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https://api.github.com/repos/huggingface/datasets/issues/5950 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5950/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5950/comments | https://api.github.com/repos/huggingface/datasets/issues/5950/events | https://github.com/huggingface/datasets/issues/5950 | 1,755,197,946 | I_kwDODunzps5onjH6 | 5,950 | Support for data with instance-wise dictionary as features | {
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"Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```"
] | 2023-06-13T15:49:00Z | 2023-06-13T15:49:00Z | null | NONE | null | ### Feature request
I notice that when loading data instances with feature type of python dictionary, the dictionary keys would be broadcast so that every instance has the same set of keys. Please see an example in the Motivation section.
It is possible to avoid this behavior, i.e., load dictionary features as it is and do not broadcast the keys among instances? Please note that these dictionaries would have to be processed dynamically at each training iteration into strings (and tokenized).
### Motivation
I am trying to load a dataset from a json file. Each instance of the dataset has a feature that is a dictionary but its keys depend on the instance. Every two instances may have different keys. For example, imagine a dataset that contains a set of math expressions from a bunch of mutually redundant expressions:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
}
},
...
{
"index": 9999,
"feature": {
"x >= 6": ["x >= 6", "x >= 0", "x >= -1"],
...
}
},
...
```
When directly loading the dataset using `data = load_dataset("json", data_files=file_paths, split='train')`, each instance would have all the keys from other instances and None as values. That is, instance of index 0 becomes:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
"x >= 6": None, # keys from other instances
...
}
},
```
This is not desirable. Moreover, issue would be raised if I attempt to combine two such datasets using `data = concatenate_datasets(multi_datasets)`, perhaps because their dictionary features contain different keys.
A solution I can think of is to store the dictionary features as a long string, and evaluate it later. Please kindly suggest any other solution using existing methods of datasets.
### Your contribution
N/A | {
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https://api.github.com/repos/huggingface/datasets/issues/5949 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5949/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5949/comments | https://api.github.com/repos/huggingface/datasets/issues/5949/events | https://github.com/huggingface/datasets/pull/5949 | 1,754,843,717 | PR_kwDODunzps5S4oPC | 5,949 | Replace metadata utils with `huggingface_hub`'s RepoCard API | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5949). 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) | 0.006635 / 0.011353 (-0.004718) | 0.004439 / 0.011008 (-0.006570) | 0.107831 / 0.038508 (0.069323) | 0.035664 / 0.023109 (0.012555) | 0.393733 / 0.275898 (0.117835) | 0.418336 / 0.323480 (0.094856) | 0.005739 / 0.007986 (-0.002247) | 0.005737 / 0.004328 (0.001408) | 0.079820 / 0.004250 (0.075569) | 0.045402 / 0.037052 (0.008349) | 0.396108 / 0.258489 (0.137619) | 0.422951 / 0.293841 (0.129110) | 0.030506 / 0.128546 (-0.098040) | 0.009785 / 0.075646 (-0.065861) | 0.375302 / 0.419271 (-0.043969) | 0.054355 / 0.043533 (0.010823) | 0.399652 / 0.255139 (0.144513) | 0.410825 / 0.283200 (0.127625) | 0.109238 / 0.141683 (-0.032445) | 1.687532 / 1.452155 (0.235378) | 1.736829 / 1.492716 (0.244113) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226514 / 0.018006 (0.208508) | 0.487010 / 0.000490 (0.486520) | 0.006436 / 0.000200 (0.006236) | 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) | 0.029097 / 0.037411 (-0.008315) | 0.122979 / 0.014526 (0.108453) | 0.129454 / 0.176557 (-0.047103) | 0.194006 / 0.737135 (-0.543129) | 0.137968 / 0.296338 (-0.158370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466425 / 0.215209 (0.251216) | 4.627307 / 2.077655 (2.549652) | 2.108840 / 1.504120 (0.604720) | 1.882547 / 1.541195 (0.341353) | 1.891077 / 1.468490 (0.422587) | 0.590646 / 4.584777 (-3.994131) | 4.176918 / 3.745712 (0.431205) | 2.071475 / 5.269862 (-3.198386) | 1.173815 / 4.565676 (-3.391862) | 0.075330 / 0.424275 (-0.348945) | 0.012944 / 0.007607 (0.005337) | 0.587080 / 0.226044 (0.361036) | 5.827053 / 2.268929 (3.558125) | 2.694258 / 55.444624 (-52.750366) | 2.276997 / 6.876477 (-4.599480) | 2.329678 / 2.142072 (0.187605) | 0.721860 / 4.805227 (-4.083367) | 0.159238 / 6.500664 (-6.341426) | 0.073013 / 0.075469 (-0.002456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345396 / 1.841788 (-0.496391) | 16.619283 / 8.074308 (8.544975) | 14.754754 / 10.191392 (4.563362) | 0.180784 / 0.680424 (-0.499639) | 0.020376 / 0.534201 (-0.513825) | 0.451010 / 0.579283 (-0.128273) | 0.481524 / 0.434364 (0.047160) | 0.564777 / 0.540337 (0.024440) | 0.683232 / 1.386936 (-0.703704) |\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) | 0.007243 / 0.011353 (-0.004110) | 0.005262 / 0.011008 (-0.005746) | 0.084090 / 0.038508 (0.045581) | 0.037429 / 0.023109 (0.014320) | 0.404038 / 0.275898 (0.128140) | 0.445040 / 0.323480 (0.121560) | 0.006220 / 0.007986 (-0.001766) | 0.004256 / 0.004328 (-0.000072) | 0.083794 / 0.004250 (0.079544) | 0.052655 / 0.037052 (0.015603) | 0.414083 / 0.258489 (0.155594) | 0.458190 / 0.293841 (0.164349) | 0.032719 / 0.128546 (-0.095828) | 0.010063 / 0.075646 (-0.065583) | 0.092281 / 0.419271 (-0.326990) | 0.053888 / 0.043533 (0.010355) | 0.407813 / 0.255139 (0.152674) | 0.431692 / 0.283200 (0.148493) | 0.119799 / 0.141683 (-0.021884) | 1.709853 / 1.452155 (0.257698) | 1.771592 / 1.492716 (0.278876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246540 / 0.018006 (0.228534) | 0.483199 / 0.000490 (0.482709) | 0.002514 / 0.000200 (0.002315) | 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) | 0.031576 / 0.037411 (-0.005835) | 0.130020 / 0.014526 (0.115495) | 0.140285 / 0.176557 (-0.036272) | 0.196164 / 0.737135 (-0.540972) | 0.143924 / 0.296338 (-0.152414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488549 / 0.215209 (0.273340) | 4.888055 / 2.077655 (2.810400) | 2.389163 / 1.504120 (0.885043) | 2.184626 / 1.541195 (0.643431) | 2.260227 / 1.468490 (0.791737) | 0.601331 / 4.584777 (-3.983446) | 4.386159 / 3.745712 (0.640447) | 3.345814 / 5.269862 (-1.924048) | 1.734360 / 4.565676 (-2.831317) | 0.073199 / 0.424275 (-0.351076) | 0.012397 / 0.007607 (0.004790) | 0.601411 / 0.226044 (0.375366) | 6.135000 / 2.268929 (3.866072) | 2.930169 / 55.444624 (-52.514456) | 2.532631 / 6.876477 (-4.343845) | 2.619351 / 2.142072 (0.477279) | 0.740954 / 4.805227 (-4.064274) | 0.162936 / 6.500664 (-6.337728) | 0.073885 / 0.075469 (-0.001585) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502493 / 1.841788 (-0.339294) | 17.026756 / 8.074308 (8.952448) | 15.880958 / 10.191392 (5.689566) | 0.167261 / 0.680424 (-0.513163) | 0.020347 / 0.534201 (-0.513854) | 0.452902 / 0.579283 (-0.126381) | 0.481614 / 0.434364 (0.047250) | 0.539893 / 0.540337 (-0.000445) | 0.653401 / 1.386936 (-0.733535) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a5781212e968e2515afdf29370a6eab6f657120 \"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) | 0.008268 / 0.011353 (-0.003084) | 0.005538 / 0.011008 (-0.005470) | 0.126136 / 0.038508 (0.087628) | 0.046100 / 0.023109 (0.022991) | 0.366882 / 0.275898 (0.090984) | 0.408912 / 0.323480 (0.085432) | 0.007090 / 0.007986 (-0.000895) | 0.004820 / 0.004328 (0.000491) | 0.091432 / 0.004250 (0.087181) | 0.058390 / 0.037052 (0.021338) | 0.368787 / 0.258489 (0.110298) | 0.419429 / 0.293841 (0.125588) | 0.034958 / 0.128546 (-0.093588) | 0.010526 / 0.075646 (-0.065120) | 0.463063 / 0.419271 (0.043791) | 0.070544 / 0.043533 (0.027011) | 0.366182 / 0.255139 (0.111043) | 0.390851 / 0.283200 (0.107652) | 0.128377 / 0.141683 (-0.013306) | 1.819385 / 1.452155 (0.367231) | 1.928834 / 1.492716 (0.436117) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228413 / 0.018006 (0.210407) | 0.485511 / 0.000490 (0.485021) | 0.005395 / 0.000200 (0.005195) | 0.000119 / 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) | 