \| 3.358003 / 55.444624 (-52.086621) \| 2.585649 / 6.876477 (-4.290827) \| 2.731129 / 2.142072 (0.589056) \| 1.088828 / 4.805227 (-3.716400) \| 0.217308 / 6.500664 (-6.283356) \| 0.076410 / 0.075469 (0.000941) \|\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) \| 1.620087 / 1.841788 (-0.221701) \| 23.145743 / 8.074308 (15.071435) \| 20.583403 / 10.191392 (10.392011) \| 0.225467 / 0.680424 (-0.454956) \| 0.029063 / 0.534201 (-0.505138) \| 0.480563 / 0.579283 (-0.098720) \| 0.539083 / 0.434364 (0.104719) \| 0.563787 / 0.540337 (0.023449) \| 0.782902 / 1.386936 (-0.604034) \|\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) \| 0.010113 / 0.011353 (-0.001239) \| 0.004997 / 0.011008 (-0.006011) \| 0.082974 / 0.038508 (0.044466) \| 0.090375 / 0.023109 (0.067266) \| 0.440273 / 0.275898 (0.164375) \| 0.476939 / 0.323480 (0.153459) \| 0.005955 / 0.007986 (-0.002031) \| 0.004375 / 0.004328 (0.000046) \| 0.080459 / 0.004250 (0.076209) \| 0.061787 / 0.037052 (0.024734) \| 0.477211 / 0.258489 (0.218722) \| 0.487164 / 0.293841 (0.193323) \| 0.054198 / 0.128546 (-0.074348) \| 0.013945 / 0.075646 (-0.061701) \| 0.093006 / 0.419271 (-0.326266) \| 0.062685 / 0.043533 (0.019152) \| 0.461373 / 0.255139 (0.206234) \| 0.475766 / 0.283200 (0.192567) \| 0.032059 / 0.141683 (-0.109623) \| 1.857989 / 1.452155 (0.405834) \| 1.837993 / 1.492716 (0.345277) \|\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) \| 0.243048 / 0.018006 (0.225042) \| 0.535850 / 0.000490 (0.535360) \| 0.007204 / 0.000200 (0.007004) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032584 / 0.037411 (-0.004827) \| 0.098151 / 0.014526 (0.083625) \| 0.109691 / 0.176557 (-0.066866) \| 0.172803 / 0.737135 (-0.564333) \| 0.110469 / 0.296338 (-0.185869) \|\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) \| 0.635086 / 0.215209 (0.419877) \| 6.500864 / 2.077655 (4.423210) \| 2.996727 / 1.504120 (1.492607) \| 2.537218 / 1.541195 (0.996023) \| 2.572310 / 1.468490 (1.103820) \| 0.870868 / 4.584777 (-3.713909) \| 4.989744 / 3.745712 (1.244032) \| 4.422174 / 5.269862 (-0.847687) \| 2.935874 / 4.565676 (-1.629803) \| 0.097118 / 0.424275 (-0.327157) \| 0.009360 / 0.007607 (0.001753) \| 0.790447 / 0.226044 (0.564403) \| 7.859519 / 2.268929 (5.590591) \| 3.975616 / 55.444624 (-51.469009) \| 3.018271 / 6.876477 (-3.858206) \| 3.111173 / 2.142072 (0.969101) \| 1.085577 / 4.805227 (-3.719651) \| 0.225719 / 6.500664 (-6.274945) \| 0.080576 / 0.075469 (0.005107) \|\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) \| 1.802284 / 1.841788 (-0.039504) \| 23.487921 / 8.074308 (15.413613) \| 20.595171 / 10.191392 (10.403779) \| 0.196610 / 0.680424 (-0.483814) \| 0.027483 / 0.534201 (-0.506718) \| 0.485840 / 0.579283 (-0.093443) \| 0.542661 / 0.434364 (0.108297) \| 0.580602 / 0.540337 (0.040265) \| 0.768195 / 1.386936 (-0.618741) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n" ]
1,818,341,584	6,062	Improve `Dataset.from_list` docstring	closed	2023-07-24T12:36:38	2023-07-24T14:43:48	2023-07-24T14:34:43	https://github.com/huggingface/datasets/pull/6062	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6062", "html_url": "https://github.com/huggingface/datasets/pull/6062", "diff_url": "https://github.com/huggingface/datasets/pull/6062.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6062.patch", "merged_at": "2023-07-24T14:34:43" }	mariosasko	true	[ "_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) \| 0.008340 / 0.011353 (-0.003013) \| 0.005053 / 0.011008 (-0.005955) \| 0.103294 / 0.038508 (0.064786) \| 0.069417 / 0.023109 (0.046308) \| 0.436922 / 0.275898 (0.161024) \| 0.461348 / 0.323480 (0.137868) \| 0.006030 / 0.007986 (-0.001955) \| 0.003727 / 0.004328 (-0.000601) \| 0.076384 / 0.004250 (0.072134) \| 0.056742 / 0.037052 (0.019689) \| 0.439996 / 0.258489 (0.181507) \| 0.469417 / 0.293841 (0.175577) \| 0.044343 / 0.128546 (-0.084203) \| 0.012634 / 0.075646 (-0.063013) \| 0.359746 / 0.419271 (-0.059525) \| 0.064842 / 0.043533 (0.021309) \| 0.425960 / 0.255139 (0.170821) \| 0.458568 / 0.283200 (0.175368) \| 0.039802 / 0.141683 (-0.101881) \| 1.687320 / 1.452155 (0.235165) \| 1.806212 / 1.492716 (0.313496) \|\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) \| 0.255484 / 0.018006 (0.237478) \| 0.563039 / 0.000490 (0.562549) \| 0.000445 / 0.000200 (0.000245) \| 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) \| 0.027511 / 0.037411 (-0.009900) \| 0.089185 / 0.014526 (0.074659) \| 0.098397 / 0.176557 (-0.078160) \| 0.163897 / 0.737135 (-0.573238) \| 0.099905 / 0.296338 (-0.196434) \|\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) \| 0.612737 / 0.215209 (0.397528) \| 6.209948 / 2.077655 (4.132294) \| 2.756060 / 1.504120 (1.251940) \| 2.402115 / 1.541195 (0.860920) \| 2.422665 / 1.468490 (0.954175) \| 0.834799 / 4.584777 (-3.749977) \| 5.251699 / 3.745712 (1.505986) \| 5.554141 / 5.269862 (0.284280) \| 3.254699 / 4.565676 (-1.310977) \| 0.095697 / 0.424275 (-0.328578) \| 0.009406 / 0.007607 (0.001799) \| 0.729025 / 0.226044 (0.502980) \| 7.195521 / 2.268929 (4.926593) \| 3.360264 / 55.444624 (-52.084361) \| 2.696764 / 6.876477 (-4.179713) \| 2.702796 / 2.142072 (0.560724) \| 0.974420 / 4.805227 (-3.830808) \| 0.195215 / 6.500664 (-6.305450) \| 0.069754 / 0.075469 (-0.005715) \|\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) \| 1.553458 / 1.841788 (-0.288330) \| 21.972436 / 8.074308 (13.898128) \| 20.027392 / 10.191392 (9.836000) \| 0.216950 / 0.680424 (-0.463474) \| 0.032196 / 0.534201 (-0.502005) \| 0.449884 / 0.579283 (-0.129399) \| 0.586213 / 0.434364 (0.151849) \| 0.537227 / 0.540337 (-0.003111) \| 0.751022 / 1.386936 (-0.635914) \|\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) \| 0.007859 / 0.011353 (-0.003493) \| 0.004762 / 0.011008 (-0.006246) \| 0.086023 / 0.038508 (0.047515) \| 0.069218 / 0.023109 (0.046109) \| 0.449312 / 0.275898 (0.173414) \| 0.481687 / 0.323480 (0.158207) \| 0.006318 / 0.007986 (-0.001668) \| 0.004063 / 0.004328 (-0.000266) \| 0.076917 / 0.004250 (0.072667) \| 0.058034 / 0.037052 (0.020981) \| 0.474265 / 0.258489 (0.215775) \| 0.497736 / 0.293841 (0.203895) \| 0.044587 / 0.128546 (-0.083959) \| 0.013880 / 0.075646 (-0.061766) \| 0.089233 / 0.419271 (-0.330038) \| 0.058760 / 0.043533 (0.015227) \| 0.439515 / 0.255139 (0.184376) \| 0.473246 / 0.283200 (0.190047) \| 0.042968 / 0.141683 (-0.098715) \| 1.802647 / 1.452155 (0.350493) \| 1.778563 / 1.492716 (0.285847) \|\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) \| 0.343741 / 0.018006 (0.325735) \| 0.567409 / 0.000490 (0.566919) \| 0.029727 / 0.000200 (0.029527) \| 0.000147 / 0.000054 (0.000092) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031021 / 0.037411 (-0.006390) \| 0.096659 / 0.014526 (0.082133) \| 0.103341 / 0.176557 (-0.073215) \| 0.169893 / 0.737135 (-0.567242) \| 0.103280 / 0.296338 (-0.193058) \|\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) \| 0.584724 / 0.215209 (0.369515) \| 5.792596 / 2.077655 (3.714941) \| 2.683133 / 1.504120 (1.179013) \| 2.367837 / 1.541195 (0.826643) \| 2.378567 / 1.468490 (0.910076) \| 0.803427 / 4.584777 (-3.781350) \| 5.179017 / 3.745712 (1.433305) \| 4.446323 / 5.269862 (-0.823538) \| 2.771731 / 4.565676 (-1.793945) \| 0.100943 / 0.424275 (-0.323332) \| 0.009875 / 0.007607 (0.002268) \| 0.725260 / 0.226044 (0.499216) \| 7.149728 / 2.268929 (4.880800) \| 3.646438 / 55.444624 (-51.798187) \| 2.793858 / 6.876477 (-4.082618) \| 2.971966 / 2.142072 (0.829894) \| 0.998147 / 4.805227 (-3.807080) \| 0.198004 / 6.500664 (-6.302660) \| 0.072581 / 0.075469 (-0.002888) \|\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) \| 1.696737 / 1.841788 (-0.145051) \| 22.615193 / 8.074308 (14.540884) \| 20.272421 / 10.191392 (10.081029) \| 0.237459 / 0.680424 (-0.442965) \| 0.034774 / 0.534201 (-0.499427) \| 0.484649 / 0.579283 (-0.094634) \| 0.590263 / 0.434364 (0.155899) \| 0.547833 / 0.540337 (0.007495) \| 0.762109 / 1.386936 (-0.624827) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bc3628b5a8f71ad7cfc014d8ba5e798f26becb7 \"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) \| 0.011183 / 0.011353 (-0.000170) \| 0.005267 / 0.011008 (-0.005741) \| 0.108506 / 0.038508 (0.069997) \| 0.083541 / 0.023109 (0.060431) \| 0.452189 / 0.275898 (0.176291) \| 0.496229 / 0.323480 (0.172749) \| 0.004951 / 0.007986 (-0.003035) \| 0.004452 / 0.004328 (0.000124) \| 0.085133 / 0.004250 (0.080883) \| 0.061291 / 0.037052 (0.024239) \| 0.450453 / 0.258489 (0.191964) \| 0.506456 / 0.293841 (0.212616) \| 0.049784 / 0.128546 (-0.078762) \| 0.014738 / 0.075646 (-0.060908) \| 0.372603 / 0.419271 (-0.046669) \| 0.065223 / 0.043533 (0.021690) \| 0.467872 / 0.255139 (0.212733) \| 0.500062 / 0.283200 (0.216862) \| 0.040911 / 0.141683 (-0.100772) \| 1.852970 / 1.452155 (0.400816) \| 2.016996 / 1.492716 (0.524280) \|\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) \| 0.262620 / 0.018006 (0.244614) \| 0.593925 / 0.000490 (0.593435) \| 0.000413 / 0.000200 (0.000213) \| 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) \| 0.035713 / 0.037411 (-0.001698) \| 0.111403 / 0.014526 (0.096878) \| 0.117259 / 0.176557 (-0.059298) \| 0.201545 / 0.737135 (-0.535590) \| 0.133111 / 0.296338 (-0.163228) \|\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) \| 0.597318 / 0.215209 (0.382109) \| 5.882691 / 2.077655 (3.805036) \| 2.572203 / 1.504120 (1.068083) \| 2.248016 / 1.541195 (0.706821) \| 2.359103 / 1.468490 (0.890613) \| 0.852023 / 4.584777 (-3.732754) \| 5.270831 / 3.745712 (1.525119) \| 4.712915 / 5.269862 (-0.556947) \| 3.124295 / 4.565676 (-1.441381) \| 0.092045 / 0.424275 (-0.332230) \| 0.007834 / 0.007607 (0.000227) \| 0.695711 / 0.226044 (0.469666) \| 7.011760 / 2.268929 (4.742831) \| 3.333300 / 55.444624 (-52.111325) \| 2.745889 / 6.876477 (-4.130587) \| 3.153458 / 2.142072 (1.011385) \| 1.011089 / 4.805227 (-3.794139) \| 0.207467 / 6.500664 (-6.293197) \| 0.079802 / 0.075469 (0.004333) \|\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) \| 1.703784 / 1.841788 (-0.138003) \| 24.414340 / 8.074308 (16.340032) \| 22.534528 / 10.191392 (12.343136) \| 0.276129 / 0.680424 (-0.404295) \| 0.027954 / 0.534201 (-0.506247) \| 0.484261 / 0.579283 (-0.095022) \| 0.605316 / 0.434364 (0.170952) \| 0.557219 / 0.540337 (0.016882) \| 0.802209 / 1.386936 (-0.584727) \|\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) \| 0.009109 / 0.011353 (-0.002244) \| 0.005376 / 0.011008 (-0.005632) \| 0.085141 / 0.038508 (0.046633) \| 0.100560 / 0.023109 (0.077450) \| 0.482673 / 0.275898 (0.206775) \| 0.551582 / 0.323480 (0.228103) \| 0.006756 / 0.007986 (-0.001229) \| 0.004171 / 0.004328 (-0.000158) \| 0.084184 / 0.004250 (0.079933) \| 0.069283 / 0.037052 (0.032230) \| 0.517722 / 0.258489 (0.259233) \| 0.542641 / 0.293841 (0.248801) \| 0.047790 / 0.128546 (-0.080756) \| 0.014063 / 0.075646 (-0.061583) \| 0.110591 / 0.419271 (-0.308680) \| 0.064373 / 0.043533 (0.020840) \| 0.496636 / 0.255139 (0.241497) \| 0.551906 / 0.283200 (0.268707) \| 0.046187 / 0.141683 (-0.095496) \| 1.864836 / 1.452155 (0.412681) \| 1.923765 / 1.492716 (0.431049) \|\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) \| 0.286558 / 0.018006 (0.268552) \| 0.610353 / 0.000490 (0.609863) \| 0.012647 / 0.000200 (0.012447) \| 0.000162 / 0.000054 (0.000107) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037099 / 0.037411 (-0.000313) \| 0.108608 / 0.014526 (0.094082) \| 0.120386 / 0.176557 (-0.056170) \| 0.183450 / 0.737135 (-0.553686) \| 0.124860 / 0.296338 (-0.171479) \|\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) \| 0.629006 / 0.215209 (0.413797) \| 6.309206 / 2.077655 (4.231551) \| 2.878558 / 1.504120 (1.374438) \| 2.616093 / 1.541195 (1.074898) \| 2.668096 / 1.468490 (1.199606) \| 0.865732 / 4.584777 (-3.719045) \| 5.312433 / 3.745712 (1.566721) \| 4.799352 / 5.269862 (-0.470509) \| 3.142207 / 4.565676 (-1.423469) \| 0.099591 / 0.424275 (-0.324684) \| 0.009159 / 0.007607 (0.001552) \| 0.730999 / 0.226044 (0.504954) \| 7.486442 / 2.268929 (5.217513) \| 3.657699 / 55.444624 (-51.786925) \| 3.080094 / 6.876477 (-3.796383) \| 3.320976 / 2.142072 (1.178904) \| 1.089324 / 4.805227 (-3.715904) \| 0.222831 / 6.500664 (-6.277833) \| 0.083976 / 0.075469 (0.008507) \|\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) \| 1.793181 / 1.841788 (-0.048607) \| 25.307444 / 8.074308 (17.233136) \| 21.321713 / 10.191392 (11.130321) \| 0.216326 / 0.680424 (-0.464098) \| 0.034298 / 0.534201 (-0.499903) \| 0.497173 / 0.579283 (-0.082110) \| 0.643550 / 0.434364 (0.209186) \| 0.581213 / 0.540337 (0.040876) \| 0.830973 / 1.386936 (-0.555963) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24875bb8494c3a7803182b08c70747b1b1a6bf4d \"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) \| 0.006886 / 0.011353 (-0.004467) \| 0.004267 / 0.011008 (-0.006741) \| 0.086182 / 0.038508 (0.047674) \| 0.083405 / 0.023109 (0.060296) \| 0.313717 / 0.275898 (0.037819) \| 0.351476 / 0.323480 (0.027996) \| 0.005702 / 0.007986 (-0.002284) \| 0.003802 / 0.004328 (-0.000526) \| 0.065759 / 0.004250 (0.061508) \| 0.060056 / 0.037052 (0.023003) \| 0.315871 / 0.258489 (0.057382) \| 0.364520 / 0.293841 (0.070679) \| 0.032067 / 0.128546 (-0.096479) \| 0.008679 / 0.075646 (-0.066967) \| 0.294968 / 0.419271 (-0.124303) \| 0.054684 / 0.043533 (0.011152) \| 0.314124 / 0.255139 (0.058985) \| 0.337312 / 0.283200 (0.054113) \| 0.025051 / 0.141683 (-0.116632) \| 1.505242 / 1.452155 (0.053087) \| 1.608263 / 1.492716 (0.115547) \|\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) \| 0.266562 / 0.018006 (0.248556) \| 0.579887 / 0.000490 (0.579397) \| 0.004161 / 0.000200 (0.003961) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031153 / 0.037411 (-0.006258) \| 0.087703 / 0.014526 (0.073177) \| 0.103864 / 0.176557 (-0.072693) \| 0.159032 / 0.737135 (-0.578104) \| 0.102482 / 0.296338 (-0.193857) \|\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) \| 0.405805 / 0.215209 (0.190596) \| 4.050669 / 2.077655 (1.973014) \| 2.064384 / 1.504120 (0.560264) \| 1.892825 / 1.541195 (0.351630) \| 2.001083 / 1.468490 (0.532593) \| 0.478174 / 4.584777 (-4.106603) \| 3.542580 / 3.745712 (-0.203132) \| 3.319205 / 5.269862 (-1.950656) \| 2.075868 / 4.565676 (-2.489808) \| 0.057345 / 0.424275 (-0.366930) \| 0.007459 / 0.007607 (-0.000148) \| 0.483564 / 0.226044 (0.257520) \| 4.827746 / 2.268929 (2.558818) \| 2.579541 / 55.444624 (-52.865083) \| 2.205125 / 6.876477 (-4.671352) \| 2.489206 / 2.142072 (0.347133) \| 0.575843 / 4.805227 (-4.229384) \| 0.133010 / 6.500664 (-6.367654) \| 0.061082 / 0.075469 (-0.014387) \|\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) \| 1.286059 / 1.841788 (-0.555729) \| 20.575173 / 8.074308 (12.500865) \| 14.351692 / 10.191392 (4.160300) \| 0.150401 / 0.680424 (-0.530022) \| 0.018678 / 0.534201 (-0.515523) \| 0.397860 / 0.579283 (-0.181423) \| 0.419474 / 0.434364 (-0.014890) \| 0.474492 / 0.540337 (-0.065846) \| 0.659510 / 1.386936 (-0.727426) \|\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) \| 0.006948 / 0.011353 (-0.004405) \| 0.004305 / 0.011008 (-0.006703) \| 0.064220 / 0.038508 (0.025712) \| 0.083251 / 0.023109 (0.060142) \| 0.388148 / 0.275898 (0.112250) \| 0.417834 / 0.323480 (0.094354) \| 0.005762 / 0.007986 (-0.002224) \| 0.003803 / 0.004328 (-0.000525) \| 0.066365 / 0.004250 (0.062114) \| 0.061808 / 0.037052 (0.024756) \| 0.390889 / 0.258489 (0.132400) \| 0.430619 / 0.293841 (0.136778) \| 0.031777 / 0.128546 (-0.096770) \| 0.008781 / 0.075646 (-0.066865) \| 0.070844 / 0.419271 (-0.348427) \| 0.050552 / 0.043533 (0.007019) \| 0.378420 / 0.255139 (0.123281) \| 0.403273 / 0.283200 (0.120074) \| 0.024578 / 0.141683 (-0.117105) \| 1.494790 / 1.452155 (0.042636) \| 1.549408 / 1.492716 (0.056692) \|\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) \| 0.302668 / 0.018006 (0.284662) \| 0.542235 / 0.000490 (0.541746) \| 0.001847 / 0.000200 (0.001647) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031947 / 0.037411 (-0.005465) \| 0.092220 / 0.014526 (0.077694) \| 0.104525 / 0.176557 (-0.072031) \| 0.162000 / 0.737135 (-0.575135) \| 0.106795 / 0.296338 (-0.189543) \|\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) \| 0.412035 / 0.215209 (0.196826) \| 4.106527 / 2.077655 (2.028872) \| 2.111529 / 1.504120 (0.607409) \| 1.953201 / 1.541195 (0.412006) \| 2.079258 / 1.468490 (0.610768) \| 0.479562 / 4.584777 (-4.105215) \| 3.606256 / 3.745712 (-0.139456) \| 5.175250 / 5.269862 (-0.094612) \| 3.292465 / 4.565676 (-1.273212) \| 0.057726 / 0.424275 (-0.366549) \| 0.008247 / 0.007607 (0.000640) \| 0.486143 / 0.226044 (0.260098) \| 4.859051 / 2.268929 (2.590123) \| 2.675629 / 55.444624 (-52.768995) \| 2.267448 / 6.876477 (-4.609029) \| 2.567639 / 2.142072 (0.425567) \| 0.580822 / 4.805227 (-4.224406) \| 0.134942 / 6.500664 (-6.365722) \| 0.063825 / 0.075469 (-0.011644) \|\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) \| 1.334421 / 1.841788 (-0.507367) \| 20.481428 / 8.074308 (12.407120) \| 14.227943 / 10.191392 (4.036551) \| 0.170711 / 0.680424 (-0.509713) \| 0.018212 / 0.534201 (-0.515989) \| 0.397212 / 0.579283 (-0.182071) \| 0.411934 / 0.434364 (-0.022430) \| 0.478019 / 0.540337 (-0.062319) \| 0.645434 / 1.386936 (-0.741502) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3d3f10886e23a65cce3bfd939b8ec0d5a5c2c1 \"CML watermark\")\n" ]
1,818,337,136	6,061	Dill 3.7 support	closed	2023-07-24T12:33:58	2023-07-24T14:13:20	2023-07-24T14:04:36	https://github.com/huggingface/datasets/pull/6061	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6061", "html_url": "https://github.com/huggingface/datasets/pull/6061", "diff_url": "https://github.com/huggingface/datasets/pull/6061.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6061.patch", "merged_at": "2023-07-24T14:04:36" }	mariosasko	true	[ "_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) \| 0.007700 / 0.011353 (-0.003653) \| 0.004680 / 0.011008 (-0.006328) \| 0.098812 / 0.038508 (0.060304) \| 0.085062 / 0.023109 (0.061952) \| 0.371472 / 0.275898 (0.095574) \| 0.412552 / 0.323480 (0.089072) \| 0.004700 / 0.007986 (-0.003285) \| 0.003765 / 0.004328 (-0.000564) \| 0.074267 / 0.004250 (0.070017) \| 0.063003 / 0.037052 (0.025951) \| 0.391842 / 0.258489 (0.133353) \| 0.436955 / 0.293841 (0.143114) \| 0.035291 / 0.128546 (-0.093255) \| 0.009309 / 0.075646 (-0.066338) \| 0.313097 / 0.419271 (-0.106174) \| 0.060098 / 0.043533 (0.016565) \| 0.350726 / 0.255139 (0.095587) \| 0.402692 / 0.283200 (0.119493) \| 0.029321 / 0.141683 (-0.112361) \| 1.671806 / 1.452155 (0.219651) \| 1.743760 / 1.492716 (0.251044) \|\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) \| 0.242281 / 0.018006 (0.224275) \| 0.505054 / 0.000490 (0.504564) \| 0.006595 / 0.000200 (0.006395) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032174 / 0.037411 (-0.005238) \| 0.094483 / 0.014526 (0.079957) \| 0.108527 / 0.176557 (-0.068030) \| 0.178983 / 0.737135 (-0.558152) \| 0.113766 / 0.296338 (-0.182572) \|\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) \| 0.419764 / 0.215209 (0.204555) \| 4.282650 / 2.077655 (2.204995) \| 2.075325 / 1.504120 (0.571205) \| 1.897668 / 1.541195 (0.356473) \| 2.027109 / 1.468490 (0.558619) \| 0.519983 / 4.584777 (-4.064794) \| 4.134603 / 3.745712 (0.388891) \| 6.586711 / 5.269862 (1.316849) \| 3.811726 / 4.565676 (-0.753951) \| 0.058628 / 0.424275 (-0.365647) \| 0.007586 / 0.007607 (-0.000021) \| 0.502180 / 0.226044 (0.276136) \| 5.101588 / 2.268929 (2.832660) \| 2.534295 / 55.444624 (-52.910330) \| 2.220170 / 6.876477 (-4.656307) \| 2.441110 / 2.142072 (0.299038) \| 0.644775 / 4.805227 (-4.160452) \| 0.144716 / 6.500664 (-6.355948) \| 0.067018 / 0.075469 (-0.008451) \|\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) \| 1.431279 / 1.841788 (-0.410508) \| 21.947814 / 8.074308 (13.873506) \| 15.548236 / 10.191392 (5.356844) \| 0.174774 / 0.680424 (-0.505650) \| 0.021182 / 0.534201 (-0.513019) \| 0.441320 / 0.579283 (-0.137963) \| 0.476685 / 0.434364 (0.042321) \| 0.506277 / 0.540337 (-0.034060) \| 0.809943 / 1.386936 (-0.576993) \|\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) \| 0.007172 / 0.011353 (-0.004181) \| 0.004358 / 0.011008 (-0.006650) \| 0.068604 / 0.038508 (0.030096) \| 0.083956 / 0.023109 (0.060847) \| 0.402579 / 0.275898 (0.126681) \| 0.444714 / 0.323480 (0.121235) \| 0.005940 / 0.007986 (-0.002046) \| 0.003607 / 0.004328 (-0.000722) \| 0.073134 / 0.004250 (0.068883) \| 0.061722 / 0.037052 (0.024669) \| 0.410957 / 0.258489 (0.152468) \| 0.458819 / 0.293841 (0.164978) \| 0.033710 / 0.128546 (-0.094836) \| 0.010230 / 0.075646 (-0.065417) \| 0.084678 / 0.419271 (-0.334593) \| 0.058203 / 0.043533 (0.014670) \| 0.444972 / 0.255139 (0.189833) \| 0.470962 / 0.283200 (0.187763) \| 0.029222 / 0.141683 (-0.112461) \| 1.671460 / 1.452155 (0.219306) \| 1.759471 / 1.492716 (0.266754) \|\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) \| 0.238894 / 0.018006 (0.220888) \| 0.493605 / 0.000490 (0.493115) \| 0.001979 / 0.000200 (0.001780) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036498 / 0.037411 (-0.000913) \| 0.095245 / 0.014526 (0.080719) \| 0.112147 / 0.176557 (-0.064409) \| 0.171128 / 0.737135 (-0.566007) \| 0.115295 / 0.296338 (-0.181044) \|\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) \| 0.461067 / 0.215209 (0.245858) \| 4.723932 / 2.077655 (2.646277) \| 2.432697 / 1.504120 (0.928578) \| 2.237302 / 1.541195 (0.696107) \| 2.351320 / 1.468490 (0.882830) \| 0.509963 / 4.584777 (-4.074813) \| 4.194817 / 3.745712 (0.449105) \| 6.689529 / 5.269862 (1.419667) \| 3.351198 / 4.565676 (-1.214478) \| 0.064563 / 0.424275 (-0.359712) \| 0.008605 / 0.007607 (0.000998) \| 0.575590 / 0.226044 (0.349546) \| 5.644179 / 2.268929 (3.375250) \| 3.021375 / 55.444624 (-52.423249) \| 2.595305 / 6.876477 (-4.281172) \| 2.839228 / 2.142072 (0.697156) \| 0.657148 / 4.805227 (-4.148079) \| 0.144831 / 6.500664 (-6.355834) \| 0.067882 / 0.075469 (-0.007587) \|\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) \| 1.595580 / 1.841788 (-0.246208) \| 22.431609 / 8.074308 (14.357301) \| 15.700845 / 10.191392 (5.509453) \| 0.164675 / 0.680424 (-0.515749) \| 0.021322 / 0.534201 (-0.512879) \| 0.455270 / 0.579283 (-0.124013) \| 0.451547 / 0.434364 (0.017183) \| 0.520955 / 0.540337 (-0.019383) \| 0.687803 / 1.386936 (-0.699133) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d19574e9f44bd3b59a3e47ca7c4ea66305a8e6b \"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) \| 0.008171 / 0.011353 (-0.003182) \| 0.005563 / 0.011008 (-0.005445) \| 0.102265 / 0.038508 (0.063757) \| 0.074755 / 0.023109 (0.051646) \| 0.431317 / 0.275898 (0.155419) \| 0.472179 / 0.323480 (0.148699) \| 0.006153 / 0.007986 (-0.001833) \| 0.003832 / 0.004328 (-0.000496) \| 0.078480 / 0.004250 (0.074230) \| 0.056250 / 0.037052 (0.019197) \| 0.432938 / 0.258489 (0.174449) \| 0.480983 / 0.293841 (0.187142) \| 0.048861 / 0.128546 (-0.079685) \| 0.016252 / 0.075646 (-0.059394) \| 0.343508 / 0.419271 (-0.075763) \| 0.065057 / 0.043533 (0.021524) \| 0.468418 / 0.255139 (0.213279) \| 0.463692 / 0.283200 (0.180492) \| 0.032912 / 0.141683 (-0.108771) \| 1.795194 / 1.452155 (0.343039) \| 1.833047 / 1.492716 (0.340331) \|\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) \| 0.197980 / 0.018006 (0.179974) \| 0.500662 / 0.000490 (0.500172) \| 0.007380 / 0.000200 (0.007181) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028323 / 0.037411 (-0.009089) \| 0.089817 / 0.014526 (0.075291) \| 0.102923 / 0.176557 (-0.073633) \| 0.173851 / 0.737135 (-0.563284) \| 0.104006 / 0.296338 (-0.192333) \|\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) \| 0.580277 / 0.215209 (0.365068) \| 5.878739 / 2.077655 (3.801085) \| 2.404673 / 1.504120 (0.900553) \| 2.071765 / 1.541195 (0.530571) \| 2.106024 / 1.468490 (0.637534) \| 0.855217 / 4.584777 (-3.729560) \| 4.918602 / 3.745712 (1.172890) \| 5.354984 / 5.269862 (0.085122) \| 3.141288 / 4.565676 (-1.424389) \| 0.099553 / 0.424275 (-0.324723) \| 0.008152 / 0.007607 (0.000545) \| 0.709857 / 0.226044 (0.483813) \| 7.144602 / 2.268929 (4.875673) \| 3.137637 / 55.444624 (-52.306987) \| 2.379851 / 6.876477 (-4.496626) \| 2.346426 / 2.142072 (0.204353) \| 1.033416 / 4.805227 (-3.771811) \| 0.213120 / 6.500664 (-6.287544) \| 0.076037 / 0.075469 (0.000568) \|\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) \| 1.597742 / 1.841788 (-0.244046) \| 21.745366 / 8.074308 (13.671058) \| 20.830698 / 10.191392 (10.639306) \| 0.238727 / 0.680424 (-0.441697) \| 0.027923 / 0.534201 (-0.506278) \| 0.466073 / 0.579283 (-0.113210) \| 0.548647 / 0.434364 (0.114283) \| 0.549245 / 0.540337 (0.008908) \| 0.977148 / 1.386936 (-0.409788) \|\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) \| 0.008252 / 0.011353 (-0.003101) \| 0.004653 / 0.011008 (-0.006356) \| 0.084012 / 0.038508 (0.045504) \| 0.077418 / 0.023109 (0.054309) \| 0.440748 / 0.275898 (0.164850) \| 0.464279 / 0.323480 (0.140799) \| 0.005762 / 0.007986 (-0.002224) \| 0.004909 / 0.004328 (0.000581) \| 0.086441 / 0.004250 (0.082190) \| 0.057883 / 0.037052 (0.020831) \| 0.466655 / 0.258489 (0.208166) \| 0.479751 / 0.293841 (0.185910) \| 0.047166 / 0.128546 (-0.081380) \| 0.014480 / 0.075646 (-0.061166) \| 0.092599 / 0.419271 (-0.326672) \| 0.062454 / 0.043533 (0.018921) \| 0.449753 / 0.255139 (0.194614) \| 0.461876 / 0.283200 (0.178676) \| 0.034828 / 0.141683 (-0.106855) \| 1.752249 / 1.452155 (0.300095) \| 1.865449 / 1.492716 (0.372732) \|\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) \| 0.245028 / 0.018006 (0.227022) \| 0.509564 / 0.000490 (0.509074) \| 0.003930 / 0.000200 (0.003730) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034746 / 0.037411 (-0.002665) \| 0.096563 / 0.014526 (0.082037) \| 0.107581 / 0.176557 (-0.068975) \| 0.184952 / 0.737135 (-0.552184) \| 0.108747 / 0.296338 (-0.187591) \|\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) \| 0.613091 / 0.215209 (0.397882) \| 5.994985 / 2.077655 (3.917330) \| 2.711276 / 1.504120 (1.207156) \| 2.415862 / 1.541195 (0.874668) \| 2.391055 / 1.468490 (0.922565) \| 0.868723 / 4.584777 (-3.716054) \| 4.953992 / 3.745712 (1.208280) \| 4.606542 / 5.269862 (-0.663319) \| 2.942162 / 4.565676 (-1.623515) \| 0.102737 / 0.424275 (-0.321538) \| 0.008634 / 0.007607 (0.001027) \| 0.722122 / 0.226044 (0.496078) \| 7.245097 / 2.268929 (4.976168) \| 3.428232 / 55.444624 (-52.016393) \| 2.709539 / 6.876477 (-4.166938) \| 2.857956 / 2.142072 (0.715884) \| 1.045594 / 4.805227 (-3.759634) \| 0.213344 / 6.500664 (-6.287320) \| 0.073601 / 0.075469 (-0.001868) \|\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) \| 1.651954 / 1.841788 (-0.189834) \| 22.458646 / 8.074308 (14.384338) \| 19.583203 / 10.191392 (9.391811) \| 0.246932 / 0.680424 (-0.433492) \| 0.025730 / 0.534201 (-0.508471) \| 0.473475 / 0.579283 (-0.105808) \| 0.521411 / 0.434364 (0.087047) \| 0.562038 / 0.540337 (0.021700) \| 0.767673 / 1.386936 (-0.619263) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3869d99628329c696f6975377f65e625dd8ef3e0 \"CML watermark\")\n", "The CI error is unrelated.", "<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) \| 0.006649 / 0.011353 (-0.004703) \| 0.003963 / 0.011008 (-0.007045) \| 0.084564 / 0.038508 (0.046056) \| 0.075668 / 0.023109 (0.052559) \| 0.314233 / 0.275898 (0.038335) \| 0.343320 / 0.323480 (0.019841) \| 0.005405 / 0.007986 (-0.002581) \| 0.003356 / 0.004328 (-0.000973) \| 0.065094 / 0.004250 (0.060844) \| 0.058774 / 0.037052 (0.021722) \| 0.320772 / 0.258489 (0.062283) \| 0.353546 / 0.293841 (0.059705) \| 0.030921 / 0.128546 (-0.097625) \| 0.008463 / 0.075646 (-0.067184) \| 0.287490 / 0.419271 (-0.131781) \| 0.053188 / 0.043533 (0.009656) \| 0.324023 / 0.255139 (0.068884) \| 0.337828 / 0.283200 (0.054628) \| 0.024764 / 0.141683 (-0.116918) \| 1.458028 / 1.452155 (0.005873) \| 1.521615 / 1.492716 (0.028899) \|\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) \| 0.209360 / 0.018006 (0.191353) \| 0.461331 / 0.000490 (0.460841) \| 0.000386 / 0.000200 (0.000186) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028405 / 0.037411 (-0.009006) \| 0.081074 / 0.014526 (0.066548) \| 0.094868 / 0.176557 (-0.081689) \| 0.151050 / 0.737135 (-0.586085) \| 0.095854 / 0.296338 (-0.200484) \|\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) \| 0.393957 / 0.215209 (0.178748) \| 3.938649 / 2.077655 (1.860994) \| 1.938190 / 1.504120 (0.434070) \| 1.766458 / 1.541195 (0.225263) \| 1.818028 / 1.468490 (0.349538) \| 0.483926 / 4.584777 (-4.100851) \| 3.641957 / 3.745712 (-0.103755) \| 4.883845 / 5.269862 (-0.386016) \| 2.960300 / 4.565676 (-1.605377) \| 0.057227 / 0.424275 (-0.367048) \| 0.007285 / 0.007607 (-0.000322) \| 0.475928 / 0.226044 (0.249884) \| 4.756757 / 2.268929 (2.487828) \| 2.502659 / 55.444624 (-52.941966) \| 2.178067 / 6.876477 (-4.698410) \| 2.378298 / 2.142072 (0.236226) \| 0.578639 / 4.805227 (-4.226588) \| 0.132512 / 6.500664 (-6.368152) \| 0.059656 / 0.075469 (-0.015813) \|\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) \| 1.272673 / 1.841788 (-0.569115) \| 19.266884 / 8.074308 (11.192576) \| 14.272930 / 10.191392 (4.081538) \| 0.165897 / 0.680424 (-0.514527) \| 0.018436 / 0.534201 (-0.515765) \| 0.395177 / 0.579283 (-0.184107) \| 0.420134 / 0.434364 (-0.014229) \| 0.460781 / 0.540337 (-0.079557) \| 0.645376 / 1.386936 (-0.741560) \|\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) \| 0.006504 / 0.011353 (-0.004849) \| 0.003942 / 0.011008 (-0.007066) \| 0.064936 / 0.038508 (0.026428) \| 0.075015 / 0.023109 (0.051905) \| 0.396871 / 0.275898 (0.120973) \| 0.423448 / 0.323480 (0.099968) \| 0.005239 / 0.007986 (-0.002747) \| 0.003265 / 0.004328 (-0.001063) \| 0.064910 / 0.004250 (0.060660) \| 0.055006 / 0.037052 (0.017953) \| 0.392818 / 0.258489 (0.134329) \| 0.429735 / 0.293841 (0.135894) \| 0.031847 / 0.128546 (-0.096699) \| 0.008626 / 0.075646 (-0.067021) \| 0.071591 / 0.419271 (-0.347681) \| 0.049006 / 0.043533 (0.005473) \| 0.384913 / 0.255139 (0.129774) \| 0.408969 / 0.283200 (0.125769) \| 0.023573 / 0.141683 (-0.118110) \| 1.490271 / 1.452155 (0.038117) \| 1.564620 / 1.492716 (0.071904) \|\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) \| 0.225917 / 0.018006 (0.207911) \| 0.450369 / 0.000490 (0.449880) \| 0.000375 / 0.000200 (0.000175) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031196 / 0.037411 (-0.006215) \| 0.090486 / 0.014526 (0.075960) \| 0.102326 / 0.176557 (-0.074231) \| 0.157483 / 0.737135 (-0.579653) \| 0.103670 / 0.296338 (-0.192668) \|\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) \| 0.417577 / 0.215209 (0.202368) \| 4.170798 / 2.077655 (2.093143) \| 2.123689 / 1.504120 (0.619569) \| 1.948231 / 1.541195 (0.407037) \| 2.040277 / 1.468490 (0.571787) \| 0.497919 / 4.584777 (-4.086858) \| 3.633270 / 3.745712 (-0.112442) \| 4.851698 / 5.269862 (-0.418164) \| 2.691992 / 4.565676 (-1.873684) \| 0.058641 / 0.424275 (-0.365634) \| 0.007719 / 0.007607 (0.000112) \| 0.500652 / 0.226044 (0.274607) \| 4.988657 / 2.268929 (2.719728) \| 2.604488 / 55.444624 (-52.840136) \| 2.329829 / 6.876477 (-4.546648) \| 2.468239 / 2.142072 (0.326167) \| 0.598724 / 4.805227 (-4.206503) \| 0.135959 / 6.500664 (-6.364706) \| 0.061088 / 0.075469 (-0.014381) \|\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) \| 1.352107 / 1.841788 (-0.489681) \| 19.973976 / 8.074308 (11.899668) \| 14.292812 / 10.191392 (4.101420) \| 0.163855 / 0.680424 (-0.516568) \| 0.018402 / 0.534201 (-0.515799) \| 0.393128 / 0.579283 (-0.186155) \| 0.407379 / 0.434364 (-0.026985) \| 0.462324 / 0.540337 (-0.078013) \| 0.607501 / 1.386936 (-0.779435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae126ac974cad3050f90106e5909232140786811 \"CML watermark\")\n" ]
1,816,614,120	6,060	Dataset.map() execute twice when in PyTorch DDP mode	closed	2023-07-22T05:06:43	2024-01-22T18:35:12	2024-01-22T18:35:12	https://github.com/huggingface/datasets/issues/6060	null	wanghaoyucn	false	[ "Sorry for asking a duplicate question about `num_proc`, I searched the forum and find the solution.\r\n\r\nBut I still can't make the trick with `torch.distributed.barrier()` to only map at the main process work. The [post on forum]( https://discuss.huggingface.co/t/slow-processing-with-map-when-using-deepspeed-or-fairscale/7229/7) didn't help.", "If it does the `map` twice then it means the hash of your map function is not some same between your two processes.\r\n\r\nCan you make sure your map functions have the same hash in different processes ?\r\n\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nprint(Hasher.hash(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)))\r\nprint(Hasher.hash(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16)))\r\n```\r\n\r\nYou can also set the fingerprint used to reload the resulting dataset by passing `new_finegrprint=` in `map`, see https://huggingface.co/docs/datasets/v2.13.1/en/about_cache#the-cache. This will force the different processes to use the same fingerprint used to locate the resulting dataset in the cache.", "Thanks for help! I find the fingerprint between processes don't have same hash:\r\n```\r\nRank 0: Gpu 0 cut_reorder_keys fingerprint c7f47f40e9a67657\r\nRank 0: Gpu 0 random_shift fingerprint 240a0ce79831e7d4\r\n\r\nRank 1: Gpu 1 cut_reorder_keys fingerprint 20edd3d9cf284001\r\nRank 1: Gpu 1 random_shift fingerprint 819f7c1c18e7733f\r\n```\r\nBut my functions only process the example one by one and don't need rank or other arguments. After all it can work in the test for dataset and dataloader.\r\nI'll try to set `new_fingerprint` to see if it works and figure out the reason of different hash.", "I finally figure it out. The fingerprint of the function will change if other key-value pairs change in `args` even the `args.num_stations_list` is not changed.\r\n\r\n```python\r\nlambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)\r\n```\r\n\r\nMy `args` contains the key `rank` which refers the rank of its GPU, so the fingerprints change among the GPUs.\r\nI use `partial` in `functools` to generate a partial function that fixs the argument `num_stations_list=args.num_stations_list`, and the fingerprint of this partial function keeps among the GPUs. Finally I can reuse the mapped cache." ]
1,816,537,176	6,059	Provide ability to load label mappings from file	open	2023-07-22T02:04:19	2024-04-16T08:07:55	null	https://github.com/huggingface/datasets/issues/6059	null	david-waterworth	false	[ "I would like this also as I have been working with a dataset with hierarchical classes. In fact, I encountered this very issue when trying to define the dataset with a script. I couldn't find a work around and reverted to hard coding the class names in the readme yaml.\r\n\r\n@david-waterworth do you envision also being able to define the hierarchical structure of the classes?", "@danielduckworth yes I did need to do that (but I ended up ditching datasets as it looks like this is a \"wont fix\"). ", "@david-waterworth Hmm, that's a shame. What are you using now? Also, I’m curious to know about the work you’re doing that involves hierarchical classes, if you don’t mind sharing." ]
1,815,131,397	6,058	laion-coco download error	closed	2023-07-21T04:24:15	2023-07-22T01:42:06	2023-07-22T01:42:06	https://github.com/huggingface/datasets/issues/6058	null	yangyijune	false	[ "This can also mean one of the files was not downloaded correctly.\r\n\r\nWe log an erroneous file's name before raising the reader's error, so this is how you can find the problematic file. Then, you should delete it and call `load_dataset` again.\r\n\r\n(I checked all the uploaded files, and they seem to be valid Parquet files, so I don't think this is a bug on their side)\r\n" ]
1,815,100,151	6,057	Why is the speed difference of gen example so big?	closed	2023-07-21T03:34:49	2023-10-04T18:06:16	2023-10-04T18:06:15	https://github.com/huggingface/datasets/issues/6057	null	pixeli99	false	[ "Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\nand share the output?" ]
1,815,086,963	6,056	Implement proper checkpointing for dataset uploading with resume function that does not require remapping shards that have already been uploaded	open	2023-07-21T03:13:21	2023-08-17T08:26:53	null	https://github.com/huggingface/datasets/pull/6056	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6056", "html_url": "https://github.com/huggingface/datasets/pull/6056", "diff_url": "https://github.com/huggingface/datasets/pull/6056.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6056.patch", "merged_at": null }	AntreasAntoniou	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is constructed to retain information about the shard_size, and total number of shards, hence ensuring that the files uploaded are not just files that have the same name but actually represent a different configuration of shard_size, and total number of shards. \r\n2. Remembering the total file size is done easily in the yaml, whereas alternatively I am not sure how one could access the file size of the uploaded files without downloading them.\r\n3. I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it. \r\n\r\nIf 1 and 2 can be achieved without an additional yaml, then I would be willing to make those changes. Let me know of any ideas. 1. could be done by changing the data file names, but I'd rather not do that as to prevent breaking existing datasets that try to upload updates to their data. ", "If the file name depends on the shard's fingerprint before mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n\r\n> I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n\r\nwhat was the issue ?", "> If the file name depends on the shard's fingerprint before mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n> \r\n> > I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n> \r\n> what was the issue ?\r\n\r\nYou are right. I was having some other issue earlier that I need more input from you guys to overcome, and when I overcame it the yaml was just legacy from before. I'll update the PR. ", "> If the file name depends on the shard's fingerprint before mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n> \r\n> > I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n> \r\n> what was the issue ?\r\n\r\nI remembered what it was, and why I needed the yaml. I needed it so it could remember the progress for a particular num_shards setup, as different num_shards would lead to different number of splits, and a user might switch between them while uploading, and I did not want the index to be conflated with one of another num_shards setup. \r\n\r\nAny idea how we deal with that without a yaml?", "If the user changes the num_shards parameters then we should re-upload everything.\r\n\r\nIt happens that the num_shards is part of the parquet file names, so it restarts the upload from scratch without having to write additional logic :)" ]
1,813,524,145	6,055	Fix host URL in The Pile datasets	open	2023-07-20T09:08:52	2023-07-20T09:09:37	null	https://github.com/huggingface/datasets/issues/6055	null	nickovchinnikov	false	[]
1,813,271,304	6,054	Multi-processed `Dataset.map` slows down a lot when `import torch`	closed	2023-07-20T06:36:14	2023-07-21T15:19:37	2023-07-21T15:19:37	https://github.com/huggingface/datasets/issues/6054	null	ShinoharaHare	false	[ "A duplicate of https://github.com/huggingface/datasets/issues/5929" ]
1,812,635,902	6,053	Change package name from "datasets" to something less generic	closed	2023-07-19T19:53:28	2024-11-20T21:22:36	2023-10-03T16:04:09	https://github.com/huggingface/datasets/issues/6053	null	jack-jjm	false	[ "This would break a lot of existing code, so we can't really do this.", "I encountered this issue while working on a large project with 6+ years history. We have a submodule named datasets in the backend, and face a big challenge incorporating huggingface datasets into the project, especially considering django app renaming and other issues.\r\nIt would be nice if the authors at least provide a recipe on how to avoid name conflict in this situation." ]
1,812,145,100	6,052	Remove `HfFileSystem` and deprecate `S3FileSystem`	closed	2023-07-19T15:00:01	2023-07-19T17:39:11	2023-07-19T17:27:17	https://github.com/huggingface/datasets/pull/6052	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6052", "html_url": "https://github.com/huggingface/datasets/pull/6052", "diff_url": "https://github.com/huggingface/datasets/pull/6052.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6052.patch", "merged_at": "2023-07-19T17:27:17" }	mariosasko	true	[ "_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) \| 0.006658 / 0.011353 (-0.004695) \| 0.004347 / 0.011008 (-0.006661) \| 0.084179 / 0.038508 (0.045671) \| 0.080842 / 0.023109 (0.057733) \| 0.321642 / 0.275898 (0.045744) \| 0.348758 / 0.323480 (0.025278) \| 0.005624 / 0.007986 (-0.002362) \| 0.003479 / 0.004328 (-0.000850) \| 0.065125 / 0.004250 (0.060875) \| 0.057624 / 0.037052 (0.020572) \| 0.323643 / 0.258489 (0.065154) \| 0.360939 / 0.293841 (0.067098) \| 0.031005 / 0.128546 (-0.097541) \| 0.008618 / 0.075646 (-0.067028) \| 0.287443 / 0.419271 (-0.131828) \| 0.052640 / 0.043533 (0.009107) \| 0.316947 / 0.255139 (0.061808) \| 0.330292 / 0.283200 (0.047093) \| 0.024393 / 0.141683 (-0.117289) \| 1.476734 / 1.452155 (0.024579) \| 1.534505 / 1.492716 (0.041789) \|\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) \| 0.273808 / 0.018006 (0.255802) \| 0.591146 / 0.000490 (0.590656) \| 0.000322 / 0.000200 (0.000122) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029992 / 0.037411 (-0.007419) \| 0.086654 / 0.014526 (0.072129) \| 0.098590 / 0.176557 (-0.077967) \| 0.157225 / 0.737135 (-0.579910) \| 0.101816 / 0.296338 (-0.194522) \|\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) \| 0.382578 / 0.215209 (0.167368) \| 3.803576 / 2.077655 (1.725922) \| 1.875136 / 1.504120 (0.371016) \| 1.704207 / 1.541195 (0.163012) \| 1.765146 / 1.468490 (0.296656) \| 0.482802 / 4.584777 (-4.101975) \| 3.571772 / 3.745712 (-0.173940) \| 3.245626 / 5.269862 (-2.024235) \| 2.051612 / 4.565676 (-2.514064) \| 0.056539 / 0.424275 (-0.367736) \| 0.007199 / 0.007607 (-0.000408) \| 0.462445 / 0.226044 (0.236401) \| 4.623800 / 2.268929 (2.354872) \| 2.318948 / 55.444624 (-53.125677) \| 1.971442 / 6.876477 (-4.905035) \| 2.225444 / 2.142072 (0.083371) \| 0.575205 / 4.805227 (-4.230022) \| 0.129243 / 6.500664 (-6.371421) \| 0.059036 / 0.075469 (-0.016433) \|\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) \| 1.266827 / 1.841788 (-0.574960) \| 20.323419 / 8.074308 (12.249110) \| 14.577603 / 10.191392 (4.386210) \| 0.162131 / 0.680424 (-0.518293) \| 0.018529 / 0.534201 (-0.515672) \| 0.395046 / 0.579283 (-0.184237) \| 0.410870 / 0.434364 (-0.023494) \| 0.455782 / 0.540337 (-0.084556) \| 0.662851 / 1.386936 (-0.724085) \|\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) \| 0.006867 / 0.011353 (-0.004486) \| 0.004197 / 0.011008 (-0.006811) \| 0.066060 / 0.038508 (0.027552) \| 0.084145 / 0.023109 (0.061036) \| 0.366740 / 0.275898 (0.090842) \| 0.402362 / 0.323480 (0.078882) \| 0.005785 / 0.007986 (-0.002200) \| 0.003551 / 0.004328 (-0.000778) \| 0.066177 / 0.004250 (0.061926) \| 0.061521 / 0.037052 (0.024468) \| 0.377807 / 0.258489 (0.119318) \| 0.413490 / 0.293841 (0.119649) \| 0.031918 / 0.128546 (-0.096628) \| 0.008767 / 0.075646 (-0.066879) \| 0.071437 / 0.419271 (-0.347835) \| 0.049237 / 0.043533 (0.005704) \| 0.365929 / 0.255139 (0.110790) \| 0.393545 / 0.283200 (0.110346) \| 0.024054 / 0.141683 (-0.117628) \| 1.524599 / 1.452155 (0.072445) \| 1.576592 / 1.492716 (0.083876) \|\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) \| 0.315181 / 0.018006 (0.297174) \| 0.535501 / 0.000490 (0.535011) \| 0.000410 / 0.000200 (0.000210) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032915 / 0.037411 (-0.004497) \| 0.089310 / 0.014526 (0.074784) \| 0.105136 / 0.176557 (-0.071421) \| 0.158572 / 0.737135 (-0.578563) \| 0.106850 / 0.296338 (-0.189489) \|\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) \| 0.419343 / 0.215209 (0.204134) \| 4.200166 / 2.077655 (2.122511) \| 2.180234 / 1.504120 (0.676114) \| 2.016885 / 1.541195 (0.475690) \| 2.131480 / 1.468490 (0.662990) \| 0.484681 / 4.584777 (-4.100096) \| 3.613535 / 3.745712 (-0.132177) \| 5.762111 / 5.269862 (0.492249) \| 3.190590 / 4.565676 (-1.375086) \| 0.057403 / 0.424275 (-0.366872) \| 0.007862 / 0.007607 (0.000255) \| 0.490857 / 0.226044 (0.264813) \| 4.911241 / 2.268929 (2.642313) \| 2.650787 / 55.444624 (-52.793838) \| 2.317060 / 6.876477 (-4.559416) \| 2.579677 / 2.142072 (0.437605) \| 0.587388 / 4.805227 (-4.217840) \| 0.148109 / 6.500664 (-6.352555) \| 0.061435 / 0.075469 (-0.014034) \|\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) \| 1.322181 / 1.841788 (-0.519606) \| 20.647184 / 8.074308 (12.572875) \| 14.907635 / 10.191392 (4.716243) \| 0.156330 / 0.680424 (-0.524094) \| 0.018719 / 0.534201 (-0.515482) \| 0.397636 / 0.579283 (-0.181647) \| 0.414107 / 0.434364 (-0.020257) \| 0.460812 / 0.540337 (-0.079526) \| 0.609568 / 1.386936 (-0.777368) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n", "This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ", "what would be the change i need to make? ", "@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n", "The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged", "I just merged the other PR so you should be good now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006303 / 0.011353 (-0.005049) \| 0.003746 / 0.011008 (-0.007262) \| 0.081083 / 0.038508 (0.042575) \| 0.067973 / 0.023109 (0.044864) \| 0.322221 / 0.275898 (0.046323) \| 0.359432 / 0.323480 (0.035952) \| 0.004891 / 0.007986 (-0.003095) \| 0.002988 / 0.004328 (-0.001341) \| 0.064068 / 0.004250 (0.059818) \| 0.052042 / 0.037052 (0.014990) \| 0.323387 / 0.258489 (0.064898) \| 0.390416 / 0.293841 (0.096575) \| 0.028090 / 0.128546 (-0.100457) \| 0.008009 / 0.075646 (-0.067638) \| 0.262288 / 0.419271 (-0.156984) \| 0.044986 / 0.043533 (0.001453) \| 0.322319 / 0.255139 (0.067180) \| 0.345323 / 0.283200 (0.062123) \| 0.021798 / 0.141683 (-0.119885) \| 1.417259 / 1.452155 (-0.034895) \| 1.490050 / 1.492716 (-0.002667) \|\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) \| 0.195902 / 0.018006 (0.177896) \| 0.490808 / 0.000490 (0.490318) \| 0.002969 / 0.000200 (0.002770) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025221 / 0.037411 (-0.012190) \| 0.075341 / 0.014526 (0.060815) \| 0.086703 / 0.176557 (-0.089853) \| 0.146953 / 0.737135 (-0.590182) \| 0.086610 / 0.296338 (-0.209728) \|\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) \| 0.434890 / 0.215209 (0.219681) \| 4.352283 / 2.077655 (2.274629) \| 2.293098 / 1.504120 (0.788979) \| 2.123023 / 1.541195 (0.581829) \| 2.179722 / 1.468490 (0.711232) \| 0.503851 / 4.584777 (-4.080926) \| 3.087991 / 3.745712 (-0.657721) \| 2.898689 / 5.269862 (-2.371173) \| 1.902813 / 4.565676 (-2.662864) \| 0.058079 / 0.424275 (-0.366196) \| 0.006600 / 0.007607 (-0.001007) \| 0.509243 / 0.226044 (0.283199) \| 5.080204 / 2.268929 (2.811275) \| 2.753594 / 55.444624 (-52.691030) \| 2.417385 / 6.876477 (-4.459091) \| 2.635470 / 2.142072 (0.493398) \| 0.591059 / 4.805227 (-4.214168) \| 0.126360 / 6.500664 (-6.374304) \| 0.062108 / 0.075469 (-0.013361) \|\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) \| 1.254398 / 1.841788 (-0.587390) \| 18.866729 / 8.074308 (10.792420) \| 14.120008 / 10.191392 (3.928616) \| 0.152388 / 0.680424 (-0.528035) \| 0.016997 / 0.534201 (-0.517204) \| 0.336435 / 0.579283 (-0.242848) \| 0.364612 / 0.434364 (-0.069752) \| 0.391434 / 0.540337 (-0.148903) \| 0.567180 / 1.386936 (-0.819756) \|\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) \| 0.006477 / 0.011353 (-0.004876) \| 0.003723 / 0.011008 (-0.007285) \| 0.062712 / 0.038508 (0.024204) \| 0.069380 / 0.023109 (0.046271) \| 0.393394 / 0.275898 (0.117496) \| 0.446903 / 0.323480 (0.123423) \| 0.004833 / 0.007986 (-0.003153) \| 0.002946 / 0.004328 (-0.001382) \| 0.062076 / 0.004250 (0.057826) \| 0.051589 / 0.037052 (0.014537) \| 0.388536 / 0.258489 (0.130047) \| 0.451406 / 0.293841 (0.157565) \| 0.027824 / 0.128546 (-0.100722) \| 0.008040 / 0.075646 (-0.067606) \| 0.067085 / 0.419271 (-0.352187) \| 0.042269 / 0.043533 (-0.001264) \| 0.363419 / 0.255139 (0.108280) \| 0.435201 / 0.283200 (0.152001) \| 0.021275 / 0.141683 (-0.120408) \| 1.439838 / 1.452155 (-0.012316) \| 1.477279 / 1.492716 (-0.015437) \|\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) \| 0.229667 / 0.018006 (0.211661) \| 0.434101 / 0.000490 (0.433611) \| 0.000652 / 0.000200 (0.000452) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026141 / 0.037411 (-0.011271) \| 0.078950 / 0.014526 (0.064424) \| 0.090143 / 0.176557 (-0.086413) \| 0.143941 / 0.737135 (-0.593195) \| 0.090465 / 0.296338 (-0.205873) \|\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) \| 0.432042 / 0.215209 (0.216833) \| 4.322134 / 2.077655 (2.244479) \| 2.242897 / 1.504120 (0.738777) \| 2.076351 / 1.541195 (0.535157) \| 2.166739 / 1.468490 (0.698249) \| 0.500833 / 4.584777 (-4.083944) \| 3.140117 / 3.745712 (-0.605595) \| 4.383050 / 5.269862 (-0.886812) \| 2.548245 / 4.565676 (-2.017432) \| 0.057521 / 0.424275 (-0.366754) \| 0.006946 / 0.007607 (-0.000662) \| 0.509613 / 0.226044 (0.283569) \| 5.114052 / 2.268929 (2.845123) \| 2.682112 / 55.444624 (-52.762512) \| 2.362385 / 6.876477 (-4.514092) \| 2.531787 / 2.142072 (0.389715) \| 0.595085 / 4.805227 (-4.210142) \| 0.130198 / 6.500664 (-6.370466) \| 0.064057 / 0.075469 (-0.011412) \|\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) \| 1.346254 / 1.841788 (-0.495534) \| 19.036911 / 8.074308 (10.962603) \| 14.478689 / 10.191392 (4.287297) \| 0.147541 / 0.680424 (-0.532883) \| 0.016851 / 0.534201 (-0.517350) \| 0.333901 / 0.579283 (-0.245382) \| 0.380012 / 0.434364 (-0.054352) \| 0.396021 / 0.540337 (-0.144317) \| 0.540612 / 1.386936 (-0.846324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n", "CI failure is unrelated. Merging.", "<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) \| 0.009498 / 0.011353 (-0.001855) \| 0.005639 / 0.011008 (-0.005369) \| 0.108731 / 0.038508 (0.070223) \| 0.094052 / 0.023109 (0.070943) \| 0.454375 / 0.275898 (0.178477) \| 0.486852 / 0.323480 (0.163372) \| 0.006627 / 0.007986 (-0.001359) \| 0.004712 / 0.004328 (0.000383) \| 0.082006 / 0.004250 (0.077756) \| 0.079394 / 0.037052 (0.042342) \| 0.450982 / 0.258489 (0.192493) \| 0.502659 / 0.293841 (0.208818) \| 0.049741 / 0.128546 (-0.078806) \| 0.014482 / 0.075646 (-0.061164) \| 0.362661 / 0.419271 (-0.056611) \| 0.068225 / 0.043533 (0.024692) \| 0.456219 / 0.255139 (0.201080) \| 0.483919 / 0.283200 (0.200719) \| 0.044490 / 0.141683 (-0.097193) \| 1.809420 / 1.452155 (0.357265) \| 1.908859 / 1.492716 (0.416143) \|\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) \| 0.267350 / 0.018006 (0.249344) \| 0.600403 / 0.000490 (0.599913) \| 0.003665 / 0.000200 (0.003465) \| 0.000162 / 0.000054 (0.000107) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032499 / 0.037411 (-0.004912) \| 0.104829 / 0.014526 (0.090303) \| 0.115809 / 0.176557 (-0.060747) \| 0.191561 / 0.737135 (-0.545574) \| 0.113454 / 0.296338 (-0.182885) \|\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) \| 0.599165 / 0.215209 (0.383956) \| 5.802947 / 2.077655 (3.725292) \| 2.477330 / 1.504120 (0.973210) \| 2.231147 / 1.541195 (0.689952) \| 2.365688 / 1.468490 (0.897197) \| 0.853912 / 4.584777 (-3.730865) \| 5.529472 / 3.745712 (1.783760) \| 6.145286 / 5.269862 (0.875424) \| 3.415871 / 4.565676 (-1.149805) \| 0.099889 / 0.424275 (-0.324386) \| 0.008933 / 0.007607 (0.001325) \| 0.704643 / 0.226044 (0.478598) \| 7.178101 / 2.268929 (4.909173) \| 3.367120 / 55.444624 (-52.077504) \| 2.795177 / 6.876477 (-4.081300) \| 2.796798 / 2.142072 (0.654726) \| 1.039097 / 4.805227 (-3.766130) \| 0.232784 / 6.500664 (-6.267881) \| 0.083608 / 0.075469 (0.008138) \|\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) \| 1.646827 / 1.841788 (-0.194961) \| 25.003419 / 8.074308 (16.929111) \| 22.165746 / 10.191392 (11.974354) \| 0.246179 / 0.680424 (-0.434245) \| 0.029304 / 0.534201 (-0.504897) \| 0.500767 / 0.579283 (-0.078516) \| 0.606501 / 0.434364 (0.172137) \| 0.564092 / 0.540337 (0.023755) \| 0.857568 / 1.386936 (-0.529368) \|\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) \| 0.009206 / 0.011353 (-0.002146) \| 0.005084 / 0.011008 (-0.005925) \| 0.081402 / 0.038508 (0.042894) \| 0.088028 / 0.023109 (0.064919) \| 0.539509 / 0.275898 (0.263611) \| 0.590759 / 0.323480 (0.267280) \| 0.006527 / 0.007986 (-0.001459) \| 0.004375 / 0.004328 (0.000047) \| 0.082327 / 0.004250 (0.078076) \| 0.065442 / 0.037052 (0.028390) \| 0.548254 / 0.258489 (0.289765) \| 0.598388 / 0.293841 (0.304547) \| 0.049409 / 0.128546 (-0.079137) \| 0.014366 / 0.075646 (-0.061280) \| 0.094568 / 0.419271 (-0.324703) \| 0.063685 / 0.043533 (0.020152) \| 0.545359 / 0.255139 (0.290220) \| 0.573358 / 0.283200 (0.290159) \| 0.036864 / 0.141683 (-0.104819) \| 1.817985 / 1.452155 (0.365830) \| 1.925188 / 1.492716 (0.432472) \|\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) \| 0.303205 / 0.018006 (0.285199) \| 0.620981 / 0.000490 (0.620491) \| 0.004910 / 0.000200 (0.004710) \| 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) \| 0.033791 / 0.037411 (-0.003620) \| 0.114974 / 0.014526 (0.100448) \| 0.117682 / 0.176557 (-0.058875) \| 0.177188 / 0.737135 (-0.559947) \| 0.126425 / 0.296338 (-0.169914) \|\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) \| 0.636932 / 0.215209 (0.421723) \| 6.289054 / 2.077655 (4.211399) \| 2.920772 / 1.504120 (1.416652) \| 2.672080 / 1.541195 (1.130885) \| 2.712271 / 1.468490 (1.243781) \| 0.889305 / 4.584777 (-3.695472) \| 5.536018 / 3.745712 (1.790306) \| 4.687564 / 5.269862 (-0.582298) \| 3.204239 / 4.565676 (-1.361437) \| 0.095546 / 0.424275 (-0.328729) \| 0.008838 / 0.007607 (0.001231) \| 0.714584 / 0.226044 (0.488540) \| 7.482663 / 2.268929 (5.213735) \| 3.621392 / 55.444624 (-51.823232) \| 2.987777 / 6.876477 (-3.888700) \| 3.312636 / 2.142072 (1.170564) \| 1.033721 / 4.805227 (-3.771506) \| 0.206292 / 6.500664 (-6.294372) \| 0.079423 / 0.075469 (0.003953) \|\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) \| 1.798645 / 1.841788 (-0.043143) \| 25.544329 / 8.074308 (17.470021) \| 23.041318 / 10.191392 (12.849926) \| 0.259067 / 0.680424 (-0.421357) \| 0.029839 / 0.534201 (-0.504362) \| 0.495583 / 0.579283 (-0.083700) \| 0.598755 / 0.434364 (0.164391) \| 0.574864 / 0.540337 (0.034527) \| 0.831160 / 1.386936 (-0.555776) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n" ]
1,811,549,650	6,051	Skipping shard in the remote repo and resume upload	closed	2023-07-19T09:25:26	2023-07-20T18:16:01	2023-07-20T18:16:00	https://github.com/huggingface/datasets/issues/6051	null	rs9000	false	[ "Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before `push_to_hub` in a faster manner and cache it to disk:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset(...)\r\nformat = dataset.format\r\ndataset = dataset.with_format(\"arrow\")\r\ndataset = dataset.map(embed_table_storage, batched=True)\r\ndataset = dataset.with_format(**format)\r\n# push_to_hub(...)\r\n```\r\n\r\n(In Datasets 3.0, these external bytes will be written to an Arrow file when generating a dataset to avoid this \"embed\" step)", "Hi, thanks, this solution saves some time.\r\nBut can't we avoid embedding all external image files bytes with each push, skipping the images that have already been pushed into the repo?\r\n\r\nEdit: Ok I missed the part of cache it manually on the disk the first time, this solves the problem. Thank you" ]
1,810,378,706	6,049	Update `ruff` version in pre-commit config	closed	2023-07-18T17:13:50	2023-12-01T14:26:19	2023-12-01T14:26:19	https://github.com/huggingface/datasets/pull/6049	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6049", "html_url": "https://github.com/huggingface/datasets/pull/6049", "diff_url": "https://github.com/huggingface/datasets/pull/6049.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6049.patch", "merged_at": null }	polinaeterna	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint.", "I've updated the `ruff`'s pre-commit version as part of https://github.com/huggingface/datasets/pull/6434, so feel free to close this PR." ]
1,809,629,346	6,048	when i use datasets.load_dataset, i encounter the http connect error!	closed	2023-07-18T10:16:34	2023-07-18T16:18:39	2023-07-18T16:18:39	https://github.com/huggingface/datasets/issues/6048	null	yangy1992	false	[ "The `audiofolder` loader is not available in version `2.3.2`, hence the error. Please run the `pip install -U datasets` command to update the `datasets` installation to make `load_dataset(\"audiofolder\", ...)` work." ]
