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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	{ "avatar_url": "https://avatars.githubusercontent.com/u/33274336?v=4", "events_url": "https://api.github.com/users/richardwth/events{/privacy}", "followers_url": "https://api.github.com/users/richardwth/followers", "following_url": "https://api.github.com/users/richardwth/following{/other_user}", "gists_url": "https://api.github.com/users/richardwth/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/richardwth", "id": 33274336, "login": "richardwth", "node_id": "MDQ6VXNlcjMzMjc0MzM2", "organizations_url": "https://api.github.com/users/richardwth/orgs", "received_events_url": "https://api.github.com/users/richardwth/received_events", "repos_url": "https://api.github.com/users/richardwth/repos", "site_admin": false, "starred_url": "https://api.github.com/users/richardwth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/richardwth/subscriptions", "type": "User", "url": "https://api.github.com/users/richardwth" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5950/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5950/timeline	null	null	null	null	false
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	open	false	null	[]	null	[ "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)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5949/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5949/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5949.diff", "html_url": "https://github.com/huggingface/datasets/pull/5949", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5949.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5949" }	true
https://api.github.com/repos/huggingface/datasets/issues/5948	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5948/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5948/comments	https://api.github.com/repos/huggingface/datasets/issues/5948/events	https://github.com/huggingface/datasets/pull/5948	1,754,794,611	PR_kwDODunzps5S4dUt	5,948	Fix sequence of array support for most dtype	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec" }	[]	open	false	null	[]	null	[ "_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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5948/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5948/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5948.diff", "html_url": "https://github.com/huggingface/datasets/pull/5948", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5948.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5948" }	true
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/8949105?v=4", "events_url": "https://api.github.com/users/wetdog/events{/privacy}", "followers_url": "https://api.github.com/users/wetdog/followers", "following_url": "https://api.github.com/users/wetdog/following{/other_user}", "gists_url": "https://api.github.com/users/wetdog/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/wetdog", "id": 8949105, "login": "wetdog", "node_id": "MDQ6VXNlcjg5NDkxMDU=", "organizations_url": "https://api.github.com/users/wetdog/orgs", "received_events_url": "https://api.github.com/users/wetdog/received_events", "repos_url": "https://api.github.com/users/wetdog/repos", "site_admin": false, "starred_url": "https://api.github.com/users/wetdog/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wetdog/subscriptions", "type": "User", "url": "https://api.github.com/users/wetdog" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "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.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5947/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5947/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5946	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5946/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5946/comments	https://api.github.com/repos/huggingface/datasets/issues/5946/events	https://github.com/huggingface/datasets/issues/5946	1,754,234,469	I_kwDODunzps5oj35l	5,946	IndexError Not Solving -> IndexError: Invalid key: ?? is out of bounds for size 0 or ??	{ "avatar_url": "https://avatars.githubusercontent.com/u/70565543?v=4", "events_url": "https://api.github.com/users/syngokhan/events{/privacy}", "followers_url": "https://api.github.com/users/syngokhan/followers", "following_url": "https://api.github.com/users/syngokhan/following{/other_user}", "gists_url": "https://api.github.com/users/syngokhan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/syngokhan", "id": 70565543, "login": "syngokhan", "node_id": "MDQ6VXNlcjcwNTY1NTQz", "organizations_url": "https://api.github.com/users/syngokhan/orgs", "received_events_url": "https://api.github.com/users/syngokhan/received_events", "repos_url": "https://api.github.com/users/syngokhan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/syngokhan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/syngokhan/subscriptions", "type": "User", "url": "https://api.github.com/users/syngokhan" }	