0.035209 / 0.037411 (-0.002203) | 0.144492 / 0.014526 (0.129967) | 0.150467 / 0.176557 (-0.026089) | 0.223861 / 0.737135 (-0.513274) | 0.156363 / 0.296338 (-0.139975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517751 / 0.215209 (0.302542) | 5.150438 / 2.077655 (3.072783) | 2.483601 / 1.504120 (0.979481) | 2.279786 / 1.541195 (0.738592) | 2.374510 / 1.468490 (0.906020) | 0.637547 / 4.584777 (-3.947230) | 4.845393 / 3.745712 (1.099681) | 2.241554 / 5.269862 (-3.028307) | 1.290105 / 4.565676 (-3.275572) | 0.079791 / 0.424275 (-0.344484) | 0.014915 / 0.007607 (0.007308) | 0.640468 / 0.226044 (0.414423) | 6.394810 / 2.268929 (4.125881) | 3.012748 / 55.444624 (-52.431876) | 2.625565 / 6.876477 (-4.250912) | 2.792435 / 2.142072 (0.650363) | 0.782284 / 4.805227 (-4.022944) | 0.171628 / 6.500664 (-6.329036) | 0.081714 / 0.075469 (0.006245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.592411 / 1.841788 (-0.249377) | 18.999604 / 8.074308 (10.925295) | 18.469946 / 10.191392 (8.278554) | 0.200878 / 0.680424 (-0.479546) | 0.021595 / 0.534201 (-0.512606) | 0.519247 / 0.579283 (-0.060036) | 0.534940 / 0.434364 (0.100576) | 0.656325 / 0.540337 (0.115987) | 0.789658 / 1.386936 (-0.597278) |\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) | 0.008093 / 0.011353 (-0.003260) | 0.005524 / 0.011008 (-0.005484) | 0.092339 / 0.038508 (0.053831) | 0.045619 / 0.023109 (0.022510) | 0.449376 / 0.275898 (0.173478) | 0.478587 / 0.323480 (0.155107) | 0.006978 / 0.007986 (-0.001007) | 0.004622 / 0.004328 (0.000294) | 0.090618 / 0.004250 (0.086368) | 0.059321 / 0.037052 (0.022269) | 0.450989 / 0.258489 (0.192500) | 0.491652 / 0.293841 (0.197811) | 0.033308 / 0.128546 (-0.095238) | 0.010677 / 0.075646 (-0.064969) | 0.099836 / 0.419271 (-0.319435) | 0.055937 / 0.043533 (0.012404) | 0.440560 / 0.255139 (0.185421) | 0.475305 / 0.283200 (0.192105) | 0.130829 / 0.141683 (-0.010854) | 1.857943 / 1.452155 (0.405789) | 1.989534 / 1.492716 (0.496818) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244715 / 0.018006 (0.226709) | 0.482866 / 0.000490 (0.482377) | 0.001100 / 0.000200 (0.000900) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036288 / 0.037411 (-0.001124) | 0.147903 / 0.014526 (0.133377) | 0.154141 / 0.176557 (-0.022416) | 0.221863 / 0.737135 (-0.515272) | 0.162319 / 0.296338 (-0.134019) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.536972 / 0.215209 (0.321763) | 5.382866 / 2.077655 (3.305211) | 2.719575 / 1.504120 (1.215456) | 2.516596 / 1.541195 (0.975401) | 2.699602 / 1.468490 (1.231112) | 0.639886 / 4.584777 (-3.944891) | 5.109746 / 3.745712 (1.364034) | 2.260206 / 5.269862 (-3.009656) | 1.305506 / 4.565676 (-3.260170) | 0.080262 / 0.424275 (-0.344013) | 0.014801 / 0.007607 (0.007194) | 0.661228 / 0.226044 (0.435184) | 6.596485 / 2.268929 (4.327557) | 3.226114 / 55.444624 (-52.218510) | 2.859776 / 6.876477 (-4.016701) | 3.059355 / 2.142072 (0.917282) | 0.793413 / 4.805227 (-4.011814) | 0.176521 / 6.500664 (-6.324143) | 0.084062 / 0.075469 (0.008593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.642085 / 1.841788 (-0.199703) | 20.355459 / 8.074308 (12.281151) | 17.979620 / 10.191392 (7.788228) | 0.229329 / 0.680424 (-0.451094) | 0.025681 / 0.534201 (-0.508520) | 0.534142 / 0.579283 (-0.045141) | 0.623439 / 0.434364 (0.189075) | 0.621938 / 0.540337 (0.081601) | 0.759038 / 1.386936 (-0.627898) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a98ff43225df344139023a5b7eb9caef610b677 \"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) | 0.007703 / 0.011353 (-0.003649) | 0.005362 / 0.011008 (-0.005646) | 0.113111 / 0.038508 (0.074602) | 0.038891 / 0.023109 (0.015782) | 0.348938 / 0.275898 (0.073040) | 0.398079 / 0.323480 (0.074599) | 0.006707 / 0.007986 (-0.001278) | 0.004489 / 0.004328 (0.000160) | 0.087194 / 0.004250 (0.082943) | 0.054268 / 0.037052 (0.017216) | 0.359949 / 0.258489 (0.101460) | 0.402959 / 0.293841 (0.109118) | 0.032508 / 0.128546 (-0.096038) | 0.010224 / 0.075646 (-0.065422) | 0.387007 / 0.419271 (-0.032264) | 0.058971 / 0.043533 (0.015439) | 0.345085 / 0.255139 (0.089946) | 0.384306 / 0.283200 (0.101107) | 0.122253 / 0.141683 (-0.019430) | 1.706353 / 1.452155 (0.254199) | 1.840780 / 1.492716 (0.348063) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254374 / 0.018006 (0.236368) | 0.497387 / 0.000490 (0.496897) | 0.012294 / 0.000200 (0.012094) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030902 / 0.037411 (-0.006509) | 0.132098 / 0.014526 (0.117573) | 0.140311 / 0.176557 (-0.036245) | 0.205887 / 0.737135 (-0.531249) | 0.143992 / 0.296338 (-0.152347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467367 / 0.215209 (0.252158) | 4.669936 / 2.077655 (2.592281) | 2.155358 / 1.504120 (0.651238) | 1.984132 / 1.541195 (0.442937) | 2.102352 / 1.468490 (0.633861) | 0.607014 / 4.584777 (-3.977763) | 4.396479 / 3.745712 (0.650767) | 4.666056 / 5.269862 (-0.603806) | 2.176649 / 4.565676 (-2.389028) | 0.072657 / 0.424275 (-0.351619) | 0.012367 / 0.007607 (0.004759) | 0.569706 / 0.226044 (0.343661) | 5.749083 / 2.268929 (3.480154) | 2.640824 / 55.444624 (-52.803801) | 2.310253 / 6.876477 (-4.566224) | 2.486748 / 2.142072 (0.344676) | 0.737891 / 4.805227 (-4.067336) | 0.163507 / 6.500664 (-6.337157) | 0.075776 / 0.075469 (0.000307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362710 / 1.841788 (-0.479078) | 17.010705 / 8.074308 (8.936396) | 15.084231 / 10.191392 (4.892839) | 0.218274 / 0.680424 (-0.462150) | 0.019555 / 0.534201 (-0.514646) | 0.456013 / 0.579283 (-0.123270) | 0.502772 / 0.434364 (0.068408) | 0.581480 / 0.540337 (0.041142) | 0.686952 / 1.386936 (-0.699984) |\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) | 0.007976 / 0.011353 (-0.003377) | 0.005141 / 0.011008 (-0.005868) | 0.086629 / 0.038508 (0.048121) | 0.039553 / 0.023109 (0.016444) | 0.433028 / 0.275898 (0.157130) | 0.463444 / 0.323480 (0.139964) | 0.006967 / 0.007986 (-0.001018) | 0.005814 / 0.004328 (0.001485) | 0.086266 / 0.004250 (0.082015) | 0.055384 / 0.037052 (0.018332) | 0.428733 / 0.258489 (0.170243) | 0.475670 / 0.293841 (0.181829) | 0.032872 / 0.128546 (-0.095674) | 0.010664 / 0.075646 (-0.064983) | 0.094357 / 0.419271 (-0.324915) | 0.058386 / 0.043533 (0.014854) | 0.431114 / 0.255139 (0.175975) | 0.441728 / 0.283200 (0.158528) | 0.131942 / 0.141683 (-0.009740) | 1.782214 / 1.452155 (0.330060) | 1.843185 / 1.492716 (0.350469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247047 / 0.018006 (0.229041) | 0.488931 / 0.000490 (0.488441) | 0.002657 / 0.000200 (0.002457) | 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) | 0.033893 / 0.037411 (-0.003518) | 0.131021 / 0.014526 (0.116495) | 0.142892 / 0.176557 (-0.033665) | 0.200955 / 0.737135 (-0.536180) | 0.151329 / 0.296338 (-0.145010) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521138 / 0.215209 (0.305929) | 5.085207 / 2.077655 (3.007552) | 2.652901 / 1.504120 (1.148781) | 2.401545 / 1.541195 (0.860350) | 2.553461 / 1.468490 (1.084971) | 0.615347 / 4.584777 (-3.969430) | 4.448038 / 3.745712 (0.702326) | 2.049997 / 5.269862 (-3.219865) | 1.190602 / 4.565676 (-3.375075) | 0.073356 / 0.424275 (-0.350919) | 0.013685 / 0.007607 (0.006078) | 0.626705 / 0.226044 (0.400660) | 6.391941 / 2.268929 (4.123012) | 3.218864 / 55.444624 (-52.225760) | 2.858808 / 6.876477 (-4.017669) | 3.005808 / 2.142072 (0.863736) | 0.740725 / 4.805227 (-4.064502) | 0.161904 / 6.500664 (-6.338760) | 0.073727 / 0.075469 (-0.001742) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.488623 / 1.841788 (-0.353164) | 17.584367 / 8.074308 (9.510059) | 16.281818 / 10.191392 (6.090426) | 0.164482 / 0.680424 (-0.515942) | 0.020197 / 0.534201 (-0.514003) | 0.456750 / 0.579283 (-0.122533) | 0.501156 / 0.434364 (0.066792) | 0.549779 / 0.540337 (0.009442) | 0.650156 / 1.386936 (-0.736780) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b6cc63b868ea4ee60502845ebec68abb943958b \"CML watermark\")\n"
] | 2023-06-13T13:03:19Z | 2023-06-13T14:42:18Z | null | CONTRIBUTOR | null | Use `huggingface_hub`'s RepoCard API instead of `DatasetMetadata` for modifying the card's YAML, and deprecate `datasets.utils.metadata` and `datasets.utils.readme`.
After removing these modules, we can also delete `datasets.utils.resources` since the moon landing repo now stores its own version of these resources for the metadata UI.