1,809,627,947	6,047	Bump dev version	closed	2023-07-18T10:15:39	2023-07-18T10:28:01	2023-07-18T10:15:52	https://github.com/huggingface/datasets/pull/6047	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6047", "html_url": "https://github.com/huggingface/datasets/pull/6047", "diff_url": "https://github.com/huggingface/datasets/pull/6047.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6047.patch", "merged_at": "2023-07-18T10:15:52" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6047). 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) \| 0.006384 / 0.011353 (-0.004969) \| 0.003872 / 0.011008 (-0.007136) \| 0.083454 / 0.038508 (0.044946) \| 0.069120 / 0.023109 (0.046011) \| 0.312573 / 0.275898 (0.036675) \| 0.345814 / 0.323480 (0.022334) \| 0.005729 / 0.007986 (-0.002257) \| 0.003225 / 0.004328 (-0.001103) \| 0.063950 / 0.004250 (0.059700) \| 0.053998 / 0.037052 (0.016946) \| 0.316492 / 0.258489 (0.058003) \| 0.350738 / 0.293841 (0.056897) \| 0.030770 / 0.128546 (-0.097776) \| 0.008474 / 0.075646 (-0.067173) \| 0.286989 / 0.419271 (-0.132282) \| 0.052473 / 0.043533 (0.008940) \| 0.314361 / 0.255139 (0.059222) \| 0.335170 / 0.283200 (0.051970) \| 0.022885 / 0.141683 (-0.118798) \| 1.465430 / 1.452155 (0.013275) \| 1.527799 / 1.492716 (0.035083) \|\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) \| 0.209377 / 0.018006 (0.191371) \| 0.455583 / 0.000490 (0.455094) \| 0.003352 / 0.000200 (0.003152) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026284 / 0.037411 (-0.011127) \| 0.080710 / 0.014526 (0.066185) \| 0.091741 / 0.176557 (-0.084816) \| 0.147602 / 0.737135 (-0.589534) \| 0.091173 / 0.296338 (-0.205166) \|\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) \| 0.386592 / 0.215209 (0.171383) \| 3.856665 / 2.077655 (1.779011) \| 1.835745 / 1.504120 (0.331625) \| 1.671814 / 1.541195 (0.130619) \| 1.711224 / 1.468490 (0.242734) \| 0.484704 / 4.584777 (-4.100073) \| 3.649239 / 3.745712 (-0.096473) \| 3.784051 / 5.269862 (-1.485810) \| 2.241195 / 4.565676 (-2.324482) \| 0.056613 / 0.424275 (-0.367662) \| 0.007140 / 0.007607 (-0.000467) \| 0.464585 / 0.226044 (0.238540) \| 4.616537 / 2.268929 (2.347609) \| 2.371969 / 55.444624 (-53.072656) \| 1.977754 / 6.876477 (-4.898723) \| 2.083385 / 2.142072 (-0.058687) \| 0.582330 / 4.805227 (-4.222897) \| 0.132744 / 6.500664 (-6.367920) \| 0.059822 / 0.075469 (-0.015647) \|\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) \| 1.259566 / 1.841788 (-0.582221) \| 18.990166 / 8.074308 (10.915858) \| 13.992069 / 10.191392 (3.800677) \| 0.160001 / 0.680424 (-0.520423) \| 0.018622 / 0.534201 (-0.515579) \| 0.392921 / 0.579283 (-0.186362) \| 0.418225 / 0.434364 (-0.016139) \| 0.471252 / 0.540337 (-0.069086) \| 0.653227 / 1.386936 (-0.733709) \|\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) \| 0.006641 / 0.011353 (-0.004712) \| 0.003738 / 0.011008 (-0.007271) \| 0.064053 / 0.038508 (0.025545) \| 0.069467 / 0.023109 (0.046357) \| 0.360625 / 0.275898 (0.084727) \| 0.394291 / 0.323480 (0.070811) \| 0.005236 / 0.007986 (-0.002750) \| 0.003304 / 0.004328 (-0.001024) \| 0.064078 / 0.004250 (0.059827) \| 0.054605 / 0.037052 (0.017552) \| 0.374567 / 0.258489 (0.116078) \| 0.411227 / 0.293841 (0.117386) \| 0.031614 / 0.128546 (-0.096933) \| 0.008323 / 0.075646 (-0.067324) \| 0.070616 / 0.419271 (-0.348656) \| 0.050077 / 0.043533 (0.006544) \| 0.362229 / 0.255139 (0.107090) \| 0.388310 / 0.283200 (0.105110) \| 0.024053 / 0.141683 (-0.117630) \| 1.508913 / 1.452155 (0.056759) \| 1.562140 / 1.492716 (0.069423) \|\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) \| 0.230172 / 0.018006 (0.212165) \| 0.449363 / 0.000490 (0.448873) \| 0.002374 / 0.000200 (0.002174) \| 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) \| 0.029813 / 0.037411 (-0.007598) \| 0.087298 / 0.014526 (0.072772) \| 0.096712 / 0.176557 (-0.079845) \| 0.152864 / 0.737135 (-0.584271) \| 0.098204 / 0.296338 (-0.198135) \|\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) \| 0.408664 / 0.215209 (0.193455) \| 4.075068 / 2.077655 (1.997414) \| 2.096365 / 1.504120 (0.592245) \| 1.936096 / 1.541195 (0.394901) \| 1.961872 / 1.468490 (0.493382) \| 0.483383 / 4.584777 (-4.101394) \| 3.686926 / 3.745712 (-0.058787) \| 4.798824 / 5.269862 (-0.471037) \| 2.652279 / 4.565676 (-1.913398) \| 0.056695 / 0.424275 (-0.367580) \| 0.007592 / 0.007607 (-0.000016) \| 0.484710 / 0.226044 (0.258665) \| 4.842153 / 2.268929 (2.573225) \| 2.636828 / 55.444624 (-52.807796) \| 2.243666 / 6.876477 (-4.632811) \| 2.375972 / 2.142072 (0.233899) \| 0.578544 / 4.805227 (-4.226683) \| 0.132579 / 6.500664 (-6.368085) \| 0.061287 / 0.075469 (-0.014182) \|\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) \| 1.360287 / 1.841788 (-0.481501) \| 19.464110 / 8.074308 (11.389802) \| 14.530875 / 10.191392 (4.339483) \| 0.149479 / 0.680424 (-0.530944) \| 0.018471 / 0.534201 (-0.515730) \| 0.395399 / 0.579283 (-0.183884) \| 0.412897 / 0.434364 (-0.021467) \| 0.465194 / 0.540337 (-0.075144) \| 0.611752 / 1.386936 (-0.775184) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79a535de98b590da7bc223a6498c59790882f14a \"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) \| 0.008986 / 0.011353 (-0.002367) \| 0.005104 / 0.011008 (-0.005905) \| 0.108371 / 0.038508 (0.069863) \| 0.091655 / 0.023109 (0.068546) \| 0.430183 / 0.275898 (0.154285) \| 0.481387 / 0.323480 (0.157907) \| 0.006662 / 0.007986 (-0.001324) \| 0.004681 / 0.004328 (0.000353) \| 0.089325 / 0.004250 (0.085075) \| 0.065096 / 0.037052 (0.028044) \| 0.435021 / 0.258489 (0.176532) \| 0.478635 / 0.293841 (0.184794) \| 0.047628 / 0.128546 (-0.080918) \| 0.013496 / 0.075646 (-0.062150) \| 0.389661 / 0.419271 (-0.029611) \| 0.082260 / 0.043533 (0.038727) \| 0.474165 / 0.255139 (0.219026) \| 0.464877 / 0.283200 (0.181677) \| 0.039784 / 0.141683 (-0.101899) \| 1.874694 / 1.452155 (0.422539) \| 1.980183 / 1.492716 (0.487467) \|\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) \| 0.254044 / 0.018006 (0.236038) \| 0.631495 / 0.000490 (0.631005) \| 0.000628 / 0.000200 (0.000428) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038773 / 0.037411 (0.001362) \| 0.103681 / 0.014526 (0.089156) \| 0.125081 / 0.176557 (-0.051476) \| 0.198345 / 0.737135 (-0.538790) \| 0.122217 / 0.296338 (-0.174121) \|\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) \| 0.611677 / 0.215209 (0.396468) \| 6.220790 / 2.077655 (4.143135) \| 2.729858 / 1.504120 (1.225739) \| 2.351944 / 1.541195 (0.810749) \| 2.449137 / 1.468490 (0.980647) \| 0.896842 / 4.584777 (-3.687935) \| 5.537491 / 3.745712 (1.791778) \| 8.480182 / 5.269862 (3.210320) \| 5.251404 / 4.565676 (0.685728) \| 0.100449 / 0.424275 (-0.323826) \| 0.009008 / 0.007607 (0.001401) \| 0.750060 / 0.226044 (0.524016) \| 7.390940 / 2.268929 (5.122011) \| 3.478256 / 55.444624 (-51.966369) \| 2.883597 / 6.876477 (-3.992880) \| 3.082256 / 2.142072 (0.940183) \| 1.114339 / 4.805227 (-3.690889) \| 0.225389 / 6.500664 (-6.275275) \| 0.083972 / 0.075469 (0.008503) \|\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) \| 1.741522 / 1.841788 (-0.100266) \| 25.674700 / 8.074308 (17.600392) \| 24.324412 / 10.191392 (14.133020) \| 0.257878 / 0.680424 (-0.422546) \| 0.038384 / 0.534201 (-0.495817) \| 0.508302 / 0.579283 (-0.070981) \| 0.612979 / 0.434364 (0.178615) \| 0.584366 / 0.540337 (0.044029) \| 0.881115 / 1.386936 (-0.505821) \|\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) \| 0.009114 / 0.011353 (-0.002239) \| 0.005333 / 0.011008 (-0.005675) \| 0.094944 / 0.038508 (0.056436) \| 0.099178 / 0.023109 (0.076068) \| 0.529813 / 0.275898 (0.253915) \| 0.551282 / 0.323480 (0.227802) \| 0.006442 / 0.007986 (-0.001543) \| 0.004283 / 0.004328 (-0.000045) \| 0.084257 / 0.004250 (0.080007) \| 0.067557 / 0.037052 (0.030504) \| 0.514733 / 0.258489 (0.256244) \| 0.568200 / 0.293841 (0.274359) \| 0.050969 / 0.128546 (-0.077577) \| 0.014495 / 0.075646 (-0.061151) \| 0.097089 / 0.419271 (-0.322182) \| 0.063142 / 0.043533 (0.019609) \| 0.513327 / 0.255139 (0.258188) \| 0.520593 / 0.283200 (0.237394) \| 0.036824 / 0.141683 (-0.104859) \| 1.954875 / 1.452155 (0.502720) \| 1.976307 / 1.492716 (0.483591) \|\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) \| 0.304070 / 0.018006 (0.286063) \| 0.611073 / 0.000490 (0.610583) \| 0.005027 / 0.000200 (0.004827) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037993 / 0.037411 (0.000582) \| 0.115876 / 0.014526 (0.101350) \| 0.118087 / 0.176557 (-0.058469) \| 0.186437 / 0.737135 (-0.550699) \| 0.129883 / 0.296338 (-0.166456) \|\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) \| 0.658292 / 0.215209 (0.443083) \| 6.618257 / 2.077655 (4.540602) \| 3.203786 / 1.504120 (1.699667) \| 2.858714 / 1.541195 (1.317519) \| 2.940974 / 1.468490 (1.472484) \| 0.856238 / 4.584777 (-3.728538) \| 5.427708 / 3.745712 (1.681996) \| 4.810048 / 5.269862 (-0.459813) \| 3.120006 / 4.565676 (-1.445671) \| 0.098098 / 0.424275 (-0.326177) \| 0.010077 / 0.007607 (0.002470) \| 0.790890 / 0.226044 (0.564845) \| 7.956679 / 2.268929 (5.687750) \| 3.955710 / 55.444624 (-51.488914) \| 3.446419 / 6.876477 (-3.430057) \| 3.541228 / 2.142072 (1.399156) \| 1.013420 / 4.805227 (-3.791808) \| 0.213741 / 6.500664 (-6.286923) \| 0.080857 / 0.075469 (0.005388) \|\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) \| 1.813265 / 1.841788 (-0.028522) \| 25.965199 / 8.074308 (17.890891) \| 21.892761 / 10.191392 (11.701369) \| 0.257843 / 0.680424 (-0.422580) \| 0.029388 / 0.534201 (-0.504813) \| 0.510609 / 0.579283 (-0.068674) \| 0.626579 / 0.434364 (0.192215) \| 0.576865 / 0.540337 (0.036528) \| 0.826610 / 1.386936 (-0.560326) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a9c00249b330f97f66ceb86c2939261091f4fe \"CML watermark\")\n" ]
1,808,154,414	6,046	Support proxy and user-agent in fsspec calls	open	2023-07-17T16:39:26	2025-06-26T18:26:27	null	https://github.com/huggingface/datasets/issues/6046	null	lhoestq	false	[ "hii @lhoestq can you assign this issue to me?\r\n", "You can reply \"#self-assign\" to this issue to automatically get assigned to it :)\r\nLet me know if you have any questions or if I can help", "#2289 ", "Actually i am quite new to figure it out how everything goes and done \r\n\r\n> You can reply \"#self-assign\" to this issue to automatically get assigned to it :)\r\n> Let me know if you have any questions or if I can help\r\n\r\nwhen i wrote #self-assign it automatically got converted to some number is it correct or i have done it some wrong way, I am quite new to open source thus wanna try to learn and explore it", "#2289 #self-assign ", "Ah yea github tries to replace the #self-assign with an issue link. I guess you can try to copy-paste instead to see if it works\r\n\r\nAnyway let me assign you manually", "thanks a lot @lhoestq ! though i have a very lil idea of the issue, i am new. as i said before, but gonna try my best shot to do it.\r\ncan you please suggest some tips or anything from your side, how basically we approach it will be really helpfull.\r\nWill try my best!", "The HfFileSystem from the `huggingface_hub` package can already read the HTTP_PROXY and HTTPS_PROXY environment variables. So the remaining thing missing is the `user_agent` that the user may include in a `DownloadConfig` object. The user agent can be used for regular http calls but also calls to the HfFileSystem.\r\n\r\n- for http, the `user_agent` isn't passed from `DownloadConfig` to `get_datasets_user_agent` in `_prepare_single_hop_path_and_storage_options` in `streaming_download_manager.py` so we need to include it\r\n- for HfFileSystem I think it requires a PR in https://github.com/huggingface/huggingface_hub to include it in the `HfFileSystem.__init__`", "Hi @lhoestq 👋🏼\n\nIs anyone currently working on this? If not, I'd like to pick it up.\n\nAs I understand it:\n- The `user_agent` from `DownloadConfig` isn't currently passed to `get_datasets_user_agent()` inside `_prepare_single_hop_path_and_storage_options`.\n- I'll update that function to include the correct `user-agent` in the `headers`.\n- For full support, we may also need a change in `huggingface_hub` to let `HfFileSystem` accept custom headers.\n\nPlease let me know if this approach sounds good or if you’d prefer it handled differently 🙌\n", "Hi @lhoestq 👋🏼 Just following up on this one!\n\nI’ve opened [PR #7631](https://github.com/huggingface/datasets/pull/7631) to pass the user_agent from DownloadConfig into the fsspec storage options via _prepare_single_hop_path_and_storage_options (for HTTP/S).\n\nFor full support in HfFileSystem, I understand we might need a corresponding PR in huggingface_hub to add user_agent handling in its __init__. If you're okay with that direction, I can also raise a PR there!\n\nLet me know if any adjustments are needed 🙌" ]
1,808,072,270	6,045	Check if column names match in Parquet loader only when config `features` are specified	closed	2023-07-17T15:50:15	2023-07-24T14:45:56	2023-07-24T14:35:03	https://github.com/huggingface/datasets/pull/6045	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6045", "html_url": "https://github.com/huggingface/datasets/pull/6045", "diff_url": "https://github.com/huggingface/datasets/pull/6045.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6045.patch", "merged_at": "2023-07-24T14:35:03" }	mariosasko	true	[ "_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) \| 0.006557 / 0.011353 (-0.004796) \| 0.004096 / 0.011008 (-0.006913) \| 0.083577 / 0.038508 (0.045069) \| 0.072092 / 0.023109 (0.048983) \| 0.319192 / 0.275898 (0.043294) \| 0.351845 / 0.323480 (0.028365) \| 0.005475 / 0.007986 (-0.002511) \| 0.003419 / 0.004328 (-0.000910) \| 0.064562 / 0.004250 (0.060311) \| 0.057930 / 0.037052 (0.020878) \| 0.326085 / 0.258489 (0.067596) \| 0.368316 / 0.293841 (0.074475) \| 0.030502 / 0.128546 (-0.098044) \| 0.008504 / 0.075646 (-0.067142) \| 0.287217 / 0.419271 (-0.132054) \| 0.052337 / 0.043533 (0.008804) \| 0.319011 / 0.255139 (0.063872) \| 0.352711 / 0.283200 (0.069511) \| 0.023278 / 0.141683 (-0.118405) \| 1.482578 / 1.452155 (0.030423) \| 1.553391 / 1.492716 (0.060675) \|\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) \| 0.199628 / 0.018006 (0.181622) \| 0.464571 / 0.000490 (0.464081) \| 0.003512 / 0.000200 (0.003312) \| 0.000072 / 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) \| 0.029109 / 0.037411 (-0.008302) \| 0.082203 / 0.014526 (0.067677) \| 0.096223 / 0.176557 (-0.080333) \| 0.155598 / 0.737135 (-0.581537) \| 0.097738 / 0.296338 (-0.198600) \|\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) \| 0.386135 / 0.215209 (0.170926) \| 3.837157 / 2.077655 (1.759502) \| 1.836869 / 1.504120 (0.332750) \| 1.680592 / 1.541195 (0.139398) \| 1.769456 / 1.468490 (0.300966) \| 0.493150 / 4.584777 (-4.091627) \| 3.589797 / 3.745712 (-0.155915) \| 3.330000 / 5.269862 (-1.939861) \| 2.059856 / 4.565676 (-2.505821) \| 0.057951 / 0.424275 (-0.366324) \| 0.007340 / 0.007607 (-0.000267) \| 0.463203 / 0.226044 (0.237159) \| 4.631514 / 2.268929 (2.362585) \| 2.329887 / 55.444624 (-53.114738) \| 2.008815 / 6.876477 (-4.867662) \| 2.199067 / 2.142072 (0.056995) \| 0.591417 / 4.805227 (-4.213810) \| 0.137154 / 6.500664 (-6.363510) \| 0.061326 / 0.075469 (-0.014143) \|\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) \| 1.269676 / 1.841788 (-0.572111) \| 19.375167 / 8.074308 (11.300858) \| 13.945419 / 10.191392 (3.754027) \| 0.146482 / 0.680424 (-0.533942) \| 0.018257 / 0.534201 (-0.515944) \| 0.391684 / 0.579283 (-0.187599) \| 0.411454 / 0.434364 (-0.022910) \| 0.466260 / 0.540337 (-0.074077) \| 0.655571 / 1.386936 (-0.731365) \|\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) \| 0.006619 / 0.011353 (-0.004734) \| 0.004102 / 0.011008 (-0.006907) \| 0.064848 / 0.038508 (0.026340) \| 0.074822 / 0.023109 (0.051713) \| 0.366535 / 0.275898 (0.090637) \| 0.395873 / 0.323480 (0.072394) \| 0.005315 / 0.007986 (-0.002670) \| 0.003270 / 0.004328 (-0.001059) \| 0.064829 / 0.004250 (0.060578) \| 0.056094 / 0.037052 (0.019042) \| 0.370355 / 0.258489 (0.111866) \| 0.406837 / 0.293841 (0.112996) \| 0.031634 / 0.128546 (-0.096912) \| 0.008569 / 0.075646 (-0.067077) \| 0.071126 / 0.419271 (-0.348145) \| 0.048629 / 0.043533 (0.005096) \| 0.365175 / 0.255139 (0.110036) \| 0.385234 / 0.283200 (0.102034) \| 0.023295 / 0.141683 (-0.118388) \| 1.466907 / 1.452155 (0.014752) \| 1.523118 / 1.492716 (0.030401) \|\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) \| 0.227872 / 0.018006 (0.209866) \| 0.451573 / 0.000490 (0.451083) \| 0.000379 / 0.000200 (0.000179) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029496 / 0.037411 (-0.007915) \| 0.086614 / 0.014526 (0.072088) \| 0.098165 / 0.176557 (-0.078392) \| 0.152218 / 0.737135 (-0.584917) \| 0.101215 / 0.296338 (-0.195123) \|\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) \| 0.407519 / 0.215209 (0.192310) \| 4.074704 / 2.077655 (1.997049) \| 2.113185 / 1.504120 (0.609065) \| 1.947461 / 1.541195 (0.406266) \| 1.998521 / 1.468490 (0.530031) \| 0.487463 / 4.584777 (-4.097313) \| 3.465423 / 3.745712 (-0.280289) \| 3.376498 / 5.269862 (-1.893363) \| 2.001533 / 4.565676 (-2.564144) \| 0.057052 / 0.424275 (-0.367223) \| 0.007325 / 0.007607 (-0.000283) \| 0.485648 / 0.226044 (0.259604) \| 4.860191 / 2.268929 (2.591262) \| 2.550340 / 55.444624 (-52.894284) \| 2.231136 / 6.876477 (-4.645341) \| 2.262539 / 2.142072 (0.120467) \| 0.591422 / 4.805227 (-4.213805) \| 0.132875 / 6.500664 (-6.367789) \| 0.062154 / 0.075469 (-0.013315) \|\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) \| 1.321834 / 1.841788 (-0.519954) \| 19.734750 / 8.074308 (11.660442) \| 14.681049 / 10.191392 (4.489657) \| 0.148894 / 0.680424 (-0.531530) \| 0.018414 / 0.534201 (-0.515787) \| 0.393377 / 0.579283 (-0.185906) \| 0.402795 / 0.434364 (-0.031569) \| 0.478624 / 0.540337 (-0.061714) \| 0.656767 / 1.386936 (-0.730169) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5a84a1fa226a4cafb3bb4387dc4b212a46caf31 \"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) \| 0.007012 / 0.011353 (-0.004341) \| 0.004120 / 0.011008 (-0.006888) \| 0.083720 / 0.038508 (0.045212) \| 0.083105 / 0.023109 (0.059996) \| 0.323803 / 0.275898 (0.047905) \| 0.340345 / 0.323480 (0.016865) \| 0.005872 / 0.007986 (-0.002113) \| 0.003528 / 0.004328 (-0.000801) \| 0.065185 / 0.004250 (0.060935) \| 0.063092 / 0.037052 (0.026040) \| 0.314900 / 0.258489 (0.056411) \| 0.349251 / 0.293841 (0.055410) \| 0.031612 / 0.128546 (-0.096934) \| 0.008541 / 0.075646 (-0.067105) \| 0.289865 / 0.419271 (-0.129407) \| 0.055264 / 0.043533 (0.011731) \| 0.309152 / 0.255139 (0.054013) \| 0.332625 / 0.283200 (0.049425) \| 0.024306 / 0.141683 (-0.117377) \| 1.489191 / 1.452155 (0.037037) \| 1.562447 / 1.492716 (0.069731) \|\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) \| 0.236681 / 0.018006 (0.218675) \| 0.567767 / 0.000490 (0.567277) \| 0.003022 / 0.000200 (0.002822) \| 0.000218 / 0.000054 (0.000164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028698 / 0.037411 (-0.008714) \| 0.081681 / 0.014526 (0.067155) \| 0.099109 / 0.176557 (-0.077447) \| 0.154381 / 0.737135 (-0.582754) \| 0.098691 / 0.296338 (-0.197648) \|\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) \| 0.397985 / 0.215209 (0.182776) \| 3.962499 / 2.077655 (1.884844) \| 1.936158 / 1.504120 (0.432038) \| 1.762339 / 1.541195 (0.221144) \| 1.837451 / 1.468490 (0.368961) \| 0.485655 / 4.584777 (-4.099122) \| 3.538341 / 3.745712 (-0.207371) \| 5.110095 / 5.269862 (-0.159767) \| 3.066152 / 4.565676 (-1.499524) \| 0.057505 / 0.424275 (-0.366770) \| 0.007334 / 0.007607 (-0.000273) \| 0.475622 / 0.226044 (0.249578) \| 4.754091 / 2.268929 (2.485162) \| 2.431379 / 55.444624 (-53.013246) \| 2.106178 / 6.876477 (-4.770298) \| 2.364305 / 2.142072 (0.222232) \| 0.614038 / 4.805227 (-4.191190) \| 0.148530 / 6.500664 (-6.352134) \| 0.061033 / 0.075469 (-0.014436) \|\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) \| 1.242345 / 1.841788 (-0.599443) \| 19.017266 / 8.074308 (10.942958) \| 13.477782 / 10.191392 (3.286390) \| 0.158513 / 0.680424 (-0.521911) \| 0.018757 / 0.534201 (-0.515444) \| 0.393773 / 0.579283 (-0.185510) \| 0.416933 / 0.434364 (-0.017431) \| 0.460012 / 0.540337 (-0.080326) \| 0.637010 / 1.386936 (-0.749926) \|\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) \| 0.006689 / 0.011353 (-0.004664) \| 0.004168 / 0.011008 (-0.006840) \| 0.065009 / 0.038508 (0.026501) \| 0.073766 / 0.023109 (0.050657) \| 0.369585 / 0.275898 (0.093687) \| 0.407945 / 0.323480 (0.084465) \| 0.005583 / 0.007986 (-0.002403) \| 0.003494 / 0.004328 (-0.000835) \| 0.065032 / 0.004250 (0.060782) \| 0.057166 / 0.037052 (0.020114) \| 0.370656 / 0.258489 (0.112166) \| 0.428381 / 0.293841 (0.134540) \| 0.031653 / 0.128546 (-0.096893) \| 0.008731 / 0.075646 (-0.066915) \| 0.071624 / 0.419271 (-0.347648) \| 0.049364 / 0.043533 (0.005832) \| 0.361824 / 0.255139 (0.106685) \| 0.387615 / 0.283200 (0.104415) \| 0.023228 / 0.141683 (-0.118455) \| 1.476204 / 1.452155 (0.024049) \| 1.553522 / 1.492716 (0.060806) \|\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) \| 0.266955 / 0.018006 (0.248948) \| 0.556566 / 0.000490 (0.556076) \| 0.000399 / 0.000200 (0.000199) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033104 / 0.037411 (-0.004307) \| 0.088067 / 0.014526 (0.073541) \| 0.103333 / 0.176557 (-0.073224) \| 0.157061 / 0.737135 (-0.580074) \| 0.105007 / 0.296338 (-0.191331) \|\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) \| 0.420826 / 0.215209 (0.205617) \| 4.201656 / 2.077655 (2.124001) \| 2.208336 / 1.504120 (0.704216) \| 2.043780 / 1.541195 (0.502585) \| 2.156215 / 1.468490 (0.687725) \| 0.490485 / 4.584777 (-4.094292) \| 3.611446 / 3.745712 (-0.134267) \| 5.293140 / 5.269862 (0.023279) \| 2.739778 / 4.565676 (-1.825899) \| 0.058175 / 0.424275 (-0.366100) \| 0.007633 / 0.007607 (0.000026) \| 0.500773 / 0.226044 (0.274729) \| 5.000900 / 2.268929 (2.731971) \| 2.721200 / 55.444624 (-52.723424) \| 2.349381 / 6.876477 (-4.527095) \| 2.386261 / 2.142072 (0.244188) \| 0.583174 / 4.805227 (-4.222053) \| 0.134558 / 6.500664 (-6.366106) \| 0.062157 / 0.075469 (-0.013312) \|\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) \| 1.351087 / 1.841788 (-0.490701) \| 20.305703 / 8.074308 (12.231395) \| 14.548518 / 10.191392 (4.357126) \| 0.173720 / 0.680424 (-0.506704) \| 0.018100 / 0.534201 (-0.516101) \| 0.395187 / 0.579283 (-0.184097) \| 0.414619 / 0.434364 (-0.019745) \| 0.462515 / 0.540337 (-0.077823) \| 0.617822 / 1.386936 (-0.769114) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#033d0a9de5c825fc9a6a9ce3c3d80eaab3493720 \"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) \| 0.006909 / 0.011353 (-0.004444) \| 0.003954 / 0.011008 (-0.007054) \| 0.084329 / 0.038508 (0.045821) \| 0.074919 / 0.023109 (0.051809) \| 0.319350 / 0.275898 (0.043451) \| 0.347264 / 0.323480 (0.023785) \| 0.005326 / 0.007986 (-0.002660) \| 0.003323 / 0.004328 (-0.001006) \| 0.064286 / 0.004250 (0.060036) \| 0.054748 / 0.037052 (0.017696) \| 0.324784 / 0.258489 (0.066295) \| 0.361445 / 0.293841 (0.067605) \| 0.031239 / 0.128546 (-0.097308) \| 0.008361 / 0.075646 (-0.067286) \| 0.287482 / 0.419271 (-0.131789) \| 0.052093 / 0.043533 (0.008560) \| 0.321454 / 0.255139 (0.066315) \| 0.337999 / 0.283200 (0.054800) \| 0.025807 / 0.141683 (-0.115876) \| 1.501838 / 1.452155 (0.049683) \| 1.574484 / 1.492716 (0.081767) \|\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) \| 0.193220 / 0.018006 (0.175214) \| 0.448105 / 0.000490 (0.447615) \| 0.002949 / 0.000200 (0.002749) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028517 / 0.037411 (-0.008894) \| 0.087281 / 0.014526 (0.072755) \| 0.098295 / 0.176557 (-0.078262) \| 0.156972 / 0.737135 (-0.580163) \| 0.101250 / 0.296338 (-0.195088) \|\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) \| 0.383734 / 0.215209 (0.168525) \| 3.821293 / 2.077655 (1.743638) \| 1.866487 / 1.504120 (0.362367) \| 1.722195 / 1.541195 (0.181000) \| 1.843762 / 1.468490 (0.375272) \| 0.484813 / 4.584777 (-4.099964) \| 3.535381 / 3.745712 (-0.210331) \| 5.502338 / 5.269862 (0.232477) \| 3.256078 / 4.565676 (-1.309599) \| 0.057312 / 0.424275 (-0.366963) \| 0.007305 / 0.007607 (-0.000302) \| 0.461523 / 0.226044 (0.235479) \| 4.611828 / 2.268929 (2.342899) \| 2.337180 / 55.444624 (-53.107445) \| 2.040956 / 6.876477 (-4.835521) \| 2.241233 / 2.142072 (0.099160) \| 0.583727 / 4.805227 (-4.221500) \| 0.132427 / 6.500664 (-6.368237) \| 0.060306 / 0.075469 (-0.015163) \|\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) \| 1.282223 / 1.841788 (-0.559565) \| 19.439745 / 8.074308 (11.365437) \| 13.627657 / 10.191392 (3.436265) \| 0.158975 / 0.680424 (-0.521449) \| 0.018599 / 0.534201 (-0.515601) \| 0.391136 / 0.579283 (-0.188147) \| 0.410947 / 0.434364 (-0.023417) \| 0.453889 / 0.540337 (-0.086448) \| 0.620928 / 1.386936 (-0.766008) \|\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) \| 0.006428 / 0.011353 (-0.004925) \| 0.003980 / 0.011008 (-0.007028) \| 0.065006 / 0.038508 (0.026498) \| 0.076541 / 0.023109 (0.053432) \| 0.358518 / 0.275898 (0.082620) \| 0.394397 / 0.323480 (0.070917) \| 0.005845 / 0.007986 (-0.002140) \| 0.003258 / 0.004328 (-0.001071) \| 0.064436 / 0.004250 (0.060186) \| 0.056691 / 0.037052 (0.019639) \| 0.367369 / 0.258489 (0.108880) \| 0.420345 / 0.293841 (0.126504) \| 0.031047 / 0.128546 (-0.097499) \| 0.008430 / 0.075646 (-0.067216) \| 0.071280 / 0.419271 (-0.347991) \| 0.048872 / 0.043533 (0.005339) \| 0.360073 / 0.255139 (0.104934) \| 0.384150 / 0.283200 (0.100951) \| 0.023189 / 0.141683 (-0.118494) \| 1.500251 / 1.452155 (0.048096) \| 1.545910 / 1.492716 (0.053194) \|\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) \| 0.224861 / 0.018006 (0.206855) \| 0.439901 / 0.000490 (0.439411) \| 0.000372 / 0.000200 (0.000172) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029914 / 0.037411 (-0.007497) \| 0.086916 / 0.014526 (0.072390) \| 0.099527 / 0.176557 (-0.077029) \| 0.153031 / 0.737135 (-0.584104) \| 0.100008 / 0.296338 (-0.196330) \|\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) \| 0.420305 / 0.215209 (0.205096) \| 4.198224 / 2.077655 (2.120569) \| 2.223807 / 1.504120 (0.719687) \| 2.058475 / 1.541195 (0.517280) \| 2.140405 / 1.468490 (0.671915) \| 0.481224 / 4.584777 (-4.103553) \| 3.593767 / 3.745712 (-0.151945) \| 5.536710 / 5.269862 (0.266849) \| 3.162048 / 4.565676 (-1.403629) \| 0.056662 / 0.424275 (-0.367614) \| 0.007301 / 0.007607 (-0.000306) \| 0.507494 / 0.226044 (0.281450) \| 5.047824 / 2.268929 (2.778896) \| 2.715167 / 55.444624 (-52.729458) \| 2.334916 / 6.876477 (-4.541560) \| 2.406615 / 2.142072 (0.264543) \| 0.572761 / 4.805227 (-4.232466) \| 0.131248 / 6.500664 (-6.369416) \| 0.062401 / 0.075469 (-0.013068) \|\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) \| 1.375896 / 1.841788 (-0.465892) \| 19.836638 / 8.074308 (11.762329) \| 14.246645 / 10.191392 (4.055253) \| 0.164975 / 0.680424 (-0.515449) \| 0.018293 / 0.534201 (-0.515908) \| 0.394196 / 0.579283 (-0.185087) \| 0.405895 / 0.434364 (-0.028469) \| 0.459221 / 0.540337 (-0.081116) \| 0.609898 / 1.386936 (-0.777038) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f89210ad839c2225b64822dfa248f68ab29ad46f \"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) \| 0.008463 / 0.011353 (-0.002890) \| 0.004754 / 0.011008 (-0.006254) \| 0.103574 / 0.038508 (0.065066) \| 0.083541 / 0.023109 (0.060432) \| 0.402498 / 0.275898 (0.126600) \| 0.434944 / 0.323480 (0.111465) \| 0.005766 / 0.007986 (-0.002219) \| 0.003823 / 0.004328 (-0.000505) \| 0.078433 / 0.004250 (0.074183) \| 0.056948 / 0.037052 (0.019895) \| 0.392539 / 0.258489 (0.134050) \| 0.447226 / 0.293841 (0.153385) \| 0.045845 / 0.128546 (-0.082701) \| 0.014043 / 0.075646 (-0.061603) \| 0.355768 / 0.419271 (-0.063503) \| 0.065492 / 0.043533 (0.021960) \| 0.408047 / 0.255139 (0.152908) \| 0.468313 / 0.283200 (0.185113) \| 0.033779 / 0.141683 (-0.107904) \| 1.772198 / 1.452155 (0.320043) \| 1.889127 / 1.492716 (0.396411) \|\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) \| 0.207107 / 0.018006 (0.189101) \| 0.533261 / 0.000490 (0.532771) \| 0.000864 / 0.000200 (0.000664) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032139 / 0.037411 (-0.005272) \| 0.102002 / 0.014526 (0.087476) \| 0.108780 / 0.176557 (-0.067777) \| 0.202857 / 0.737135 (-0.534278) \| 0.110378 / 0.296338 (-0.185960) \|\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) \| 0.582814 / 0.215209 (0.367605) \| 5.870683 / 2.077655 (3.793028) \| 2.510290 / 1.504120 (1.006171) \| 2.146337 / 1.541195 (0.605142) \| 2.239278 / 1.468490 (0.770788) \| 0.861205 / 4.584777 (-3.723572) \| 5.177394 / 3.745712 (1.431682) \| 8.550713 / 5.269862 (3.280852) \| 4.867715 / 4.565676 (0.302038) \| 0.096665 / 0.424275 (-0.327610) \| 0.008702 / 0.007607 (0.001095) \| 0.748908 / 0.226044 (0.522863) \| 7.302815 / 2.268929 (5.033887) \| 3.205045 / 55.444624 (-52.239580) \| 2.743914 / 6.876477 (-4.132562) \| 2.831240 / 2.142072 (0.689167) \| 1.103912 / 4.805227 (-3.701315) \| 0.246075 / 6.500664 (-6.254589) \| 0.092092 / 0.075469 (0.016623) \|\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) \| 1.591331 / 1.841788 (-0.250457) \| 23.085848 / 8.074308 (15.011540) \| 22.887963 / 10.191392 (12.696571) \| 0.212735 / 0.680424 (-0.467689) \| 0.027400 / 0.534201 (-0.506801) \| 0.493822 / 0.579283 (-0.085461) \| 0.574485 / 0.434364 (0.140121) \| 0.574873 / 0.540337 (0.034536) \| 0.826178 / 1.386936 (-0.560758) \|\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) \| 0.009155 / 0.011353 (-0.002198) \| 0.004976 / 0.011008 (-0.006032) \| 0.079308 / 0.038508 (0.040799) \| 0.093959 / 0.023109 (0.070850) \| 0.449110 / 0.275898 (0.173212) \| 0.493356 / 0.323480 (0.169876) \| 0.006317 / 0.007986 (-0.001669) \| 0.004179 / 0.004328 (-0.000150) \| 0.076991 / 0.004250 (0.072740) \| 0.061977 / 0.037052 (0.024924) \| 0.493823 / 0.258489 (0.235333) \| 0.491609 / 0.293841 (0.197768) \| 0.049552 / 0.128546 (-0.078994) \| 0.015174 / 0.075646 (-0.060472) \| 0.090431 / 0.419271 (-0.328841) \| 0.061597 / 0.043533 (0.018064) \| 0.467672 / 0.255139 (0.212533) \| 0.490542 / 0.283200 (0.207342) \| 0.035048 / 0.141683 (-0.106635) \| 1.807939 / 1.452155 (0.355784) \| 1.854859 / 1.492716 (0.362142) \|\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) \| 0.236672 / 0.018006 (0.218666) \| 0.542236 / 0.000490 (0.541746) \| 0.016334 / 0.000200 (0.016134) \| 0.000220 / 0.000054 (0.000165) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032051 / 0.037411 (-0.005360) \| 0.115352 / 0.014526 (0.100826) \| 0.125115 / 0.176557 (-0.051441) \| 0.173670 / 0.737135 (-0.563466) \| 0.117832 / 0.296338 (-0.178507) \|\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) \| 0.631513 / 0.215209 (0.416304) \| 6.371688 / 2.077655 (4.294033) \| 2.867240 / 1.504120 (1.363120) \| 2.454907 / 1.541195 (0.913713) \| 2.518860 / 1.468490 (1.050370) \| 0.879973 / 4.584777 (-3.704804) \| 5.170263 / 3.745712 (1.424551) \| 7.986429 / 5.269862 (2.716567) \| 4.828095 / 4.565676 (0.262418) \| 0.097808 / 0.424275 (-0.326468) \| 0.010541 / 0.007607 (0.002934) \| 0.745601 / 0.226044 (0.519557) \| 7.631683 / 2.268929 (5.362755) \| 3.524255 / 55.444624 (-51.920369) \| 2.866199 / 6.876477 (-4.010278) \| 2.982483 / 2.142072 (0.840410) \| 1.148957 / 4.805227 (-3.656270) \| 0.217067 / 6.500664 (-6.283598) \| 0.074357 / 0.075469 (-0.001112) \|\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) \| 1.714917 / 1.841788 (-0.126871) \| 24.151348 / 8.074308 (16.077040) \| 21.993604 / 10.191392 (11.802212) \| 0.234883 / 0.680424 (-0.445541) \| 0.028182 / 0.534201 (-0.506019) \| 0.474050 / 0.579283 (-0.105233) \| 0.557012 / 0.434364 (0.122648) \| 0.537823 / 0.540337 (-0.002514) \| 0.741488 / 1.386936 (-0.645448) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2e5a7a01a952a17d0424e93c3be2b4a5ffca7da \"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) \| 0.007640 / 0.011353 (-0.003713) \| 0.004776 / 0.011008 (-0.006232) \| 0.101582 / 0.038508 (0.063074) \| 0.085113 / 0.023109 (0.062003) \| 0.376000 / 0.275898 (0.100102) \| 0.421117 / 0.323480 (0.097637) \| 0.006095 / 0.007986 (-0.001891) \| 0.003884 / 0.004328 (-0.000445) \| 0.077263 / 0.004250 (0.073013) \| 0.065262 / 0.037052 (0.028210) \| 0.384041 / 0.258489 (0.125552) \| 0.442229 / 0.293841 (0.148388) \| 0.035706 / 0.128546 (-0.092840) \| 0.009996 / 0.075646 (-0.065651) \| 0.344925 / 0.419271 (-0.074346) \| 0.062358 / 0.043533 (0.018825) \| 0.371738 / 0.255139 (0.116599) \| 0.407093 / 0.283200 (0.123894) \| 0.026996 / 0.141683 (-0.114687) \| 1.762705 / 1.452155 (0.310550) \| 1.846777 / 1.492716 (0.354061) \|\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) \| 0.219660 / 0.018006 (0.201653) \| 0.521795 / 0.000490 (0.521305) \| 0.005344 / 0.000200 (0.005145) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036027 / 0.037411 (-0.001385) \| 0.100309 / 0.014526 (0.085784) \| 0.113041 / 0.176557 (-0.063515) \| 0.190037 / 0.737135 (-0.547099) \| 0.114552 / 0.296338 (-0.181786) \|\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) \| 0.466364 / 0.215209 (0.251154) \| 4.638745 / 2.077655 (2.561090) \| 2.317875 / 1.504120 (0.813755) \| 2.099241 / 1.541195 (0.558046) \| 2.149827 / 1.468490 (0.681337) \| 0.578913 / 4.584777 (-4.005864) \| 4.281866 / 3.745712 (0.536154) \| 3.778453 / 5.269862 (-1.491408) \| 2.411704 / 4.565676 (-2.153972) \| 0.068556 / 0.424275 (-0.355719) \| 0.008779 / 0.007607 (0.001172) \| 0.553165 / 0.226044 (0.327121) \| 5.524520 / 2.268929 (3.255591) \| 2.848444 / 55.444624 (-52.596181) \| 2.468591 / 6.876477 (-4.407885) \| 2.652117 / 2.142072 (0.510045) \| 0.694124 / 4.805227 (-4.111103) \| 0.157087 / 6.500664 (-6.343577) \| 0.070706 / 0.075469 (-0.004763) \|\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) \| 1.492031 / 1.841788 (-0.349757) \| 23.086596 / 8.074308 (15.012288) \| 16.791351 / 10.191392 (6.599959) \| 0.203932 / 0.680424 (-0.476492) \| 0.021736 / 0.534201 (-0.512464) \| 0.468344 / 0.579283 (-0.110939) \| 0.493790 / 0.434364 (0.059426) \| 0.563226 / 0.540337 (0.022889) \| 0.780384 / 1.386936 (-0.606553) \|\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) \| 0.007980 / 0.011353 (-0.003373) \| 0.004696 / 0.011008 (-0.006312) \| 0.076712 / 0.038508 (0.038204) \| 0.095915 / 0.023109 (0.072805) \| 0.433615 / 0.275898 (0.157717) \| 0.482477 / 0.323480 (0.158997) \| 0.007029 / 0.007986 (-0.000957) \| 0.003842 / 0.004328 (-0.000487) \| 0.076331 / 0.004250 (0.072081) \| 0.069755 / 0.037052 (0.032703) \| 0.458914 / 0.258489 (0.200425) \| 0.486155 / 0.293841 (0.192314) \| 0.036966 / 0.128546 (-0.091580) \| 0.010082 / 0.075646 (-0.065564) \| 0.083886 / 0.419271 (-0.335385) \| 0.059329 / 0.043533 (0.015796) \| 0.453782 / 0.255139 (0.198643) \| 0.459508 / 0.283200 (0.176308) \| 0.028400 / 0.141683 (-0.113283) \| 1.796406 / 1.452155 (0.344251) \| 1.881161 / 1.492716 (0.388445) \|\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) \| 0.235053 / 0.018006 (0.217047) \| 0.501907 / 0.000490 (0.501417) \| 0.005211 / 0.000200 (0.005011) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037752 / 0.037411 (0.000341) \| 0.107299 / 0.014526 (0.092773) \| 0.120307 / 0.176557 (-0.056250) \| 0.187542 / 0.737135 (-0.549593) \| 0.121805 / 0.296338 (-0.174533) \|\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) \| 0.490039 / 0.215209 (0.274830) \| 4.919169 / 2.077655 (2.841515) \| 2.520610 / 1.504120 (1.016490) \| 2.324473 / 1.541195 (0.783279) \| 2.421195 / 1.468490 (0.952705) \| 0.576314 / 4.584777 (-4.008463) \| 4.304752 / 3.745712 (0.559040) \| 3.881151 / 5.269862 (-1.388710) \| 2.409777 / 4.565676 (-2.155900) \| 0.067400 / 0.424275 (-0.356875) \| 0.009235 / 0.007607 (0.001627) \| 0.586601 / 0.226044 (0.360556) \| 5.850080 / 2.268929 (3.581152) \| 3.064859 / 55.444624 (-52.379766) \| 2.701734 / 6.876477 (-4.174743) \| 2.926190 / 2.142072 (0.784117) \| 0.698511 / 4.805227 (-4.106716) \| 0.158273 / 6.500664 (-6.342392) \| 0.074530 / 0.075469 (-0.000939) \|\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) \| 1.607113 / 1.841788 (-0.234674) \| 23.499279 / 8.074308 (15.424971) \| 17.049509 / 10.191392 (6.858117) \| 0.175689 / 0.680424 (-0.504735) \| 0.021762 / 0.534201 (-0.512439) \| 0.491450 / 0.579283 (-0.087833) \| 0.487557 / 0.434364 (0.053193) \| 0.570104 / 0.540337 (0.029766) \| 0.761527 / 1.386936 (-0.625409) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7096c59e6a8f4d5b16f3b906075f9e2ed83bbb25 \"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) \| 0.008725 / 0.011353 (-0.002628) \| 0.005156 / 0.011008 (-0.005852) \| 0.095147 / 0.038508 (0.056639) \| 0.084916 / 0.023109 (0.061807) \| 0.390769 / 0.275898 (0.114871) \| 0.434716 / 0.323480 (0.111237) \| 0.005982 / 0.007986 (-0.002004) \| 0.004323 / 0.004328 (-0.000006) \| 0.074712 / 0.004250 (0.070461) \| 0.058889 / 0.037052 (0.021837) \| 0.403997 / 0.258489 (0.145508) \| 0.443361 / 0.293841 (0.149520) \| 0.045908 / 0.128546 (-0.082639) \| 0.013562 / 0.075646 (-0.062085) \| 0.330683 / 0.419271 (-0.088588) \| 0.064821 / 0.043533 (0.021288) \| 0.407202 / 0.255139 (0.152063) \| 0.409930 / 0.283200 (0.126730) \| 0.032693 / 0.141683 (-0.108990) \| 1.630181 / 1.452155 (0.178026) \| 1.729680 / 1.492716 (0.236963) \|\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) \| 0.261240 / 0.018006 (0.243234) \| 0.581850 / 0.000490 (0.581360) \| 0.002997 / 0.000200 (0.002797) \| 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) \| 0.029279 / 0.037411 (-0.008133) \| 0.085004 / 0.014526 (0.070478) \| 0.127782 / 0.176557 (-0.048774) \| 0.168852 / 0.737135 (-0.568283) \| 0.098697 / 0.296338 (-0.197641) \|\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) \| 0.546417 / 0.215209 (0.331208) \| 5.602186 / 2.077655 (3.524531) \| 2.597049 / 1.504120 (1.092930) \| 2.384880 / 1.541195 (0.843685) \| 2.444516 / 1.468490 (0.976026) \| 0.796562 / 4.584777 (-3.788214) \| 5.239440 / 3.745712 (1.493727) \| 7.087768 / 5.269862 (1.817906) \| 4.308476 / 4.565676 (-0.257200) \| 0.091215 / 0.424275 (-0.333060) \| 0.007942 / 0.007607 (0.000335) \| 0.690059 / 0.226044 (0.464015) \| 6.727809 / 2.268929 (4.458880) \| 3.294522 / 55.444624 (-52.150103) \| 2.604088 / 6.876477 (-4.272389) \| 2.786970 / 2.142072 (0.644898) \| 0.918817 / 4.805227 (-3.886410) \| 0.191451 / 6.500664 (-6.309213) \| 0.069557 / 0.075469 (-0.005912) \|\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) \| 1.486377 / 1.841788 (-0.355411) \| 22.363470 / 8.074308 (14.289162) \| 19.963684 / 10.191392 (9.772292) \| 0.204161 / 0.680424 (-0.476263) \| 0.034570 / 0.534201 (-0.499631) \| 0.467937 / 0.579283 (-0.111346) \| 0.564870 / 0.434364 (0.130506) \| 0.511133 / 0.540337 (-0.029204) \| 0.777084 / 1.386936 (-0.609852) \|\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) \| 0.008612 / 0.011353 (-0.002741) \| 0.004993 / 0.011008 (-0.006015) \| 0.080769 / 0.038508 (0.042261) \| 0.075923 / 0.023109 (0.052814) \| 0.442271 / 0.275898 (0.166373) \| 0.495625 / 0.323480 (0.172146) \| 0.006467 / 0.007986 (-0.001518) \| 0.004001 / 0.004328 (-0.000328) \| 0.077309 / 0.004250 (0.073059) \| 0.063466 / 0.037052 (0.026414) \| 0.452460 / 0.258489 (0.193971) \| 0.494063 / 0.293841 (0.200223) \| 0.045751 / 0.128546 (-0.082796) \| 0.013402 / 0.075646 (-0.062245) \| 0.085760 / 0.419271 (-0.333511) \| 0.056532 / 0.043533 (0.012999) \| 0.440596 / 0.255139 (0.185457) \| 0.459540 / 0.283200 (0.176340) \| 0.035897 / 0.141683 (-0.105786) \| 1.728264 / 1.452155 (0.276109) \| 1.808142 / 1.492716 (0.315426) \|\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) \| 0.285094 / 0.018006 (0.267088) \| 0.598440 / 0.000490 (0.597950) \| 0.003476 / 0.000200 (0.003276) \| 0.000103 / 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) \| 0.035106 / 0.037411 (-0.002305) \| 0.091724 / 0.014526 (0.077198) \| 0.122803 / 0.176557 (-0.053754) \| 0.182114 / 0.737135 (-0.555022) \| 0.116196 / 0.296338 (-0.180143) \|\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) \| 0.585420 / 0.215209 (0.370211) \| 5.790370 / 2.077655 (3.712715) \| 2.833247 / 1.504120 (1.329127) \| 2.627949 / 1.541195 (1.086755) \| 2.643050 / 1.468490 (1.174560) \| 0.792036 / 4.584777 (-3.792741) \| 5.145084 / 3.745712 (1.399372) \| 4.423679 / 5.269862 (-0.846182) \| 2.802778 / 4.565676 (-1.762898) \| 0.093983 / 0.424275 (-0.330292) \| 0.009260 / 0.007607 (0.001652) \| 0.720302 / 0.226044 (0.494258) \| 7.116959 / 2.268929 (4.848031) \| 3.574782 / 55.444624 (-51.869843) \| 3.009330 / 6.876477 (-3.867147) \| 3.126488 / 2.142072 (0.984415) \| 0.949144 / 4.805227 (-3.856083) \| 0.195143 / 6.500664 (-6.305521) \| 0.072490 / 0.075469 (-0.002979) \|\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) \| 1.626368 / 1.841788 (-0.215419) \| 23.683021 / 8.074308 (15.608713) \| 20.085297 / 10.191392 (9.893905) \| 0.267057 / 0.680424 (-0.413367) \| 0.028306 / 0.534201 (-0.505894) \| 0.478448 / 0.579283 (-0.100835) \| 0.597619 / 0.434364 (0.163256) \| 0.544737 / 0.540337 (0.004399) \| 0.761805 / 1.386936 (-0.625131) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac53b590916c8d859fabcc2ef23c12add7f22f7 \"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) \| 0.009359 / 0.011353 (-0.001994) \| 0.004848 / 0.011008 (-0.006160) \| 0.099471 / 0.038508 (0.060963) \| 0.079483 / 0.023109 (0.056373) \| 0.375281 / 0.275898 (0.099383) \| 0.415566 / 0.323480 (0.092086) \| 0.006317 / 0.007986 (-0.001669) \| 0.005145 / 0.004328 (0.000817) \| 0.080345 / 0.004250 (0.076094) \| 0.064540 / 0.037052 (0.027487) \| 0.385897 / 0.258489 (0.127408) \| 0.432576 / 0.293841 (0.138735) \| 0.055109 / 0.128546 (-0.073437) \| 0.014166 / 0.075646 (-0.061480) \| 0.350870 / 0.419271 (-0.068402) \| 0.087483 / 0.043533 (0.043950) \| 0.402288 / 0.255139 (0.147149) \| 0.391997 / 0.283200 (0.108798) \| 0.045233 / 0.141683 (-0.096450) \| 1.795002 / 1.452155 (0.342847) \| 1.839063 / 1.492716 (0.346347) \|\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) \| 0.220851 / 0.018006 (0.202845) \| 0.513391 / 0.000490 (0.512901) \| 0.003740 / 0.000200 (0.003540) \| 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) \| 0.035287 / 0.037411 (-0.002124) \| 0.090670 / 0.014526 (0.076144) \| 0.115651 / 0.176557 (-0.060905) \| 0.180469 / 0.737135 (-0.556667) \| 0.106955 / 0.296338 (-0.189384) \|\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) \| 0.632381 / 0.215209 (0.417172) \| 6.185151 / 2.077655 (4.107497) \| 2.548263 / 1.504120 (1.044143) \| 2.194931 / 1.541195 (0.653737) \| 2.368685 / 1.468490 (0.900194) \| 0.956467 / 4.584777 (-3.628310) \| 5.280904 / 3.745712 (1.535192) \| 4.783057 / 5.269862 (-0.486805) \| 3.218493 / 4.565676 (-1.347184) \| 0.103545 / 0.424275 (-0.320730) \| 0.008424 / 0.007607 (0.000817) \| 0.736303 / 0.226044 (0.510259) \| 7.354305 / 2.268929 (5.085376) \| 3.280670 / 55.444624 (-52.163954) \| 2.478628 / 6.876477 (-4.397848) \| 2.623290 / 2.142072 (0.481217) \| 1.033064 / 4.805227 (-3.772163) \| 0.206496 / 6.500664 (-6.294168) \| 0.066449 / 0.075469 (-0.009020) \|\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) \| 1.508756 / 1.841788 (-0.333031) \| 21.866012 / 8.074308 (13.791704) \| 21.887761 / 10.191392 (11.696369) \| 0.231415 / 0.680424 (-0.449008) \| 0.028917 / 0.534201 (-0.505284) \| 0.468761 / 0.579283 (-0.110522) \| 0.568236 / 0.434364 (0.133872) \| 0.550156 / 0.540337 (0.009818) \| 0.783197 / 1.386936 (-0.603739) \|\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) \| 0.009413 / 0.011353 (-0.001939) \| 0.004951 / 0.011008 (-0.006058) \| 0.071402 / 0.038508 (0.032893) \| 0.068455 / 0.023109 (0.045346) \| 0.425216 / 0.275898 (0.149318) \| 0.431928 / 0.323480 (0.108448) \| 0.006477 / 0.007986 (-0.001509) \| 0.003891 / 0.004328 (-0.000437) \| 0.076898 / 0.004250 (0.072647) \| 0.057522 / 0.037052 (0.020470) \| 0.449585 / 0.258489 (0.191096) \| 0.431356 / 0.293841 (0.137515) \| 0.049728 / 0.128546 (-0.078818) \| 0.014456 / 0.075646 (-0.061190) \| 0.084618 / 0.419271 (-0.334653) \| 0.064482 / 0.043533 (0.020949) \| 0.456377 / 0.255139 (0.201238) \| 0.433949 / 0.283200 (0.150749) \| 0.036577 / 0.141683 (-0.105106) \| 1.819742 / 1.452155 (0.367588) \| 1.694691 / 1.492716 (0.201975) \|\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) \| 0.224610 / 0.018006 (0.206604) \| 0.494586 / 0.000490 (0.494096) \| 0.004506 / 0.000200 (0.004307) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033172 / 0.037411 (-0.004239) \| 0.100562 / 0.014526 (0.086036) \| 0.116499 / 0.176557 (-0.060058) \| 0.153717 / 0.737135 (-0.583418) \| 0.140047 / 0.296338 (-0.156291) \|\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) \| 0.635922 / 0.215209 (0.420713) \| 6.359792 / 2.077655 (4.282137) \| 2.689083 / 1.504120 (1.184963) \| 2.330574 / 1.541195 (0.789380) \| 2.583535 / 1.468490 (1.115044) \| 0.902737 / 4.584777 (-3.682040) \| 5.136586 / 3.745712 (1.390874) \| 4.570824 / 5.269862 (-0.699037) \| 3.029953 / 4.565676 (-1.535724) \| 0.103961 / 0.424275 (-0.320314) \| 0.007908 / 0.007607 (0.000301) \| 0.723290 / 0.226044 (0.497246) \| 7.678599 / 2.268929 (5.409671) \| 3.342522 / 55.444624 (-52.102102) \| 2.774659 / 6.876477 (-4.101817) \| 2.966496 / 2.142072 (0.824423) \| 1.025395 / 4.805227 (-3.779832) \| 0.222246 / 6.500664 (-6.278418) \| 0.072455 / 0.075469 (-0.003014) \|\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) \| 1.603637 / 1.841788 (-0.238151) \| 21.387722 / 8.074308 (13.313414) \| 22.855221 / 10.191392 (12.663829) \| 0.222147 / 0.680424 (-0.458277) \| 0.030763 / 0.534201 (-0.503438) \| 0.472586 / 0.579283 (-0.106697) \| 0.560161 / 0.434364 (0.125797) \| 0.551941 / 0.540337 (0.011604) \| 0.711254 / 1.386936 (-0.675682) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85cf123e553ff282b43ad1d1877ba2c40d206d52 \"CML watermark\")\n" ]
1,808,057,906	6,044	Rename "pattern" to "path" in YAML data_files configs	closed	2023-07-17T15:41:16	2023-07-19T16:59:55	2023-07-19T16:48:06	https://github.com/huggingface/datasets/pull/6044	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6044", "html_url": "https://github.com/huggingface/datasets/pull/6044", "diff_url": "https://github.com/huggingface/datasets/pull/6044.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6044.patch", "merged_at": "2023-07-19T16:48:06" }	lhoestq	true	[ "_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) \| 0.006543 / 0.011353 (-0.004809) \| 0.004085 / 0.011008 (-0.006924) \| 0.083989 / 0.038508 (0.045481) \| 0.074733 / 0.023109 (0.051623) \| 0.310839 / 0.275898 (0.034941) \| 0.333540 / 0.323480 (0.010060) \| 0.005566 / 0.007986 (-0.002419) \| 0.003461 / 0.004328 (-0.000868) \| 0.065194 / 0.004250 (0.060943) \| 0.057007 / 0.037052 (0.019954) \| 0.325633 / 0.258489 (0.067144) \| 0.351665 / 0.293841 (0.057824) \| 0.030561 / 0.128546 (-0.097985) \| 0.008579 / 0.075646 (-0.067068) \| 0.287457 / 0.419271 (-0.131815) \| 0.063554 / 0.043533 (0.020021) \| 0.309182 / 0.255139 (0.054043) \| 0.327809 / 0.283200 (0.044609) \| 0.034470 / 0.141683 (-0.107213) \| 1.452098 / 1.452155 (-0.000057) \| 1.527130 / 1.492716 (0.034414) \|\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) \| 0.241736 / 0.018006 (0.223729) \| 0.552432 / 0.000490 (0.551943) \| 0.004085 / 0.000200 (0.003885) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027290 / 0.037411 (-0.010121) \| 0.081250 / 0.014526 (0.066724) \| 0.094739 / 0.176557 (-0.081818) \| 0.150424 / 0.737135 (-0.586711) \| 0.095488 / 0.296338 (-0.200851) \|\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) \| 0.377245 / 0.215209 (0.162036) \| 3.781021 / 2.077655 (1.703366) \| 1.820092 / 1.504120 (0.315972) \| 1.654420 / 1.541195 (0.113225) \| 1.751256 / 1.468490 (0.282766) \| 0.475161 / 4.584777 (-4.109616) \| 3.603462 / 3.745712 (-0.142251) \| 5.437837 / 5.269862 (0.167975) \| 3.305598 / 4.565676 (-1.260079) \| 0.055856 / 0.424275 (-0.368419) \| 0.007259 / 0.007607 (-0.000348) \| 0.454205 / 0.226044 (0.228161) \| 4.544157 / 2.268929 (2.275229) \| 2.296776 / 55.444624 (-53.147848) \| 1.951017 / 6.876477 (-4.925459) \| 2.128759 / 2.142072 (-0.013313) \| 0.590354 / 4.805227 (-4.214873) \| 0.129974 / 6.500664 (-6.370690) \| 0.059506 / 0.075469 (-0.015963) \|\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) \| 1.285866 / 1.841788 (-0.555921) \| 19.419446 / 8.074308 (11.345138) \| 13.985108 / 10.191392 (3.793716) \| 0.146803 / 0.680424 (-0.533620) \| 0.018176 / 0.534201 (-0.516025) \| 0.392345 / 0.579283 (-0.186938) \| 0.405394 / 0.434364 (-0.028970) \| 0.454649 / 0.540337 (-0.085688) \| 0.633075 / 1.386936 (-0.753861) \|\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) \| 0.006497 / 0.011353 (-0.004855) \| 0.004092 / 0.011008 (-0.006916) \| 0.064908 / 0.038508 (0.026400) \| 0.073494 / 0.023109 (0.050385) \| 0.382227 / 0.275898 (0.106329) \| 0.407320 / 0.323480 (0.083840) \| 0.005653 / 0.007986 (-0.002332) \| 0.003500 / 0.004328 (-0.000829) \| 0.064570 / 0.004250 (0.060320) \| 0.058733 / 0.037052 (0.021681) \| 0.385702 / 0.258489 (0.127213) \| 0.426463 / 0.293841 (0.132622) \| 0.031073 / 0.128546 (-0.097473) \| 0.008710 / 0.075646 (-0.066936) \| 0.071378 / 0.419271 (-0.347893) \| 0.050141 / 0.043533 (0.006608) \| 0.377769 / 0.255139 (0.122630) \| 0.395016 / 0.283200 (0.111816) \| 0.025158 / 0.141683 (-0.116525) \| 1.470503 / 1.452155 (0.018348) \| 1.532742 / 1.492716 (0.040026) \|\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) \| 0.214249 / 0.018006 (0.196243) \| 0.583580 / 0.000490 (0.583090) \| 0.004027 / 0.000200 (0.003828) \| 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) \| 0.030186 / 0.037411 (-0.007226) \| 0.086927 / 0.014526 (0.072401) \| 0.102060 / 0.176557 (-0.074497) \| 0.156281 / 0.737135 (-0.580855) \| 0.100825 / 0.296338 (-0.195514) \|\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) \| 0.419942 / 0.215209 (0.204733) \| 4.183797 / 2.077655 (2.106142) \| 2.205079 / 1.504120 (0.700959) \| 2.071219 / 1.541195 (0.530024) \| 2.194047 / 1.468490 (0.725557) \| 0.478768 / 4.584777 (-4.106009) \| 3.584864 / 3.745712 (-0.160848) \| 3.371635 / 5.269862 (-1.898227) \| 2.022134 / 4.565676 (-2.543542) \| 0.056553 / 0.424275 (-0.367722) \| 0.007231 / 0.007607 (-0.000376) \| 0.493158 / 0.226044 (0.267113) \| 4.934370 / 2.268929 (2.665441) \| 2.699593 / 55.444624 (-52.745031) \| 2.396371 / 6.876477 (-4.480105) \| 2.438052 / 2.142072 (0.295979) \| 0.589578 / 4.805227 (-4.215649) \| 0.147234 / 6.500664 (-6.353430) \| 0.062049 / 0.075469 (-0.013420) \|\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) \| 1.318246 / 1.841788 (-0.523542) \| 19.829025 / 8.074308 (11.754717) \| 14.314825 / 10.191392 (4.123433) \| 0.168309 / 0.680424 (-0.512115) \| 0.018596 / 0.534201 (-0.515605) \| 0.397540 / 0.579283 (-0.181743) \| 0.421280 / 0.434364 (-0.013084) \| 0.479917 / 0.540337 (-0.060421) \| 0.643494 / 1.386936 (-0.743442) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5be59becaa65f1fa08129091b8c778823e4a50ac \"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) \| 0.008349 / 0.011353 (-0.003004) \| 0.005362 / 0.011008 (-0.005646) \| 0.100777 / 0.038508 (0.062269) \| 0.078719 / 0.023109 (0.055609) \| 0.398105 / 0.275898 (0.122207) \| 0.444189 / 0.323480 (0.120709) \| 0.006834 / 0.007986 (-0.001152) \| 0.004642 / 0.004328 (0.000314) \| 0.076284 / 0.004250 (0.072034) \| 0.062738 / 0.037052 (0.025685) \| 0.409532 / 0.258489 (0.151043) \| 0.447218 / 0.293841 (0.153377) \| 0.052996 / 0.128546 (-0.075550) \| 0.012977 / 0.075646 (-0.062669) \| 0.347687 / 0.419271 (-0.071585) \| 0.068076 / 0.043533 (0.024543) \| 0.394526 / 0.255139 (0.139387) \| 0.434110 / 0.283200 (0.150910) \| 0.041719 / 0.141683 (-0.099963) \| 1.759109 / 1.452155 (0.306955) \| 1.866049 / 1.492716 (0.373333) \|\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) \| 0.287633 / 0.018006 (0.269627) \| 0.611540 / 0.000490 (0.611051) \| 0.005388 / 0.000200 (0.005188) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027394 / 0.037411 (-0.010017) \| 0.089796 / 0.014526 (0.075270) \| 0.106931 / 0.176557 (-0.069625) \| 0.173560 / 0.737135 (-0.563575) \| 0.106948 / 0.296338 (-0.189391) \|\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) \| 0.575156 / 0.215209 (0.359947) \| 5.674170 / 2.077655 (3.596516) \| 2.463090 / 1.504120 (0.958971) \| 2.128245 / 1.541195 (0.587050) \| 2.118982 / 1.468490 (0.650492) \| 0.876976 / 4.584777 (-3.707801) \| 5.238229 / 3.745712 (1.492517) \| 4.548788 / 5.269862 (-0.721074) \| 2.905243 / 4.565676 (-1.660433) \| 0.090750 / 0.424275 (-0.333525) \| 0.008266 / 0.007607 (0.000659) \| 0.693305 / 0.226044 (0.467260) \| 7.126970 / 2.268929 (4.858041) \| 3.152131 / 55.444624 (-52.292494) \| 2.532118 / 6.876477 (-4.344359) \| 2.678442 / 2.142072 (0.536369) \| 0.932745 / 4.805227 (-3.872483) \| 0.196290 / 6.500664 (-6.304374) \| 0.074082 / 0.075469 (-0.001387) \|\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) \| 1.599636 / 1.841788 (-0.242152) \| 23.271435 / 8.074308 (15.197127) \| 19.696709 / 10.191392 (9.505317) \| 0.222668 / 0.680424 (-0.457756) \| 0.029088 / 0.534201 (-0.505113) \| 0.492477 / 0.579283 (-0.086806) \| 0.580578 / 0.434364 (0.146214) \| 0.558852 / 0.540337 (0.018514) \| 0.762083 / 1.386936 (-0.624853) \|\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) \| 0.009021 / 0.011353 (-0.002332) \| 0.005011 / 0.011008 (-0.005997) \| 0.076504 / 0.038508 (0.037996) \| 0.077303 / 0.023109 (0.054193) \| 0.480660 / 0.275898 (0.204762) \| 0.493944 / 0.323480 (0.170464) \| 0.006339 / 0.007986 (-0.001646) \| 0.004302 / 0.004328 (-0.000026) \| 0.076228 / 0.004250 (0.071978) \| 0.060805 / 0.037052 (0.023753) \| 0.477539 / 0.258489 (0.219050) \| 0.496799 / 0.293841 (0.202958) \| 0.049495 / 0.128546 (-0.079052) \| 0.013333 / 0.075646 (-0.062313) \| 0.087217 / 0.419271 (-0.332055) \| 0.061451 / 0.043533 (0.017918) \| 0.485169 / 0.255139 (0.230030) \| 0.487348 / 0.283200 (0.204149) \| 0.035874 / 0.141683 (-0.105809) \| 1.829137 / 1.452155 (0.376982) \| 1.906151 / 1.492716 (0.413435) \|\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) \| 0.304526 / 0.018006 (0.286520) \| 0.627499 / 0.000490 (0.627009) \| 0.003786 / 0.000200 (0.003586) \| 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) \| 0.035512 / 0.037411 (-0.001899) \| 0.096684 / 0.014526 (0.082158) \| 0.111879 / 0.176557 (-0.064678) \| 0.171489 / 0.737135 (-0.565647) \| 0.112175 / 0.296338 (-0.184164) \|\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) \| 0.604791 / 0.215209 (0.389582) \| 6.089137 / 2.077655 (4.011482) \| 2.883237 / 1.504120 (1.379117) \| 2.561109 / 1.541195 (1.019914) \| 2.542400 / 1.468490 (1.073910) \| 0.852828 / 4.584777 (-3.731949) \| 5.236812 / 3.745712 (1.491100) \| 4.756429 / 5.269862 (-0.513432) \| 2.885660 / 4.565676 (-1.680016) \| 0.095643 / 0.424275 (-0.328632) \| 0.008403 / 0.007607 (0.000796) \| 0.727707 / 0.226044 (0.501663) \| 7.428002 / 2.268929 (5.159074) \| 3.816051 / 55.444624 (-51.628573) \| 2.971057 / 6.876477 (-3.905420) \| 2.915965 / 2.142072 (0.773893) \| 1.006553 / 4.805227 (-3.798674) \| 0.201840 / 6.500664 (-6.298824) \| 0.080795 / 0.075469 (0.005326) \|\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) \| 1.794951 / 1.841788 (-0.046837) \| 23.624556 / 8.074308 (15.550248) \| 21.856195 / 10.191392 (11.664802) \| 0.253043 / 0.680424 (-0.427381) \| 0.031201 / 0.534201 (-0.503000) \| 0.461641 / 0.579283 (-0.117642) \| 0.577789 / 0.434364 (0.143425) \| 0.569197 / 0.540337 (0.028860) \| 0.780111 / 1.386936 (-0.606825) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4904f14459c862f0ab525ec034a636177be5dee4 \"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) \| 0.007646 / 0.011353 (-0.003707) \| 0.004750 / 0.011008 (-0.006258) \| 0.097981 / 0.038508 (0.059473) \| 0.088989 / 0.023109 (0.065880) \| 0.377732 / 0.275898 (0.101834) \| 0.406805 / 0.323480 (0.083325) \| 0.006389 / 0.007986 (-0.001597) \| 0.003854 / 0.004328 (-0.000474) \| 0.073977 / 0.004250 (0.069727) \| 0.066497 / 0.037052 (0.029444) \| 0.371498 / 0.258489 (0.113009) \| 0.417352 / 0.293841 (0.123511) \| 0.036326 / 0.128546 (-0.092220) \| 0.009876 / 0.075646 (-0.065770) \| 0.330142 / 0.419271 (-0.089130) \| 0.062423 / 0.043533 (0.018890) \| 0.369375 / 0.255139 (0.114236) \| 0.406048 / 0.283200 (0.122848) \| 0.026564 / 0.141683 (-0.115119) \| 1.713295 / 1.452155 (0.261140) \| 1.797493 / 1.492716 (0.304777) \|\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) \| 0.231889 / 0.018006 (0.213882) \| 0.512497 / 0.000490 (0.512007) \| 0.000390 / 0.000200 (0.000190) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033978 / 0.037411 (-0.003433) \| 0.100117 / 0.014526 (0.085592) \| 0.112460 / 0.176557 (-0.064097) \| 0.179936 / 0.737135 (-0.557200) \| 0.114277 / 0.296338 (-0.182061) \|\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) \| 0.461320 / 0.215209 (0.246111) \| 4.563180 / 2.077655 (2.485526) \| 2.249474 / 1.504120 (0.745354) \| 2.100450 / 1.541195 (0.559255) \| 2.231080 / 1.468490 (0.762590) \| 0.567907 / 4.584777 (-4.016870) \| 4.117233 / 3.745712 (0.371521) \| 4.943159 / 5.269862 (-0.326703) \| 3.112299 / 4.565676 (-1.453377) \| 0.065500 / 0.424275 (-0.358775) \| 0.008407 / 0.007607 (0.000800) \| 0.545928 / 0.226044 (0.319883) \| 5.508058 / 2.268929 (3.239129) \| 2.834645 / 55.444624 (-52.609980) \| 2.440328 / 6.876477 (-4.436148) \| 2.680483 / 2.142072 (0.538410) \| 0.697191 / 4.805227 (-4.108036) \| 0.176646 / 6.500664 (-6.324018) \| 0.073608 / 0.075469 (-0.001861) \|\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) \| 1.451865 / 1.841788 (-0.389922) \| 22.752595 / 8.074308 (14.678287) \| 15.543338 / 10.191392 (5.351946) \| 0.214644 / 0.680424 (-0.465780) \| 0.022050 / 0.534201 (-0.512151) \| 0.463898 / 0.579283 (-0.115385) \| 0.481691 / 0.434364 (0.047327) \| 0.549715 / 0.540337 (0.009378) \| 0.773595 / 1.386936 (-0.613341) \|\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) \| 0.007541 / 0.011353 (-0.003812) \| 0.004715 / 0.011008 (-0.006293) \| 0.076782 / 0.038508 (0.038274) \| 0.086242 / 0.023109 (0.063133) \| 0.458053 / 0.275898 (0.182155) \| 0.503097 / 0.323480 (0.179617) \| 0.006262 / 0.007986 (-0.001724) \| 0.003882 / 0.004328 (-0.000447) \| 0.075669 / 0.004250 (0.071419) \| 0.066004 / 0.037052 (0.028952) \| 0.469439 / 0.258489 (0.210950) \| 0.529744 / 0.293841 (0.235903) \| 0.037228 / 0.128546 (-0.091319) \| 0.009794 / 0.075646 (-0.065852) \| 0.082464 / 0.419271 (-0.336808) \| 0.058797 / 0.043533 (0.015264) \| 0.452069 / 0.255139 (0.196930) \| 0.488246 / 0.283200 (0.205046) \| 0.029324 / 0.141683 (-0.112359) \| 1.742237 / 1.452155 (0.290082) \| 1.839676 / 1.492716 (0.346959) \|\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) \| 0.228106 / 0.018006 (0.210100) \| 0.491632 / 0.000490 (0.491142) \| 0.004993 / 0.000200 (0.004793) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035413 / 0.037411 (-0.001999) \| 0.104617 / 0.014526 (0.090091) \| 0.121948 / 0.176557 (-0.054609) \| 0.186233 / 0.737135 (-0.550902) \| 0.121574 / 0.296338 (-0.174764) \|\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) \| 0.473849 / 0.215209 (0.258640) \| 4.788312 / 2.077655 (2.710657) \| 2.470535 / 1.504120 (0.966415) \| 2.270393 / 1.541195 (0.729198) \| 2.361096 / 1.468490 (0.892606) \| 0.556184 / 4.584777 (-4.028593) \| 4.216852 / 3.745712 (0.471140) \| 3.901718 / 5.269862 (-1.368143) \| 2.355209 / 4.565676 (-2.210467) \| 0.066708 / 0.424275 (-0.357567) \| 0.008709 / 0.007607 (0.001102) \| 0.571714 / 0.226044 (0.345669) \| 5.663150 / 2.268929 (3.394221) \| 3.025769 / 55.444624 (-52.418855) \| 2.652554 / 6.876477 (-4.223923) \| 2.750555 / 2.142072 (0.608483) \| 0.681536 / 4.805227 (-4.123691) \| 0.157187 / 6.500664 (-6.343477) \| 0.073533 / 0.075469 (-0.001936) \|\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) \| 1.604630 / 1.841788 (-0.237158) \| 22.735629 / 8.074308 (14.661321) \| 16.762347 / 10.191392 (6.570955) \| 0.175514 / 0.680424 (-0.504910) \| 0.021497 / 0.534201 (-0.512704) \| 0.461438 / 0.579283 (-0.117845) \| 0.476184 / 0.434364 (0.041820) \| 0.571048 / 0.540337 (0.030710) \| 0.747086 / 1.386936 (-0.639850) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ea38fc40ee2b10d3b5c6df09b09ad05e02a2cff \"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) \| 0.006889 / 0.011353 (-0.004464) \| 0.004241 / 0.011008 (-0.006767) \| 0.084542 / 0.038508 (0.046034) \| 0.080484 / 0.023109 (0.057374) \| 0.309356 / 0.275898 (0.033458) \| 0.338548 / 0.323480 (0.015068) \| 0.004904 / 0.007986 (-0.003082) \| 0.005220 / 0.004328 (0.000892) \| 0.065501 / 0.004250 (0.061251) \| 0.062095 / 0.037052 (0.025043) \| 0.317332 / 0.258489 (0.058843) \| 0.364797 / 0.293841 (0.070956) \| 0.030492 / 0.128546 (-0.098054) \| 0.008991 / 0.075646 (-0.066656) \| 0.288274 / 0.419271 (-0.130998) \| 0.052582 / 0.043533 (0.009049) \| 0.310838 / 0.255139 (0.055699) \| 0.346304 / 0.283200 (0.063104) \| 0.027968 / 0.141683 (-0.113715) \| 1.509727 / 1.452155 (0.057573) \| 1.577410 / 1.492716 (0.084694) \|\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) \| 0.269725 / 0.018006 (0.251719) \| 0.627685 / 0.000490 (0.627195) \| 0.000419 / 0.000200 (0.000219) \| 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) \| 0.031022 / 0.037411 (-0.006389) \| 0.081858 / 0.014526 (0.067332) \| 0.099477 / 0.176557 (-0.077080) \| 0.162981 / 0.737135 (-0.574154) \| 0.101987 / 0.296338 (-0.194351) \|\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) \| 0.386297 / 0.215209 (0.171088) \| 3.845321 / 2.077655 (1.767666) \| 1.834446 / 1.504120 (0.330326) \| 1.699730 / 1.541195 (0.158536) \| 1.764342 / 1.468490 (0.295852) \| 0.486423 / 4.584777 (-4.098354) \| 3.527595 / 3.745712 (-0.218117) \| 4.137034 / 5.269862 (-1.132827) \| 2.590457 / 4.565676 (-1.975219) \| 0.057598 / 0.424275 (-0.366677) \| 0.007318 / 0.007607 (-0.000289) \| 0.460775 / 0.226044 (0.234730) \| 4.627576 / 2.268929 (2.358647) \| 2.402566 / 55.444624 (-53.042059) \| 2.011392 / 6.876477 (-4.865085) \| 2.223915 / 2.142072 (0.081842) \| 0.623217 / 4.805227 (-4.182011) \| 0.148875 / 6.500664 (-6.351789) \| 0.059799 / 0.075469 (-0.015671) \|\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) \| 1.290768 / 1.841788 (-0.551020) \| 20.455083 / 8.074308 (12.380775) \| 13.469846 / 10.191392 (3.278454) \| 0.170329 / 0.680424 (-0.510095) \| 0.018409 / 0.534201 (-0.515792) \| 0.394356 / 0.579283 (-0.184927) \| 0.422685 / 0.434364 (-0.011679) \| 0.476241 / 0.540337 (-0.064096) \| 0.662682 / 1.386936 (-0.724254) \|\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) \| 0.006724 / 0.011353 (-0.004629) \| 0.004508 / 0.011008 (-0.006500) \| 0.065304 / 0.038508 (0.026796) \| 0.080243 / 0.023109 (0.057133) \| 0.384545 / 0.275898 (0.108647) \| 0.415234 / 0.323480 (0.091754) \| 0.006361 / 0.007986 (-0.001624) \| 0.004193 / 0.004328 (-0.000135) \| 0.065940 / 0.004250 (0.061689) \| 0.063633 / 0.037052 (0.026581) \| 0.392799 / 0.258489 (0.134310) \| 0.443618 / 0.293841 (0.149777) \| 0.031134 / 0.128546 (-0.097412) \| 0.009058 / 0.075646 (-0.066588) \| 0.071051 / 0.419271 (-0.348221) \| 0.049096 / 0.043533 (0.005563) \| 0.379526 / 0.255139 (0.124387) \| 0.403370 / 0.283200 (0.120171) \| 0.026378 / 0.141683 (-0.115305) \| 1.457879 / 1.452155 (0.005724) \| 1.562890 / 1.492716 (0.070174) \|\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) \| 0.304416 / 0.018006 (0.286410) \| 0.626046 / 0.000490 (0.625557) \| 0.000469 / 0.000200 (0.000269) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032979 / 0.037411 (-0.004433) \| 0.086769 / 0.014526 (0.072243) \| 0.108188 / 0.176557 (-0.068369) \| 0.163077 / 0.737135 (-0.574058) \| 0.106276 / 0.296338 (-0.190062) \|\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) \| 0.406922 / 0.215209 (0.191713) \| 4.052828 / 2.077655 (1.975174) \| 2.084802 / 1.504120 (0.580682) \| 1.927263 / 1.541195 (0.386069) \| 1.956078 / 1.468490 (0.487587) \| 0.480110 / 4.584777 (-4.104667) \| 3.553022 / 3.745712 (-0.192691) \| 3.554450 / 5.269862 (-1.715411) \| 2.082681 / 4.565676 (-2.482995) \| 0.056711 / 0.424275 (-0.367564) \| 0.007374 / 0.007607 (-0.000234) \| 0.480555 / 0.226044 (0.254510) \| 4.795851 / 2.268929 (2.526923) \| 2.606675 / 55.444624 (-52.837949) \| 2.249964 / 6.876477 (-4.626512) \| 2.274234 / 2.142072 (0.132162) \| 0.571767 / 4.805227 (-4.233461) \| 0.133312 / 6.500664 (-6.367352) \| 0.061703 / 0.075469 (-0.013766) \|\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) \| 1.354308 / 1.841788 (-0.487479) \| 20.959352 / 8.074308 (12.885044) \| 14.158420 / 10.191392 (3.967028) \| 0.197959 / 0.680424 (-0.482465) \| 0.018412 / 0.534201 (-0.515789) \| 0.394307 / 0.579283 (-0.184976) \| 0.402455 / 0.434364 (-0.031909) \| 0.463314 / 0.540337 (-0.077024) \| 0.621050 / 1.386936 (-0.765886) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7298d4d1b169442a8d0bc8c1667298bb89ca501 \"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) \| 0.007179 / 0.011353 (-0.004174) \| 0.004318 / 0.011008 (-0.006690) \| 0.085209 / 0.038508 (0.046701) \| 0.089989 / 0.023109 (0.066880) \| 0.328188 / 0.275898 (0.052290) \| 0.346027 / 0.323480 (0.022547) \| 0.005711 / 0.007986 (-0.002275) \| 0.003703 / 0.004328 (-0.000625) \| 0.065419 / 0.004250 (0.061169) \| 0.065354 / 0.037052 (0.028301) \| 0.314531 / 0.258489 (0.056042) \| 0.354357 / 0.293841 (0.060516) \| 0.030918 / 0.128546 (-0.097628) \| 0.008632 / 0.075646 (-0.067015) \| 0.286817 / 0.419271 (-0.132455) \| 0.065267 / 0.043533 (0.021735) \| 0.310918 / 0.255139 (0.055779) \| 0.330497 / 0.283200 (0.047298) \| 0.035695 / 0.141683 (-0.105988) \| 1.471101 / 1.452155 (0.018947) \| 1.538658 / 1.492716 (0.045942) \|\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) \| 0.254314 / 0.018006 (0.236308) \| 0.591413 / 0.000490 (0.590923) \| 0.006082 / 0.000200 (0.005882) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031843 / 0.037411 (-0.005568) \| 0.089968 / 0.014526 (0.075442) \| 0.101838 / 0.176557 (-0.074718) \| 0.164401 / 0.737135 (-0.572734) \| 0.103785 / 0.296338 (-0.192554) \|\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) \| 0.380486 / 0.215209 (0.165277) \| 3.798868 / 2.077655 (1.721213) \| 1.824645 / 1.504120 (0.320525) \| 1.660804 / 1.541195 (0.119610) \| 1.784793 / 1.468490 (0.316303) \| 0.487222 / 4.584777 (-4.097555) \| 3.560580 / 3.745712 (-0.185132) \| 5.392662 / 5.269862 (0.122800) \| 3.295327 / 4.565676 (-1.270350) \| 0.057699 / 0.424275 (-0.366576) \| 0.007559 / 0.007607 (-0.000048) \| 0.459655 / 0.226044 (0.233611) \| 4.587583 / 2.268929 (2.318654) \| 2.304845 / 55.444624 (-53.139779) \| 1.966433 / 6.876477 (-4.910044) \| 2.254591 / 2.142072 (0.112519) \| 0.582978 / 4.805227 (-4.222250) \| 0.133455 / 6.500664 (-6.367210) \| 0.061924 / 0.075469 (-0.013546) \|\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) \| 1.275685 / 1.841788 (-0.566103) \| 20.814545 / 8.074308 (12.740237) \| 13.753567 / 10.191392 (3.562175) \| 0.164076 / 0.680424 (-0.516348) \| 0.018768 / 0.534201 (-0.515433) \| 0.390991 / 0.579283 (-0.188293) \| 0.404417 / 0.434364 (-0.029947) \| 0.457522 / 0.540337 (-0.082815) \| 0.624654 / 1.386936 (-0.762282) \|\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) \| 0.007435 / 0.011353 (-0.003918) \| 0.004255 / 0.011008 (-0.006754) \| 0.066134 / 0.038508 (0.027626) \| 0.086035 / 0.023109 (0.062925) \| 0.364688 / 0.275898 (0.088790) \| 0.403895 / 0.323480 (0.080415) \| 0.005868 / 0.007986 (-0.002117) \| 0.003634 / 0.004328 (-0.000694) \| 0.065803 / 0.004250 (0.061553) \| 0.065113 / 0.037052 (0.028061) \| 0.370057 / 0.258489 (0.111568) \| 0.412634 / 0.293841 (0.118793) \| 0.031660 / 0.128546 (-0.096886) \| 0.008699 / 0.075646 (-0.066947) \| 0.070618 / 0.419271 (-0.348654) \| 0.050814 / 0.043533 (0.007281) \| 0.362320 / 0.255139 (0.107181) \| 0.383863 / 0.283200 (0.100663) \| 0.027980 / 0.141683 (-0.113703) \| 1.486389 / 1.452155 (0.034234) \| 1.595534 / 1.492716 (0.102817) \|\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) \| 0.300991 / 0.018006 (0.282985) \| 0.565265 / 0.000490 (0.564775) \| 0.000400 / 0.000200 (0.000200) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034942 / 0.037411 (-0.002470) \| 0.092498 / 0.014526 (0.077972) \| 0.106737 / 0.176557 (-0.069819) \| 0.165400 / 0.737135 (-0.571735) \| 0.107809 / 0.296338 (-0.188529) \|\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) \| 0.412156 / 0.215209 (0.196947) \| 4.116747 / 2.077655 (2.039092) \| 2.199612 / 1.504120 (0.695492) \| 2.049310 / 1.541195 (0.508115) \| 2.174342 / 1.468490 (0.705852) \| 0.482794 / 4.584777 (-4.101983) \| 3.561344 / 3.745712 (-0.184368) \| 3.465935 / 5.269862 (-1.803926) \| 2.076595 / 4.565676 (-2.489081) \| 0.056242 / 0.424275 (-0.368033) \| 0.007371 / 0.007607 (-0.000236) \| 0.489135 / 0.226044 (0.263091) \| 4.895691 / 2.268929 (2.626763) \| 2.626936 / 55.444624 (-52.817688) \| 2.306658 / 6.876477 (-4.569818) \| 2.421705 / 2.142072 (0.279633) \| 0.599547 / 4.805227 (-4.205680) \| 0.133627 / 6.500664 (-6.367037) \| 0.063830 / 0.075469 (-0.011639) \|\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) \| 1.383039 / 1.841788 (-0.458748) \| 21.005346 / 8.074308 (12.931038) \| 14.911083 / 10.191392 (4.719691) \| 0.190995 / 0.680424 (-0.489429) \| 0.018510 / 0.534201 (-0.515691) \| 0.396346 / 0.579283 (-0.182937) \| 0.411496 / 0.434364 (-0.022868) \| 0.470972 / 0.540337 (-0.069366) \| 0.615670 / 1.386936 (-0.771266) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6d2ba47759d8acbf3d750b1cc4d89b195b1f9c9 \"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) \| 0.007249 / 0.011353 (-0.004104) \| 0.004261 / 0.011008 (-0.006747) \| 0.100645 / 0.038508 (0.062137) \| 0.078522 / 0.023109 (0.055413) \| 0.423526 / 0.275898 (0.147628) \| 0.439541 / 0.323480 (0.116061) \| 0.005812 / 0.007986 (-0.002173) \| 0.003615 / 0.004328 (-0.000713) \| 0.075908 / 0.004250 (0.071658) \| 0.062490 / 0.037052 (0.025437) \| 0.414941 / 0.258489 (0.156452) \| 0.447267 / 0.293841 (0.153426) \| 0.035127 / 0.128546 (-0.093419) \| 0.009642 / 0.075646 (-0.066004) \| 0.354093 / 0.419271 (-0.065179) \| 0.060970 / 0.043533 (0.017437) \| 0.418579 / 0.255139 (0.163440) \| 0.427972 / 0.283200 (0.144772) \| 0.025838 / 0.141683 (-0.115845) \| 1.778349 / 1.452155 (0.326194) \| 1.845965 / 1.492716 (0.353249) \|\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) \| 0.227304 / 0.018006 (0.209298) \| 0.571833 / 0.000490 (0.571343) \| 0.001328 / 0.000200 (0.001128) \| 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) \| 0.031343 / 0.037411 (-0.006068) \| 0.096400 / 0.014526 (0.081875) \| 0.106881 / 0.176557 (-0.069676) \| 0.175449 / 0.737135 (-0.561686) \| 0.108751 / 0.296338 (-0.187588) \|\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) \| 0.480204 / 0.215209 (0.264995) \| 4.622063 / 2.077655 (2.544408) \| 2.211505 / 1.504120 (0.707385) \| 2.065154 / 1.541195 (0.523959) \| 2.159446 / 1.468490 (0.690956) \| 0.584571 / 4.584777 (-4.000206) \| 4.392449 / 3.745712 (0.646737) \| 4.790166 / 5.269862 (-0.479695) \| 2.840615 / 4.565676 (-1.725062) \| 0.070845 / 0.424275 (-0.353430) \| 0.009112 / 0.007607 (0.001505) \| 0.580251 / 0.226044 (0.354207) \| 5.660311 / 2.268929 (3.391382) \| 2.836136 / 55.444624 (-52.608489) \| 2.412859 / 6.876477 (-4.463618) \| 2.556710 / 2.142072 (0.414637) \| 0.691946 / 4.805227 (-4.113282) \| 0.160123 / 6.500664 (-6.340541) \| 0.072593 / 0.075469 (-0.002876) \|\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) \| 1.547339 / 1.841788 (-0.294448) \| 21.724793 / 8.074308 (13.650485) \| 16.315304 / 10.191392 (6.123912) \| 0.188733 / 0.680424 (-0.491690) \| 0.022109 / 0.534201 (-0.512092) \| 0.481623 / 0.579283 (-0.097660) \| 0.464316 / 0.434364 (0.029952) \| 0.557953 / 0.540337 (0.017615) \| 0.756023 / 1.386936 (-0.630913) \|\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) \| 0.008637 / 0.011353 (-0.002716) \| 0.005286 / 0.011008 (-0.005723) \| 0.091387 / 0.038508 (0.052879) \| 0.114092 / 0.023109 (0.090983) \| 0.457547 / 0.275898 (0.181649) \| 0.506878 / 0.323480 (0.183398) \| 0.006849 / 0.007986 (-0.001137) \| 0.004255 / 0.004328 (-0.000073) \| 0.079556 / 0.004250 (0.075306) \| 0.077729 / 0.037052 (0.040677) \| 0.454094 / 0.258489 (0.195605) \| 0.515812 / 0.293841 (0.221971) \| 0.038271 / 0.128546 (-0.090275) \| 0.010110 / 0.075646 (-0.065536) \| 0.094254 / 0.419271 (-0.325017) \| 0.065392 / 0.043533 (0.021860) \| 0.459749 / 0.255139 (0.204610) \| 0.489829 / 0.283200 (0.206629) \| 0.040393 / 0.141683 (-0.101290) \| 1.810414 / 1.452155 (0.358259) \| 1.913212 / 1.492716 (0.420496) \|\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) \| 0.236898 / 0.018006 (0.218891) \| 0.513118 / 0.000490 (0.512628) \| 0.004432 / 0.000200 (0.004232) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035074 / 0.037411 (-0.002337) \| 0.102384 / 0.014526 (0.087858) \| 0.117326 / 0.176557 (-0.059231) \| 0.182596 / 0.737135 (-0.554539) \| 0.116384 / 0.296338 (-0.179955) \|\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) \| 0.514544 / 0.215209 (0.299335) \| 5.152930 / 2.077655 (3.075275) \| 2.624477 / 1.504120 (1.120357) \| 2.363209 / 1.541195 (0.822014) \| 2.436060 / 1.468490 (0.967570) \| 0.592523 / 4.584777 (-3.992254) \| 4.209668 / 3.745712 (0.463956) \| 6.284372 / 5.269862 (1.014511) \| 3.667303 / 4.565676 (-0.898374) \| 0.067017 / 0.424275 (-0.357259) \| 0.008607 / 0.007607 (0.001000) \| 0.600840 / 0.226044 (0.374796) \| 5.992630 / 2.268929 (3.723701) \| 3.114532 / 55.444624 (-52.330093) \| 2.693242 / 6.876477 (-4.183235) \| 2.767187 / 2.142072 (0.625115) \| 0.687591 / 4.805227 (-4.117636) \| 0.158477 / 6.500664 (-6.342187) \| 0.075504 / 0.075469 (0.000034) \|\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) \| 1.605039 / 1.841788 (-0.236749) \| 21.524730 / 8.074308 (13.450422) \| 17.014643 / 10.191392 (6.823251) \| 0.201580 / 0.680424 (-0.478843) \| 0.023028 / 0.534201 (-0.511173) \| 0.483801 / 0.579283 (-0.095482) \| 0.490221 / 0.434364 (0.055857) \| 0.589292 / 0.540337 (0.048955) \| 0.758532 / 1.386936 (-0.628404) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c9c24d1d90f0c2db043ae2bc39f7c292454a58c \"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) \| 0.008080 / 0.011353 (-0.003273) \| 0.004859 / 0.011008 (-0.006149) \| 0.101895 / 0.038508 (0.063387) \| 0.091168 / 0.023109 (0.068059) \| 0.378914 / 0.275898 (0.103016) \| 0.417172 / 0.323480 (0.093692) \| 0.006314 / 0.007986 (-0.001672) \| 0.004069 / 0.004328 (-0.000259) \| 0.076566 / 0.004250 (0.072315) \| 0.070986 / 0.037052 (0.033934) \| 0.380935 / 0.258489 (0.122446) \| 0.417131 / 0.293841 (0.123290) \| 0.036343 / 0.128546 (-0.092203) \| 0.009996 / 0.075646 (-0.065650) \| 0.346386 / 0.419271 (-0.072886) \| 0.063162 / 0.043533 (0.019630) \| 0.372620 / 0.255139 (0.117481) \| 0.404902 / 0.283200 (0.121702) \| 0.028217 / 0.141683 (-0.113466) \| 1.793875 / 1.452155 (0.341721) \| 1.836284 / 1.492716 (0.343568) \|\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) \| 0.223830 / 0.018006 (0.205823) \| 0.503643 / 0.000490 (0.503153) \| 0.004957 / 0.000200 (0.004757) \| 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) \| 0.035455 / 0.037411 (-0.001957) \| 0.108015 / 0.014526 (0.093489) \| 0.116887 / 0.176557 (-0.059669) \| 0.188174 / 0.737135 (-0.548961) \| 0.117217 / 0.296338 (-0.179121) \|\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) \| 0.471681 / 0.215209 (0.256472) \| 4.694509 / 2.077655 (2.616855) \| 2.369539 / 1.504120 (0.865419) \| 2.176839 / 1.541195 (0.635644) \| 2.300536 / 1.468490 (0.832045) \| 0.575689 / 4.584777 (-4.009088) \| 4.232765 / 3.745712 (0.487053) \| 4.766775 / 5.269862 (-0.503087) \| 2.864667 / 4.565676 (-1.701010) \| 0.069390 / 0.424275 (-0.354885) \| 0.008822 / 0.007607 (0.001214) \| 0.559620 / 0.226044 (0.333576) \| 5.580401 / 2.268929 (3.311472) \| 2.920293 / 55.444624 (-52.524331) \| 2.552166 / 6.876477 (-4.324311) \| 2.795890 / 2.142072 (0.653818) \| 0.687863 / 4.805227 (-4.117364) \| 0.159129 / 6.500664 (-6.341535) \| 0.073475 / 0.075469 (-0.001994) \|\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) \| 1.505892 / 1.841788 (-0.335896) \| 24.127650 / 8.074308 (16.053342) \| 16.758238 / 10.191392 (6.566846) \| 0.200555 / 0.680424 (-0.479869) \| 0.021596 / 0.534201 (-0.512605) \| 0.480668 / 0.579283 (-0.098615) \| 0.483528 / 0.434364 (0.049164) \| 0.571241 / 0.540337 (0.030903) \| 0.790547 / 1.386936 (-0.596390) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007997 / 0.011353 (-0.003356) \| 0.004842 / 0.011008 (-0.006166) \| 0.077190 / 0.038508 (0.038681) \| 0.092765 / 0.023109 (0.069656) \| 0.457475 / 0.275898 (0.181577) \| 0.523914 / 0.323480 (0.200434) \| 0.006349 / 0.007986 (-0.001637) \| 0.003902 / 0.004328 (-0.000427) \| 0.075860 / 0.004250 (0.071609) \| 0.069708 / 0.037052 (0.032656) \| 0.459612 / 0.258489 (0.201123) \| 0.555028 / 0.293841 (0.261187) \| 0.036854 / 0.128546 (-0.091692) \| 0.010078 / 0.075646 (-0.065568) \| 0.083871 / 0.419271 (-0.335400) \| 0.061221 / 0.043533 (0.017689) \| 0.435737 / 0.255139 (0.180598) \| 0.509700 / 0.283200 (0.226500) \| 0.038091 / 0.141683 (-0.103592) \| 1.777161 / 1.452155 (0.325006) \| 1.859603 / 1.492716 (0.366886) \|\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) \| 0.250020 / 0.018006 (0.232014) \| 0.486198 / 0.000490 (0.485708) \| 0.007080 / 0.000200 (0.006880) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038163 / 0.037411 (0.000751) \| 0.110812 / 0.014526 (0.096286) \| 0.122489 / 0.176557 (-0.054068) \| 0.188215 / 0.737135 (-0.548920) \| 0.122375 / 0.296338 (-0.173963) \|\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) \| 0.484534 / 0.215209 (0.269325) \| 4.828654 / 2.077655 (2.751000) \| 2.545102 / 1.504120 (1.040982) \| 2.368867 / 1.541195 (0.827672) \| 2.458042 / 1.468490 (0.989552) \| 0.576372 / 4.584777 (-4.008404) \| 4.814033 / 3.745712 (1.068321) \| 6.175972 / 5.269862 (0.906110) \| 4.033422 / 4.565676 (-0.532254) \| 0.068544 / 0.424275 (-0.355731) \| 0.008906 / 0.007607 (0.001299) \| 0.581767 / 0.226044 (0.355723) \| 5.808623 / 2.268929 (3.539695) \| 3.120312 / 55.444624 (-52.324313) \| 2.774834 / 6.876477 (-4.101642) \| 2.770413 / 2.142072 (0.628340) \| 0.692715 / 4.805227 (-4.112512) \| 0.158883 / 6.500664 (-6.341782) \| 0.075894 / 0.075469 (0.000425) \|\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) \| 1.631250 / 1.841788 (-0.210538) \| 24.693250 / 8.074308 (16.618942) \| 17.434790 / 10.191392 (7.243398) \| 0.196456 / 0.680424 (-0.483968) \| 0.022505 / 0.534201 (-0.511696) \| 0.474788 / 0.579283 (-0.104495) \| 0.500947 / 0.434364 (0.066583) \| 0.553596 / 0.540337 (0.013259) \| 0.737767 / 1.386936 (-0.649169) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f87d6e6394bf4b390ccc82235eb7667f874e5d43 \"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) \| 0.006629 / 0.011353 (-0.004724) \| 0.004115 / 0.011008 (-0.006894) \| 0.083934 / 0.038508 (0.045426) \| 0.074952 / 0.023109 (0.051843) \| 0.313069 / 0.275898 (0.037171) \| 0.345878 / 0.323480 (0.022398) \| 0.006034 / 0.007986 (-0.001952) \| 0.003413 / 0.004328 (-0.000916) \| 0.065130 / 0.004250 (0.060880) \| 0.057363 / 0.037052 (0.020310) \| 0.314483 / 0.258489 (0.055994) \| 0.352626 / 0.293841 (0.058785) \| 0.031325 / 0.128546 (-0.097221) \| 0.008577 / 0.075646 (-0.067069) \| 0.288137 / 0.419271 (-0.131135) \| 0.053651 / 0.043533 (0.010118) \| 0.313006 / 0.255139 (0.057867) \| 0.338668 / 0.283200 (0.055468) \| 0.023709 / 0.141683 (-0.117974) \| 1.481209 / 1.452155 (0.029054) \| 1.559801 / 1.492716 (0.067085) \|\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) \| 0.211543 / 0.018006 (0.193537) \| 0.452185 / 0.000490 (0.451696) \| 0.003177 / 0.000200 (0.002977) \| 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) \| 0.028821 / 0.037411 (-0.008591) \| 0.083290 / 0.014526 (0.068765) \| 0.097478 / 0.176557 (-0.079079) \| 0.153506 / 0.737135 (-0.583629) \| 0.097054 / 0.296338 (-0.199284) \|\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) \| 0.385847 / 0.215209 (0.170638) \| 3.835629 / 2.077655 (1.757974) \| 1.880938 / 1.504120 (0.376819) \| 1.711848 / 1.541195 (0.170653) \| 1.785099 / 1.468490 (0.316609) \| 0.486256 / 4.584777 (-4.098521) \| 3.629026 / 3.745712 (-0.116686) \| 3.321578 / 5.269862 (-1.948283) \| 2.024314 / 4.565676 (-2.541363) \| 0.058097 / 0.424275 (-0.366179) \| 0.007724 / 0.007607 (0.000117) \| 0.458293 / 0.226044 (0.232249) \| 4.581314 / 2.268929 (2.312386) \| 2.314379 / 55.444624 (-53.130246) \| 1.966089 / 6.876477 (-4.910387) \| 2.203824 / 2.142072 (0.061752) \| 0.611581 / 4.805227 (-4.193647) \| 0.149166 / 6.500664 (-6.351498) \| 0.059825 / 0.075469 (-0.015644) \|\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) \| 1.235546 / 1.841788 (-0.606242) \| 19.747439 / 8.074308 (11.673131) \| 14.628383 / 10.191392 (4.436991) \| 0.193074 / 0.680424 (-0.487350) \| 0.020327 / 0.534201 (-0.513874) \| 0.397051 / 0.579283 (-0.182232) \| 0.418491 / 0.434364 (-0.015873) \| 0.462055 / 0.540337 (-0.078282) \| 0.637524 / 1.386936 (-0.749412) \|\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) \| 0.007069 / 0.011353 (-0.004284) \| 0.004106 / 0.011008 (-0.006902) \| 0.065818 / 0.038508 (0.027310) \| 0.077101 / 0.023109 (0.053991) \| 0.363323 / 0.275898 (0.087425) \| 0.399463 / 0.323480 (0.075983) \| 0.005540 / 0.007986 (-0.002446) \| 0.003480 / 0.004328 (-0.000849) \| 0.065176 / 0.004250 (0.060926) \| 0.060867 / 0.037052 (0.023815) \| 0.365763 / 0.258489 (0.107273) \| 0.407789 / 0.293841 (0.113949) \| 0.032018 / 0.128546 (-0.096528) \| 0.008550 / 0.075646 (-0.067096) \| 0.071750 / 0.419271 (-0.347521) \| 0.050625 / 0.043533 (0.007092) \| 0.361434 / 0.255139 (0.106295) \| 0.384799 / 0.283200 (0.101599) \| 0.026104 / 0.141683 (-0.115579) \| 1.496093 / 1.452155 (0.043938) \| 1.592909 / 1.492716 (0.100193) \|\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) \| 0.185794 / 0.018006 (0.167787) \| 0.453379 / 0.000490 (0.452890) \| 0.004365 / 0.000200 (0.004165) \| 0.000092 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031666 / 0.037411 (-0.005746) \| 0.088323 / 0.014526 (0.073798) \| 0.104602 / 0.176557 (-0.071954) \| 0.159827 / 0.737135 (-0.577308) \| 0.103725 / 0.296338 (-0.192614) \|\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) \| 0.413509 / 0.215209 (0.198300) \| 4.126071 / 2.077655 (2.048416) \| 2.137088 / 1.504120 (0.632968) \| 1.981034 / 1.541195 (0.439839) \| 2.063660 / 1.468490 (0.595170) \| 0.478798 / 4.584777 (-4.105979) \| 3.642801 / 3.745712 (-0.102911) \| 3.428994 / 5.269862 (-1.840867) \| 2.031902 / 4.565676 (-2.533774) \| 0.056244 / 0.424275 (-0.368032) \| 0.007365 / 0.007607 (-0.000242) \| 0.484371 / 0.226044 (0.258327) \| 4.838537 / 2.268929 (2.569608) \| 2.559497 / 55.444624 (-52.885127) \| 2.251863 / 6.876477 (-4.624614) \| 2.339227 / 2.142072 (0.197155) \| 0.607228 / 4.805227 (-4.198000) \| 0.133877 / 6.500664 (-6.366787) \| 0.062049 / 0.075469 (-0.013420) \|\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) \| 1.350389 / 1.841788 (-0.491399) \| 20.060359 / 8.074308 (11.986051) \| 14.305675 / 10.191392 (4.114283) \| 0.165642 / 0.680424 (-0.514782) \| 0.018206 / 0.534201 (-0.515994) \| 0.396907 / 0.579283 (-0.182376) \| 0.431896 / 0.434364 (-0.002468) \| 0.475778 / 0.540337 (-0.064559) \| 0.644688 / 1.386936 (-0.742248) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f6fa96ae5de873a49ef28739e8f64edf8b18cae \"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) \| 0.009048 / 0.011353 (-0.002305) \| 0.005787 / 0.011008 (-0.005221) \| 0.111617 / 0.038508 (0.073109) \| 0.087603 / 0.023109 (0.064494) \| 0.446481 / 0.275898 (0.170583) \| 0.491726 / 0.323480 (0.168247) \| 0.007052 / 0.007986 (-0.000934) \| 0.004481 / 0.004328 (0.000152) \| 0.084331 / 0.004250 (0.080081) \| 0.072006 / 0.037052 (0.034953) \| 0.454238 / 0.258489 (0.195749) \| 0.496749 / 0.293841 (0.202908) \| 0.049027 / 0.128546 (-0.079520) \| 0.014005 / 0.075646 (-0.061641) \| 0.372550 / 0.419271 (-0.046722) \| 0.071414 / 0.043533 (0.027881) \| 0.459432 / 0.255139 (0.204293) \| 0.467332 / 0.283200 (0.184133) \| 0.037539 / 0.141683 (-0.104144) \| 1.869179 / 1.452155 (0.417024) \| 1.983641 / 1.492716 (0.490925) \|\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) \| 0.265426 / 0.018006 (0.247419) \| 0.672527 / 0.000490 (0.672037) \| 0.001152 / 0.000200 (0.000953) \| 0.000181 / 0.000054 (0.000127) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032967 / 0.037411 (-0.004445) \| 0.103023 / 0.014526 (0.088497) \| 0.115978 / 0.176557 (-0.060578) \| 0.191698 / 0.737135 (-0.545438) \| 0.117867 / 0.296338 (-0.178471) \|\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) \| 0.602208 / 0.215209 (0.386999) \| 6.147784 / 2.077655 (4.070129) \| 2.768933 / 1.504120 (1.264813) \| 2.415619 / 1.541195 (0.874424) \| 2.456159 / 1.468490 (0.987669) \| 0.836270 / 4.584777 (-3.748507) \| 5.447754 / 3.745712 (1.702042) \| 7.751825 / 5.269862 (2.481963) \| 4.591892 / 4.565676 (0.026215) \| 0.108269 / 0.424275 (-0.316006) \| 0.009626 / 0.007607 (0.002019) \| 0.719260 / 0.226044 (0.493216) \| 7.313442 / 2.268929 (5.044514) \| 3.490739 / 55.444624 (-51.953885) \| 2.743543 / 6.876477 (-4.132934) \| 3.035071 / 2.142072 (0.892999) \| 1.042791 / 4.805227 (-3.762436) \| 0.217080 / 6.500664 (-6.283584) \| 0.084286 / 0.075469 (0.008817) \|\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) \| 1.655427 / 1.841788 (-0.186361) \| 25.386536 / 8.074308 (17.312228) \| 21.740666 / 10.191392 (11.549274) \| 0.246388 / 0.680424 (-0.434036) \| 0.029723 / 0.534201 (-0.504478) \| 0.491537 / 0.579283 (-0.087746) \| 0.603495 / 0.434364 (0.169131) \| 0.573938 / 0.540337 (0.033600) \| 0.981875 / 1.386936 (-0.405061) \|\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) \| 0.009664 / 0.011353 (-0.001689) \| 0.006446 / 0.011008 (-0.004562) \| 0.085113 / 0.038508 (0.046605) \| 0.094533 / 0.023109 (0.071424) \| 0.498388 / 0.275898 (0.222490) \| 0.540127 / 0.323480 (0.216647) \| 0.007316 / 0.007986 (-0.000670) \| 0.004252 / 0.004328 (-0.000077) \| 0.086292 / 0.004250 (0.082041) \| 0.067956 / 0.037052 (0.030903) \| 0.507664 / 0.258489 (0.249175) \| 0.554324 / 0.293841 (0.260483) \| 0.050107 / 0.128546 (-0.078439) \| 0.014277 / 0.075646 (-0.061370) \| 0.098838 / 0.419271 (-0.320433) \| 0.066053 / 0.043533 (0.022521) \| 0.491090 / 0.255139 (0.235951) \| 0.537432 / 0.283200 (0.254232) \| 0.035937 / 0.141683 (-0.105746) \| 1.820715 / 1.452155 (0.368561) \| 1.996268 / 1.492716 (0.503552) \|\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) \| 0.300859 / 0.018006 (0.282852) \| 0.610958 / 0.000490 (0.610468) \| 0.000474 / 0.000200 (0.000274) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036372 / 0.037411 (-0.001039) \| 0.109115 / 0.014526 (0.094589) \| 0.122802 / 0.176557 (-0.053755) \| 0.187092 / 0.737135 (-0.550044) \| 0.123432 / 0.296338 (-0.172906) \|\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) \| 0.646979 / 0.215209 (0.431770) \| 6.577713 / 2.077655 (4.500058) \| 3.004606 / 1.504120 (1.500486) \| 2.661183 / 1.541195 (1.119989) \| 2.726717 / 1.468490 (1.258227) \| 0.889497 / 4.584777 (-3.695280) \| 5.485055 / 3.745712 (1.739343) \| 4.852043 / 5.269862 (-0.417819) \| 3.177392 / 4.565676 (-1.388285) \| 0.099796 / 0.424275 (-0.324479) \| 0.009868 / 0.007607 (0.002261) \| 0.819919 / 0.226044 (0.593874) \| 7.911255 / 2.268929 (5.642326) \| 3.839877 / 55.444624 (-51.604747) \| 3.088663 / 6.876477 (-3.787813) \| 3.371184 / 2.142072 (1.229112) \| 1.072762 / 4.805227 (-3.732466) \| 0.224536 / 6.500664 (-6.276128) \| 0.083415 / 0.075469 (0.007946) \|\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) \| 1.754426 / 1.841788 (-0.087361) \| 25.546690 / 8.074308 (17.472382) \| 22.998252 / 10.191392 (12.806860) \| 0.258019 / 0.680424 (-0.422405) \| 0.030104 / 0.534201 (-0.504097) \| 0.518406 / 0.579283 (-0.060877) \| 0.605753 / 0.434364 (0.171389) \| 0.599630 / 0.540337 (0.059292) \| 0.819042 / 1.386936 (-0.567894) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#350f4fd6caabbdfacb5fbf9193ab255c3d0daa4c \"CML watermark\")\n" ]
1,807,771,750	6,043	Compression kwargs have no effect when saving datasets as csv	open	2023-07-17T13:19:21	2023-07-22T17:34:18	null	https://github.com/huggingface/datasets/issues/6043	null	exs-avianello	false	[ "Hello @exs-avianello, I have reproduced the bug successfully and have understood the problem. But I am confused regarding this part of the statement, \"`pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`\".\r\n\r\nCan you please elaborate on it?\r\n\r\nThanks!", "Hi @aryanxk02 ! Sure, what I actually meant is that when passing a path-like `path_or_buf` here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/arrow_dataset.py#L4708-L4714 \r\n\r\nit gets converted to a file object behind the scenes here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L92-L94\r\n\r\nand the eventual pandas `.to_csv()` calls that write to it always get `path_or_buf=None`, making pandas ignore the `compression` kwarg in the `to_csv_kwargs`\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L107-L109", "@exs-avianello When `path_or_buf` is set to None, the `to_csv()` method will return the CSV data as a string instead of saving it to a file. Hence the compression doesn't take place. I think setting `path_or_buf=self.path_or_buf` should work. What you say?" ]
1,807,516,762	6,042	Fix unused DatasetInfosDict code in push_to_hub	closed	2023-07-17T11:03:09	2023-07-18T16:17:52	2023-07-18T16:08:42	https://github.com/huggingface/datasets/pull/6042	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6042", "html_url": "https://github.com/huggingface/datasets/pull/6042", "diff_url": "https://github.com/huggingface/datasets/pull/6042.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6042.patch", "merged_at": "2023-07-18T16:08:42" }	lhoestq	true	[ "_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) \| 0.008634 / 0.011353 (-0.002719) \| 0.005147 / 0.011008 (-0.005861) \| 0.102865 / 0.038508 (0.064357) \| 0.080245 / 0.023109 (0.057136) \| 0.401288 / 0.275898 (0.125390) \| 0.419708 / 0.323480 (0.096228) \| 0.006342 / 0.007986 (-0.001644) \| 0.003998 / 0.004328 (-0.000330) \| 0.078880 / 0.004250 (0.074630) \| 0.068199 / 0.037052 (0.031147) \| 0.389573 / 0.258489 (0.131084) \| 0.417292 / 0.293841 (0.123451) \| 0.048856 / 0.128546 (-0.079691) \| 0.014165 / 0.075646 (-0.061481) \| 0.348063 / 0.419271 (-0.071209) \| 0.067547 / 0.043533 (0.024014) \| 0.402251 / 0.255139 (0.147112) \| 0.419478 / 0.283200 (0.136278) \| 0.034846 / 0.141683 (-0.106837) \| 1.773493 / 1.452155 (0.321338) \| 1.930546 / 1.492716 (0.437830) \|\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) \| 0.211835 / 0.018006 (0.193829) \| 0.545311 / 0.000490 (0.544821) \| 0.006766 / 0.000200 (0.006566) \| 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) \| 0.035406 / 0.037411 (-0.002006) \| 0.100769 / 0.014526 (0.086243) \| 0.108667 / 0.176557 (-0.067890) \| 0.193099 / 0.737135 (-0.544036) \| 0.113539 / 0.296338 (-0.182799) \|\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) \| 0.586935 / 0.215209 (0.371726) \| 5.895245 / 2.077655 (3.817591) \| 2.528375 / 1.504120 (1.024255) \| 2.228617 / 1.541195 (0.687423) \| 2.295799 / 1.468490 (0.827309) \| 0.859272 / 4.584777 (-3.725505) \| 5.033434 / 3.745712 (1.287722) \| 7.546587 / 5.269862 (2.276726) \| 4.457137 / 4.565676 (-0.108539) \| 0.099626 / 0.424275 (-0.324649) \| 0.009296 / 0.007607 (0.001689) \| 0.713498 / 0.226044 (0.487454) \| 7.409385 / 2.268929 (5.140456) \| 3.361418 / 55.444624 (-52.083206) \| 2.681111 / 6.876477 (-4.195366) \| 2.849598 / 2.142072 (0.707526) \| 1.114863 / 4.805227 (-3.690364) \| 0.215494 / 6.500664 (-6.285170) \| 0.075807 / 0.075469 (0.000338) \|\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) \| 1.606458 / 1.841788 (-0.235330) \| 23.751096 / 8.074308 (15.676788) \| 21.279110 / 10.191392 (11.087718) \| 0.220785 / 0.680424 (-0.459639) \| 0.032688 / 0.534201 (-0.501513) \| 0.530948 / 0.579283 (-0.048335) \| 0.630056 / 0.434364 (0.195693) \| 0.572743 / 0.540337 (0.032405) \| 0.771853 / 1.386936 (-0.615083) \|\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) \| 0.008693 / 0.011353 (-0.002660) \| 0.004750 / 0.011008 (-0.006259) \| 0.079764 / 0.038508 (0.041256) \| 0.082096 / 0.023109 (0.058987) \| 0.467198 / 0.275898 (0.191300) \| 0.532361 / 0.323480 (0.208881) \| 0.005836 / 0.007986 (-0.002149) \| 0.004333 / 0.004328 (0.000005) \| 0.080444 / 0.004250 (0.076194) \| 0.065883 / 0.037052 (0.028831) \| 0.464871 / 0.258489 (0.206382) \| 0.575026 / 0.293841 (0.281185) \| 0.057807 / 0.128546 (-0.070739) \| 0.017462 / 0.075646 (-0.058185) \| 0.093667 / 0.419271 (-0.325605) \| 0.071466 / 0.043533 (0.027933) \| 0.495846 / 0.255139 (0.240707) \| 0.526100 / 0.283200 (0.242900) \| 0.034852 / 0.141683 (-0.106831) \| 1.884152 / 1.452155 (0.431998) \| 1.922681 / 1.492716 (0.429965) \|\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) \| 0.250969 / 0.018006 (0.232963) \| 0.504979 / 0.000490 (0.504489) \| 0.000466 / 0.000200 (0.000266) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032411 / 0.037411 (-0.005000) \| 0.093184 / 0.014526 (0.078658) \| 0.110798 / 0.176557 (-0.065759) \| 0.165741 / 0.737135 (-0.571394) \| 0.111022 / 0.296338 (-0.185317) \|\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) \| 0.661284 / 0.215209 (0.446075) \| 6.622388 / 2.077655 (4.544733) \| 3.095705 / 1.504120 (1.591585) \| 2.745698 / 1.541195 (1.204503) \| 2.694103 / 1.468490 (1.225612) \| 0.862154 / 4.584777 (-3.722623) \| 5.109985 / 3.745712 (1.364273) \| 5.040362 / 5.269862 (-0.229499) \| 3.072837 / 4.565676 (-1.492840) \| 0.110421 / 0.424275 (-0.313854) \| 0.008476 / 0.007607 (0.000869) \| 0.910020 / 0.226044 (0.683975) \| 8.123626 / 2.268929 (5.854698) \| 3.813811 / 55.444624 (-51.630813) \| 3.017244 / 6.876477 (-3.859232) \| 3.061222 / 2.142072 (0.919150) \| 1.073548 / 4.805227 (-3.731680) \| 0.216327 / 6.500664 (-6.284338) \| 0.072977 / 0.075469 (-0.002492) \|\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) \| 1.722482 / 1.841788 (-0.119305) \| 23.706716 / 8.074308 (15.632407) \| 23.192134 / 10.191392 (13.000742) \| 0.276733 / 0.680424 (-0.403691) \| 0.033538 / 0.534201 (-0.500663) \| 0.602083 / 0.579283 (0.022799) \| 0.578718 / 0.434364 (0.144354) \| 0.558311 / 0.540337 (0.017974) \| 0.740341 / 1.386936 (-0.646595) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac575b8ed57dac60d7ba33a616894f38601f84a \"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) \| 0.006862 / 0.011353 (-0.004491) \| 0.004223 / 0.011008 (-0.006786) \| 0.085931 / 0.038508 (0.047423) \| 0.081437 / 0.023109 (0.058328) \| 0.349542 / 0.275898 (0.073644) \| 0.379881 / 0.323480 (0.056401) \| 0.005651 / 0.007986 (-0.002334) \| 0.003662 / 0.004328 (-0.000666) \| 0.065251 / 0.004250 (0.061001) \| 0.061599 / 0.037052 (0.024547) \| 0.359681 / 0.258489 (0.101192) \| 0.392502 / 0.293841 (0.098661) \| 0.031300 / 0.128546 (-0.097246) \| 0.008591 / 0.075646 (-0.067055) \| 0.288577 / 0.419271 (-0.130694) \| 0.062920 / 0.043533 (0.019388) \| 0.348989 / 0.255139 (0.093850) \| 0.362769 / 0.283200 (0.079569) \| 0.030087 / 0.141683 (-0.111596) \| 1.480748 / 1.452155 (0.028594) \| 1.580413 / 1.492716 (0.087697) \|\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) \| 0.205804 / 0.018006 (0.187798) \| 0.455386 / 0.000490 (0.454897) \| 0.003134 / 0.000200 (0.002934) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030252 / 0.037411 (-0.007159) \| 0.087566 / 0.014526 (0.073041) \| 0.098209 / 0.176557 (-0.078347) \| 0.155816 / 0.737135 (-0.581319) \| 0.098938 / 0.296338 (-0.197401) \|\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) \| 0.386688 / 0.215209 (0.171479) \| 3.852777 / 2.077655 (1.775123) \| 1.938688 / 1.504120 (0.434568) \| 1.779234 / 1.541195 (0.238039) \| 1.864262 / 1.468490 (0.395772) \| 0.482472 / 4.584777 (-4.102305) \| 3.658060 / 3.745712 (-0.087652) \| 5.206489 / 5.269862 (-0.063373) \| 3.262498 / 4.565676 (-1.303179) \| 0.057523 / 0.424275 (-0.366752) \| 0.007365 / 0.007607 (-0.000242) \| 0.466886 / 0.226044 (0.240841) \| 4.671026 / 2.268929 (2.402097) \| 2.380357 / 55.444624 (-53.064268) \| 2.096590 / 6.876477 (-4.779887) \| 2.274415 / 2.142072 (0.132342) \| 0.579705 / 4.805227 (-4.225522) \| 0.134522 / 6.500664 (-6.366142) \| 0.062232 / 0.075469 (-0.013237) \|\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) \| 1.245965 / 1.841788 (-0.595823) \| 20.115180 / 8.074308 (12.040872) \| 14.602983 / 10.191392 (4.411591) \| 0.146890 / 0.680424 (-0.533533) \| 0.018424 / 0.534201 (-0.515777) \| 0.393941 / 0.579283 (-0.185342) \| 0.413785 / 0.434364 (-0.020579) \| 0.453344 / 0.540337 (-0.086993) \| 0.655446 / 1.386936 (-0.731490) \|\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) \| 0.006807 / 0.011353 (-0.004546) \| 0.004083 / 0.011008 (-0.006925) \| 0.065389 / 0.038508 (0.026881) \| 0.081056 / 0.023109 (0.057947) \| 0.362823 / 0.275898 (0.086925) \| 0.401928 / 0.323480 (0.078448) \| 0.005452 / 0.007986 (-0.002533) \| 0.003413 / 0.004328 (-0.000915) \| 0.065238 / 0.004250 (0.060987) \| 0.057264 / 0.037052 (0.020211) \| 0.375713 / 0.258489 (0.117224) \| 0.407858 / 0.293841 (0.114017) \| 0.031580 / 0.128546 (-0.096966) \| 0.008643 / 0.075646 (-0.067003) \| 0.071693 / 0.419271 (-0.347578) \| 0.049392 / 0.043533 (0.005859) \| 0.370194 / 0.255139 (0.115055) \| 0.384647 / 0.283200 (0.101447) \| 0.024805 / 0.141683 (-0.116877) \| 1.509511 / 1.452155 (0.057356) \| 1.560193 / 1.492716 (0.067477) \|\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) \| 0.234442 / 0.018006 (0.216436) \| 0.458818 / 0.000490 (0.458329) \| 0.000407 / 0.000200 (0.000207) \| 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) \| 0.031661 / 0.037411 (-0.005750) \| 0.093143 / 0.014526 (0.078618) \| 0.102205 / 0.176557 (-0.074352) \| 0.155850 / 0.737135 (-0.581286) \| 0.104345 / 0.296338 (-0.191994) \|\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) \| 0.419641 / 0.215209 (0.204432) \| 4.200808 / 2.077655 (2.123153) \| 2.218227 / 1.504120 (0.714107) \| 2.052604 / 1.541195 (0.511409) \| 2.150611 / 1.468490 (0.682121) \| 0.482665 / 4.584777 (-4.102112) \| 3.606541 / 3.745712 (-0.139172) \| 3.310637 / 5.269862 (-1.959224) \| 2.070200 / 4.565676 (-2.495476) \| 0.056586 / 0.424275 (-0.367689) \| 0.007826 / 0.007607 (0.000218) \| 0.491037 / 0.226044 (0.264992) \| 4.901538 / 2.268929 (2.632610) \| 2.676402 / 55.444624 (-52.768223) \| 2.363935 / 6.876477 (-4.512542) \| 2.587813 / 2.142072 (0.445741) \| 0.579302 / 4.805227 (-4.225926) \| 0.132792 / 6.500664 (-6.367873) \| 0.061865 / 0.075469 (-0.013604) \|\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) \| 1.354315 / 1.841788 (-0.487473) \| 20.874516 / 8.074308 (12.800208) \| 14.863559 / 10.191392 (4.672167) \| 0.183635 / 0.680424 (-0.496789) \| 0.018636 / 0.534201 (-0.515565) \| 0.395317 / 0.579283 (-0.183966) \| 0.410598 / 0.434364 (-0.023766) \| 0.476485 / 0.540337 (-0.063853) \| 0.643246 / 1.386936 (-0.743690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4472a8795c603a95eef7c8f15cb04f1290cc8d11 \"CML watermark\")\n" ]
1,807,441,055	6,041	Flatten repository_structure docs on yaml	closed	2023-07-17T10:15:10	2023-07-17T10:24:51	2023-07-17T10:16:22	https://github.com/huggingface/datasets/pull/6041	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6041", "html_url": "https://github.com/huggingface/datasets/pull/6041", "diff_url": "https://github.com/huggingface/datasets/pull/6041.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6041.patch", "merged_at": "2023-07-17T10:16:22" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6041). 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) \| 0.007587 / 0.011353 (-0.003766) \| 0.004469 / 0.011008 (-0.006540) \| 0.098028 / 0.038508 (0.059520) \| 0.086378 / 0.023109 (0.063269) \| 0.412290 / 0.275898 (0.136392) \| 0.449912 / 0.323480 (0.126432) \| 0.004769 / 0.007986 (-0.003217) \| 0.003708 / 0.004328 (-0.000621) \| 0.075541 / 0.004250 (0.071290) \| 0.063821 / 0.037052 (0.026768) \| 0.417213 / 0.258489 (0.158724) \| 0.471954 / 0.293841 (0.178113) \| 0.036243 / 0.128546 (-0.092303) \| 0.009540 / 0.075646 (-0.066106) \| 0.339043 / 0.419271 (-0.080228) \| 0.061853 / 0.043533 (0.018320) \| 0.418510 / 0.255139 (0.163371) \| 0.462372 / 0.283200 (0.179173) \| 0.027328 / 0.141683 (-0.114355) \| 1.745114 / 1.452155 (0.292959) \| 1.879839 / 1.492716 (0.387123) \|\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) \| 0.211042 / 0.018006 (0.193035) \| 0.512865 / 0.000490 (0.512375) \| 0.008744 / 0.000200 (0.008544) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032493 / 0.037411 (-0.004918) \| 0.096472 / 0.014526 (0.081946) \| 0.110340 / 0.176557 (-0.066216) \| 0.183195 / 0.737135 (-0.553940) \| 0.112829 / 0.296338 (-0.183510) \|\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) \| 0.478040 / 0.215209 (0.262830) \| 4.743776 / 2.077655 (2.666121) \| 2.389770 / 1.504120 (0.885650) \| 2.168468 / 1.541195 (0.627274) \| 2.238154 / 1.468490 (0.769663) \| 0.572308 / 4.584777 (-4.012469) \| 4.154783 / 3.745712 (0.409071) \| 3.771509 / 5.269862 (-1.498353) \| 2.384828 / 4.565676 (-2.180848) \| 0.068122 / 0.424275 (-0.356153) \| 0.008573 / 0.007607 (0.000965) \| 0.560300 / 0.226044 (0.334256) \| 5.591163 / 2.268929 (3.322235) \| 2.929660 / 55.444624 (-52.514965) \| 2.517721 / 6.876477 (-4.358756) \| 2.762285 / 2.142072 (0.620213) \| 0.687193 / 4.805227 (-4.118034) \| 0.157839 / 6.500664 (-6.342825) \| 0.071862 / 0.075469 (-0.003607) \|\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) \| 1.484788 / 1.841788 (-0.357000) \| 21.696071 / 8.074308 (13.621763) \| 15.476166 / 10.191392 (5.284774) \| 0.185034 / 0.680424 (-0.495390) \| 0.021181 / 0.534201 (-0.513020) \| 0.463324 / 0.579283 (-0.115959) \| 0.502455 / 0.434364 (0.068091) \| 0.559880 / 0.540337 (0.019543) \| 0.767281 / 1.386936 (-0.619655) \|\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) \| 0.004331 / 0.011008 (-0.006677) \| 0.075023 / 0.038508 (0.036515) \| 0.085474 / 0.023109 (0.062365) \| 0.464900 / 0.275898 (0.189002) \| 0.503348 / 0.323480 (0.179868) \| 0.006885 / 0.007986 (-0.001101) \| 0.003647 / 0.004328 (-0.000681) \| 0.074874 / 0.004250 (0.070623) \| 0.071076 / 0.037052 (0.034024) \| 0.465495 / 0.258489 (0.207006) \| 0.506418 / 0.293841 (0.212577) \| 0.038900 / 0.128546 (-0.089647) \| 0.009467 / 0.075646 (-0.066180) \| 0.082547 / 0.419271 (-0.336724) \| 0.058457 / 0.043533 (0.014924) \| 0.459114 / 0.255139 (0.203975) \| 0.484872 / 0.283200 (0.201673) \| 0.027443 / 0.141683 (-0.114240) \| 1.713996 / 1.452155 (0.261841) \| 1.893639 / 1.492716 (0.400922) \|\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) \| 0.248693 / 0.018006 (0.230687) \| 0.488805 / 0.000490 (0.488315) \| 0.000421 / 0.000200 (0.000221) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034886 / 0.037411 (-0.002525) \| 0.103215 / 0.014526 (0.088689) \| 0.116422 / 0.176557 (-0.060134) \| 0.182789 / 0.737135 (-0.554346) \| 0.117788 / 0.296338 (-0.178550) \|\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) \| 0.482782 / 0.215209 (0.267573) \| 4.802895 / 2.077655 (2.725241) \| 2.489823 / 1.504120 (0.985703) \| 2.324005 / 1.541195 (0.782810) \| 2.457674 / 1.468490 (0.989184) \| 0.566980 / 4.584777 (-4.017797) \| 4.117359 / 3.745712 (0.371647) \| 3.841180 / 5.269862 (-1.428681) \| 2.322410 / 4.565676 (-2.243266) \| 0.066367 / 0.424275 (-0.357908) \| 0.008501 / 0.007607 (0.000894) \| 0.561453 / 0.226044 (0.335408) \| 5.694861 / 2.268929 (3.425932) \| 3.129829 / 55.444624 (-52.314796) \| 2.647375 / 6.876477 (-4.229102) \| 2.673071 / 2.142072 (0.530998) \| 0.676120 / 4.805227 (-4.129108) \| 0.153483 / 6.500664 (-6.347181) \| 0.070797 / 0.075469 (-0.004672) \|\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) \| 1.575697 / 1.841788 (-0.266091) \| 22.447462 / 8.074308 (14.373154) \| 15.964906 / 10.191392 (5.773514) \| 0.218343 / 0.680424 (-0.462081) \| 0.021051 / 0.534201 (-0.513150) \| 0.466079 / 0.579283 (-0.113204) \| 0.493190 / 0.434364 (0.058826) \| 0.565929 / 0.540337 (0.025592) \| 0.768638 / 1.386936 (-0.618298) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#104bafffef7ddc775ec2d0b10b2b262466041eb7 \"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) \| 0.006268 / 0.011353 (-0.005085) \| 0.003715 / 0.011008 (-0.007293) \| 0.080628 / 0.038508 (0.042120) \| 0.070294 / 0.023109 (0.047185) \| 0.404749 / 0.275898 (0.128851) \| 0.434130 / 0.323480 (0.110650) \| 0.005533 / 0.007986 (-0.002452) \| 0.002980 / 0.004328 (-0.001349) \| 0.063016 / 0.004250 (0.058766) \| 0.051667 / 0.037052 (0.014615) \| 0.403859 / 0.258489 (0.145370) \| 0.437913 / 0.293841 (0.144073) \| 0.027518 / 0.128546 (-0.101029) \| 0.007991 / 0.075646 (-0.067655) \| 0.260723 / 0.419271 (-0.158548) \| 0.046580 / 0.043533 (0.003047) \| 0.405453 / 0.255139 (0.150314) \| 0.428390 / 0.283200 (0.145190) \| 0.022774 / 0.141683 (-0.118909) \| 1.488204 / 1.452155 (0.036049) \| 1.536557 / 1.492716 (0.043841) \|\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) \| 0.185864 / 0.018006 (0.167858) \| 0.431388 / 0.000490 (0.430898) \| 0.003743 / 0.000200 (0.003543) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024062 / 0.037411 (-0.013350) \| 0.075749 / 0.014526 (0.061224) \| 0.083519 / 0.176557 (-0.093037) \| 0.147965 / 0.737135 (-0.589170) \| 0.085635 / 0.296338 (-0.210703) \|\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) \| 0.400455 / 0.215209 (0.185246) \| 4.084294 / 2.077655 (2.006640) \| 1.928795 / 1.504120 (0.424675) \| 1.743205 / 1.541195 (0.202010) \| 1.811233 / 1.468490 (0.342743) \| 0.504976 / 4.584777 (-4.079801) \| 3.073134 / 3.745712 (-0.672578) \| 2.816357 / 5.269862 (-2.453505) \| 1.857462 / 4.565676 (-2.708214) \| 0.058329 / 0.424275 (-0.365946) \| 0.006850 / 0.007607 (-0.000757) \| 0.466017 / 0.226044 (0.239973) \| 4.660158 / 2.268929 (2.391230) \| 2.396614 / 55.444624 (-53.048010) \| 2.007491 / 6.876477 (-4.868986) \| 2.206997 / 2.142072 (0.064925) \| 0.592233 / 4.805227 (-4.212994) \| 0.125364 / 6.500664 (-6.375300) \| 0.061166 / 0.075469 (-0.014303) \|\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) \| 1.290148 / 1.841788 (-0.551640) \| 18.317462 / 8.074308 (10.243154) \| 13.465142 / 10.191392 (3.273750) \| 0.149696 / 0.680424 (-0.530728) \| 0.017120 / 0.534201 (-0.517081) \| 0.334818 / 0.579283 (-0.244465) \| 0.363976 / 0.434364 (-0.070388) \| 0.388271 / 0.540337 (-0.152066) \| 0.542383 / 1.386936 (-0.844553) \|\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) \| 0.006029 / 0.011353 (-0.005324) \| 0.003656 / 0.011008 (-0.007352) \| 0.063518 / 0.038508 (0.025010) \| 0.058214 / 0.023109 (0.035105) \| 0.435987 / 0.275898 (0.160089) \| 0.442769 / 0.323480 (0.119289) \| 0.004675 / 0.007986 (-0.003310) \| 0.002911 / 0.004328 (-0.001418) \| 0.063020 / 0.004250 (0.058769) \| 0.049422 / 0.037052 (0.012369) \| 0.435521 / 0.258489 (0.177032) \| 0.478251 / 0.293841 (0.184411) \| 0.027294 / 0.128546 (-0.101252) \| 0.008073 / 0.075646 (-0.067574) \| 0.068397 / 0.419271 (-0.350875) \| 0.044796 / 0.043533 (0.001263) \| 0.416646 / 0.255139 (0.161507) \| 0.435021 / 0.283200 (0.151821) \| 0.024686 / 0.141683 (-0.116997) \| 1.495650 / 1.452155 (0.043496) \| 1.495846 / 1.492716 (0.003130) \|\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) \| 0.211205 / 0.018006 (0.193199) \| 0.414497 / 0.000490 (0.414007) \| 0.001704 / 0.000200 (0.001504) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025237 / 0.037411 (-0.012174) \| 0.077291 / 0.014526 (0.062765) \| 0.085736 / 0.176557 (-0.090821) \| 0.141059 / 0.737135 (-0.596076) \| 0.087620 / 0.296338 (-0.208719) \|\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) \| 0.421995 / 0.215209 (0.206786) \| 4.158503 / 2.077655 (2.080849) \| 2.313598 / 1.504120 (0.809479) \| 2.183553 / 1.541195 (0.642359) \| 2.279656 / 1.468490 (0.811166) \| 0.500146 / 4.584777 (-4.084631) \| 3.092654 / 3.745712 (-0.653059) \| 4.371616 / 5.269862 (-0.898245) \| 2.605096 / 4.565676 (-1.960581) \| 0.057658 / 0.424275 (-0.366617) \| 0.006574 / 0.007607 (-0.001033) \| 0.491455 / 0.226044 (0.265411) \| 4.926730 / 2.268929 (2.657801) \| 2.635749 / 55.444624 (-52.808875) \| 2.255780 / 6.876477 (-4.620697) \| 2.305547 / 2.142072 (0.163474) \| 0.589027 / 4.805227 (-4.216200) \| 0.126229 / 6.500664 (-6.374435) \| 0.063268 / 0.075469 (-0.012201) \|\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) \| 1.299102 / 1.841788 (-0.542686) \| 18.547417 / 8.074308 (10.473109) \| 13.860030 / 10.191392 (3.668638) \| 0.145482 / 0.680424 (-0.534942) \| 0.016543 / 0.534201 (-0.517658) \| 0.330788 / 0.579283 (-0.248496) \| 0.362020 / 0.434364 (-0.072344) \| 0.380635 / 0.540337 (-0.159703) \| 0.517375 / 1.386936 (-0.869561) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf602e0193baca21e283babbac9622ae36d1e6b6 \"CML watermark\")\n" ]
1,807,410,238	6,040	Fix legacy_dataset_infos	closed	2023-07-17T09:56:21	2023-07-17T10:24:34	2023-07-17T10:16:03	https://github.com/huggingface/datasets/pull/6040	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6040", "html_url": "https://github.com/huggingface/datasets/pull/6040", "diff_url": "https://github.com/huggingface/datasets/pull/6040.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6040.patch", "merged_at": "2023-07-17T10:16:03" }	lhoestq	true	[ "_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) \| 0.006087 / 0.011353 (-0.005265) \| 0.003567 / 0.011008 (-0.007442) \| 0.079668 / 0.038508 (0.041160) \| 0.063647 / 0.023109 (0.040538) \| 0.323082 / 0.275898 (0.047184) \| 0.348679 / 0.323480 (0.025199) \| 0.004726 / 0.007986 (-0.003259) \| 0.002955 / 0.004328 (-0.001373) \| 0.062724 / 0.004250 (0.058473) \| 0.050194 / 0.037052 (0.013142) \| 0.321407 / 0.258489 (0.062918) \| 0.355053 / 0.293841 (0.061212) \| 0.026992 / 0.128546 (-0.101554) \| 0.007994 / 0.075646 (-0.067653) \| 0.260562 / 0.419271 (-0.158710) \| 0.050933 / 0.043533 (0.007400) \| 0.316644 / 0.255139 (0.061505) \| 0.336759 / 0.283200 (0.053560) \| 0.022581 / 0.141683 (-0.119101) \| 1.481259 / 1.452155 (0.029104) \| 1.535191 / 1.492716 (0.042475) \|\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) \| 0.194111 / 0.018006 (0.176104) \| 0.448146 / 0.000490 (0.447656) \| 0.000321 / 0.000200 (0.000121) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023908 / 0.037411 (-0.013503) \| 0.073316 / 0.014526 (0.058790) \| 0.085588 / 0.176557 (-0.090968) \| 0.145377 / 0.737135 (-0.591759) \| 0.084788 / 0.296338 (-0.211550) \|\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) \| 0.439327 / 0.215209 (0.224118) \| 4.384833 / 2.077655 (2.307179) \| 2.322943 / 1.504120 (0.818823) \| 2.147737 / 1.541195 (0.606542) \| 2.226725 / 1.468490 (0.758235) \| 0.502957 / 4.584777 (-4.081820) \| 3.098106 / 3.745712 (-0.647606) \| 4.194642 / 5.269862 (-1.075220) \| 2.598820 / 4.565676 (-1.966856) \| 0.057942 / 0.424275 (-0.366333) \| 0.006857 / 0.007607 (-0.000750) \| 0.511517 / 0.226044 (0.285472) \| 5.121797 / 2.268929 (2.852868) \| 2.756506 / 55.444624 (-52.688118) \| 2.424602 / 6.876477 (-4.451875) \| 2.608342 / 2.142072 (0.466270) \| 0.589498 / 4.805227 (-4.215729) \| 0.126065 / 6.500664 (-6.374600) \| 0.061456 / 0.075469 (-0.014013) \|\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) \| 1.239928 / 1.841788 (-0.601860) \| 18.423532 / 8.074308 (10.349224) \| 13.935148 / 10.191392 (3.743756) \| 0.129913 / 0.680424 (-0.550511) \| 0.016744 / 0.534201 (-0.517457) \| 0.333468 / 0.579283 (-0.245815) \| 0.359615 / 0.434364 (-0.074749) \| 0.383678 / 0.540337 (-0.156659) \| 0.533007 / 1.386936 (-0.853929) \|\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) \| 0.005980 / 0.011353 (-0.005373) \| 0.003640 / 0.011008 (-0.007368) \| 0.062500 / 0.038508 (0.023992) \| 0.059843 / 0.023109 (0.036733) \| 0.360993 / 0.275898 (0.085095) \| 0.401981 / 0.323480 (0.078501) \| 0.005495 / 0.007986 (-0.002490) \| 0.002862 / 0.004328 (-0.001467) \| 0.062491 / 0.004250 (0.058240) \| 0.050778 / 0.037052 (0.013726) \| 0.371007 / 0.258489 (0.112518) \| 0.405154 / 0.293841 (0.111313) \| 0.027390 / 0.128546 (-0.101156) \| 0.008042 / 0.075646 (-0.067604) \| 0.067590 / 0.419271 (-0.351681) \| 0.042485 / 0.043533 (-0.001048) \| 0.361305 / 0.255139 (0.106166) \| 0.388669 / 0.283200 (0.105469) \| 0.024143 / 0.141683 (-0.117540) \| 1.451508 / 1.452155 (-0.000647) \| 1.490431 / 1.492716 (-0.002285) \|\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) \| 0.175976 / 0.018006 (0.157970) \| 0.428923 / 0.000490 (0.428434) \| 0.002099 / 0.000200 (0.001899) \| 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) \| 0.026346 / 0.037411 (-0.011065) \| 0.078084 / 0.014526 (0.063558) \| 0.087287 / 0.176557 (-0.089269) \| 0.144179 / 0.737135 (-0.592957) \| 0.088286 / 0.296338 (-0.208053) \|\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) \| 0.450436 / 0.215209 (0.235227) \| 4.488801 / 2.077655 (2.411146) \| 2.479303 / 1.504120 (0.975184) \| 2.305396 / 1.541195 (0.764201) \| 2.370370 / 1.468490 (0.901879) \| 0.502355 / 4.584777 (-4.082422) \| 3.094733 / 3.745712 (-0.650979) \| 4.062367 / 5.269862 (-1.207495) \| 2.587506 / 4.565676 (-1.978170) \| 0.058245 / 0.424275 (-0.366030) \| 0.006487 / 0.007607 (-0.001120) \| 0.524147 / 0.226044 (0.298102) \| 5.236876 / 2.268929 (2.967947) \| 2.897134 / 55.444624 (-52.547490) \| 2.574631 / 6.876477 (-4.301846) \| 2.620307 / 2.142072 (0.478235) \| 0.586963 / 4.805227 (-4.218265) \| 0.125761 / 6.500664 (-6.374903) \| 0.062264 / 0.075469 (-0.013205) \|\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) \| 1.299668 / 1.841788 (-0.542120) \| 19.004441 / 8.074308 (10.930133) \| 13.841867 / 10.191392 (3.650475) \| 0.159674 / 0.680424 (-0.520750) \| 0.016699 / 0.534201 (-0.517502) \| 0.331868 / 0.579283 (-0.247415) \| 0.344604 / 0.434364 (-0.089760) \| 0.379391 / 0.540337 (-0.160947) \| 0.514790 / 1.386936 (-0.872146) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#47a006a90e9711b33db70b0ef2d2cefaadfa2179 \"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) \| 0.005792 / 0.011353 (-0.005561) \| 0.003519 / 0.011008 (-0.007489) \| 0.079133 / 0.038508 (0.040625) \| 0.057858 / 0.023109 (0.034749) \| 0.314206 / 0.275898 (0.038308) \| 0.346939 / 0.323480 (0.023459) \| 0.004583 / 0.007986 (-0.003403) \| 0.002824 / 0.004328 (-0.001504) \| 0.061652 / 0.004250 (0.057402) \| 0.048520 / 0.037052 (0.011467) \| 0.318018 / 0.258489 (0.059529) \| 0.350350 / 0.293841 (0.056509) \| 0.026284 / 0.128546 (-0.102262) \| 0.007827 / 0.075646 (-0.067819) \| 0.259624 / 0.419271 (-0.159647) \| 0.052318 / 0.043533 (0.008786) \| 0.317400 / 0.255139 (0.062261) \| 0.340530 / 0.283200 (0.057331) \| 0.025181 / 0.141683 (-0.116501) \| 1.459208 / 1.452155 (0.007053) \| 1.529158 / 1.492716 (0.036442) \|\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) \| 0.169692 / 0.018006 (0.151686) \| 0.432638 / 0.000490 (0.432148) \| 0.003675 / 0.000200 (0.003475) \| 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) \| 0.022956 / 0.037411 (-0.014456) \| 0.071860 / 0.014526 (0.057334) \| 0.082159 / 0.176557 (-0.094398) \| 0.142560 / 0.737135 (-0.594576) \| 0.082333 / 0.296338 (-0.214006) \|\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) \| 0.397923 / 0.215209 (0.182714) \| 3.958757 / 2.077655 (1.881102) \| 1.925837 / 1.504120 (0.421717) \| 1.758114 / 1.541195 (0.216919) \| 1.808845 / 1.468490 (0.340354) \| 0.501116 / 4.584777 (-4.083661) \| 3.007739 / 3.745712 (-0.737973) \| 3.295755 / 5.269862 (-1.974106) \| 2.123843 / 4.565676 (-2.441833) \| 0.057174 / 0.424275 (-0.367101) \| 0.006426 / 0.007607 (-0.001182) \| 0.468196 / 0.226044 (0.242152) \| 4.677392 / 2.268929 (2.408464) \| 2.334179 / 55.444624 (-53.110446) \| 1.989283 / 6.876477 (-4.887194) \| 2.140091 / 2.142072 (-0.001981) \| 0.590700 / 4.805227 (-4.214527) \| 0.124066 / 6.500664 (-6.376598) \| 0.059931 / 0.075469 (-0.015538) \|\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) \| 1.224547 / 1.841788 (-0.617240) \| 17.866979 / 8.074308 (9.792671) \| 13.142009 / 10.191392 (2.950617) \| 0.147081 / 0.680424 (-0.533343) \| 0.016777 / 0.534201 (-0.517424) \| 0.327766 / 0.579283 (-0.251517) \| 0.343988 / 0.434364 (-0.090376) \| 0.383268 / 0.540337 (-0.157070) \| 0.528109 / 1.386936 (-0.858827) \|\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) \| 0.006145 / 0.011353 (-0.005208) \| 0.003634 / 0.011008 (-0.007374) \| 0.062887 / 0.038508 (0.024379) \| 0.062659 / 0.023109 (0.039550) \| 0.362962 / 0.275898 (0.087064) \| 0.405149 / 0.323480 (0.081669) \| 0.004821 / 0.007986 (-0.003164) \| 0.002888 / 0.004328 (-0.001441) \| 0.062982 / 0.004250 (0.058732) \| 0.051929 / 0.037052 (0.014877) \| 0.366825 / 0.258489 (0.108336) \| 0.409830 / 0.293841 (0.115989) \| 0.027263 / 0.128546 (-0.101283) \| 0.007972 / 0.075646 (-0.067674) \| 0.067413 / 0.419271 (-0.351858) \| 0.044233 / 0.043533 (0.000700) \| 0.365087 / 0.255139 (0.109948) \| 0.393845 / 0.283200 (0.110646) \| 0.027740 / 0.141683 (-0.113943) \| 1.497896 / 1.452155 (0.045741) \| 1.549419 / 1.492716 (0.056703) \|\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) \| 0.225510 / 0.018006 (0.207503) \| 0.417054 / 0.000490 (0.416564) \| 0.002184 / 0.000200 (0.001984) \| 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) \| 0.025503 / 0.037411 (-0.011908) \| 0.076164 / 0.014526 (0.061638) \| 0.086110 / 0.176557 (-0.090446) \| 0.140387 / 0.737135 (-0.596748) \| 0.086956 / 0.296338 (-0.209382) \|\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) \| 0.469667 / 0.215209 (0.254458) \| 4.689915 / 2.077655 (2.612261) \| 2.685000 / 1.504120 (1.180880) \| 2.516160 / 1.541195 (0.974965) \| 2.531733 / 1.468490 (1.063243) \| 0.501675 / 4.584777 (-4.083102) \| 3.000579 / 3.745712 (-0.745133) \| 2.853376 / 5.269862 (-2.416486) \| 1.810677 / 4.565676 (-2.754999) \| 0.057632 / 0.424275 (-0.366643) \| 0.006390 / 0.007607 (-0.001217) \| 0.543986 / 0.226044 (0.317941) \| 5.432837 / 2.268929 (3.163908) \| 3.138797 / 55.444624 (-52.305827) \| 2.813141 / 6.876477 (-4.063336) \| 2.803681 / 2.142072 (0.661609) \| 0.588736 / 4.805227 (-4.216491) \| 0.125696 / 6.500664 (-6.374968) \| 0.062492 / 0.075469 (-0.012977) \|\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) \| 1.337163 / 1.841788 (-0.504624) \| 18.611715 / 8.074308 (10.537407) \| 13.953016 / 10.191392 (3.761624) \| 0.154670 / 0.680424 (-0.525754) \| 0.016523 / 0.534201 (-0.517678) \| 0.333898 / 0.579283 (-0.245385) \| 0.336520 / 0.434364 (-0.097844) \| 0.389032 / 0.540337 (-0.151305) \| 0.529202 / 1.386936 (-0.857734) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01d4b3330f2cc243a3f3b0cd61ec5558466c40fd \"CML watermark\")\n" ]
1,806,508,451	6,039	Loading column subset from parquet file produces error since version 2.13	closed	2023-07-16T09:13:07	2023-07-24T14:35:04	2023-07-24T14:35:04	https://github.com/huggingface/datasets/issues/6039	null	kklemon	false	[]
1,805,960,244	6,038	File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?	closed	2023-07-15T07:58:08	2023-07-24T11:54:15	2023-07-24T11:54:15	https://github.com/huggingface/datasets/issues/6038	null	BaiMeiyingxue	false	[ "Instead of writing the loading script, you can use the built-in loader to [load JSON files](https://huggingface.co/docs/datasets/loading#json):\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"json\", data_files={\"train\": os.path.join(data_dir[\"train\"]), \"dev\": os.path.join(data_dir[\"dev\"])})\r\n```" ]
1,805,887,184	6,037	Documentation links to examples are broken	closed	2023-07-15T04:54:50	2023-07-17T22:35:14	2023-07-17T15:10:32	https://github.com/huggingface/datasets/issues/6037	null	david-waterworth	false	[ "These docs are outdated (version 1.2.1 is over two years old). Please refer to [this](https://huggingface.co/docs/datasets/dataset_script) version instead.\r\n\r\nInitially, we hosted datasets in this repo, but now you can find them [on the HF Hub](https://huggingface.co/datasets) (e.g. the [`ag_news`](https://huggingface.co/datasets/ag_news/blob/main/ag_news.py) script)", "Sorry I thought I'd selected the latest version." ]
1,805,138,898	6,036	Deprecate search API	open	2023-07-14T16:22:09	2023-09-07T16:44:32	null	https://github.com/huggingface/datasets/pull/6036	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6036", "html_url": "https://github.com/huggingface/datasets/pull/6036", "diff_url": "https://github.com/huggingface/datasets/pull/6036.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6036.patch", "merged_at": null }	mariosasko	true	[ "<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) \| 0.005746 / 0.011353 (-0.005607) \| 0.003461 / 0.011008 (-0.007548) \| 0.078672 / 0.038508 (0.040164) \| 0.056800 / 0.023109 (0.033691) \| 0.312853 / 0.275898 (0.036955) \| 0.346715 / 0.323480 (0.023235) \| 0.004516 / 0.007986 (-0.003469) \| 0.002872 / 0.004328 (-0.001457) \| 0.061264 / 0.004250 (0.057013) \| 0.046606 / 0.037052 (0.009553) \| 0.320080 / 0.258489 (0.061591) \| 0.350390 / 0.293841 (0.056550) \| 0.026445 / 0.128546 (-0.102101) \| 0.007710 / 0.075646 (-0.067936) \| 0.259519 / 0.419271 (-0.159752) \| 0.043935 / 0.043533 (0.000402) \| 0.320015 / 0.255139 (0.064876) \| 0.339799 / 0.283200 (0.056599) \| 0.018638 / 0.141683 (-0.123044) \| 1.463393 / 1.452155 (0.011239) \| 1.496977 / 1.492716 (0.004261) \|\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) \| 0.185175 / 0.018006 (0.167168) \| 0.420734 / 0.000490 (0.420245) \| 0.002569 / 0.000200 (0.002369) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022335 / 0.037411 (-0.015077) \| 0.071686 / 0.014526 (0.057161) \| 0.079906 / 0.176557 (-0.096650) \| 0.140386 / 0.737135 (-0.596749) \| 0.079712 / 0.296338 (-0.216627) \|\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) \| 0.392643 / 0.215209 (0.177434) \| 3.917934 / 2.077655 (1.840279) \| 1.906808 / 1.504120 (0.402688) \| 1.729564 / 1.541195 (0.188369) \| 1.751533 / 1.468490 (0.283043) \| 0.496810 / 4.584777 (-4.087967) \| 3.047405 / 3.745712 (-0.698307) \| 4.361766 / 5.269862 (-0.908095) \| 2.660845 / 4.565676 (-1.904832) \| 0.056951 / 0.424275 (-0.367324) \| 0.006277 / 0.007607 (-0.001330) \| 0.466357 / 0.226044 (0.240312) \| 4.660457 / 2.268929 (2.391529) \| 2.328590 / 55.444624 (-53.116034) \| 1.986140 / 6.876477 (-4.890337) \| 2.096182 / 2.142072 (-0.045891) \| 0.581685 / 4.805227 (-4.223542) \| 0.123643 / 6.500664 (-6.377021) \| 0.060286 / 0.075469 (-0.015183) \|\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) \| 1.237024 / 1.841788 (-0.604763) \| 17.778533 / 8.074308 (9.704225) \| 13.202205 / 10.191392 (3.010813) \| 0.141301 / 0.680424 (-0.539123) \| 0.016453 / 0.534201 (-0.517748) \| 0.329173 / 0.579283 (-0.250110) \| 0.349945 / 0.434364 (-0.084419) \| 0.375319 / 0.540337 (-0.165018) \| 0.530394 / 1.386936 (-0.856542) \|\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) \| 0.005863 / 0.011353 (-0.005489) \| 0.003578 / 0.011008 (-0.007430) \| 0.062719 / 0.038508 (0.024211) \| 0.056192 / 0.023109 (0.033082) \| 0.422812 / 0.275898 (0.146914) \| 0.454316 / 0.323480 (0.130836) \| 0.004446 / 0.007986 (-0.003540) \| 0.002808 / 0.004328 (-0.001521) \| 0.062819 / 0.004250 (0.058569) \| 0.046243 / 0.037052 (0.009190) \| 0.445858 / 0.258489 (0.187369) \| 0.463750 / 0.293841 (0.169909) \| 0.027504 / 0.128546 (-0.101042) \| 0.007897 / 0.075646 (-0.067749) \| 0.068248 / 0.419271 (-0.351024) \| 0.041921 / 0.043533 (-0.001612) \| 0.413314 / 0.255139 (0.158175) \| 0.441619 / 0.283200 (0.158419) \| 0.019246 / 0.141683 (-0.122437) \| 1.457069 / 1.452155 (0.004914) \| 1.524168 / 1.492716 (0.031452) \|\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) \| 0.237785 / 0.018006 (0.219779) \| 0.418455 / 0.000490 (0.417965) \| 0.002301 / 0.000200 (0.002101) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025630 / 0.037411 (-0.011781) \| 0.076673 / 0.014526 (0.062147) \| 0.084877 / 0.176557 (-0.091680) \| 0.137528 / 0.737135 (-0.599607) \| 0.085261 / 0.296338 (-0.211077) \|\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) \| 0.419040 / 0.215209 (0.203831) \| 4.183022 / 2.077655 (2.105368) \| 2.157852 / 1.504120 (0.653732) \| 1.966177 / 1.541195 (0.424982) \| 2.019612 / 1.468490 (0.551122) \| 0.497415 / 4.584777 (-4.087362) \| 3.102873 / 3.745712 (-0.642839) \| 4.526336 / 5.269862 (-0.743525) \| 2.991503 / 4.565676 (-1.574174) \| 0.057235 / 0.424275 (-0.367040) \| 0.006735 / 0.007607 (-0.000872) \| 0.498255 / 0.226044 (0.272211) \| 4.957364 / 2.268929 (2.688435) \| 2.632643 / 55.444624 (-52.811981) \| 2.249788 / 6.876477 (-4.626688) \| 2.289134 / 2.142072 (0.147062) \| 0.583581 / 4.805227 (-4.221646) \| 0.126046 / 6.500664 (-6.374618) \| 0.062966 / 0.075469 (-0.012504) \|\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) \| 1.295215 / 1.841788 (-0.546573) \| 18.554020 / 8.074308 (10.479711) \| 13.683273 / 10.191392 (3.491881) \| 0.132266 / 0.680424 (-0.548158) \| 0.016376 / 0.534201 (-0.517825) \| 0.334495 / 0.579283 (-0.244788) \| 0.347106 / 0.434364 (-0.087258) \| 0.387531 / 0.540337 (-0.152806) \| 0.525745 / 1.386936 (-0.861191) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22aa0bf179602e4bf3f44a9de5d180579bb377e \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6036). All of your documentation changes will be reflected on that endpoint.", "I don't think `transformers` should have any dataset indexing code. So before deprecating I'd be in favor of finding a suitable replacement. Not sure about the stats of the RAG model that uses `datasets` indexing though", "The RAG downloads stats are decent (over 20k downloads last month).\r\n\r\nI think it's suboptimal to maintain an API that only a single model uses. One option is to put this code into a separate lib. However, `langchain` and `docarray` already provide a unified interface to vector stores, so I don't see this as an impactful project. Considering how specific this model is, I think we should go with the simplest solution and combine an index with a dataset in Transformers (this wouldn't require too much code).", "What about migrating to the [datasets-server](https://github.com/huggingface/datasets-server) search feature instead? Would make more sense from a product perspective ", "I don't think it's a good idea:\r\n- using datasets-server would require to upload the data and to not control the indexing, whereas the current feature is about using a local index that you control\r\n- faiss indexes are vector indexes that are not supported by datasets-server, and they are also very customised. For instance RAG uses DPR embeddings and cosine similarity\r\n- FTS is only done for the first 5GB of data for now in datasets-server\r\n\r\nI think a better option would be to integrate with open source search tools such as docarray.\r\nAnd if we want to make the datasets-server search available in python we can build an integration in docarray and/or in huggingface_hub.", "`llama_index` is another popular tool in this space.\r\n\r\n@lhoestq \r\n> I think a better option would be to integrate with open source search tools such as docarray.\r\nAnd if we want to make the datasets-server search available in python we can build an integration in docarray and/or in huggingface_hub.\r\n\r\nI don't think these integrations would be popular unless we integrate them with the Hub \"UI-wise\" (e.g., through a widget), so they can wait IMO. Also, FAISS supports `fsspec` already with the callback reader/writer, so this doesn't require a specific integration. ", "After discussing it a bit with @lhoestq, do we need to deprecate the search API? While I understand it's imperfect, it looks like this will result in significant work to update it everywhere, so I'd favor keeping it until there's an obviously better alternative; this way we can focus on different things in the meantime.", "FAISS/ES are simple to use (probably the main reason why they are so popular), so creating \"better alternatives\" is not easy - they usually add more complexity (as is the case here, `langchain`, etc.)\r\n\r\nSo, instead of waiting for better alternatives, IMO it makes more sense to wait for the RAG model to be deprecated in Transformers (less than 1,000 cumulated downloads over all checkpoints in the past 30 days) before deprecating this API here.\r\n\r\nIn the meantime, we should make it clear that the vector search API is in maintenance mode (no new features, etc.).\r\n\r\nHow does that sound?" ]