[]	open	false	null	[]	null	[ "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"	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5946/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5946/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5945	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5945/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5945/comments	https://api.github.com/repos/huggingface/datasets/issues/5945/events	https://github.com/huggingface/datasets/issues/5945	1,754,084,577	I_kwDODunzps5ojTTh	5,945	Failing to upload dataset to the hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/77382661?v=4", "events_url": "https://api.github.com/users/Ar770/events{/privacy}", "followers_url": "https://api.github.com/users/Ar770/followers", "following_url": "https://api.github.com/users/Ar770/following{/other_user}", "gists_url": "https://api.github.com/users/Ar770/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Ar770", "id": 77382661, "login": "Ar770", "node_id": "MDQ6VXNlcjc3MzgyNjYx", "organizations_url": "https://api.github.com/users/Ar770/orgs", "received_events_url": "https://api.github.com/users/Ar770/received_events", "repos_url": "https://api.github.com/users/Ar770/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Ar770/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ar770/subscriptions", "type": "User", "url": "https://api.github.com/users/Ar770" }	[]	open	false	null	[]	null	[ "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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5945/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5945/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5944	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5944/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5944/comments	https://api.github.com/repos/huggingface/datasets/issues/5944/events	https://github.com/huggingface/datasets/pull/5944	1,752,882,200	PR_kwDODunzps5Sx7O4	5,944	Arrow dataset builder to be able to load and stream Arrow datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/10278877?v=4", "events_url": "https://api.github.com/users/mariusz-jachimowicz-83/events{/privacy}", "followers_url": "https://api.github.com/users/mariusz-jachimowicz-83/followers", "following_url": "https://api.github.com/users/mariusz-jachimowicz-83/following{/other_user}", "gists_url": "https://api.github.com/users/mariusz-jachimowicz-83/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariusz-jachimowicz-83", "id": 10278877, "login": "mariusz-jachimowicz-83", "node_id": "MDQ6VXNlcjEwMjc4ODc3", "organizations_url": "https://api.github.com/users/mariusz-jachimowicz-83/orgs", "received_events_url": "https://api.github.com/users/mariusz-jachimowicz-83/received_events", "repos_url": "https://api.github.com/users/mariusz-jachimowicz-83/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariusz-jachimowicz-83/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariusz-jachimowicz-83/subscriptions", "type": "User", "url": "https://api.github.com/users/mariusz-jachimowicz-83" }	[]	closed	false	null	[]	null	[ "_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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5944/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5944/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5944.diff", "html_url": "https://github.com/huggingface/datasets/pull/5944", "merged_at": "2023-06-13T17:29:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/5944.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5944" }	true
https://api.github.com/repos/huggingface/datasets/issues/5943	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5943/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5943/comments	https://api.github.com/repos/huggingface/datasets/issues/5943/events	https://github.com/huggingface/datasets/issues/5943	1,752,824,336	I_kwDODunzps5oefoQ	5,943	Language `lzh` is not shown on the web interface	{ "avatar_url": "https://avatars.githubusercontent.com/u/68557794?v=4", "events_url": "https://api.github.com/users/ayaka14732/events{/privacy}", "followers_url": "https://api.github.com/users/ayaka14732/followers", "following_url": "https://api.github.com/users/ayaka14732/following{/other_user}", "gists_url": "https://api.github.com/users/ayaka14732/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ayaka14732", "id": 68557794, "login": "ayaka14732", "node_id": "MDQ6VXNlcjY4NTU3Nzk0", "organizations_url": "https://api.github.com/users/ayaka14732/orgs", "received_events_url": "https://api.github.com/users/ayaka14732/received_events", "repos_url": "https://api.github.com/users/ayaka14732/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ayaka14732/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ayaka14732/subscriptions", "type": "User", "url": "https://api.github.com/users/ayaka14732" }	[]	open	false	null	[]	null	[ "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: ![](https://github.com/huggingface/datasets/assets/68557794/761b7781-ddb3-41f4-a08d-bd73af1b1b3c) On the metadata section of a certain dataset, languages are shown in their names, but `lzh` is shown as "lzh". ![](https://github.com/huggingface/datasets/assets/68557794/f795fc60-bc38-4072-8b41-e141ef888f6c) 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"	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5943/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5943/timeline	null	null	null	null	false