PS: this change requires bumping `huggingface_hub` to 0.13.0 (Transformers requires 0.14.0, so should be ok) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007220 / 0.011353 (-0.004133) | 0.004558 / 0.011008 (-0.006451) | 0.116647 / 0.038508 (0.078139) | 0.046845 / 0.023109 (0.023736) | 0.352429 / 0.275898 (0.076531) | 0.429739 / 0.323480 (0.106259) | 0.006620 / 0.007986 (-0.001366) | 0.003731 / 0.004328 (-0.000597) | 0.088683 / 0.004250 (0.084433) | 0.070583 / 0.037052 (0.033530) | 0.366699 / 0.258489 (0.108210) | 0.420730 / 0.293841 (0.126889) | 0.037342 / 0.128546 (-0.091204) | 0.010041 / 0.075646 (-0.065605) | 0.383477 / 0.419271 (-0.035795) | 0.060279 / 0.043533 (0.016746) | 0.349988 / 0.255139 (0.094849) | 0.371423 / 0.283200 (0.088224) | 0.026725 / 0.141683 (-0.114958) | 1.736886 / 1.452155 (0.284731) | 1.812874 / 1.492716 (0.320157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253256 / 0.018006 (0.235250) | 0.563470 / 0.000490 (0.562980) | 0.010475 / 0.000200 (0.010275) | 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) | 0.030518 / 0.037411 (-0.006893) | 0.133324 / 0.014526 (0.118798) | 0.137095 / 0.176557 (-0.039461) | 0.202227 / 0.737135 (-0.534909) | 0.144195 / 0.296338 (-0.152143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480870 / 0.215209 (0.265661) | 4.822713 / 2.077655 (2.745058) | 2.124183 / 1.504120 (0.620064) | 1.910733 / 1.541195 (0.369538) | 1.970266 / 1.468490 (0.501776) | 0.624695 / 4.584777 (-3.960082) | 4.459659 / 3.745712 (0.713947) | 2.210123 / 5.269862 (-3.059739) | 1.300520 / 4.565676 (-3.265157) | 0.077096 / 0.424275 (-0.347180) | 0.013333 / 0.007607 (0.005726) | 0.596841 / 0.226044 (0.370797) | 5.917397 / 2.268929 (3.648469) | 2.699397 / 55.444624 (-52.745228) | 2.274833 / 6.876477 (-4.601644) | 2.525376 / 2.142072 (0.383304) | 0.755718 / 4.805227 (-4.049510) | 0.163587 / 6.500664 (-6.337077) | 0.072817 / 0.075469 (-0.002653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.524306 / 1.841788 (-0.317481) | 18.843312 / 8.074308 (10.769004) | 15.694644 / 10.191392 (5.503252) | 0.177400 / 0.680424 (-0.503024) | 0.020104 / 0.534201 (-0.514097) | 0.466421 / 0.579283 (-0.112862) | 0.537274 / 0.434364 (0.102910) | 0.576920 / 0.540337 (0.036583) | 0.718889 / 1.386936 (-0.668047) |\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) | 0.007671 / 0.011353 (-0.003682) | 0.004850 / 0.011008 (-0.006158) | 0.090085 / 0.038508 (0.051576) | 0.052023 / 0.023109 (0.028914) | 0.508575 / 0.275898 (0.232677) | 0.590024 / 0.323480 (0.266544) | 0.004564 / 0.007986 (-0.003422) | 0.005345 / 0.004328 (0.001017) | 0.087904 / 0.004250 (0.083653) | 0.064446 / 0.037052 (0.027394) | 0.525625 / 0.258489 (0.267136) | 0.584307 / 0.293841 (0.290466) | 0.037221 / 0.128546 (-0.091325) | 0.010588 / 0.075646 (-0.065059) | 0.098612 / 0.419271 (-0.320659) | 0.059597 / 0.043533 (0.016064) | 0.488064 / 0.255139 (0.232925) | 0.522330 / 0.283200 (0.239131) | 0.030004 / 0.141683 (-0.111679) | 1.732512 / 1.452155 (0.280357) | 1.809027 / 1.492716 (0.316310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218741 / 0.018006 (0.200735) | 0.494946 / 0.000490 (0.494456) | 0.004580 / 0.000200 (0.004380) | 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) | 0.034916 / 0.037411 (-0.002495) | 0.133695 / 0.014526 (0.119169) | 0.147964 / 0.176557 (-0.028592) | 0.213210 / 0.737135 (-0.523926) | 0.148850 / 0.296338 (-0.147488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.508855 / 0.215209 (0.293646) | 5.065088 / 2.077655 (2.987433) | 2.473110 / 1.504120 (0.968990) | 2.259765 / 1.541195 (0.718570) | 2.359189 / 1.468490 (0.890699) | 0.639082 / 4.584777 (-3.945695) | 4.768195 / 3.745712 (1.022482) | 2.253803 / 5.269862 (-3.016059) | 1.442996 / 4.565676 (-3.122680) | 0.078761 / 0.424275 (-0.345514) | 0.013936 / 0.007607 (0.006329) | 0.625977 / 0.226044 (0.399933) | 6.260817 / 2.268929 (3.991888) | 3.149640 / 55.444624 (-52.294985) | 2.753555 / 6.876477 (-4.122921) | 2.831872 / 2.142072 (0.689799) | 0.781294 / 4.805227 (-4.023933) | 0.169109 / 6.500664 (-6.331555) | 0.075810 / 0.075469 (0.000341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.533282 / 1.841788 (-0.308506) | 19.460579 / 8.074308 (11.386271) | 17.250424 / 10.191392 (7.059032) | 0.193485 / 0.680424 (-0.486939) | 0.020650 / 0.534201 (-0.513551) | 0.472110 / 0.579283 (-0.107173) | 0.532276 / 0.434364 (0.097912) | 0.613152 / 0.540337 (0.072814) | 0.684684 / 1.386936 (-0.702252) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#650a86ee122209d4a8c8e8068c01ebfd3ba553f5 \"CML watermark\")\n"
] | 2023-06-13T12:38:59Z | 2023-06-13T15:48:13Z | null | CONTRIBUTOR | null | Fixes #5936
Also, a related fix to #5927 | {
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https://api.github.com/repos/huggingface/datasets/issues/5947 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5947/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5947/comments | https://api.github.com/repos/huggingface/datasets/issues/5947/events | https://github.com/huggingface/datasets/issues/5947 | 1,754,359,316 | I_kwDODunzps5okWYU | 5,947 | Return the audio filename when decoding fails due to corrupt files | {
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"Hi ! The audio data don't always exist as files on disk - the blobs are often stored in the Arrow files. For now I'd suggest disabling decoding with `.cast_column(\"audio\", Audio(decode=False))` and apply your own decoding that handles corrupted files (maybe to filter them out ?)\r\n\r\ncc @sanchit-gandhi since it's related to our discussion about allowing users to make decoding return `None` and show a warning when there are corrupted files",
"Thanks @lhoestq, I wasn't aware of the decode flag. It makes more sense as you say to show a warning when there are corrupted files together with some metadata of the file that allows to filter them from the dataset.\r\n\r\nMy workaround was to catch the LibsndfileError and generate a dummy audio with an unsual sample rate to filter it later. However returning `None` seems better. \r\n\r\n`try:\r\n array, sampling_rate = sf.read(file)\r\nexcept sf.LibsndfileError:\r\n print(\"bad file\")\r\n array = np.array([0.0])\r\n sampling_rate = 99.000` \r\n\r\n"
] | 2023-06-13T08:44:09Z | 2023-06-13T08:44:32Z | null | NONE | null | ### Feature request
Return the audio filename when the audio decoding fails. Although currently there are some checks for mp3 and opus formats with the library version there are still cases when the audio decoding could fail, eg. Corrupt file.
### Motivation
When you try to load an object file dataset and the decoding fails you can't know which file is corrupt
```
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f5ab7e38290>: Format not recognised.
```
### Your contribution
Make a PR to Add exceptions for LIbsndfileError to return the audio filename or path when soundfile decoding fails. | {
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"https://colab.research.google.com/#scrollTo=AQ_HCYruWIHU&fileId=https%3A//huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb\r\n\r\nI ran the same administration exactly the same but got the same error",
"Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq"
] | 2023-06-13T07:34:15Z | 2023-06-13T11:06:44Z | null | NONE | null | ### Describe the bug
in <cell line: 1>:1 │
│ │
│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1537 in train │
│ │
│ 1534 │ │ inner_training_loop = find_executable_batch_size( │
│ 1535 │ │ │ self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size │
│ 1536 │ │ ) │
│ ❱ 1537 │ │ return inner_training_loop( │
│ 1538 │ │ │ args=args, │
│ 1539 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │
│ 1540 │ │ │ trial=trial, │
│ │
│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1789 in _inner_training_loop │
│ │
│ 1786 │ │ │ │ rng_to_sync = True │
│ 1787 │ │ │ │
│ 1788 │ │ │ step = -1 │
│ ❱ 1789 │ │ │ for step, inputs in enumerate(epoch_iterator): │
│ 1790 │ │ │ │ total_batched_samples += 1 │
│ 1791 │ │ │ │ if rng_to_sync: │
│ 1792 │ │ │ │ │ self._load_rng_state(resume_from_checkpoint) │
│ │
│ /usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py:377 in __iter__ │
│ │
│ 374 │ │ dataloader_iter = super().__iter__() │
│ 375 │ │ # We iterate one batch ahead to check when we are at the end │
│ 376 │ │ try: │
│ ❱ 377 │ │ │ current_batch = next(dataloader_iter) │
│ 378 │ │ except StopIteration: │
│ 379 │ │ │ yield │
│ 380 │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:633 in __next__ │
│ │
│ 630 │ │ │ if self._sampler_iter is None: │
│ 631 │ │ │ │ # TODO(https://github.com/pytorch/pytorch/issues/76750) │
│ 632 │ │ │ │ self._reset() # type: ignore[call-arg] │
│ ❱ 633 │ │ │ data = self._next_data() │
│ 634 │ │ │ self._num_yielded += 1 │
│ 635 │ │ │ if self._dataset_kind == _DatasetKind.Iterable and \ │
│ 636 │ │ │ │ │ self._IterableDataset_len_called is not None and \ │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:677 in _next_data │
│ │
│ 674 │ │
│ 675 │ def _next_data(self): │
│ 676 │ │ index = self._next_index() # may raise StopIteration │
│ ❱ 677 │ │ data = self._dataset_fetcher.fetch(index) # may raise StopIteration │
│ 678 │ │ if self._pin_memory: │
│ 679 │ │ │ data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) │
│ 680 │ │ return data │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py:49 in fetch │
│ │
│ 46 │ def fetch(self, possibly_batched_index): │
│ 47 │ │ if self.auto_collation: │
│ 48 │ │ │ if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__: │
│ ❱ 49 │ │ │ │ data = self.dataset.__getitems__(possibly_batched_index) │
│ 50 │ │ │ else: │
│ 51 │ │ │ │ data = [self.dataset[idx] for idx in possibly_batched_index] │
│ 52 │ │ else: │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2782 in __getitems__ │
│ │
│ 2779 │ │
│ 2780 │ def __getitems__(self, keys: List) -> List: │
│ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │
│ ❱ 2782 │ │ batch = self.__getitem__(keys) │
│ 2783 │ │ n_examples = len(batch[next(iter(batch))]) │
│ 2784 │ │ return [{col: array[i] for col, array in batch.items()} for i in range(n_example │
│ 2785 │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2778 in __getitem__ │
│ │
│ 2775 │ │
│ 2776 │ def __getitem__(self, key): # noqa: F811 │
│ 2777 │ │ """Can be used to index columns (by string names) or rows (by integer index or i │
│ ❱ 2778 │ │ return self._getitem(key) │
│ 2779 │ │
│ 2780 │ def __getitems__(self, keys: List) -> List: │
│ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2762 in _getitem │
│ │
│ 2759 │ │ format_kwargs = kwargs["format_kwargs"] if "format_kwargs" in kwargs else self._ │
│ 2760 │ │ format_kwargs = format_kwargs if format_kwargs is not None else {} │
│ 2761 │ │ formatter = get_formatter(format_type, features=self._info.features, **format_kw │
│ ❱ 2762 │ │ pa_subtable = query_table(self._data, key, indices=self._indices if self._indice │
│ 2763 │ │ formatted_output = format_table( │
│ 2764 │ │ │ pa_subtable, key, formatter=formatter, format_columns=format_columns, output │
│ 2765 │ │ ) │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:578 in query_table │
│ │
│ 575 │ │ _check_valid_column_key(key, table.column_names) │
│ 576 │ else: │
│ 577 │ │ size = indices.num_rows if indices is not None else table.num_rows │
│ ❱ 578 │ │ _check_valid_index_key(key, size) │
│ 579 │ # Query the main table │
│ 580 │ if indices is None: │
│ 581 │ │ pa_subtable = _query_table(table, key) │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:531 in │
│ _check_valid_index_key │
│ │
│ 528 │ │ │ _check_valid_index_key(min(key), size=size) │
│ 529 │ elif isinstance(key, Iterable): │
│ 530 │ │ if len(key) > 0: │
│ ❱ 531 │ │ │ _check_valid_index_key(int(max(key)), size=size) │
│ 532 │ │ │ _check_valid_index_key(int(min(key)), size=size) │
│ 533 │ else: │
│ 534 │ │ _raise_bad_key_type(key) │
│ │
│ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:521 in │
│ _check_valid_index_key │
│ │
│ 518 def _check_valid_index_key(key: Union[int, slice, range, Iterable], size: int) -> None: │
│ 519 │ if isinstance(key, int): │
│ 520 │ │ if (key < 0 and key + size < 0) or (key >= size): │
│ ❱ 521 │ │ │ raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") │
│ 522 │ │ return │
│ 523 │ elif isinstance(key, slice): │
│ 524 │ │ pass
### Steps to reproduce the bug
``
import json
import os
from pprint import pprint
import bitsandbytes as bnb
import pandas as pd
import torch
import torch.nn as nn
import transformers
from datasets import Dataset,load_dataset
from peft import (
LoraConfig,
PeftConfig,
PeftModel,
get_peft_model,
prepare_model_for_kbit_training
)
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
MODEL_NAME = "tiiuae/falcon-7b"
bnb_config = BitsAndBytesConfig(
load_in_4bit = True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map = "auto",
trust_remote_code = True,
quantization_config = bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token
model.gradient_checkpointing_enable()
model = prepare_model_for_kbit_training(model)
config = LoraConfig(
r = 16,
lora_alpha = 32,
target_modules = ["query_key_value"],
lora_dropout = 0.05,
bias = "none",
task_type = "CASUAL_LM"
)
model = get_peft_model(model,config)
print_trainable_parameters(model)
def generate_prompt(data_point):
return f"""
<human>: {data_point["question"]}
<assistant>: {data_point["answer"]}
""".strip()
def generate_and_tokenize_prompt(data_point):
full_prompt = generate_prompt(data_point)
tokenized_full_prompt = tokenizer(full_prompt, padding = True, truncation = True,return_tensors = None)
return dict({
"input_ids" : tokenized_full_prompt["input_ids"],
"attention_mask" : tokenized_full_prompt["attention_mask"]
})
data = data["train"].shuffle().map(generate_and_tokenize_prompt, batched = False)
OUTPUT_DIR = "experiments"
trainings_args = transformers.TrainingArguments(
per_device_train_batch_size = 1,
gradient_accumulation_steps = 4,
num_train_epochs = 1,
learning_rate = 2e-4,
fp16 = True,
save_total_limit = 3,
logging_steps = 1,
output_dir = OUTPUT_DIR,
max_steps = 80,
optim = "paged_adamw_8bit",
lr_scheduler_type = "cosine",
warmup_ratio = 0.05,
#remove_unused_columns=True
)
trainer = transformers.Trainer(
model = model,
train_dataset = data,
args = trainings_args,
data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False
trainer.train()
IndexError: Invalid key: 32 is out of bounds for size 0
DataSet Format is like :
[{"question": "How can I create an account?", "answer": "To create an account, click on the 'Sign Up' button on the top right corner of our website and follow the instructions to complete the registration process."}, .... ]
### Expected behavior
-
### Environment info
!pip install -q pip
!pip install -q bitsandbytes==0.39.0
!pip install -q torch==2.0.1
!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q git+https://github.com/huggingface/peft.git
!pip install -q git+https://github.com/huggingface/accelerate.git
!pip install -q datasets
!pip install -q loralib==0.1.1
!pip install -q einops==0.6.1
import json
import os
from pprint import pprint
import bitsandbytes as bnb
import pandas as pd
import torch
import torch.nn as nn
import transformers
from datasets import Dataset,load_dataset
from peft import (
LoraConfig,
PeftConfig,
PeftModel,
get_peft_model,
prepare_model_for_kbit_training
)
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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"Hi ! Feel free to re-run your code later, it will resume automatically where you left"
] | 2023-06-13T05:46:46Z | 2023-06-13T05:46:46Z | null | NONE | null | ### Describe the bug
Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work.