1,805,087,687	6,035	Dataset representation	open	2023-07-14T15:42:37	2023-07-19T19:41:35	null	https://github.com/huggingface/datasets/pull/6035	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6035", "html_url": "https://github.com/huggingface/datasets/pull/6035", "diff_url": "https://github.com/huggingface/datasets/pull/6035.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6035.patch", "merged_at": null }	Ganryuu	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6035). All of your documentation changes will be reflected on that endpoint." ]
1,804,501,361	6,034	load_dataset hangs on WSL	closed	2023-07-14T09:03:10	2023-07-14T14:48:29	2023-07-14T14:48:29	https://github.com/huggingface/datasets/issues/6034	null	Andy-Zhou2	false	[ "Even if a dataset is cached, we still make requests to check whether the cache is up-to-date. [This](https://huggingface.co/docs/datasets/v2.13.1/en/loading#offline) section in the docs explains how to avoid them and directly load the cached version.", "Thanks - that works! However it doesn't resolve the original issue (but I am not sure if it is a WSL problem)", "We use `requests` to make HTTP requests (and `aiohttp` in the streaming mode), so I don't think we can provide much help regarding the socket issue (it probably has something to do with WSL). " ]
1,804,482,051	6,033	`map` function doesn't fully utilize `input_columns`.	closed	2023-07-14T08:49:28	2023-07-14T09:16:04	2023-07-14T09:16:04	https://github.com/huggingface/datasets/issues/6033	null	kwonmha	false	[]
1,804,358,679	6,032	DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info	open	2023-07-14T07:22:55	2023-09-11T13:50:41	null	https://github.com/huggingface/datasets/issues/6032	null	codingl2k1	false	[ "`HfApi` comes from the `huggingface_hub` package. You can use [this](https://huggingface.co/docs/huggingface_hub/v0.16.3/en/package_reference/utilities#huggingface_hub.configure_http_backend) utility to change the `huggingface_hub`'s `Session` proxies (see the example).\r\n\r\nWe plan to implement https://github.com/huggingface/datasets/issues/5080 and make this behavior more consistent eventually.", "> this\r\n\r\nThanks. I will try `huggingface_hub.configure_http_backend` to change session's config.", "@mariosasko are you saying if I do the following:\r\n\r\n```\r\ndef backend_factory() -> requests.Session:\r\n session = requests.Session()\r\n session.proxies = {\r\n \"https\": \"127.0.0.1:8887\",\r\n \"http\": \"127.0.0.1:8887\",\r\n }\r\n session.verify = \"/etc/ssl/certs/ca-certificates.crt\"\r\n return session\r\n\r\n# Set it as the default session factory\r\nconfigure_http_backend(backend_factory=backend_factory)\r\n```\r\n\r\nwhich works nicely with transformer library:\r\n\r\n```\r\ndef download_gpt_2_model():\r\n tokenizer = GPT2Tokenizer.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n text = \"Replace me by any text you'd like.\"\r\n encoded_input = tokenizer(text, return_tensors=\"pt\")\r\n print(encoded_input)\r\n\r\n model = GPT2Model.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n output = model(**encoded_input)\r\n```\r\n\r\nshould work for datasets library as well ?\r\n\r\nIn my case if I just do:\r\n\r\n```\r\ndef download_sts12_sts_dataset():\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n\r\n```\r\nI am getting:\r\n`ConnectionError: Couldn't reach https://huggingface.co/datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f429e87a3a0>, 'Connection to huggingface.co timed out. (connect timeout=100)'))\")))`\r\n\r\nwhich is typical when the proxy server is not defined. Looks like what is set in configure_http_backend(backend_factory=backend_factory) is ignore.\r\n\r\nIf I use env variable instead, it is working \r\n```\r\ndef download_sts12_sts_dataset():\r\n\r\n os.environ[\"https_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"http_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"REQUESTS_CA_BUNDLE\"] = \"/etc/ssl/certs/ca-certificates.crt\"\r\n\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n```\r\n\r\nShould I add something ?\r\n\r\nI am using `huggingface_hub 0.15.1`, `datasets 2.13.0`, `transformers 4.30.2`", "`huggingface_hub.configure_http_backend` works for `transformers` because they only use the `huggingface_hub` lib for downloads. Our download logic is a bit more complex (e.g., we also support downloading non-Hub files), so we are not aligned with them yet. In the meantime, it's best to use the env vars.", "@mariosasko I fully understand that the logic for dataset is different. I see 2 issues with the current implementation of the env variables:\r\n\r\n- having the same https_proxy/http_prox/no_proxy env variables for all tools is not good in some case. For example I have 2 differents proxy server. In 2019 we had discussion with the Tensorflow teams and they recommended to do the following: TFDS_HTTP_PROXY, TFDS_HTTPS_PROXY ...\r\n- with recent version of requests, it is not possible to deactivate TLS interception (verify=false) by using env variable. This is useful to debug things and in some case TLS is not working and you need to ignore verifying the SSL certificate (probably not recommended) \r\n\r\nOne of the best way is to able to pass our requests.Session() directly\r\n```\r\nimport openai\r\nsession = requests.Session()\r\nsession.cert = CERT\r\nsession.verify = False\r\nopenai.requestssession = session\r\n```\r\n\r\nMy 2 cents in this discussion" ]
1,804,183,858	6,031	Argument type for map function changes when using `input_columns` for `IterableDataset`	closed	2023-07-14T05:11:14	2023-07-14T14:44:15	2023-07-14T14:44:15	https://github.com/huggingface/datasets/issues/6031	null	kwonmha	false	[ "Yes, this is intended." ]
1,803,864,744	6,030	fixed typo in comment	closed	2023-07-13T22:49:57	2023-07-14T14:21:58	2023-07-14T14:13:38	https://github.com/huggingface/datasets/pull/6030	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6030", "html_url": "https://github.com/huggingface/datasets/pull/6030", "diff_url": "https://github.com/huggingface/datasets/pull/6030.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6030.patch", "merged_at": "2023-07-14T14:13:38" }	NightMachinery	true	[ "_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) \| 0.005855 / 0.011353 (-0.005498) \| 0.003556 / 0.011008 (-0.007452) \| 0.079430 / 0.038508 (0.040922) \| 0.056754 / 0.023109 (0.033645) \| 0.311718 / 0.275898 (0.035820) \| 0.346731 / 0.323480 (0.023251) \| 0.004414 / 0.007986 (-0.003571) \| 0.002835 / 0.004328 (-0.001493) \| 0.062138 / 0.004250 (0.057888) \| 0.044259 / 0.037052 (0.007206) \| 0.314681 / 0.258489 (0.056192) \| 0.359802 / 0.293841 (0.065961) \| 0.026684 / 0.128546 (-0.101862) \| 0.008023 / 0.075646 (-0.067623) \| 0.260148 / 0.419271 (-0.159123) \| 0.043734 / 0.043533 (0.000202) \| 0.312081 / 0.255139 (0.056942) \| 0.340004 / 0.283200 (0.056805) \| 0.019559 / 0.141683 (-0.122124) \| 1.488758 / 1.452155 (0.036604) \| 1.510828 / 1.492716 (0.018111) \|\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) \| 0.181376 / 0.018006 (0.163370) \| 0.441726 / 0.000490 (0.441236) \| 0.001722 / 0.000200 (0.001522) \| 0.000066 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023760 / 0.037411 (-0.013651) \| 0.071847 / 0.014526 (0.057321) \| 0.082642 / 0.176557 (-0.093915) \| 0.145555 / 0.737135 (-0.591580) \| 0.084554 / 0.296338 (-0.211784) \|\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) \| 0.401688 / 0.215209 (0.186479) \| 4.000994 / 2.077655 (1.923339) \| 2.047109 / 1.504120 (0.542989) \| 1.891874 / 1.541195 (0.350679) \| 1.970599 / 1.468490 (0.502109) \| 0.500646 / 4.584777 (-4.084131) \| 3.006623 / 3.745712 (-0.739089) \| 4.248359 / 5.269862 (-1.021503) \| 2.613946 / 4.565676 (-1.951730) \| 0.057921 / 0.424275 (-0.366354) \| 0.006407 / 0.007607 (-0.001200) \| 0.470676 / 0.226044 (0.244631) \| 4.722280 / 2.268929 (2.453352) \| 2.448530 / 55.444624 (-52.996095) \| 2.175841 / 6.876477 (-4.700635) \| 2.352287 / 2.142072 (0.210214) \| 0.589049 / 4.805227 (-4.216179) \| 0.125145 / 6.500664 (-6.375519) \| 0.060829 / 0.075469 (-0.014640) \|\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) \| 1.189225 / 1.841788 (-0.652563) \| 16.753085 / 8.074308 (8.678777) \| 13.086512 / 10.191392 (2.895120) \| 0.132371 / 0.680424 (-0.548052) \| 0.016933 / 0.534201 (-0.517268) \| 0.328258 / 0.579283 (-0.251025) \| 0.344074 / 0.434364 (-0.090290) \| 0.374042 / 0.540337 (-0.166296) \| 0.515307 / 1.386936 (-0.871629) \|\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) \| 0.005963 / 0.011353 (-0.005390) \| 0.003484 / 0.011008 (-0.007525) \| 0.062618 / 0.038508 (0.024110) \| 0.057217 / 0.023109 (0.034108) \| 0.426760 / 0.275898 (0.150862) \| 0.464422 / 0.323480 (0.140942) \| 0.005276 / 0.007986 (-0.002709) \| 0.002872 / 0.004328 (-0.001456) \| 0.062636 / 0.004250 (0.058385) \| 0.045953 / 0.037052 (0.008900) \| 0.433221 / 0.258489 (0.174732) \| 0.475087 / 0.293841 (0.181246) \| 0.027217 / 0.128546 (-0.101329) \| 0.007965 / 0.075646 (-0.067681) \| 0.067749 / 0.419271 (-0.351522) \| 0.041235 / 0.043533 (-0.002298) \| 0.425424 / 0.255139 (0.170285) \| 0.453390 / 0.283200 (0.170190) \| 0.020217 / 0.141683 (-0.121466) \| 1.436354 / 1.452155 (-0.015801) \| 1.492372 / 1.492716 (-0.000345) \|\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) \| 0.226896 / 0.018006 (0.208889) \| 0.411935 / 0.000490 (0.411445) \| 0.000356 / 0.000200 (0.000156) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024705 / 0.037411 (-0.012706) \| 0.076232 / 0.014526 (0.061706) \| 0.086949 / 0.176557 (-0.089608) \| 0.141867 / 0.737135 (-0.595269) \| 0.088199 / 0.296338 (-0.208140) \|\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) \| 0.419748 / 0.215209 (0.204539) \| 4.198597 / 2.077655 (2.120942) \| 2.338477 / 1.504120 (0.834357) \| 2.195741 / 1.541195 (0.654547) \| 2.278145 / 1.468490 (0.809655) \| 0.502365 / 4.584777 (-4.082412) \| 2.987773 / 3.745712 (-0.757939) \| 2.896526 / 5.269862 (-2.373336) \| 1.841610 / 4.565676 (-2.724067) \| 0.058032 / 0.424275 (-0.366243) \| 0.006470 / 0.007607 (-0.001137) \| 0.496969 / 0.226044 (0.270925) \| 4.960984 / 2.268929 (2.692056) \| 2.648615 / 55.444624 (-52.796009) \| 2.286846 / 6.876477 (-4.589631) \| 2.320176 / 2.142072 (0.178104) \| 0.600550 / 4.805227 (-4.204678) \| 0.125652 / 6.500664 (-6.375012) \| 0.062177 / 0.075469 (-0.013292) \|\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) \| 1.293063 / 1.841788 (-0.548725) \| 18.294204 / 8.074308 (10.219896) \| 13.720502 / 10.191392 (3.529110) \| 0.146480 / 0.680424 (-0.533944) \| 0.016965 / 0.534201 (-0.517236) \| 0.330137 / 0.579283 (-0.249146) \| 0.352051 / 0.434364 (-0.082313) \| 0.381754 / 0.540337 (-0.158584) \| 0.517935 / 1.386936 (-0.869001) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#269fcd31a2e759c65ffd5952ecef13e6a0d92574 \"CML watermark\")\n" ]
1,803,460,046	6,029	[docs] Fix link	closed	2023-07-13T17:24:12	2023-07-13T17:47:41	2023-07-13T17:38:59	https://github.com/huggingface/datasets/pull/6029	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6029", "html_url": "https://github.com/huggingface/datasets/pull/6029", "diff_url": "https://github.com/huggingface/datasets/pull/6029.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6029.patch", "merged_at": "2023-07-13T17:38:59" }	stevhliu	true	[ "_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) \| 0.007039 / 0.011353 (-0.004314) \| 0.004175 / 0.011008 (-0.006833) \| 0.085426 / 0.038508 (0.046918) \| 0.079818 / 0.023109 (0.056709) \| 0.321924 / 0.275898 (0.046026) \| 0.345482 / 0.323480 (0.022002) \| 0.005510 / 0.007986 (-0.002475) \| 0.003452 / 0.004328 (-0.000877) \| 0.065158 / 0.004250 (0.060907) \| 0.058843 / 0.037052 (0.021791) \| 0.316280 / 0.258489 (0.057791) \| 0.351666 / 0.293841 (0.057825) \| 0.031190 / 0.128546 (-0.097357) \| 0.008500 / 0.075646 (-0.067147) \| 0.289595 / 0.419271 (-0.129676) \| 0.053798 / 0.043533 (0.010265) \| 0.315804 / 0.255139 (0.060665) \| 0.334957 / 0.283200 (0.051757) \| 0.024350 / 0.141683 (-0.117332) \| 1.515753 / 1.452155 (0.063599) \| 1.556215 / 1.492716 (0.063499) \|\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) \| 0.210378 / 0.018006 (0.192372) \| 0.469309 / 0.000490 (0.468820) \| 0.002890 / 0.000200 (0.002690) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030214 / 0.037411 (-0.007197) \| 0.088492 / 0.014526 (0.073966) \| 0.098684 / 0.176557 (-0.077873) \| 0.156077 / 0.737135 (-0.581058) \| 0.098814 / 0.296338 (-0.197525) \|\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) \| 0.404548 / 0.215209 (0.189339) \| 4.026173 / 2.077655 (1.948518) \| 2.043216 / 1.504120 (0.539096) \| 1.880997 / 1.541195 (0.339802) \| 1.975205 / 1.468490 (0.506715) \| 0.489395 / 4.584777 (-4.095382) \| 3.684097 / 3.745712 (-0.061615) \| 5.126934 / 5.269862 (-0.142928) \| 3.092153 / 4.565676 (-1.473524) \| 0.057668 / 0.424275 (-0.366607) \| 0.007372 / 0.007607 (-0.000235) \| 0.479647 / 0.226044 (0.253603) \| 4.780207 / 2.268929 (2.511278) \| 2.533457 / 55.444624 (-52.911168) \| 2.182126 / 6.876477 (-4.694351) \| 2.431834 / 2.142072 (0.289761) \| 0.591760 / 4.805227 (-4.213467) \| 0.135450 / 6.500664 (-6.365214) \| 0.063218 / 0.075469 (-0.012251) \|\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) \| 1.262053 / 1.841788 (-0.579734) \| 20.246992 / 8.074308 (12.172684) \| 14.638222 / 10.191392 (4.446830) \| 0.150021 / 0.680424 (-0.530403) \| 0.018680 / 0.534201 (-0.515521) \| 0.395215 / 0.579283 (-0.184068) \| 0.421270 / 0.434364 (-0.013094) \| 0.458845 / 0.540337 (-0.081492) \| 0.634488 / 1.386936 (-0.752448) \|\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) \| 0.007080 / 0.011353 (-0.004273) \| 0.004112 / 0.011008 (-0.006896) \| 0.066426 / 0.038508 (0.027918) \| 0.090088 / 0.023109 (0.066978) \| 0.400191 / 0.275898 (0.124293) \| 0.429614 / 0.323480 (0.106134) \| 0.005428 / 0.007986 (-0.002558) \| 0.003501 / 0.004328 (-0.000827) \| 0.065056 / 0.004250 (0.060806) \| 0.061643 / 0.037052 (0.024590) \| 0.398619 / 0.258489 (0.140130) \| 0.445497 / 0.293841 (0.151657) \| 0.031703 / 0.128546 (-0.096843) \| 0.008708 / 0.075646 (-0.066938) \| 0.071561 / 0.419271 (-0.347711) \| 0.050684 / 0.043533 (0.007151) \| 0.385361 / 0.255139 (0.130222) \| 0.409349 / 0.283200 (0.126149) \| 0.027388 / 0.141683 (-0.114295) \| 1.473021 / 1.452155 (0.020866) \| 1.525246 / 1.492716 (0.032529) \|\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) \| 0.237710 / 0.018006 (0.219704) \| 0.468719 / 0.000490 (0.468230) \| 0.000385 / 0.000200 (0.000185) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032539 / 0.037411 (-0.004872) \| 0.095324 / 0.014526 (0.080798) \| 0.102248 / 0.176557 (-0.074308) \| 0.156096 / 0.737135 (-0.581039) \| 0.103458 / 0.296338 (-0.192881) \|\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) \| 0.416226 / 0.215209 (0.201017) \| 4.141044 / 2.077655 (2.063389) \| 2.143732 / 1.504120 (0.639612) \| 2.001020 / 1.541195 (0.459825) \| 2.091194 / 1.468490 (0.622704) \| 0.489977 / 4.584777 (-4.094800) \| 3.579615 / 3.745712 (-0.166097) \| 3.438082 / 5.269862 (-1.831780) \| 2.069031 / 4.565676 (-2.496645) \| 0.056994 / 0.424275 (-0.367281) \| 0.007362 / 0.007607 (-0.000245) \| 0.493077 / 0.226044 (0.267033) \| 4.922622 / 2.268929 (2.653694) \| 2.627083 / 55.444624 (-52.817541) \| 2.301141 / 6.876477 (-4.575336) \| 2.356794 / 2.142072 (0.214722) \| 0.583792 / 4.805227 (-4.221436) \| 0.133707 / 6.500664 (-6.366958) \| 0.062892 / 0.075469 (-0.012577) \|\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) \| 1.364908 / 1.841788 (-0.476880) \| 20.641219 / 8.074308 (12.566911) \| 14.848528 / 10.191392 (4.657136) \| 0.174207 / 0.680424 (-0.506217) \| 0.018206 / 0.534201 (-0.515995) \| 0.413742 / 0.579283 (-0.165541) \| 0.419940 / 0.434364 (-0.014424) \| 0.458543 / 0.540337 (-0.081794) \| 0.616518 / 1.386936 (-0.770418) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b2202c3e7cdde05920078f01864964556427da \"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) \| 0.006875 / 0.011353 (-0.004478) \| 0.003489 / 0.011008 (-0.007519) \| 0.082077 / 0.038508 (0.043569) \| 0.103011 / 0.023109 (0.079902) \| 0.370572 / 0.275898 (0.094674) \| 0.416400 / 0.323480 (0.092920) \| 0.004048 / 0.007986 (-0.003938) \| 0.003563 / 0.004328 (-0.000765) \| 0.062666 / 0.004250 (0.058416) \| 0.063664 / 0.037052 (0.026612) \| 0.374206 / 0.258489 (0.115717) \| 0.425590 / 0.293841 (0.131749) \| 0.028174 / 0.128546 (-0.100373) \| 0.007906 / 0.075646 (-0.067741) \| 0.266251 / 0.419271 (-0.153020) \| 0.045923 / 0.043533 (0.002390) \| 0.376746 / 0.255139 (0.121607) \| 0.401950 / 0.283200 (0.118750) \| 0.024628 / 0.141683 (-0.117054) \| 1.441903 / 1.452155 (-0.010252) \| 1.537494 / 1.492716 (0.044777) \|\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) \| 0.214696 / 0.018006 (0.196690) \| 0.425626 / 0.000490 (0.425137) \| 0.003370 / 0.000200 (0.003170) \| 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) \| 0.023133 / 0.037411 (-0.014279) \| 0.072374 / 0.014526 (0.057848) \| 0.081255 / 0.176557 (-0.095301) \| 0.146960 / 0.737135 (-0.590175) \| 0.081748 / 0.296338 (-0.214590) \|\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) \| 0.390683 / 0.215209 (0.175473) \| 3.893166 / 2.077655 (1.815511) \| 1.884321 / 1.504120 (0.380201) \| 1.701899 / 1.541195 (0.160704) \| 1.737839 / 1.468490 (0.269349) \| 0.497008 / 4.584777 (-4.087769) \| 3.041211 / 3.745712 (-0.704501) \| 3.519947 / 5.269862 (-1.749915) \| 2.015085 / 4.565676 (-2.550592) \| 0.057685 / 0.424275 (-0.366590) \| 0.006415 / 0.007607 (-0.001192) \| 0.465565 / 0.226044 (0.239520) \| 4.635224 / 2.268929 (2.366295) \| 2.297941 / 55.444624 (-53.146683) \| 1.946670 / 6.876477 (-4.929807) \| 2.078527 / 2.142072 (-0.063546) \| 0.584101 / 4.805227 (-4.221126) \| 0.126488 / 6.500664 (-6.374176) \| 0.060819 / 0.075469 (-0.014650) \|\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) \| 1.223400 / 1.841788 (-0.618388) \| 17.960923 / 8.074308 (9.886615) \| 13.187683 / 10.191392 (2.996291) \| 0.129258 / 0.680424 (-0.551166) \| 0.016601 / 0.534201 (-0.517600) \| 0.330028 / 0.579283 (-0.249255) \| 0.353861 / 0.434364 (-0.080503) \| 0.376022 / 0.540337 (-0.164315) \| 0.518145 / 1.386936 (-0.868791) \|\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) \| 0.006015 / 0.011353 (-0.005338) \| 0.003605 / 0.011008 (-0.007403) \| 0.062169 / 0.038508 (0.023661) \| 0.056094 / 0.023109 (0.032985) \| 0.353085 / 0.275898 (0.077187) \| 0.393744 / 0.323480 (0.070265) \| 0.004672 / 0.007986 (-0.003313) \| 0.002859 / 0.004328 (-0.001469) \| 0.062992 / 0.004250 (0.058742) \| 0.049767 / 0.037052 (0.012714) \| 0.356850 / 0.258489 (0.098361) \| 0.403731 / 0.293841 (0.109890) \| 0.026664 / 0.128546 (-0.101882) \| 0.008026 / 0.075646 (-0.067621) \| 0.067944 / 0.419271 (-0.351327) \| 0.042133 / 0.043533 (-0.001400) \| 0.353865 / 0.255139 (0.098726) \| 0.383461 / 0.283200 (0.100261) \| 0.021250 / 0.141683 (-0.120433) \| 1.428102 / 1.452155 (-0.024053) \| 1.481061 / 1.492716 (-0.011655) \|\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) \| 0.223552 / 0.018006 (0.205546) \| 0.402390 / 0.000490 (0.401900) \| 0.000721 / 0.000200 (0.000521) \| 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) \| 0.025065 / 0.037411 (-0.012347) \| 0.075537 / 0.014526 (0.061011) \| 0.083519 / 0.176557 (-0.093037) \| 0.137068 / 0.737135 (-0.600068) \| 0.084165 / 0.296338 (-0.212173) \|\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) \| 0.420176 / 0.215209 (0.204967) \| 4.206226 / 2.077655 (2.128571) \| 2.168089 / 1.504120 (0.663969) \| 1.987299 / 1.541195 (0.446104) \| 2.029489 / 1.468490 (0.560999) \| 0.495822 / 4.584777 (-4.088955) \| 3.106580 / 3.745712 (-0.639132) \| 3.833215 / 5.269862 (-1.436647) \| 2.450450 / 4.565676 (-2.115226) \| 0.056979 / 0.424275 (-0.367296) \| 0.006514 / 0.007607 (-0.001093) \| 0.503646 / 0.226044 (0.277601) \| 5.035035 / 2.268929 (2.766106) \| 2.608245 / 55.444624 (-52.836379) \| 2.245492 / 6.876477 (-4.630985) \| 2.262868 / 2.142072 (0.120795) \| 0.590736 / 4.805227 (-4.214491) \| 0.124637 / 6.500664 (-6.376027) \| 0.061442 / 0.075469 (-0.014027) \|\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) \| 1.316736 / 1.841788 (-0.525052) \| 17.948635 / 8.074308 (9.874327) \| 13.752442 / 10.191392 (3.561050) \| 0.144107 / 0.680424 (-0.536317) \| 0.017112 / 0.534201 (-0.517089) \| 0.336537 / 0.579283 (-0.242746) \| 0.347832 / 0.434364 (-0.086532) \| 0.392944 / 0.540337 (-0.147393) \| 0.534455 / 1.386936 (-0.852481) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#406b2212263c0d33f267e35b917f410ff6b3bc00 \"CML watermark\")\n" ]
1,803,294,981	6,028	Use new hffs	closed	2023-07-13T15:41:44	2023-07-17T17:09:39	2023-07-17T17:01:00	https://github.com/huggingface/datasets/pull/6028	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6028", "html_url": "https://github.com/huggingface/datasets/pull/6028", "diff_url": "https://github.com/huggingface/datasets/pull/6028.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6028.patch", "merged_at": "2023-07-17T17:01:00" }	lhoestq	true	[ "_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) \| 0.006665 / 0.011353 (-0.004688) \| 0.004376 / 0.011008 (-0.006633) \| 0.085529 / 0.038508 (0.047021) \| 0.076372 / 0.023109 (0.053263) \| 0.310019 / 0.275898 (0.034121) \| 0.341404 / 0.323480 (0.017924) \| 0.005666 / 0.007986 (-0.002320) \| 0.003763 / 0.004328 (-0.000566) \| 0.064678 / 0.004250 (0.060427) \| 0.059283 / 0.037052 (0.022231) \| 0.316194 / 0.258489 (0.057704) \| 0.349397 / 0.293841 (0.055557) \| 0.031199 / 0.128546 (-0.097347) \| 0.008724 / 0.075646 (-0.066923) \| 0.300236 / 0.419271 (-0.119035) \| 0.068872 / 0.043533 (0.025339) \| 0.308521 / 0.255139 (0.053382) \| 0.331292 / 0.283200 (0.048092) \| 0.028236 / 0.141683 (-0.113447) \| 1.501365 / 1.452155 (0.049211) \| 1.554334 / 1.492716 (0.061618) \|\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) \| 0.238291 / 0.018006 (0.220285) \| 0.565069 / 0.000490 (0.564580) \| 0.001626 / 0.000200 (0.001426) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029777 / 0.037411 (-0.007634) \| 0.082873 / 0.014526 (0.068347) \| 0.099619 / 0.176557 (-0.076937) \| 0.156572 / 0.737135 (-0.580563) \| 0.099887 / 0.296338 (-0.196452) \|\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) \| 0.401017 / 0.215209 (0.185808) \| 3.827192 / 2.077655 (1.749537) \| 1.861554 / 1.504120 (0.357434) \| 1.699869 / 1.541195 (0.158674) \| 1.720043 / 1.468490 (0.251553) \| 0.486757 / 4.584777 (-4.098020) \| 3.638125 / 3.745712 (-0.107587) \| 5.844959 / 5.269862 (0.575097) \| 3.454901 / 4.565676 (-1.110775) \| 0.057650 / 0.424275 (-0.366625) \| 0.007341 / 0.007607 (-0.000266) \| 0.462698 / 0.226044 (0.236654) \| 4.633472 / 2.268929 (2.364544) \| 2.287607 / 55.444624 (-53.157017) \| 2.057318 / 6.876477 (-4.819159) \| 2.203657 / 2.142072 (0.061584) \| 0.598136 / 4.805227 (-4.207091) \| 0.134012 / 6.500664 (-6.366653) \| 0.060824 / 0.075469 (-0.014645) \|\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) \| 1.277752 / 1.841788 (-0.564036) \| 20.013398 / 8.074308 (11.939089) \| 14.372993 / 10.191392 (4.181601) \| 0.169991 / 0.680424 (-0.510433) \| 0.018344 / 0.534201 (-0.515857) \| 0.396985 / 0.579283 (-0.182299) \| 0.416289 / 0.434364 (-0.018075) \| 0.458658 / 0.540337 (-0.081680) \| 0.692980 / 1.386936 (-0.693956) \|\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) \| 0.006689 / 0.011353 (-0.004664) \| 0.004393 / 0.011008 (-0.006615) \| 0.064069 / 0.038508 (0.025561) \| 0.080717 / 0.023109 (0.057607) \| 0.370090 / 0.275898 (0.094191) \| 0.400432 / 0.323480 (0.076952) \| 0.005613 / 0.007986 (-0.002372) \| 0.003641 / 0.004328 (-0.000687) \| 0.064771 / 0.004250 (0.060520) \| 0.057555 / 0.037052 (0.020502) \| 0.392156 / 0.258489 (0.133667) \| 0.409842 / 0.293841 (0.116001) \| 0.031500 / 0.128546 (-0.097047) \| 0.008786 / 0.075646 (-0.066860) \| 0.070342 / 0.419271 (-0.348929) \| 0.048646 / 0.043533 (0.005113) \| 0.360914 / 0.255139 (0.105775) \| 0.387626 / 0.283200 (0.104426) \| 0.022787 / 0.141683 (-0.118896) \| 1.508915 / 1.452155 (0.056761) \| 1.539719 / 1.492716 (0.047002) \|\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) \| 0.257985 / 0.018006 (0.239979) \| 0.550990 / 0.000490 (0.550501) \| 0.000407 / 0.000200 (0.000207) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030183 / 0.037411 (-0.007228) \| 0.086882 / 0.014526 (0.072356) \| 0.102382 / 0.176557 (-0.074175) \| 0.154745 / 0.737135 (-0.582390) \| 0.104008 / 0.296338 (-0.192331) \|\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) \| 0.426284 / 0.215209 (0.211075) \| 4.240812 / 2.077655 (2.163158) \| 2.261240 / 1.504120 (0.757120) \| 2.085905 / 1.541195 (0.544710) \| 2.160374 / 1.468490 (0.691883) \| 0.481126 / 4.584777 (-4.103651) \| 3.516234 / 3.745712 (-0.229478) \| 3.325322 / 5.269862 (-1.944539) \| 2.043307 / 4.565676 (-2.522369) \| 0.056663 / 0.424275 (-0.367612) \| 0.007786 / 0.007607 (0.000179) \| 0.497614 / 0.226044 (0.271570) \| 4.974529 / 2.268929 (2.705600) \| 2.700018 / 55.444624 (-52.744606) \| 2.393778 / 6.876477 (-4.482699) \| 2.628202 / 2.142072 (0.486130) \| 0.594316 / 4.805227 (-4.210911) \| 0.147092 / 6.500664 (-6.353572) \| 0.062207 / 0.075469 (-0.013262) \|\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) \| 1.315676 / 1.841788 (-0.526112) \| 20.749251 / 8.074308 (12.674943) \| 14.371553 / 10.191392 (4.180160) \| 0.170249 / 0.680424 (-0.510175) \| 0.018478 / 0.534201 (-0.515722) \| 0.395710 / 0.579283 (-0.183573) \| 0.409706 / 0.434364 (-0.024658) \| 0.463454 / 0.540337 (-0.076884) \| 0.615657 / 1.386936 (-0.771279) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5a752d8e8ca0a6ed118b024ba03c1b4a2881177 \"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) \| 0.007224 / 0.011353 (-0.004129) \| 0.004506 / 0.011008 (-0.006503) \| 0.096729 / 0.038508 (0.058221) \| 0.082394 / 0.023109 (0.059284) \| 0.390954 / 0.275898 (0.115056) \| 0.416647 / 0.323480 (0.093167) \| 0.005894 / 0.007986 (-0.002092) \| 0.003756 / 0.004328 (-0.000572) \| 0.075800 / 0.004250 (0.071549) \| 0.062683 / 0.037052 (0.025631) \| 0.398959 / 0.258489 (0.140470) \| 0.436624 / 0.293841 (0.142783) \| 0.034650 / 0.128546 (-0.093896) \| 0.009655 / 0.075646 (-0.065991) \| 0.315761 / 0.419271 (-0.103511) \| 0.060957 / 0.043533 (0.017424) \| 0.385649 / 0.255139 (0.130510) \| 0.394022 / 0.283200 (0.110822) \| 0.024601 / 0.141683 (-0.117082) \| 1.729586 / 1.452155 (0.277431) \| 1.724153 / 1.492716 (0.231437) \|\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) \| 0.207070 / 0.018006 (0.189063) \| 0.466502 / 0.000490 (0.466012) \| 0.010739 / 0.000200 (0.010540) \| 0.000214 / 0.000054 (0.000160) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031633 / 0.037411 (-0.005779) \| 0.095345 / 0.014526 (0.080819) \| 0.105399 / 0.176557 (-0.071157) \| 0.174173 / 0.737135 (-0.562962) \| 0.104207 / 0.296338 (-0.192132) \|\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) \| 0.435312 / 0.215209 (0.220103) \| 4.265600 / 2.077655 (2.187946) \| 2.056500 / 1.504120 (0.552380) \| 1.848023 / 1.541195 (0.306828) \| 1.946156 / 1.468490 (0.477666) \| 0.557788 / 4.584777 (-4.026989) \| 4.070289 / 3.745712 (0.324577) \| 3.608027 / 5.269862 (-1.661835) \| 2.214556 / 4.565676 (-2.351121) \| 0.062623 / 0.424275 (-0.361652) \| 0.008083 / 0.007607 (0.000476) \| 0.491782 / 0.226044 (0.265738) \| 4.989963 / 2.268929 (2.721035) \| 2.575867 / 55.444624 (-52.868757) \| 2.208045 / 6.876477 (-4.668431) \| 2.364184 / 2.142072 (0.222112) \| 0.633925 / 4.805227 (-4.171302) \| 0.144323 / 6.500664 (-6.356341) \| 0.067505 / 0.075469 (-0.007965) \|\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) \| 1.467219 / 1.841788 (-0.374569) \| 22.334967 / 8.074308 (14.260659) \| 15.715747 / 10.191392 (5.524355) \| 0.175443 / 0.680424 (-0.504980) \| 0.026165 / 0.534201 (-0.508036) \| 0.490675 / 0.579283 (-0.088608) \| 0.509211 / 0.434364 (0.074847) \| 0.586303 / 0.540337 (0.045965) \| 0.785052 / 1.386936 (-0.601884) \|\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) \| 0.007893 / 0.011353 (-0.003460) \| 0.004577 / 0.011008 (-0.006431) \| 0.075781 / 0.038508 (0.037273) \| 0.095492 / 0.023109 (0.072382) \| 0.433259 / 0.275898 (0.157361) \| 0.469386 / 0.323480 (0.145906) \| 0.006317 / 0.007986 (-0.001669) \| 0.003708 / 0.004328 (-0.000621) \| 0.074417 / 0.004250 (0.070167) \| 0.068605 / 0.037052 (0.031552) \| 0.448701 / 0.258489 (0.190212) \| 0.469131 / 0.293841 (0.175290) \| 0.036647 / 0.128546 (-0.091899) \| 0.010077 / 0.075646 (-0.065570) \| 0.082457 / 0.419271 (-0.336815) \| 0.063255 / 0.043533 (0.019722) \| 0.428144 / 0.255139 (0.173005) \| 0.451872 / 0.283200 (0.168672) \| 0.033953 / 0.141683 (-0.107730) \| 1.781752 / 1.452155 (0.329597) \| 1.869014 / 1.492716 (0.376297) \|\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) \| 0.223596 / 0.018006 (0.205590) \| 0.470307 / 0.000490 (0.469818) \| 0.005059 / 0.000200 (0.004859) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038804 / 0.037411 (0.001393) \| 0.117879 / 0.014526 (0.103353) \| 0.140701 / 0.176557 (-0.035855) \| 0.194672 / 0.737135 (-0.542463) \| 0.132806 / 0.296338 (-0.163533) \|\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) \| 0.510109 / 0.215209 (0.294900) \| 4.729457 / 2.077655 (2.651803) \| 2.512113 / 1.504120 (1.007993) \| 2.302553 / 1.541195 (0.761358) \| 2.420462 / 1.468490 (0.951972) \| 0.531682 / 4.584777 (-4.053095) \| 4.061208 / 3.745712 (0.315496) \| 3.588542 / 5.269862 (-1.681320) \| 2.203187 / 4.565676 (-2.362489) \| 0.065791 / 0.424275 (-0.358484) \| 0.008839 / 0.007607 (0.001232) \| 0.562041 / 0.226044 (0.335997) \| 5.702340 / 2.268929 (3.433412) \| 3.127609 / 55.444624 (-52.317015) \| 2.823060 / 6.876477 (-4.053417) \| 2.898675 / 2.142072 (0.756603) \| 0.659589 / 4.805227 (-4.145638) \| 0.148798 / 6.500664 (-6.351866) \| 0.070787 / 0.075469 (-0.004682) \|\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) \| 1.478317 / 1.841788 (-0.363471) \| 21.995400 / 8.074308 (13.921092) \| 16.770729 / 10.191392 (6.579337) \| 0.226333 / 0.680424 (-0.454091) \| 0.021835 / 0.534201 (-0.512366) \| 0.460373 / 0.579283 (-0.118910) \| 0.479494 / 0.434364 (0.045130) \| 0.529470 / 0.540337 (-0.010868) \| 0.718066 / 1.386936 (-0.668870) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9a717b8eb80b0e50b25818127f79a35e0866fb14 \"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) \| 0.007824 / 0.011353 (-0.003529) \| 0.004601 / 0.011008 (-0.006407) \| 0.100025 / 0.038508 (0.061517) \| 0.096046 / 0.023109 (0.072936) \| 0.376226 / 0.275898 (0.100328) \| 0.410905 / 0.323480 (0.087425) \| 0.006048 / 0.007986 (-0.001938) \| 0.003817 / 0.004328 (-0.000511) \| 0.076624 / 0.004250 (0.072374) \| 0.066390 / 0.037052 (0.029338) \| 0.380098 / 0.258489 (0.121609) \| 0.413603 / 0.293841 (0.119762) \| 0.036546 / 0.128546 (-0.092001) \| 0.009881 / 0.075646 (-0.065765) \| 0.344338 / 0.419271 (-0.074934) \| 0.061882 / 0.043533 (0.018350) \| 0.368568 / 0.255139 (0.113429) \| 0.397133 / 0.283200 (0.113934) \| 0.027255 / 0.141683 (-0.114428) \| 1.795099 / 1.452155 (0.342945) \| 1.852443 / 1.492716 (0.359727) \|\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) \| 0.247436 / 0.018006 (0.229430) \| 0.494119 / 0.000490 (0.493629) \| 0.004359 / 0.000200 (0.004159) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034765 / 0.037411 (-0.002647) \| 0.104541 / 0.014526 (0.090015) \| 0.113898 / 0.176557 (-0.062659) \| 0.183634 / 0.737135 (-0.553501) \| 0.116423 / 0.296338 (-0.179916) \|\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) \| 0.458747 / 0.215209 (0.243538) \| 4.555740 / 2.077655 (2.478085) \| 2.217240 / 1.504120 (0.713121) \| 2.039879 / 1.541195 (0.498684) \| 2.088581 / 1.468490 (0.620091) \| 0.588063 / 4.584777 (-3.996714) \| 4.238226 / 3.745712 (0.492514) \| 4.768060 / 5.269862 (-0.501802) \| 2.857117 / 4.565676 (-1.708560) \| 0.068742 / 0.424275 (-0.355533) \| 0.008667 / 0.007607 (0.001059) \| 0.549294 / 0.226044 (0.323249) \| 5.464635 / 2.268929 (3.195706) \| 2.744435 / 55.444624 (-52.700189) \| 2.347660 / 6.876477 (-4.528816) \| 2.616816 / 2.142072 (0.474743) \| 0.703701 / 4.805227 (-4.101526) \| 0.159749 / 6.500664 (-6.340915) \| 0.071990 / 0.075469 (-0.003479) \|\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) \| 1.486599 / 1.841788 (-0.355188) \| 22.745438 / 8.074308 (14.671130) \| 16.822332 / 10.191392 (6.630940) \| 0.184730 / 0.680424 (-0.495694) \| 0.021267 / 0.534201 (-0.512934) \| 0.467108 / 0.579283 (-0.112176) \| 0.472674 / 0.434364 (0.038311) \| 0.548094 / 0.540337 (0.007756) \| 0.735885 / 1.386936 (-0.651051) \|\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) \| 0.007746 / 0.011353 (-0.003607) \| 0.004585 / 0.011008 (-0.006423) \| 0.076943 / 0.038508 (0.038435) \| 0.087473 / 0.023109 (0.064363) \| 0.480099 / 0.275898 (0.204201) \| 0.495271 / 0.323480 (0.171791) \| 0.006348 / 0.007986 (-0.001638) \| 0.003902 / 0.004328 (-0.000426) \| 0.077586 / 0.004250 (0.073335) \| 0.066467 / 0.037052 (0.029415) \| 0.468741 / 0.258489 (0.210252) \| 0.506778 / 0.293841 (0.212937) \| 0.036877 / 0.128546 (-0.091669) \| 0.010102 / 0.075646 (-0.065545) \| 0.084419 / 0.419271 (-0.334852) \| 0.058721 / 0.043533 (0.015188) \| 0.453633 / 0.255139 (0.198494) \| 0.481171 / 0.283200 (0.197971) \| 0.028716 / 0.141683 (-0.112967) \| 1.853048 / 1.452155 (0.400893) \| 1.885847 / 1.492716 (0.393130) \|\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) \| 0.192136 / 0.018006 (0.174130) \| 0.484481 / 0.000490 (0.483991) \| 0.002951 / 0.000200 (0.002751) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037949 / 0.037411 (0.000538) \| 0.108364 / 0.014526 (0.093838) \| 0.119542 / 0.176557 (-0.057014) \| 0.188542 / 0.737135 (-0.548593) \| 0.122011 / 0.296338 (-0.174327) \|\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) \| 0.483135 / 0.215209 (0.267926) \| 4.849715 / 2.077655 (2.772060) \| 2.497736 / 1.504120 (0.993616) \| 2.314243 / 1.541195 (0.773048) \| 2.412739 / 1.468490 (0.944249) \| 0.564137 / 4.584777 (-4.020639) \| 4.242273 / 3.745712 (0.496561) \| 6.337843 / 5.269862 (1.067982) \| 3.923250 / 4.565676 (-0.642426) \| 0.066464 / 0.424275 (-0.357811) \| 0.009217 / 0.007607 (0.001610) \| 0.575667 / 0.226044 (0.349623) \| 5.746187 / 2.268929 (3.477258) \| 3.069655 / 55.444624 (-52.374969) \| 2.674798 / 6.876477 (-4.201679) \| 2.956535 / 2.142072 (0.814463) \| 0.701043 / 4.805227 (-4.104185) \| 0.157241 / 6.500664 (-6.343423) \| 0.073175 / 0.075469 (-0.002294) \|\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) \| 1.609943 / 1.841788 (-0.231844) \| 23.478594 / 8.074308 (15.404286) \| 17.454437 / 10.191392 (7.263045) \| 0.186422 / 0.680424 (-0.494002) \| 0.021703 / 0.534201 (-0.512498) \| 0.471704 / 0.579283 (-0.107579) \| 0.480553 / 0.434364 (0.046189) \| 0.552881 / 0.540337 (0.012544) \| 0.722515 / 1.386936 (-0.664421) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84645f80049cd00d9e0d4908faf3c3203fdcf21d \"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) \| 0.007542 / 0.011353 (-0.003811) \| 0.004692 / 0.011008 (-0.006316) \| 0.099155 / 0.038508 (0.060647) \| 0.089365 / 0.023109 (0.066256) \| 0.370870 / 0.275898 (0.094972) \| 0.422152 / 0.323480 (0.098673) \| 0.006223 / 0.007986 (-0.001763) \| 0.003852 / 0.004328 (-0.000476) \| 0.075438 / 0.004250 (0.071188) \| 0.065973 / 0.037052 (0.028921) \| 0.381513 / 0.258489 (0.123024) \| 0.416196 / 0.293841 (0.122355) \| 0.035483 / 0.128546 (-0.093063) \| 0.009884 / 0.075646 (-0.065762) \| 0.341290 / 0.419271 (-0.077982) \| 0.060546 / 0.043533 (0.017014) \| 0.365101 / 0.255139 (0.109962) \| 0.391058 / 0.283200 (0.107859) \| 0.026325 / 0.141683 (-0.115358) \| 1.815168 / 1.452155 (0.363013) \| 1.834711 / 1.492716 (0.341994) \|\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) \| 0.222177 / 0.018006 (0.204171) \| 0.501151 / 0.000490 (0.500662) \| 0.010202 / 0.000200 (0.010002) \| 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) \| 0.034043 / 0.037411 (-0.003368) \| 0.097884 / 0.014526 (0.083358) \| 0.114022 / 0.176557 (-0.062534) \| 0.186200 / 0.737135 (-0.550935) \| 0.115555 / 0.296338 (-0.180783) \|\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) \| 0.485857 / 0.215209 (0.270648) \| 4.959263 / 2.077655 (2.881608) \| 2.501085 / 1.504120 (0.996965) \| 2.234660 / 1.541195 (0.693465) \| 2.238585 / 1.468490 (0.770095) \| 0.645431 / 4.584777 (-3.939345) \| 4.434311 / 3.745712 (0.688599) \| 4.771491 / 5.269862 (-0.498371) \| 2.778963 / 4.565676 (-1.786714) \| 0.075615 / 0.424275 (-0.348660) \| 0.009502 / 0.007607 (0.001895) \| 0.546539 / 0.226044 (0.320495) \| 5.464242 / 2.268929 (3.195314) \| 2.894101 / 55.444624 (-52.550524) \| 2.513761 / 6.876477 (-4.362715) \| 2.719843 / 2.142072 (0.577770) \| 0.678828 / 4.805227 (-4.126399) \| 0.157839 / 6.500664 (-6.342825) \| 0.071305 / 0.075469 (-0.004164) \|\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) \| 1.496879 / 1.841788 (-0.344909) \| 22.214452 / 8.074308 (14.140144) \| 17.707541 / 10.191392 (7.516149) \| 0.197008 / 0.680424 (-0.483416) \| 0.024883 / 0.534201 (-0.509318) \| 0.493611 / 0.579283 (-0.085672) \| 0.500677 / 0.434364 (0.066313) \| 0.569381 / 0.540337 (0.029044) \| 0.773950 / 1.386936 (-0.612986) \|\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) \| 0.007337 / 0.011353 (-0.004015) \| 0.004572 / 0.011008 (-0.006436) \| 0.091123 / 0.038508 (0.052615) \| 0.079762 / 0.023109 (0.056652) \| 0.450527 / 0.275898 (0.174629) \| 0.525097 / 0.323480 (0.201617) \| 0.005873 / 0.007986 (-0.002112) \| 0.003797 / 0.004328 (-0.000532) \| 0.076259 / 0.004250 (0.072009) \| 0.062745 / 0.037052 (0.025692) \| 0.465553 / 0.258489 (0.207064) \| 0.546026 / 0.293841 (0.252186) \| 0.035638 / 0.128546 (-0.092909) \| 0.010086 / 0.075646 (-0.065560) \| 0.109269 / 0.419271 (-0.310002) \| 0.056765 / 0.043533 (0.013233) \| 0.440887 / 0.255139 (0.185748) \| 0.513325 / 0.283200 (0.230125) \| 0.027206 / 0.141683 (-0.114476) \| 1.863564 / 1.452155 (0.411409) \| 1.918206 / 1.492716 (0.425490) \|\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) \| 0.266479 / 0.018006 (0.248473) \| 0.487971 / 0.000490 (0.487481) \| 0.012246 / 0.000200 (0.012046) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035281 / 0.037411 (-0.002130) \| 0.102991 / 0.014526 (0.088465) \| 0.114638 / 0.176557 (-0.061919) \| 0.184117 / 0.737135 (-0.553018) \| 0.117943 / 0.296338 (-0.178396) \|\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) \| 0.497897 / 0.215209 (0.282688) \| 4.973806 / 2.077655 (2.896151) \| 2.596146 / 1.504120 (1.092026) \| 2.419694 / 1.541195 (0.878499) \| 2.525784 / 1.468490 (1.057294) \| 0.568021 / 4.584777 (-4.016756) \| 4.296431 / 3.745712 (0.550719) \| 3.690682 / 5.269862 (-1.579179) \| 2.345965 / 4.565676 (-2.219712) \| 0.066859 / 0.424275 (-0.357416) \| 0.009093 / 0.007607 (0.001486) \| 0.582616 / 0.226044 (0.356571) \| 5.826528 / 2.268929 (3.557600) \| 3.253222 / 55.444624 (-52.191403) \| 2.798447 / 6.876477 (-4.078030) \| 3.054609 / 2.142072 (0.912537) \| 0.678816 / 4.805227 (-4.126411) \| 0.157966 / 6.500664 (-6.342698) \| 0.073797 / 0.075469 (-0.001672) \|\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) \| 1.599480 / 1.841788 (-0.242308) \| 23.249738 / 8.074308 (15.175430) \| 16.965406 / 10.191392 (6.774014) \| 0.171390 / 0.680424 (-0.509034) \| 0.021810 / 0.534201 (-0.512391) \| 0.483339 / 0.579283 (-0.095944) \| 0.496615 / 0.434364 (0.062251) \| 0.583786 / 0.540337 (0.043448) \| 0.741699 / 1.386936 (-0.645237) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7935cd2e564f5d1c66ed1acf731703724ba7a287 \"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) \| 0.006054 / 0.011353 (-0.005299) \| 0.003706 / 0.011008 (-0.007302) \| 0.080060 / 0.038508 (0.041552) \| 0.061479 / 0.023109 (0.038370) \| 0.327981 / 0.275898 (0.052083) \| 0.356930 / 0.323480 (0.033450) \| 0.004671 / 0.007986 (-0.003315) \| 0.002901 / 0.004328 (-0.001428) \| 0.062425 / 0.004250 (0.058174) \| 0.046310 / 0.037052 (0.009258) \| 0.323657 / 0.258489 (0.065168) \| 0.370130 / 0.293841 (0.076289) \| 0.027151 / 0.128546 (-0.101395) \| 0.007850 / 0.075646 (-0.067797) \| 0.262300 / 0.419271 (-0.156971) \| 0.045456 / 0.043533 (0.001923) \| 0.325569 / 0.255139 (0.070430) \| 0.352962 / 0.283200 (0.069762) \| 0.020156 / 0.141683 (-0.121527) \| 1.429404 / 1.452155 (-0.022750) \| 1.615032 / 1.492716 (0.122316) \|\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) \| 0.187309 / 0.018006 (0.169303) \| 0.428848 / 0.000490 (0.428358) \| 0.003599 / 0.000200 (0.003399) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023260 / 0.037411 (-0.014151) \| 0.072467 / 0.014526 (0.057941) \| 0.082398 / 0.176557 (-0.094159) \| 0.142573 / 0.737135 (-0.594562) \| 0.082570 / 0.296338 (-0.213768) \|\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) \| 0.426503 / 0.215209 (0.211294) \| 4.267875 / 2.077655 (2.190220) \| 2.189762 / 1.504120 (0.685642) \| 2.027992 / 1.541195 (0.486798) \| 2.053211 / 1.468490 (0.584721) \| 0.503850 / 4.584777 (-4.080927) \| 3.086444 / 3.745712 (-0.659268) \| 3.319492 / 5.269862 (-1.950370) \| 2.070714 / 4.565676 (-2.494962) \| 0.057591 / 0.424275 (-0.366684) \| 0.006407 / 0.007607 (-0.001200) \| 0.501145 / 0.226044 (0.275100) \| 5.017753 / 2.268929 (2.748825) \| 2.643145 / 55.444624 (-52.801479) \| 2.327440 / 6.876477 (-4.549037) \| 2.460250 / 2.142072 (0.318178) \| 0.589397 / 4.805227 (-4.215830) \| 0.124948 / 6.500664 (-6.375716) \| 0.060450 / 0.075469 (-0.015020) \|\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) \| 1.279870 / 1.841788 (-0.561918) \| 18.115908 / 8.074308 (10.041600) \| 13.570032 / 10.191392 (3.378640) \| 0.132981 / 0.680424 (-0.547442) \| 0.016942 / 0.534201 (-0.517259) \| 0.333591 / 0.579283 (-0.245692) \| 0.358844 / 0.434364 (-0.075520) \| 0.395748 / 0.540337 (-0.144590) \| 0.546213 / 1.386936 (-0.840723) \|\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) \| 0.006062 / 0.011353 (-0.005291) \| 0.003673 / 0.011008 (-0.007336) \| 0.064726 / 0.038508 (0.026218) \| 0.061854 / 0.023109 (0.038745) \| 0.385343 / 0.275898 (0.109445) \| 0.441284 / 0.323480 (0.117805) \| 0.004830 / 0.007986 (-0.003156) \| 0.002909 / 0.004328 (-0.001420) \| 0.063874 / 0.004250 (0.059624) \| 0.049331 / 0.037052 (0.012278) \| 0.418484 / 0.258489 (0.159995) \| 0.451397 / 0.293841 (0.157556) \| 0.027665 / 0.128546 (-0.100881) \| 0.008088 / 0.075646 (-0.067558) \| 0.069625 / 0.419271 (-0.349646) \| 0.043437 / 0.043533 (-0.000095) \| 0.359789 / 0.255139 (0.104650) \| 0.430206 / 0.283200 (0.147007) \| 0.022308 / 0.141683 (-0.119375) \| 1.461030 / 1.452155 (0.008875) \| 1.513683 / 1.492716 (0.020966) \|\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) \| 0.230958 / 0.018006 (0.212952) \| 0.417553 / 0.000490 (0.417063) \| 0.000802 / 0.000200 (0.000602) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025421 / 0.037411 (-0.011991) \| 0.077156 / 0.014526 (0.062630) \| 0.087533 / 0.176557 (-0.089024) \| 0.138048 / 0.737135 (-0.599087) \| 0.089358 / 0.296338 (-0.206981) \|\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) \| 0.439172 / 0.215209 (0.223963) \| 4.409509 / 2.077655 (2.331854) \| 2.491270 / 1.504120 (0.987150) \| 2.308446 / 1.541195 (0.767252) \| 2.378440 / 1.468490 (0.909950) \| 0.499834 / 4.584777 (-4.084943) \| 3.083168 / 3.745712 (-0.662544) \| 2.867543 / 5.269862 (-2.402318) \| 1.876354 / 4.565676 (-2.689323) \| 0.057092 / 0.424275 (-0.367183) \| 0.006955 / 0.007607 (-0.000653) \| 0.513799 / 0.226044 (0.287754) \| 5.126660 / 2.268929 (2.857731) \| 2.917348 / 55.444624 (-52.527277) \| 2.508035 / 6.876477 (-4.368441) \| 2.698089 / 2.142072 (0.556016) \| 0.586828 / 4.805227 (-4.218399) \| 0.124740 / 6.500664 (-6.375924) \| 0.062276 / 0.075469 (-0.013193) \|\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) \| 1.291624 / 1.841788 (-0.550164) \| 18.199968 / 8.074308 (10.125660) \| 13.888139 / 10.191392 (3.696747) \| 0.162955 / 0.680424 (-0.517469) \| 0.017343 / 0.534201 (-0.516858) \| 0.334683 / 0.579283 (-0.244600) \| 0.352708 / 0.434364 (-0.081656) \| 0.400629 / 0.540337 (-0.139708) \| 0.539497 / 1.386936 (-0.847439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7976db7fe22c6b93a869488d07b8137ea6a0db4 \"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) \| 0.007500 / 0.011353 (-0.003853) \| 0.004498 / 0.011008 (-0.006510) \| 0.100239 / 0.038508 (0.061731) \| 0.083424 / 0.023109 (0.060315) \| 0.366664 / 0.275898 (0.090766) \| 0.406641 / 0.323480 (0.083161) \| 0.004577 / 0.007986 (-0.003409) \| 0.004809 / 0.004328 (0.000480) \| 0.076898 / 0.004250 (0.072647) \| 0.064021 / 0.037052 (0.026969) \| 0.375836 / 0.258489 (0.117347) \| 0.413008 / 0.293841 (0.119167) \| 0.036010 / 0.128546 (-0.092537) \| 0.009655 / 0.075646 (-0.065991) \| 0.342595 / 0.419271 (-0.076677) \| 0.061846 / 0.043533 (0.018313) \| 0.376543 / 0.255139 (0.121404) \| 0.395858 / 0.283200 (0.112659) \| 0.026792 / 0.141683 (-0.114891) \| 1.775569 / 1.452155 (0.323414) \| 1.865077 / 1.492716 (0.372360) \|\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) \| 0.221521 / 0.018006 (0.203514) \| 0.474604 / 0.000490 (0.474114) \| 0.004354 / 0.000200 (0.004154) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032947 / 0.037411 (-0.004464) \| 0.100454 / 0.014526 (0.085928) \| 0.111955 / 0.176557 (-0.064602) \| 0.179752 / 0.737135 (-0.557383) \| 0.114282 / 0.296338 (-0.182056) \|\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) \| 0.458261 / 0.215209 (0.243052) \| 4.563536 / 2.077655 (2.485881) \| 2.231928 / 1.504120 (0.727808) \| 2.036751 / 1.541195 (0.495556) \| 2.170413 / 1.468490 (0.701923) \| 0.570825 / 4.584777 (-4.013952) \| 4.505762 / 3.745712 (0.760050) \| 5.033461 / 5.269862 (-0.236401) \| 2.704989 / 4.565676 (-1.860687) \| 0.067011 / 0.424275 (-0.357264) \| 0.008568 / 0.007607 (0.000961) \| 0.545151 / 0.226044 (0.319106) \| 5.438984 / 2.268929 (3.170055) \| 2.771818 / 55.444624 (-52.672806) \| 2.393082 / 6.876477 (-4.483395) \| 2.467173 / 2.142072 (0.325101) \| 0.678849 / 4.805227 (-4.126379) \| 0.160480 / 6.500664 (-6.340184) \| 0.073681 / 0.075469 (-0.001788) \|\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) \| 1.532272 / 1.841788 (-0.309516) \| 22.548741 / 8.074308 (14.474433) \| 17.091044 / 10.191392 (6.899652) \| 0.172100 / 0.680424 (-0.508324) \| 0.022220 / 0.534201 (-0.511981) \| 0.467871 / 0.579283 (-0.111412) \| 0.491135 / 0.434364 (0.056771) \| 0.548433 / 0.540337 (0.008096) \| 0.733340 / 1.386936 (-0.653596) \|\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) \| 0.007593 / 0.011353 (-0.003760) \| 0.004656 / 0.011008 (-0.006352) \| 0.076940 / 0.038508 (0.038431) \| 0.085183 / 0.023109 (0.062073) \| 0.447178 / 0.275898 (0.171280) \| 0.469545 / 0.323480 (0.146065) \| 0.006023 / 0.007986 (-0.001962) \| 0.003808 / 0.004328 (-0.000520) \| 0.076767 / 0.004250 (0.072517) \| 0.065713 / 0.037052 (0.028661) \| 0.445573 / 0.258489 (0.187084) \| 0.481689 / 0.293841 (0.187848) \| 0.036893 / 0.128546 (-0.091654) \| 0.009976 / 0.075646 (-0.065670) \| 0.084443 / 0.419271 (-0.334829) \| 0.058829 / 0.043533 (0.015297) \| 0.429291 / 0.255139 (0.174152) \| 0.454016 / 0.283200 (0.170816) \| 0.027289 / 0.141683 (-0.114394) \| 1.806786 / 1.452155 (0.354632) \| 1.887680 / 1.492716 (0.394964) \|\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) \| 0.241012 / 0.018006 (0.223006) \| 0.470629 / 0.000490 (0.470139) \| 0.003213 / 0.000200 (0.003013) \| 0.000107 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036896 / 0.037411 (-0.000515) \| 0.106932 / 0.014526 (0.092406) \| 0.120333 / 0.176557 (-0.056223) \| 0.186271 / 0.737135 (-0.550865) \| 0.121581 / 0.296338 (-0.174758) \|\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) \| 0.507782 / 0.215209 (0.292573) \| 5.062932 / 2.077655 (2.985278) \| 2.689539 / 1.504120 (1.185419) \| 2.482978 / 1.541195 (0.941784) \| 2.561320 / 1.468490 (1.092830) \| 0.570664 / 4.584777 (-4.014113) \| 4.346051 / 3.745712 (0.600339) \| 6.479374 / 5.269862 (1.209513) \| 4.096483 / 4.565676 (-0.469194) \| 0.067564 / 0.424275 (-0.356711) \| 0.009147 / 0.007607 (0.001540) \| 0.596059 / 0.226044 (0.370015) \| 5.963223 / 2.268929 (3.694295) \| 3.201039 / 55.444624 (-52.243585) \| 2.816581 / 6.876477 (-4.059896) \| 3.047821 / 2.142072 (0.905748) \| 0.687749 / 4.805227 (-4.117478) \| 0.158174 / 6.500664 (-6.342490) \| 0.073329 / 0.075469 (-0.002140) \|\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) \| 1.601346 / 1.841788 (-0.240441) \| 23.712210 / 8.074308 (15.637902) \| 16.567272 / 10.191392 (6.375880) \| 0.224745 / 0.680424 (-0.455679) \| 0.021662 / 0.534201 (-0.512539) \| 0.471427 / 0.579283 (-0.107856) \| 0.498751 / 0.434364 (0.064387) \| 0.572047 / 0.540337 (0.031710) \| 0.821868 / 1.386936 (-0.565068) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#34d0c9027c750adc89f3d04a6bf2e9cb95915da4 \"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) \| 0.006371 / 0.011353 (-0.004981) \| 0.003749 / 0.011008 (-0.007259) \| 0.084155 / 0.038508 (0.045647) \| 0.072450 / 0.023109 (0.049340) \| 0.308002 / 0.275898 (0.032104) \| 0.340471 / 0.323480 (0.016991) \| 0.005054 / 0.007986 (-0.002931) \| 0.003176 / 0.004328 (-0.001152) \| 0.064867 / 0.004250 (0.060616) \| 0.054305 / 0.037052 (0.017252) \| 0.321047 / 0.258489 (0.062558) \| 0.345999 / 0.293841 (0.052158) \| 0.030507 / 0.128546 (-0.098039) \| 0.008299 / 0.075646 (-0.067347) \| 0.287682 / 0.419271 (-0.131590) \| 0.052048 / 0.043533 (0.008515) \| 0.308322 / 0.255139 (0.053183) \| 0.333220 / 0.283200 (0.050020) \| 0.022698 / 0.141683 (-0.118985) \| 1.474033 / 1.452155 (0.021879) \| 1.544790 / 1.492716 (0.052074) \|\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) \| 0.200612 / 0.018006 (0.182606) \| 0.450934 / 0.000490 (0.450445) \| 0.005383 / 0.000200 (0.005183) \| 0.000200 / 0.000054 (0.000145) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027759 / 0.037411 (-0.009652) \| 0.080935 / 0.014526 (0.066409) \| 0.093041 / 0.176557 (-0.083516) \| 0.148643 / 0.737135 (-0.588492) \| 0.093463 / 0.296338 (-0.202876) \|\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) \| 0.381653 / 0.215209 (0.166444) \| 3.810699 / 2.077655 (1.733044) \| 1.866858 / 1.504120 (0.362738) \| 1.716985 / 1.541195 (0.175790) \| 1.788071 / 1.468490 (0.319581) \| 0.481130 / 4.584777 (-4.103647) \| 3.529798 / 3.745712 (-0.215914) \| 3.982037 / 5.269862 (-1.287824) \| 2.324866 / 4.565676 (-2.240811) \| 0.056767 / 0.424275 (-0.367508) \| 0.007306 / 0.007607 (-0.000301) \| 0.459472 / 0.226044 (0.233428) \| 4.602808 / 2.268929 (2.333879) \| 2.332014 / 55.444624 (-53.112610) \| 2.044858 / 6.876477 (-4.831619) \| 2.204165 / 2.142072 (0.062093) \| 0.577946 / 4.805227 (-4.227281) \| 0.130900 / 6.500664 (-6.369764) \| 0.059054 / 0.075469 (-0.016415) \|\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) \| 1.245211 / 1.841788 (-0.596576) \| 19.176397 / 8.074308 (11.102089) \| 13.995280 / 10.191392 (3.803888) \| 0.171743 / 0.680424 (-0.508681) \| 0.018038 / 0.534201 (-0.516163) \| 0.392338 / 0.579283 (-0.186945) \| 0.419370 / 0.434364 (-0.014994) \| 0.477829 / 0.540337 (-0.062508) \| 0.677409 / 1.386936 (-0.709527) \|\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) \| 0.006513 / 0.011353 (-0.004840) \| 0.003984 / 0.011008 (-0.007024) \| 0.064516 / 0.038508 (0.026008) \| 0.070504 / 0.023109 (0.047395) \| 0.384509 / 0.275898 (0.108611) \| 0.410564 / 0.323480 (0.087084) \| 0.005310 / 0.007986 (-0.002675) \| 0.003268 / 0.004328 (-0.001061) \| 0.064684 / 0.004250 (0.060433) \| 0.055367 / 0.037052 (0.018315) \| 0.399108 / 0.258489 (0.140619) \| 0.422740 / 0.293841 (0.128900) \| 0.031624 / 0.128546 (-0.096922) \| 0.008617 / 0.075646 (-0.067030) \| 0.070929 / 0.419271 (-0.348342) \| 0.049146 / 0.043533 (0.005613) \| 0.385492 / 0.255139 (0.130353) \| 0.407434 / 0.283200 (0.124234) \| 0.021972 / 0.141683 (-0.119711) \| 1.496135 / 1.452155 (0.043980) \| 1.533739 / 1.492716 (0.041023) \|\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) \| 0.226218 / 0.018006 (0.208211) \| 0.443176 / 0.000490 (0.442686) \| 0.000376 / 0.000200 (0.000176) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030315 / 0.037411 (-0.007097) \| 0.086416 / 0.014526 (0.071890) \| 0.097725 / 0.176557 (-0.078831) \| 0.150407 / 0.737135 (-0.586728) \| 0.099914 / 0.296338 (-0.196424) \|\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) \| 0.409807 / 0.215209 (0.194598) \| 4.099086 / 2.077655 (2.021431) \| 2.103160 / 1.504120 (0.599040) \| 1.927927 / 1.541195 (0.386733) \| 1.977751 / 1.468490 (0.509261) \| 0.476995 / 4.584777 (-4.107781) \| 3.521835 / 3.745712 (-0.223877) \| 3.237695 / 5.269862 (-2.032167) \| 1.995953 / 4.565676 (-2.569724) \| 0.056208 / 0.424275 (-0.368068) \| 0.007660 / 0.007607 (0.000053) \| 0.483537 / 0.226044 (0.257492) \| 4.833974 / 2.268929 (2.565046) \| 2.589115 / 55.444624 (-52.855510) \| 2.228076 / 6.876477 (-4.648401) \| 2.395271 / 2.142072 (0.253198) \| 0.577534 / 4.805227 (-4.227694) \| 0.131432 / 6.500664 (-6.369232) \| 0.060999 / 0.075469 (-0.014471) \|\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) \| 1.356043 / 1.841788 (-0.485745) \| 19.470401 / 8.074308 (11.396093) \| 14.091266 / 10.191392 (3.899874) \| 0.166809 / 0.680424 (-0.513615) \| 0.018782 / 0.534201 (-0.515419) \| 0.394916 / 0.579283 (-0.184367) \| 0.411378 / 0.434364 (-0.022986) \| 0.466886 / 0.540337 (-0.073451) \| 0.617369 / 1.386936 (-0.769567) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#601ae6c7baff33a600fd10b12940966024fd2221 \"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) \| 0.007590 / 0.011353 (-0.003762) \| 0.004068 / 0.011008 (-0.006941) \| 0.105479 / 0.038508 (0.066971) \| 0.085614 / 0.023109 (0.062505) \| 0.384325 / 0.275898 (0.108427) \| 0.467867 / 0.323480 (0.144387) \| 0.004652 / 0.007986 (-0.003333) \| 0.005445 / 0.004328 (0.001117) \| 0.079604 / 0.004250 (0.075353) \| 0.066031 / 0.037052 (0.028978) \| 0.426184 / 0.258489 (0.167695) \| 0.480712 / 0.293841 (0.186871) \| 0.037837 / 0.128546 (-0.090709) \| 0.009765 / 0.075646 (-0.065882) \| 0.351316 / 0.419271 (-0.067955) \| 0.063634 / 0.043533 (0.020101) \| 0.420297 / 0.255139 (0.165158) \| 0.449169 / 0.283200 (0.165969) \| 0.030947 / 0.141683 (-0.110736) \| 1.840184 / 1.452155 (0.388029) \| 1.934074 / 1.492716 (0.441357) \|\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) \| 0.223483 / 0.018006 (0.205477) \| 0.521086 / 0.000490 (0.520596) \| 0.000379 / 0.000200 (0.000179) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032011 / 0.037411 (-0.005400) \| 0.101474 / 0.014526 (0.086948) \| 0.108652 / 0.176557 (-0.067904) \| 0.173340 / 0.737135 (-0.563796) \| 0.114186 / 0.296338 (-0.182153) \|\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) \| 0.478020 / 0.215209 (0.262811) \| 4.645400 / 2.077655 (2.567746) \| 2.590763 / 1.504120 (1.086643) \| 2.383002 / 1.541195 (0.841807) \| 2.482550 / 1.468490 (1.014060) \| 0.572417 / 4.584777 (-4.012360) \| 4.233436 / 3.745712 (0.487724) \| 4.858823 / 5.269862 (-0.411038) \| 2.838913 / 4.565676 (-1.726764) \| 0.070010 / 0.424275 (-0.354265) \| 0.009602 / 0.007607 (0.001995) \| 0.538735 / 0.226044 (0.312691) \| 5.534340 / 2.268929 (3.265411) \| 2.915006 / 55.444624 (-52.529619) \| 2.625132 / 6.876477 (-4.251345) \| 2.537838 / 2.142072 (0.395766) \| 0.667870 / 4.805227 (-4.137357) \| 0.146330 / 6.500664 (-6.354334) \| 0.071631 / 0.075469 (-0.003838) \|\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) \| 1.594686 / 1.841788 (-0.247101) \| 22.311113 / 8.074308 (14.236804) \| 17.603983 / 10.191392 (7.412591) \| 0.195995 / 0.680424 (-0.484428) \| 0.022254 / 0.534201 (-0.511947) \| 0.479661 / 0.579283 (-0.099622) \| 0.463626 / 0.434364 (0.029262) \| 0.483465 / 0.540337 (-0.056873) \| 0.676141 / 1.386936 (-0.710795) \|\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) \| 0.006146 / 0.011353 (-0.005207) \| 0.004856 / 0.011008 (-0.006152) \| 0.067506 / 0.038508 (0.028998) \| 0.073968 / 0.023109 (0.050859) \| 0.470013 / 0.275898 (0.194115) \| 0.479022 / 0.323480 (0.155542) \| 0.005972 / 0.007986 (-0.002014) \| 0.003846 / 0.004328 (-0.000483) \| 0.075141 / 0.004250 (0.070890) \| 0.058597 / 0.037052 (0.021544) \| 0.481454 / 0.258489 (0.222965) \| 0.515634 / 0.293841 (0.221793) \| 0.034979 / 0.128546 (-0.093567) \| 0.010385 / 0.075646 (-0.065261) \| 0.072649 / 0.419271 (-0.346622) \| 0.058183 / 0.043533 (0.014650) \| 0.462138 / 0.255139 (0.206999) \| 0.476093 / 0.283200 (0.192893) \| 0.032918 / 0.141683 (-0.108765) \| 1.820530 / 1.452155 (0.368375) \| 1.626360 / 1.492716 (0.133644) \|\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) \| 0.208970 / 0.018006 (0.190964) \| 0.492478 / 0.000490 (0.491988) \| 0.005487 / 0.000200 (0.005287) \| 0.000140 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037896 / 0.037411 (0.000484) \| 0.089752 / 0.014526 (0.075227) \| 0.107445 / 0.176557 (-0.069111) \| 0.181260 / 0.737135 (-0.555876) \| 0.105700 / 0.296338 (-0.190639) \|\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) \| 0.495031 / 0.215209 (0.279821) \| 4.806939 / 2.077655 (2.729284) \| 2.227928 / 1.504120 (0.723808) \| 2.067117 / 1.541195 (0.525922) \| 2.348982 / 1.468490 (0.880492) \| 0.567201 / 4.584777 (-4.017576) \| 4.166592 / 3.745712 (0.420880) \| 3.654329 / 5.269862 (-1.615533) \| 2.331092 / 4.565676 (-2.234584) \| 0.062212 / 0.424275 (-0.362063) \| 0.008775 / 0.007607 (0.001168) \| 0.515413 / 0.226044 (0.289369) \| 5.449300 / 2.268929 (3.180371) \| 3.206574 / 55.444624 (-52.238050) \| 2.600455 / 6.876477 (-4.276022) \| 3.041162 / 2.142072 (0.899089) \| 0.681899 / 4.805227 (-4.123328) \| 0.155400 / 6.500664 (-6.345265) \| 0.073933 / 0.075469 (-0.001537) \|\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) \| 1.572329 / 1.841788 (-0.269459) \| 23.638519 / 8.074308 (15.564211) \| 17.145663 / 10.191392 (6.954271) \| 0.232690 / 0.680424 (-0.447734) \| 0.028620 / 0.534201 (-0.505581) \| 0.488105 / 0.579283 (-0.091178) \| 0.490365 / 0.434364 (0.056001) \| 0.599501 / 0.540337 (0.059164) \| 0.708101 / 1.386936 (-0.678835) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a761315900880a25b347ad19b78bd567cfce1f0 \"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) \| 0.005947 / 0.011353 (-0.005406) \| 0.003577 / 0.011008 (-0.007431) \| 0.081631 / 0.038508 (0.043122) \| 0.058651 / 0.023109 (0.035541) \| 0.342742 / 0.275898 (0.066843) \| 0.384130 / 0.323480 (0.060650) \| 0.004620 / 0.007986 (-0.003366) \| 0.002885 / 0.004328 (-0.001444) \| 0.063698 / 0.004250 (0.059448) \| 0.048953 / 0.037052 (0.011901) \| 0.367880 / 0.258489 (0.109391) \| 0.407050 / 0.293841 (0.113209) \| 0.027242 / 0.128546 (-0.101305) \| 0.007914 / 0.075646 (-0.067733) \| 0.262156 / 0.419271 (-0.157116) \| 0.044750 / 0.043533 (0.001218) \| 0.351613 / 0.255139 (0.096474) \| 0.380284 / 0.283200 (0.097084) \| 0.020080 / 0.141683 (-0.121603) \| 1.498101 / 1.452155 (0.045946) \| 1.543608 / 1.492716 (0.050892) \|\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) \| 0.180014 / 0.018006 (0.162008) \| 0.436172 / 0.000490 (0.435682) \| 0.003694 / 0.000200 (0.003494) \| 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) \| 0.024389 / 0.037411 (-0.013022) \| 0.072874 / 0.014526 (0.058348) \| 0.083469 / 0.176557 (-0.093088) \| 0.144600 / 0.737135 (-0.592536) \| 0.084229 / 0.296338 (-0.212110) \|\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) \| 0.391636 / 0.215209 (0.176427) \| 3.906941 / 2.077655 (1.829286) \| 1.901944 / 1.504120 (0.397825) \| 1.762702 / 1.541195 (0.221507) \| 1.817970 / 1.468490 (0.349480) \| 0.500345 / 4.584777 (-4.084432) \| 3.011351 / 3.745712 (-0.734361) \| 4.417763 / 5.269862 (-0.852098) \| 2.689744 / 4.565676 (-1.875933) \| 0.057765 / 0.424275 (-0.366511) \| 0.006412 / 0.007607 (-0.001195) \| 0.468156 / 0.226044 (0.242112) \| 4.664975 / 2.268929 (2.396047) \| 2.323355 / 55.444624 (-53.121270) \| 1.984280 / 6.876477 (-4.892197) \| 2.165215 / 2.142072 (0.023142) \| 0.586950 / 4.805227 (-4.218278) \| 0.124363 / 6.500664 (-6.376301) \| 0.060702 / 0.075469 (-0.014767) \|\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) \| 1.238870 / 1.841788 (-0.602917) \| 18.587360 / 8.074308 (10.513052) \| 13.831674 / 10.191392 (3.640282) \| 0.143542 / 0.680424 (-0.536882) \| 0.016913 / 0.534201 (-0.517288) \| 0.332314 / 0.579283 (-0.246969) \| 0.345419 / 0.434364 (-0.088945) \| 0.381257 / 0.540337 (-0.159081) \| 0.537844 / 1.386936 (-0.849092) \|\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) \| 0.006294 / 0.011353 (-0.005059) \| 0.003714 / 0.011008 (-0.007294) \| 0.062684 / 0.038508 (0.024176) \| 0.063520 / 0.023109 (0.040411) \| 0.389591 / 0.275898 (0.113693) \| 0.444278 / 0.323480 (0.120798) \| 0.004825 / 0.007986 (-0.003160) \| 0.003010 / 0.004328 (-0.001318) \| 0.062767 / 0.004250 (0.058517) \| 0.051739 / 0.037052 (0.014686) \| 0.434299 / 0.258489 (0.175810) \| 0.452003 / 0.293841 (0.158162) \| 0.027375 / 0.128546 (-0.101171) \| 0.008135 / 0.075646 (-0.067511) \| 0.067401 / 0.419271 (-0.351871) \| 0.042752 / 0.043533 (-0.000780) \| 0.367633 / 0.255139 (0.112494) \| 0.433039 / 0.283200 (0.149840) \| 0.021086 / 0.141683 (-0.120597) \| 1.488024 / 1.452155 (0.035870) \| 1.507767 / 1.492716 (0.015050) \|\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) \| 0.230046 / 0.018006 (0.212040) \| 0.428085 / 0.000490 (0.427595) \| 0.002188 / 0.000200 (0.001988) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026705 / 0.037411 (-0.010706) \| 0.082466 / 0.014526 (0.067940) \| 0.089378 / 0.176557 (-0.087179) \| 0.147287 / 0.737135 (-0.589849) \| 0.090426 / 0.296338 (-0.205913) \|\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) \| 0.430882 / 0.215209 (0.215672) \| 4.296224 / 2.077655 (2.218569) \| 2.229982 / 1.504120 (0.725862) \| 2.048506 / 1.541195 (0.507311) \| 2.129514 / 1.468490 (0.661024) \| 0.502964 / 4.584777 (-4.081813) \| 3.048125 / 3.745712 (-0.697587) \| 4.208636 / 5.269862 (-1.061226) \| 2.594015 / 4.565676 (-1.971661) \| 0.057967 / 0.424275 (-0.366308) \| 0.006875 / 0.007607 (-0.000732) \| 0.513872 / 0.226044 (0.287828) \| 5.126435 / 2.268929 (2.857506) \| 2.691278 / 55.444624 (-52.753346) \| 2.361723 / 6.876477 (-4.514754) \| 2.511213 / 2.142072 (0.369141) \| 0.593558 / 4.805227 (-4.211670) \| 0.129332 / 6.500664 (-6.371332) \| 0.064051 / 0.075469 (-0.011418) \|\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) \| 1.289049 / 1.841788 (-0.552739) \| 18.912363 / 8.074308 (10.838055) \| 14.226500 / 10.191392 (4.035108) \| 0.131392 / 0.680424 (-0.549032) \| 0.016750 / 0.534201 (-0.517451) \| 0.330078 / 0.579283 (-0.249205) \| 0.347588 / 0.434364 (-0.086776) \| 0.383234 / 0.540337 (-0.157103) \| 0.510967 / 1.386936 (-0.875969) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7892beb30bab0633b84398c5ea43d7e69fe38cc \"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) \| 0.005974 / 0.011353 (-0.005379) \| 0.003691 / 0.011008 (-0.007317) \| 0.079410 / 0.038508 (0.040902) \| 0.061769 / 0.023109 (0.038660) \| 0.323310 / 0.275898 (0.047412) \| 0.354325 / 0.323480 (0.030845) \| 0.004794 / 0.007986 (-0.003191) \| 0.002899 / 0.004328 (-0.001430) \| 0.062104 / 0.004250 (0.057854) \| 0.048973 / 0.037052 (0.011921) \| 0.326497 / 0.258489 (0.068008) \| 0.361347 / 0.293841 (0.067506) \| 0.026741 / 0.128546 (-0.101805) \| 0.007936 / 0.075646 (-0.067710) \| 0.259168 / 0.419271 (-0.160104) \| 0.044859 / 0.043533 (0.001327) \| 0.319342 / 0.255139 (0.064203) \| 0.343711 / 0.283200 (0.060511) \| 0.022298 / 0.141683 (-0.119384) \| 1.451595 / 1.452155 (-0.000560) \| 1.573730 / 1.492716 (0.081014) \|\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) \| 0.173086 / 0.018006 (0.155080) \| 0.432400 / 0.000490 (0.431910) \| 0.003739 / 0.000200 (0.003539) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024477 / 0.037411 (-0.012934) \| 0.073463 / 0.014526 (0.058937) \| 0.083410 / 0.176557 (-0.093146) \| 0.144760 / 0.737135 (-0.592376) \| 0.084199 / 0.296338 (-0.212140) \|\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) \| 0.388251 / 0.215209 (0.173042) \| 3.875375 / 2.077655 (1.797720) \| 1.875515 / 1.504120 (0.371395) \| 1.729282 / 1.541195 (0.188087) \| 1.784732 / 1.468490 (0.316242) \| 0.496985 / 4.584777 (-4.087792) \| 3.030276 / 3.745712 (-0.715436) \| 2.813192 / 5.269862 (-2.456669) \| 1.868647 / 4.565676 (-2.697030) \| 0.057376 / 0.424275 (-0.366899) \| 0.006463 / 0.007607 (-0.001144) \| 0.462153 / 0.226044 (0.236108) \| 4.586583 / 2.268929 (2.317654) \| 2.287730 / 55.444624 (-53.156894) \| 1.972177 / 6.876477 (-4.904299) \| 2.151592 / 2.142072 (0.009520) \| 0.587169 / 4.805227 (-4.218058) \| 0.127063 / 6.500664 (-6.373601) \| 0.060297 / 0.075469 (-0.015172) \|\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) \| 1.267651 / 1.841788 (-0.574136) \| 18.426011 / 8.074308 (10.351703) \| 14.050470 / 10.191392 (3.859078) \| 0.148063 / 0.680424 (-0.532361) \| 0.017112 / 0.534201 (-0.517089) \| 0.330051 / 0.579283 (-0.249232) \| 0.358730 / 0.434364 (-0.075634) \| 0.392365 / 0.540337 (-0.147972) \| 0.534650 / 1.386936 (-0.852286) \|\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) \| 0.005936 / 0.011353 (-0.005417) \| 0.003652 / 0.011008 (-0.007356) \| 0.063066 / 0.038508 (0.024558) \| 0.060617 / 0.023109 (0.037507) \| 0.388293 / 0.275898 (0.112395) \| 0.411422 / 0.323480 (0.087942) \| 0.004691 / 0.007986 (-0.003295) \| 0.002857 / 0.004328 (-0.001472) \| 0.064198 / 0.004250 (0.059947) \| 0.049124 / 0.037052 (0.012071) \| 0.403601 / 0.258489 (0.145112) \| 0.413619 / 0.293841 (0.119778) \| 0.027279 / 0.128546 (-0.101267) \| 0.008072 / 0.075646 (-0.067575) \| 0.067890 / 0.419271 (-0.351381) \| 0.041866 / 0.043533 (-0.001667) \| 0.393438 / 0.255139 (0.138299) \| 0.402865 / 0.283200 (0.119666) \| 0.023381 / 0.141683 (-0.118302) \| 1.496324 / 1.452155 (0.044170) \| 1.538080 / 1.492716 (0.045364) \|\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) \| 0.212065 / 0.018006 (0.194059) \| 0.410511 / 0.000490 (0.410021) \| 0.001236 / 0.000200 (0.001036) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026012 / 0.037411 (-0.011399) \| 0.076592 / 0.014526 (0.062066) \| 0.085963 / 0.176557 (-0.090594) \| 0.137803 / 0.737135 (-0.599332) \| 0.087594 / 0.296338 (-0.208745) \|\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) \| 0.434283 / 0.215209 (0.219074) \| 4.345478 / 2.077655 (2.267824) \| 2.400954 / 1.504120 (0.896834) \| 2.282024 / 1.541195 (0.740829) \| 2.414247 / 1.468490 (0.945757) \| 0.501855 / 4.584777 (-4.082922) \| 3.059433 / 3.745712 (-0.686279) \| 2.811288 / 5.269862 (-2.458574) \| 1.856839 / 4.565676 (-2.708838) \| 0.058017 / 0.424275 (-0.366258) \| 0.006844 / 0.007607 (-0.000763) \| 0.515376 / 0.226044 (0.289332) \| 5.148775 / 2.268929 (2.879847) \| 2.930807 / 55.444624 (-52.513817) \| 2.520532 / 6.876477 (-4.355944) \| 2.746299 / 2.142072 (0.604227) \| 0.590102 / 4.805227 (-4.215125) \| 0.125747 / 6.500664 (-6.374917) \| 0.061873 / 0.075469 (-0.013597) \|\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) \| 1.306247 / 1.841788 (-0.535541) \| 18.366048 / 8.074308 (10.291740) \| 13.855617 / 10.191392 (3.664225) \| 0.150124 / 0.680424 (-0.530300) \| 0.017189 / 0.534201 (-0.517012) \| 0.336285 / 0.579283 (-0.242998) \| 0.344985 / 0.434364 (-0.089379) \| 0.397973 / 0.540337 (-0.142364) \| 0.536142 / 1.386936 (-0.850794) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ae24cf12054b4a512f198979b1ca7707bb99d56 \"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) \| 0.006401 / 0.011353 (-0.004952) \| 0.003789 / 0.011008 (-0.007219) \| 0.079516 / 0.038508 (0.041008) \| 0.068279 / 0.023109 (0.045170) \| 0.295691 / 0.275898 (0.019793) \| 0.327208 / 0.323480 (0.003728) \| 0.005070 / 0.007986 (-0.002915) \| 0.003044 / 0.004328 (-0.001285) \| 0.061411 / 0.004250 (0.057161) \| 0.053227 / 0.037052 (0.016175) \| 0.297368 / 0.258489 (0.038879) \| 0.334740 / 0.293841 (0.040899) \| 0.029459 / 0.128546 (-0.099087) \| 0.008080 / 0.075646 (-0.067566) \| 0.267344 / 0.419271 (-0.151927) \| 0.049877 / 0.043533 (0.006344) \| 0.293853 / 0.255139 (0.038714) \| 0.319819 / 0.283200 (0.036620) \| 0.022593 / 0.141683 (-0.119089) \| 1.459054 / 1.452155 (0.006900) \| 1.471250 / 1.492716 (-0.021466) \|\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) \| 0.194326 / 0.018006 (0.176320) \| 0.443565 / 0.000490 (0.443075) \| 0.003745 / 0.000200 (0.003545) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026640 / 0.037411 (-0.010772) \| 0.077630 / 0.014526 (0.063104) \| 0.089364 / 0.176557 (-0.087192) \| 0.147327 / 0.737135 (-0.589809) \| 0.089603 / 0.296338 (-0.206735) \|\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) \| 0.373758 / 0.215209 (0.158549) \| 3.746778 / 2.077655 (1.669123) \| 1.814991 / 1.504120 (0.310871) \| 1.645650 / 1.541195 (0.104455) \| 1.690752 / 1.468490 (0.222262) \| 0.472117 / 4.584777 (-4.112660) \| 3.457346 / 3.745712 (-0.288367) \| 3.138869 / 5.269862 (-2.130993) \| 1.934924 / 4.565676 (-2.630753) \| 0.055709 / 0.424275 (-0.368566) \| 0.006680 / 0.007607 (-0.000927) \| 0.446874 / 0.226044 (0.220829) \| 4.458409 / 2.268929 (2.189480) \| 2.253932 / 55.444624 (-53.190693) \| 2.007240 / 6.876477 (-4.869237) \| 2.081687 / 2.142072 (-0.060386) \| 0.563379 / 4.805227 (-4.241848) \| 0.128694 / 6.500664 (-6.371970) \| 0.057409 / 0.075469 (-0.018060) \|\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) \| 1.212231 / 1.841788 (-0.629556) \| 18.519121 / 8.074308 (10.444813) \| 13.582243 / 10.191392 (3.390851) \| 0.142488 / 0.680424 (-0.537936) \| 0.017421 / 0.534201 (-0.516780) \| 0.366864 / 0.579283 (-0.212419) \| 0.401467 / 0.434364 (-0.032897) \| 0.443659 / 0.540337 (-0.096679) \| 0.618854 / 1.386936 (-0.768082) \|\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) \| 0.006121 / 0.011353 (-0.005232) \| 0.003690 / 0.011008 (-0.007318) \| 0.060340 / 0.038508 (0.021832) \| 0.067215 / 0.023109 (0.044106) \| 0.382846 / 0.275898 (0.106948) \| 0.415774 / 0.323480 (0.092294) \| 0.004868 / 0.007986 (-0.003118) \| 0.003108 / 0.004328 (-0.001221) \| 0.060572 / 0.004250 (0.056321) \| 0.050453 / 0.037052 (0.013401) \| 0.400494 / 0.258489 (0.142005) \| 0.424368 / 0.293841 (0.130527) \| 0.030279 / 0.128546 (-0.098267) \| 0.008151 / 0.075646 (-0.067495) \| 0.066707 / 0.419271 (-0.352564) \| 0.046118 / 0.043533 (0.002585) \| 0.386697 / 0.255139 (0.131558) \| 0.410156 / 0.283200 (0.126957) \| 0.020688 / 0.141683 (-0.120995) \| 1.418162 / 1.452155 (-0.033993) \| 1.463057 / 1.492716 (-0.029659) \|\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) \| 0.216081 / 0.018006 (0.198075) \| 0.440541 / 0.000490 (0.440051) \| 0.000371 / 0.000200 (0.000171) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027763 / 0.037411 (-0.009648) \| 0.082316 / 0.014526 (0.067791) \| 0.094086 / 0.176557 (-0.082471) \| 0.144738 / 0.737135 (-0.592398) \| 0.094837 / 0.296338 (-0.201501) \|\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) \| 0.396277 / 0.215209 (0.181068) \| 3.958791 / 2.077655 (1.881136) \| 2.021367 / 1.504120 (0.517247) \| 1.860112 / 1.541195 (0.318917) \| 1.886032 / 1.468490 (0.417541) \| 0.468536 / 4.584777 (-4.116241) \| 3.417950 / 3.745712 (-0.327762) \| 4.849991 / 5.269862 (-0.419871) \| 2.773935 / 4.565676 (-1.791742) \| 0.055813 / 0.424275 (-0.368462) \| 0.007053 / 0.007607 (-0.000554) \| 0.470167 / 0.226044 (0.244122) \| 4.702969 / 2.268929 (2.434041) \| 2.474161 / 55.444624 (-52.970464) \| 2.171256 / 6.876477 (-4.705220) \| 2.315373 / 2.142072 (0.173301) \| 0.589195 / 4.805227 (-4.216032) \| 0.128237 / 6.500664 (-6.372427) \| 0.058641 / 0.075469 (-0.016828) \|\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) \| 1.292947 / 1.841788 (-0.548841) \| 18.851300 / 8.074308 (10.776992) \| 14.089764 / 10.191392 (3.898372) \| 0.164853 / 0.680424 (-0.515571) \| 0.017281 / 0.534201 (-0.516920) \| 0.359112 / 0.579283 (-0.220171) \| 0.386696 / 0.434364 (-0.047668) \| 0.428222 / 0.540337 (-0.112115) \| 0.568659 / 1.386936 (-0.818277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#563864ded894b468e2ba3f677ef79c5ab3fe65df \"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) \| 0.006051 / 0.011353 (-0.005301) \| 0.003654 / 0.011008 (-0.007355) \| 0.080081 / 0.038508 (0.041572) \| 0.062925 / 0.023109 (0.039815) \| 0.358097 / 0.275898 (0.082199) \| 0.405728 / 0.323480 (0.082248) \| 0.005359 / 0.007986 (-0.002627) \| 0.002820 / 0.004328 (-0.001508) \| 0.063108 / 0.004250 (0.058858) \| 0.049627 / 0.037052 (0.012575) \| 0.397870 / 0.258489 (0.139381) \| 0.437157 / 0.293841 (0.143316) \| 0.027707 / 0.128546 (-0.100839) \| 0.007911 / 0.075646 (-0.067735) \| 0.260991 / 0.419271 (-0.158280) \| 0.044771 / 0.043533 (0.001238) \| 0.340230 / 0.255139 (0.085091) \| 0.384925 / 0.283200 (0.101725) \| 0.021369 / 0.141683 (-0.120314) \| 1.431439 / 1.452155 (-0.020715) \| 1.478794 / 1.492716 (-0.013922) \|\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) \| 0.182626 / 0.018006 (0.164620) \| 0.435551 / 0.000490 (0.435061) \| 0.003015 / 0.000200 (0.002815) \| 0.000064 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024703 / 0.037411 (-0.012708) \| 0.073640 / 0.014526 (0.059114) \| 0.084598 / 0.176557 (-0.091959) \| 0.145810 / 0.737135 (-0.591325) \| 0.085125 / 0.296338 (-0.211213) \|\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) \| 0.394539 / 0.215209 (0.179330) \| 3.945882 / 2.077655 (1.868227) \| 1.947166 / 1.504120 (0.443046) \| 1.763305 / 1.541195 (0.222111) \| 1.816208 / 1.468490 (0.347718) \| 0.498880 / 4.584777 (-4.085897) \| 3.098283 / 3.745712 (-0.647429) \| 2.823474 / 5.269862 (-2.446388) \| 1.873993 / 4.565676 (-2.691684) \| 0.058097 / 0.424275 (-0.366179) \| 0.006488 / 0.007607 (-0.001119) \| 0.466711 / 0.226044 (0.240667) \| 4.671520 / 2.268929 (2.402592) \| 2.363381 / 55.444624 (-53.081243) \| 2.052092 / 6.876477 (-4.824385) \| 2.209212 / 2.142072 (0.067140) \| 0.594650 / 4.805227 (-4.210577) \| 0.125604 / 6.500664 (-6.375060) \| 0.061511 / 0.075469 (-0.013958) \|\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) \| 1.226564 / 1.841788 (-0.615224) \| 18.583605 / 8.074308 (10.509297) \| 13.993091 / 10.191392 (3.801699) \| 0.146185 / 0.680424 (-0.534239) \| 0.016839 / 0.534201 (-0.517362) \| 0.334116 / 0.579283 (-0.245167) \| 0.360780 / 0.434364 (-0.073584) \| 0.386008 / 0.540337 (-0.154329) \| 0.643278 / 1.386936 (-0.743658) \|\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) \| 0.006174 / 0.011353 (-0.005179) \| 0.003658 / 0.011008 (-0.007350) \| 0.063250 / 0.038508 (0.024742) \| 0.063542 / 0.023109 (0.040433) \| 0.366845 / 0.275898 (0.090947) \| 0.409794 / 0.323480 (0.086314) \| 0.005678 / 0.007986 (-0.002308) \| 0.003061 / 0.004328 (-0.001268) \| 0.063561 / 0.004250 (0.059311) \| 0.052648 / 0.037052 (0.015596) \| 0.378096 / 0.258489 (0.119607) \| 0.410706 / 0.293841 (0.116865) \| 0.027668 / 0.128546 (-0.100878) \| 0.008045 / 0.075646 (-0.067601) \| 0.068290 / 0.419271 (-0.350981) \| 0.042602 / 0.043533 (-0.000930) \| 0.364976 / 0.255139 (0.109837) \| 0.395599 / 0.283200 (0.112400) \| 0.022733 / 0.141683 (-0.118950) \| 1.522473 / 1.452155 (0.070319) \| 1.515891 / 1.492716 (0.023175) \|\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) \| 0.232554 / 0.018006 (0.214547) \| 0.420702 / 0.000490 (0.420213) \| 0.002161 / 0.000200 (0.001961) \| 0.000064 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026276 / 0.037411 (-0.011135) \| 0.078504 / 0.014526 (0.063978) \| 0.088989 / 0.176557 (-0.087567) \| 0.144044 / 0.737135 (-0.593091) \| 0.091074 / 0.296338 (-0.205265) \|\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) \| 0.420189 / 0.215209 (0.204980) \| 4.189596 / 2.077655 (2.111941) \| 2.316425 / 1.504120 (0.812305) \| 2.186877 / 1.541195 (0.645682) \| 2.259065 / 1.468490 (0.790575) \| 0.502827 / 4.584777 (-4.081950) \| 3.135266 / 3.745712 (-0.610446) \| 2.838808 / 5.269862 (-2.431053) \| 1.876519 / 4.565676 (-2.689158) \| 0.057802 / 0.424275 (-0.366473) \| 0.006824 / 0.007607 (-0.000784) \| 0.500213 / 0.226044 (0.274168) \| 4.999798 / 2.268929 (2.730869) \| 2.627713 / 55.444624 (-52.816911) \| 2.344263 / 6.876477 (-4.532214) \| 2.415449 / 2.142072 (0.273376) \| 0.593082 / 4.805227 (-4.212145) \| 0.125787 / 6.500664 (-6.374877) \| 0.062699 / 0.075469 (-0.012770) \|\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) \| 1.308219 / 1.841788 (-0.533569) \| 18.703099 / 8.074308 (10.628791) \| 13.976234 / 10.191392 (3.784842) \| 0.144037 / 0.680424 (-0.536387) \| 0.016592 / 0.534201 (-0.517609) \| 0.333078 / 0.579283 (-0.246206) \| 0.342317 / 0.434364 (-0.092047) \| 0.396837 / 0.540337 (-0.143500) \| 0.532641 / 1.386936 (-0.854295) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14f6edd9222e577dccb962ed5338b79b73502fa5 \"CML watermark\")\n" ]
1,803,008,486	6,027	Delete `task_templates` in `IterableDataset` when they are no longer valid	closed	2023-07-13T13:16:17	2023-07-13T14:06:20	2023-07-13T13:57:35	https://github.com/huggingface/datasets/pull/6027	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6027", "html_url": "https://github.com/huggingface/datasets/pull/6027", "diff_url": "https://github.com/huggingface/datasets/pull/6027.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6027.patch", "merged_at": "2023-07-13T13:57:35" }	mariosasko	true	[ "_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) \| 0.008698 / 0.011353 (-0.002655) \| 0.005250 / 0.011008 (-0.005758) \| 0.104101 / 0.038508 (0.065593) \| 0.085021 / 0.023109 (0.061912) \| 0.426653 / 0.275898 (0.150755) \| 0.460449 / 0.323480 (0.136969) \| 0.005222 / 0.007986 (-0.002763) \| 0.006280 / 0.004328 (0.001951) \| 0.083458 / 0.004250 (0.079207) \| 0.066132 / 0.037052 (0.029079) \| 0.433416 / 0.258489 (0.174927) \| 0.482718 / 0.293841 (0.188877) \| 0.048872 / 0.128546 (-0.079675) \| 0.013699 / 0.075646 (-0.061948) \| 0.365660 / 0.419271 (-0.053611) \| 0.071008 / 0.043533 (0.027475) \| 0.428688 / 0.255139 (0.173549) \| 0.443554 / 0.283200 (0.160354) \| 0.035901 / 0.141683 (-0.105782) \| 1.829296 / 1.452155 (0.377141) \| 1.862351 / 1.492716 (0.369635) \|\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) \| 0.236284 / 0.018006 (0.218278) \| 0.584075 / 0.000490 (0.583585) \| 0.004634 / 0.000200 (0.004434) \| 0.000125 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034723 / 0.037411 (-0.002688) \| 0.100989 / 0.014526 (0.086464) \| 0.113722 / 0.176557 (-0.062834) \| 0.187659 / 0.737135 (-0.549477) \| 0.113937 / 0.296338 (-0.182401) \|\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) \| 0.587500 / 0.215209 (0.372291) \| 5.847371 / 2.077655 (3.769716) \| 2.599691 / 1.504120 (1.095571) \| 2.246187 / 1.541195 (0.704992) \| 2.419126 / 1.468490 (0.950636) \| 0.847327 / 4.584777 (-3.737450) \| 5.230438 / 3.745712 (1.484726) \| 7.539021 / 5.269862 (2.269160) \| 4.617473 / 4.565676 (0.051797) \| 0.103620 / 0.424275 (-0.320655) \| 0.009195 / 0.007607 (0.001588) \| 0.714247 / 0.226044 (0.488203) \| 7.331621 / 2.268929 (5.062693) \| 3.416575 / 55.444624 (-52.028049) \| 2.649467 / 6.876477 (-4.227009) \| 2.928091 / 2.142072 (0.786018) \| 1.002155 / 4.805227 (-3.803072) \| 0.210790 / 6.500664 (-6.289874) \| 0.081303 / 0.075469 (0.005834) \|\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) \| 1.655431 / 1.841788 (-0.186357) \| 24.069595 / 8.074308 (15.995287) \| 20.923766 / 10.191392 (10.732374) \| 0.232021 / 0.680424 (-0.448403) \| 0.026355 / 0.534201 (-0.507846) \| 0.496830 / 0.579283 (-0.082453) \| 0.582620 / 0.434364 (0.148257) \| 0.551227 / 0.540337 (0.010890) \| 0.756389 / 1.386936 (-0.630547) \|\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) \| 0.009329 / 0.011353 (-0.002024) \| 0.005045 / 0.011008 (-0.005964) \| 0.082116 / 0.038508 (0.043608) \| 0.082420 / 0.023109 (0.059311) \| 0.502513 / 0.275898 (0.226615) \| 0.526098 / 0.323480 (0.202618) \| 0.007468 / 0.007986 (-0.000517) \| 0.005477 / 0.004328 (0.001148) \| 0.082617 / 0.004250 (0.078367) \| 0.070292 / 0.037052 (0.033239) \| 0.503290 / 0.258489 (0.244801) \| 0.541631 / 0.293841 (0.247790) \| 0.050826 / 0.128546 (-0.077721) \| 0.014699 / 0.075646 (-0.060948) \| 0.094441 / 0.419271 (-0.324830) \| 0.065034 / 0.043533 (0.021501) \| 0.486778 / 0.255139 (0.231639) \| 0.516907 / 0.283200 (0.233707) \| 0.045140 / 0.141683 (-0.096543) \| 1.831676 / 1.452155 (0.379521) \| 1.910865 / 1.492716 (0.418149) \|\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) \| 0.286818 / 0.018006 (0.268812) \| 0.558621 / 0.000490 (0.558131) \| 0.002830 / 0.000200 (0.002630) \| 0.000148 / 0.000054 (0.000094) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036716 / 0.037411 (-0.000696) \| 0.107830 / 0.014526 (0.093305) \| 0.116368 / 0.176557 (-0.060188) \| 0.178401 / 0.737135 (-0.558734) \| 0.124729 / 0.296338 (-0.171609) \|\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) \| 0.633557 / 0.215209 (0.418348) \| 6.423135 / 2.077655 (4.345480) \| 2.981883 / 1.504120 (1.477763) \| 2.755592 / 1.541195 (1.214398) \| 2.769337 / 1.468490 (1.300847) \| 0.836219 / 4.584777 (-3.748558) \| 5.302030 / 3.745712 (1.556318) \| 7.463960 / 5.269862 (2.194098) \| 4.427254 / 4.565676 (-0.138422) \| 0.095990 / 0.424275 (-0.328285) \| 0.009264 / 0.007607 (0.001657) \| 0.770642 / 0.226044 (0.544597) \| 7.779667 / 2.268929 (5.510739) \| 3.799115 / 55.444624 (-51.645509) \| 3.212560 / 6.876477 (-3.663917) \| 3.281657 / 2.142072 (1.139584) \| 1.044981 / 4.805227 (-3.760246) \| 0.210693 / 6.500664 (-6.289971) \| 0.079466 / 0.075469 (0.003997) \|\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) \| 1.793155 / 1.841788 (-0.048632) \| 24.691127 / 8.074308 (16.616819) \| 22.083150 / 10.191392 (11.891758) \| 0.242246 / 0.680424 (-0.438178) \| 0.028001 / 0.534201 (-0.506200) \| 0.494061 / 0.579283 (-0.085222) \| 0.599288 / 0.434364 (0.164924) \| 0.552101 / 0.540337 (0.011764) \| 0.784093 / 1.386936 (-0.602843) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd429c39604af34bc3a3ba1f463329b23fcbc1e3 \"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) \| 0.006658 / 0.011353 (-0.004695) \| 0.004044 / 0.011008 (-0.006965) \| 0.085844 / 0.038508 (0.047336) \| 0.077147 / 0.023109 (0.054038) \| 0.344387 / 0.275898 (0.068489) \| 0.376718 / 0.323480 (0.053238) \| 0.005537 / 0.007986 (-0.002448) \| 0.003452 / 0.004328 (-0.000876) \| 0.065326 / 0.004250 (0.061076) \| 0.057639 / 0.037052 (0.020587) \| 0.352363 / 0.258489 (0.093873) \| 0.378939 / 0.293841 (0.085098) \| 0.031259 / 0.128546 (-0.097287) \| 0.008464 / 0.075646 (-0.067183) \| 0.289076 / 0.419271 (-0.130195) \| 0.052991 / 0.043533 (0.009459) \| 0.346053 / 0.255139 (0.090914) \| 0.362761 / 0.283200 (0.079561) \| 0.023501 / 0.141683 (-0.118182) \| 1.478312 / 1.452155 (0.026157) \| 1.545437 / 1.492716 (0.052721) \|\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) \| 0.202964 / 0.018006 (0.184957) \| 0.534793 / 0.000490 (0.534303) \| 0.006025 / 0.000200 (0.005825) \| 0.000225 / 0.000054 (0.000171) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029418 / 0.037411 (-0.007993) \| 0.084297 / 0.014526 (0.069771) \| 0.096702 / 0.176557 (-0.079855) \| 0.157355 / 0.737135 (-0.579781) \| 0.097858 / 0.296338 (-0.198480) \|\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) \| 0.380728 / 0.215209 (0.165519) \| 3.787712 / 2.077655 (1.710057) \| 1.836393 / 1.504120 (0.332273) \| 1.678415 / 1.541195 (0.137220) \| 1.781800 / 1.468490 (0.313310) \| 0.478677 / 4.584777 (-4.106100) \| 3.614080 / 3.745712 (-0.131632) \| 3.255637 / 5.269862 (-2.014225) \| 2.063642 / 4.565676 (-2.502035) \| 0.056470 / 0.424275 (-0.367805) \| 0.007408 / 0.007607 (-0.000199) \| 0.459155 / 0.226044 (0.233111) \| 4.586679 / 2.268929 (2.317750) \| 2.305737 / 55.444624 (-53.138888) \| 1.954755 / 6.876477 (-4.921721) \| 2.190809 / 2.142072 (0.048737) \| 0.572426 / 4.805227 (-4.232802) \| 0.130349 / 6.500664 (-6.370315) \| 0.059346 / 0.075469 (-0.016124) \|\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) \| 1.253671 / 1.841788 (-0.588117) \| 19.509015 / 8.074308 (11.434706) \| 13.951349 / 10.191392 (3.759957) \| 0.171038 / 0.680424 (-0.509386) \| 0.018826 / 0.534201 (-0.515375) \| 0.394642 / 0.579283 (-0.184642) \| 0.419614 / 0.434364 (-0.014750) \| 0.470931 / 0.540337 (-0.069406) \| 0.643858 / 1.386936 (-0.743078) \|\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) \| 0.006765 / 0.011353 (-0.004587) \| 0.003955 / 0.011008 (-0.007053) \| 0.064377 / 0.038508 (0.025869) \| 0.076980 / 0.023109 (0.053871) \| 0.368675 / 0.275898 (0.092777) \| 0.403746 / 0.323480 (0.080267) \| 0.005303 / 0.007986 (-0.002683) \| 0.003257 / 0.004328 (-0.001072) \| 0.064154 / 0.004250 (0.059903) \| 0.056975 / 0.037052 (0.019923) \| 0.376718 / 0.258489 (0.118229) \| 0.416291 / 0.293841 (0.122450) \| 0.031444 / 0.128546 (-0.097102) \| 0.008532 / 0.075646 (-0.067115) \| 0.070455 / 0.419271 (-0.348816) \| 0.049032 / 0.043533 (0.005499) \| 0.361413 / 0.255139 (0.106274) \| 0.384648 / 0.283200 (0.101448) \| 0.024050 / 0.141683 (-0.117633) \| 1.514330 / 1.452155 (0.062176) \| 1.585424 / 1.492716 (0.092708) \|\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) \| 0.214701 / 0.018006 (0.196695) \| 0.447706 / 0.000490 (0.447216) \| 0.000373 / 0.000200 (0.000173) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031007 / 0.037411 (-0.006404) \| 0.090545 / 0.014526 (0.076019) \| 0.100611 / 0.176557 (-0.075945) \| 0.154847 / 0.737135 (-0.582289) \| 0.102864 / 0.296338 (-0.193475) \|\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) \| 0.427740 / 0.215209 (0.212531) \| 4.273143 / 2.077655 (2.195488) \| 2.294906 / 1.504120 (0.790786) \| 2.138460 / 1.541195 (0.597265) \| 2.274126 / 1.468490 (0.805636) \| 0.486559 / 4.584777 (-4.098218) \| 3.565554 / 3.745712 (-0.180158) \| 3.377659 / 5.269862 (-1.892202) \| 2.029883 / 4.565676 (-2.535793) \| 0.057303 / 0.424275 (-0.366972) \| 0.007314 / 0.007607 (-0.000293) \| 0.504263 / 0.226044 (0.278219) \| 5.041196 / 2.268929 (2.772268) \| 2.819273 / 55.444624 (-52.625351) \| 2.421479 / 6.876477 (-4.454998) \| 2.503063 / 2.142072 (0.360991) \| 0.581467 / 4.805227 (-4.223760) \| 0.133532 / 6.500664 (-6.367132) \| 0.062504 / 0.075469 (-0.012965) \|\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) \| 1.328765 / 1.841788 (-0.513022) \| 20.131672 / 8.074308 (12.057363) \| 14.312895 / 10.191392 (4.121503) \| 0.191199 / 0.680424 (-0.489225) \| 0.018522 / 0.534201 (-0.515679) \| 0.393121 / 0.579283 (-0.186162) \| 0.413122 / 0.434364 (-0.021242) \| 0.469312 / 0.540337 (-0.071026) \| 0.633140 / 1.386936 (-0.753796) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dbf6c103f5844de40431478e7e4a64fbf2c2c067 \"CML watermark\")\n" ]
1,802,929,222	6,026	Fix style with ruff 0.0.278	closed	2023-07-13T12:34:24	2023-07-13T12:46:26	2023-07-13T12:37:01	https://github.com/huggingface/datasets/pull/6026	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6026", "html_url": "https://github.com/huggingface/datasets/pull/6026", "diff_url": "https://github.com/huggingface/datasets/pull/6026.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6026.patch", "merged_at": "2023-07-13T12:37:01" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6026). 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) \| 0.006444 / 0.011353 (-0.004909) \| 0.003768 / 0.011008 (-0.007240) \| 0.079625 / 0.038508 (0.041117) \| 0.064490 / 0.023109 (0.041381) \| 0.313858 / 0.275898 (0.037960) \| 0.350810 / 0.323480 (0.027330) \| 0.004804 / 0.007986 (-0.003182) \| 0.002904 / 0.004328 (-0.001425) \| 0.061728 / 0.004250 (0.057477) \| 0.052265 / 0.037052 (0.015213) \| 0.321246 / 0.258489 (0.062757) \| 0.353873 / 0.293841 (0.060032) \| 0.027510 / 0.128546 (-0.101036) \| 0.007942 / 0.075646 (-0.067704) \| 0.260518 / 0.419271 (-0.158754) \| 0.045686 / 0.043533 (0.002153) \| 0.316821 / 0.255139 (0.061682) \| 0.337086 / 0.283200 (0.053886) \| 0.022188 / 0.141683 (-0.119495) \| 1.427345 / 1.452155 (-0.024810) \| 1.476059 / 1.492716 (-0.016657) \|\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) \| 0.189640 / 0.018006 (0.171634) \| 0.429724 / 0.000490 (0.429235) \| 0.005314 / 0.000200 (0.005114) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024412 / 0.037411 (-0.013000) \| 0.073488 / 0.014526 (0.058962) \| 0.083843 / 0.176557 (-0.092714) \| 0.147849 / 0.737135 (-0.589286) \| 0.085465 / 0.296338 (-0.210873) \|\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) \| 0.405314 / 0.215209 (0.190105) \| 4.071471 / 2.077655 (1.993816) \| 1.916252 / 1.504120 (0.412132) \| 1.721616 / 1.541195 (0.180422) \| 1.807187 / 1.468490 (0.338697) \| 0.498045 / 4.584777 (-4.086732) \| 3.057526 / 3.745712 (-0.688187) \| 4.451424 / 5.269862 (-0.818437) \| 2.764020 / 4.565676 (-1.801656) \| 0.057665 / 0.424275 (-0.366610) \| 0.006679 / 0.007607 (-0.000928) \| 0.485733 / 0.226044 (0.259688) \| 4.844367 / 2.268929 (2.575438) \| 2.435359 / 55.444624 (-53.009265) \| 2.111478 / 6.876477 (-4.764999) \| 2.377448 / 2.142072 (0.235375) \| 0.587997 / 4.805227 (-4.217230) \| 0.125545 / 6.500664 (-6.375120) \| 0.061509 / 0.075469 (-0.013960) \|\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) \| 1.229210 / 1.841788 (-0.612577) \| 18.553994 / 8.074308 (10.479686) \| 14.037877 / 10.191392 (3.846485) \| 0.144230 / 0.680424 (-0.536194) \| 0.016891 / 0.534201 (-0.517310) \| 0.329039 / 0.579283 (-0.250244) \| 0.357269 / 0.434364 (-0.077095) \| 0.384222 / 0.540337 (-0.156115) \| 0.521292 / 1.386936 (-0.865644) \|\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) \| 0.006359 / 0.011353 (-0.004994) \| 0.003721 / 0.011008 (-0.007287) \| 0.062047 / 0.038508 (0.023539) \| 0.065267 / 0.023109 (0.042158) \| 0.360164 / 0.275898 (0.084266) \| 0.402292 / 0.323480 (0.078812) \| 0.005603 / 0.007986 (-0.002382) \| 0.002966 / 0.004328 (-0.001363) \| 0.062580 / 0.004250 (0.058330) \| 0.053634 / 0.037052 (0.016582) \| 0.362210 / 0.258489 (0.103721) \| 0.404285 / 0.293841 (0.110444) \| 0.027567 / 0.128546 (-0.100979) \| 0.008119 / 0.075646 (-0.067528) \| 0.067577 / 0.419271 (-0.351694) \| 0.042867 / 0.043533 (-0.000666) \| 0.361576 / 0.255139 (0.106437) \| 0.389061 / 0.283200 (0.105862) \| 0.021923 / 0.141683 (-0.119760) \| 1.446259 / 1.452155 (-0.005895) \| 1.490724 / 1.492716 (-0.001992) \|\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) \| 0.206433 / 0.018006 (0.188427) \| 0.424178 / 0.000490 (0.423688) \| 0.002340 / 0.000200 (0.002140) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024955 / 0.037411 (-0.012456) \| 0.077446 / 0.014526 (0.062920) \| 0.088540 / 0.176557 (-0.088017) \| 0.141225 / 0.737135 (-0.595910) \| 0.089747 / 0.296338 (-0.206592) \|\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) \| 0.443738 / 0.215209 (0.228529) \| 4.208887 / 2.077655 (2.131233) \| 2.155127 / 1.504120 (0.651007) \| 2.028178 / 1.541195 (0.486983) \| 2.084903 / 1.468490 (0.616413) \| 0.497530 / 4.584777 (-4.087247) \| 3.069012 / 3.745712 (-0.676700) \| 3.025184 / 5.269862 (-2.244678) \| 1.904687 / 4.565676 (-2.660990) \| 0.057526 / 0.424275 (-0.366749) \| 0.006482 / 0.007607 (-0.001125) \| 0.494692 / 0.226044 (0.268647) \| 4.944437 / 2.268929 (2.675508) \| 2.655989 / 55.444624 (-52.788635) \| 2.331677 / 6.876477 (-4.544800) \| 2.382396 / 2.142072 (0.240324) \| 0.582019 / 4.805227 (-4.223209) \| 0.125866 / 6.500664 (-6.374799) \| 0.062908 / 0.075469 (-0.012561) \|\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) \| 1.294612 / 1.841788 (-0.547176) \| 19.016152 / 8.074308 (10.941844) \| 14.088828 / 10.191392 (3.897436) \| 0.160842 / 0.680424 (-0.519582) \| 0.017054 / 0.534201 (-0.517146) \| 0.333647 / 0.579283 (-0.245636) \| 0.348094 / 0.434364 (-0.086270) \| 0.394970 / 0.540337 (-0.145367) \| 0.551141 / 1.386936 (-0.835795) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e9cfe886792b30b5000808072a0f91ec8536749 \"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) \| 0.007442 / 0.011353 (-0.003911) \| 0.004302 / 0.011008 (-0.006707) \| 0.087159 / 0.038508 (0.048651) \| 0.095094 / 0.023109 (0.071985) \| 0.315422 / 0.275898 (0.039524) \| 0.346672 / 0.323480 (0.023192) \| 0.005811 / 0.007986 (-0.002174) \| 0.003597 / 0.004328 (-0.000731) \| 0.066400 / 0.004250 (0.062150) \| 0.065947 / 0.037052 (0.028894) \| 0.323269 / 0.258489 (0.064780) \| 0.353309 / 0.293841 (0.059468) \| 0.032268 / 0.128546 (-0.096278) \| 0.008696 / 0.075646 (-0.066950) \| 0.291486 / 0.419271 (-0.127786) \| 0.054609 / 0.043533 (0.011076) \| 0.321061 / 0.255139 (0.065922) \| 0.336907 / 0.283200 (0.053707) \| 0.027338 / 0.141683 (-0.114345) \| 1.496442 / 1.452155 (0.044287) \| 1.576946 / 1.492716 (0.084229) \|\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) \| 0.229140 / 0.018006 (0.211134) \| 0.487500 / 0.000490 (0.487010) \| 0.002425 / 0.000200 (0.002225) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029351 / 0.037411 (-0.008060) \| 0.089610 / 0.014526 (0.075084) \| 0.097880 / 0.176557 (-0.078676) \| 0.155947 / 0.737135 (-0.581189) \| 0.098593 / 0.296338 (-0.197745) \|\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) \| 0.382911 / 0.215209 (0.167702) \| 3.820363 / 2.077655 (1.742708) \| 1.866385 / 1.504120 (0.362265) \| 1.712910 / 1.541195 (0.171716) \| 1.813863 / 1.468490 (0.345373) \| 0.484884 / 4.584777 (-4.099893) \| 3.678911 / 3.745712 (-0.066801) \| 5.249908 / 5.269862 (-0.019953) \| 3.099614 / 4.565676 (-1.466063) \| 0.057449 / 0.424275 (-0.366826) \| 0.007728 / 0.007607 (0.000120) \| 0.462123 / 0.226044 (0.236078) \| 4.603942 / 2.268929 (2.335014) \| 2.380957 / 55.444624 (-53.063668) \| 2.059621 / 6.876477 (-4.816856) \| 2.293764 / 2.142072 (0.151691) \| 0.636471 / 4.805227 (-4.168756) \| 0.150112 / 6.500664 (-6.350552) \| 0.063705 / 0.075469 (-0.011764) \|\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) \| 1.358099 / 1.841788 (-0.483689) \| 20.193750 / 8.074308 (12.119442) \| 14.297350 / 10.191392 (4.105958) \| 0.164477 / 0.680424 (-0.515947) \| 0.018259 / 0.534201 (-0.515942) \| 0.399010 / 0.579283 (-0.180273) \| 0.417306 / 0.434364 (-0.017058) \| 0.456961 / 0.540337 (-0.083377) \| 0.631068 / 1.386936 (-0.755868) \|\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) \| 0.007324 / 0.011353 (-0.004028) \| 0.004463 / 0.011008 (-0.006545) \| 0.066148 / 0.038508 (0.027640) \| 0.093909 / 0.023109 (0.070799) \| 0.399122 / 0.275898 (0.123224) \| 0.430226 / 0.323480 (0.106746) \| 0.005505 / 0.007986 (-0.002481) \| 0.003579 / 0.004328 (-0.000749) \| 0.066529 / 0.004250 (0.062278) \| 0.063471 / 0.037052 (0.026418) \| 0.406351 / 0.258489 (0.147862) \| 0.439987 / 0.293841 (0.146146) \| 0.032640 / 0.128546 (-0.095906) \| 0.008770 / 0.075646 (-0.066877) \| 0.072592 / 0.419271 (-0.346680) \| 0.050429 / 0.043533 (0.006896) \| 0.390873 / 0.255139 (0.135734) \| 0.412438 / 0.283200 (0.129239) \| 0.027113 / 0.141683 (-0.114570) \| 1.458281 / 1.452155 (0.006126) \| 1.536819 / 1.492716 (0.044103) \|\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) \| 0.228309 / 0.018006 (0.210303) \| 0.454042 / 0.000490 (0.453552) \| 0.000387 / 0.000200 (0.000187) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029573 / 0.037411 (-0.007838) \| 0.086433 / 0.014526 (0.071907) \| 0.097992 / 0.176557 (-0.078565) \| 0.152464 / 0.737135 (-0.584671) \| 0.099901 / 0.296338 (-0.196437) \|\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) \| 0.413807 / 0.215209 (0.198598) \| 4.126395 / 2.077655 (2.048740) \| 2.113544 / 1.504120 (0.609424) \| 1.967829 / 1.541195 (0.426635) \| 2.037123 / 1.468490 (0.568633) \| 0.489403 / 4.584777 (-4.095374) \| 3.689508 / 3.745712 (-0.056204) \| 3.503909 / 5.269862 (-1.765952) \| 2.113812 / 4.565676 (-2.451864) \| 0.057988 / 0.424275 (-0.366287) \| 0.007336 / 0.007607 (-0.000271) \| 0.490840 / 0.226044 (0.264795) \| 4.885040 / 2.268929 (2.616112) \| 2.627864 / 55.444624 (-52.816760) \| 2.231467 / 6.876477 (-4.645010) \| 2.251307 / 2.142072 (0.109235) \| 0.577370 / 4.805227 (-4.227857) \| 0.131770 / 6.500664 (-6.368895) \| 0.061313 / 0.075469 (-0.014156) \|\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) \| 1.362052 / 1.841788 (-0.479735) \| 21.332694 / 8.074308 (13.258386) \| 15.562019 / 10.191392 (5.370627) \| 0.170874 / 0.680424 (-0.509550) \| 0.019226 / 0.534201 (-0.514975) \| 0.400311 / 0.579283 (-0.178972) \| 0.423060 / 0.434364 (-0.011304) \| 0.469946 / 0.540337 (-0.070391) \| 0.647745 / 1.386936 (-0.739191) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aec567c2f224f192e6e1f9799e3afc755eb517b2 \"CML watermark\")\n" ]
1,801,852,601	6,025	Using a dataset for a use other than it was intended for.	closed	2023-07-12T22:33:17	2023-07-13T13:57:36	2023-07-13T13:57:36	https://github.com/huggingface/datasets/issues/6025	null	surya-narayanan	false	[ "I've opened a PR with a fix. In the meantime, you can avoid the error by deleting `task_templates` with `dataset.info.task_templates = None` before the `interleave_datasets` call.\r\n` " ]
1,801,708,808	6,024	Don't reference self in Spark._validate_cache_dir	closed	2023-07-12T20:31:16	2023-07-13T16:58:32	2023-07-13T12:37:09	https://github.com/huggingface/datasets/pull/6024	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6024", "html_url": "https://github.com/huggingface/datasets/pull/6024", "diff_url": "https://github.com/huggingface/datasets/pull/6024.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6024.patch", "merged_at": "2023-07-13T12:37:09" }	maddiedawson	true	[ "Ptal @lhoestq :) I tested this manually on a multi-node Databricks cluster", "Hm looks like the check_code_quality failures are unrelated to me change... https://github.com/huggingface/datasets/actions/runs/5536162850/jobs/10103451883?pr=6024", "_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) \| 0.005952 / 0.011353 (-0.005400) \| 0.003585 / 0.011008 (-0.007424) \| 0.079163 / 0.038508 (0.040655) \| 0.057926 / 0.023109 (0.034817) \| 0.326647 / 0.275898 (0.050749) \| 0.383485 / 0.323480 (0.060005) \| 0.004530 / 0.007986 (-0.003456) \| 0.002821 / 0.004328 (-0.001508) \| 0.062071 / 0.004250 (0.057820) \| 0.048023 / 0.037052 (0.010971) \| 0.329368 / 0.258489 (0.070879) \| 0.390877 / 0.293841 (0.097036) \| 0.026959 / 0.128546 (-0.101588) \| 0.007911 / 0.075646 (-0.067735) \| 0.259956 / 0.419271 (-0.159315) \| 0.044582 / 0.043533 (0.001049) \| 0.320537 / 0.255139 (0.065398) \| 0.373814 / 0.283200 (0.090614) \| 0.020275 / 0.141683 (-0.121408) \| 1.532128 / 1.452155 (0.079973) \| 1.595031 / 1.492716 (0.102315) \|\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) \| 0.186127 / 0.018006 (0.168120) \| 0.428586 / 0.000490 (0.428097) \| 0.005180 / 0.000200 (0.004980) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024876 / 0.037411 (-0.012536) \| 0.072169 / 0.014526 (0.057643) \| 0.082015 / 0.176557 (-0.094542) \| 0.147467 / 0.737135 (-0.589668) \| 0.082769 / 0.296338 (-0.213570) \|\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) \| 0.410625 / 0.215209 (0.195416) \| 4.116742 / 2.077655 (2.039088) \| 2.172291 / 1.504120 (0.668171) \| 2.022462 / 1.541195 (0.481268) \| 2.048142 / 1.468490 (0.579651) \| 0.503152 / 4.584777 (-4.081625) \| 3.019135 / 3.745712 (-0.726577) \| 3.589451 / 5.269862 (-1.680410) \| 2.206876 / 4.565676 (-2.358801) \| 0.057687 / 0.424275 (-0.366588) \| 0.006560 / 0.007607 (-0.001047) \| 0.475585 / 0.226044 (0.249541) \| 4.784344 / 2.268929 (2.515416) \| 2.506322 / 55.444624 (-52.938302) \| 2.168251 / 6.876477 (-4.708225) \| 2.324453 / 2.142072 (0.182381) \| 0.590609 / 4.805227 (-4.214618) \| 0.124178 / 6.500664 (-6.376486) \| 0.059197 / 0.075469 (-0.016272) \|\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) \| 1.212359 / 1.841788 (-0.629429) \| 17.915843 / 8.074308 (9.841535) \| 13.128330 / 10.191392 (2.936938) \| 0.144805 / 0.680424 (-0.535618) \| 0.016889 / 0.534201 (-0.517312) \| 0.344056 / 0.579283 (-0.235227) \| 0.359370 / 0.434364 (-0.074994) \| 0.404199 / 0.540337 (-0.136138) \| 0.549117 / 1.386936 (-0.837819) \|\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) \| 0.005914 / 0.011353 (-0.005439) \| 0.003565 / 0.011008 (-0.007443) \| 0.061575 / 0.038508 (0.023067) \| 0.057677 / 0.023109 (0.034568) \| 0.359753 / 0.275898 (0.083855) \| 0.394135 / 0.323480 (0.070655) \| 0.004648 / 0.007986 (-0.003338) \| 0.002795 / 0.004328 (-0.001534) \| 0.061877 / 0.004250 (0.057626) \| 0.049673 / 0.037052 (0.012621) \| 0.363120 / 0.258489 (0.104631) \| 0.402685 / 0.293841 (0.108844) \| 0.027021 / 0.128546 (-0.101525) \| 0.008006 / 0.075646 (-0.067641) \| 0.067398 / 0.419271 (-0.351874) \| 0.044442 / 0.043533 (0.000909) \| 0.364851 / 0.255139 (0.109712) \| 0.387219 / 0.283200 (0.104019) \| 0.027267 / 0.141683 (-0.114416) \| 1.466675 / 1.452155 (0.014520) \| 1.512607 / 1.492716 (0.019891) \|\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) \| 0.206156 / 0.018006 (0.188150) \| 0.410877 / 0.000490 (0.410387) \| 0.003061 / 0.000200 (0.002861) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024869 / 0.037411 (-0.012542) \| 0.075736 / 0.014526 (0.061210) \| 0.083922 / 0.176557 (-0.092634) \| 0.139510 / 0.737135 (-0.597626) \| 0.087685 / 0.296338 (-0.208654) \|\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) \| 0.414473 / 0.215209 (0.199264) \| 4.150633 / 2.077655 (2.072979) \| 2.132892 / 1.504120 (0.628773) \| 1.964072 / 1.541195 (0.422878) \| 2.003353 / 1.468490 (0.534863) \| 0.498012 / 4.584777 (-4.086765) \| 3.010135 / 3.745712 (-0.735577) \| 2.841130 / 5.269862 (-2.428732) \| 1.826013 / 4.565676 (-2.739664) \| 0.057443 / 0.424275 (-0.366832) \| 0.006374 / 0.007607 (-0.001234) \| 0.490337 / 0.226044 (0.264292) \| 4.889628 / 2.268929 (2.620700) \| 2.575626 / 55.444624 (-52.868998) \| 2.246522 / 6.876477 (-4.629955) \| 2.276183 / 2.142072 (0.134110) \| 0.581465 / 4.805227 (-4.223763) \| 0.123877 / 6.500664 (-6.376787) \| 0.060339 / 0.075469 (-0.015130) \|\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) \| 1.333202 / 1.841788 (-0.508585) \| 18.363558 / 8.074308 (10.289250) \| 14.109356 / 10.191392 (3.917964) \| 0.147358 / 0.680424 (-0.533066) \| 0.016813 / 0.534201 (-0.517388) \| 0.334815 / 0.579283 (-0.244468) \| 0.366576 / 0.434364 (-0.067788) \| 0.397223 / 0.540337 (-0.143115) \| 0.547893 / 1.386936 (-0.839043) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ac60bcbebe9ddac70264951b1d584c93003cdf \"CML watermark\")\n" ]
1,801,272,420	6,023	Fix `ClassLabel` min max check for `None` values	closed	2023-07-12T15:46:12	2023-07-12T16:29:26	2023-07-12T16:18:04	https://github.com/huggingface/datasets/pull/6023	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6023", "html_url": "https://github.com/huggingface/datasets/pull/6023", "diff_url": "https://github.com/huggingface/datasets/pull/6023.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6023.patch", "merged_at": "2023-07-12T16:18:04" }	mariosasko	true	[ "_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" ]
1,800,092,589	6,022	Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int'	closed	2023-07-12T03:20:17	2023-07-12T16:18:06	2023-07-12T16:18:05	https://github.com/huggingface/datasets/issues/6022	null	codingl2k1	false	[ "Thanks for reporting! I've opened a PR with a fix." ]
1,799,785,904	6,021	[docs] Update return statement of index search	closed	2023-07-11T21:33:32	2023-07-12T17:13:02	2023-07-12T17:03:00	https://github.com/huggingface/datasets/pull/6021	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6021", "html_url": "https://github.com/huggingface/datasets/pull/6021", "diff_url": "https://github.com/huggingface/datasets/pull/6021.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6021.patch", "merged_at": "2023-07-12T17:03:00" }	stevhliu	true	[ "_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) \| 0.007697 / 0.011353 (-0.003656) \| 0.004233 / 0.011008 (-0.006776) \| 0.087890 / 0.038508 (0.049382) \| 0.065305 / 0.023109 (0.042196) \| 0.366919 / 0.275898 (0.091020) \| 0.399656 / 0.323480 (0.076176) \| 0.006753 / 0.007986 (-0.001232) \| 0.003428 / 0.004328 (-0.000900) \| 0.070180 / 0.004250 (0.065930) \| 0.054164 / 0.037052 (0.017112) \| 0.377130 / 0.258489 (0.118641) \| 0.403456 / 0.293841 (0.109615) \| 0.042639 / 0.128546 (-0.085907) \| 0.012396 / 0.075646 (-0.063250) \| 0.314235 / 0.419271 (-0.105036) \| 0.061976 / 0.043533 (0.018443) \| 0.376959 / 0.255139 (0.121820) \| 0.433313 / 0.283200 (0.150113) \| 0.031253 / 0.141683 (-0.110430) \| 1.555749 / 1.452155 (0.103594) \| 1.643905 / 1.492716 (0.151189) \|\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) \| 0.208630 / 0.018006 (0.190624) \| 0.519532 / 0.000490 (0.519042) \| 0.003719 / 0.000200 (0.003519) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027403 / 0.037411 (-0.010008) \| 0.080990 / 0.014526 (0.066464) \| 0.090424 / 0.176557 (-0.086133) \| 0.153922 / 0.737135 (-0.583213) \| 0.098156 / 0.296338 (-0.198183) \|\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) \| 0.519453 / 0.215209 (0.304244) \| 5.100089 / 2.077655 (3.022434) \| 2.212165 / 1.504120 (0.708045) \| 1.894405 / 1.541195 (0.353210) \| 1.922914 / 1.468490 (0.454424) \| 0.762443 / 4.584777 (-3.822334) \| 4.669214 / 3.745712 (0.923502) \| 5.016066 / 5.269862 (-0.253796) \| 3.128821 / 4.565676 (-1.436856) \| 0.091541 / 0.424275 (-0.332734) \| 0.007582 / 0.007607 (-0.000026) \| 0.652753 / 0.226044 (0.426709) \| 6.601375 / 2.268929 (4.332446) \| 3.076948 / 55.444624 (-52.367677) \| 2.250544 / 6.876477 (-4.625933) \| 2.404059 / 2.142072 (0.261987) \| 0.994917 / 4.805227 (-3.810311) \| 0.200318 / 6.500664 (-6.300346) \| 0.069354 / 0.075469 (-0.006115) \|\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) \| 1.482559 / 1.841788 (-0.359229) \| 20.722092 / 8.074308 (12.647784) \| 17.703217 / 10.191392 (7.511825) \| 0.215370 / 0.680424 (-0.465053) \| 0.028208 / 0.534201 (-0.505993) \| 0.425992 / 0.579283 (-0.153291) \| 0.492785 / 0.434364 (0.058421) \| 0.474154 / 0.540337 (-0.066183) \| 0.644599 / 1.386936 (-0.742337) \|\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) \| 0.008372 / 0.011353 (-0.002981) \| 0.004543 / 0.011008 (-0.006465) \| 0.070564 / 0.038508 (0.032056) \| 0.066855 / 0.023109 (0.043746) \| 0.386724 / 0.275898 (0.110826) \| 0.432184 / 0.323480 (0.108704) \| 0.005250 / 0.007986 (-0.002736) \| 0.003630 / 0.004328 (-0.000698) \| 0.069310 / 0.004250 (0.065060) \| 0.055759 / 0.037052 (0.018707) \| 0.375789 / 0.258489 (0.117299) \| 0.417335 / 0.293841 (0.123494) \| 0.043424 / 0.128546 (-0.085122) \| 0.013106 / 0.075646 (-0.062541) \| 0.087836 / 0.419271 (-0.331436) \| 0.057770 / 0.043533 (0.014237) \| 0.396694 / 0.255139 (0.141555) \| 0.439350 / 0.283200 (0.156150) \| 0.031660 / 0.141683 (-0.110023) \| 1.571339 / 1.452155 (0.119185) \| 1.667169 / 1.492716 (0.174452) \|\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) \| 0.180534 / 0.018006 (0.162528) \| 0.540027 / 0.000490 (0.539537) \| 0.003573 / 0.000200 (0.003373) \| 0.000141 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031380 / 0.037411 (-0.006032) \| 0.083762 / 0.014526 (0.069236) \| 0.098166 / 0.176557 (-0.078390) \| 0.160761 / 0.737135 (-0.576374) \| 0.097683 / 0.296338 (-0.198656) \|\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) \| 0.568074 / 0.215209 (0.352865) \| 5.660544 / 2.077655 (3.582889) \| 2.416698 / 1.504120 (0.912578) \| 2.177096 / 1.541195 (0.635901) \| 2.206178 / 1.468490 (0.737688) \| 0.844864 / 4.584777 (-3.739912) \| 4.793636 / 3.745712 (1.047923) \| 7.062387 / 5.269862 (1.792525) \| 4.201228 / 4.565676 (-0.364449) \| 0.091997 / 0.424275 (-0.332279) \| 0.007881 / 0.007607 (0.000274) \| 0.679466 / 0.226044 (0.453422) \| 6.580268 / 2.268929 (4.311340) \| 3.229907 / 55.444624 (-52.214717) \| 2.524877 / 6.876477 (-4.351600) \| 2.463796 / 2.142072 (0.321723) \| 0.975627 / 4.805227 (-3.829600) \| 0.186670 / 6.500664 (-6.313994) \| 0.065307 / 0.075469 (-0.010163) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.501447 / 1.841788 (-0.340340) \| 21.231037 / 8.074308 (13.156729) \| 17.591671 / 10.191392 (7.400279) \| 0.212745 / 0.680424 (-0.467679) \| 0.026100 / 0.534201 (-0.508101) \| 0.428391 / 0.579283 (-0.150892) \| 0.535268 / 0.434364 (0.100904) \| 0.506733 / 0.540337 (-0.033604) \| 0.660832 / 1.386936 (-0.726104) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#962537d7ee9191438ef47a4185d0ba626b2ee949 \"CML watermark\")\n" ]
1,799,720,536	6,020	Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs	open	2023-07-11T20:40:38	2024-10-27T06:30:13	null	https://github.com/huggingface/datasets/issues/6020	null	kheyer	false	[ "This scenario currently requires explicitly passing the target features (to avoid the error): \r\n```python\r\nimport datasets\r\n\r\n...\r\n\r\nfeatures = dataset.features\r\nfeatures[\"output\"] = = [{\"test\": datasets.Value(\"int64\")}]\r\ntest2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32, features=features)\r\n```", "I just encountered the same error in the same situation (multiprocessing with variable length outputs).\r\n\r\nThe funny (or dangerous?) thing is, that this error only showed up when testing with a small test dataset (16 examples, ValueError with `num_proc` >1) but the same code works fine for the full dataset (~70k examples).\r\n\r\n@mariosasko Any idea on how to do that with a nested feature with lists of variable lengths containing dicts?\r\n\r\nEDIT: Was able to narrow it down: >200 Examples: no error, <150 Examples: Error. \r\nNow idea what to make of this but pretty obvious that this is a bug....", "This error also occurs while concatenating the datasets.", "I'm running into the same error, is there any working workaround for this that doesnt involve using a larger subset or reducing the number of workers? I couldn't get the `features` set mentioned above to work..." ]
1,799,532,822	6,019	Improve logging	closed	2023-07-11T18:30:23	2023-07-12T19:34:14	2023-07-12T17:19:28	https://github.com/huggingface/datasets/pull/6019	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6019", "html_url": "https://github.com/huggingface/datasets/pull/6019", "diff_url": "https://github.com/huggingface/datasets/pull/6019.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6019.patch", "merged_at": "2023-07-12T17:19:28" }	mariosasko	true	[ "_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) \| 0.007782 / 0.011353 (-0.003571) \| 0.004451 / 0.011008 (-0.006557) \| 0.099928 / 0.038508 (0.061420) \| 0.081534 / 0.023109 (0.058425) \| 0.379382 / 0.275898 (0.103484) \| 0.410652 / 0.323480 (0.087172) \| 0.005967 / 0.007986 (-0.002019) \| 0.003702 / 0.004328 (-0.000627) \| 0.076359 / 0.004250 (0.072109) \| 0.066721 / 0.037052 (0.029669) \| 0.383595 / 0.258489 (0.125106) \| 0.423854 / 0.293841 (0.130013) \| 0.032796 / 0.128546 (-0.095750) \| 0.009728 / 0.075646 (-0.065918) \| 0.344347 / 0.419271 (-0.074925) \| 0.056320 / 0.043533 (0.012788) \| 0.379974 / 0.255139 (0.124835) \| 0.401294 / 0.283200 (0.118094) \| 0.024110 / 0.141683 (-0.117572) \| 1.804194 / 1.452155 (0.352039) \| 1.860240 / 1.492716 (0.367523) \|\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) \| 0.233803 / 0.018006 (0.215797) \| 0.506893 / 0.000490 (0.506404) \| 0.003894 / 0.000200 (0.003694) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033328 / 0.037411 (-0.004083) \| 0.098661 / 0.014526 (0.084136) \| 0.114971 / 0.176557 (-0.061586) \| 0.186815 / 0.737135 (-0.550321) \| 0.115490 / 0.296338 (-0.180848) \|\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) \| 0.422590 / 0.215209 (0.207381) \| 4.277189 / 2.077655 (2.199535) \| 2.095565 / 1.504120 (0.591445) \| 2.040825 / 1.541195 (0.499630) \| 2.162562 / 1.468490 (0.694072) \| 0.578602 / 4.584777 (-4.006175) \| 4.203474 / 3.745712 (0.457762) \| 6.674595 / 5.269862 (1.404734) \| 3.913251 / 4.565676 (-0.652426) \| 0.067777 / 0.424275 (-0.356498) \| 0.008716 / 0.007607 (0.001109) \| 0.548704 / 0.226044 (0.322660) \| 5.162120 / 2.268929 (2.893192) \| 2.600250 / 55.444624 (-52.844374) \| 2.232730 / 6.876477 (-4.643747) \| 2.485617 / 2.142072 (0.343544) \| 0.650872 / 4.805227 (-4.154355) \| 0.148022 / 6.500664 (-6.352642) \| 0.064795 / 0.075469 (-0.010674) \|\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) \| 1.399439 / 1.841788 (-0.442349) \| 22.438959 / 8.074308 (14.364651) \| 16.447831 / 10.191392 (6.256439) \| 0.202003 / 0.680424 (-0.478421) \| 0.026200 / 0.534201 (-0.508001) \| 0.472966 / 0.579283 (-0.106317) \| 0.491621 / 0.434364 (0.057257) \| 0.551580 / 0.540337 (0.011242) \| 0.751420 / 1.386936 (-0.635516) \|\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) \| 0.007241 / 0.011353 (-0.004112) \| 0.004434 / 0.011008 (-0.006574) \| 0.075872 / 0.038508 (0.037364) \| 0.080094 / 0.023109 (0.056985) \| 0.459244 / 0.275898 (0.183346) \| 0.492482 / 0.323480 (0.169002) \| 0.005791 / 0.007986 (-0.002194) \| 0.003657 / 0.004328 (-0.000671) \| 0.075214 / 0.004250 (0.070964) \| 0.064208 / 0.037052 (0.027156) \| 0.464195 / 0.258489 (0.205706) \| 0.497809 / 0.293841 (0.203968) \| 0.036301 / 0.128546 (-0.092245) \| 0.009855 / 0.075646 (-0.065791) \| 0.080826 / 0.419271 (-0.338445) \| 0.056700 / 0.043533 (0.013167) \| 0.452850 / 0.255139 (0.197711) \| 0.490738 / 0.283200 (0.207538) \| 0.024145 / 0.141683 (-0.117538) \| 1.689911 / 1.452155 (0.237757) \| 1.789803 / 1.492716 (0.297087) \|\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) \| 0.247741 / 0.018006 (0.229735) \| 0.486769 / 0.000490 (0.486279) \| 0.000418 / 0.000200 (0.000218) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036317 / 0.037411 (-0.001094) \| 0.104943 / 0.014526 (0.090417) \| 0.120972 / 0.176557 (-0.055585) \| 0.188461 / 0.737135 (-0.548674) \| 0.120926 / 0.296338 (-0.175412) \|\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) \| 0.465788 / 0.215209 (0.250579) \| 4.662369 / 2.077655 (2.584714) \| 2.442241 / 1.504120 (0.938121) \| 2.266328 / 1.541195 (0.725133) \| 2.438998 / 1.468490 (0.970508) \| 0.531384 / 4.584777 (-4.053393) \| 4.125286 / 3.745712 (0.379574) \| 3.920912 / 5.269862 (-1.348950) \| 2.292149 / 4.565676 (-2.273528) \| 0.070146 / 0.424275 (-0.354129) \| 0.008887 / 0.007607 (0.001280) \| 0.598181 / 0.226044 (0.372137) \| 5.726454 / 2.268929 (3.457526) \| 3.081836 / 55.444624 (-52.362788) \| 2.683508 / 6.876477 (-4.192969) \| 2.587350 / 2.142072 (0.445278) \| 0.604736 / 4.805227 (-4.200491) \| 0.141303 / 6.500664 (-6.359362) \| 0.065020 / 0.075469 (-0.010449) \|\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) \| 1.481850 / 1.841788 (-0.359938) \| 22.259592 / 8.074308 (14.185284) \| 16.304290 / 10.191392 (6.112898) \| 0.173514 / 0.680424 (-0.506909) \| 0.021590 / 0.534201 (-0.512611) \| 0.471753 / 0.579283 (-0.107531) \| 0.472132 / 0.434364 (0.037768) \| 0.563344 / 0.540337 (0.023007) \| 0.738509 / 1.386936 (-0.648427) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb7ae56dbd814945a4982c63bf0e50859a7b93a \"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) \| 0.005910 / 0.011353 (-0.005443) \| 0.004372 / 0.011008 (-0.006636) \| 0.081583 / 0.038508 (0.043075) \| 0.069598 / 0.023109 (0.046488) \| 0.346360 / 0.275898 (0.070462) \| 0.360733 / 0.323480 (0.037254) \| 0.004725 / 0.007986 (-0.003261) \| 0.003106 / 0.004328 (-0.001222) \| 0.059916 / 0.004250 (0.055666) \| 0.053242 / 0.037052 (0.016189) \| 0.353551 / 0.258489 (0.095062) \| 0.373052 / 0.293841 (0.079211) \| 0.029036 / 0.128546 (-0.099510) \| 0.007894 / 0.075646 (-0.067753) \| 0.284131 / 0.419271 (-0.135140) \| 0.049348 / 0.043533 (0.005815) \| 0.347409 / 0.255139 (0.092270) \| 0.355029 / 0.283200 (0.071830) \| 0.022511 / 0.141683 (-0.119171) \| 1.454495 / 1.452155 (0.002340) \| 1.439551 / 1.492716 (-0.053166) \|\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) \| 0.218889 / 0.018006 (0.200883) \| 0.478734 / 0.000490 (0.478244) \| 0.003758 / 0.000200 (0.003558) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025759 / 0.037411 (-0.011653) \| 0.082511 / 0.014526 (0.067985) \| 0.087578 / 0.176557 (-0.088979) \| 0.137760 / 0.737135 (-0.599375) \| 0.093312 / 0.296338 (-0.203027) \|\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) \| 0.378963 / 0.215209 (0.163754) \| 3.645846 / 2.077655 (1.568191) \| 1.741135 / 1.504120 (0.237015) \| 1.599166 / 1.541195 (0.057972) \| 1.610817 / 1.468490 (0.142327) \| 0.459209 / 4.584777 (-4.125568) \| 3.484857 / 3.745712 (-0.260855) \| 3.928109 / 5.269862 (-1.341752) \| 2.419784 / 4.565676 (-2.145892) \| 0.051987 / 0.424275 (-0.372288) \| 0.006495 / 0.007607 (-0.001112) \| 0.427311 / 0.226044 (0.201267) \| 4.226378 / 2.268929 (1.957450) \| 2.212331 / 55.444624 (-53.232293) \| 1.916213 / 6.876477 (-4.960264) \| 1.978809 / 2.142072 (-0.163263) \| 0.547351 / 4.805227 (-4.257876) \| 0.121110 / 6.500664 (-6.379554) \| 0.054163 / 0.075469 (-0.021306) \|\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) \| 1.228594 / 1.841788 (-0.613193) \| 19.410901 / 8.074308 (11.336593) \| 13.014722 / 10.191392 (2.823330) \| 0.156449 / 0.680424 (-0.523975) \| 0.021032 / 0.534201 (-0.513169) \| 0.403976 / 0.579283 (-0.175307) \| 0.413885 / 0.434364 (-0.020479) \| 0.470465 / 0.540337 (-0.069873) \| 0.641322 / 1.386936 (-0.745614) \|\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) \| 0.007210 / 0.011353 (-0.004143) \| 0.003824 / 0.011008 (-0.007185) \| 0.058227 / 0.038508 (0.019719) \| 0.076211 / 0.023109 (0.053102) \| 0.336626 / 0.275898 (0.060728) \| 0.420542 / 0.323480 (0.097062) \| 0.006178 / 0.007986 (-0.001808) \| 0.003332 / 0.004328 (-0.000997) \| 0.058073 / 0.004250 (0.053823) \| 0.062485 / 0.037052 (0.025432) \| 0.386175 / 0.258489 (0.127686) \| 0.415659 / 0.293841 (0.121818) \| 0.031264 / 0.128546 (-0.097282) \| 0.007502 / 0.075646 (-0.068144) \| 0.072079 / 0.419271 (-0.347192) \| 0.055860 / 0.043533 (0.012327) \| 0.343508 / 0.255139 (0.088369) \| 0.437844 / 0.283200 (0.154645) \| 0.032852 / 0.141683 (-0.108831) \| 1.409241 / 1.452155 (-0.042913) \| 1.623949 / 1.492716 (0.131233) \|\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) \| 0.207511 / 0.018006 (0.189504) \| 0.464149 / 0.000490 (0.463660) \| 0.003248 / 0.000200 (0.003048) \| 0.000226 / 0.000054 (0.000172) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030767 / 0.037411 (-0.006645) \| 0.079169 / 0.014526 (0.064643) \| 0.093111 / 0.176557 (-0.083445) \| 0.153369 / 0.737135 (-0.583767) \| 0.092939 / 0.296338 (-0.203400) \|\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) \| 0.375602 / 0.215209 (0.160392) \| 3.968612 / 2.077655 (1.890957) \| 2.081749 / 1.504120 (0.577629) \| 1.899772 / 1.541195 (0.358577) \| 1.847923 / 1.468490 (0.379433) \| 0.442867 / 4.584777 (-4.141910) \| 3.646664 / 3.745712 (-0.099048) \| 5.870600 / 5.269862 (0.600739) \| 3.356698 / 4.565676 (-1.208979) \| 0.051422 / 0.424275 (-0.372853) \| 0.006006 / 0.007607 (-0.001601) \| 0.442439 / 0.226044 (0.216395) \| 4.466256 / 2.268929 (2.197328) \| 2.483832 / 55.444624 (-52.960792) \| 2.105612 / 6.876477 (-4.770865) \| 2.060650 / 2.142072 (-0.081422) \| 0.531119 / 4.805227 (-4.274108) \| 0.123436 / 6.500664 (-6.377228) \| 0.059838 / 0.075469 (-0.015632) \|\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) \| 1.283042 / 1.841788 (-0.558746) \| 19.688251 / 8.074308 (11.613943) \| 13.346386 / 10.191392 (3.154994) \| 0.197463 / 0.680424 (-0.482961) \| 0.018484 / 0.534201 (-0.515717) \| 0.391727 / 0.579283 (-0.187556) \| 0.425061 / 0.434364 (-0.009303) \| 0.448177 / 0.540337 (-0.092160) \| 0.653694 / 1.386936 (-0.733242) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01604752fe89d290479fa406b1a24ac1f346826e \"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) \| 0.008966 / 0.011353 (-0.002387) \| 0.005195 / 0.011008 (-0.005813) \| 0.102879 / 0.038508 (0.064371) \| 0.090902 / 0.023109 (0.067792) \| 0.434397 / 0.275898 (0.158498) \| 0.454013 / 0.323480 (0.130534) \| 0.008507 / 0.007986 (0.000521) \| 0.005000 / 0.004328 (0.000671) \| 0.075789 / 0.004250 (0.071538) \| 0.067608 / 0.037052 (0.030555) \| 0.435091 / 0.258489 (0.176602) \| 0.469411 / 0.293841 (0.175570) \| 0.050859 / 0.128546 (-0.077687) \| 0.013560 / 0.075646 (-0.062086) \| 0.345473 / 0.419271 (-0.073799) \| 0.094974 / 0.043533 (0.051441) \| 0.429626 / 0.255139 (0.174487) \| 0.434290 / 0.283200 (0.151090) \| 0.052269 / 0.141683 (-0.089413) \| 1.700549 / 1.452155 (0.248395) \| 1.890693 / 1.492716 (0.397976) \|\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) \| 0.296618 / 0.018006 (0.278612) \| 0.613908 / 0.000490 (0.613419) \| 0.000484 / 0.000200 (0.000284) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034346 / 0.037411 (-0.003065) \| 0.096836 / 0.014526 (0.082310) \| 0.113332 / 0.176557 (-0.063224) \| 0.194464 / 0.737135 (-0.542671) \| 0.111732 / 0.296338 (-0.184606) \|\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) \| 0.624954 / 0.215209 (0.409745) \| 6.442193 / 2.077655 (4.364538) \| 2.818331 / 1.504120 (1.314211) \| 2.529607 / 1.541195 (0.988413) \| 2.549026 / 1.468490 (1.080536) \| 0.967367 / 4.584777 (-3.617410) \| 5.446885 / 3.745712 (1.701173) \| 6.259099 / 5.269862 (0.989237) \| 3.652936 / 4.565676 (-0.912740) \| 0.106420 / 0.424275 (-0.317855) \| 0.011293 / 0.007607 (0.003686) \| 0.772026 / 0.226044 (0.545982) \| 7.823986 / 2.268929 (5.555057) \| 3.725328 / 55.444624 (-51.719297) \| 2.851489 / 6.876477 (-4.024988) \| 3.013722 / 2.142072 (0.871649) \| 1.045090 / 4.805227 (-3.760137) \| 0.213174 / 6.500664 (-6.287490) \| 0.077104 / 0.075469 (0.001635) \|\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) \| 1.657135 / 1.841788 (-0.184652) \| 24.547604 / 8.074308 (16.473296) \| 19.989533 / 10.191392 (9.798141) \| 0.257139 / 0.680424 (-0.423285) \| 0.028448 / 0.534201 (-0.505753) \| 0.490801 / 0.579283 (-0.088482) \| 0.628072 / 0.434364 (0.193708) \| 0.584873 / 0.540337 (0.044536) \| 0.825258 / 1.386936 (-0.561678) \|\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) \| 0.009258 / 0.011353 (-0.002095) \| 0.005660 / 0.011008 (-0.005348) \| 0.080577 / 0.038508 (0.042069) \| 0.095786 / 0.023109 (0.072676) \| 0.473334 / 0.275898 (0.197436) \| 0.527962 / 0.323480 (0.204482) \| 0.006537 / 0.007986 (-0.001449) \| 0.004411 / 0.004328 (0.000083) \| 0.080702 / 0.004250 (0.076452) \| 0.077020 / 0.037052 (0.039968) \| 0.483205 / 0.258489 (0.224716) \| 0.556916 / 0.293841 (0.263076) \| 0.047670 / 0.128546 (-0.080877) \| 0.016647 / 0.075646 (-0.058999) \| 0.090653 / 0.419271 (-0.328619) \| 0.062122 / 0.043533 (0.018589) \| 0.498326 / 0.255139 (0.243187) \| 0.546572 / 0.283200 (0.263372) \| 0.037525 / 0.141683 (-0.104157) \| 1.869520 / 1.452155 (0.417365) \| 1.915335 / 1.492716 (0.422619) \|\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) \| 0.248287 / 0.018006 (0.230281) \| 0.611440 / 0.000490 (0.610950) \| 0.004102 / 0.000200 (0.003902) \| 0.000132 / 0.000054 (0.000078) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038228 / 0.037411 (0.000817) \| 0.103510 / 0.014526 (0.088984) \| 0.114337 / 0.176557 (-0.062219) \| 0.189662 / 0.737135 (-0.547473) \| 0.119078 / 0.296338 (-0.177260) \|\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) \| 0.606622 / 0.215209 (0.391413) \| 6.053900 / 2.077655 (3.976246) \| 2.857972 / 1.504120 (1.353852) \| 2.549756 / 1.541195 (1.008561) \| 2.584557 / 1.468490 (1.116067) \| 0.930431 / 4.584777 (-3.654346) \| 5.524077 / 3.745712 (1.778365) \| 7.858406 / 5.269862 (2.588545) \| 4.890697 / 4.565676 (0.325020) \| 0.095356 / 0.424275 (-0.328919) \| 0.008614 / 0.007607 (0.001007) \| 0.774227 / 0.226044 (0.548182) \| 7.470215 / 2.268929 (5.201287) \| 3.784820 / 55.444624 (-51.659805) \| 3.199364 / 6.876477 (-3.677113) \| 3.212002 / 2.142072 (1.069929) \| 1.054104 / 4.805227 (-3.751123) \| 0.226044 / 6.500664 (-6.274620) \| 0.092237 / 0.075469 (0.016768) \|\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) \| 1.801054 / 1.841788 (-0.040734) \| 24.220404 / 8.074308 (16.146096) \| 21.652936 / 10.191392 (11.461544) \| 0.247004 / 0.680424 (-0.433420) \| 0.029651 / 0.534201 (-0.504550) \| 0.475702 / 0.579283 (-0.103581) \| 0.621121 / 0.434364 (0.186757) \| 0.570489 / 0.540337 (0.030151) \| 0.768840 / 1.386936 (-0.618096) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2fc21eda345643fb57d1d1167ebed9043310911 \"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) \| 0.009223 / 0.011353 (-0.002130) \| 0.005750 / 0.011008 (-0.005258) \| 0.105264 / 0.038508 (0.066756) \| 0.088478 / 0.023109 (0.065369) \| 0.461119 / 0.275898 (0.185221) \| 0.481115 / 0.323480 (0.157636) \| 0.006366 / 0.007986 (-0.001619) \| 0.004515 / 0.004328 (0.000186) \| 0.079296 / 0.004250 (0.075045) \| 0.063483 / 0.037052 (0.026430) \| 0.444490 / 0.258489 (0.186001) \| 0.496474 / 0.293841 (0.202634) \| 0.048568 / 0.128546 (-0.079978) \| 0.013574 / 0.075646 (-0.062073) \| 0.379213 / 0.419271 (-0.040059) \| 0.086464 / 0.043533 (0.042932) \| 0.437526 / 0.255139 (0.182387) \| 0.447117 / 0.283200 (0.163917) \| 0.049502 / 0.141683 (-0.092180) \| 1.749146 / 1.452155 (0.296992) \| 1.831082 / 1.492716 (0.338365) \|\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) \| 0.268205 / 0.018006 (0.250199) \| 0.627406 / 0.000490 (0.626917) \| 0.005439 / 0.000200 (0.005239) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030564 / 0.037411 (-0.006848) \| 0.096365 / 0.014526 (0.081840) \| 0.117484 / 0.176557 (-0.059072) \| 0.189104 / 0.737135 (-0.548032) \| 0.118073 / 0.296338 (-0.178266) \|\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) \| 0.618229 / 0.215209 (0.403019) \| 6.437853 / 2.077655 (4.360199) \| 2.789946 / 1.504120 (1.285826) \| 2.339245 / 1.541195 (0.798050) \| 2.588779 / 1.468490 (1.120289) \| 0.921008 / 4.584777 (-3.663769) \| 5.402940 / 3.745712 (1.657227) \| 4.818783 / 5.269862 (-0.451078) \| 3.162259 / 4.565676 (-1.403417) \| 0.108501 / 0.424275 (-0.315774) \| 0.009384 / 0.007607 (0.001777) \| 0.766811 / 0.226044 (0.540766) \| 7.624629 / 2.268929 (5.355701) \| 3.442420 / 55.444624 (-52.002204) \| 2.759967 / 6.876477 (-4.116510) \| 3.049644 / 2.142072 (0.907572) \| 1.113308 / 4.805227 (-3.691919) \| 0.223923 / 6.500664 (-6.276741) \| 0.079156 / 0.075469 (0.003687) \|\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) \| 1.683318 / 1.841788 (-0.158470) \| 25.062141 / 8.074308 (16.987833) \| 21.777131 / 10.191392 (11.585739) \| 0.266939 / 0.680424 (-0.413485) \| 0.029670 / 0.534201 (-0.504531) \| 0.476761 / 0.579283 (-0.102522) \| 0.622080 / 0.434364 (0.187716) \| 0.601781 / 0.540337 (0.061443) \| 0.785126 / 1.386936 (-0.601811) \|\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) \| 0.010198 / 0.011353 (-0.001155) \| 0.005777 / 0.011008 (-0.005231) \| 0.083003 / 0.038508 (0.044495) \| 0.093411 / 0.023109 (0.070302) \| 0.496178 / 0.275898 (0.220280) \| 0.554670 / 0.323480 (0.231190) \| 0.008351 / 0.007986 (0.000365) \| 0.004678 / 0.004328 (0.000350) \| 0.083631 / 0.004250 (0.079381) \| 0.075538 / 0.037052 (0.038485) \| 0.492410 / 0.258489 (0.233921) \| 0.545209 / 0.293841 (0.251368) \| 0.048365 / 0.128546 (-0.080181) \| 0.014219 / 0.075646 (-0.061427) \| 0.100749 / 0.419271 (-0.318523) \| 0.063431 / 0.043533 (0.019898) \| 0.511115 / 0.255139 (0.255976) \| 0.532965 / 0.283200 (0.249765) \| 0.037968 / 0.141683 (-0.103715) \| 1.940268 / 1.452155 (0.488113) \| 2.032934 / 1.492716 (0.540217) \|\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) \| 0.238179 / 0.018006 (0.220172) \| 0.605767 / 0.000490 (0.605277) \| 0.004033 / 0.000200 (0.003833) \| 0.000125 / 0.000054 (0.000071) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036436 / 0.037411 (-0.000975) \| 0.108034 / 0.014526 (0.093509) \| 0.118624 / 0.176557 (-0.057933) \| 0.183079 / 0.737135 (-0.554056) \| 0.121739 / 0.296338 (-0.174600) \|\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) \| 0.630538 / 0.215209 (0.415329) \| 6.552184 / 2.077655 (4.474529) \| 3.003412 / 1.504120 (1.499292) \| 2.669026 / 1.541195 (1.127832) \| 2.791109 / 1.468490 (1.322619) \| 0.884003 / 4.584777 (-3.700774) \| 5.538660 / 3.745712 (1.792947) \| 5.126708 / 5.269862 (-0.143154) \| 3.120825 / 4.565676 (-1.444852) \| 0.101178 / 0.424275 (-0.323097) \| 0.009027 / 0.007607 (0.001420) \| 0.785914 / 0.226044 (0.559869) \| 7.994720 / 2.268929 (5.725792) \| 4.061996 / 55.444624 (-51.382629) \| 3.263230 / 6.876477 (-3.613247) \| 3.288622 / 2.142072 (1.146550) \| 1.141867 / 4.805227 (-3.663360) \| 0.255287 / 6.500664 (-6.245378) \| 0.100637 / 0.075469 (0.025168) \|\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) \| 1.769821 / 1.841788 (-0.071967) \| 24.994008 / 8.074308 (16.919700) \| 21.765971 / 10.191392 (11.574579) \| 0.268493 / 0.680424 (-0.411931) \| 0.028047 / 0.534201 (-0.506154) \| 0.489472 / 0.579283 (-0.089811) \| 0.594809 / 0.434364 (0.160445) \| 0.613578 / 0.540337 (0.073241) \| 0.879360 / 1.386936 (-0.507576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b85b1154aef2a9ab4d558f60d91623f2cc1583c4 \"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) \| 0.006003 / 0.011353 (-0.005350) \| 0.003590 / 0.011008 (-0.007418) \| 0.084657 / 0.038508 (0.046149) \| 0.057884 / 0.023109 (0.034775) \| 0.318347 / 0.275898 (0.042449) \| 0.345976 / 0.323480 (0.022496) \| 0.004706 / 0.007986 (-0.003279) \| 0.002921 / 0.004328 (-0.001407) \| 0.061850 / 0.004250 (0.057600) \| 0.050558 / 0.037052 (0.013505) \| 0.320877 / 0.258489 (0.062388) \| 0.356062 / 0.293841 (0.062222) \| 0.027511 / 0.128546 (-0.101035) \| 0.007954 / 0.075646 (-0.067693) \| 0.260290 / 0.419271 (-0.158981) \| 0.051207 / 0.043533 (0.007674) \| 0.334423 / 0.255139 (0.079284) \| 0.338575 / 0.283200 (0.055375) \| 0.022330 / 0.141683 (-0.119353) \| 1.445446 / 1.452155 (-0.006709) \| 1.500626 / 1.492716 (0.007910) \|\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) \| 0.192440 / 0.018006 (0.174433) \| 0.428455 / 0.000490 (0.427965) \| 0.000318 / 0.000200 (0.000118) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022933 / 0.037411 (-0.014478) \| 0.072795 / 0.014526 (0.058269) \| 0.081149 / 0.176557 (-0.095407) \| 0.142941 / 0.737135 (-0.594195) \| 0.082410 / 0.296338 (-0.213928) \|\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) \| 0.405220 / 0.215209 (0.190011) \| 4.048585 / 2.077655 (1.970931) \| 2.027908 / 1.504120 (0.523788) \| 1.887828 / 1.541195 (0.346633) \| 2.131780 / 1.468490 (0.663290) \| 0.502847 / 4.584777 (-4.081930) \| 3.069498 / 3.745712 (-0.676215) \| 4.094774 / 5.269862 (-1.175088) \| 2.544004 / 4.565676 (-2.021673) \| 0.059540 / 0.424275 (-0.364735) \| 0.006501 / 0.007607 (-0.001106) \| 0.477218 / 0.226044 (0.251173) \| 4.764961 / 2.268929 (2.496032) \| 2.434594 / 55.444624 (-53.010030) \| 2.104833 / 6.876477 (-4.771644) \| 2.263059 / 2.142072 (0.120987) \| 0.591755 / 4.805227 (-4.213472) \| 0.131167 / 6.500664 (-6.369497) \| 0.061808 / 0.075469 (-0.013661) \|\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) \| 1.345364 / 1.841788 (-0.496424) \| 18.122584 / 8.074308 (10.048276) \| 13.318689 / 10.191392 (3.127297) \| 0.144526 / 0.680424 (-0.535898) \| 0.016997 / 0.534201 (-0.517204) \| 0.336036 / 0.579283 (-0.243247) \| 0.359532 / 0.434364 (-0.074832) \| 0.386945 / 0.540337 (-0.153392) \| 0.538659 / 1.386936 (-0.848277) \|\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) \| 0.006088 / 0.011353 (-0.005265) \| 0.003684 / 0.011008 (-0.007324) \| 0.062340 / 0.038508 (0.023832) \| 0.058461 / 0.023109 (0.035352) \| 0.360134 / 0.275898 (0.084236) \| 0.393298 / 0.323480 (0.069818) \| 0.004664 / 0.007986 (-0.003322) \| 0.002909 / 0.004328 (-0.001420) \| 0.062668 / 0.004250 (0.058418) \| 0.050145 / 0.037052 (0.013092) \| 0.361897 / 0.258489 (0.103408) \| 0.402008 / 0.293841 (0.108167) \| 0.027491 / 0.128546 (-0.101055) \| 0.008113 / 0.075646 (-0.067534) \| 0.068114 / 0.419271 (-0.351157) \| 0.043303 / 0.043533 (-0.000230) \| 0.360569 / 0.255139 (0.105430) \| 0.387144 / 0.283200 (0.103944) \| 0.020194 / 0.141683 (-0.121489) \| 1.418066 / 1.452155 (-0.034089) \| 1.475640 / 1.492716 (-0.017076) \|\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) \| 0.200291 / 0.018006 (0.182285) \| 0.432298 / 0.000490 (0.431809) \| 0.003303 / 0.000200 (0.003103) \| 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) \| 0.027749 / 0.037411 (-0.009662) \| 0.081890 / 0.014526 (0.067364) \| 0.094319 / 0.176557 (-0.082238) \| 0.148646 / 0.737135 (-0.588490) \| 0.091830 / 0.296338 (-0.204509) \|\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) \| 0.433546 / 0.215209 (0.218337) \| 4.326855 / 2.077655 (2.249200) \| 2.230186 / 1.504120 (0.726066) \| 2.052524 / 1.541195 (0.511329) \| 2.117270 / 1.468490 (0.648779) \| 0.500331 / 4.584777 (-4.084446) \| 3.113662 / 3.745712 (-0.632050) \| 2.931540 / 5.269862 (-2.338322) \| 1.853615 / 4.565676 (-2.712062) \| 0.058250 / 0.424275 (-0.366025) \| 0.006546 / 0.007607 (-0.001061) \| 0.508850 / 0.226044 (0.282806) \| 5.081809 / 2.268929 (2.812880) \| 2.687037 / 55.444624 (-52.757588) \| 2.369317 / 6.876477 (-4.507160) \| 2.383549 / 2.142072 (0.241477) \| 0.587039 / 4.805227 (-4.218188) \| 0.125858 / 6.500664 (-6.374806) \| 0.062522 / 0.075469 (-0.012947) \|\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) \| 1.294929 / 1.841788 (-0.546858) \| 18.056312 / 8.074308 (9.982004) \| 13.755117 / 10.191392 (3.563725) \| 0.132037 / 0.680424 (-0.548387) \| 0.016866 / 0.534201 (-0.517335) \| 0.339040 / 0.579283 (-0.240243) \| 0.364371 / 0.434364 (-0.069993) \| 0.399533 / 0.540337 (-0.140804) \| 0.564524 / 1.386936 (-0.822412) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#64b811c13a7982015d7e078e3d693ce5359a05a2 \"CML watermark\")\n", "@lhoestq This bar comes from: https://github.com/huggingface/datasets/blob/b8067c0262073891180869f700ebef5ac3dc5cce/src/datasets/builder.py#L1156-L1166\r\n\r\nDo you prefer not showing it or, e.g., having `desc=\"Generating splits\"`?", "No strong opinion. Since there is a \"Generating\" progress bar already, maybe it can be \"Preparing splits\" (ref to download_and_prepare)", "<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) \| 0.006348 / 0.011353 (-0.005005) \| 0.003721 / 0.011008 (-0.007287) \| 0.084039 / 0.038508 (0.045531) \| 0.067627 / 0.023109 (0.044517) \| 0.308372 / 0.275898 (0.032474) \| 0.335131 / 0.323480 (0.011652) \| 0.005157 / 0.007986 (-0.002829) \| 0.003266 / 0.004328 (-0.001062) \| 0.065374 / 0.004250 (0.061124) \| 0.055550 / 0.037052 (0.018498) \| 0.314001 / 0.258489 (0.055512) \| 0.350510 / 0.293841 (0.056669) \| 0.030859 / 0.128546 (-0.097688) \| 0.008286 / 0.075646 (-0.067361) \| 0.287122 / 0.419271 (-0.132149) \| 0.051494 / 0.043533 (0.007961) \| 0.309868 / 0.255139 (0.054729) \| 0.325845 / 0.283200 (0.042645) \| 0.022622 / 0.141683 (-0.119061) \| 1.468730 / 1.452155 (0.016575) \| 1.547871 / 1.492716 (0.055155) \|\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) \| 0.202763 / 0.018006 (0.184757) \| 0.456403 / 0.000490 (0.455914) \| 0.003116 / 0.000200 (0.002916) \| 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) \| 0.027297 / 0.037411 (-0.010114) \| 0.081204 / 0.014526 (0.066678) \| 0.094274 / 0.176557 (-0.082282) \| 0.154391 / 0.737135 (-0.582744) \| 0.094312 / 0.296338 (-0.202026) \|\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) \| 0.387382 / 0.215209 (0.172173) \| 3.865597 / 2.077655 (1.787943) \| 1.855959 / 1.504120 (0.351839) \| 1.685411 / 1.541195 (0.144216) \| 1.732127 / 1.468490 (0.263637) \| 0.482230 / 4.584777 (-4.102547) \| 3.664947 / 3.745712 (-0.080765) \| 5.114379 / 5.269862 (-0.155482) \| 3.102803 / 4.565676 (-1.462873) \| 0.056509 / 0.424275 (-0.367766) \| 0.007230 / 0.007607 (-0.000377) \| 0.456788 / 0.226044 (0.230744) \| 4.575831 / 2.268929 (2.306902) \| 2.335249 / 55.444624 (-53.109375) \| 2.003805 / 6.876477 (-4.872672) \| 2.141788 / 2.142072 (-0.000285) \| 0.577501 / 4.805227 (-4.227726) \| 0.130264 / 6.500664 (-6.370400) \| 0.058889 / 0.075469 (-0.016580) \|\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) \| 1.252673 / 1.841788 (-0.589115) \| 18.676897 / 8.074308 (10.602589) \| 13.988101 / 10.191392 (3.796709) \| 0.151376 / 0.680424 (-0.529048) \| 0.018104 / 0.534201 (-0.516097) \| 0.388413 / 0.579283 (-0.190870) \| 0.414841 / 0.434364 (-0.019523) \| 0.456078 / 0.540337 (-0.084259) \| 0.641715 / 1.386936 (-0.745221) \|\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) \| 0.006315 / 0.011353 (-0.005038) \| 0.003847 / 0.011008 (-0.007162) \| 0.063989 / 0.038508 (0.025481) \| 0.068244 / 0.023109 (0.045135) \| 0.416201 / 0.275898 (0.140303) \| 0.438446 / 0.323480 (0.114966) \| 0.005820 / 0.007986 (-0.002166) \| 0.003165 / 0.004328 (-0.001163) \| 0.064143 / 0.004250 (0.059892) \| 0.056529 / 0.037052 (0.019477) \| 0.414916 / 0.258489 (0.156427) \| 0.450771 / 0.293841 (0.156930) \| 0.030611 / 0.128546 (-0.097935) \| 0.008289 / 0.075646 (-0.067357) \| 0.070725 / 0.419271 (-0.348546) \| 0.047998 / 0.043533 (0.004465) \| 0.405609 / 0.255139 (0.150470) \| 0.421895 / 0.283200 (0.138696) \| 0.022135 / 0.141683 (-0.119548) \| 1.444238 / 1.452155 (-0.007916) \| 1.515823 / 1.492716 (0.023107) \|\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) \| 0.227043 / 0.018006 (0.209037) \| 0.439732 / 0.000490 (0.439242) \| 0.001267 / 0.000200 (0.001067) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029082 / 0.037411 (-0.008329) \| 0.086201 / 0.014526 (0.071675) \| 0.098653 / 0.176557 (-0.077903) \| 0.152574 / 0.737135 (-0.584561) \| 0.100696 / 0.296338 (-0.195642) \|\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) \| 0.411243 / 0.215209 (0.196034) \| 4.100170 / 2.077655 (2.022515) \| 2.118310 / 1.504120 (0.614190) \| 1.935646 / 1.541195 (0.394451) \| 1.970798 / 1.468490 (0.502307) \| 0.478635 / 4.584777 (-4.106142) \| 3.589396 / 3.745712 (-0.156316) \| 3.312462 / 5.269862 (-1.957399) \| 1.963081 / 4.565676 (-2.602595) \| 0.056392 / 0.424275 (-0.367883) \| 0.007134 / 0.007607 (-0.000473) \| 0.485131 / 0.226044 (0.259086) \| 4.838946 / 2.268929 (2.570017) \| 2.624550 / 55.444624 (-52.820075) \| 2.223046 / 6.876477 (-4.653431) \| 2.230642 / 2.142072 (0.088570) \| 0.594892 / 4.805227 (-4.210335) \| 0.130523 / 6.500664 (-6.370141) \| 0.059585 / 0.075469 (-0.015884) \|\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) \| 1.329941 / 1.841788 (-0.511847) \| 19.199057 / 8.074308 (11.124748) \| 14.166009 / 10.191392 (3.974617) \| 0.190595 / 0.680424 (-0.489829) \| 0.018419 / 0.534201 (-0.515782) \| 0.392031 / 0.579283 (-0.187252) \| 0.409395 / 0.434364 (-0.024969) \| 0.475930 / 0.540337 (-0.064408) \| 0.654412 / 1.386936 (-0.732524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42fdfbd567674d075c3a9148ec3c95221eb62cfe \"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) \| 0.007500 / 0.011353 (-0.003853) \| 0.004328 / 0.011008 (-0.006681) \| 0.086718 / 0.038508 (0.048209) \| 0.098638 / 0.023109 (0.075529) \| 0.335308 / 0.275898 (0.059409) \| 0.369163 / 0.323480 (0.045683) \| 0.005733 / 0.007986 (-0.002253) \| 0.003738 / 0.004328 (-0.000590) \| 0.066452 / 0.004250 (0.062202) \| 0.066245 / 0.037052 (0.029192) \| 0.337609 / 0.258489 (0.079120) \| 0.388584 / 0.293841 (0.094744) \| 0.031742 / 0.128546 (-0.096804) \| 0.008721 / 0.075646 (-0.066925) \| 0.290820 / 0.419271 (-0.128452) \| 0.053323 / 0.043533 (0.009790) \| 0.329192 / 0.255139 (0.074053) \| 0.350560 / 0.283200 (0.067360) \| 0.025402 / 0.141683 (-0.116281) \| 1.476174 / 1.452155 (0.024020) \| 1.578194 / 1.492716 (0.085478) \|\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) \| 0.256160 / 0.018006 (0.238154) \| 0.560315 / 0.000490 (0.559825) \| 0.005287 / 0.000200 (0.005088) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029164 / 0.037411 (-0.008247) \| 0.084881 / 0.014526 (0.070356) \| 0.100979 / 0.176557 (-0.075577) \| 0.156539 / 0.737135 (-0.580597) \| 0.101510 / 0.296338 (-0.194828) \|\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) \| 0.381138 / 0.215209 (0.165929) \| 3.791573 / 2.077655 (1.713918) \| 1.841954 / 1.504120 (0.337834) \| 1.672463 / 1.541195 (0.131268) \| 1.785769 / 1.468490 (0.317279) \| 0.483263 / 4.584777 (-4.101514) \| 3.617391 / 3.745712 (-0.128322) \| 5.607794 / 5.269862 (0.337933) \| 3.359530 / 4.565676 (-1.206147) \| 0.056826 / 0.424275 (-0.367449) \| 0.007375 / 0.007607 (-0.000232) \| 0.455853 / 0.226044 (0.229809) \| 4.548965 / 2.268929 (2.280037) \| 2.412716 / 55.444624 (-53.031908) \| 1.991456 / 6.876477 (-4.885021) \| 2.242851 / 2.142072 (0.100778) \| 0.573070 / 4.805227 (-4.232157) \| 0.134658 / 6.500664 (-6.366006) \| 0.061539 / 0.075469 (-0.013930) \|\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) \| 1.278306 / 1.841788 (-0.563481) \| 20.634317 / 8.074308 (12.560009) \| 15.164246 / 10.191392 (4.972854) \| 0.167487 / 0.680424 (-0.512937) \| 0.019006 / 0.534201 (-0.515195) \| 0.394617 / 0.579283 (-0.184666) \| 0.423385 / 0.434364 (-0.010979) \| 0.469968 / 0.540337 (-0.070370) \| 0.630058 / 1.386936 (-0.756878) \|\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) \| 0.006793 / 0.011353 (-0.004559) \| 0.004260 / 0.011008 (-0.006748) \| 0.065398 / 0.038508 (0.026890) \| 0.077850 / 0.023109 (0.054741) \| 0.371754 / 0.275898 (0.095855) \| 0.400652 / 0.323480 (0.077172) \| 0.005729 / 0.007986 (-0.002256) \| 0.003660 / 0.004328 (-0.000669) \| 0.065119 / 0.004250 (0.060869) \| 0.060714 / 0.037052 (0.023661) \| 0.384592 / 0.258489 (0.126103) \| 0.412806 / 0.293841 (0.118965) \| 0.031865 / 0.128546 (-0.096681) \| 0.008807 / 0.075646 (-0.066839) \| 0.071156 / 0.419271 (-0.348115) \| 0.049571 / 0.043533 (0.006038) \| 0.367381 / 0.255139 (0.112242) \| 0.386713 / 0.283200 (0.103513) \| 0.024838 / 0.141683 (-0.116845) \| 1.492986 / 1.452155 (0.040831) \| 1.559243 / 1.492716 (0.066526) \|\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) \| 0.269737 / 0.018006 (0.251730) \| 0.565177 / 0.000490 (0.564687) \| 0.000404 / 0.000200 (0.000204) \| 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) \| 0.031631 / 0.037411 (-0.005780) \| 0.087289 / 0.014526 (0.072764) \| 0.102798 / 0.176557 (-0.073759) \| 0.158977 / 0.737135 (-0.578158) \| 0.105495 / 0.296338 (-0.190843) \|\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) \| 0.425067 / 0.215209 (0.209858) \| 4.243121 / 2.077655 (2.165466) \| 2.234567 / 1.504120 (0.730447) \| 2.070810 / 1.541195 (0.529615) \| 2.176802 / 1.468490 (0.708312) \| 0.484987 / 4.584777 (-4.099790) \| 3.647000 / 3.745712 (-0.098712) \| 3.574843 / 5.269862 (-1.695019) \| 2.092581 / 4.565676 (-2.473095) \| 0.057299 / 0.424275 (-0.366976) \| 0.007480 / 0.007607 (-0.000128) \| 0.507838 / 0.226044 (0.281794) \| 5.076594 / 2.268929 (2.807666) \| 2.718858 / 55.444624 (-52.725766) \| 2.362793 / 6.876477 (-4.513684) \| 2.451962 / 2.142072 (0.309890) \| 0.581355 / 4.805227 (-4.223872) \| 0.133723 / 6.500664 (-6.366941) \| 0.061896 / 0.075469 (-0.013573) \|\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) \| 1.325814 / 1.841788 (-0.515974) \| 20.614502 / 8.074308 (12.540194) \| 14.769422 / 10.191392 (4.578029) \| 0.193797 / 0.680424 (-0.486627) \| 0.018379 / 0.534201 (-0.515822) \| 0.394153 / 0.579283 (-0.185130) \| 0.409585 / 0.434364 (-0.024779) \| 0.479107 / 0.540337 (-0.061231) \| 0.668397 / 1.386936 (-0.718539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2d892237169bad5512c91cae453d257ebefc201 \"CML watermark\")\n", "In the end, I decided to remove the progress bar to avoid having it displayed when loading a cached dataset.", "<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) \| 0.006673 / 0.011353 (-0.004680) \| 0.004162 / 0.011008 (-0.006846) \| 0.084017 / 0.038508 (0.045509) \| 0.079536 / 0.023109 (0.056426) \| 0.313594 / 0.275898 (0.037695) \| 0.349200 / 0.323480 (0.025720) \| 0.005544 / 0.007986 (-0.002441) \| 0.003472 / 0.004328 (-0.000857) \| 0.064742 / 0.004250 (0.060491) \| 0.056857 / 0.037052 (0.019805) \| 0.318635 / 0.258489 (0.060146) \| 0.354378 / 0.293841 (0.060537) \| 0.030856 / 0.128546 (-0.097690) \| 0.008759 / 0.075646 (-0.066887) \| 0.287760 / 0.419271 (-0.131511) \| 0.052307 / 0.043533 (0.008775) \| 0.316396 / 0.255139 (0.061257) \| 0.351408 / 0.283200 (0.068208) \| 0.024914 / 0.141683 (-0.116769) \| 1.484592 / 1.452155 (0.032437) \| 1.560662 / 1.492716 (0.067945) \|\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) \| 0.280938 / 0.018006 (0.262932) \| 0.580236 / 0.000490 (0.579747) \| 0.003369 / 0.000200 (0.003169) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028736 / 0.037411 (-0.008675) \| 0.082916 / 0.014526 (0.068390) \| 0.097761 / 0.176557 (-0.078796) \| 0.153515 / 0.737135 (-0.583620) \| 0.099282 / 0.296338 (-0.197057) \|\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) \| 0.401244 / 0.215209 (0.186035) \| 4.019866 / 2.077655 (1.942211) \| 2.029642 / 1.504120 (0.525522) \| 1.849591 / 1.541195 (0.308396) \| 1.946829 / 1.468490 (0.478339) \| 0.479750 / 4.584777 (-4.105027) \| 3.482822 / 3.745712 (-0.262890) \| 3.955859 / 5.269862 (-1.314003) \| 2.370747 / 4.565676 (-2.194930) \| 0.056905 / 0.424275 (-0.367370) \| 0.007319 / 0.007607 (-0.000288) \| 0.485310 / 0.226044 (0.259266) \| 4.858228 / 2.268929 (2.589299) \| 2.500476 / 55.444624 (-52.944148) \| 2.171156 / 6.876477 (-4.705320) \| 2.427266 / 2.142072 (0.285194) \| 0.570199 / 4.805227 (-4.235029) \| 0.130855 / 6.500664 (-6.369809) \| 0.060269 / 0.075469 (-0.015200) \|\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) \| 1.258044 / 1.841788 (-0.583743) \| 20.218657 / 8.074308 (12.144349) \| 13.597970 / 10.191392 (3.406578) \| 0.167656 / 0.680424 (-0.512768) \| 0.018137 / 0.534201 (-0.516064) \| 0.395309 / 0.579283 (-0.183975) \| 0.406325 / 0.434364 (-0.028039) \| 0.467457 / 0.540337 (-0.072880) \| 0.613636 / 1.386936 (-0.773300) \|\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) \| 0.006846 / 0.011353 (-0.004507) \| 0.004207 / 0.011008 (-0.006802) \| 0.064525 / 0.038508 (0.026017) \| 0.081329 / 0.023109 (0.058220) \| 0.399838 / 0.275898 (0.123940) \| 0.431305 / 0.323480 (0.107825) \| 0.005859 / 0.007986 (-0.002127) \| 0.003568 / 0.004328 (-0.000760) \| 0.065262 / 0.004250 (0.061011) \| 0.064796 / 0.037052 (0.027744) \| 0.406858 / 0.258489 (0.148369) \| 0.440971 / 0.293841 (0.147130) \| 0.031421 / 0.128546 (-0.097125) \| 0.008777 / 0.075646 (-0.066870) \| 0.071418 / 0.419271 (-0.347853) \| 0.049263 / 0.043533 (0.005730) \| 0.384279 / 0.255139 (0.129140) \| 0.410745 / 0.283200 (0.127546) \| 0.024467 / 0.141683 (-0.117216) \| 1.522379 / 1.452155 (0.070224) \| 1.581636 / 1.492716 (0.088920) \|\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) \| 0.276161 / 0.018006 (0.258155) \| 0.548842 / 0.000490 (0.548352) \| 0.004523 / 0.000200 (0.004324) \| 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) \| 0.030747 / 0.037411 (-0.006664) \| 0.087493 / 0.014526 (0.072967) \| 0.106563 / 0.176557 (-0.069993) \| 0.162949 / 0.737135 (-0.574186) \| 0.105303 / 0.296338 (-0.191036) \|\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) \| 0.425854 / 0.215209 (0.210645) \| 4.244797 / 2.077655 (2.167142) \| 2.269006 / 1.504120 (0.764886) \| 2.097428 / 1.541195 (0.556234) \| 2.181038 / 1.468490 (0.712548) \| 0.477286 / 4.584777 (-4.107491) \| 3.591452 / 3.745712 (-0.154260) \| 3.481281 / 5.269862 (-1.788580) \| 2.066895 / 4.565676 (-2.498782) \| 0.056576 / 0.424275 (-0.367699) \| 0.007409 / 0.007607 (-0.000199) \| 0.498411 / 0.226044 (0.272367) \| 4.994873 / 2.268929 (2.725945) \| 2.749148 / 55.444624 (-52.695476) \| 2.378544 / 6.876477 (-4.497932) \| 2.452859 / 2.142072 (0.310786) \| 0.571340 / 4.805227 (-4.233887) \| 0.132174 / 6.500664 (-6.368490) \| 0.061507 / 0.075469 (-0.013962) \|\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) \| 1.370773 / 1.841788 (-0.471015) \| 20.493342 / 8.074308 (12.419034) \| 14.809886 / 10.191392 (4.618494) \| 0.175730 / 0.680424 (-0.504693) \| 0.018617 / 0.534201 (-0.515583) \| 0.393808 / 0.579283 (-0.185476) \| 0.416419 / 0.434364 (-0.017945) \| 0.477183 / 0.540337 (-0.063155) \| 0.668060 / 1.386936 (-0.718876) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2de7a2a4af5d94b0f98a7a6db94e78984af40602 \"CML watermark\")\n", "Nice one :)" ]
1,799,411,999	6,018	test1	closed	2023-07-11T17:25:49	2023-07-20T10:11:41	2023-07-20T10:11:41	https://github.com/huggingface/datasets/pull/6018	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6018", "html_url": "https://github.com/huggingface/datasets/pull/6018", "diff_url": "https://github.com/huggingface/datasets/pull/6018.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6018.patch", "merged_at": null }	ognjenovicj	true	[ "We no longer host datasets in this repo. You should use the HF Hub instead." ]
1,799,309,132	6,017	Switch to huggingface_hub's HfFileSystem	closed	2023-07-11T16:24:40	2023-07-17T17:01:01	2023-07-17T17:01:01	https://github.com/huggingface/datasets/issues/6017	null	lhoestq	false	[]
1,798,968,033	6,016	Dataset string representation enhancement	open	2023-07-11T13:38:25	2023-07-16T10:26:18	null	https://github.com/huggingface/datasets/pull/6016	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6016", "html_url": "https://github.com/huggingface/datasets/pull/6016", "diff_url": "https://github.com/huggingface/datasets/pull/6016.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6016.patch", "merged_at": null }	Ganryuu	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6016). All of your documentation changes will be reflected on that endpoint.", "It we could have something similar to Polars, that would be great.\r\n\r\nThis is what Polars outputs: \r\n* `__repr__`/`__str__` :\r\n```\r\nshape: (67_349, 3)\r\n┌───────┬───────────────────────────────────┬───────┐\r\n│ idx ┆ sentence ┆ label │\r\n│ --- ┆ --- ┆ --- │\r\n│ i32 ┆ str ┆ i64 │\r\n╞═══════╪═══════════════════════════════════╪═══════╡\r\n│ 0 ┆ hide new secretions from the par… ┆ 0 │\r\n│ 1 ┆ contains no wit , only labored g… ┆ 0 │\r\n│ 2 ┆ that loves its characters and co… ┆ 1 │\r\n│ 3 ┆ remains utterly satisfied to rem… ┆ 0 │\r\n│ … ┆ … ┆ … │\r\n│ 67345 ┆ anguish , anger and frustration ┆ 0 │\r\n│ 67346 ┆ at achieving the modest , crowd-… ┆ 1 │\r\n│ 67347 ┆ a patient viewer ┆ 1 │\r\n│ 67348 ┆ this new jangle of noise , mayhe… ┆ 0 │\r\n└───────┴───────────────────────────────────┴───────┘\r\n```\r\n\r\n* `_repr_html_`:\r\n<img width=\"251\" alt=\"Screenshot 2023-07-12 at 18 25 58\" src=\"https://github.com/huggingface/datasets/assets/47462742/5d04519d-f302-4411-9fbc-7445bdf53b23\">\r\n\r\n" ]
1,798,807,893	6,015	Add metadata ui screenshot in docs	closed	2023-07-11T12:16:29	2023-07-11T16:07:28	2023-07-11T15:56:46	https://github.com/huggingface/datasets/pull/6015	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6015", "html_url": "https://github.com/huggingface/datasets/pull/6015", "diff_url": "https://github.com/huggingface/datasets/pull/6015.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6015.patch", "merged_at": "2023-07-11T15:56:46" }	lhoestq	true	[ "_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) \| 0.007633 / 0.011353 (-0.003720) \| 0.004666 / 0.011008 (-0.006343) \| 0.097768 / 0.038508 (0.059260) \| 0.085153 / 0.023109 (0.062044) \| 0.400315 / 0.275898 (0.124417) \| 0.452903 / 0.323480 (0.129423) \| 0.006227 / 0.007986 (-0.001759) \| 0.003814 / 0.004328 (-0.000515) \| 0.074586 / 0.004250 (0.070336) \| 0.064295 / 0.037052 (0.027242) \| 0.408082 / 0.258489 (0.149593) \| 0.446921 / 0.293841 (0.153080) \| 0.034593 / 0.128546 (-0.093953) \| 0.009191 / 0.075646 (-0.066456) \| 0.337099 / 0.419271 (-0.082173) \| 0.075320 / 0.043533 (0.031787) \| 0.403488 / 0.255139 (0.148349) \| 0.435309 / 0.283200 (0.152109) \| 0.035675 / 0.141683 (-0.106008) \| 1.732642 / 1.452155 (0.280487) \| 1.770238 / 1.492716 (0.277522) \|\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) \| 0.235879 / 0.018006 (0.217873) \| 0.500330 / 0.000490 (0.499841) \| 0.005221 / 0.000200 (0.005021) \| 0.000150 / 0.000054 (0.000096) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032479 / 0.037411 (-0.004933) \| 0.095873 / 0.014526 (0.081348) \| 0.107118 / 0.176557 (-0.069438) \| 0.173809 / 0.737135 (-0.563326) \| 0.109832 / 0.296338 (-0.186507) \|\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) \| 0.444342 / 0.215209 (0.229133) \| 4.459010 / 2.077655 (2.381355) \| 2.209687 / 1.504120 (0.705567) \| 2.007556 / 1.541195 (0.466362) \| 2.113683 / 1.468490 (0.645193) \| 0.544281 / 4.584777 (-4.040496) \| 4.037151 / 3.745712 (0.291439) \| 4.852644 / 5.269862 (-0.417217) \| 3.134126 / 4.565676 (-1.431550) \| 0.066815 / 0.424275 (-0.357460) \| 0.008836 / 0.007607 (0.001229) \| 0.560904 / 0.226044 (0.334859) \| 5.302760 / 2.268929 (3.033832) \| 2.750182 / 55.444624 (-52.694442) \| 2.322595 / 6.876477 (-4.553882) \| 2.547486 / 2.142072 (0.405414) \| 0.665766 / 4.805227 (-4.139461) \| 0.151613 / 6.500664 (-6.349051) \| 0.071155 / 0.075469 (-0.004314) \|\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) \| 1.473717 / 1.841788 (-0.368071) \| 22.584179 / 8.074308 (14.509871) \| 15.888001 / 10.191392 (5.696609) \| 0.181073 / 0.680424 (-0.499351) \| 0.021395 / 0.534201 (-0.512806) \| 0.452693 / 0.579283 (-0.126590) \| 0.447709 / 0.434364 (0.013345) \| 0.529599 / 0.540337 (-0.010738) \| 0.699241 / 1.386936 (-0.687695) \|\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) \| 0.007917 / 0.011353 (-0.003436) \| 0.004544 / 0.011008 (-0.006464) \| 0.074566 / 0.038508 (0.036058) \| 0.087530 / 0.023109 (0.064421) \| 0.419753 / 0.275898 (0.143854) \| 0.452352 / 0.323480 (0.128872) \| 0.005882 / 0.007986 (-0.002104) \| 0.003904 / 0.004328 (-0.000425) \| 0.073539 / 0.004250 (0.069289) \| 0.071320 / 0.037052 (0.034267) \| 0.432899 / 0.258489 (0.174409) \| 0.470365 / 0.293841 (0.176524) \| 0.036198 / 0.128546 (-0.092348) \| 0.009342 / 0.075646 (-0.066304) \| 0.080970 / 0.419271 (-0.338301) \| 0.058769 / 0.043533 (0.015236) \| 0.413397 / 0.255139 (0.158258) \| 0.448362 / 0.283200 (0.165162) \| 0.034177 / 0.141683 (-0.107506) \| 1.706217 / 1.452155 (0.254063) \| 1.776743 / 1.492716 (0.284026) \|\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) \| 0.198779 / 0.018006 (0.180773) \| 0.499862 / 0.000490 (0.499372) \| 0.003891 / 0.000200 (0.003692) \| 0.000108 / 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) \| 0.034671 / 0.037411 (-0.002740) \| 0.103165 / 0.014526 (0.088639) \| 0.115813 / 0.176557 (-0.060744) \| 0.177407 / 0.737135 (-0.559728) \| 0.117733 / 0.296338 (-0.178606) \|\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) \| 0.476859 / 0.215209 (0.261650) \| 4.823063 / 2.077655 (2.745409) \| 2.524133 / 1.504120 (1.020013) \| 2.374482 / 1.541195 (0.833288) \| 2.518047 / 1.468490 (1.049557) \| 0.559131 / 4.584777 (-4.025646) \| 4.126213 / 3.745712 (0.380501) \| 6.488570 / 5.269862 (1.218708) \| 3.816540 / 4.565676 (-0.749137) \| 0.064742 / 0.424275 (-0.359533) \| 0.008476 / 0.007607 (0.000869) \| 0.576432 / 0.226044 (0.350387) \| 5.835133 / 2.268929 (3.566205) \| 3.237833 / 55.444624 (-52.206791) \| 2.726596 / 6.876477 (-4.149880) \| 2.799212 / 2.142072 (0.657139) \| 0.661628 / 4.805227 (-4.143599) \| 0.153997 / 6.500664 (-6.346667) \| 0.070621 / 0.075469 (-0.004848) \|\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) \| 1.648505 / 1.841788 (-0.193282) \| 22.454019 / 8.074308 (14.379711) \| 16.077098 / 10.191392 (5.885706) \| 0.217875 / 0.680424 (-0.462549) \| 0.021285 / 0.534201 (-0.512916) \| 0.459837 / 0.579283 (-0.119446) \| 0.476211 / 0.434364 (0.041847) \| 0.525903 / 0.540337 (-0.014435) \| 0.717224 / 1.386936 (-0.669712) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"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) \| 0.008929 / 0.011353 (-0.002424) \| 0.004188 / 0.011008 (-0.006820) \| 0.097030 / 0.038508 (0.058522) \| 0.071363 / 0.023109 (0.048254) \| 0.333116 / 0.275898 (0.057218) \| 0.371272 / 0.323480 (0.047792) \| 0.006430 / 0.007986 (-0.001555) \| 0.003689 / 0.004328 (-0.000639) \| 0.068666 / 0.004250 (0.064416) \| 0.057562 / 0.037052 (0.020510) \| 0.347208 / 0.258489 (0.088719) \| 0.390514 / 0.293841 (0.096673) \| 0.050560 / 0.128546 (-0.077987) \| 0.013372 / 0.075646 (-0.062275) \| 0.311345 / 0.419271 (-0.107927) \| 0.068990 / 0.043533 (0.025457) \| 0.363026 / 0.255139 (0.107887) \| 0.379793 / 0.283200 (0.096593) \| 0.036891 / 0.141683 (-0.104792) \| 1.583481 / 1.452155 (0.131327) \| 1.688727 / 1.492716 (0.196011) \|\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) \| 0.209777 / 0.018006 (0.191771) \| 0.507267 / 0.000490 (0.506777) \| 0.003637 / 0.000200 (0.003438) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029309 / 0.037411 (-0.008102) \| 0.088386 / 0.014526 (0.073861) \| 0.104974 / 0.176557 (-0.071582) \| 0.171999 / 0.737135 (-0.565137) \| 0.110797 / 0.296338 (-0.185542) \|\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) \| 0.543465 / 0.215209 (0.328256) \| 5.361491 / 2.077655 (3.283836) \| 2.348712 / 1.504120 (0.844592) \| 2.012527 / 1.541195 (0.471332) \| 2.069776 / 1.468490 (0.601286) \| 0.874262 / 4.584777 (-3.710515) \| 4.877317 / 3.745712 (1.131605) \| 5.327459 / 5.269862 (0.057597) \| 3.336823 / 4.565676 (-1.228854) \| 0.100456 / 0.424275 (-0.323819) \| 0.008503 / 0.007607 (0.000895) \| 0.692009 / 0.226044 (0.465965) \| 6.912731 / 2.268929 (4.643802) \| 3.110548 / 55.444624 (-52.334076) \| 2.443665 / 6.876477 (-4.432811) \| 2.528713 / 2.142072 (0.386641) \| 1.076358 / 4.805227 (-3.728869) \| 0.220352 / 6.500664 (-6.280312) \| 0.080293 / 0.075469 (0.004824) \|\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) \| 1.538444 / 1.841788 (-0.303344) \| 21.121221 / 8.074308 (13.046913) \| 19.810609 / 10.191392 (9.619216) \| 0.225406 / 0.680424 (-0.455018) \| 0.026652 / 0.534201 (-0.507549) \| 0.430372 / 0.579283 (-0.148911) \| 0.510722 / 0.434364 (0.076358) \| 0.514347 / 0.540337 (-0.025991) \| 0.686050 / 1.386936 (-0.700886) \|\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) \| 0.007675 / 0.011353 (-0.003678) \| 0.004542 / 0.011008 (-0.006466) \| 0.069655 / 0.038508 (0.031147) \| 0.069338 / 0.023109 (0.046229) \| 0.436505 / 0.275898 (0.160607) \| 0.481806 / 0.323480 (0.158326) \| 0.005315 / 0.007986 (-0.002670) \| 0.004455 / 0.004328 (0.000127) \| 0.072674 / 0.004250 (0.068424) \| 0.058088 / 0.037052 (0.021035) \| 0.445825 / 0.258489 (0.187336) \| 0.501706 / 0.293841 (0.207865) \| 0.047123 / 0.128546 (-0.081424) \| 0.012943 / 0.075646 (-0.062703) \| 0.093491 / 0.419271 (-0.325780) \| 0.060169 / 0.043533 (0.016637) \| 0.436530 / 0.255139 (0.181391) \| 0.466873 / 0.283200 (0.183674) \| 0.040453 / 0.141683 (-0.101230) \| 1.586438 / 1.452155 (0.134283) \| 1.671081 / 1.492716 (0.178365) \|\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) \| 0.180607 / 0.018006 (0.162601) \| 0.520145 / 0.000490 (0.519655) \| 0.004824 / 0.000200 (0.004624) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029308 / 0.037411 (-0.008103) \| 0.093652 / 0.014526 (0.079126) \| 0.102332 / 0.176557 (-0.074224) \| 0.162414 / 0.737135 (-0.574721) \| 0.098017 / 0.296338 (-0.198321) \|\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) \| 0.583949 / 0.215209 (0.368740) \| 6.035191 / 2.077655 (3.957536) \| 2.801274 / 1.504120 (1.297155) \| 2.566150 / 1.541195 (1.024955) \| 2.437122 / 1.468490 (0.968632) \| 0.865038 / 4.584777 (-3.719739) \| 4.841727 / 3.745712 (1.096015) \| 4.683919 / 5.269862 (-0.585943) \| 2.941240 / 4.565676 (-1.624437) \| 0.104888 / 0.424275 (-0.319387) \| 0.007747 / 0.007607 (0.000140) \| 0.780041 / 0.226044 (0.553997) \| 7.771314 / 2.268929 (5.502385) \| 3.680814 / 55.444624 (-51.763811) \| 2.938472 / 6.876477 (-3.938004) \| 2.981740 / 2.142072 (0.839668) \| 1.065411 / 4.805227 (-3.739816) \| 0.222265 / 6.500664 (-6.278399) \| 0.082428 / 0.075469 (0.006959) \|\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) \| 1.626774 / 1.841788 (-0.215014) \| 21.618284 / 8.074308 (13.543976) \| 20.596743 / 10.191392 (10.405351) \| 0.240969 / 0.680424 (-0.439454) \| 0.025630 / 0.534201 (-0.508570) \| 0.481981 / 0.579283 (-0.097302) \| 0.547914 / 0.434364 (0.113550) \| 0.522296 / 0.540337 (-0.018041) \| 0.729174 / 1.386936 (-0.657762) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n" ]
1,798,213,816	6,014	Request to Share/Update Dataset Viewer Code	closed	2023-07-11T06:36:09	2024-07-20T07:29:08	2023-09-25T12:01:17	https://github.com/huggingface/datasets/issues/6014	null	lilyorlilypad	false	[ "Hi ! The huggingface/dataset-viewer code was not maintained anymore because we switched to a new dataset viewer that is deployed available for each dataset the Hugging Face website.\r\n\r\nWhat are you using this old repository for ?", "I think these parts are outdated:\r\n\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L126-L131\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L145-L150\r\n\r\nTo make the viewer work, the first one should be replaced with the following:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nconfs = builder_cls.BUILDER_CONFIGS\r\n```\r\nAnd the second one:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nif conf:\r\n builder_instance = builder_cls(name=conf, cache_dir=path if path_to_datasets is not None else None)\r\nelse:\r\n builder_instance = builder_cls(cache_dir=path if path_to_datasets is not None else None)\r\n```\r\n\r\nBut as @lhoestq suggested, it's better to use the `datasets-server` API nowadays to [fetch the rows](https://huggingface.co/docs/datasets-server/rows).", "> The dataset viewer on the Hugging Face website is incredibly useful\r\n\r\n@mariosasko i think @lilyorlilypad wants to run the new dataset-viewer, not the old one", "> wants to run the new dataset-viewer, not the old one\r\n\r\nThanks for the clarification for me. I do want to run the new dataset-viewer. ", "It should be possible to run it locally using the HF datasets-server API (docs [here](https://huggingface.co/docs/datasets-server)) but the front end part is not open source (yet ?)\r\n\r\nThe back-end is open source though if you're interested: https://github.com/huggingface/datasets-server\r\nIt automatically converts datasets on HF to Parquet, which is the format we use to power the viewer.", "the new frontend would probably be hard to open source, as is, as it's quite intertwined with the Hub's code.\r\n\r\nHowever, at some point it would be amazing to have a community-driven open source implementation of a frontend to datasets-server! ", "For the frontend viewer, see https://github.com/huggingface/datasets/issues/6139.\r\n\r\nAlso mentioned in https://github.com/huggingface/datasets-server/issues/213 and https://github.com/huggingface/datasets-server/issues/441\r\n\r\nClosing as a duplicate of https://github.com/huggingface/datasets/issues/6139", "Hi team,\r\n\r\nI'm currently researching the Dataset Viewer project and would like to understand more about the frontend technologies used. Specifically, I'm interested in knowing:\r\n\r\nWhich frontend framework is being utilized (e.g., React, Vue, etc.)?\r\nAre there any specific libraries or components being used for UI (e.g., Material-UI, Ant Design)?\r\nAny other notable frontend tools or technologies that are part of this project?\r\nYour assistance in providing these details would be greatly appreciated. Thank you for your time and effort!\r\n\r\nBest regards", "@jacob-rodgers-max we use https://svelte.dev/", "> @jacob-rodgers-max we use https://svelte.dev/\r\n\r\nThank you very much for your prompt and detailed response!" ]
1,796,083,437	6,013	[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage	open	2023-07-10T06:42:20	2025-06-19T06:30:38	null	https://github.com/huggingface/datasets/issues/6013	null	NightMachinery	false	[ "You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_columns=ds.column_names)\r\nds_combined = concatenate_datasets([ds, ds_new], axis=1)\r\n```\r\n\r\nDoing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize.", "Hi @mariosasko 👋 I’d like to start working on this issue." ]
1,795,575,432	6,012	[FR] Transform Chaining, Lazy Mapping	open	2023-07-09T21:40:21	2025-01-20T14:06:28	null	https://github.com/huggingface/datasets/issues/6012	null	NightMachinery	false	[ "You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ", "> You can use `with_transform` to get a new dataset object.\r\n> \r\n> Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.\r\n\r\nI read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\n`with_transform` still does not chain AFAIU.", "> I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\nYou must cache an `IterableDataset` to disk to load it as a `Dataset`. One way to do this is with `Dataset.from_generator`:\r\n```python\r\nfrom functools import partial\r\nfrom datasets import Dataset\r\n\r\ndef gen_from_iterable_dataset(iterable_ds)\r\n yield from iterable_ds\r\n\r\nds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n```\r\n\r\n> with_transform still does not chain AFAIU.\r\n\r\nYes, not supported yet - the solution is to combine the transforms into a single one.", "I wonder if it would be beneficial to have a dedicated method to do that ? Maybe a `.save_to_disk()` so that the user can reload the resulting dataset later ?", "> ```python\r\n> from functools import partial\r\n> from datasets import Dataset\r\n> \r\n> def gen_from_iterable_dataset(iterable_ds)\r\n> yield from iterable_ds\r\n> \r\n> ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n> ```\r\n\r\n@mariosasko With these complex mapping functions, what hash will be used to cache this dataset?\r\n", "The params passed to `Dataset.from_generator` will be used to compute the hash (`partial` encapsulates the `iterable_ds` value, so changing it will also change the hash)", "Hi, I think this feature would be very useful. I want to concatenate large datasets with heterogeneous columns. I dislike `map` since I don't want multiple copy of that datasets locally. I tried to use \"set_transform\" on each dataset to convert it to a standard features format, but `datasets.concatenate_datasets` ignores the updated format of the datasets. A work around is to use `torch.utils.data.ConcatDataset`. Is there a neat way to do it using HF datasets?", "@mariosasko These features would be handy for large datasets. A typical use case is video datasets: We have millions of videos, each stored in some OSS so they require some custom loading logic.\n\n1) Due to the memory limit, loading the videos a priori into the memory is infeasible. But we can postpone video loading until they are needed with lazy mapping.\n2) With chained transforms, we can allow the users to specify their custom video preprocessing logic while keeping the loading logic the same.", "FYI lazy map is available for `IterableDataset`(map is applied on-the-fly when iterating on the dataset):\n\n```python\nds = load_dataset(...streaming=True)\n# or\nds = Dataset.from_list(...).to_iterable_dataset()\n# or\nds = IterableDataset.from_generator(...)\n\n# Then you can chain many map/filter/shuffle/etc.\nds = ds.map(...).filter(...).map(...)\n\n# The map functions are applied on-the-fly when iterating on the dataset\nfor example in ds:\n ..." ]
1,795,296,568	6,011	Documentation: wiki_dpr Dataset has no metric_type for Faiss Index	closed	2023-07-09T08:30:19	2023-07-11T03:02:36	2023-07-11T03:02:36	https://github.com/huggingface/datasets/issues/6011	null	YichiRockyZhang	false	[ "Hi! You can do `ds.get_index(\"embeddings\").faiss_index.metric_type` to get the metric type and then match the result with the FAISS metric [enum](https://github.com/facebookresearch/faiss/blob/43d86e30736ede853c384b24667fc3ab897d6ba9/faiss/MetricType.h#L22-L36) (should be L2).", "Ah! Thank you for pointing this out. FYI: the enum indicates it's using the inner product. Using `torch.inner` or `torch.dot` still produces a discrepancy compared to the built-in score. I think this is because of the compression/quantization that occurs with the FAISS index." ]
1,793,838,152	6,010	Improve `Dataset`'s string representation	open	2023-07-07T16:38:03	2023-09-01T03:45:07	null	https://github.com/huggingface/datasets/issues/6010	null	mariosasko	false	[ "I want to take a shot at this if possible ", "Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas.", "@mariosasko are there any other similar issues that I could work on? I see this has been already solved. " ]
1,792,059,808	6,009	Fix cast for dictionaries with no keys	closed	2023-07-06T18:48:14	2023-07-07T14:13:00	2023-07-07T14:01:13	https://github.com/huggingface/datasets/pull/6009	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6009", "html_url": "https://github.com/huggingface/datasets/pull/6009", "diff_url": "https://github.com/huggingface/datasets/pull/6009.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6009.patch", "merged_at": "2023-07-07T14:01:13" }	mariosasko	true	[ "_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) \| 0.006961 / 0.011353 (-0.004392) \| 0.004390 / 0.011008 (-0.006618) \| 0.103249 / 0.038508 (0.064741) \| 0.048084 / 0.023109 (0.024975) \| 0.351213 / 0.275898 (0.075315) \| 0.416918 / 0.323480 (0.093439) \| 0.005539 / 0.007986 (-0.002446) \| 0.003555 / 0.004328 (-0.000774) \| 0.079306 / 0.004250 (0.075055) \| 0.066937 / 0.037052 (0.029884) \| 0.382601 / 0.258489 (0.124112) \| 0.406125 / 0.293841 (0.112284) \| 0.032269 / 0.128546 (-0.096277) \| 0.009133 / 0.075646 (-0.066514) \| 0.354449 / 0.419271 (-0.064822) \| 0.068978 / 0.043533 (0.025445) \| 0.352314 / 0.255139 (0.097175) \| 0.390398 / 0.283200 (0.107199) \| 0.025640 / 0.141683 (-0.116043) \| 1.553865 / 1.452155 (0.101710) \| 1.601292 / 1.492716 (0.108576) \|\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) \| 0.208310 / 0.018006 (0.190303) \| 0.440076 / 0.000490 (0.439586) \| 0.000363 / 0.000200 (0.000163) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029173 / 0.037411 (-0.008238) \| 0.111323 / 0.014526 (0.096797) \| 0.123001 / 0.176557 (-0.053556) \| 0.180180 / 0.737135 (-0.556955) \| 0.125804 / 0.296338 (-0.170534) \|\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) \| 0.419919 / 0.215209 (0.204710) \| 4.194515 / 2.077655 (2.116860) \| 1.881234 / 1.504120 (0.377114) \| 1.672914 / 1.541195 (0.131720) \| 1.723102 / 1.468490 (0.254612) \| 0.543584 / 4.584777 (-4.041193) \| 3.822477 / 3.745712 (0.076765) \| 1.837946 / 5.269862 (-3.431915) \| 1.094975 / 4.565676 (-3.470701) \| 0.066788 / 0.424275 (-0.357487) \| 0.011689 / 0.007607 (0.004082) \| 0.520983 / 0.226044 (0.294938) \| 5.209245 / 2.268929 (2.940316) \| 2.392916 / 55.444624 (-53.051708) \| 2.060042 / 6.876477 (-4.816434) \| 2.162291 / 2.142072 (0.020219) \| 0.668472 / 4.805227 (-4.136755) \| 0.144373 / 6.500664 (-6.356291) \| 0.066152 / 0.075469 (-0.009318) \|\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) \| 1.251256 / 1.841788 (-0.590532) \| 15.161338 / 8.074308 (7.087030) \| 14.416133 / 10.191392 (4.224741) \| 0.166145 / 0.680424 (-0.514279) \| 0.018168 / 0.534201 (-0.516033) \| 0.433364 / 0.579283 (-0.145919) \| 0.417484 / 0.434364 (-0.016880) \| 0.502543 / 0.540337 (-0.037794) \| 0.602904 / 1.386936 (-0.784032) \|\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) \| 0.006946 / 0.011353 (-0.004407) \| 0.004248 / 0.011008 (-0.006761) \| 0.079707 / 0.038508 (0.041199) \| 0.046226 / 0.023109 (0.023117) \| 0.375864 / 0.275898 (0.099966) \| 0.430740 / 0.323480 (0.107260) \| 0.006222 / 0.007986 (-0.001764) \| 0.003474 / 0.004328 (-0.000854) \| 0.079622 / 0.004250 (0.075372) \| 0.066666 / 0.037052 (0.029613) \| 0.379487 / 0.258489 (0.120998) \| 0.423002 / 0.293841 (0.129161) \| 0.032836 / 0.128546 (-0.095710) \| 0.008976 / 0.075646 (-0.066670) \| 0.086578 / 0.419271 (-0.332693) \| 0.055651 / 0.043533 (0.012118) \| 0.360787 / 0.255139 (0.105648) \| 0.384265 / 0.283200 (0.101065) \| 0.025350 / 0.141683 (-0.116333) \| 1.547880 / 1.452155 (0.095725) \| 1.605850 / 1.492716 (0.113134) \|\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) \| 0.184227 / 0.018006 (0.166220) \| 0.442071 / 0.000490 (0.441582) \| 0.002887 / 0.000200 (0.002687) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031923 / 0.037411 (-0.005488) \| 0.119093 / 0.014526 (0.104568) \| 0.128704 / 0.176557 (-0.047853) \| 0.187065 / 0.737135 (-0.550070) \| 0.134135 / 0.296338 (-0.162204) \|\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) \| 0.455731 / 0.215209 (0.240522) \| 4.562911 / 2.077655 (2.485256) \| 2.247431 / 1.504120 (0.743311) \| 2.053346 / 1.541195 (0.512151) \| 2.049611 / 1.468490 (0.581121) \| 0.546069 / 4.584777 (-4.038708) \| 3.821852 / 3.745712 (0.076140) \| 3.358497 / 5.269862 (-1.911364) \| 1.667697 / 4.565676 (-2.897979) \| 0.067968 / 0.424275 (-0.356307) \| 0.012344 / 0.007607 (0.004737) \| 0.550864 / 0.226044 (0.324820) \| 5.496867 / 2.268929 (3.227939) \| 2.680031 / 55.444624 (-52.764594) \| 2.328673 / 6.876477 (-4.547804) \| 2.436754 / 2.142072 (0.294682) \| 0.681195 / 4.805227 (-4.124033) \| 0.148761 / 6.500664 (-6.351904) \| 0.067716 / 0.075469 (-0.007753) \|\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) \| 1.353798 / 1.841788 (-0.487990) \| 15.992965 / 8.074308 (7.918657) \| 14.051539 / 10.191392 (3.860147) \| 0.181087 / 0.680424 (-0.499337) \| 0.018653 / 0.534201 (-0.515548) \| 0.433499 / 0.579283 (-0.145784) \| 0.428845 / 0.434364 (-0.005519) \| 0.501100 / 0.540337 (-0.039238) \| 0.603666 / 1.386936 (-0.783270) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10cfa871a2f387fe9c6360e1873ea74c6d69ff67 \"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) \| 0.010983 / 0.011353 (-0.000370) \| 0.005630 / 0.011008 (-0.005378) \| 0.109967 / 0.038508 (0.071458) \| 0.101580 / 0.023109 (0.078471) \| 0.490205 / 0.275898 (0.214307) \| 0.534653 / 0.323480 (0.211173) \| 0.008365 / 0.007986 (0.000379) \| 0.004317 / 0.004328 (-0.000012) \| 0.082429 / 0.004250 (0.078179) \| 0.080556 / 0.037052 (0.043504) \| 0.494627 / 0.258489 (0.236138) \| 0.544189 / 0.293841 (0.250348) \| 0.049419 / 0.128546 (-0.079127) \| 0.014033 / 0.075646 (-0.061613) \| 0.370406 / 0.419271 (-0.048866) \| 0.083468 / 0.043533 (0.039935) \| 0.463829 / 0.255139 (0.208690) \| 0.507516 / 0.283200 (0.224316) \| 0.053266 / 0.141683 (-0.088417) \| 1.778680 / 1.452155 (0.326525) \| 1.916616 / 1.492716 (0.423900) \|\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) \| 0.267646 / 0.018006 (0.249640) \| 0.617824 / 0.000490 (0.617334) \| 0.007720 / 0.000200 (0.007520) \| 0.000139 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034464 / 0.037411 (-0.002948) \| 0.113626 / 0.014526 (0.099100) \| 0.118911 / 0.176557 (-0.057646) \| 0.194701 / 0.737135 (-0.542434) \| 0.123431 / 0.296338 (-0.172907) \|\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) \| 0.606073 / 0.215209 (0.390863) \| 6.086393 / 2.077655 (4.008738) \| 2.568712 / 1.504120 (1.064593) \| 2.260801 / 1.541195 (0.719606) \| 2.411798 / 1.468490 (0.943307) \| 0.876433 / 4.584777 (-3.708344) \| 5.521280 / 3.745712 (1.775568) \| 5.969722 / 5.269862 (0.699861) \| 3.671028 / 4.565676 (-0.894649) \| 0.097082 / 0.424275 (-0.327193) \| 0.011354 / 0.007607 (0.003747) \| 0.713842 / 0.226044 (0.487798) \| 7.291172 / 2.268929 (5.022244) \| 3.315272 / 55.444624 (-52.129352) \| 2.777487 / 6.876477 (-4.098990) \| 3.025449 / 2.142072 (0.883377) \| 1.014115 / 4.805227 (-3.791112) \| 0.217928 / 6.500664 (-6.282736) \| 0.083097 / 0.075469 (0.007627) \|\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) \| 1.640060 / 1.841788 (-0.201728) \| 25.342172 / 8.074308 (17.267864) \| 22.776510 / 10.191392 (12.585118) \| 0.227300 / 0.680424 (-0.453124) \| 0.032233 / 0.534201 (-0.501968) \| 0.507547 / 0.579283 (-0.071736) \| 0.647044 / 0.434364 (0.212680) \| 0.607019 / 0.540337 (0.066682) \| 0.823548 / 1.386936 (-0.563388) \|\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) \| 0.009576 / 0.011353 (-0.001777) \| 0.009322 / 0.011008 (-0.001687) \| 0.087184 / 0.038508 (0.048676) \| 0.100795 / 0.023109 (0.077685) \| 0.492138 / 0.275898 (0.216240) \| 0.528386 / 0.323480 (0.204906) \| 0.006689 / 0.007986 (-0.001296) \| 0.004735 / 0.004328 (0.000406) \| 0.085519 / 0.004250 (0.081269) \| 0.072648 / 0.037052 (0.035595) \| 0.496068 / 0.258489 (0.237579) \| 0.549634 / 0.293841 (0.255793) \| 0.049709 / 0.128546 (-0.078837) \| 0.015077 / 0.075646 (-0.060569) \| 0.099445 / 0.419271 (-0.319826) \| 0.068080 / 0.043533 (0.024547) \| 0.500426 / 0.255139 (0.245287) \| 0.531437 / 0.283200 (0.248238) \| 0.053176 / 0.141683 (-0.088507) \| 1.827942 / 1.452155 (0.375787) \| 1.914286 / 1.492716 (0.421570) \|\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) \| 0.247658 / 0.018006 (0.229652) \| 0.590805 / 0.000490 (0.590315) \| 0.005319 / 0.000200 (0.005119) \| 0.000165 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036993 / 0.037411 (-0.000418) \| 0.112944 / 0.014526 (0.098419) \| 0.118964 / 0.176557 (-0.057593) \| 0.194867 / 0.737135 (-0.542269) \| 0.120816 / 0.296338 (-0.175523) \|\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) \| 0.638062 / 0.215209 (0.422853) \| 6.246785 / 2.077655 (4.169130) \| 2.957779 / 1.504120 (1.453659) \| 2.739118 / 1.541195 (1.197924) \| 2.795362 / 1.468490 (1.326872) \| 0.890532 / 4.584777 (-3.694245) \| 5.508198 / 3.745712 (1.762486) \| 5.222315 / 5.269862 (-0.047547) \| 3.152731 / 4.565676 (-1.412946) \| 0.098344 / 0.424275 (-0.325931) \| 0.008800 / 0.007607 (0.001193) \| 0.757889 / 0.226044 (0.531845) \| 7.545715 / 2.268929 (5.276787) \| 3.694536 / 55.444624 (-51.750088) \| 3.112872 / 6.876477 (-3.763605) \| 3.182358 / 2.142072 (1.040285) \| 1.028171 / 4.805227 (-3.777056) \| 0.215223 / 6.500664 (-6.285441) \| 0.085856 / 0.075469 (0.010387) \|\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) \| 1.853138 / 1.841788 (0.011350) \| 25.939672 / 8.074308 (17.865364) \| 23.118029 / 10.191392 (12.926637) \| 0.250599 / 0.680424 (-0.429825) \| 0.029942 / 0.534201 (-0.504259) \| 0.508748 / 0.579283 (-0.070535) \| 0.593966 / 0.434364 (0.159602) \| 0.605499 / 0.540337 (0.065162) \| 0.863827 / 1.386936 (-0.523109) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d15950d99677e9473cdcd31cfd83aa17e313e28 \"CML watermark\")\n" ]
1,789,869,344	6,008	Dataset.from_generator consistently freezes at ~1000 rows	closed	2023-07-05T16:06:48	2023-07-10T13:46:39	2023-07-10T13:46:39	https://github.com/huggingface/datasets/issues/6008	null	andreemic	false	[ "By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n\r\nOur support for multi-dim arrays could be better, and we plan to improve it as part of https://github.com/huggingface/datasets/issues/5272.", "> By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n> \r\n> Our support for multi-dim arrays could be better, and we plan to improve it as part of #5272.\r\n\r\nThanks for the explanation! The Image array was just for demonstration, I use PIL Images in practice. Does that make a difference? What's the best approach for a dataset with PIL Images as rows?", "It's best to use the `datasets.Image()` feature type for PIL images (to save space) :)" ]
1,789,782,693	6,007	Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset	open	2023-07-05T15:16:50	2024-02-07T22:22:35	null	https://github.com/huggingface/datasets/issues/6007	null	silverriver	false	[ "This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier.", "I am afraid int32 is not the reason for this error.\r\n\r\nI have submitted a commit to use int64 for all ints in the dataset:\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/857ac00d9eab96a6708ad6a82bd9001686042a9e\r\n\r\nand I have updated my env to the latest datasets release:\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 2.13.1\r\n- Platform: macOS-13.2.1-arm64-arm-64bit\r\n- Python version: 3.11.2\r\n- Huggingface_hub version: 0.13.4\r\n- PyArrow version: 11.0.0\r\n- Pandas version: 1.5.3\r\n\r\nBut the error still exist\r\n\r\n```\r\nDownloading and preparing dataset mnbvc/news_peoples_daily to /Users/silver/.cache/huggingface/datasets/liwu___mnbvc/news_peoples_daily/0.0.1/ee380f6309fe9b8b0d1fb14d77118f132444f22c8c4b28bf5c1645312688e051...\r\nDownloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 12/12 [00:00<00:00, 9070.40it/s]\r\nExtracting data files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 12/12 [00:00<00:00, 2697.16it/s]\r\n---------------------------------------------------------------------------\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1647, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1646 example = self.info.features.encode_example(record) if self.info.features is not None else record\r\n-> 1647 writer.write(example, key)\r\n 1648 num_examples_progress_update += 1\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:490, in ArrowWriter.write(self, example, key, writer_batch_size)\r\n 488 self.hkey_record = []\r\n--> 490 self.write_examples_on_file()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1656, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1655 num_shards = shard_id + 1\r\n-> 1656 num_examples, num_bytes = writer.finalize()\r\n 1657 writer.close()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream)\r\n 583 self.hkey_record = []\r\n--> 584 self.write_examples_on_file()\r\n 585 # If schema is known, infer features even if no examples were written\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nDatasetGenerationError Traceback (most recent call last)\r\nCell In[2], line 1\r\n----> 1 dataset = load_dataset(\"liwu/MNBVC\", 'news_peoples_daily', split='train')\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/load.py:1809, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs)\r\n 1806 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1808 # Download and prepare data\r\n-> 1809 builder_instance.download_and_prepare(\r\n 1810 download_config=download_config,\r\n 1811 download_mode=download_mode,\r\n 1812 verification_mode=verification_mode,\r\n 1813 try_from_hf_gcs=try_from_hf_gcs,\r\n 1814 num_proc=num_proc,\r\n 1815 storage_options=storage_options,\r\n 1816 )\r\n 1818 # Build dataset for splits\r\n 1819 keep_in_memory = (\r\n 1820 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1821 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:909, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs)\r\n 907 if num_proc is not None:\r\n 908 prepare_split_kwargs[\"num_proc\"] = num_proc\r\n--> 909 self._download_and_prepare(\r\n 910 dl_manager=dl_manager,\r\n 911 verification_mode=verification_mode,\r\n 912 prepare_split_kwargs,\r\n 913 download_and_prepare_kwargs,\r\n 914 )\r\n 915 # Sync info\r\n 916 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1670, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_splits_kwargs)\r\n 1669 def _download_and_prepare(self, dl_manager, verification_mode, prepare_splits_kwargs):\r\n-> 1670 super()._download_and_prepare(\r\n 1671 dl_manager,\r\n 1672 verification_mode,\r\n 1673 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS\r\n 1674 or verification_mode == VerificationMode.ALL_CHECKS,\r\n 1675 prepare_splits_kwargs,\r\n 1676 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1004, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs)\r\n 1000 split_dict.add(split_generator.split_info)\r\n 1002 try:\r\n 1003 # Prepare split will record examples associated to the split\r\n-> 1004 self._prepare_split(split_generator, prepare_split_kwargs)\r\n 1005 except OSError as e:\r\n 1006 raise OSError(\r\n 1007 \"Cannot find data file. \"\r\n 1008 + (self.manual_download_instructions or \"\")\r\n 1009 + \"\\nOriginal error:\\n\"\r\n 1010 + str(e)\r\n 1011 ) from None\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1508, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\r\n 1506 job_id = 0\r\n 1507 with pbar:\r\n-> 1508 for job_id, done, content in self._prepare_split_single(\r\n 1509 gen_kwargs=gen_kwargs, job_id=job_id, _prepare_split_args\r\n 1510 ):\r\n 1511 if done:\r\n 1512 result = content\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1665, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1663 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:\r\n 1664 e = e.__context__\r\n-> 1665 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n 1667 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nBesides, it works fine when I am using streamed dataset.", "`simhash` is the problematic column - it has values such as `18329103420363166823` that are out of the int64 range. You can fix this by setting the feature type to `Value(\"string\")` (it's advised to use this type for hash values in general)\r\n\r\n> Besides, it works fine when I am using streamed dataset.\r\n\r\nStreaming yields Python dictionaries from the script without converting them to the Arrow representation, as this conversion step is not that cheap performance-wise.", "i am using uint64 for simhash\r\n\r\nuint64 ranges up to about 3.69E19.\r\n\r\n18329103420363166823 is less than this value.\r\n\r\nmoreover, our simhash algorithm use 64 bits. it should fit in uint64.\r\n\r\n\r\n\r\n", "You are right. I overlooked the feature type.\r\n\r\nThis is a reproducer:\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.arrow_writer import TypedSequence\r\n\r\npa.array(TypedSequence([18329103420363166823], type=Value(\"uint64\")))\r\n```\r\n\r\n`pa.array([18329103420363166823])` also fails with the same error, so it seems PyArrow does not always infer the correct type as NumPy does (`uint64` in this case).\r\n\r\nI'll report this issue in the Arrow repo.\r\n\r\n`pa.array([18329103420363166823], pa.uint64)` works, so maybe we can implement a temporary fix (supporting complex input such as `[{\"image\": pil_image, \"num\": uint64_value}]` would be hard though).\r\n\r\nIn the meantime, you should be able to bypass this error by returning the `simhash` values as NumPy scalars in the script:\r\n```python\r\ndef _generate_examples(self, ...):\r\n ...\r\n yield {..., \"simhash\": np.uint64(simhash), ...}\r\n```", "Thank you for checking this issue in detail.\r\n\r\nHowever, it seems that using `np.uint64(simhash)` does not work. The same issue still exists.\r\n\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/1e44f1e400b7e61052647d44c99cdae3bae9c830\r\n\r\nAnyway, we decide to use string type for these simhash values. Hope pyarrow can fix their bug soon.", "Arrow issue: https://github.com/apache/arrow/issues/36520", "May be something read your training data line by line.\r\nThen your training data just only one line. \r\nIt is so large.\r\nI guess.\r\n" ]