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`	{ "avatar_url": "https://avatars.githubusercontent.com/u/84066822?v=4", "events_url": "https://api.github.com/users/graelo/events{/privacy}", "followers_url": "https://api.github.com/users/graelo/followers", "following_url": "https://api.github.com/users/graelo/following{/other_user}", "gists_url": "https://api.github.com/users/graelo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/graelo", "id": 84066822, "login": "graelo", "node_id": "MDQ6VXNlcjg0MDY2ODIy", "organizations_url": "https://api.github.com/users/graelo/orgs", "received_events_url": "https://api.github.com/users/graelo/received_events", "repos_url": "https://api.github.com/users/graelo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/graelo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/graelo/subscriptions", "type": "User", "url": "https://api.github.com/users/graelo" }	[]	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5942/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5942/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5942.diff", "html_url": "https://github.com/huggingface/datasets/pull/5942", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5942.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5942" }	true
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/19569322?v=4", "events_url": "https://api.github.com/users/xyx361100238/events{/privacy}", "followers_url": "https://api.github.com/users/xyx361100238/followers", "following_url": "https://api.github.com/users/xyx361100238/following{/other_user}", "gists_url": "https://api.github.com/users/xyx361100238/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/xyx361100238", "id": 19569322, "login": "xyx361100238", "node_id": "MDQ6VXNlcjE5NTY5MzIy", "organizations_url": "https://api.github.com/users/xyx361100238/orgs", "received_events_url": "https://api.github.com/users/xyx361100238/received_events", "repos_url": "https://api.github.com/users/xyx361100238/repos", "site_admin": false, "starred_url": "https://api.github.com/users/xyx361100238/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xyx361100238/subscriptions", "type": "User", "url": "https://api.github.com/users/xyx361100238" }	[]	open	false	null	[]	null	[ "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： ![image](https://github.com/huggingface/datasets/assets/19569322/d9b08eee-95fe-4741-a346-b70416c948f8) ### 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>	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5941/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5941/timeline	null	null	null	null	false
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	.	{ "avatar_url": "https://avatars.githubusercontent.com/u/103381497?v=4", "events_url": "https://api.github.com/users/flckv/events{/privacy}", "followers_url": "https://api.github.com/users/flckv/followers", "following_url": "https://api.github.com/users/flckv/following{/other_user}", "gists_url": "https://api.github.com/users/flckv/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/flckv", "id": 103381497, "login": "flckv", "node_id": "U_kgDOBil5-Q", "organizations_url": "https://api.github.com/users/flckv/orgs", "received_events_url": "https://api.github.com/users/flckv/received_events", "repos_url": "https://api.github.com/users/flckv/repos", "site_admin": false, "starred_url": "https://api.github.com/users/flckv/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/flckv/subscriptions", "type": "User", "url": "https://api.github.com/users/flckv" }	[]	closed	false	null	[]	null	[]	2023-06-09T14:01:34Z	2023-06-12T12:19:34Z	2023-06-12T12:19:19Z	NONE	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5939/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5939/timeline	null	completed	null	null	false
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	open	false	null	[]	null	[ "_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	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5938/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5938/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5938.diff", "html_url": "https://github.com/huggingface/datasets/pull/5938", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5938.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5938" }	true