From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable.
Please help.
I'm trying to upload the dataset for almost a week.
Thanks
### Steps to reproduce the bug
not relevant
### Expected behavior
Be able to upload thedataset
### Environment info
python: 3.9 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ",
"Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead",
"<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) | 0.003788 / 0.011008 (-0.007220) | 0.098524 / 0.038508 (0.060016) | 0.031786 / 0.023109 (0.008677) | 0.307799 / 0.275898 (0.031901) | 0.337329 / 0.323480 (0.013849) | 0.003650 / 0.007986 (-0.004336) | 0.003731 / 0.004328 (-0.000598) | 0.076816 / 0.004250 (0.072566) | 0.041888 / 0.037052 (0.004835) | 0.310702 / 0.258489 (0.052213) | 0.343846 / 0.293841 (0.050005) | 0.027841 / 0.128546 (-0.100705) | 0.008312 / 0.075646 (-0.067334) | 0.320230 / 0.419271 (-0.099042) | 0.047378 / 0.043533 (0.003845) | 0.308683 / 0.255139 (0.053544) | 0.335129 / 0.283200 (0.051930) | 0.096294 / 0.141683 (-0.045389) | 1.485521 / 1.452155 (0.033366) | 1.559868 / 1.492716 (0.067152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197376 / 0.018006 (0.179370) | 0.430461 / 0.000490 (0.429972) | 0.004152 / 0.000200 (0.003953) | 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) | 0.023660 / 0.037411 (-0.013751) | 0.103128 / 0.014526 (0.088602) | 0.107549 / 0.176557 (-0.069008) | 0.175934 / 0.737135 (-0.561201) | 0.112210 / 0.296338 (-0.184129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415804 / 0.215209 (0.200595) | 4.216333 / 2.077655 (2.138679) | 1.910354 / 1.504120 (0.406234) | 1.712689 / 1.541195 (0.171494) | 1.754705 / 1.468490 (0.286215) | 0.554647 / 4.584777 (-4.030130) | 3.393592 / 3.745712 (-0.352120) | 1.737504 / 5.269862 (-3.532358) | 1.021213 / 4.565676 (-3.544464) | 0.066908 / 0.424275 (-0.357367) | 0.011446 / 0.007607 (0.003839) | 0.524630 / 0.226044 (0.298585) | 5.243005 / 2.268929 (2.974077) | 2.349685 / 55.444624 (-53.094939) | 2.027457 / 6.876477 (-4.849020) | 2.131053 / 2.142072 (-0.011020) | 0.669070 / 4.805227 (-4.136157) | 0.136317 / 6.500664 (-6.364347) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254102 / 1.841788 (-0.587686) | 13.790492 / 8.074308 (5.716184) | 14.197772 / 10.191392 (4.006380) | 0.143989 / 0.680424 (-0.536434) | 0.016577 / 0.534201 (-0.517624) | 0.375437 / 0.579283 (-0.203846) | 0.398995 / 0.434364 (-0.035369) | 0.445287 / 0.540337 (-0.095050) | 0.538632 / 1.386936 (-0.848304) |\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) | 0.006251 / 0.011353 (-0.005101) | 0.004019 / 0.011008 (-0.006989) | 0.077985 / 0.038508 (0.039477) | 0.028705 / 0.023109 (0.005596) | 0.417360 / 0.275898 (0.141462) | 0.463964 / 0.323480 (0.140484) | 0.003489 / 0.007986 (-0.004497) | 0.003032 / 0.004328 (-0.001296) | 0.077953 / 0.004250 (0.073702) | 0.040104 / 0.037052 (0.003051) | 0.405242 / 0.258489 (0.146753) | 0.475029 / 0.293841 (0.181188) | 0.028113 / 0.128546 (-0.100433) | 0.008610 / 0.075646 (-0.067036) | 0.084847 / 0.419271 (-0.334424) | 0.048227 / 0.043533 (0.004694) | 0.417235 / 0.255139 (0.162096) | 0.450470 / 0.283200 (0.167270) | 0.096978 / 0.141683 (-0.044705) | 1.514688 / 1.452155 (0.062533) | 1.560205 / 1.492716 (0.067488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235125 / 0.018006 (0.217119) | 0.409904 / 0.000490 (0.409414) | 0.002474 / 0.000200 (0.002275) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025152 / 0.037411 (-0.012259) | 0.103517 / 0.014526 (0.088991) | 0.110154 / 0.176557 (-0.066402) | 0.161431 / 0.737135 (-0.575704) | 0.114891 / 0.296338 (-0.181448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456077 / 0.215209 (0.240868) | 4.541171 / 2.077655 (2.463517) | 2.297912 / 1.504120 (0.793792) | 2.079337 / 1.541195 (0.538143) | 2.121291 / 1.468490 (0.652801) | 0.560172 / 4.584777 (-4.024605) | 3.421122 / 3.745712 (-0.324590) | 1.764675 / 5.269862 (-3.505186) | 1.043482 / 4.565676 (-3.522195) | 0.067652 / 0.424275 (-0.356623) | 0.011181 / 0.007607 (0.003574) | 0.557232 / 0.226044 (0.331188) | 5.607851 / 2.268929 (3.338922) | 2.783715 / 55.444624 (-52.660909) | 2.380943 / 6.876477 (-4.495534) | 2.378316 / 2.142072 (0.236244) | 0.674356 / 4.805227 (-4.130871) | 0.135912 / 6.500664 (-6.364752) | 0.067009 / 0.075469 (-0.008460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309002 / 1.841788 (-0.532786) | 14.464073 / 8.074308 (6.389765) | 14.418727 / 10.191392 (4.227335) | 0.148486 / 0.680424 (-0.531938) | 0.016650 / 0.534201 (-0.517551) | 0.368786 / 0.579283 (-0.210497) | 0.395026 / 0.434364 (-0.039338) | 0.433565 / 0.540337 (-0.106772) | 0.526603 / 1.386936 (-0.860333) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n"
] | 2023-06-12T14:21:49Z | 2023-06-13T17:36:02Z | 2023-06-13T17:29:01Z | CONTRIBUTOR | null | This adds a Arrow dataset builder to be able to load and stream from already preprocessed Arrow files.
It's related to https://github.com/huggingface/datasets/issues/3035 | {
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"Hi! I've opened a PR with a fix."
] | 2023-06-12T13:57:27Z | 2023-06-12T15:11:01Z | null | NONE | null | ### Describe the bug
Despite its popularity, the language `lzh` (Literary Chinese) is not shown on the left panel on the dataset page:
On the metadata section of a certain dataset, languages are shown in their names, but `lzh` is shown as "lzh".
While we can see that the name does exist in the `languages.json` file:
https://github.com/huggingface/datasets/blob/7fcbe5b1575c8d162b65b9397b3dfda995a4e048/src/datasets/utils/resources/languages.json#L3880
### Steps to reproduce the bug
Open the link <https://huggingface.co/datasets?language=language:lzh> and <https://huggingface.co/datasets/Ayaka/ORCHESTRA-simple-1M>
### Expected behavior
1. "Literary Chinese" should be shown on the left panel
2. "lzh" should be displayed as "Literary Chinese"
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https://api.github.com/repos/huggingface/datasets/issues/5942 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5942/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5942/comments | https://api.github.com/repos/huggingface/datasets/issues/5942/events | https://github.com/huggingface/datasets/pull/5942 | 1,752,021,681 | PR_kwDODunzps5Su-V4 | 5,942 | Pass datasets-cli additional args as kwargs to DatasetBuilder in `run_beam.py` | {
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} | [] | open | false | null | [] | null | [] | 2023-06-12T06:50:50Z | 2023-06-12T06:51:27Z | null | NONE | null | Hi,
Following this <https://discuss.huggingface.co/t/how-to-preprocess-a-wikipedia-dataset-using-dataflowrunner/41991/3>, here is a simple PR to pass any additional args to datasets-cli as kwargs in the DatasetBuilder in `run_beam.py`.
I also took the liberty to add missing setup steps to the `beam.mdx` docs in order to help everyone.