1,788,855,582	6,006	NotADirectoryError when loading gigawords	closed	2023-07-05T06:23:41	2023-07-05T06:31:02	2023-07-05T06:31:01	https://github.com/huggingface/datasets/issues/6006	null	xipq	false	[ "issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence." ]
1,788,103,576	6,005	Drop Python 3.7 support	closed	2023-07-04T15:02:37	2023-07-06T15:32:41	2023-07-06T15:22:43	https://github.com/huggingface/datasets/pull/6005	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6005", "html_url": "https://github.com/huggingface/datasets/pull/6005", "diff_url": "https://github.com/huggingface/datasets/pull/6005.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6005.patch", "merged_at": "2023-07-06T15:22:43" }	mariosasko	true	[ "<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) \| 0.006152 / 0.011353 (-0.005200) \| 0.003916 / 0.011008 (-0.007092) \| 0.097355 / 0.038508 (0.058847) \| 0.037228 / 0.023109 (0.014119) \| 0.315753 / 0.275898 (0.039855) \| 0.387949 / 0.323480 (0.064470) \| 0.004804 / 0.007986 (-0.003181) \| 0.002975 / 0.004328 (-0.001353) \| 0.076932 / 0.004250 (0.072682) \| 0.053497 / 0.037052 (0.016445) \| 0.331143 / 0.258489 (0.072654) \| 0.388347 / 0.293841 (0.094506) \| 0.027535 / 0.128546 (-0.101011) \| 0.008509 / 0.075646 (-0.067137) \| 0.312639 / 0.419271 (-0.106632) \| 0.047212 / 0.043533 (0.003679) \| 0.316875 / 0.255139 (0.061736) \| 0.352191 / 0.283200 (0.068992) \| 0.021380 / 0.141683 (-0.120303) \| 1.541401 / 1.452155 (0.089247) \| 1.519420 / 1.492716 (0.026704) \|\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) \| 0.206332 / 0.018006 (0.188326) \| 0.412252 / 0.000490 (0.411762) \| 0.005119 / 0.000200 (0.004919) \| 0.000077 / 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) \| 0.023856 / 0.037411 (-0.013556) \| 0.098216 / 0.014526 (0.083691) \| 0.106553 / 0.176557 (-0.070003) \| 0.168767 / 0.737135 (-0.568369) \| 0.109244 / 0.296338 (-0.187094) \|\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) \| 0.457580 / 0.215209 (0.242371) \| 4.583246 / 2.077655 (2.505591) \| 2.296356 / 1.504120 (0.792236) \| 2.096216 / 1.541195 (0.555021) \| 2.159086 / 1.468490 (0.690596) \| 0.557905 / 4.584777 (-4.026872) \| 3.345910 / 3.745712 (-0.399802) \| 1.767436 / 5.269862 (-3.502426) \| 1.021583 / 4.565676 (-3.544094) \| 0.067265 / 0.424275 (-0.357011) \| 0.011411 / 0.007607 (0.003804) \| 0.559841 / 0.226044 (0.333797) \| 5.586892 / 2.268929 (3.317963) \| 2.735520 / 55.444624 (-52.709104) \| 2.429393 / 6.876477 (-4.447084) \| 2.544901 / 2.142072 (0.402829) \| 0.667603 / 4.805227 (-4.137625) \| 0.136244 / 6.500664 (-6.364421) \| 0.066961 / 0.075469 (-0.008508) \|\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) \| 1.206529 / 1.841788 (-0.635259) \| 13.988306 / 8.074308 (5.913998) \| 13.481813 / 10.191392 (3.290421) \| 0.161901 / 0.680424 (-0.518523) \| 0.016850 / 0.534201 (-0.517351) \| 0.367657 / 0.579283 (-0.211626) \| 0.393343 / 0.434364 (-0.041021) \| 0.465288 / 0.540337 (-0.075050) \| 0.559888 / 1.386936 (-0.827048) \|\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) \| 0.005956 / 0.011353 (-0.005397) \| 0.003734 / 0.011008 (-0.007274) \| 0.077841 / 0.038508 (0.039333) \| 0.036532 / 0.023109 (0.013422) \| 0.438923 / 0.275898 (0.163025) \| 0.490133 / 0.323480 (0.166653) \| 0.004651 / 0.007986 (-0.003335) \| 0.002881 / 0.004328 (-0.001448) \| 0.077868 / 0.004250 (0.073618) \| 0.051700 / 0.037052 (0.014647) \| 0.448018 / 0.258489 (0.189529) \| 0.500304 / 0.293841 (0.206464) \| 0.029051 / 0.128546 (-0.099496) \| 0.008498 / 0.075646 (-0.067148) \| 0.082932 / 0.419271 (-0.336339) \| 0.043665 / 0.043533 (0.000132) \| 0.431613 / 0.255139 (0.176474) \| 0.458749 / 0.283200 (0.175549) \| 0.021951 / 0.141683 (-0.119731) \| 1.556043 / 1.452155 (0.103888) \| 1.588391 / 1.492716 (0.095675) \|\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) \| 0.220674 / 0.018006 (0.202667) \| 0.415408 / 0.000490 (0.414918) \| 0.002613 / 0.000200 (0.002413) \| 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) \| 0.025548 / 0.037411 (-0.011863) \| 0.103633 / 0.014526 (0.089107) \| 0.115193 / 0.176557 (-0.061364) \| 0.163971 / 0.737135 (-0.573164) \| 0.114754 / 0.296338 (-0.181585) \|\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) \| 0.456823 / 0.215209 (0.241614) \| 4.569950 / 2.077655 (2.492296) \| 2.196339 / 1.504120 (0.692219) \| 1.985822 / 1.541195 (0.444628) \| 2.044083 / 1.468490 (0.575593) \| 0.567919 / 4.584777 (-4.016858) \| 3.397515 / 3.745712 (-0.348197) \| 1.741087 / 5.269862 (-3.528775) \| 1.041237 / 4.565676 (-3.524440) \| 0.068963 / 0.424275 (-0.355313) \| 0.011677 / 0.007607 (0.004070) \| 0.565010 / 0.226044 (0.338966) \| 5.625886 / 2.268929 (3.356957) \| 2.670658 / 55.444624 (-52.773967) \| 2.300279 / 6.876477 (-4.576198) \| 2.392178 / 2.142072 (0.250106) \| 0.680226 / 4.805227 (-4.125001) \| 0.139119 / 6.500664 (-6.361545) \| 0.067953 / 0.075469 (-0.007516) \|\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) \| 1.303280 / 1.841788 (-0.538507) \| 14.458686 / 8.074308 (6.384378) \| 14.409369 / 10.191392 (4.217977) \| 0.144581 / 0.680424 (-0.535843) \| 0.016634 / 0.534201 (-0.517567) \| 0.364607 / 0.579283 (-0.214676) \| 0.394521 / 0.434364 (-0.039843) \| 0.433417 / 0.540337 (-0.106921) \| 0.527127 / 1.386936 (-0.859809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006245 / 0.011353 (-0.005108) \| 0.003871 / 0.011008 (-0.007138) \| 0.098823 / 0.038508 (0.060315) \| 0.039853 / 0.023109 (0.016744) \| 0.314989 / 0.275898 (0.039091) \| 0.376733 / 0.323480 (0.053254) \| 0.004754 / 0.007986 (-0.003232) \| 0.002971 / 0.004328 (-0.001357) \| 0.078451 / 0.004250 (0.074201) \| 0.053160 / 0.037052 (0.016107) \| 0.324443 / 0.258489 (0.065954) \| 0.361488 / 0.293841 (0.067647) \| 0.027942 / 0.128546 (-0.100604) \| 0.008535 / 0.075646 (-0.067111) \| 0.315526 / 0.419271 (-0.103745) \| 0.045706 / 0.043533 (0.002174) \| 0.329614 / 0.255139 (0.074475) \| 0.336339 / 0.283200 (0.053139) \| 0.021278 / 0.141683 (-0.120405) \| 1.529710 / 1.452155 (0.077555) \| 1.566833 / 1.492716 (0.074116) \|\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) \| 0.215263 / 0.018006 (0.197257) \| 0.440320 / 0.000490 (0.439830) \| 0.002627 / 0.000200 (0.002427) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023971 / 0.037411 (-0.013441) \| 0.100549 / 0.014526 (0.086023) \| 0.106995 / 0.176557 (-0.069561) \| 0.169630 / 0.737135 (-0.567505) \| 0.111614 / 0.296338 (-0.184724) \|\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) \| 0.424911 / 0.215209 (0.209702) \| 4.246920 / 2.077655 (2.169266) \| 1.923321 / 1.504120 (0.419202) \| 1.714795 / 1.541195 (0.173600) \| 1.772906 / 1.468490 (0.304416) \| 0.554676 / 4.584777 (-4.030101) \| 3.478896 / 3.745712 (-0.266816) \| 2.800494 / 5.269862 (-2.469368) \| 1.382630 / 4.565676 (-3.183047) \| 0.067271 / 0.424275 (-0.357004) \| 0.010967 / 0.007607 (0.003360) \| 0.526769 / 0.226044 (0.300725) \| 5.288564 / 2.268929 (3.019636) \| 2.337459 / 55.444624 (-53.107165) \| 1.999975 / 6.876477 (-4.876502) \| 2.102680 / 2.142072 (-0.039392) \| 0.672181 / 4.805227 (-4.133046) \| 0.135097 / 6.500664 (-6.365567) \| 0.066950 / 0.075469 (-0.008519) \|\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) \| 1.264365 / 1.841788 (-0.577423) \| 14.282440 / 8.074308 (6.208132) \| 14.220200 / 10.191392 (4.028808) \| 0.139055 / 0.680424 (-0.541369) \| 0.016681 / 0.534201 (-0.517520) \| 0.367936 / 0.579283 (-0.211348) \| 0.393959 / 0.434364 (-0.040404) \| 0.424438 / 0.540337 (-0.115900) \| 0.508065 / 1.386936 (-0.878872) \|\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) \| 0.006514 / 0.011353 (-0.004839) \| 0.003890 / 0.011008 (-0.007118) \| 0.078871 / 0.038508 (0.040363) \| 0.038080 / 0.023109 (0.014971) \| 0.358282 / 0.275898 (0.082384) \| 0.430654 / 0.323480 (0.107174) \| 0.005712 / 0.007986 (-0.002273) \| 0.003030 / 0.004328 (-0.001299) \| 0.078636 / 0.004250 (0.074386) \| 0.057771 / 0.037052 (0.020719) \| 0.368814 / 0.258489 (0.110325) \| 0.437047 / 0.293841 (0.143206) \| 0.029470 / 0.128546 (-0.099076) \| 0.008523 / 0.075646 (-0.067124) \| 0.083334 / 0.419271 (-0.335938) \| 0.044505 / 0.043533 (0.000972) \| 0.357484 / 0.255139 (0.102345) \| 0.393839 / 0.283200 (0.110639) \| 0.023340 / 0.141683 (-0.118343) \| 1.561033 / 1.452155 (0.108878) \| 1.595560 / 1.492716 (0.102844) \|\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) \| 0.204149 / 0.018006 (0.186143) \| 0.442747 / 0.000490 (0.442257) \| 0.003105 / 0.000200 (0.002905) \| 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) \| 0.027002 / 0.037411 (-0.010409) \| 0.105595 / 0.014526 (0.091070) \| 0.108695 / 0.176557 (-0.067861) \| 0.163182 / 0.737135 (-0.573953) \| 0.114999 / 0.296338 (-0.181339) \|\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) \| 0.483713 / 0.215209 (0.268504) \| 4.836063 / 2.077655 (2.758409) \| 2.488072 / 1.504120 (0.983952) \| 2.289556 / 1.541195 (0.748361) \| 2.342912 / 1.468490 (0.874422) \| 0.565937 / 4.584777 (-4.018840) \| 3.479085 / 3.745712 (-0.266627) \| 1.770922 / 5.269862 (-3.498940) \| 1.046084 / 4.565676 (-3.519592) \| 0.067857 / 0.424275 (-0.356418) \| 0.011283 / 0.007607 (0.003676) \| 0.592966 / 0.226044 (0.366921) \| 5.932842 / 2.268929 (3.663914) \| 2.956252 / 55.444624 (-52.488372) \| 2.602704 / 6.876477 (-4.273772) \| 2.715625 / 2.142072 (0.573552) \| 0.674299 / 4.805227 (-4.130929) \| 0.136039 / 6.500664 (-6.364625) \| 0.067629 / 0.075469 (-0.007840) \|\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) \| 1.333734 / 1.841788 (-0.508054) \| 14.561943 / 8.074308 (6.487634) \| 14.455385 / 10.191392 (4.263993) \| 0.132020 / 0.680424 (-0.548404) \| 0.016893 / 0.534201 (-0.517308) \| 0.367146 / 0.579283 (-0.212137) \| 0.399623 / 0.434364 (-0.034741) \| 0.432658 / 0.540337 (-0.107680) \| 0.530475 / 1.386936 (-0.856461) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"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) \| 0.006045 / 0.011353 (-0.005308) \| 0.003906 / 0.011008 (-0.007103) \| 0.097558 / 0.038508 (0.059050) \| 0.038827 / 0.023109 (0.015718) \| 0.393564 / 0.275898 (0.117666) \| 0.442459 / 0.323480 (0.118980) \| 0.004792 / 0.007986 (-0.003194) \| 0.002984 / 0.004328 (-0.001345) \| 0.076419 / 0.004250 (0.072169) \| 0.053606 / 0.037052 (0.016554) \| 0.409743 / 0.258489 (0.151254) \| 0.445753 / 0.293841 (0.151912) \| 0.027753 / 0.128546 (-0.100793) \| 0.008428 / 0.075646 (-0.067219) \| 0.310267 / 0.419271 (-0.109004) \| 0.057582 / 0.043533 (0.014049) \| 0.396624 / 0.255139 (0.141485) \| 0.416288 / 0.283200 (0.133089) \| 0.029048 / 0.141683 (-0.112635) \| 1.495362 / 1.452155 (0.043207) \| 1.546331 / 1.492716 (0.053615) \|\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) \| 0.203832 / 0.018006 (0.185826) \| 0.423649 / 0.000490 (0.423160) \| 0.004533 / 0.000200 (0.004333) \| 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) \| 0.023084 / 0.037411 (-0.014328) \| 0.100503 / 0.014526 (0.085977) \| 0.105058 / 0.176557 (-0.071499) \| 0.168506 / 0.737135 (-0.568629) \| 0.112019 / 0.296338 (-0.184320) \|\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) \| 0.425877 / 0.215209 (0.210668) \| 4.251278 / 2.077655 (2.173624) \| 1.931339 / 1.504120 (0.427219) \| 1.730578 / 1.541195 (0.189383) \| 1.750637 / 1.468490 (0.282147) \| 0.559307 / 4.584777 (-4.025470) \| 3.461665 / 3.745712 (-0.284047) \| 2.826959 / 5.269862 (-2.442903) \| 1.418448 / 4.565676 (-3.147229) \| 0.067881 / 0.424275 (-0.356394) \| 0.011394 / 0.007607 (0.003787) \| 0.533226 / 0.226044 (0.307181) \| 5.341849 / 2.268929 (3.072921) \| 2.367832 / 55.444624 (-53.076792) \| 2.027240 / 6.876477 (-4.849236) \| 2.095852 / 2.142072 (-0.046220) \| 0.673790 / 4.805227 (-4.131437) \| 0.136044 / 6.500664 (-6.364620) \| 0.066350 / 0.075469 (-0.009119) \|\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) \| 1.203740 / 1.841788 (-0.638048) \| 13.720879 / 8.074308 (5.646571) \| 13.405939 / 10.191392 (3.214547) \| 0.146792 / 0.680424 (-0.533632) \| 0.016844 / 0.534201 (-0.517357) \| 0.373455 / 0.579283 (-0.205828) \| 0.394596 / 0.434364 (-0.039768) \| 0.464715 / 0.540337 (-0.075623) \| 0.558931 / 1.386936 (-0.828005) \|\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) \| 0.006118 / 0.011353 (-0.005235) \| 0.003817 / 0.011008 (-0.007191) \| 0.077494 / 0.038508 (0.038985) \| 0.037507 / 0.023109 (0.014398) \| 0.387030 / 0.275898 (0.111132) \| 0.437352 / 0.323480 (0.113872) \| 0.004810 / 0.007986 (-0.003176) \| 0.002935 / 0.004328 (-0.001394) \| 0.077143 / 0.004250 (0.072892) \| 0.053986 / 0.037052 (0.016933) \| 0.393164 / 0.258489 (0.134675) \| 0.449603 / 0.293841 (0.155762) \| 0.029303 / 0.128546 (-0.099244) \| 0.008481 / 0.075646 (-0.067165) \| 0.083363 / 0.419271 (-0.335908) \| 0.043877 / 0.043533 (0.000344) \| 0.378175 / 0.255139 (0.123036) \| 0.403996 / 0.283200 (0.120797) \| 0.021688 / 0.141683 (-0.119995) \| 1.541606 / 1.452155 (0.089452) \| 1.552996 / 1.492716 (0.060280) \|\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) \| 0.236759 / 0.018006 (0.218752) \| 0.416221 / 0.000490 (0.415732) \| 0.000862 / 0.000200 (0.000662) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025543 / 0.037411 (-0.011868) \| 0.101731 / 0.014526 (0.087206) \| 0.108482 / 0.176557 (-0.068075) \| 0.160290 / 0.737135 (-0.576845) \| 0.111392 / 0.296338 (-0.184946) \|\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) \| 0.457767 / 0.215209 (0.242558) \| 4.565976 / 2.077655 (2.488321) \| 2.245413 / 1.504120 (0.741294) \| 2.031458 / 1.541195 (0.490264) \| 2.073193 / 1.468490 (0.604702) \| 0.560461 / 4.584777 (-4.024316) \| 3.422536 / 3.745712 (-0.323176) \| 2.977017 / 5.269862 (-2.292845) \| 1.377021 / 4.565676 (-3.188655) \| 0.068444 / 0.424275 (-0.355831) \| 0.011036 / 0.007607 (0.003429) \| 0.571501 / 0.226044 (0.345456) \| 5.702652 / 2.268929 (3.433723) \| 2.727132 / 55.444624 (-52.717492) \| 2.399269 / 6.876477 (-4.477208) \| 2.574281 / 2.142072 (0.432208) \| 0.682600 / 4.805227 (-4.122627) \| 0.136943 / 6.500664 (-6.363722) \| 0.067126 / 0.075469 (-0.008343) \|\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) \| 1.322196 / 1.841788 (-0.519592) \| 14.239509 / 8.074308 (6.165201) \| 14.235779 / 10.191392 (4.044387) \| 0.148262 / 0.680424 (-0.532162) \| 0.016566 / 0.534201 (-0.517635) \| 0.364034 / 0.579283 (-0.215249) \| 0.399157 / 0.434364 (-0.035207) \| 0.426348 / 0.540337 (-0.113990) \| 0.520804 / 1.386936 (-0.866132) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"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) \| 0.007808 / 0.011353 (-0.003545) \| 0.004706 / 0.011008 (-0.006303) \| 0.100530 / 0.038508 (0.062022) \| 0.052052 / 0.023109 (0.028943) \| 0.419300 / 0.275898 (0.143402) \| 0.488451 / 0.323480 (0.164971) \| 0.006350 / 0.007986 (-0.001636) \| 0.003875 / 0.004328 (-0.000453) \| 0.076489 / 0.004250 (0.072238) \| 0.077554 / 0.037052 (0.040502) \| 0.435863 / 0.258489 (0.177373) \| 0.483241 / 0.293841 (0.189400) \| 0.037518 / 0.128546 (-0.091028) \| 0.009857 / 0.075646 (-0.065789) \| 0.340933 / 0.419271 (-0.078339) \| 0.087046 / 0.043533 (0.043514) \| 0.410721 / 0.255139 (0.155582) \| 0.428995 / 0.283200 (0.145795) \| 0.041701 / 0.141683 (-0.099982) \| 1.821017 / 1.452155 (0.368862) \| 1.837021 / 1.492716 (0.344305) \|\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) \| 0.228444 / 0.018006 (0.210438) \| 0.480446 / 0.000490 (0.479956) \| 0.004963 / 0.000200 (0.004763) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032485 / 0.037411 (-0.004926) \| 0.096500 / 0.014526 (0.081974) \| 0.111547 / 0.176557 (-0.065010) \| 0.178842 / 0.737135 (-0.558294) \| 0.111099 / 0.296338 (-0.185240) \|\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) \| 0.467159 / 0.215209 (0.251950) \| 4.701676 / 2.077655 (2.624021) \| 2.390560 / 1.504120 (0.886440) \| 2.197722 / 1.541195 (0.656528) \| 2.264705 / 1.468490 (0.796215) \| 0.568667 / 4.584777 (-4.016110) \| 4.200724 / 3.745712 (0.455012) \| 3.777625 / 5.269862 (-1.492236) \| 2.372451 / 4.565676 (-2.193225) \| 0.067562 / 0.424275 (-0.356714) \| 0.008947 / 0.007607 (0.001340) \| 0.556910 / 0.226044 (0.330865) \| 5.528927 / 2.268929 (3.259998) \| 2.902780 / 55.444624 (-52.541844) \| 2.507933 / 6.876477 (-4.368544) \| 2.734627 / 2.142072 (0.592554) \| 0.683305 / 4.805227 (-4.121922) \| 0.158288 / 6.500664 (-6.342376) \| 0.071252 / 0.075469 (-0.004217) \|\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) \| 1.487502 / 1.841788 (-0.354286) \| 22.193341 / 8.074308 (14.119033) \| 15.922607 / 10.191392 (5.731215) \| 0.172189 / 0.680424 (-0.508235) \| 0.021502 / 0.534201 (-0.512699) \| 0.471198 / 0.579283 (-0.108085) \| 0.475979 / 0.434364 (0.041615) \| 0.544675 / 0.540337 (0.004338) \| 0.756102 / 1.386936 (-0.630834) \|\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) \| 0.007635 / 0.011353 (-0.003717) \| 0.004614 / 0.011008 (-0.006394) \| 0.075852 / 0.038508 (0.037344) \| 0.049700 / 0.023109 (0.026591) \| 0.425957 / 0.275898 (0.150059) \| 0.512590 / 0.323480 (0.189110) \| 0.006921 / 0.007986 (-0.001065) \| 0.003714 / 0.004328 (-0.000615) \| 0.075536 / 0.004250 (0.071286) \| 0.070206 / 0.037052 (0.033153) \| 0.455706 / 0.258489 (0.197217) \| 0.512231 / 0.293841 (0.218390) \| 0.036685 / 0.128546 (-0.091861) \| 0.009793 / 0.075646 (-0.065853) \| 0.084208 / 0.419271 (-0.335064) \| 0.065262 / 0.043533 (0.021729) \| 0.423761 / 0.255139 (0.168622) \| 0.456791 / 0.283200 (0.173591) \| 0.044539 / 0.141683 (-0.097144) \| 1.797029 / 1.452155 (0.344874) \| 1.864124 / 1.492716 (0.371408) \|\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) \| 0.366840 / 0.018006 (0.348834) \| 0.479254 / 0.000490 (0.478765) \| 0.070383 / 0.000200 (0.070183) \| 0.000762 / 0.000054 (0.000707) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034233 / 0.037411 (-0.003178) \| 0.103140 / 0.014526 (0.088614) \| 0.117099 / 0.176557 (-0.059457) \| 0.178532 / 0.737135 (-0.558603) \| 0.120092 / 0.296338 (-0.176247) \|\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) \| 0.492993 / 0.215209 (0.277784) \| 4.878776 / 2.077655 (2.801121) \| 2.566666 / 1.504120 (1.062547) \| 2.356383 / 1.541195 (0.815188) \| 2.454723 / 1.468490 (0.986233) \| 0.571432 / 4.584777 (-4.013345) \| 4.240554 / 3.745712 (0.494842) \| 7.509259 / 5.269862 (2.239398) \| 4.040294 / 4.565676 (-0.525382) \| 0.067409 / 0.424275 (-0.356866) \| 0.008657 / 0.007607 (0.001050) \| 0.585751 / 0.226044 (0.359707) \| 5.967668 / 2.268929 (3.698739) \| 3.195573 / 55.444624 (-52.249052) \| 2.839772 / 6.876477 (-4.036704) \| 2.806319 / 2.142072 (0.664246) \| 0.681502 / 4.805227 (-4.123725) \| 0.158673 / 6.500664 (-6.341991) \| 0.073224 / 0.075469 (-0.002245) \|\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) \| 1.623335 / 1.841788 (-0.218453) \| 22.490806 / 8.074308 (14.416498) \| 16.762435 / 10.191392 (6.571043) \| 0.180961 / 0.680424 (-0.499463) \| 0.022716 / 0.534201 (-0.511485) \| 0.472910 / 0.579283 (-0.106373) \| 0.471616 / 0.434364 (0.037252) \| 0.548192 / 0.540337 (0.007854) \| 0.734357 / 1.386936 (-0.652579) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"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) \| 0.005858 / 0.011353 (-0.005495) \| 0.003512 / 0.011008 (-0.007497) \| 0.079739 / 0.038508 (0.041231) \| 0.057736 / 0.023109 (0.034627) \| 0.317640 / 0.275898 (0.041742) \| 0.354157 / 0.323480 (0.030677) \| 0.004772 / 0.007986 (-0.003214) \| 0.002824 / 0.004328 (-0.001504) \| 0.063288 / 0.004250 (0.059037) \| 0.049542 / 0.037052 (0.012489) \| 0.323974 / 0.258489 (0.065485) \| 0.372149 / 0.293841 (0.078308) \| 0.026841 / 0.128546 (-0.101705) \| 0.007846 / 0.075646 (-0.067800) \| 0.262546 / 0.419271 (-0.156725) \| 0.051952 / 0.043533 (0.008420) \| 0.319439 / 0.255139 (0.064300) \| 0.343862 / 0.283200 (0.060663) \| 0.027021 / 0.141683 (-0.114662) \| 1.445211 / 1.452155 (-0.006944) \| 1.485006 / 1.492716 (-0.007711) \|\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) \| 0.183174 / 0.018006 (0.165167) \| 0.422794 / 0.000490 (0.422304) \| 0.004148 / 0.000200 (0.003948) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023037 / 0.037411 (-0.014374) \| 0.071300 / 0.014526 (0.056775) \| 0.083022 / 0.176557 (-0.093535) \| 0.146215 / 0.737135 (-0.590920) \| 0.082549 / 0.296338 (-0.213789) \|\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) \| 0.422846 / 0.215209 (0.207637) \| 4.215280 / 2.077655 (2.137626) \| 2.256802 / 1.504120 (0.752682) \| 2.056867 / 1.541195 (0.515673) \| 2.102478 / 1.468490 (0.633988) \| 0.497552 / 4.584777 (-4.087225) \| 3.049716 / 3.745712 (-0.695996) \| 4.209227 / 5.269862 (-1.060635) \| 2.599947 / 4.565676 (-1.965730) \| 0.059131 / 0.424275 (-0.365144) \| 0.006459 / 0.007607 (-0.001148) \| 0.495047 / 0.226044 (0.269003) \| 4.952332 / 2.268929 (2.683404) \| 2.675260 / 55.444624 (-52.769365) \| 2.333223 / 6.876477 (-4.543254) \| 2.449573 / 2.142072 (0.307500) \| 0.583420 / 4.805227 (-4.221807) \| 0.125140 / 6.500664 (-6.375524) \| 0.060209 / 0.075469 (-0.015260) \|\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) \| 1.215033 / 1.841788 (-0.626755) \| 18.101107 / 8.074308 (10.026799) \| 13.489222 / 10.191392 (3.297830) \| 0.147122 / 0.680424 (-0.533302) \| 0.016567 / 0.534201 (-0.517634) \| 0.329909 / 0.579283 (-0.249374) \| 0.340952 / 0.434364 (-0.093412) \| 0.379166 / 0.540337 (-0.161172) \| 0.510767 / 1.386936 (-0.876169) \|\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) \| 0.005942 / 0.011353 (-0.005411) \| 0.003628 / 0.011008 (-0.007380) \| 0.061975 / 0.038508 (0.023467) \| 0.058331 / 0.023109 (0.035221) \| 0.393277 / 0.275898 (0.117379) \| 0.410740 / 0.323480 (0.087261) \| 0.004546 / 0.007986 (-0.003440) \| 0.002826 / 0.004328 (-0.001503) \| 0.062216 / 0.004250 (0.057966) \| 0.049801 / 0.037052 (0.012748) \| 0.394070 / 0.258489 (0.135581) \| 0.414407 / 0.293841 (0.120566) \| 0.027161 / 0.128546 (-0.101385) \| 0.007901 / 0.075646 (-0.067746) \| 0.066778 / 0.419271 (-0.352493) \| 0.041354 / 0.043533 (-0.002179) \| 0.379432 / 0.255139 (0.124293) \| 0.402966 / 0.283200 (0.119766) \| 0.020279 / 0.141683 (-0.121404) \| 1.416986 / 1.452155 (-0.035169) \| 1.474335 / 1.492716 (-0.018382) \|\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) \| 0.226147 / 0.018006 (0.208140) \| 0.404361 / 0.000490 (0.403871) \| 0.000358 / 0.000200 (0.000158) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025105 / 0.037411 (-0.012306) \| 0.075849 / 0.014526 (0.061323) \| 0.084781 / 0.176557 (-0.091775) \| 0.137415 / 0.737135 (-0.599720) \| 0.086288 / 0.296338 (-0.210051) \|\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) \| 0.445925 / 0.215209 (0.230716) \| 4.453478 / 2.077655 (2.375823) \| 2.419048 / 1.504120 (0.914928) \| 2.246363 / 1.541195 (0.705168) \| 2.304022 / 1.468490 (0.835532) \| 0.499132 / 4.584777 (-4.085645) \| 3.001336 / 3.745712 (-0.744376) \| 2.902593 / 5.269862 (-2.367269) \| 1.819843 / 4.565676 (-2.745834) \| 0.057210 / 0.424275 (-0.367065) \| 0.006338 / 0.007607 (-0.001269) \| 0.523280 / 0.226044 (0.297236) \| 5.235969 / 2.268929 (2.967040) \| 2.897585 / 55.444624 (-52.547039) \| 2.541586 / 6.876477 (-4.334891) \| 2.564233 / 2.142072 (0.422160) \| 0.584714 / 4.805227 (-4.220513) \| 0.124611 / 6.500664 (-6.376053) \| 0.061774 / 0.075469 (-0.013695) \|\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) \| 1.349799 / 1.841788 (-0.491988) \| 18.225076 / 8.074308 (10.150768) \| 13.781518 / 10.191392 (3.590126) \| 0.130562 / 0.680424 (-0.549862) \| 0.016434 / 0.534201 (-0.517767) \| 0.331607 / 0.579283 (-0.247676) \| 0.343456 / 0.434364 (-0.090908) \| 0.380437 / 0.540337 (-0.159900) \| 0.522793 / 1.386936 (-0.864143) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"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) \| 0.013721 / 0.011353 (0.002368) \| 0.005715 / 0.011008 (-0.005293) \| 0.090116 / 0.038508 (0.051608) \| 0.087185 / 0.023109 (0.064075) \| 0.427813 / 0.275898 (0.151915) \| 0.390614 / 0.323480 (0.067135) \| 0.006976 / 0.007986 (-0.001009) \| 0.004231 / 0.004328 (-0.000098) \| 0.078320 / 0.004250 (0.074070) \| 0.066235 / 0.037052 (0.029183) \| 0.439904 / 0.258489 (0.181415) \| 0.424119 / 0.293841 (0.130278) \| 0.050362 / 0.128546 (-0.078184) \| 0.014992 / 0.075646 (-0.060654) \| 0.293519 / 0.419271 (-0.125753) \| 0.066906 / 0.043533 (0.023373) \| 0.449657 / 0.255139 (0.194518) \| 0.393800 / 0.283200 (0.110600) \| 0.032258 / 0.141683 (-0.109425) \| 1.539534 / 1.452155 (0.087379) \| 1.675292 / 1.492716 (0.182576) \|\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) \| 0.210515 / 0.018006 (0.192508) \| 0.506817 / 0.000490 (0.506327) \| 0.001938 / 0.000200 (0.001738) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026019 / 0.037411 (-0.011393) \| 0.080635 / 0.014526 (0.066109) \| 0.103050 / 0.176557 (-0.073507) \| 0.160597 / 0.737135 (-0.576538) \| 0.095844 / 0.296338 (-0.200495) \|\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) \| 0.506359 / 0.215209 (0.291150) \| 5.041586 / 2.077655 (2.963931) \| 2.198288 / 1.504120 (0.694168) \| 1.987544 / 1.541195 (0.446349) \| 1.866790 / 1.468490 (0.398300) \| 0.681642 / 4.584777 (-3.903135) \| 4.719306 / 3.745712 (0.973593) \| 7.669869 / 5.269862 (2.400008) \| 4.466082 / 4.565676 (-0.099595) \| 0.092974 / 0.424275 (-0.331301) \| 0.008196 / 0.007607 (0.000589) \| 0.707656 / 0.226044 (0.481612) \| 6.974507 / 2.268929 (4.705579) \| 3.254206 / 55.444624 (-52.190418) \| 2.499019 / 6.876477 (-4.377457) \| 2.509089 / 2.142072 (0.367017) \| 0.915952 / 4.805227 (-3.889276) \| 0.192119 / 6.500664 (-6.308545) \| 0.065473 / 0.075469 (-0.009996) \|\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) \| 1.309078 / 1.841788 (-0.532710) \| 19.660348 / 8.074308 (11.586040) \| 16.659582 / 10.191392 (6.468190) \| 0.194315 / 0.680424 (-0.486109) \| 0.027773 / 0.534201 (-0.506428) \| 0.401241 / 0.579283 (-0.178042) \| 0.515799 / 0.434364 (0.081435) \| 0.488772 / 0.540337 (-0.051566) \| 0.604790 / 1.386936 (-0.782146) \|\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) \| 0.006823 / 0.011353 (-0.004530) \| 0.003940 / 0.011008 (-0.007068) \| 0.061533 / 0.038508 (0.023025) \| 0.065241 / 0.023109 (0.042132) \| 0.411790 / 0.275898 (0.135892) \| 0.475720 / 0.323480 (0.152241) \| 0.005376 / 0.007986 (-0.002609) \| 0.003433 / 0.004328 (-0.000895) \| 0.065703 / 0.004250 (0.061452) \| 0.050736 / 0.037052 (0.013683) \| 0.435890 / 0.258489 (0.177401) \| 0.436698 / 0.293841 (0.142857) \| 0.040357 / 0.128546 (-0.088189) \| 0.011578 / 0.075646 (-0.064069) \| 0.072831 / 0.419271 (-0.346440) \| 0.055698 / 0.043533 (0.012165) \| 0.408225 / 0.255139 (0.153086) \| 0.439551 / 0.283200 (0.156352) \| 0.030469 / 0.141683 (-0.111214) \| 1.443866 / 1.452155 (-0.008289) \| 1.502022 / 1.492716 (0.009306) \|\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) \| 0.290338 / 0.018006 (0.272332) \| 0.540726 / 0.000490 (0.540236) \| 0.003244 / 0.000200 (0.003044) \| 0.000170 / 0.000054 (0.000116) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030865 / 0.037411 (-0.006547) \| 0.090866 / 0.014526 (0.076340) \| 0.106224 / 0.176557 (-0.070332) \| 0.166583 / 0.737135 (-0.570553) \| 0.104448 / 0.296338 (-0.191891) \|\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) \| 0.518025 / 0.215209 (0.302816) \| 6.027065 / 2.077655 (3.949410) \| 2.671840 / 1.504120 (1.167720) \| 2.273949 / 1.541195 (0.732754) \| 2.414892 / 1.468490 (0.946402) \| 0.774318 / 4.584777 (-3.810459) \| 5.020364 / 3.745712 (1.274652) \| 4.146927 / 5.269862 (-1.122934) \| 2.584598 / 4.565676 (-1.981078) \| 0.089519 / 0.424275 (-0.334756) \| 0.009181 / 0.007607 (0.001574) \| 0.654467 / 0.226044 (0.428423) \| 6.421595 / 2.268929 (4.152666) \| 3.091589 / 55.444624 (-52.353036) \| 2.554798 / 6.876477 (-4.321679) \| 2.441354 / 2.142072 (0.299282) \| 0.943386 / 4.805227 (-3.861841) \| 0.173641 / 6.500664 (-6.327023) \| 0.072209 / 0.075469 (-0.003260) \|\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) \| 1.557147 / 1.841788 (-0.284641) \| 19.980747 / 8.074308 (11.906439) \| 17.816813 / 10.191392 (7.625421) \| 0.212078 / 0.680424 (-0.468346) \| 0.025435 / 0.534201 (-0.508766) \| 0.396200 / 0.579283 (-0.183084) \| 0.546249 / 0.434364 (0.111885) \| 0.459632 / 0.540337 (-0.080705) \| 0.616548 / 1.386936 (-0.770388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#535e972a70a3d4f8490a7e1a77ac43d5a4ab2655 \"CML watermark\")\n" ]
1,786,636,368	6,004	Misc improvements	closed	2023-07-03T18:29:14	2023-07-06T17:04:11	2023-07-06T16:55:25	https://github.com/huggingface/datasets/pull/6004	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6004", "html_url": "https://github.com/huggingface/datasets/pull/6004", "diff_url": "https://github.com/huggingface/datasets/pull/6004.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6004.patch", "merged_at": "2023-07-06T16:55:25" }	mariosasko	true	[ "<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) \| 0.006897 / 0.011353 (-0.004456) \| 0.004207 / 0.011008 (-0.006802) \| 0.104828 / 0.038508 (0.066320) \| 0.048054 / 0.023109 (0.024945) \| 0.373991 / 0.275898 (0.098093) \| 0.426740 / 0.323480 (0.103260) \| 0.005540 / 0.007986 (-0.002446) \| 0.003531 / 0.004328 (-0.000797) \| 0.079304 / 0.004250 (0.075053) \| 0.066996 / 0.037052 (0.029944) \| 0.370675 / 0.258489 (0.112186) \| 0.414154 / 0.293841 (0.120313) \| 0.031567 / 0.128546 (-0.096979) \| 0.008843 / 0.075646 (-0.066803) \| 0.357426 / 0.419271 (-0.061845) \| 0.067040 / 0.043533 (0.023508) \| 0.362384 / 0.255139 (0.107245) \| 0.376056 / 0.283200 (0.092856) \| 0.032985 / 0.141683 (-0.108697) \| 1.560603 / 1.452155 (0.108448) \| 1.619024 / 1.492716 (0.126308) \|\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) \| 0.229059 / 0.018006 (0.211053) \| 0.440513 / 0.000490 (0.440023) \| 0.004647 / 0.000200 (0.004447) \| 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) \| 0.029517 / 0.037411 (-0.007894) \| 0.120974 / 0.014526 (0.106448) \| 0.125070 / 0.176557 (-0.051486) \| 0.184695 / 0.737135 (-0.552441) \| 0.130244 / 0.296338 (-0.166095) \|\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) \| 0.436930 / 0.215209 (0.221721) \| 4.356118 / 2.077655 (2.278463) \| 2.049169 / 1.504120 (0.545049) \| 1.842898 / 1.541195 (0.301703) \| 1.918948 / 1.468490 (0.450458) \| 0.553573 / 4.584777 (-4.031204) \| 3.883195 / 3.745712 (0.137483) \| 3.209780 / 5.269862 (-2.060081) \| 1.551707 / 4.565676 (-3.013970) \| 0.068181 / 0.424275 (-0.356094) \| 0.012370 / 0.007607 (0.004762) \| 0.539899 / 0.226044 (0.313854) \| 5.380008 / 2.268929 (3.111079) \| 2.518178 / 55.444624 (-52.926446) \| 2.174190 / 6.876477 (-4.702286) \| 2.317812 / 2.142072 (0.175740) \| 0.674154 / 4.805227 (-4.131073) \| 0.149313 / 6.500664 (-6.351351) \| 0.068297 / 0.075469 (-0.007172) \|\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) \| 1.261426 / 1.841788 (-0.580362) \| 15.316378 / 8.074308 (7.242070) \| 13.573512 / 10.191392 (3.382120) \| 0.190022 / 0.680424 (-0.490401) \| 0.018697 / 0.534201 (-0.515504) \| 0.448122 / 0.579283 (-0.131161) \| 0.435044 / 0.434364 (0.000681) \| 0.550065 / 0.540337 (0.009728) \| 0.653547 / 1.386936 (-0.733389) \|\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) \| 0.007116 / 0.011353 (-0.004237) \| 0.004375 / 0.011008 (-0.006633) \| 0.081793 / 0.038508 (0.043285) \| 0.047980 / 0.023109 (0.024871) \| 0.392185 / 0.275898 (0.116287) \| 0.462263 / 0.323480 (0.138783) \| 0.005574 / 0.007986 (-0.002412) \| 0.003552 / 0.004328 (-0.000776) \| 0.080413 / 0.004250 (0.076162) \| 0.065539 / 0.037052 (0.028487) \| 0.413137 / 0.258489 (0.154648) \| 0.467377 / 0.293841 (0.173536) \| 0.034386 / 0.128546 (-0.094160) \| 0.009183 / 0.075646 (-0.066464) \| 0.087542 / 0.419271 (-0.331730) \| 0.053954 / 0.043533 (0.010421) \| 0.385096 / 0.255139 (0.129957) \| 0.404900 / 0.283200 (0.121701) \| 0.025908 / 0.141683 (-0.115775) \| 1.550159 / 1.452155 (0.098005) \| 1.598794 / 1.492716 (0.106078) \|\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) \| 0.246222 / 0.018006 (0.228216) \| 0.441095 / 0.000490 (0.440605) \| 0.006863 / 0.000200 (0.006663) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032179 / 0.037411 (-0.005233) \| 0.120112 / 0.014526 (0.105586) \| 0.129326 / 0.176557 (-0.047230) \| 0.184542 / 0.737135 (-0.552593) \| 0.135038 / 0.296338 (-0.161300) \|\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) \| 0.459002 / 0.215209 (0.243793) \| 4.580258 / 2.077655 (2.502604) \| 2.296689 / 1.504120 (0.792569) \| 2.104338 / 1.541195 (0.563143) \| 2.182896 / 1.468490 (0.714406) \| 0.546447 / 4.584777 (-4.038330) \| 3.854047 / 3.745712 (0.108335) \| 1.873829 / 5.269862 (-3.396032) \| 1.116484 / 4.565676 (-3.449193) \| 0.067158 / 0.424275 (-0.357117) \| 0.012035 / 0.007607 (0.004428) \| 0.556642 / 0.226044 (0.330597) \| 5.574436 / 2.268929 (3.305508) \| 2.828223 / 55.444624 (-52.616402) \| 2.519851 / 6.876477 (-4.356626) \| 2.668594 / 2.142072 (0.526521) \| 0.675989 / 4.805227 (-4.129238) \| 0.146075 / 6.500664 (-6.354589) \| 0.067788 / 0.075469 (-0.007681) \|\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) \| 1.345958 / 1.841788 (-0.495830) \| 15.672748 / 8.074308 (7.598440) \| 14.937583 / 10.191392 (4.746191) \| 0.163479 / 0.680424 (-0.516945) \| 0.018364 / 0.534201 (-0.515837) \| 0.433296 / 0.579283 (-0.145987) \| 0.432463 / 0.434364 (-0.001901) \| 0.512000 / 0.540337 (-0.028338) \| 0.619397 / 1.386936 (-0.767539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0832d48a07ed00b406271f4b4439e6d54ae38ebf \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010097 / 0.011353 (-0.001256) \| 0.005070 / 0.011008 (-0.005939) \| 0.118638 / 0.038508 (0.080130) \| 0.043651 / 0.023109 (0.020542) \| 0.356074 / 0.275898 (0.080176) \| 0.414578 / 0.323480 (0.091098) \| 0.005939 / 0.007986 (-0.002046) \| 0.004927 / 0.004328 (0.000598) \| 0.089545 / 0.004250 (0.085294) \| 0.067533 / 0.037052 (0.030481) \| 0.371550 / 0.258489 (0.113061) \| 0.417808 / 0.293841 (0.123967) \| 0.045186 / 0.128546 (-0.083361) \| 0.015763 / 0.075646 (-0.059883) \| 0.393304 / 0.419271 (-0.025967) \| 0.065123 / 0.043533 (0.021591) \| 0.345057 / 0.255139 (0.089918) \| 0.378809 / 0.283200 (0.095610) \| 0.033243 / 0.141683 (-0.108440) \| 1.679956 / 1.452155 (0.227802) \| 1.775456 / 1.492716 (0.282739) \|\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) \| 0.229723 / 0.018006 (0.211717) \| 0.554630 / 0.000490 (0.554140) \| 0.008729 / 0.000200 (0.008529) \| 0.000183 / 0.000054 (0.000129) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027284 / 0.037411 (-0.010128) \| 0.114741 / 0.014526 (0.100215) \| 0.129188 / 0.176557 (-0.047369) \| 0.189270 / 0.737135 (-0.547866) \| 0.126000 / 0.296338 (-0.170339) \|\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) \| 0.580417 / 0.215209 (0.365208) \| 5.829337 / 2.077655 (3.751683) \| 2.421191 / 1.504120 (0.917071) \| 2.063673 / 1.541195 (0.522479) \| 2.133427 / 1.468490 (0.664937) \| 0.830964 / 4.584777 (-3.753813) \| 5.107139 / 3.745712 (1.361427) \| 4.599451 / 5.269862 (-0.670410) \| 2.406502 / 4.565676 (-2.159175) \| 0.100422 / 0.424275 (-0.323853) \| 0.011850 / 0.007607 (0.004243) \| 0.741881 / 0.226044 (0.515836) \| 7.425689 / 2.268929 (5.156760) \| 3.068948 / 55.444624 (-52.375676) \| 2.496292 / 6.876477 (-4.380184) \| 2.566420 / 2.142072 (0.424348) \| 1.093084 / 4.805227 (-3.712144) \| 0.224106 / 6.500664 (-6.276558) \| 0.084549 / 0.075469 (0.009080) \|\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) \| 1.416315 / 1.841788 (-0.425473) \| 16.306901 / 8.074308 (8.232593) \| 19.792419 / 10.191392 (9.601027) \| 0.224223 / 0.680424 (-0.456201) \| 0.026385 / 0.534201 (-0.507816) \| 0.463460 / 0.579283 (-0.115823) \| 0.598385 / 0.434364 (0.164021) \| 0.543981 / 0.540337 (0.003644) \| 0.647454 / 1.386936 (-0.739482) \|\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) \| 0.009470 / 0.011353 (-0.001883) \| 0.004800 / 0.011008 (-0.006208) \| 0.094276 / 0.038508 (0.055768) \| 0.045157 / 0.023109 (0.022048) \| 0.397302 / 0.275898 (0.121404) \| 0.474213 / 0.323480 (0.150733) \| 0.005826 / 0.007986 (-0.002160) \| 0.003724 / 0.004328 (-0.000605) \| 0.090060 / 0.004250 (0.085809) \| 0.066671 / 0.037052 (0.029618) \| 0.439560 / 0.258489 (0.181071) \| 0.468598 / 0.293841 (0.174757) \| 0.044549 / 0.128546 (-0.083997) \| 0.014000 / 0.075646 (-0.061646) \| 0.110457 / 0.419271 (-0.308815) \| 0.065898 / 0.043533 (0.022365) \| 0.408101 / 0.255139 (0.152962) \| 0.433473 / 0.283200 (0.150273) \| 0.038438 / 0.141683 (-0.103245) \| 1.767781 / 1.452155 (0.315626) \| 1.791575 / 1.492716 (0.298859) \|\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) \| 0.230257 / 0.018006 (0.212251) \| 0.492280 / 0.000490 (0.491790) \| 0.005110 / 0.000200 (0.004910) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028854 / 0.037411 (-0.008557) \| 0.111702 / 0.014526 (0.097176) \| 0.122040 / 0.176557 (-0.054517) \| 0.179103 / 0.737135 (-0.558032) \| 0.128869 / 0.296338 (-0.167470) \|\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) \| 0.634795 / 0.215209 (0.419586) \| 6.204760 / 2.077655 (4.127105) \| 2.692479 / 1.504120 (1.188359) \| 2.324260 / 1.541195 (0.783066) \| 2.380640 / 1.468490 (0.912149) \| 0.887827 / 4.584777 (-3.696950) \| 5.251648 / 3.745712 (1.505935) \| 2.632767 / 5.269862 (-2.637095) \| 1.745721 / 4.565676 (-2.819955) \| 0.108364 / 0.424275 (-0.315911) \| 0.013409 / 0.007607 (0.005802) \| 0.783427 / 0.226044 (0.557383) \| 7.765144 / 2.268929 (5.496216) \| 3.340686 / 55.444624 (-52.103938) \| 2.715340 / 6.876477 (-4.161137) \| 2.768604 / 2.142072 (0.626531) \| 1.119746 / 4.805227 (-3.685481) \| 0.210804 / 6.500664 (-6.289860) \| 0.072600 / 0.075469 (-0.002869) \|\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) \| 1.517334 / 1.841788 (-0.324454) \| 17.046837 / 8.074308 (8.972529) \| 19.371090 / 10.191392 (9.179698) \| 0.194275 / 0.680424 (-0.486148) \| 0.026712 / 0.534201 (-0.507488) \| 0.462731 / 0.579283 (-0.116552) \| 0.568958 / 0.434364 (0.134595) \| 0.555707 / 0.540337 (0.015370) \| 0.663654 / 1.386936 (-0.723283) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d20476b1d4c8e11e0ffafc1570cbf4bd19011cf \"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) \| 0.006423 / 0.011353 (-0.004930) \| 0.003882 / 0.011008 (-0.007126) \| 0.082976 / 0.038508 (0.044468) \| 0.071281 / 0.023109 (0.048171) \| 0.311367 / 0.275898 (0.035469) \| 0.348228 / 0.323480 (0.024748) \| 0.005315 / 0.007986 (-0.002671) \| 0.003326 / 0.004328 (-0.001003) \| 0.064641 / 0.004250 (0.060391) \| 0.056134 / 0.037052 (0.019081) \| 0.314071 / 0.258489 (0.055582) \| 0.360534 / 0.293841 (0.066693) \| 0.030642 / 0.128546 (-0.097904) \| 0.008301 / 0.075646 (-0.067345) \| 0.285820 / 0.419271 (-0.133451) \| 0.069241 / 0.043533 (0.025708) \| 0.313995 / 0.255139 (0.058856) \| 0.336656 / 0.283200 (0.053457) \| 0.031686 / 0.141683 (-0.109997) \| 1.467627 / 1.452155 (0.015472) \| 1.536493 / 1.492716 (0.043777) \|\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) \| 0.196518 / 0.018006 (0.178512) \| 0.458235 / 0.000490 (0.457745) \| 0.005599 / 0.000200 (0.005399) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027371 / 0.037411 (-0.010040) \| 0.080986 / 0.014526 (0.066460) \| 0.093296 / 0.176557 (-0.083260) \| 0.150592 / 0.737135 (-0.586543) \| 0.094150 / 0.296338 (-0.202188) \|\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) \| 0.379412 / 0.215209 (0.164202) \| 3.797927 / 2.077655 (1.720272) \| 1.830654 / 1.504120 (0.326534) \| 1.669569 / 1.541195 (0.128374) \| 1.746738 / 1.468490 (0.278248) \| 0.479536 / 4.584777 (-4.105241) \| 3.592867 / 3.745712 (-0.152845) \| 5.468098 / 5.269862 (0.198237) \| 3.268013 / 4.565676 (-1.297663) \| 0.056635 / 0.424275 (-0.367640) \| 0.007224 / 0.007607 (-0.000383) \| 0.456681 / 0.226044 (0.230636) \| 4.566736 / 2.268929 (2.297807) \| 2.362831 / 55.444624 (-53.081793) \| 1.965141 / 6.876477 (-4.911336) \| 2.156905 / 2.142072 (0.014833) \| 0.572543 / 4.805227 (-4.232684) \| 0.132203 / 6.500664 (-6.368461) \| 0.059254 / 0.075469 (-0.016215) \|\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) \| 1.256134 / 1.841788 (-0.585654) \| 19.905438 / 8.074308 (11.831130) \| 14.179556 / 10.191392 (3.988164) \| 0.168043 / 0.680424 (-0.512381) \| 0.018215 / 0.534201 (-0.515986) \| 0.392740 / 0.579283 (-0.186543) \| 0.398397 / 0.434364 (-0.035967) \| 0.463806 / 0.540337 (-0.076531) \| 0.616248 / 1.386936 (-0.770688) \|\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) \| 0.006564 / 0.011353 (-0.004789) \| 0.003923 / 0.011008 (-0.007085) \| 0.063929 / 0.038508 (0.025421) \| 0.073780 / 0.023109 (0.050671) \| 0.360242 / 0.275898 (0.084344) \| 0.395078 / 0.323480 (0.071598) \| 0.005265 / 0.007986 (-0.002720) \| 0.003229 / 0.004328 (-0.001100) \| 0.064094 / 0.004250 (0.059843) \| 0.057468 / 0.037052 (0.020416) \| 0.369530 / 0.258489 (0.111041) \| 0.411159 / 0.293841 (0.117318) \| 0.031278 / 0.128546 (-0.097268) \| 0.008424 / 0.075646 (-0.067222) \| 0.070411 / 0.419271 (-0.348860) \| 0.048714 / 0.043533 (0.005181) \| 0.361280 / 0.255139 (0.106141) \| 0.382468 / 0.283200 (0.099269) \| 0.023059 / 0.141683 (-0.118624) \| 1.452369 / 1.452155 (0.000215) \| 1.519192 / 1.492716 (0.026475) \|\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) \| 0.223745 / 0.018006 (0.205739) \| 0.442086 / 0.000490 (0.441596) \| 0.000379 / 0.000200 (0.000179) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030919 / 0.037411 (-0.006493) \| 0.088483 / 0.014526 (0.073958) \| 0.101165 / 0.176557 (-0.075391) \| 0.154332 / 0.737135 (-0.582804) \| 0.103030 / 0.296338 (-0.193309) \|\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) \| 0.414520 / 0.215209 (0.199311) \| 4.126754 / 2.077655 (2.049099) \| 2.142677 / 1.504120 (0.638557) \| 1.995300 / 1.541195 (0.454106) \| 2.101678 / 1.468490 (0.633188) \| 0.481099 / 4.584777 (-4.103678) \| 3.562813 / 3.745712 (-0.182900) \| 3.392463 / 5.269862 (-1.877399) \| 1.983943 / 4.565676 (-2.581734) \| 0.056594 / 0.424275 (-0.367681) \| 0.007216 / 0.007607 (-0.000391) \| 0.495085 / 0.226044 (0.269041) \| 4.955640 / 2.268929 (2.686712) \| 2.629434 / 55.444624 (-52.815191) \| 2.269577 / 6.876477 (-4.606900) \| 2.357708 / 2.142072 (0.215635) \| 0.612370 / 4.805227 (-4.192857) \| 0.131169 / 6.500664 (-6.369495) \| 0.061029 / 0.075469 (-0.014440) \|\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) \| 1.339438 / 1.841788 (-0.502350) \| 19.757611 / 8.074308 (11.683303) \| 14.246254 / 10.191392 (4.054862) \| 0.170750 / 0.680424 (-0.509674) \| 0.018192 / 0.534201 (-0.516009) \| 0.395693 / 0.579283 (-0.183590) \| 0.411003 / 0.434364 (-0.023361) \| 0.478531 / 0.540337 (-0.061806) \| 0.650291 / 1.386936 (-0.736645) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e34d06d746688dd5d26e4c85517b7e1a2f361ca \"CML watermark\")\n" ]
1,786,554,110	6,003	interleave_datasets & DataCollatorForLanguageModeling having a conflict ?	open	2023-07-03T17:15:31	2023-07-03T17:15:31	null	https://github.com/huggingface/datasets/issues/6003	null	PonteIneptique	false	[]
1,786,053,060	6,002	Add KLUE-MRC metrics	closed	2023-07-03T12:11:10	2023-07-09T11:57:20	2023-07-09T11:57:20	https://github.com/huggingface/datasets/pull/6002	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6002", "html_url": "https://github.com/huggingface/datasets/pull/6002", "diff_url": "https://github.com/huggingface/datasets/pull/6002.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6002.patch", "merged_at": null }	ingyuseong	true	[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]
1,782,516,627	6,001	Align `column_names` type check with type hint in `sort`	closed	2023-06-30T13:15:50	2023-06-30T14:18:32	2023-06-30T14:11:24	https://github.com/huggingface/datasets/pull/6001	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6001", "html_url": "https://github.com/huggingface/datasets/pull/6001", "diff_url": "https://github.com/huggingface/datasets/pull/6001.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6001.patch", "merged_at": "2023-06-30T14:11:24" }	mariosasko	true	[ "_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) \| 0.006038 / 0.011353 (-0.005315) \| 0.003797 / 0.011008 (-0.007211) \| 0.097686 / 0.038508 (0.059178) \| 0.035235 / 0.023109 (0.012126) \| 0.317294 / 0.275898 (0.041396) \| 0.377682 / 0.323480 (0.054202) \| 0.003485 / 0.007986 (-0.004501) \| 0.003603 / 0.004328 (-0.000725) \| 0.077268 / 0.004250 (0.073017) \| 0.054649 / 0.037052 (0.017597) \| 0.322293 / 0.258489 (0.063804) \| 0.372277 / 0.293841 (0.078436) \| 0.027927 / 0.128546 (-0.100619) \| 0.008495 / 0.075646 (-0.067151) \| 0.313078 / 0.419271 (-0.106193) \| 0.046974 / 0.043533 (0.003441) \| 0.313848 / 0.255139 (0.058709) \| 0.338454 / 0.283200 (0.055255) \| 0.020462 / 0.141683 (-0.121221) \| 1.473027 / 1.452155 (0.020873) \| 1.539468 / 1.492716 (0.046752) \|\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) \| 0.221429 / 0.018006 (0.203423) \| 0.412044 / 0.000490 (0.411555) \| 0.005866 / 0.000200 (0.005666) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022870 / 0.037411 (-0.014541) \| 0.099129 / 0.014526 (0.084603) \| 0.103463 / 0.176557 (-0.073094) \| 0.164969 / 0.737135 (-0.572166) \| 0.110000 / 0.296338 (-0.186339) \|\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) \| 0.431311 / 0.215209 (0.216102) \| 4.293562 / 2.077655 (2.215907) \| 1.961209 / 1.504120 (0.457089) \| 1.733680 / 1.541195 (0.192485) \| 1.793171 / 1.468490 (0.324681) \| 0.568566 / 4.584777 (-4.016211) \| 3.401794 / 3.745712 (-0.343918) \| 1.827949 / 5.269862 (-3.441913) \| 1.055963 / 4.565676 (-3.509714) \| 0.068459 / 0.424275 (-0.355816) \| 0.011586 / 0.007607 (0.003979) \| 0.533936 / 0.226044 (0.307891) \| 5.347637 / 2.268929 (3.078708) \| 2.378056 / 55.444624 (-53.066569) \| 2.032159 / 6.876477 (-4.844318) \| 2.159064 / 2.142072 (0.016991) \| 0.674528 / 4.805227 (-4.130699) \| 0.136859 / 6.500664 (-6.363805) \| 0.066629 / 0.075469 (-0.008840) \|\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) \| 1.218084 / 1.841788 (-0.623704) \| 14.141710 / 8.074308 (6.067402) \| 13.588415 / 10.191392 (3.397023) \| 0.155104 / 0.680424 (-0.525320) \| 0.017160 / 0.534201 (-0.517041) \| 0.375558 / 0.579283 (-0.203725) \| 0.386293 / 0.434364 (-0.048071) \| 0.459476 / 0.540337 (-0.080862) \| 0.548561 / 1.386936 (-0.838375) \|\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) \| 0.005878 / 0.011353 (-0.005475) \| 0.003750 / 0.011008 (-0.007259) \| 0.077720 / 0.038508 (0.039212) \| 0.034955 / 0.023109 (0.011846) \| 0.357480 / 0.275898 (0.081582) \| 0.418210 / 0.323480 (0.094730) \| 0.004566 / 0.007986 (-0.003419) \| 0.002918 / 0.004328 (-0.001410) \| 0.076517 / 0.004250 (0.072266) \| 0.050202 / 0.037052 (0.013150) \| 0.368166 / 0.258489 (0.109677) \| 0.415681 / 0.293841 (0.121840) \| 0.029496 / 0.128546 (-0.099050) \| 0.008547 / 0.075646 (-0.067099) \| 0.083037 / 0.419271 (-0.336234) \| 0.045001 / 0.043533 (0.001468) \| 0.356503 / 0.255139 (0.101364) \| 0.383747 / 0.283200 (0.100547) \| 0.025071 / 0.141683 (-0.116612) \| 1.541985 / 1.452155 (0.089830) \| 1.594710 / 1.492716 (0.101994) \|\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) \| 0.204491 / 0.018006 (0.186484) \| 0.408686 / 0.000490 (0.408196) \| 0.002505 / 0.000200 (0.002305) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024446 / 0.037411 (-0.012965) \| 0.101432 / 0.014526 (0.086906) \| 0.108105 / 0.176557 (-0.068452) \| 0.161195 / 0.737135 (-0.575940) \| 0.112671 / 0.296338 (-0.183667) \|\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) \| 0.459697 / 0.215209 (0.244488) \| 4.570071 / 2.077655 (2.492416) \| 2.211547 / 1.504120 (0.707427) \| 1.996651 / 1.541195 (0.455457) \| 2.015621 / 1.468490 (0.547131) \| 0.567423 / 4.584777 (-4.017354) \| 3.408027 / 3.745712 (-0.337685) \| 2.913824 / 5.269862 (-2.356038) \| 1.423223 / 4.565676 (-3.142453) \| 0.068740 / 0.424275 (-0.355535) \| 0.010997 / 0.007607 (0.003390) \| 0.567340 / 0.226044 (0.341296) \| 5.666280 / 2.268929 (3.397351) \| 2.804934 / 55.444624 (-52.639690) \| 2.430761 / 6.876477 (-4.445716) \| 2.451820 / 2.142072 (0.309748) \| 0.681926 / 4.805227 (-4.123301) \| 0.137761 / 6.500664 (-6.362903) \| 0.067173 / 0.075469 (-0.008296) \|\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) \| 1.329853 / 1.841788 (-0.511934) \| 14.436232 / 8.074308 (6.361924) \| 14.398645 / 10.191392 (4.207253) \| 0.147421 / 0.680424 (-0.533002) \| 0.016743 / 0.534201 (-0.517458) \| 0.364964 / 0.579283 (-0.214319) \| 0.387072 / 0.434364 (-0.047292) \| 0.423892 / 0.540337 (-0.116445) \| 0.521304 / 1.386936 (-0.865632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"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) \| 0.006463 / 0.011353 (-0.004889) \| 0.003923 / 0.011008 (-0.007086) \| 0.102096 / 0.038508 (0.063588) \| 0.040230 / 0.023109 (0.017121) \| 0.384688 / 0.275898 (0.108789) \| 0.445574 / 0.323480 (0.122094) \| 0.003590 / 0.007986 (-0.004395) \| 0.004023 / 0.004328 (-0.000306) \| 0.080125 / 0.004250 (0.075875) \| 0.057406 / 0.037052 (0.020354) \| 0.395049 / 0.258489 (0.136560) \| 0.438065 / 0.293841 (0.144224) \| 0.028963 / 0.128546 (-0.099583) \| 0.008693 / 0.075646 (-0.066954) \| 0.317158 / 0.419271 (-0.102114) \| 0.047930 / 0.043533 (0.004397) \| 0.382442 / 0.255139 (0.127303) \| 0.410665 / 0.283200 (0.127466) \| 0.020127 / 0.141683 (-0.121555) \| 1.558554 / 1.452155 (0.106400) \| 1.590959 / 1.492716 (0.098242) \|\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) \| 0.208826 / 0.018006 (0.190820) \| 0.432037 / 0.000490 (0.431547) \| 0.006509 / 0.000200 (0.006309) \| 0.000285 / 0.000054 (0.000230) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023460 / 0.037411 (-0.013951) \| 0.099070 / 0.014526 (0.084545) \| 0.105771 / 0.176557 (-0.070785) \| 0.166683 / 0.737135 (-0.570452) \| 0.108755 / 0.296338 (-0.187583) \|\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) \| 0.424324 / 0.215209 (0.209115) \| 4.225696 / 2.077655 (2.148042) \| 1.910955 / 1.504120 (0.406835) \| 1.704493 / 1.541195 (0.163298) \| 1.782784 / 1.468490 (0.314293) \| 0.562927 / 4.584777 (-4.021850) \| 3.380163 / 3.745712 (-0.365550) \| 1.779641 / 5.269862 (-3.490221) \| 1.029134 / 4.565676 (-3.536543) \| 0.068325 / 0.424275 (-0.355950) \| 0.011528 / 0.007607 (0.003921) \| 0.530141 / 0.226044 (0.304097) \| 5.323443 / 2.268929 (3.054514) \| 2.346956 / 55.444624 (-53.097668) \| 2.013335 / 6.876477 (-4.863142) \| 2.118531 / 2.142072 (-0.023541) \| 0.675206 / 4.805227 (-4.130021) \| 0.135473 / 6.500664 (-6.365191) \| 0.064804 / 0.075469 (-0.010665) \|\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) \| 1.240179 / 1.841788 (-0.601608) \| 14.692449 / 8.074308 (6.618141) \| 13.672223 / 10.191392 (3.480831) \| 0.147748 / 0.680424 (-0.532676) \| 0.017119 / 0.534201 (-0.517082) \| 0.369481 / 0.579283 (-0.209802) \| 0.390133 / 0.434364 (-0.044231) \| 0.458768 / 0.540337 (-0.081569) \| 0.548989 / 1.386936 (-0.837947) \|\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) \| 0.006319 / 0.011353 (-0.005034) \| 0.003975 / 0.011008 (-0.007033) \| 0.077886 / 0.038508 (0.039378) \| 0.038322 / 0.023109 (0.015213) \| 0.379851 / 0.275898 (0.103953) \| 0.456749 / 0.323480 (0.133269) \| 0.005320 / 0.007986 (-0.002665) \| 0.003135 / 0.004328 (-0.001194) \| 0.078272 / 0.004250 (0.074022) \| 0.059919 / 0.037052 (0.022866) \| 0.430062 / 0.258489 (0.171573) \| 0.477432 / 0.293841 (0.183591) \| 0.029713 / 0.128546 (-0.098833) \| 0.008704 / 0.075646 (-0.066942) \| 0.082488 / 0.419271 (-0.336784) \| 0.044667 / 0.043533 (0.001134) \| 0.354910 / 0.255139 (0.099771) \| 0.434637 / 0.283200 (0.151438) \| 0.026402 / 0.141683 (-0.115281) \| 1.528825 / 1.452155 (0.076671) \| 1.548209 / 1.492716 (0.055493) \|\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) \| 0.237988 / 0.018006 (0.219982) \| 0.420402 / 0.000490 (0.419913) \| 0.003098 / 0.000200 (0.002898) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026253 / 0.037411 (-0.011159) \| 0.106137 / 0.014526 (0.091611) \| 0.110273 / 0.176557 (-0.066284) \| 0.165316 / 0.737135 (-0.571819) \| 0.115720 / 0.296338 (-0.180619) \|\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) \| 0.454244 / 0.215209 (0.239035) \| 4.526018 / 2.077655 (2.448364) \| 2.395985 / 1.504120 (0.891865) \| 2.234822 / 1.541195 (0.693627) \| 2.370235 / 1.468490 (0.901745) \| 0.567607 / 4.584777 (-4.017169) \| 3.650156 / 3.745712 (-0.095556) \| 3.360094 / 5.269862 (-1.909768) \| 1.415252 / 4.565676 (-3.150424) \| 0.068012 / 0.424275 (-0.356263) \| 0.011135 / 0.007607 (0.003528) \| 0.561967 / 0.226044 (0.335923) \| 5.621819 / 2.268929 (3.352890) \| 2.676912 / 55.444624 (-52.767712) \| 2.338306 / 6.876477 (-4.538171) \| 2.430888 / 2.142072 (0.288815) \| 0.684576 / 4.805227 (-4.120651) \| 0.138923 / 6.500664 (-6.361741) \| 0.069933 / 0.075469 (-0.005536) \|\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) \| 1.313383 / 1.841788 (-0.528405) \| 15.125088 / 8.074308 (7.050780) \| 14.801501 / 10.191392 (4.610109) \| 0.134235 / 0.680424 (-0.546189) \| 0.017058 / 0.534201 (-0.517143) \| 0.365166 / 0.579283 (-0.214117) \| 0.395415 / 0.434364 (-0.038949) \| 0.419355 / 0.540337 (-0.120983) \| 0.513411 / 1.386936 (-0.873525) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n" ]
1,782,456,878	6,000	Pin `joblib` to avoid `joblibspark` test failures	closed	2023-06-30T12:36:54	2023-06-30T13:17:05	2023-06-30T13:08:27	https://github.com/huggingface/datasets/pull/6000	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6000", "html_url": "https://github.com/huggingface/datasets/pull/6000", "diff_url": "https://github.com/huggingface/datasets/pull/6000.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6000.patch", "merged_at": "2023-06-30T13:08:27" }	mariosasko	true	[ "<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) \| 0.006722 / 0.011353 (-0.004631) \| 0.004425 / 0.011008 (-0.006583) \| 0.100850 / 0.038508 (0.062341) \| 0.040816 / 0.023109 (0.017707) \| 0.348823 / 0.275898 (0.072925) \| 0.446285 / 0.323480 (0.122805) \| 0.005738 / 0.007986 (-0.002247) \| 0.003517 / 0.004328 (-0.000811) \| 0.078824 / 0.004250 (0.074574) \| 0.064695 / 0.037052 (0.027643) \| 0.389894 / 0.258489 (0.131405) \| 0.416107 / 0.293841 (0.122266) \| 0.028850 / 0.128546 (-0.099696) \| 0.009011 / 0.075646 (-0.066635) \| 0.323117 / 0.419271 (-0.096154) \| 0.049162 / 0.043533 (0.005629) \| 0.340144 / 0.255139 (0.085005) \| 0.382072 / 0.283200 (0.098872) \| 0.023160 / 0.141683 (-0.118523) \| 1.549218 / 1.452155 (0.097063) \| 1.581266 / 1.492716 (0.088550) \|\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) \| 0.293360 / 0.018006 (0.275353) \| 0.602189 / 0.000490 (0.601700) \| 0.004608 / 0.000200 (0.004408) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028144 / 0.037411 (-0.009267) \| 0.107088 / 0.014526 (0.092562) \| 0.112188 / 0.176557 (-0.064369) \| 0.174669 / 0.737135 (-0.562466) \| 0.116359 / 0.296338 (-0.179980) \|\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) \| 0.422911 / 0.215209 (0.207702) \| 4.231524 / 2.077655 (2.153869) \| 1.906711 / 1.504120 (0.402591) \| 1.706841 / 1.541195 (0.165646) \| 1.792066 / 1.468490 (0.323576) \| 0.559221 / 4.584777 (-4.025556) \| 3.434280 / 3.745712 (-0.311433) \| 1.918714 / 5.269862 (-3.351148) \| 1.073070 / 4.565676 (-3.492606) \| 0.067891 / 0.424275 (-0.356384) \| 0.011927 / 0.007607 (0.004320) \| 0.530843 / 0.226044 (0.304799) \| 5.309213 / 2.268929 (3.040285) \| 2.439246 / 55.444624 (-53.005378) \| 2.101245 / 6.876477 (-4.775231) \| 2.177436 / 2.142072 (0.035363) \| 0.672150 / 4.805227 (-4.133077) \| 0.137571 / 6.500664 (-6.363093) \| 0.068343 / 0.075469 (-0.007126) \|\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) \| 1.265262 / 1.841788 (-0.576525) \| 14.988021 / 8.074308 (6.913713) \| 13.611677 / 10.191392 (3.420285) \| 0.171389 / 0.680424 (-0.509035) \| 0.017681 / 0.534201 (-0.516520) \| 0.377542 / 0.579283 (-0.201741) \| 0.399475 / 0.434364 (-0.034889) \| 0.469553 / 0.540337 (-0.070785) \| 0.561888 / 1.386936 (-0.825048) \|\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) \| 0.006782 / 0.011353 (-0.004571) \| 0.004412 / 0.011008 (-0.006597) \| 0.078594 / 0.038508 (0.040086) \| 0.039930 / 0.023109 (0.016820) \| 0.371879 / 0.275898 (0.095981) \| 0.444910 / 0.323480 (0.121430) \| 0.005707 / 0.007986 (-0.002279) \| 0.003901 / 0.004328 (-0.000427) \| 0.080125 / 0.004250 (0.075875) \| 0.063977 / 0.037052 (0.026925) \| 0.382781 / 0.258489 (0.124292) \| 0.441791 / 0.293841 (0.147950) \| 0.030428 / 0.128546 (-0.098118) \| 0.009008 / 0.075646 (-0.066638) \| 0.084447 / 0.419271 (-0.334824) \| 0.044432 / 0.043533 (0.000899) \| 0.365686 / 0.255139 (0.110547) \| 0.394312 / 0.283200 (0.111113) \| 0.024508 / 0.141683 (-0.117175) \| 1.577020 / 1.452155 (0.124865) \| 1.630259 / 1.492716 (0.137543) \|\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) \| 0.307960 / 0.018006 (0.289953) \| 0.591473 / 0.000490 (0.590983) \| 0.008098 / 0.000200 (0.007898) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029567 / 0.037411 (-0.007845) \| 0.112773 / 0.014526 (0.098247) \| 0.117362 / 0.176557 (-0.059194) \| 0.174293 / 0.737135 (-0.562843) \| 0.123156 / 0.296338 (-0.173182) \|\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) \| 0.457475 / 0.215209 (0.242266) \| 4.599067 / 2.077655 (2.521412) \| 2.262638 / 1.504120 (0.758518) \| 2.124943 / 1.541195 (0.583748) \| 2.339912 / 1.468490 (0.871422) \| 0.566264 / 4.584777 (-4.018513) \| 3.489261 / 3.745712 (-0.256451) \| 1.925151 / 5.269862 (-3.344711) \| 1.099389 / 4.565676 (-3.466287) \| 0.068232 / 0.424275 (-0.356043) \| 0.011660 / 0.007607 (0.004052) \| 0.571227 / 0.226044 (0.345183) \| 5.702059 / 2.268929 (3.433130) \| 2.837701 / 55.444624 (-52.606924) \| 2.605468 / 6.876477 (-4.271008) \| 2.818396 / 2.142072 (0.676323) \| 0.681856 / 4.805227 (-4.123371) \| 0.141401 / 6.500664 (-6.359263) \| 0.069728 / 0.075469 (-0.005741) \|\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) \| 1.354935 / 1.841788 (-0.486853) \| 15.437404 / 8.074308 (7.363095) \| 15.415193 / 10.191392 (5.223801) \| 0.153459 / 0.680424 (-0.526964) \| 0.017190 / 0.534201 (-0.517011) \| 0.367256 / 0.579283 (-0.212027) \| 0.392709 / 0.434364 (-0.041655) \| 0.426125 / 0.540337 (-0.114213) \| 0.522612 / 1.386936 (-0.864324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009183 / 0.011353 (-0.002170) \| 0.005232 / 0.011008 (-0.005776) \| 0.120349 / 0.038508 (0.081841) \| 0.044715 / 0.023109 (0.021606) \| 0.361519 / 0.275898 (0.085621) \| 0.463702 / 0.323480 (0.140223) \| 0.005842 / 0.007986 (-0.002144) \| 0.004041 / 0.004328 (-0.000288) \| 0.096953 / 0.004250 (0.092703) \| 0.070593 / 0.037052 (0.033540) \| 0.409790 / 0.258489 (0.151301) \| 0.477452 / 0.293841 (0.183611) \| 0.045827 / 0.128546 (-0.082719) \| 0.014038 / 0.075646 (-0.061608) \| 0.421317 / 0.419271 (0.002045) \| 0.065276 / 0.043533 (0.021743) \| 0.360074 / 0.255139 (0.104935) \| 0.409147 / 0.283200 (0.125947) \| 0.032444 / 0.141683 (-0.109238) \| 1.739257 / 1.452155 (0.287102) \| 1.831408 / 1.492716 (0.338692) \|\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) \| 0.274852 / 0.018006 (0.256846) \| 0.596320 / 0.000490 (0.595830) \| 0.006399 / 0.000200 (0.006199) \| 0.000133 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031400 / 0.037411 (-0.006012) \| 0.127052 / 0.014526 (0.112526) \| 0.134269 / 0.176557 (-0.042288) \| 0.225998 / 0.737135 (-0.511137) \| 0.150019 / 0.296338 (-0.146319) \|\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) \| 0.654202 / 0.215209 (0.438993) \| 6.216735 / 2.077655 (4.139081) \| 2.440214 / 1.504120 (0.936094) \| 2.150575 / 1.541195 (0.609380) \| 2.124790 / 1.468490 (0.656300) \| 0.923514 / 4.584777 (-3.661263) \| 5.556924 / 3.745712 (1.811212) \| 2.843886 / 5.269862 (-2.425975) \| 1.834232 / 4.565676 (-2.731444) \| 0.111735 / 0.424275 (-0.312540) \| 0.014823 / 0.007607 (0.007216) \| 0.820503 / 0.226044 (0.594459) \| 7.887737 / 2.268929 (5.618809) \| 3.120307 / 55.444624 (-52.324317) \| 2.405856 / 6.876477 (-4.470621) \| 2.411239 / 2.142072 (0.269167) \| 1.071283 / 4.805227 (-3.733944) \| 0.227738 / 6.500664 (-6.272926) \| 0.073516 / 0.075469 (-0.001953) \|\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) \| 1.531806 / 1.841788 (-0.309982) \| 18.547661 / 8.074308 (10.473353) \| 21.083922 / 10.191392 (10.892530) \| 0.241706 / 0.680424 (-0.438718) \| 0.034169 / 0.534201 (-0.500032) \| 0.497514 / 0.579283 (-0.081769) \| 0.599801 / 0.434364 (0.165437) \| 0.576465 / 0.540337 (0.036127) \| 0.673509 / 1.386936 (-0.713427) \|\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) \| 0.007558 / 0.011353 (-0.003795) \| 0.005001 / 0.011008 (-0.006008) \| 0.093809 / 0.038508 (0.055301) \| 0.039792 / 0.023109 (0.016683) \| 0.456869 / 0.275898 (0.180971) \| 0.493370 / 0.323480 (0.169891) \| 0.005561 / 0.007986 (-0.002424) \| 0.003982 / 0.004328 (-0.000346) \| 0.085421 / 0.004250 (0.081170) \| 0.059817 / 0.037052 (0.022765) \| 0.468040 / 0.258489 (0.209550) \| 0.514853 / 0.293841 (0.221012) \| 0.044267 / 0.128546 (-0.084279) \| 0.012674 / 0.075646 (-0.062972) \| 0.098324 / 0.419271 (-0.320948) \| 0.056604 / 0.043533 (0.013071) \| 0.432200 / 0.255139 (0.177061) \| 0.459812 / 0.283200 (0.176612) \| 0.033872 / 0.141683 (-0.107811) \| 1.618576 / 1.452155 (0.166421) \| 1.676562 / 1.492716 (0.183846) \|\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) \| 0.230625 / 0.018006 (0.212619) \| 0.600558 / 0.000490 (0.600068) \| 0.003419 / 0.000200 (0.003219) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026916 / 0.037411 (-0.010496) \| 0.103003 / 0.014526 (0.088478) \| 0.117078 / 0.176557 (-0.059478) \| 0.169359 / 0.737135 (-0.567776) \| 0.120305 / 0.296338 (-0.176034) \|\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) \| 0.616877 / 0.215209 (0.401668) \| 6.157232 / 2.077655 (4.079577) \| 2.869219 / 1.504120 (1.365099) \| 2.381410 / 1.541195 (0.840216) \| 2.417357 / 1.468490 (0.948867) \| 0.914947 / 4.584777 (-3.669830) \| 5.718526 / 3.745712 (1.972814) \| 2.757253 / 5.269862 (-2.512609) \| 1.794122 / 4.565676 (-2.771554) \| 0.108423 / 0.424275 (-0.315852) \| 0.013378 / 0.007607 (0.005771) \| 0.831067 / 0.226044 (0.605023) \| 8.478946 / 2.268929 (6.210018) \| 3.685937 / 55.444624 (-51.758687) \| 2.867472 / 6.876477 (-4.009005) \| 2.895975 / 2.142072 (0.753903) \| 1.137547 / 4.805227 (-3.667681) \| 0.213891 / 6.500664 (-6.286773) \| 0.075825 / 0.075469 (0.000356) \|\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) \| 1.621193 / 1.841788 (-0.220594) \| 17.322110 / 8.074308 (9.247802) \| 21.804016 / 10.191392 (11.612624) \| 0.243692 / 0.680424 (-0.436732) \| 0.030331 / 0.534201 (-0.503870) \| 0.492186 / 0.579283 (-0.087097) \| 0.632583 / 0.434364 (0.198219) \| 0.576265 / 0.540337 (0.035927) \| 0.713165 / 1.386936 (-0.673771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"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) \| 0.008916 / 0.011353 (-0.002437) \| 0.004737 / 0.011008 (-0.006271) \| 0.134271 / 0.038508 (0.095763) \| 0.054472 / 0.023109 (0.031363) \| 0.380942 / 0.275898 (0.105044) \| 0.474138 / 0.323480 (0.150658) \| 0.007917 / 0.007986 (-0.000068) \| 0.003748 / 0.004328 (-0.000580) \| 0.092765 / 0.004250 (0.088515) \| 0.077873 / 0.037052 (0.040821) \| 0.397533 / 0.258489 (0.139043) \| 0.454737 / 0.293841 (0.160896) \| 0.039901 / 0.128546 (-0.088645) \| 0.010188 / 0.075646 (-0.065458) \| 0.447312 / 0.419271 (0.028040) \| 0.068684 / 0.043533 (0.025151) \| 0.371554 / 0.255139 (0.116415) \| 0.459655 / 0.283200 (0.176455) \| 0.027157 / 0.141683 (-0.114526) \| 1.874643 / 1.452155 (0.422488) \| 2.014800 / 1.492716 (0.522083) \|\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) \| 0.227079 / 0.018006 (0.209073) \| 0.483241 / 0.000490 (0.482751) \| 0.012404 / 0.000200 (0.012204) \| 0.000409 / 0.000054 (0.000354) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033135 / 0.037411 (-0.004277) \| 0.137782 / 0.014526 (0.123257) \| 0.142951 / 0.176557 (-0.033605) \| 0.209825 / 0.737135 (-0.527311) \| 0.152438 / 0.296338 (-0.143900) \|\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) \| 0.513066 / 0.215209 (0.297857) \| 5.122776 / 2.077655 (3.045121) \| 2.399270 / 1.504120 (0.895150) \| 2.180143 / 1.541195 (0.638949) \| 2.286395 / 1.468490 (0.817905) \| 0.641866 / 4.584777 (-3.942911) \| 4.694922 / 3.745712 (0.949210) \| 2.543390 / 5.269862 (-2.726472) \| 1.398592 / 4.565676 (-3.167084) \| 0.088662 / 0.424275 (-0.335613) \| 0.015854 / 0.007607 (0.008247) \| 0.688891 / 0.226044 (0.462847) \| 6.370148 / 2.268929 (4.101220) \| 2.949974 / 55.444624 (-52.494650) \| 2.538049 / 6.876477 (-4.338428) \| 2.699380 / 2.142072 (0.557308) \| 0.792670 / 4.805227 (-4.012557) \| 0.169126 / 6.500664 (-6.331538) \| 0.078511 / 0.075469 (0.003042) \|\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) \| 1.609119 / 1.841788 (-0.232669) \| 18.785069 / 8.074308 (10.710761) \| 16.670783 / 10.191392 (6.479391) \| 0.213081 / 0.680424 (-0.467343) \| 0.023904 / 0.534201 (-0.510296) \| 0.567720 / 0.579283 (-0.011564) \| 0.505806 / 0.434364 (0.071442) \| 0.649466 / 0.540337 (0.109129) \| 0.773174 / 1.386936 (-0.613762) \|\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) \| 0.008036 / 0.011353 (-0.003317) \| 0.004808 / 0.011008 (-0.006201) \| 0.094316 / 0.038508 (0.055808) \| 0.056174 / 0.023109 (0.033065) \| 0.481618 / 0.275898 (0.205720) \| 0.565300 / 0.323480 (0.241820) \| 0.006339 / 0.007986 (-0.001646) \| 0.003950 / 0.004328 (-0.000379) \| 0.093389 / 0.004250 (0.089139) \| 0.076163 / 0.037052 (0.039111) \| 0.489013 / 0.258489 (0.230524) \| 0.565451 / 0.293841 (0.271611) \| 0.039392 / 0.128546 (-0.089155) \| 0.010553 / 0.075646 (-0.065093) \| 0.101406 / 0.419271 (-0.317865) \| 0.062355 / 0.043533 (0.018822) \| 0.470461 / 0.255139 (0.215322) \| 0.502574 / 0.283200 (0.219375) \| 0.030196 / 0.141683 (-0.111486) \| 1.893926 / 1.452155 (0.441771) \| 1.958902 / 1.492716 (0.466185) \|\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) \| 0.198074 / 0.018006 (0.180068) \| 0.476828 / 0.000490 (0.476338) \| 0.003457 / 0.000200 (0.003257) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037576 / 0.037411 (0.000165) \| 0.146663 / 0.014526 (0.132138) \| 0.152969 / 0.176557 (-0.023588) \| 0.218683 / 0.737135 (-0.518452) \| 0.161552 / 0.296338 (-0.134786) \|\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) \| 0.525988 / 0.215209 (0.310779) \| 5.234673 / 2.077655 (3.157018) \| 2.571668 / 1.504120 (1.067548) \| 2.339760 / 1.541195 (0.798565) \| 2.422886 / 1.468490 (0.954395) \| 0.651537 / 4.584777 (-3.933240) \| 4.811148 / 3.745712 (1.065436) \| 4.451165 / 5.269862 (-0.818697) \| 2.016283 / 4.565676 (-2.549394) \| 0.096393 / 0.424275 (-0.327882) \| 0.015222 / 0.007607 (0.007615) \| 0.739132 / 0.226044 (0.513087) \| 6.813327 / 2.268929 (4.544399) \| 3.169018 / 55.444624 (-52.275606) \| 2.783120 / 6.876477 (-4.093356) \| 2.918979 / 2.142072 (0.776907) \| 0.797476 / 4.805227 (-4.007751) \| 0.171038 / 6.500664 (-6.329626) \| 0.079878 / 0.075469 (0.004409) \|\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) \| 1.595082 / 1.841788 (-0.246705) \| 19.685844 / 8.074308 (11.611536) \| 17.518989 / 10.191392 (7.327597) \| 0.220015 / 0.680424 (-0.460409) \| 0.026351 / 0.534201 (-0.507850) \| 0.578977 / 0.579283 (-0.000306) \| 0.549564 / 0.434364 (0.115200) \| 0.667564 / 0.540337 (0.127227) \| 0.802121 / 1.386936 (-0.584815) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]