https://api.github.com/repos/huggingface/datasets/issues/5937	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5937/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5937/comments	https://api.github.com/repos/huggingface/datasets/issues/5937/events	https://github.com/huggingface/datasets/pull/5937	1,749,388,597	PR_kwDODunzps5SmLIs	5,937	Avoid parallel redownload in cache	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	open	false	null	[]	null	[ "_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.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5937/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5937/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5937.diff", "html_url": "https://github.com/huggingface/datasets/pull/5937", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5937.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5937" }	true
https://api.github.com/repos/huggingface/datasets/issues/5936	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5936/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5936/comments	https://api.github.com/repos/huggingface/datasets/issues/5936/events	https://github.com/huggingface/datasets/issues/5936	1,748,424,388	I_kwDODunzps5oNtbE	5,936	Sequence of array not supported for most dtype	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec" }	[]	open	false	null	[]	null	[ "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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5936/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5936/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5935	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5935/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5935/comments	https://api.github.com/repos/huggingface/datasets/issues/5935/events	https://github.com/huggingface/datasets/pull/5935	1,748,090,220	PR_kwDODunzps5Sh9Mg	5,935	Better row group size in push_to_hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	[ "<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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5935/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5935/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5935.diff", "html_url": "https://github.com/huggingface/datasets/pull/5935", "merged_at": "2023-06-09T17:40:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/5935.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5935" }	true
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/21087104?v=4", "events_url": "https://api.github.com/users/Laurent2916/events{/privacy}", "followers_url": "https://api.github.com/users/Laurent2916/followers", "following_url": "https://api.github.com/users/Laurent2916/following{/other_user}", "gists_url": "https://api.github.com/users/Laurent2916/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Laurent2916", "id": 21087104, "login": "Laurent2916", "node_id": "MDQ6VXNlcjIxMDg3MTA0", "organizations_url": "https://api.github.com/users/Laurent2916/orgs", "received_events_url": "https://api.github.com/users/Laurent2916/received_events", "repos_url": "https://api.github.com/users/Laurent2916/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Laurent2916/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Laurent2916/subscriptions", "type": "User", "url": "https://api.github.com/users/Laurent2916" }	[]	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.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5934/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5934/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5934.diff", "html_url": "https://github.com/huggingface/datasets/pull/5934", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5934.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5934" }	true
https://api.github.com/repos/huggingface/datasets/issues/5933	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5933/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5933/comments	https://api.github.com/repos/huggingface/datasets/issues/5933/events	https://github.com/huggingface/datasets/pull/5933	1,747,382,500	PR_kwDODunzps5Sfi5J	5,933	Fix `to_numpy` when None values in the sequence	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec" }	[]	closed	false	null	[]	null	[ "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.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5933/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5933/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5933.diff", "html_url": "https://github.com/huggingface/datasets/pull/5933", "merged_at": "2023-06-09T13:23:48Z", "patch_url": "https://github.com/huggingface/datasets/pull/5933.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5933" }	true