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5941 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5941/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5941/comments | https://api.github.com/repos/huggingface/datasets/issues/5941/events | https://github.com/huggingface/datasets/issues/5941 | 1,751,838,897 | I_kwDODunzps5oavCx | 5,941 | Load Data Sets Too Slow In Train Seq2seq Model | {
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"Hi ! you can speed it up using multiprocessing by passing `num_proc=` to `load_dataset()`",
"already did，but not useful for step Generating train split，it works in step \"Resolving data files\" & \"Downloading data files\" ",
"@mariosasko some advice ， thanks！"
] | 2023-06-12T03:58:43Z | 2023-06-12T03:58:43Z | null | NONE | null | ### Describe the bug
step 'Generating train split' in load_dataset is too slow：
### Steps to reproduce the bug
Data： own data，16K16B Mono wav
Oficial Script:[ run_speech_recognition_seq2seq.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py)
Add Code：
if data_args.data_path is not None:
print(data_args.data_path)
raw_datasets = load_dataset("audiofolder", data_dir=data_args.data_path, cache_dir=model_args.cache_dir)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
raw_datasets = raw_datasets["train"].train_test_split(test_size=0.005, shuffle=True)
（change cache_dir to other path ，ex:/DATA/cache）
### Expected behavior
load data fast,at least 1000+
`Generating train split: 387875 examples [32:24:45, 1154.83 examples/s]`
### Environment info
- `transformers` version: 4.28.0.dev0
- Platform: Linux-5.4.0-149-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.16
- Huggingface_hub version: 0.13.2
- PyTorch version (GPU?): 1.13.1+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in> | {
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https://api.github.com/repos/huggingface/datasets/issues/5939 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5939/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5939/comments | https://api.github.com/repos/huggingface/datasets/issues/5939/events | https://github.com/huggingface/datasets/issues/5939 | 1,749,955,883 | I_kwDODunzps5oTjUr | 5,939 | . | {
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https://api.github.com/repos/huggingface/datasets/issues/5938 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5938/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5938/comments | https://api.github.com/repos/huggingface/datasets/issues/5938/events | https://github.com/huggingface/datasets/pull/5938 | 1,749,462,851 | PR_kwDODunzps5SmbkI | 5,938 | Make get_from_cache use custom temp filename that is locked | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007241 / 0.011353 (-0.004112) | 0.004574 / 0.011008 (-0.006434) | 0.120481 / 0.038508 (0.081973) | 0.040492 / 0.023109 (0.017383) | 0.391399 / 0.275898 (0.115501) | 0.422844 / 0.323480 (0.099365) | 0.004441 / 0.007986 (-0.003545) | 0.004544 / 0.004328 (0.000216) | 0.089482 / 0.004250 (0.085231) | 0.052939 / 0.037052 (0.015887) | 0.393649 / 0.258489 (0.135160) | 0.433852 / 0.293841 (0.140011) | 0.035882 / 0.128546 (-0.092664) | 0.010172 / 0.075646 (-0.065474) | 0.410331 / 0.419271 (-0.008940) | 0.061481 / 0.043533 (0.017948) | 0.405066 / 0.255139 (0.149927) | 0.417732 / 0.283200 (0.134532) | 0.121647 / 0.141683 (-0.020035) | 1.790624 / 1.452155 (0.338469) | 1.863398 / 1.492716 (0.370681) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250650 / 0.018006 (0.232644) | 0.489044 / 0.000490 (0.488554) | 0.010421 / 0.000200 (0.010222) | 0.000106 / 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) | 0.030340 / 0.037411 (-0.007071) | 0.128318 / 0.014526 (0.113792) | 0.140463 / 0.176557 (-0.036093) | 0.205762 / 0.737135 (-0.531373) | 0.147996 / 0.296338 (-0.148342) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493158 / 0.215209 (0.277949) | 4.858346 / 2.077655 (2.780691) | 2.242942 / 1.504120 (0.738822) | 2.010092 / 1.541195 (0.468897) | 2.076765 / 1.468490 (0.608275) | 0.636669 / 4.584777 (-3.948108) | 4.478027 / 3.745712 (0.732314) | 2.157843 / 5.269862 (-3.112019) | 1.305133 / 4.565676 (-3.260543) | 0.079220 / 0.424275 (-0.345055) | 0.013858 / 0.007607 (0.006251) | 0.604501 / 0.226044 (0.378457) | 5.950071 / 2.268929 (3.681143) | 2.738373 / 55.444624 (-52.706251) | 2.380275 / 6.876477 (-4.496201) | 2.517108 / 2.142072 (0.375035) | 0.772249 / 4.805227 (-4.032979) | 0.169874 / 6.500664 (-6.330790) | 0.078026 / 0.075469 (0.002557) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.450200 / 1.841788 (-0.391588) | 17.810965 / 8.074308 (9.736657) | 15.518998 / 10.191392 (5.327606) | 0.200469 / 0.680424 (-0.479954) | 0.020777 / 0.534201 (-0.513424) | 0.504556 / 0.579283 (-0.074727) | 0.518493 / 0.434364 (0.084129) | 0.615335 / 0.540337 (0.074998) | 0.754065 / 1.386936 (-0.632871) |\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) | 0.007224 / 0.011353 (-0.004129) | 0.004663 / 0.011008 (-0.006345) | 0.092151 / 0.038508 (0.053643) | 0.038359 / 0.023109 (0.015250) | 0.486413 / 0.275898 (0.210515) | 0.521596 / 0.323480 (0.198116) | 0.004207 / 0.007986 (-0.003778) | 0.003745 / 0.004328 (-0.000583) | 0.089840 / 0.004250 (0.085589) | 0.050996 / 0.037052 (0.013943) | 0.498090 / 0.258489 (0.239601) | 0.533647 / 0.293841 (0.239806) | 0.035151 / 0.128546 (-0.093395) | 0.010293 / 0.075646 (-0.065354) | 0.099056 / 0.419271 (-0.320215) | 0.057365 / 0.043533 (0.013833) | 0.470652 / 0.255139 (0.215513) | 0.509801 / 0.283200 (0.226602) | 0.115650 / 0.141683 (-0.026033) | 1.810860 / 1.452155 (0.358705) | 1.896775 / 1.492716 (0.404059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261887 / 0.018006 (0.243880) | 0.489919 / 0.000490 (0.489430) | 0.006117 / 0.000200 (0.005917) | 0.000134 / 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) | 0.035033 / 0.037411 (-0.002378) | 0.141093 / 0.014526 (0.126567) | 0.152613 / 0.176557 (-0.023943) | 0.218351 / 0.737135 (-0.518785) | 0.158366 / 0.296338 (-0.137972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.542219 / 0.215209 (0.327010) | 5.479358 / 2.077655 (3.401703) | 2.749586 / 1.504120 (1.245466) | 2.537686 / 1.541195 (0.996491) | 2.582351 / 1.468490 (1.113861) | 0.636750 / 4.584777 (-3.948027) | 4.537501 / 3.745712 (0.791789) | 2.141392 / 5.269862 (-3.128469) | 1.279711 / 4.565676 (-3.285965) | 0.079227 / 0.424275 (-0.345048) | 0.014141 / 0.007607 (0.006534) | 0.662070 / 0.226044 (0.436025) | 6.572144 / 2.268929 (4.303215) | 3.321349 / 55.444624 (-52.123275) | 2.928219 / 6.876477 (-3.948258) | 3.002732 / 2.142072 (0.860659) | 0.773808 / 4.805227 (-4.031419) | 0.166017 / 6.500664 (-6.334647) | 0.076424 / 0.075469 (0.000955) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584325 / 1.841788 (-0.257463) | 18.359247 / 8.074308 (10.284938) | 16.977875 / 10.191392 (6.786483) | 0.195381 / 0.680424 (-0.485043) | 0.021048 / 0.534201 (-0.513153) | 0.512237 / 0.579283 (-0.067047) | 0.511435 / 0.434364 (0.077071) | 0.592856 / 0.540337 (0.052518) | 0.711905 / 1.386936 (-0.675031) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d536e37b21a6dd5c122b6d8113994ec50846c5b5 \"CML watermark\")\n"
] | 2023-06-09T09:01:13Z | 2023-06-12T06:09:56Z | null | MEMBER | null | This PR ensures that the temporary filename created is the same as the one that is locked, while writing to the cache.
This PR stops using `tempfile` to generate the temporary filename.
Additionally, the behavior now is aligned for both `resume_download` `True` and `False`.
Refactor temp_file_manager so that it uses the filename that is locked:
- Use: `cache_path + ".incomplete"`, when the locked one is `cache_path + ".lock"`
Before it was using `tempfile` inside `cache_dir`, which was not locked: although very improbable name collision (8 random characters), this was not impossible when huge number of multiple processes.