1,781,851,513	5,999	Getting a 409 error while loading xglue dataset	closed	2023-06-30T04:13:54	2023-06-30T05:57:23	2023-06-30T05:57:22	https://github.com/huggingface/datasets/issues/5999	null	Praful932	false	[ "Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5" ]
1,781,805,018	5,998	The current implementation has a potential bug in the sort method	closed	2023-06-30T03:16:57	2023-06-30T14:21:03	2023-06-30T14:11:25	https://github.com/huggingface/datasets/issues/5998	null	wangyuxinwhy	false	[ "Thanks for reporting, @wangyuxinwhy. " ]
1,781,582,818	5,997	extend the map function so it can wrap around long text that does not fit in the context window	open	2023-06-29T22:15:21	2023-07-03T17:58:52	null	https://github.com/huggingface/datasets/issues/5997	null	siddhsql	false	[ "I just noticed the [docs](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2881C11-L2881C200) say:\r\n\r\n>If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.\r\n\r\nso maybe this is a bug then.", "All the values in a batch must be of the same length. So one solution is dropping all the input columns:\r\n```python\r\ndata = data.map(lambda samples: tokenizer(samples[\"text\"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nAnother is padding/transforming the input columns to the tokenizer output's length (447). " ]
1,779,294,374	5,996	Deprecate `use_auth_token` in favor of `token`	closed	2023-06-28T16:26:38	2023-07-05T15:22:20	2023-07-03T16:03:33	https://github.com/huggingface/datasets/pull/5996	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5996", "html_url": "https://github.com/huggingface/datasets/pull/5996", "diff_url": "https://github.com/huggingface/datasets/pull/5996.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5996.patch", "merged_at": "2023-07-03T16:03:33" }	mariosasko	true	[ "_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) \| 0.006134 / 0.011353 (-0.005219) \| 0.003816 / 0.011008 (-0.007193) \| 0.098226 / 0.038508 (0.059718) \| 0.036830 / 0.023109 (0.013721) \| 0.314551 / 0.275898 (0.038653) \| 0.372251 / 0.323480 (0.048771) \| 0.004762 / 0.007986 (-0.003224) \| 0.003041 / 0.004328 (-0.001287) \| 0.077651 / 0.004250 (0.073401) \| 0.052445 / 0.037052 (0.015393) \| 0.324632 / 0.258489 (0.066143) \| 0.365724 / 0.293841 (0.071883) \| 0.028069 / 0.128546 (-0.100477) \| 0.008444 / 0.075646 (-0.067203) \| 0.312767 / 0.419271 (-0.106505) \| 0.047773 / 0.043533 (0.004240) \| 0.305317 / 0.255139 (0.050178) \| 0.332007 / 0.283200 (0.048807) \| 0.018985 / 0.141683 (-0.122698) \| 1.538022 / 1.452155 (0.085868) \| 1.575898 / 1.492716 (0.083182) \|\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) \| 0.204780 / 0.018006 (0.186774) \| 0.428125 / 0.000490 (0.427635) \| 0.003454 / 0.000200 (0.003254) \| 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) \| 0.025064 / 0.037411 (-0.012348) \| 0.099419 / 0.014526 (0.084893) \| 0.111068 / 0.176557 (-0.065489) \| 0.169775 / 0.737135 (-0.567361) \| 0.112067 / 0.296338 (-0.184271) \|\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) \| 0.429642 / 0.215209 (0.214433) \| 4.275556 / 2.077655 (2.197901) \| 1.914658 / 1.504120 (0.410539) \| 1.706556 / 1.541195 (0.165361) \| 1.754228 / 1.468490 (0.285738) \| 0.563669 / 4.584777 (-4.021108) \| 3.391501 / 3.745712 (-0.354211) \| 1.791517 / 5.269862 (-3.478345) \| 1.030704 / 4.565676 (-3.534973) \| 0.070882 / 0.424275 (-0.353393) \| 0.011351 / 0.007607 (0.003744) \| 0.529438 / 0.226044 (0.303394) \| 5.294316 / 2.268929 (3.025387) \| 2.344653 / 55.444624 (-53.099972) \| 1.997468 / 6.876477 (-4.879009) \| 2.108932 / 2.142072 (-0.033140) \| 0.676794 / 4.805227 (-4.128433) \| 0.135058 / 6.500664 (-6.365607) \| 0.065857 / 0.075469 (-0.009612) \|\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) \| 1.231864 / 1.841788 (-0.609924) \| 13.986694 / 8.074308 (5.912386) \| 13.306600 / 10.191392 (3.115208) \| 0.145520 / 0.680424 (-0.534904) \| 0.016717 / 0.534201 (-0.517484) \| 0.366303 / 0.579283 (-0.212980) \| 0.391637 / 0.434364 (-0.042727) \| 0.425445 / 0.540337 (-0.114892) \| 0.507719 / 1.386936 (-0.879217) \|\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) \| 0.006236 / 0.011353 (-0.005116) \| 0.003766 / 0.011008 (-0.007242) \| 0.076794 / 0.038508 (0.038286) \| 0.037210 / 0.023109 (0.014101) \| 0.378387 / 0.275898 (0.102489) \| 0.425456 / 0.323480 (0.101977) \| 0.004694 / 0.007986 (-0.003291) \| 0.002921 / 0.004328 (-0.001407) \| 0.076985 / 0.004250 (0.072735) \| 0.052188 / 0.037052 (0.015136) \| 0.394385 / 0.258489 (0.135896) \| 0.432527 / 0.293841 (0.138686) \| 0.029091 / 0.128546 (-0.099455) \| 0.008364 / 0.075646 (-0.067282) \| 0.082583 / 0.419271 (-0.336689) \| 0.042928 / 0.043533 (-0.000605) \| 0.375321 / 0.255139 (0.120182) \| 0.391719 / 0.283200 (0.108519) \| 0.019388 / 0.141683 (-0.122295) \| 1.550644 / 1.452155 (0.098489) \| 1.604882 / 1.492716 (0.112166) \|\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) \| 0.236859 / 0.018006 (0.218853) \| 0.418528 / 0.000490 (0.418039) \| 0.000388 / 0.000200 (0.000188) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.100644 / 0.014526 (0.086118) \| 0.109102 / 0.176557 (-0.067455) \| 0.161694 / 0.737135 (-0.575441) \| 0.112088 / 0.296338 (-0.184250) \|\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) \| 0.484128 / 0.215209 (0.268919) \| 4.849952 / 2.077655 (2.772297) \| 2.512769 / 1.504120 (1.008649) \| 2.303295 / 1.541195 (0.762100) \| 2.356699 / 1.468490 (0.888209) \| 0.564181 / 4.584777 (-4.020596) \| 3.421393 / 3.745712 (-0.324319) \| 2.570875 / 5.269862 (-2.698987) \| 1.474307 / 4.565676 (-3.091370) \| 0.068035 / 0.424275 (-0.356240) \| 0.011300 / 0.007607 (0.003693) \| 0.587867 / 0.226044 (0.361823) \| 5.862447 / 2.268929 (3.593519) \| 3.004017 / 55.444624 (-52.440607) \| 2.664989 / 6.876477 (-4.211488) \| 2.740020 / 2.142072 (0.597948) \| 0.680840 / 4.805227 (-4.124387) \| 0.137001 / 6.500664 (-6.363663) \| 0.068098 / 0.075469 (-0.007371) \|\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) \| 1.297362 / 1.841788 (-0.544426) \| 14.207891 / 8.074308 (6.133583) \| 14.087562 / 10.191392 (3.896170) \| 0.149514 / 0.680424 (-0.530910) \| 0.016566 / 0.534201 (-0.517635) \| 0.367602 / 0.579283 (-0.211681) \| 0.400692 / 0.434364 (-0.033671) \| 0.432907 / 0.540337 (-0.107431) \| 0.525924 / 1.386936 (-0.861012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ec069feaaf6c28d4e4df76d344693b591a74c3f \"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) \| 0.006223 / 0.011353 (-0.005130) \| 0.003672 / 0.011008 (-0.007336) \| 0.097451 / 0.038508 (0.058943) \| 0.036243 / 0.023109 (0.013133) \| 0.375650 / 0.275898 (0.099752) \| 0.431652 / 0.323480 (0.108172) \| 0.004758 / 0.007986 (-0.003227) \| 0.002941 / 0.004328 (-0.001387) \| 0.077383 / 0.004250 (0.073132) \| 0.055342 / 0.037052 (0.018289) \| 0.390335 / 0.258489 (0.131846) \| 0.427867 / 0.293841 (0.134026) \| 0.027619 / 0.128546 (-0.100927) \| 0.008244 / 0.075646 (-0.067402) \| 0.313499 / 0.419271 (-0.105773) \| 0.054987 / 0.043533 (0.011454) \| 0.394044 / 0.255139 (0.138905) \| 0.398784 / 0.283200 (0.115584) \| 0.026499 / 0.141683 (-0.115184) \| 1.496907 / 1.452155 (0.044753) \| 1.554465 / 1.492716 (0.061749) \|\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) \| 0.241197 / 0.018006 (0.223190) \| 0.427856 / 0.000490 (0.427366) \| 0.006264 / 0.000200 (0.006065) \| 0.000218 / 0.000054 (0.000164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025550 / 0.037411 (-0.011862) \| 0.104426 / 0.014526 (0.089901) \| 0.110310 / 0.176557 (-0.066246) \| 0.173813 / 0.737135 (-0.563322) \| 0.112129 / 0.296338 (-0.184209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458806 / 0.215209 (0.243597) \| 4.576351 / 2.077655 (2.498697) \| 2.265670 / 1.504120 (0.761550) \| 2.073230 / 1.541195 (0.532035) \| 2.135283 / 1.468490 (0.666793) \| 0.562506 / 4.584777 (-4.022271) \| 3.375101 / 3.745712 (-0.370611) \| 1.734393 / 5.269862 (-3.535469) \| 1.026622 / 4.565676 (-3.539054) \| 0.068144 / 0.424275 (-0.356131) \| 0.011092 / 0.007607 (0.003485) \| 0.562779 / 0.226044 (0.336734) \| 5.608256 / 2.268929 (3.339328) \| 2.706468 / 55.444624 (-52.738157) \| 2.381607 / 6.876477 (-4.494869) \| 2.451027 / 2.142072 (0.308954) \| 0.671590 / 4.805227 (-4.133637) \| 0.135749 / 6.500664 (-6.364915) \| 0.065389 / 0.075469 (-0.010080) \|\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) \| 1.244806 / 1.841788 (-0.596981) \| 14.042150 / 8.074308 (5.967841) \| 14.246612 / 10.191392 (4.055220) \| 0.134309 / 0.680424 (-0.546114) \| 0.017082 / 0.534201 (-0.517119) \| 0.366043 / 0.579283 (-0.213240) \| 0.400748 / 0.434364 (-0.033616) \| 0.425695 / 0.540337 (-0.114643) \| 0.509355 / 1.386936 (-0.877581) \|\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) \| 0.006134 / 0.011353 (-0.005219) \| 0.003980 / 0.011008 (-0.007028) \| 0.078353 / 0.038508 (0.039845) \| 0.038011 / 0.023109 (0.014902) \| 0.375784 / 0.275898 (0.099886) \| 0.433619 / 0.323480 (0.110139) \| 0.004897 / 0.007986 (-0.003088) \| 0.002981 / 0.004328 (-0.001347) \| 0.077362 / 0.004250 (0.073112) \| 0.056108 / 0.037052 (0.019056) \| 0.395984 / 0.258489 (0.137495) \| 0.427397 / 0.293841 (0.133556) \| 0.029325 / 0.128546 (-0.099221) \| 0.008498 / 0.075646 (-0.067148) \| 0.082478 / 0.419271 (-0.336794) \| 0.044085 / 0.043533 (0.000552) \| 0.389923 / 0.255139 (0.134784) \| 0.391180 / 0.283200 (0.107980) \| 0.022452 / 0.141683 (-0.119231) \| 1.507758 / 1.452155 (0.055603) \| 1.530459 / 1.492716 (0.037743) \|\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) \| 0.230928 / 0.018006 (0.212922) \| 0.408484 / 0.000490 (0.407995) \| 0.000806 / 0.000200 (0.000606) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025183 / 0.037411 (-0.012228) \| 0.102292 / 0.014526 (0.087766) \| 0.108142 / 0.176557 (-0.068415) \| 0.161172 / 0.737135 (-0.575963) \| 0.114476 / 0.296338 (-0.181862) \|\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) \| 0.482978 / 0.215209 (0.267769) \| 4.816103 / 2.077655 (2.738448) \| 2.505567 / 1.504120 (1.001447) \| 2.302598 / 1.541195 (0.761404) \| 2.371238 / 1.468490 (0.902748) \| 0.567467 / 4.584777 (-4.017310) \| 3.363407 / 3.745712 (-0.382306) \| 1.746213 / 5.269862 (-3.523649) \| 1.035468 / 4.565676 (-3.530208) \| 0.068431 / 0.424275 (-0.355844) \| 0.011069 / 0.007607 (0.003462) \| 0.598241 / 0.226044 (0.372196) \| 5.953927 / 2.268929 (3.684999) \| 3.007493 / 55.444624 (-52.437132) \| 2.629399 / 6.876477 (-4.247078) \| 2.737201 / 2.142072 (0.595129) \| 0.682456 / 4.805227 (-4.122771) \| 0.137613 / 6.500664 (-6.363051) \| 0.067941 / 0.075469 (-0.007528) \|\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) \| 1.306015 / 1.841788 (-0.535772) \| 14.359240 / 8.074308 (6.284932) \| 14.187601 / 10.191392 (3.996209) \| 0.138612 / 0.680424 (-0.541812) \| 0.016708 / 0.534201 (-0.517493) \| 0.366365 / 0.579283 (-0.212918) \| 0.396982 / 0.434364 (-0.037382) \| 0.426939 / 0.540337 (-0.113398) \| 0.520064 / 1.386936 (-0.866872) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21d0fd041a5eca02d3ee787396216ac613c662ac \"CML watermark\")\n", "They use `token` and emit a deprecation warning if `use_auth_token` is passed instead (see https://github.com/huggingface/transformers/blob/78a2b19fc84ed55c65f4bf20a901edb7ceb73c5f/src/transformers/modeling_utils.py#L1933). \r\n\r\nI think we can update the `examples` scripts after merging this PR.", "> I think we can update the examples scripts after merging this PR.\r\n\r\nWe should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the `token` arg", "> We should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the token arg\r\n\r\nThis would avoid the warning only for the latest `datasets` release. TBH, I don't think this is worth the hassle, considering how simple it is to remove it.", "<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) \| 0.007644 / 0.011353 (-0.003709) \| 0.004667 / 0.011008 (-0.006341) \| 0.117347 / 0.038508 (0.078839) \| 0.050620 / 0.023109 (0.027510) \| 0.415402 / 0.275898 (0.139504) \| 0.485898 / 0.323480 (0.162418) \| 0.005848 / 0.007986 (-0.002138) \| 0.003736 / 0.004328 (-0.000592) \| 0.089798 / 0.004250 (0.085547) \| 0.069344 / 0.037052 (0.032292) \| 0.441684 / 0.258489 (0.183195) \| 0.468972 / 0.293841 (0.175131) \| 0.036637 / 0.128546 (-0.091909) \| 0.010219 / 0.075646 (-0.065427) \| 0.394293 / 0.419271 (-0.024978) \| 0.061462 / 0.043533 (0.017929) \| 0.409448 / 0.255139 (0.154309) \| 0.431557 / 0.283200 (0.148358) \| 0.027795 / 0.141683 (-0.113888) \| 1.837844 / 1.452155 (0.385690) \| 1.862683 / 1.492716 (0.369967) \|\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) \| 0.230500 / 0.018006 (0.212494) \| 0.483139 / 0.000490 (0.482649) \| 0.006517 / 0.000200 (0.006317) \| 0.000143 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033152 / 0.037411 (-0.004259) \| 0.133673 / 0.014526 (0.119147) \| 0.143853 / 0.176557 (-0.032704) \| 0.215254 / 0.737135 (-0.521882) \| 0.150676 / 0.296338 (-0.145662) \|\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) \| 0.503796 / 0.215209 (0.288587) \| 5.049981 / 2.077655 (2.972326) \| 2.399427 / 1.504120 (0.895307) \| 2.167635 / 1.541195 (0.626441) \| 2.257448 / 1.468490 (0.788958) \| 0.641298 / 4.584777 (-3.943479) \| 4.828676 / 3.745712 (1.082964) \| 4.346069 / 5.269862 (-0.923793) \| 2.103890 / 4.565676 (-2.461786) \| 0.079115 / 0.424275 (-0.345160) \| 0.013377 / 0.007607 (0.005770) \| 0.621207 / 0.226044 (0.395162) \| 6.190939 / 2.268929 (3.922011) \| 2.920129 / 55.444624 (-52.524495) \| 2.549225 / 6.876477 (-4.327252) \| 2.719221 / 2.142072 (0.577149) \| 0.790949 / 4.805227 (-4.014278) \| 0.172032 / 6.500664 (-6.328632) \| 0.077779 / 0.075469 (0.002310) \|\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) \| 1.432572 / 1.841788 (-0.409216) \| 21.000031 / 8.074308 (12.925723) \| 17.555093 / 10.191392 (7.363701) \| 0.166646 / 0.680424 (-0.513778) \| 0.020451 / 0.534201 (-0.513750) \| 0.488767 / 0.579283 (-0.090516) \| 0.737036 / 0.434364 (0.302672) \| 0.621694 / 0.540337 (0.081356) \| 0.732074 / 1.386936 (-0.654862) \|\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) \| 0.008198 / 0.011353 (-0.003155) \| 0.004987 / 0.011008 (-0.006021) \| 0.090714 / 0.038508 (0.052206) \| 0.053379 / 0.023109 (0.030270) \| 0.425199 / 0.275898 (0.149301) \| 0.514036 / 0.323480 (0.190556) \| 0.006043 / 0.007986 (-0.001943) \| 0.003888 / 0.004328 (-0.000441) \| 0.088294 / 0.004250 (0.084043) \| 0.073024 / 0.037052 (0.035971) \| 0.435983 / 0.258489 (0.177494) \| 0.514293 / 0.293841 (0.220452) \| 0.039451 / 0.128546 (-0.089095) \| 0.010439 / 0.075646 (-0.065207) \| 0.096885 / 0.419271 (-0.322387) \| 0.060165 / 0.043533 (0.016632) \| 0.421053 / 0.255139 (0.165914) \| 0.455545 / 0.283200 (0.172345) \| 0.027234 / 0.141683 (-0.114449) \| 1.768975 / 1.452155 (0.316820) \| 1.842853 / 1.492716 (0.350137) \|\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) \| 0.278940 / 0.018006 (0.260933) \| 0.480709 / 0.000490 (0.480219) \| 0.000436 / 0.000200 (0.000236) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034900 / 0.037411 (-0.002511) \| 0.144893 / 0.014526 (0.130368) \| 0.149567 / 0.176557 (-0.026989) \| 0.213200 / 0.737135 (-0.523935) \| 0.156735 / 0.296338 (-0.139604) \|\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) \| 0.535897 / 0.215209 (0.320687) \| 5.336998 / 2.077655 (3.259343) \| 2.685854 / 1.504120 (1.181734) \| 2.470177 / 1.541195 (0.928983) \| 2.547495 / 1.468490 (1.079004) \| 0.642830 / 4.584777 (-3.941947) \| 4.595866 / 3.745712 (0.850154) \| 2.186696 / 5.269862 (-3.083165) \| 1.317969 / 4.565676 (-3.247708) \| 0.079268 / 0.424275 (-0.345007) \| 0.013792 / 0.007607 (0.006185) \| 0.662236 / 0.226044 (0.436192) \| 6.604775 / 2.268929 (4.335847) \| 3.355888 / 55.444624 (-52.088736) \| 2.968911 / 6.876477 (-3.907565) \| 3.121862 / 2.142072 (0.979790) \| 0.794752 / 4.805227 (-4.010475) \| 0.170800 / 6.500664 (-6.329864) \| 0.078393 / 0.075469 (0.002924) \|\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) \| 1.601605 / 1.841788 (-0.240183) \| 20.743553 / 8.074308 (12.669245) \| 17.543968 / 10.191392 (7.352576) \| 0.221884 / 0.680424 (-0.458540) \| 0.020779 / 0.534201 (-0.513422) \| 0.479677 / 0.579283 (-0.099606) \| 0.516207 / 0.434364 (0.081843) \| 0.564046 / 0.540337 (0.023709) \| 0.711336 / 1.386936 (-0.675600) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bb4346434912eb405ce3f3e9f21dc25a2fe85 \"CML watermark\")\n", "Yes, sounds great! Thanks", "yup" ]
1,777,088,925	5,995	Support returning dataframe in map transform	closed	2023-06-27T14:15:08	2023-06-28T13:56:02	2023-06-28T13:46:33	https://github.com/huggingface/datasets/pull/5995	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5995", "html_url": "https://github.com/huggingface/datasets/pull/5995", "diff_url": "https://github.com/huggingface/datasets/pull/5995.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5995.patch", "merged_at": "2023-06-28T13:46:33" }	mariosasko	true	[ "_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) \| 0.009725 / 0.011353 (-0.001628) \| 0.006014 / 0.011008 (-0.004994) \| 0.136039 / 0.038508 (0.097531) \| 0.049685 / 0.023109 (0.026576) \| 0.492967 / 0.275898 (0.217068) \| 0.553775 / 0.323480 (0.230295) \| 0.007421 / 0.007986 (-0.000564) \| 0.004686 / 0.004328 (0.000357) \| 0.106639 / 0.004250 (0.102389) \| 0.073483 / 0.037052 (0.036431) \| 0.507194 / 0.258489 (0.248705) \| 0.535760 / 0.293841 (0.241919) \| 0.049666 / 0.128546 (-0.078880) \| 0.014139 / 0.075646 (-0.061507) \| 0.435459 / 0.419271 (0.016188) \| 0.076026 / 0.043533 (0.032493) \| 0.454542 / 0.255139 (0.199403) \| 0.512724 / 0.283200 (0.229524) \| 0.034969 / 0.141683 (-0.106713) \| 1.881048 / 1.452155 (0.428893) \| 1.959915 / 1.492716 (0.467199) \|\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) \| 0.265322 / 0.018006 (0.247316) \| 0.573963 / 0.000490 (0.573474) \| 0.017493 / 0.000200 (0.017293) \| 0.000637 / 0.000054 (0.000582) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028712 / 0.037411 (-0.008699) \| 0.149554 / 0.014526 (0.135029) \| 0.130013 / 0.176557 (-0.046544) \| 0.203408 / 0.737135 (-0.533727) \| 0.144778 / 0.296338 (-0.151561) \|\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) \| 0.664198 / 0.215209 (0.448989) \| 6.418054 / 2.077655 (4.340399) \| 2.602338 / 1.504120 (1.098219) \| 2.212992 / 1.541195 (0.671797) \| 2.214309 / 1.468490 (0.745819) \| 0.914772 / 4.584777 (-3.670005) \| 5.824831 / 3.745712 (2.079119) \| 2.865381 / 5.269862 (-2.404481) \| 1.906020 / 4.565676 (-2.659657) \| 0.106947 / 0.424275 (-0.317328) \| 0.013467 / 0.007607 (0.005860) \| 0.834556 / 0.226044 (0.608512) \| 8.237078 / 2.268929 (5.968150) \| 3.380919 / 55.444624 (-52.063705) \| 2.656713 / 6.876477 (-4.219764) \| 2.834941 / 2.142072 (0.692869) \| 1.151241 / 4.805227 (-3.653986) \| 0.220860 / 6.500664 (-6.279804) \| 0.080781 / 0.075469 (0.005312) \|\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) \| 1.655128 / 1.841788 (-0.186660) \| 18.696108 / 8.074308 (10.621800) \| 22.882108 / 10.191392 (12.690716) \| 0.236041 / 0.680424 (-0.444383) \| 0.031073 / 0.534201 (-0.503128) \| 0.525263 / 0.579283 (-0.054021) \| 0.632933 / 0.434364 (0.198569) \| 0.707228 / 0.540337 (0.166890) \| 0.753508 / 1.386936 (-0.633428) \|\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) \| 0.009875 / 0.011353 (-0.001478) \| 0.005135 / 0.011008 (-0.005873) \| 0.101307 / 0.038508 (0.062799) \| 0.044895 / 0.023109 (0.021786) \| 0.497824 / 0.275898 (0.221926) \| 0.573098 / 0.323480 (0.249618) \| 0.006669 / 0.007986 (-0.001317) \| 0.004289 / 0.004328 (-0.000039) \| 0.105824 / 0.004250 (0.101573) \| 0.061002 / 0.037052 (0.023950) \| 0.510127 / 0.258489 (0.251638) \| 0.581387 / 0.293841 (0.287546) \| 0.052843 / 0.128546 (-0.075703) \| 0.015506 / 0.075646 (-0.060140) \| 0.116057 / 0.419271 (-0.303215) \| 0.063444 / 0.043533 (0.019912) \| 0.479366 / 0.255139 (0.224227) \| 0.518419 / 0.283200 (0.235220) \| 0.034876 / 0.141683 (-0.106806) \| 2.018446 / 1.452155 (0.566292) \| 1.960755 / 1.492716 (0.468039) \|\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) \| 0.269077 / 0.018006 (0.251070) \| 0.606059 / 0.000490 (0.605569) \| 0.000488 / 0.000200 (0.000288) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032465 / 0.037411 (-0.004946) \| 0.136517 / 0.014526 (0.121991) \| 0.147740 / 0.176557 (-0.028816) \| 0.193802 / 0.737135 (-0.543334) \| 0.151876 / 0.296338 (-0.144462) \|\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) \| 0.709866 / 0.215209 (0.494657) \| 6.848193 / 2.077655 (4.770538) \| 3.310853 / 1.504120 (1.806733) \| 2.940813 / 1.541195 (1.399619) \| 2.934934 / 1.468490 (1.466444) \| 0.927104 / 4.584777 (-3.657673) \| 5.921607 / 3.745712 (2.175895) \| 4.926558 / 5.269862 (-0.343303) \| 2.853269 / 4.565676 (-1.712407) \| 0.120278 / 0.424275 (-0.303998) \| 0.015468 / 0.007607 (0.007861) \| 0.820509 / 0.226044 (0.594464) \| 8.263136 / 2.268929 (5.994208) \| 3.780214 / 55.444624 (-51.664410) \| 3.108482 / 6.876477 (-3.767995) \| 3.101544 / 2.142072 (0.959471) \| 1.165539 / 4.805227 (-3.639688) \| 0.229215 / 6.500664 (-6.271449) \| 0.079862 / 0.075469 (0.004393) \|\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) \| 1.775071 / 1.841788 (-0.066717) \| 19.327621 / 8.074308 (11.253313) \| 23.057537 / 10.191392 (12.866145) \| 0.250649 / 0.680424 (-0.429775) \| 0.029767 / 0.534201 (-0.504434) \| 0.554774 / 0.579283 (-0.024509) \| 0.651919 / 0.434364 (0.217555) \| 0.651641 / 0.540337 (0.111304) \| 0.762386 / 1.386936 (-0.624550) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fdc3ce7060366f480621e8640903c9ab476164e7 \"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) \| 0.005997 / 0.011353 (-0.005356) \| 0.003892 / 0.011008 (-0.007116) \| 0.098020 / 0.038508 (0.059512) \| 0.042584 / 0.023109 (0.019475) \| 0.317909 / 0.275898 (0.042011) \| 0.395042 / 0.323480 (0.071563) \| 0.005358 / 0.007986 (-0.002628) \| 0.003266 / 0.004328 (-0.001062) \| 0.076698 / 0.004250 (0.072447) \| 0.062331 / 0.037052 (0.025279) \| 0.334900 / 0.258489 (0.076411) \| 0.379355 / 0.293841 (0.085514) \| 0.030815 / 0.128546 (-0.097731) \| 0.008596 / 0.075646 (-0.067050) \| 0.327739 / 0.419271 (-0.091533) \| 0.054061 / 0.043533 (0.010528) \| 0.311044 / 0.255139 (0.055905) \| 0.336705 / 0.283200 (0.053506) \| 0.022785 / 0.141683 (-0.118898) \| 1.516793 / 1.452155 (0.064639) \| 1.590435 / 1.492716 (0.097719) \|\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) \| 0.289157 / 0.018006 (0.271151) \| 0.531074 / 0.000490 (0.530585) \| 0.004672 / 0.000200 (0.004472) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026173 / 0.037411 (-0.011238) \| 0.105723 / 0.014526 (0.091197) \| 0.118010 / 0.176557 (-0.058547) \| 0.178062 / 0.737135 (-0.559073) \| 0.120059 / 0.296338 (-0.176279) \|\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) \| 0.410870 / 0.215209 (0.195661) \| 4.042183 / 2.077655 (1.964528) \| 1.830059 / 1.504120 (0.325939) \| 1.638996 / 1.541195 (0.097802) \| 1.701368 / 1.468490 (0.232878) \| 0.529915 / 4.584777 (-4.054861) \| 3.693308 / 3.745712 (-0.052404) \| 1.827875 / 5.269862 (-3.441986) \| 1.063237 / 4.565676 (-3.502440) \| 0.065368 / 0.424275 (-0.358907) \| 0.010986 / 0.007607 (0.003379) \| 0.509399 / 0.226044 (0.283354) \| 5.092739 / 2.268929 (2.823810) \| 2.293490 / 55.444624 (-53.151135) \| 1.958742 / 6.876477 (-4.917735) \| 2.024985 / 2.142072 (-0.117088) \| 0.646978 / 4.805227 (-4.158249) \| 0.138616 / 6.500664 (-6.362048) \| 0.062101 / 0.075469 (-0.013368) \|\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) \| 1.202016 / 1.841788 (-0.639772) \| 14.493204 / 8.074308 (6.418896) \| 12.992160 / 10.191392 (2.800768) \| 0.188922 / 0.680424 (-0.491502) \| 0.017594 / 0.534201 (-0.516606) \| 0.399917 / 0.579283 (-0.179367) \| 0.429760 / 0.434364 (-0.004604) \| 0.497906 / 0.540337 (-0.042431) \| 0.608745 / 1.386936 (-0.778191) \|\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) \| 0.006164 / 0.011353 (-0.005189) \| 0.003980 / 0.011008 (-0.007028) \| 0.074676 / 0.038508 (0.036168) \| 0.041337 / 0.023109 (0.018228) \| 0.400981 / 0.275898 (0.125083) \| 0.448791 / 0.323480 (0.125312) \| 0.004063 / 0.007986 (-0.003923) \| 0.004443 / 0.004328 (0.000114) \| 0.075011 / 0.004250 (0.070760) \| 0.056494 / 0.037052 (0.019441) \| 0.402054 / 0.258489 (0.143565) \| 0.446122 / 0.293841 (0.152281) \| 0.031752 / 0.128546 (-0.096794) \| 0.008835 / 0.075646 (-0.066811) \| 0.081226 / 0.419271 (-0.338046) \| 0.051501 / 0.043533 (0.007969) \| 0.383674 / 0.255139 (0.128535) \| 0.405524 / 0.283200 (0.122325) \| 0.025929 / 0.141683 (-0.115754) \| 1.492985 / 1.452155 (0.040830) \| 1.541601 / 1.492716 (0.048885) \|\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) \| 0.305149 / 0.018006 (0.287142) \| 0.497259 / 0.000490 (0.496770) \| 0.000420 / 0.000200 (0.000220) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027933 / 0.037411 (-0.009479) \| 0.111900 / 0.014526 (0.097374) \| 0.124879 / 0.176557 (-0.051678) \| 0.178952 / 0.737135 (-0.558184) \| 0.127698 / 0.296338 (-0.168640) \|\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) \| 0.448525 / 0.215209 (0.233316) \| 4.486791 / 2.077655 (2.409137) \| 2.256687 / 1.504120 (0.752567) \| 2.061078 / 1.541195 (0.519884) \| 2.078924 / 1.468490 (0.610434) \| 0.534412 / 4.584777 (-4.050365) \| 3.721098 / 3.745712 (-0.024614) \| 1.818735 / 5.269862 (-3.451127) \| 1.104198 / 4.565676 (-3.461479) \| 0.066277 / 0.424275 (-0.357998) \| 0.011441 / 0.007607 (0.003834) \| 0.550140 / 0.226044 (0.324095) \| 5.498079 / 2.268929 (3.229150) \| 2.717398 / 55.444624 (-52.727227) \| 2.410194 / 6.876477 (-4.466283) \| 2.405304 / 2.142072 (0.263231) \| 0.665432 / 4.805227 (-4.139796) \| 0.141488 / 6.500664 (-6.359177) \| 0.064051 / 0.075469 (-0.011419) \|\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) \| 1.272334 / 1.841788 (-0.569454) \| 14.901608 / 8.074308 (6.827300) \| 14.287857 / 10.191392 (4.096465) \| 0.165337 / 0.680424 (-0.515086) \| 0.017402 / 0.534201 (-0.516799) \| 0.398120 / 0.579283 (-0.181163) \| 0.416539 / 0.434364 (-0.017825) \| 0.463890 / 0.540337 (-0.076447) \| 0.567909 / 1.386936 (-0.819027) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#504ec0f2e00ee38e0993ed1e4f1e10f1eefaea0d \"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) \| 0.009434 / 0.011353 (-0.001919) \| 0.005567 / 0.011008 (-0.005441) \| 0.122652 / 0.038508 (0.084144) \| 0.050177 / 0.023109 (0.027067) \| 0.384292 / 0.275898 (0.108394) \| 0.446608 / 0.323480 (0.123128) \| 0.006502 / 0.007986 (-0.001484) \| 0.004523 / 0.004328 (0.000194) \| 0.100581 / 0.004250 (0.096331) \| 0.073615 / 0.037052 (0.036563) \| 0.420179 / 0.258489 (0.161690) \| 0.474631 / 0.293841 (0.180790) \| 0.047942 / 0.128546 (-0.080604) \| 0.013864 / 0.075646 (-0.061783) \| 0.419384 / 0.419271 (0.000112) \| 0.088317 / 0.043533 (0.044784) \| 0.379620 / 0.255139 (0.124481) \| 0.412639 / 0.283200 (0.129440) \| 0.048947 / 0.141683 (-0.092736) \| 1.823498 / 1.452155 (0.371343) \| 1.966629 / 1.492716 (0.473913) \|\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) \| 0.300669 / 0.018006 (0.282663) \| 0.593499 / 0.000490 (0.593009) \| 0.007247 / 0.000200 (0.007047) \| 0.000114 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030556 / 0.037411 (-0.006856) \| 0.119252 / 0.014526 (0.104726) \| 0.131403 / 0.176557 (-0.045153) \| 0.201845 / 0.737135 (-0.535291) \| 0.139350 / 0.296338 (-0.156989) \|\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) \| 0.652400 / 0.215209 (0.437191) \| 6.536540 / 2.077655 (4.458886) \| 2.644565 / 1.504120 (1.140445) \| 2.245181 / 1.541195 (0.703986) \| 2.316030 / 1.468490 (0.847540) \| 0.922535 / 4.584777 (-3.662242) \| 5.469065 / 3.745712 (1.723353) \| 2.800489 / 5.269862 (-2.469373) \| 1.749042 / 4.565676 (-2.816635) \| 0.108444 / 0.424275 (-0.315831) \| 0.015651 / 0.007607 (0.008044) \| 0.846085 / 0.226044 (0.620041) \| 8.018460 / 2.268929 (5.749531) \| 3.338710 / 55.444624 (-52.105914) \| 2.675998 / 6.876477 (-4.200479) \| 2.918550 / 2.142072 (0.776478) \| 1.135145 / 4.805227 (-3.670082) \| 0.215165 / 6.500664 (-6.285499) \| 0.082066 / 0.075469 (0.006597) \|\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) \| 1.561661 / 1.841788 (-0.280127) \| 18.519035 / 8.074308 (10.444727) \| 19.046300 / 10.191392 (8.854908) \| 0.236890 / 0.680424 (-0.443534) \| 0.027681 / 0.534201 (-0.506520) \| 0.511998 / 0.579283 (-0.067285) \| 0.591627 / 0.434364 (0.157264) \| 0.562021 / 0.540337 (0.021683) \| 0.679354 / 1.386936 (-0.707582) \|\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) \| 0.009643 / 0.011353 (-0.001710) \| 0.005768 / 0.011008 (-0.005241) \| 0.104430 / 0.038508 (0.065922) \| 0.050044 / 0.023109 (0.026935) \| 0.464117 / 0.275898 (0.188219) \| 0.518439 / 0.323480 (0.194959) \| 0.006935 / 0.007986 (-0.001051) \| 0.004316 / 0.004328 (-0.000013) \| 0.094330 / 0.004250 (0.090080) \| 0.071451 / 0.037052 (0.034399) \| 0.492248 / 0.258489 (0.233759) \| 0.555740 / 0.293841 (0.261899) \| 0.047836 / 0.128546 (-0.080711) \| 0.014788 / 0.075646 (-0.060859) \| 0.107590 / 0.419271 (-0.311682) \| 0.064396 / 0.043533 (0.020863) \| 0.451529 / 0.255139 (0.196390) \| 0.475025 / 0.283200 (0.191826) \| 0.040006 / 0.141683 (-0.101677) \| 1.797107 / 1.452155 (0.344953) \| 1.879261 / 1.492716 (0.386545) \|\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) \| 0.298458 / 0.018006 (0.280451) \| 0.613022 / 0.000490 (0.612532) \| 0.003582 / 0.000200 (0.003382) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030179 / 0.037411 (-0.007232) \| 0.123286 / 0.014526 (0.108760) \| 0.132070 / 0.176557 (-0.044486) \| 0.190883 / 0.737135 (-0.546252) \| 0.138526 / 0.296338 (-0.157812) \|\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) \| 0.666908 / 0.215209 (0.451699) \| 6.489035 / 2.077655 (4.411381) \| 2.897027 / 1.504120 (1.392907) \| 2.565150 / 1.541195 (1.023956) \| 2.504827 / 1.468490 (1.036336) \| 0.916112 / 4.584777 (-3.668665) \| 5.651751 / 3.745712 (1.906039) \| 2.743382 / 5.269862 (-2.526479) \| 1.773338 / 4.565676 (-2.792338) \| 0.128764 / 0.424275 (-0.295511) \| 0.013140 / 0.007607 (0.005533) \| 0.803281 / 0.226044 (0.577236) \| 8.258874 / 2.268929 (5.989945) \| 3.633260 / 55.444624 (-51.811364) \| 2.878827 / 6.876477 (-3.997649) \| 2.977178 / 2.142072 (0.835106) \| 1.130467 / 4.805227 (-3.674760) \| 0.226381 / 6.500664 (-6.274283) \| 0.081550 / 0.075469 (0.006081) \|\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) \| 1.842927 / 1.841788 (0.001139) \| 18.411520 / 8.074308 (10.337212) \| 21.118228 / 10.191392 (10.926836) \| 0.231526 / 0.680424 (-0.448898) \| 0.029300 / 0.534201 (-0.504901) \| 0.527450 / 0.579283 (-0.051834) \| 0.618873 / 0.434364 (0.184509) \| 0.593314 / 0.540337 (0.052976) \| 0.734430 / 1.386936 (-0.652506) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d2b8854c265b4dc202e480427890f472b34ea15 \"CML watermark\")\n" ]
1,776,829,004	5,994	Fix select_columns columns order	closed	2023-06-27T12:32:46	2023-06-27T15:40:47	2023-06-27T15:32:43	https://github.com/huggingface/datasets/pull/5994	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5994", "html_url": "https://github.com/huggingface/datasets/pull/5994", "diff_url": "https://github.com/huggingface/datasets/pull/5994.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5994.patch", "merged_at": "2023-06-27T15:32:43" }	lhoestq	true	[ "<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) \| 0.005969 / 0.011353 (-0.005384) \| 0.003687 / 0.011008 (-0.007321) \| 0.100843 / 0.038508 (0.062335) \| 0.036912 / 0.023109 (0.013803) \| 0.312389 / 0.275898 (0.036491) \| 0.370335 / 0.323480 (0.046855) \| 0.003434 / 0.007986 (-0.004552) \| 0.003710 / 0.004328 (-0.000619) \| 0.076899 / 0.004250 (0.072648) \| 0.053647 / 0.037052 (0.016594) \| 0.324825 / 0.258489 (0.066336) \| 0.367711 / 0.293841 (0.073870) \| 0.028079 / 0.128546 (-0.100467) \| 0.008326 / 0.075646 (-0.067320) \| 0.312342 / 0.419271 (-0.106930) \| 0.047423 / 0.043533 (0.003890) \| 0.321063 / 0.255139 (0.065924) \| 0.336508 / 0.283200 (0.053308) \| 0.019973 / 0.141683 (-0.121710) \| 1.529334 / 1.452155 (0.077179) \| 1.573746 / 1.492716 (0.081030) \|\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) \| 0.210849 / 0.018006 (0.192843) \| 0.418798 / 0.000490 (0.418309) \| 0.007347 / 0.000200 (0.007147) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022718 / 0.037411 (-0.014694) \| 0.098400 / 0.014526 (0.083874) \| 0.106590 / 0.176557 (-0.069967) \| 0.168460 / 0.737135 (-0.568675) \| 0.108401 / 0.296338 (-0.187938) \|\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) \| 0.443066 / 0.215209 (0.227857) \| 4.416658 / 2.077655 (2.339003) \| 2.088844 / 1.504120 (0.584724) \| 1.879564 / 1.541195 (0.338369) \| 1.933815 / 1.468490 (0.465325) \| 0.565085 / 4.584777 (-4.019692) \| 3.412440 / 3.745712 (-0.333273) \| 1.754686 / 5.269862 (-3.515175) \| 1.024576 / 4.565676 (-3.541100) \| 0.067909 / 0.424275 (-0.356366) \| 0.011054 / 0.007607 (0.003447) \| 0.534748 / 0.226044 (0.308703) \| 5.351457 / 2.268929 (3.082529) \| 2.517368 / 55.444624 (-52.927256) \| 2.182762 / 6.876477 (-4.693715) \| 2.238205 / 2.142072 (0.096133) \| 0.672962 / 4.805227 (-4.132265) \| 0.136098 / 6.500664 (-6.364566) \| 0.066534 / 0.075469 (-0.008935) \|\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) \| 1.281241 / 1.841788 (-0.560547) \| 13.872881 / 8.074308 (5.798573) \| 13.161023 / 10.191392 (2.969631) \| 0.130011 / 0.680424 (-0.550412) \| 0.016759 / 0.534201 (-0.517442) \| 0.359802 / 0.579283 (-0.219481) \| 0.392577 / 0.434364 (-0.041787) \| 0.427742 / 0.540337 (-0.112595) \| 0.522241 / 1.386936 (-0.864695) \|\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) \| 0.005985 / 0.011353 (-0.005368) \| 0.003705 / 0.011008 (-0.007304) \| 0.077699 / 0.038508 (0.039191) \| 0.035686 / 0.023109 (0.012577) \| 0.420356 / 0.275898 (0.144458) \| 0.476753 / 0.323480 (0.153273) \| 0.003510 / 0.007986 (-0.004475) \| 0.002807 / 0.004328 (-0.001521) \| 0.077151 / 0.004250 (0.072901) \| 0.046420 / 0.037052 (0.009368) \| 0.391781 / 0.258489 (0.133292) \| 0.461128 / 0.293841 (0.167287) \| 0.027847 / 0.128546 (-0.100699) \| 0.008322 / 0.075646 (-0.067324) \| 0.082768 / 0.419271 (-0.336503) \| 0.042629 / 0.043533 (-0.000904) \| 0.405745 / 0.255139 (0.150606) \| 0.430797 / 0.283200 (0.147598) \| 0.019832 / 0.141683 (-0.121851) \| 1.556208 / 1.452155 (0.104054) \| 1.612166 / 1.492716 (0.119450) \|\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) \| 0.230633 / 0.018006 (0.212626) \| 0.401667 / 0.000490 (0.401178) \| 0.000776 / 0.000200 (0.000576) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024959 / 0.037411 (-0.012452) \| 0.100560 / 0.014526 (0.086034) \| 0.109175 / 0.176557 (-0.067382) \| 0.159919 / 0.737135 (-0.577217) \| 0.112810 / 0.296338 (-0.183528) \|\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) \| 0.460601 / 0.215209 (0.245392) \| 4.620039 / 2.077655 (2.542385) \| 2.257900 / 1.504120 (0.753780) \| 2.039192 / 1.541195 (0.497997) \| 2.064451 / 1.468490 (0.595961) \| 0.557887 / 4.584777 (-4.026890) \| 3.356100 / 3.745712 (-0.389612) \| 1.703578 / 5.269862 (-3.566284) \| 1.024984 / 4.565676 (-3.540693) \| 0.067602 / 0.424275 (-0.356673) \| 0.011450 / 0.007607 (0.003842) \| 0.563230 / 0.226044 (0.337186) \| 5.632150 / 2.268929 (3.363221) \| 2.698701 / 55.444624 (-52.745924) \| 2.363218 / 6.876477 (-4.513259) \| 2.363997 / 2.142072 (0.221925) \| 0.671260 / 4.805227 (-4.133967) \| 0.136166 / 6.500664 (-6.364499) \| 0.067094 / 0.075469 (-0.008375) \|\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) \| 1.303030 / 1.841788 (-0.538757) \| 14.137277 / 8.074308 (6.062969) \| 13.937631 / 10.191392 (3.746239) \| 0.162626 / 0.680424 (-0.517798) \| 0.016687 / 0.534201 (-0.517514) \| 0.363657 / 0.579283 (-0.215626) \| 0.392021 / 0.434364 (-0.042343) \| 0.427275 / 0.540337 (-0.113062) \| 0.512192 / 1.386936 (-0.874744) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42603528d9bd8c3ab287ed0eadc7fa3d1ef4cfd8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005974 / 0.011353 (-0.005378) \| 0.003947 / 0.011008 (-0.007061) \| 0.098604 / 0.038508 (0.060096) \| 0.036947 / 0.023109 (0.013838) \| 0.311844 / 0.275898 (0.035946) \| 0.375243 / 0.323480 (0.051763) \| 0.003453 / 0.007986 (-0.004533) \| 0.003834 / 0.004328 (-0.000495) \| 0.077943 / 0.004250 (0.073692) \| 0.052956 / 0.037052 (0.015904) \| 0.320812 / 0.258489 (0.062323) \| 0.373963 / 0.293841 (0.080122) \| 0.028382 / 0.128546 (-0.100164) \| 0.008525 / 0.075646 (-0.067121) \| 0.311306 / 0.419271 (-0.107965) \| 0.047029 / 0.043533 (0.003496) \| 0.309933 / 0.255139 (0.054794) \| 0.335114 / 0.283200 (0.051915) \| 0.019629 / 0.141683 (-0.122054) \| 1.569771 / 1.452155 (0.117617) \| 1.585899 / 1.492716 (0.093182) \|\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) \| 0.216565 / 0.018006 (0.198559) \| 0.426717 / 0.000490 (0.426228) \| 0.003609 / 0.000200 (0.003409) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023079 / 0.037411 (-0.014332) \| 0.096954 / 0.014526 (0.082428) \| 0.105398 / 0.176557 (-0.071158) \| 0.165433 / 0.737135 (-0.571703) \| 0.109703 / 0.296338 (-0.186636) \|\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) \| 0.456227 / 0.215209 (0.241018) \| 4.529857 / 2.077655 (2.452202) \| 2.214054 / 1.504120 (0.709934) \| 2.029716 / 1.541195 (0.488521) \| 2.081175 / 1.468490 (0.612685) \| 0.563642 / 4.584777 (-4.021135) \| 3.355393 / 3.745712 (-0.390320) \| 1.765938 / 5.269862 (-3.503924) \| 1.039062 / 4.565676 (-3.526615) \| 0.067952 / 0.424275 (-0.356323) \| 0.011044 / 0.007607 (0.003437) \| 0.556935 / 0.226044 (0.330890) \| 5.588167 / 2.268929 (3.319239) \| 2.667217 / 55.444624 (-52.777407) \| 2.337383 / 6.876477 (-4.539094) \| 2.429590 / 2.142072 (0.287517) \| 0.676972 / 4.805227 (-4.128256) \| 0.135782 / 6.500664 (-6.364882) \| 0.066323 / 0.075469 (-0.009146) \|\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) \| 1.237358 / 1.841788 (-0.604429) \| 13.910492 / 8.074308 (5.836184) \| 13.227275 / 10.191392 (3.035883) \| 0.146857 / 0.680424 (-0.533567) \| 0.016991 / 0.534201 (-0.517210) \| 0.363637 / 0.579283 (-0.215646) \| 0.392462 / 0.434364 (-0.041902) \| 0.450009 / 0.540337 (-0.090329) \| 0.536077 / 1.386936 (-0.850859) \|\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) \| 0.006067 / 0.011353 (-0.005286) \| 0.003851 / 0.011008 (-0.007158) \| 0.078462 / 0.038508 (0.039954) \| 0.036221 / 0.023109 (0.013112) \| 0.389195 / 0.275898 (0.113297) \| 0.428710 / 0.323480 (0.105230) \| 0.004645 / 0.007986 (-0.003341) \| 0.002973 / 0.004328 (-0.001355) \| 0.078299 / 0.004250 (0.074048) \| 0.047076 / 0.037052 (0.010024) \| 0.375673 / 0.258489 (0.117184) \| 0.432352 / 0.293841 (0.138511) \| 0.028212 / 0.128546 (-0.100334) \| 0.008475 / 0.075646 (-0.067172) \| 0.083902 / 0.419271 (-0.335369) \| 0.046699 / 0.043533 (0.003166) \| 0.364502 / 0.255139 (0.109363) \| 0.389792 / 0.283200 (0.106592) \| 0.025266 / 0.141683 (-0.116417) \| 1.517458 / 1.452155 (0.065303) \| 1.543634 / 1.492716 (0.050918) \|\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) \| 0.236479 / 0.018006 (0.218472) \| 0.411528 / 0.000490 (0.411038) \| 0.005213 / 0.000200 (0.005013) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025764 / 0.037411 (-0.011647) \| 0.103174 / 0.014526 (0.088648) \| 0.110609 / 0.176557 (-0.065948) \| 0.164630 / 0.737135 (-0.572506) \| 0.114863 / 0.296338 (-0.181475) \|\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) \| 0.457155 / 0.215209 (0.241946) \| 4.550675 / 2.077655 (2.473021) \| 2.350473 / 1.504120 (0.846353) \| 2.204919 / 1.541195 (0.663724) \| 2.076724 / 1.468490 (0.608234) \| 0.563107 / 4.584777 (-4.021670) \| 3.390669 / 3.745712 (-0.355043) \| 1.741111 / 5.269862 (-3.528751) \| 1.033268 / 4.565676 (-3.532408) \| 0.068400 / 0.424275 (-0.355875) \| 0.011607 / 0.007607 (0.004000) \| 0.561944 / 0.226044 (0.335900) \| 5.620224 / 2.268929 (3.351296) \| 2.705241 / 55.444624 (-52.739384) \| 2.344520 / 6.876477 (-4.531957) \| 2.386119 / 2.142072 (0.244046) \| 0.681583 / 4.805227 (-4.123644) \| 0.137272 / 6.500664 (-6.363392) \| 0.069217 / 0.075469 (-0.006252) \|\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) \| 1.322690 / 1.841788 (-0.519098) \| 14.464953 / 8.074308 (6.390645) \| 14.269350 / 10.191392 (4.077958) \| 0.158879 / 0.680424 (-0.521545) \| 0.016722 / 0.534201 (-0.517479) \| 0.360299 / 0.579283 (-0.218984) \| 0.391609 / 0.434364 (-0.042755) \| 0.420507 / 0.540337 (-0.119831) \| 0.512822 / 1.386936 (-0.874114) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca68191900d97b29abb3c2c4ba0502fe30d137d1 \"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) \| 0.007106 / 0.011353 (-0.004247) \| 0.005224 / 0.011008 (-0.005784) \| 0.127563 / 0.038508 (0.089055) \| 0.055067 / 0.023109 (0.031958) \| 0.418660 / 0.275898 (0.142761) \| 0.487891 / 0.323480 (0.164411) \| 0.005712 / 0.007986 (-0.002274) \| 0.004585 / 0.004328 (0.000256) \| 0.090994 / 0.004250 (0.086743) \| 0.071837 / 0.037052 (0.034784) \| 0.446957 / 0.258489 (0.188468) \| 0.475966 / 0.293841 (0.182125) \| 0.038062 / 0.128546 (-0.090484) \| 0.010056 / 0.075646 (-0.065590) \| 0.406796 / 0.419271 (-0.012475) \| 0.066542 / 0.043533 (0.023009) \| 0.413676 / 0.255139 (0.158537) \| 0.448624 / 0.283200 (0.165424) \| 0.030332 / 0.141683 (-0.111351) \| 1.895307 / 1.452155 (0.443152) \| 1.904411 / 1.492716 (0.411694) \|\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) \| 0.221246 / 0.018006 (0.203240) \| 0.461288 / 0.000490 (0.460799) \| 0.005957 / 0.000200 (0.005757) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029255 / 0.037411 (-0.008156) \| 0.131299 / 0.014526 (0.116773) \| 0.135814 / 0.176557 (-0.040742) \| 0.201342 / 0.737135 (-0.535793) \| 0.141748 / 0.296338 (-0.154591) \|\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) \| 0.463936 / 0.215209 (0.248727) \| 4.709621 / 2.077655 (2.631966) \| 2.093844 / 1.504120 (0.589724) \| 1.897963 / 1.541195 (0.356768) \| 1.927865 / 1.468490 (0.459375) \| 0.610879 / 4.584777 (-3.973898) \| 4.481370 / 3.745712 (0.735658) \| 2.112235 / 5.269862 (-3.157627) \| 1.203349 / 4.565676 (-3.362327) \| 0.074828 / 0.424275 (-0.349447) \| 0.013121 / 0.007607 (0.005514) \| 0.580894 / 0.226044 (0.354849) \| 5.801872 / 2.268929 (3.532943) \| 2.579950 / 55.444624 (-52.864674) \| 2.251569 / 6.876477 (-4.624908) \| 2.421305 / 2.142072 (0.279232) \| 0.760938 / 4.805227 (-4.044289) \| 0.169554 / 6.500664 (-6.331110) \| 0.077499 / 0.075469 (0.002030) \|\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) \| 1.410419 / 1.841788 (-0.431368) \| 17.442331 / 8.074308 (9.368023) \| 15.782183 / 10.191392 (5.590791) \| 0.180649 / 0.680424 (-0.499775) \| 0.021790 / 0.534201 (-0.512411) \| 0.511040 / 0.579283 (-0.068243) \| 0.510472 / 0.434364 (0.076108) \| 0.607141 / 0.540337 (0.066804) \| 0.724794 / 1.386936 (-0.662142) \|\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) \| 0.007280 / 0.011353 (-0.004073) \| 0.004712 / 0.011008 (-0.006296) \| 0.089225 / 0.038508 (0.050717) \| 0.053157 / 0.023109 (0.030048) \| 0.431949 / 0.275898 (0.156051) \| 0.478128 / 0.323480 (0.154648) \| 0.006181 / 0.007986 (-0.001804) \| 0.003387 / 0.004328 (-0.000941) \| 0.083741 / 0.004250 (0.079490) \| 0.071610 / 0.037052 (0.034557) \| 0.414698 / 0.258489 (0.156209) \| 0.484422 / 0.293841 (0.190581) \| 0.034988 / 0.128546 (-0.093558) \| 0.009831 / 0.075646 (-0.065816) \| 0.089644 / 0.419271 (-0.329628) \| 0.057053 / 0.043533 (0.013520) \| 0.413144 / 0.255139 (0.158005) \| 0.445464 / 0.283200 (0.162264) \| 0.026109 / 0.141683 (-0.115574) \| 1.842899 / 1.452155 (0.390745) \| 1.923774 / 1.492716 (0.431057) \|\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) \| 0.245051 / 0.018006 (0.227045) \| 0.460444 / 0.000490 (0.459954) \| 0.000444 / 0.000200 (0.000244) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034835 / 0.037411 (-0.002577) \| 0.130078 / 0.014526 (0.115553) \| 0.147012 / 0.176557 (-0.029544) \| 0.203097 / 0.737135 (-0.534038) \| 0.149636 / 0.296338 (-0.146702) \|\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) \| 0.521664 / 0.215209 (0.306455) \| 5.283865 / 2.077655 (3.206210) \| 2.456701 / 1.504120 (0.952581) \| 2.266059 / 1.541195 (0.724864) \| 2.295387 / 1.468490 (0.826897) \| 0.613200 / 4.584777 (-3.971577) \| 4.526107 / 3.745712 (0.780394) \| 2.047327 / 5.269862 (-3.222535) \| 1.261063 / 4.565676 (-3.304614) \| 0.070402 / 0.424275 (-0.353873) \| 0.014128 / 0.007607 (0.006521) \| 0.620929 / 0.226044 (0.394884) \| 6.109127 / 2.268929 (3.840198) \| 3.081406 / 55.444624 (-52.363218) \| 2.658224 / 6.876477 (-4.218253) \| 2.671974 / 2.142072 (0.529902) \| 0.744081 / 4.805227 (-4.061146) \| 0.161498 / 6.500664 (-6.339166) \| 0.075148 / 0.075469 (-0.000321) \|\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) \| 1.585640 / 1.841788 (-0.256148) \| 17.884321 / 8.074308 (9.810013) \| 15.938937 / 10.191392 (5.747545) \| 0.220818 / 0.680424 (-0.459605) \| 0.021452 / 0.534201 (-0.512749) \| 0.499747 / 0.579283 (-0.079536) \| 0.512318 / 0.434364 (0.077954) \| 0.562853 / 0.540337 (0.022515) \| 0.678512 / 1.386936 (-0.708424) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa50937d82256827aee3dbd749c7a23555e05e38 \"CML watermark\")\n" ]
1,776,643,555	5,993	ValueError: Table schema does not match schema used to create file	closed	2023-06-27T10:54:07	2023-06-27T15:36:42	2023-06-27T15:32:44	https://github.com/huggingface/datasets/issues/5993	null	exs-avianello	false	[ "We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)", "Thank you very much @lhoestq ! 🚀 " ]
1,776,460,964	5,992	speedup	closed	2023-06-27T09:17:58	2023-06-27T09:23:07	2023-06-27T09:18:04	https://github.com/huggingface/datasets/pull/5992	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5992", "html_url": "https://github.com/huggingface/datasets/pull/5992", "diff_url": "https://github.com/huggingface/datasets/pull/5992.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5992.patch", "merged_at": null }	qgallouedec	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5992). All of your documentation changes will be reflected on that endpoint." ]
1,774,456,518	5,991	`map` with any joblib backend	open	2023-06-26T10:33:42	2025-06-26T18:32:56	null	https://github.com/huggingface/datasets/issues/5991	null	lhoestq	false	[ "#self-assign\n\nHi @lhoestq 👋🏼\n\nI’d like to work on this!\n\nPlanning to support progress tracking with `map()` using any joblib backend (like \"loky\") by replacing the Queue-based approach in `iflatmap_unordered` with a file-based progress tracking mechanism (e.g. shared temp file with periodic updates).\n\nThis would allow the progress bar to work even in backends where inter-process Queues aren't supported. Let me know if this sounds good — I’ll get started!\n" ]
1,774,134,091	5,989	Set a rule on the config and split names	open	2023-06-26T07:34:14	2023-07-19T14:22:54	null	https://github.com/huggingface/datasets/issues/5989	null	severo	false	[ "in this case we need to decide what to do with the existing datasets with white space characters (there shouldn't be a lot of them I think)", "I imagine that we should stop supporting them, and help the user fix them?", "See a report where the datasets server fails: https://huggingface.co/datasets/poloclub/diffusiondb/discussions/2#6374ff55b93cbdf65675f564\r\n\r\nThe config name is `random_10k [2m]`!" ]
1,773,257,828	5,988	ConnectionError: Couldn't reach dataset_infos.json	closed	2023-06-25T12:39:31	2023-07-07T13:20:57	2023-07-07T13:20:57	https://github.com/huggingface/datasets/issues/5988	null	yulingao	false	[ "Unfortunately, I can't reproduce the error. What does the following code return for you?\r\n```python\r\nimport requests\r\nfrom huggingface_hub import hf_hub_url\r\nr = requests.get(hf_hub_url(\"codeparrot/codeparrot-clean-train\", \"dataset_infos.json\", repo_type=\"dataset\"))\r\n```\r\n\r\nAlso, can you provide more info about your network (region, proxies, etc.)?" ]
1,773,047,909	5,987	Why max_shard_size is not supported in load_dataset and passed to download_and_prepare	closed	2023-06-25T04:19:13	2023-06-29T16:06:08	2023-06-29T16:06:08	https://github.com/huggingface/datasets/issues/5987	null	npuichigo	false	[ "Can you explain your use case for `max_shard_size`? \r\n\r\nOn some systems, there is a limit to the size of a memory-mapped file, so we could consider exposing this parameter in `load_dataset`.", "In my use case, users may choose a proper size to balance the cost and benefit of using large shard size. (On azure blob or hdfs which may automatically download the shard from background)", "But `load_dataset` doesn't support caching (and reading) Arrow datasets from remote storage. \r\n\r\n`load_datset_builder` + `download_and_prepare` is not equal to `load_dataset`. The latter has one more step, `builder.as_dataset`, that memory-maps Arrow files, which only works for local files.", "Thanks. So if I want to use `IterableDataset` and control the size of single arrow file, how should I organize the data loader? Maybe `load_dataset_build` + `download_and_prepare` + `builder.as_dataset` + `dataset.to_iterable_dataset`?", "Yes, this should work.\r\n\r\nI think we can expose `max_shard_size` in `load_dataset`, so feel free to open a PR." ]
1,772,233,111	5,986	Make IterableDataset.from_spark more efficient	closed	2023-06-23T22:18:20	2023-07-07T10:05:58	2023-07-07T09:56:09	https://github.com/huggingface/datasets/pull/5986	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5986", "html_url": "https://github.com/huggingface/datasets/pull/5986", "diff_url": "https://github.com/huggingface/datasets/pull/5986.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5986.patch", "merged_at": "2023-07-07T09:56:09" }	mathewjacob1002	true	[ "@lhoestq would you be able to review this please and also approve the workflow?", "Sounds good to me :) feel free to run `make style` to apply code formatting", "_The documentation is not available anymore as the PR was closed or merged._", "cool ! I think we can merge once all comments have been addressed", "@lhoestq I just addressed the comments and I think we can move ahead with this! \r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007734 / 0.011353 (-0.003619) \| 0.004608 / 0.011008 (-0.006400) \| 0.094466 / 0.038508 (0.055958) \| 0.086477 / 0.023109 (0.063368) \| 0.410311 / 0.275898 (0.134413) \| 0.455560 / 0.323480 (0.132080) \| 0.006112 / 0.007986 (-0.001874) \| 0.003845 / 0.004328 (-0.000483) \| 0.072506 / 0.004250 (0.068256) \| 0.066721 / 0.037052 (0.029669) \| 0.409967 / 0.258489 (0.151478) \| 0.460480 / 0.293841 (0.166639) \| 0.036700 / 0.128546 (-0.091847) \| 0.009854 / 0.075646 (-0.065792) \| 0.320936 / 0.419271 (-0.098335) \| 0.061002 / 0.043533 (0.017469) \| 0.413963 / 0.255139 (0.158824) \| 0.426787 / 0.283200 (0.143588) \| 0.029182 / 0.141683 (-0.112501) \| 1.685136 / 1.452155 (0.232981) \| 1.754590 / 1.492716 (0.261873) \|\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) \| 0.222698 / 0.018006 (0.204692) \| 0.505929 / 0.000490 (0.505440) \| 0.005291 / 0.000200 (0.005091) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032527 / 0.037411 (-0.004884) \| 0.094842 / 0.014526 (0.080317) \| 0.110138 / 0.176557 (-0.066418) \| 0.193786 / 0.737135 (-0.543349) \| 0.112593 / 0.296338 (-0.183745) \|\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) \| 0.441671 / 0.215209 (0.226461) \| 4.392961 / 2.077655 (2.315306) \| 2.161111 / 1.504120 (0.656991) \| 1.967080 / 1.541195 (0.425885) \| 2.065411 / 1.468490 (0.596920) \| 0.561080 / 4.584777 (-4.023697) \| 4.159612 / 3.745712 (0.413900) \| 6.435248 / 5.269862 (1.165386) \| 3.732338 / 4.565676 (-0.833339) \| 0.066156 / 0.424275 (-0.358119) \| 0.008030 / 0.007607 (0.000423) \| 0.532182 / 0.226044 (0.306137) \| 5.315142 / 2.268929 (3.046213) \| 2.680157 / 55.444624 (-52.764467) \| 2.303799 / 6.876477 (-4.572677) \| 2.530911 / 2.142072 (0.388838) \| 0.669504 / 4.805227 (-4.135723) \| 0.151940 / 6.500664 (-6.348724) \| 0.066999 / 0.075469 (-0.008470) \|\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) \| 1.424275 / 1.841788 (-0.417513) \| 21.550742 / 8.074308 (13.476434) \| 16.031414 / 10.191392 (5.840022) \| 0.194681 / 0.680424 (-0.485743) \| 0.020389 / 0.534201 (-0.513812) \| 0.429808 / 0.579283 (-0.149475) \| 0.457503 / 0.434364 (0.023139) \| 0.511522 / 0.540337 (-0.028816) \| 0.682621 / 1.386936 (-0.704315) \|\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) \| 0.007519 / 0.011353 (-0.003834) \| 0.004445 / 0.011008 (-0.006563) \| 0.071946 / 0.038508 (0.033438) \| 0.082982 / 0.023109 (0.059873) \| 0.459938 / 0.275898 (0.184040) \| 0.504875 / 0.323480 (0.181395) \| 0.005805 / 0.007986 (-0.002181) \| 0.003740 / 0.004328 (-0.000589) \| 0.071998 / 0.004250 (0.067747) \| 0.062580 / 0.037052 (0.025527) \| 0.462263 / 0.258489 (0.203774) \| 0.506355 / 0.293841 (0.212514) \| 0.036321 / 0.128546 (-0.092225) \| 0.009830 / 0.075646 (-0.065816) \| 0.079810 / 0.419271 (-0.339461) \| 0.055291 / 0.043533 (0.011758) \| 0.464093 / 0.255139 (0.208954) \| 0.481109 / 0.283200 (0.197910) \| 0.026909 / 0.141683 (-0.114774) \| 1.652538 / 1.452155 (0.200383) \| 1.750713 / 1.492716 (0.257997) \|\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) \| 0.267552 / 0.018006 (0.249546) \| 0.502021 / 0.000490 (0.501531) \| 0.001635 / 0.000200 (0.001435) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033747 / 0.037411 (-0.003665) \| 0.104242 / 0.014526 (0.089716) \| 0.113829 / 0.176557 (-0.062728) \| 0.176242 / 0.737135 (-0.560893) \| 0.117002 / 0.296338 (-0.179336) \|\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) \| 0.476731 / 0.215209 (0.261522) \| 4.727054 / 2.077655 (2.649399) \| 2.589396 / 1.504120 (1.085276) \| 2.511180 / 1.541195 (0.969985) \| 2.634122 / 1.468490 (1.165632) \| 0.563840 / 4.584777 (-4.020937) \| 4.140212 / 3.745712 (0.394500) \| 6.188789 / 5.269862 (0.918928) \| 3.716897 / 4.565676 (-0.848780) \| 0.065823 / 0.424275 (-0.358452) \| 0.007705 / 0.007607 (0.000098) \| 0.566580 / 0.226044 (0.340535) \| 5.653306 / 2.268929 (3.384377) \| 3.028756 / 55.444624 (-52.415868) \| 2.592319 / 6.876477 (-4.284158) \| 2.614250 / 2.142072 (0.472178) \| 0.667135 / 4.805227 (-4.138093) \| 0.153455 / 6.500664 (-6.347209) \| 0.069321 / 0.075469 (-0.006148) \|\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) \| 1.541978 / 1.841788 (-0.299810) \| 21.747360 / 8.074308 (13.673052) \| 15.963657 / 10.191392 (5.772265) \| 0.192843 / 0.680424 (-0.487581) \| 0.020702 / 0.534201 (-0.513499) \| 0.433620 / 0.579283 (-0.145663) \| 0.467327 / 0.434364 (0.032963) \| 0.507398 / 0.540337 (-0.032940) \| 0.692797 / 1.386936 (-0.694140) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#396cf9419d12e3150e2051793b10f2c813780a90 \"CML watermark\")\n" ]

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