Maybe related to "Stale file handle" issues caused by `tempfile`:
- [ ] https://huggingface.co/datasets/tapaco/discussions/4
- [ ] https://huggingface.co/datasets/xcsr/discussions/1
- [ ] https://huggingface.co/datasets/covost2/discussions/3
```
Error code: ConfigNamesError
Exception: OSError
Message: [Errno 116] Stale file handle
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 61, in compute_config_names_response
for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1188, in dataset_module_factory
return HubDatasetModuleFactoryWithScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 907, in get_module
dataset_readme_path = self.download_dataset_readme_file()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 896, in download_dataset_readme_file
return cached_path(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 183, in cached_path
output_path = get_from_cache(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 611, in get_from_cache
http_get(
File "/usr/local/lib/python3.9/tempfile.py", line 496, in __exit__
result = self.file.__exit__(exc, value, tb)
OSError: [Errno 116] Stale file handle
```
- the stale file handle error can be raised when `tempfile` tries to close (when exiting its context manager) a filename that has been already closed by other process
- note that `tempfile` filenames are randomly generated but not locked in our code
CC: @severo | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006157 / 0.011353 (-0.005196) | 0.003790 / 0.011008 (-0.007219) | 0.097889 / 0.038508 (0.059381) | 0.029038 / 0.023109 (0.005929) | 0.306918 / 0.275898 (0.031020) | 0.339637 / 0.323480 (0.016157) | 0.003526 / 0.007986 (-0.004460) | 0.003102 / 0.004328 (-0.001227) | 0.076908 / 0.004250 (0.072658) | 0.039254 / 0.037052 (0.002201) | 0.309197 / 0.258489 (0.050708) | 0.345635 / 0.293841 (0.051794) | 0.027954 / 0.128546 (-0.100593) | 0.008510 / 0.075646 (-0.067136) | 0.314674 / 0.419271 (-0.104598) | 0.057102 / 0.043533 (0.013569) | 0.307495 / 0.255139 (0.052356) | 0.329501 / 0.283200 (0.046302) | 0.098450 / 0.141683 (-0.043233) | 1.480102 / 1.452155 (0.027948) | 1.550554 / 1.492716 (0.057838) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207440 / 0.018006 (0.189434) | 0.426560 / 0.000490 (0.426071) | 0.003250 / 0.000200 (0.003050) | 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) | 0.023777 / 0.037411 (-0.013634) | 0.103905 / 0.014526 (0.089379) | 0.108324 / 0.176557 (-0.068233) | 0.167223 / 0.737135 (-0.569913) | 0.113529 / 0.296338 (-0.182810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426770 / 0.215209 (0.211561) | 4.251806 / 2.077655 (2.174151) | 2.010426 / 1.504120 (0.506306) | 1.858630 / 1.541195 (0.317435) | 1.941318 / 1.468490 (0.472828) | 0.558056 / 4.584777 (-4.026721) | 3.399107 / 3.745712 (-0.346606) | 1.758386 / 5.269862 (-3.511476) | 1.036305 / 4.565676 (-3.529372) | 0.067094 / 0.424275 (-0.357182) | 0.011167 / 0.007607 (0.003560) | 0.526705 / 0.226044 (0.300661) | 5.250319 / 2.268929 (2.981390) | 2.496723 / 55.444624 (-52.947902) | 2.154013 / 6.876477 (-4.722464) | 2.394724 / 2.142072 (0.252652) | 0.669723 / 4.805227 (-4.135504) | 0.136367 / 6.500664 (-6.364297) | 0.067080 / 0.075469 (-0.008389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269700 / 1.841788 (-0.572088) | 14.099775 / 8.074308 (6.025467) | 14.422936 / 10.191392 (4.231544) | 0.132344 / 0.680424 (-0.548080) | 0.016744 / 0.534201 (-0.517457) | 0.378286 / 0.579283 (-0.200997) | 0.392282 / 0.434364 (-0.042082) | 0.437648 / 0.540337 (-0.102689) | 0.528554 / 1.386936 (-0.858382) |\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) | 0.006086 / 0.011353 (-0.005267) | 0.003769 / 0.011008 (-0.007239) | 0.077414 / 0.038508 (0.038906) | 0.027806 / 0.023109 (0.004697) | 0.360333 / 0.275898 (0.084434) | 0.404725 / 0.323480 (0.081245) | 0.003443 / 0.007986 (-0.004543) | 0.004434 / 0.004328 (0.000106) | 0.077309 / 0.004250 (0.073059) | 0.040441 / 0.037052 (0.003388) | 0.358627 / 0.258489 (0.100138) | 0.415246 / 0.293841 (0.121405) | 0.027718 / 0.128546 (-0.100829) | 0.008495 / 0.075646 (-0.067151) | 0.082874 / 0.419271 (-0.336397) | 0.042323 / 0.043533 (-0.001210) | 0.354895 / 0.255139 (0.099756) | 0.390032 / 0.283200 (0.106832) | 0.092377 / 0.141683 (-0.049306) | 1.492817 / 1.452155 (0.040662) | 1.551859 / 1.492716 (0.059143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198921 / 0.018006 (0.180915) | 0.417699 / 0.000490 (0.417209) | 0.001349 / 0.000200 (0.001149) | 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) | 0.026349 / 0.037411 (-0.011062) | 0.105712 / 0.014526 (0.091186) | 0.111792 / 0.176557 (-0.064765) | 0.163677 / 0.737135 (-0.573459) | 0.116864 / 0.296338 (-0.179474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447532 / 0.215209 (0.232323) | 4.468770 / 2.077655 (2.391116) | 2.403820 / 1.504120 (0.899700) | 2.273640 / 1.541195 (0.732445) | 2.337505 / 1.468490 (0.869015) | 0.560729 / 4.584777 (-4.024048) | 3.389165 / 3.745712 (-0.356547) | 2.697614 / 5.269862 (-2.572247) | 1.351909 / 4.565676 (-3.213768) | 0.068089 / 0.424275 (-0.356186) | 0.011639 / 0.007607 (0.004032) | 0.555277 / 0.226044 (0.329233) | 5.559291 / 2.268929 (3.290363) | 2.657609 / 55.444624 (-52.787015) | 2.346667 / 6.876477 (-4.529809) | 2.615823 / 2.142072 (0.473751) | 0.668662 / 4.805227 (-4.136566) | 0.136593 / 6.500664 (-6.364071) | 0.068384 / 0.075469 (-0.007085) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312089 / 1.841788 (-0.529699) | 14.477510 / 8.074308 (6.403202) | 14.231432 / 10.191392 (4.040040) | 0.132015 / 0.680424 (-0.548409) | 0.016908 / 0.534201 (-0.517293) | 0.368315 / 0.579283 (-0.210968) | 0.397964 / 0.434364 (-0.036400) | 0.432446 / 0.540337 (-0.107891) | 0.526349 / 1.386936 (-0.860587) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78b4d55c3cfc60e309eb033d3ed0aba5e796b6ce \"CML watermark\")\n"
] | 2023-06-09T08:18:36Z | 2023-06-09T10:02:27Z | null | MEMBER | null | Avoid parallel redownload in cache by retrying inside the lock if path exists. | {
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"Related, `float16` is the only dtype not supported by `Array2D` (probably by every `ArrayND`):\r\n\r\n```python\r\nfrom datasets import Array2D, Features, Dataset\r\n\r\nimport numpy as np\r\n\r\nfor dtype in [\r\n \"bool\", # ok\r\n \"int8\", # ok\r\n \"int16\", # ok\r\n \"int32\", # ok\r\n \"int64\", # ok\r\n \"uint8\", # ok\r\n \"uint16\", # ok\r\n \"uint32\", # ok\r\n \"uint64\", # ok\r\n \"float16\", # failed\r\n \"float32\", # ok\r\n \"float64\", # ok\r\n]:\r\n features = Features({\"foo\": Array2D(dtype=dtype, shape=(3, 4))})\r\n array = np.zeros((3, 4), dtype=dtype)\r\n try:\r\n dataset = Dataset.from_dict({\"foo\": [array]}, features=features)\r\n except Exception as e:\r\n print(f\"Failed for dtype={dtype}\")\r\n```",
"Here's something I can't explain:\r\n\r\nWhen an array is encoded in the `from_dict` method, the numpy array is converted to a list (thus losing the original dtype, which is transfromed to the nearest builtin Python type)\r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/features/features.py#L524-L525\r\n\r\nHowever, later on, this same data is written to memory, and it seems authorized that the data is an array (or in this case, a list of arrays). \r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/arrow_writer.py#L185-L186\r\n\r\nSo the question is: why convert it to a Python list? This seems to be quite expensive both in terms of write time (all data is copied) and memory (e.g., an int8 is converted to an int64).\r\n\r\nFinally, if I try to remove this step, it solves all the previous problems, and it seems to me that it doesn't break anything (the CI passes without problem).",
"Arrow only support 1d numpy arrays, so we convert multidim arrays to lists of 1s arrays (and keep the dtype).\r\n\r\nThough you noticed that it's concerting to lists and lose the dtype. If it's the case then it's a bug.",
"Ok the conversion to list shouldn't be there indeed ! Could you open a PR to remove it ?"
] | 2023-06-08T18:18:07Z | 2023-06-13T09:25:34Z | null | CONTRIBUTOR | null | ### Describe the bug
Create a dataset composed of sequence of array fails for most dtypes (see code below).
### Steps to reproduce the bug
```python
from datasets import Sequence, Array2D, Features, Dataset
import numpy as np
for dtype in [
"bool", # ok
"int8", # failed
"int16", # failed
"int32", # failed
"int64", # ok
"uint8", # failed
"uint16", # failed
"uint32", # failed
"uint64", # failed
"float16", # failed
"float32", # failed
"float64", # ok
]:
features = Features({"foo": Sequence(Array2D(dtype=dtype, shape=(2, 2)))})
sequence = [
[[1.0, 2.0], [3.0, 4.0]],
[[5.0, 6.0], [7.0, 8.0]],
]
array = np.array(sequence, dtype=dtype)
try:
dataset = Dataset.from_dict({"foo": [array]}, features=features)
except Exception as e:
print(f"Failed for dtype={dtype}")
```
Traceback for `dtype="int8"`:
```
Traceback (most recent call last):
File "/home/qgallouedec/datasets/a.py", line 29, in <module>
raise e
File "/home/qgallouedec/datasets/a.py", line 26, in <module>
dataset = Dataset.from_dict({"foo": [array]}, features=features)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 899, in from_dict
pa_table = InMemoryTable.from_pydict(mapping=mapping)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 799, in from_pydict
return cls(pa.Table.from_pydict(*args, **kwargs))
File "pyarrow/table.pxi", line 3725, in pyarrow.lib.Table.from_pydict
File "pyarrow/table.pxi", line 5254, in pyarrow.lib._from_pydict
File "pyarrow/array.pxi", line 350, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 236, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 204, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2091, in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2139, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper
return func(array, *args, **kwargs)
File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast
return pa_type.wrap_array(array)
File "pyarrow/types.pxi", line 879, in pyarrow.lib.BaseExtensionType.wrap_array
TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: int8>>, got list<item: list<item: int64>>
```
### Expected behavior
Not to fail.
### Environment info
- Python 3.10.6
- datasets: master branch
- Numpy: 1.23.4 | {
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"<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) | 0.007489 / 0.011353 (-0.003864) | 0.004914 / 0.011008 (-0.006095) | 0.111626 / 0.038508 (0.073117) | 0.037920 / 0.023109 (0.014811) | 0.350571 / 0.275898 (0.074673) | 0.389667 / 0.323480 (0.066187) | 0.006309 / 0.007986 (-0.001676) | 0.005488 / 0.004328 (0.001160) | 0.083962 / 0.004250 (0.079712) | 0.050728 / 0.037052 (0.013675) | 0.360997 / 0.258489 (0.102508) | 0.392736 / 0.293841 (0.098895) | 0.031975 / 0.128546 (-0.096571) | 0.009941 / 0.075646 (-0.065705) | 0.379840 / 0.419271 (-0.039432) | 0.056522 / 0.043533 (0.012989) | 0.359379 / 0.255139 (0.104240) | 0.384487 / 0.283200 (0.101287) | 0.117523 / 0.141683 (-0.024160) | 1.683639 / 1.452155 (0.231485) | 1.791645 / 1.492716 (0.298929) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236862 / 0.018006 (0.218856) | 0.481208 / 0.000490 (0.480719) | 0.007455 / 0.000200 (0.007255) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030854 / 0.037411 (-0.006557) | 0.126892 / 0.014526 (0.112367) | 0.139207 / 0.176557 (-0.037350) | 0.206447 / 0.737135 (-0.530689) | 0.143095 / 0.296338 (-0.153244) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474677 / 0.215209 (0.259468) | 4.699534 / 2.077655 (2.621879) | 2.152102 / 1.504120 (0.647983) | 1.934815 / 1.541195 (0.393620) | 1.986448 / 1.468490 (0.517958) | 0.607184 / 4.584777 (-3.977593) | 4.480385 / 3.745712 (0.734673) | 2.074729 / 5.269862 (-3.195132) | 1.182383 / 4.565676 (-3.383294) | 0.075624 / 0.424275 (-0.348651) | 0.014046 / 0.007607 (0.006439) | 0.598859 / 0.226044 (0.372814) | 5.959551 / 2.268929 (3.690622) | 2.700851 / 55.444624 (-52.743773) | 2.303775 / 6.876477 (-4.572702) | 2.456441 / 2.142072 (0.314369) | 0.747185 / 4.805227 (-4.058042) | 0.165787 / 6.500664 (-6.334878) | 0.075817 / 0.075469 (0.000348) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.411859 / 1.841788 (-0.429928) | 17.375495 / 8.074308 (9.301187) | 15.187098 / 10.191392 (4.995706) | 0.169953 / 0.680424 (-0.510471) | 0.020204 / 0.534201 (-0.513997) | 0.461424 / 0.579283 (-0.117859) | 0.494443 / 0.434364 (0.060080) | 0.544583 / 0.540337 (0.004246) | 0.648231 / 1.386936 (-0.738705) |\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) | 0.007785 / 0.011353 (-0.003568) | 0.005314 / 0.011008 (-0.005694) | 0.087273 / 0.038508 (0.048765) | 0.037810 / 0.023109 (0.014701) | 0.425473 / 0.275898 (0.149575) | 0.459976 / 0.323480 (0.136497) | 0.007270 / 0.007986 (-0.000716) | 0.004631 / 0.004328 (0.000303) | 0.087063 / 0.004250 (0.082812) | 0.052630 / 0.037052 (0.015578) | 0.432384 / 0.258489 (0.173895) | 0.500291 / 0.293841 (0.206450) | 0.033144 / 0.128546 (-0.095402) | 0.010101 / 0.075646 (-0.065545) | 0.096068 / 0.419271 (-0.323204) | 0.062750 / 0.043533 (0.019217) | 0.419308 / 0.255139 (0.164169) | 0.437099 / 0.283200 (0.153900) | 0.122289 / 0.141683 (-0.019394) | 1.737829 / 1.452155 (0.285674) | 1.851481 / 1.492716 (0.358765) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014277 / 0.018006 (-0.003729) | 0.489835 / 0.000490 (0.489345) | 0.008423 / 0.000200 (0.008223) | 0.000188 / 0.000054 (0.000134) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032966 / 0.037411 (-0.004445) | 0.130069 / 0.014526 (0.115544) | 0.144372 / 0.176557 (-0.032185) | 0.200400 / 0.737135 (-0.536735) | 0.149384 / 0.296338 (-0.146954) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.511542 / 0.215209 (0.296333) | 5.093879 / 2.077655 (3.016225) | 2.572088 / 1.504120 (1.067968) | 2.339118 / 1.541195 (0.797923) | 2.441637 / 1.468490 (0.973147) | 0.614818 / 4.584777 (-3.969959) | 4.724441 / 3.745712 (0.978729) | 5.431978 / 5.269862 (0.162116) | 2.257794 / 4.565676 (-2.307883) | 0.078109 / 0.424275 (-0.346166) | 0.013821 / 0.007607 (0.006214) | 0.639232 / 0.226044 (0.413188) | 6.424623 / 2.268929 (4.155694) | 3.163018 / 55.444624 (-52.281606) | 2.756786 / 6.876477 (-4.119690) | 2.808655 / 2.142072 (0.666583) | 0.745843 / 4.805227 (-4.059385) | 0.165562 / 6.500664 (-6.335102) | 0.076610 / 0.075469 (0.001141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.738630 / 1.841788 (-0.103158) | 18.073573 / 8.074308 (9.999265) | 16.482820 / 10.191392 (6.291428) | 0.213233 / 0.680424 (-0.467191) | 0.022839 / 0.534201 (-0.511362) | 0.487043 / 0.579283 (-0.092240) | 0.512518 / 0.434364 (0.078154) | 0.549365 / 0.540337 (0.009028) | 0.656612 / 1.386936 (-0.730324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#288e92b03bd4ec91c10c8a529b32631cfaba9fb7 \"CML watermark\")\n",
"Good idea!\r\n\r\nI was wondering: if we want to optimize the balance between the size of downloading a row group, and the number of rows in the group, would it make sense to compute the row group size by checking the average size of the rows?\r\n\r\neg. 32x32 images could have a larger row group size than full HD images, no? Relying on the size would even remove the need to check the column types.\r\n\r\n(in this proposal, we could use the computed row group size, eg 837, or use the nearest row group size in a list of values: 10, 100, 1000, 10000)",
"Probably, but I would go for a simpler solution first :p",
"Sure! I wanted to understand if the idea made sense or not, but it's not for this PR.",
"I think it will be more useful for people who use the viewer and won't impact sequential io that much.",
"DuckDB [paragraph](https://duckdb.org/docs/data/parquet/tips.html#selecting-a-row_group_size) that explains how to choose the `row_group_size`. Our default shard size is 500 MB in `push_to_hub`, so, ideally, we should aim for 64 MB row groups (and make this part configurable for power users 🙂).\r\n\r\nSo, before merging this PR, let's add a TODO or open an issue as a reminder that this can be improved.",
"I moved the config values, improved the features check and mentioned the improvements we could do in the docstring :)",
"_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) | 0.006211 / 0.011353 (-0.005141) | 0.004244 / 0.011008 (-0.006764) | 0.097941 / 0.038508 (0.059433) | 0.028564 / 0.023109 (0.005455) | 0.299651 / 0.275898 (0.023753) | 0.340694 / 0.323480 (0.017214) | 0.005161 / 0.007986 (-0.002824) | 0.004764 / 0.004328 (0.000435) | 0.075505 / 0.004250 (0.071255) | 0.039656 / 0.037052 (0.002603) | 0.309242 / 0.258489 (0.050753) | 0.350783 / 0.293841 (0.056942) | 0.025145 / 0.128546 (-0.103401) | 0.008498 / 0.075646 (-0.067148) | 0.317657 / 0.419271 (-0.101615) | 0.043926 / 0.043533 (0.000394) | 0.305915 / 0.255139 (0.050776) | 0.331630 / 0.283200 (0.048430) | 0.088564 / 0.141683 (-0.053119) | 1.533175 / 1.452155 (0.081021) | 1.581017 / 1.492716 (0.088301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206032 / 0.018006 (0.188025) | 0.433446 / 0.000490 (0.432956) | 0.003955 / 0.000200 (0.003755) | 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) | 0.023468 / 0.037411 (-0.013943) | 0.103292 / 0.014526 (0.088766) | 0.107234 / 0.176557 (-0.069322) | 0.168525 / 0.737135 (-0.568610) | 0.113218 / 0.296338 (-0.183120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431085 / 0.215209 (0.215875) | 4.302082 / 2.077655 (2.224427) | 2.068290 / 1.504120 (0.564171) | 1.850718 / 1.541195 (0.309523) | 1.964261 / 1.468490 (0.495771) | 0.547562 / 4.584777 (-4.037215) | 3.410739 / 3.745712 (-0.334974) | 1.779640 / 5.269862 (-3.490221) | 1.005466 / 4.565676 (-3.560210) | 0.066250 / 0.424275 (-0.358025) | 0.011877 / 0.007607 (0.004270) | 0.525185 / 0.226044 (0.299141) | 5.234786 / 2.268929 (2.965857) | 2.398045 / 55.444624 (-53.046580) | 2.073020 / 6.876477 (-4.803457) | 2.210753 / 2.142072 (0.068680) | 0.654897 / 4.805227 (-4.150331) | 0.134639 / 6.500664 (-6.366025) | 0.067050 / 0.075469 (-0.008419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180210 / 1.841788 (-0.661577) | 13.613091 / 8.074308 (5.538783) | 13.441837 / 10.191392 (3.250445) | 0.146048 / 0.680424 (-0.534376) | 0.016505 / 0.534201 (-0.517696) | 0.363210 / 0.579283 (-0.216073) | 0.405484 / 0.434364 (-0.028880) | 0.428712 / 0.540337 (-0.111625) | 0.522300 / 1.386936 (-0.864636) |\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) | 0.006147 / 0.011353 (-0.005206) | 0.004161 / 0.011008 (-0.006847) | 0.075861 / 0.038508 (0.037353) | 0.027948 / 0.023109 (0.004839) | 0.362466 / 0.275898 (0.086568) | 0.398227 / 0.323480 (0.074747) | 0.005014 / 0.007986 (-0.002972) | 0.004772 / 0.004328 (0.000444) | 0.075674 / 0.004250 (0.071423) | 0.039158 / 0.037052 (0.002106) | 0.363567 / 0.258489 (0.105078) | 0.410378 / 0.293841 (0.116537) | 0.025510 / 0.128546 (-0.103036) | 0.008528 / 0.075646 (-0.067118) | 0.081803 / 0.419271 (-0.337468) | 0.040954 / 0.043533 (-0.002579) | 0.358492 / 0.255139 (0.103353) | 0.381345 / 0.283200 (0.098145) | 0.092347 / 0.141683 (-0.049336) | 1.567695 / 1.452155 (0.115540) | 1.668412 / 1.492716 (0.175696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203367 / 0.018006 (0.185360) | 0.424642 / 0.000490 (0.424152) | 0.002451 / 0.000200 (0.002251) | 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) | 0.026129 / 0.037411 (-0.011282) | 0.102564 / 0.014526 (0.088039) | 0.110583 / 0.176557 (-0.065973) | 0.164332 / 0.737135 (-0.572804) | 0.115706 / 0.296338 (-0.180632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468925 / 0.215209 (0.253716) | 4.657266 / 2.077655 (2.579612) | 2.423280 / 1.504120 (0.919160) | 2.236284 / 1.541195 (0.695089) | 2.323019 / 1.468490 (0.854529) | 0.548120 / 4.584777 (-4.036657) | 3.455602 / 3.745712 (-0.290110) | 1.730421 / 5.269862 (-3.539441) | 1.006089 / 4.565676 (-3.559588) | 0.067478 / 0.424275 (-0.356797) | 0.011465 / 0.007607 (0.003857) | 0.574235 / 0.226044 (0.348190) | 5.744404 / 2.268929 (3.475475) | 2.882225 / 55.444624 (-52.562400) | 2.618246 / 6.876477 (-4.258231) | 2.642920 / 2.142072 (0.500847) | 0.661441 / 4.805227 (-4.143787) | 0.137358 / 6.500664 (-6.363306) | 0.070372 / 0.075469 (-0.005097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333815 / 1.841788 (-0.507973) | 14.689667 / 8.074308 (6.615359) | 14.362294 / 10.191392 (4.170902) | 0.152011 / 0.680424 (-0.528413) | 0.016869 / 0.534201 (-0.517332) | 0.370433 / 0.579283 (-0.208851) | 0.399642 / 0.434364 (-0.034722) | 0.433759 / 0.540337 (-0.106578) | 0.525443 / 1.386936 (-0.861493) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e9f9a88edd9055b5c540e3d83b5a11d48f8ba8 \"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) | 0.006564 / 0.011353 (-0.004789) | 0.004350 / 0.011008 (-0.006658) | 0.096277 / 0.038508 (0.057769) | 0.032956 / 0.023109 (0.009847) | 0.303675 / 0.275898 (0.027777) | 0.336384 / 0.323480 (0.012904) | 0.005789 / 0.007986 (-0.002197) | 0.003957 / 0.004328 (-0.000371) | 0.073990 / 0.004250 (0.069740) | 0.050974 / 0.037052 (0.013922) | 0.321754 / 0.258489 (0.063265) | 0.349489 / 0.293841 (0.055648) | 0.031138 / 0.128546 (-0.097409) | 0.009000 / 0.075646 (-0.066646) | 0.325445 / 0.419271 (-0.093826) | 0.070173 / 0.043533 (0.026640) | 0.304706 / 0.255139 (0.049567) | 0.321803 / 0.283200 (0.038603) | 0.109405 / 0.141683 (-0.032278) | 1.489812 / 1.452155 (0.037657) | 1.577729 / 1.492716 (0.085013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287187 / 0.018006 (0.269181) | 0.527625 / 0.000490 (0.527135) | 0.006533 / 0.000200 (0.006333) | 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) | 0.026659 / 0.037411 (-0.010752) | 0.106236 / 0.014526 (0.091710) | 0.118615 / 0.176557 (-0.057941) | 0.173156 / 0.737135 (-0.563979) | 0.122883 / 0.296338 (-0.173456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407189 / 0.215209 (0.191980) | 4.055732 / 2.077655 (1.978078) | 1.865594 / 1.504120 (0.361474) | 1.664325 / 1.541195 (0.123130) | 1.668961 / 1.468490 (0.200471) | 0.521207 / 4.584777 (-4.063570) | 3.740424 / 3.745712 (-0.005288) | 3.431973 / 5.269862 (-1.837889) | 1.636669 / 4.565676 (-2.929008) | 0.065271 / 0.424275 (-0.359005) | 0.012151 / 0.007607 (0.004544) | 0.514233 / 0.226044 (0.288189) | 5.110150 / 2.268929 (2.841222) | 2.264340 / 55.444624 (-53.180284) | 1.940428 / 6.876477 (-4.936049) | 2.042286 / 2.142072 (-0.099787) | 0.639200 / 4.805227 (-4.166028) | 0.139537 / 6.500664 (-6.361127) | 0.063195 / 0.075469 (-0.012274) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.179501 / 1.841788 (-0.662286) | 14.600133 / 8.074308 (6.525825) | 14.902137 / 10.191392 (4.710745) | 0.144509 / 0.680424 (-0.535915) | 0.017449 / 0.534201 (-0.516752) | 0.393135 / 0.579283 (-0.186148) | 0.413103 / 0.434364 (-0.021261) | 0.459897 / 0.540337 (-0.080440) | 0.552602 / 1.386936 (-0.834334) |\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) | 0.006891 / 0.011353 (-0.004462) | 0.004633 / 0.011008 (-0.006375) | 0.073093 / 0.038508 (0.034585) | 0.032509 / 0.023109 (0.009399) | 0.348332 / 0.275898 (0.072434) | 0.381920 / 0.323480 (0.058440) | 0.005978 / 0.007986 (-0.002007) | 0.005360 / 0.004328 (0.001032) | 0.074307 / 0.004250 (0.070056) | 0.049668 / 0.037052 (0.012615) | 0.354713 / 0.258489 (0.096224) | 0.398521 / 0.293841 (0.104681) | 0.032013 / 0.128546 (-0.096534) | 0.008890 / 0.075646 (-0.066756) | 0.080013 / 0.419271 (-0.339259) | 0.051820 / 0.043533 (0.008288) | 0.349730 / 0.255139 (0.094591) | 0.369267 / 0.283200 (0.086067) | 0.103874 / 0.141683 (-0.037809) | 1.484148 / 1.452155 (0.031993) | 1.573927 / 1.492716 (0.081211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009699 / 0.018006 (-0.008307) | 0.511176 / 0.000490 (0.510686) | 0.002938 / 0.000200 (0.002738) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027847 / 0.037411 (-0.009564) | 0.111565 / 0.014526 (0.097039) | 0.120625 / 0.176557 (-0.055932) | 0.172130 / 0.737135 (-0.565006) | 0.125949 / 0.296338 (-0.170389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430634 / 0.215209 (0.215424) | 4.315377 / 2.077655 (2.237722) | 2.070764 / 1.504120 (0.566644) | 1.881962 / 1.541195 (0.340767) | 1.904053 / 1.468490 (0.435563) | 0.524973 / 4.584777 (-4.059804) | 3.718359 / 3.745712 (-0.027353) | 3.415344 / 5.269862 (-1.854518) | 1.224568 / 4.565676 (-3.341108) | 0.065593 / 0.424275 (-0.358682) | 0.011643 / 0.007607 (0.004036) | 0.537050 / 0.226044 (0.311006) | 5.352155 / 2.268929 (3.083226) | 2.557361 / 55.444624 (-52.887263) | 2.217770 / 6.876477 (-4.658707) | 2.194975 / 2.142072 (0.052902) | 0.635142 / 4.805227 (-4.170085) | 0.140642 / 6.500664 (-6.360022) | 0.064690 / 0.075469 (-0.010779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266125 / 1.841788 (-0.575663) | 14.836413 / 8.074308 (6.762105) | 14.446870 / 10.191392 (4.255478) | 0.191545 / 0.680424 (-0.488878) | 0.017433 / 0.534201 (-0.516768) | 0.392296 / 0.579283 (-0.186987) | 0.420698 / 0.434364 (-0.013666) | 0.463225 / 0.540337 (-0.077112) | 0.556127 / 1.386936 (-0.830809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fcbe5b1575c8d162b65b9397b3dfda995a4e048 \"CML watermark\")\n"
] | 2023-06-08T15:01:15Z | 2023-06-09T17:47:37Z | 2023-06-09T17:40:09Z | MEMBER | null | This is a very simple change that improves `to_parquet` to use a more reasonable row group size for image and audio datasets.
This is especially useful for `push_to_hub` and will provide a better experience with the dataset viewer on HF | {
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https://api.github.com/repos/huggingface/datasets/issues/5934 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5934/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5934/comments | https://api.github.com/repos/huggingface/datasets/issues/5934/events | https://github.com/huggingface/datasets/pull/5934 | 1,747,904,840 | PR_kwDODunzps5ShUxQ | 5,934 | Modify levels of some logging messages | {
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} | [] | open | false | null | [] | null | [] | 2023-06-08T13:31:44Z | 2023-06-08T13:31:44Z | null | CONTRIBUTOR | null | Some warning messages didn't quite sound like warnings so I modified their logging levels to info. | {
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"I just added the same test with dynamic shape",
"_The documentation is not available anymore as the PR was closed or merged._",
"Awesome ! I'm merging now if you don't mind :)\r\nWe should probably give you permissions to merge your own PRs when you have an approval",
"<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) | 0.009980 / 0.011353 (-0.001373) | 0.005709 / 0.011008 (-0.005300) | 0.132185 / 0.038508 (0.093677) | 0.039299 / 0.023109 (0.016190) | 0.400168 / 0.275898 (0.124270) | 0.470582 / 0.323480 (0.147102) | 0.007753 / 0.007986 (-0.000233) | 0.005196 / 0.004328 (0.000868) | 0.093698 / 0.004250 (0.089448) | 0.052631 / 0.037052 (0.015579) | 0.430347 / 0.258489 (0.171858) | 0.460162 / 0.293841 (0.166321) | 0.057511 / 0.128546 (-0.071035) | 0.013944 / 0.075646 (-0.061702) | 0.459008 / 0.419271 (0.039737) | 0.075532 / 0.043533 (0.031999) | 0.405165 / 0.255139 (0.150026) | 0.456142 / 0.283200 (0.172942) | 0.117309 / 0.141683 (-0.024374) | 1.945787 / 1.452155 (0.493633) | 2.067162 / 1.492716 (0.574446) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285755 / 0.018006 (0.267749) | 0.619965 / 0.000490 (0.619476) | 0.005071 / 0.000200 (0.004871) | 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) | 0.031112 / 0.037411 (-0.006299) | 0.128514 / 0.014526 (0.113988) | 0.137161 / 0.176557 (-0.039396) | 0.211363 / 0.737135 (-0.525772) | 0.151045 / 0.296338 (-0.145293) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.609361 / 0.215209 (0.394152) | 6.124844 / 2.077655 (4.047189) | 2.440757 / 1.504120 (0.936637) | 2.034495 / 1.541195 (0.493300) | 2.047192 / 1.468490 (0.578702) | 0.883171 / 4.584777 (-3.701606) | 5.470552 / 3.745712 (1.724840) | 4.401696 / 5.269862 (-0.868165) | 2.378674 / 4.565676 (-2.187003) | 0.108065 / 0.424275 (-0.316210) | 0.013239 / 0.007607 (0.005632) | 0.830957 / 0.226044 (0.604913) | 8.090659 / 2.268929 (5.821731) | 3.289203 / 55.444624 (-52.155422) | 2.500777 / 6.876477 (-4.375700) | 2.561440 / 2.142072 (0.419367) | 1.064893 / 4.805227 (-3.740334) | 0.220486 / 6.500664 (-6.280178) | 0.079507 / 0.075469 (0.004038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.544334 / 1.841788 (-0.297454) | 17.878997 / 8.074308 (9.804689) | 18.952191 / 10.191392 (8.760799) | 0.245166 / 0.680424 (-0.435258) | 0.028022 / 0.534201 (-0.506179) | 0.517828 / 0.579283 (-0.061455) | 0.618988 / 0.434364 (0.184624) | 0.589742 / 0.540337 (0.049405) | 0.670902 / 1.386936 (-0.716034) |\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) | 0.009616 / 0.011353 (-0.001737) | 0.006098 / 0.011008 (-0.004911) | 0.100301 / 0.038508 (0.061793) | 0.037792 / 0.023109 (0.014683) | 0.484667 / 0.275898 (0.208769) | 0.519286 / 0.323480 (0.195806) | 0.007427 / 0.007986 (-0.000558) | 0.007172 / 0.004328 (0.002844) | 0.104429 / 0.004250 (0.100179) | 0.056567 / 0.037052 (0.019515) | 0.502641 / 0.258489 (0.244152) | 0.549629 / 0.293841 (0.255788) | 0.049574 / 0.128546 (-0.078972) | 0.015223 / 0.075646 (-0.060424) | 0.113947 / 0.419271 (-0.305324) | 0.064585 / 0.043533 (0.021053) | 0.512962 / 0.255139 (0.257823) | 0.507218 / 0.283200 (0.224019) | 0.122194 / 0.141683 (-0.019488) | 1.927821 / 1.452155 (0.475667) | 2.051161 / 1.492716 (0.558445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291350 / 0.018006 (0.273344) | 0.588099 / 0.000490 (0.587610) | 0.001368 / 0.000200 (0.001168) | 0.000153 / 0.000054 (0.000099) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030604 / 0.037411 (-0.006807) | 0.126810 / 0.014526 (0.112285) | 0.139309 / 0.176557 (-0.037248) | 0.208030 / 0.737135 (-0.529105) | 0.138985 / 0.296338 (-0.157353) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.681254 / 0.215209 (0.466045) | 6.753856 / 2.077655 (4.676201) | 2.780704 / 1.504120 (1.276585) | 2.475205 / 1.541195 (0.934010) | 2.486784 / 1.468490 (1.018294) | 0.879223 / 4.584777 (-3.705554) | 5.662294 / 3.745712 (1.916582) | 2.698705 / 5.269862 (-2.571156) | 1.660620 / 4.565676 (-2.905057) | 0.112218 / 0.424275 (-0.312057) | 0.014211 / 0.007607 (0.006604) | 0.796957 / 0.226044 (0.570913) | 8.180897 / 2.268929 (5.911969) | 3.540419 / 55.444624 (-51.904205) | 2.899467 / 6.876477 (-3.977010) | 2.870306 / 2.142072 (0.728233) | 1.069537 / 4.805227 (-3.735690) | 0.211281 / 6.500664 (-6.289383) | 0.078898 / 0.075469 (0.003429) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.666790 / 1.841788 (-0.174998) | 18.302127 / 8.074308 (10.227819) | 21.317546 / 10.191392 (11.126153) | 0.242795 / 0.680424 (-0.437629) | 0.026754 / 0.534201 (-0.507447) | 0.493375 / 0.579283 (-0.085908) | 0.605400 / 0.434364 (0.171036) | 0.586888 / 0.540337 (0.046550) | 0.722809 / 1.386936 (-0.664127) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce2328e7b1d62998b22510492530af55d4493b73 \"CML watermark\")\n"
] | 2023-06-08T08:38:56Z | 2023-06-09T13:49:41Z | 2023-06-09T13:23:48Z | CONTRIBUTOR | null | Closes #5927
I've realized that the error was overlooked during testing due to the presence of only one None value in the sequence.
Unfortunately, it was the only case where the function works as expected. When the sequence contained more than one None value, the function failed. Consequently, I've updated the tests to include sequences with multiple None values. | {
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