https://api.github.com/repos/huggingface/datasets/issues/5932	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5932/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5932/comments	https://api.github.com/repos/huggingface/datasets/issues/5932/events	https://github.com/huggingface/datasets/pull/5932	1,746,249,161	PR_kwDODunzps5Sbrzo	5,932	[doc build] Use secrets	{ "avatar_url": "https://avatars.githubusercontent.com/u/11827707?v=4", "events_url": "https://api.github.com/users/mishig25/events{/privacy}", "followers_url": "https://api.github.com/users/mishig25/followers", "following_url": "https://api.github.com/users/mishig25/following{/other_user}", "gists_url": "https://api.github.com/users/mishig25/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mishig25", "id": 11827707, "login": "mishig25", "node_id": "MDQ6VXNlcjExODI3NzA3", "organizations_url": "https://api.github.com/users/mishig25/orgs", "received_events_url": "https://api.github.com/users/mishig25/received_events", "repos_url": "https://api.github.com/users/mishig25/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mishig25/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mishig25/subscriptions", "type": "User", "url": "https://api.github.com/users/mishig25" }	[]	closed	false	null	[]	null	[ "<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) \| 0.008499 / 0.011353 (-0.002854) \| 0.006155 / 0.011008 (-0.004853) \| 0.124032 / 0.038508 (0.085524) \| 0.037337 / 0.023109 (0.014228) \| 0.389274 / 0.275898 (0.113376) \| 0.427736 / 0.323480 (0.104257) \| 0.006929 / 0.007986 (-0.001057) \| 0.005017 / 0.004328 (0.000689) \| 0.096356 / 0.004250 (0.092105) \| 0.055694 / 0.037052 (0.018642) \| 0.391417 / 0.258489 (0.132928) \| 0.448098 / 0.293841 (0.154257) \| 0.042442 / 0.128546 (-0.086105) \| 0.013456 / 0.075646 (-0.062190) \| 0.423502 / 0.419271 (0.004230) \| 0.062919 / 0.043533 (0.019386) \| 0.384317 / 0.255139 (0.129178) \| 0.410851 / 0.283200 (0.127652) \| 0.112807 / 0.141683 (-0.028875) \| 1.746050 / 1.452155 (0.293895) \| 1.977974 / 1.492716 (0.485257) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306382 / 0.018006 (0.288375) \| 0.620310 / 0.000490 (0.619820) \| 0.009309 / 0.000200 (0.009109) \| 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) \| 0.026900 / 0.037411 (-0.010511) \| 0.140125 / 0.014526 (0.125599) \| 0.136295 / 0.176557 (-0.040261) \| 0.207721 / 0.737135 (-0.529414) \| 0.146328 / 0.296338 (-0.150011) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616712 / 0.215209 (0.401503) \| 6.237820 / 2.077655 (4.160166) \| 2.503809 / 1.504120 (0.999689) \| 2.129739 / 1.541195 (0.588544) \| 2.160768 / 1.468490 (0.692277) \| 0.971273 / 4.584777 (-3.613504) \| 5.687161 / 3.745712 (1.941449) \| 2.738148 / 5.269862 (-2.531713) \| 1.692695 / 4.565676 (-2.872981) \| 0.113701 / 0.424275 (-0.310574) \| 0.014809 / 0.007607 (0.007202) \| 0.774795 / 0.226044 (0.548750) \| 7.660012 / 2.268929 (5.391083) \| 3.253036 / 55.444624 (-52.191588) \| 2.607498 / 6.876477 (-4.268979) \| 2.681678 / 2.142072 (0.539606) \| 1.095275 / 4.805227 (-3.709952) \| 0.239078 / 6.500664 (-6.261586) \| 0.081034 / 0.075469 (0.005565) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.574547 / 1.841788 (-0.267240) \| 18.323566 / 8.074308 (10.249258) \| 19.274482 / 10.191392 (9.083090) \| 0.210275 / 0.680424 (-0.470149) \| 0.031843 / 0.534201 (-0.502358) \| 0.514843 / 0.579283 (-0.064440) \| 0.633782 / 0.434364 (0.199418) \| 0.588569 / 0.540337 (0.048232) \| 0.721401 / 1.386936 (-0.665535) \|\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) \| 0.008866 / 0.011353 (-0.002487) \| 0.006460 / 0.011008 (-0.004548) \| 0.121337 / 0.038508 (0.082829) \| 0.033896 / 0.023109 (0.010786) \| 0.455702 / 0.275898 (0.179804) \| 0.509685 / 0.323480 (0.186205) \| 0.007650 / 0.007986 (-0.000336) \| 0.005578 / 0.004328 (0.001250) \| 0.098505 / 0.004250 (0.094255) \| 0.056122 / 0.037052 (0.019069) \| 0.478483 / 0.258489 (0.219994) \| 0.560008 / 0.293841 (0.266167) \| 0.044926 / 0.128546 (-0.083620) \| 0.014562 / 0.075646 (-0.061085) \| 0.115027 / 0.419271 (-0.304244) \| 0.066494 / 0.043533 (0.022961) \| 0.463434 / 0.255139 (0.208296) \| 0.513856 / 0.283200 (0.230656) \| 0.126436 / 0.141683 (-0.015247) \| 1.874729 / 1.452155 (0.422575) \| 1.925080 / 1.492716 (0.432364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012672 / 0.018006 (-0.005334) \| 0.615797 / 0.000490 (0.615307) \| 0.001606 / 0.000200 (0.001406) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031104 / 0.037411 (-0.006307) \| 0.130107 / 0.014526 (0.115581) \| 0.140587 / 0.176557 (-0.035970) \| 0.205081 / 0.737135 (-0.532054) \| 0.144068 / 0.296338 (-0.152270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.646549 / 0.215209 (0.431340) \| 6.403962 / 2.077655 (4.326307) \| 2.812594 / 1.504120 (1.308474) \| 2.478480 / 1.541195 (0.937285) \| 2.552385 / 1.468490 (1.083895) \| 0.991987 / 4.584777 (-3.592790) \| 5.777917 / 3.745712 (2.032205) \| 5.697830 / 5.269862 (0.427969) \| 2.370583 / 4.565676 (-2.195094) \| 0.109905 / 0.424275 (-0.314370) \| 0.013801 / 0.007607 (0.006193) \| 0.799932 / 0.226044 (0.573888) \| 8.155672 / 2.268929 (5.886743) \| 3.711662 / 55.444624 (-51.732963) \| 3.042164 / 6.876477 (-3.834312) \| 3.073549 / 2.142072 (0.931477) \| 1.137515 / 4.805227 (-3.667712) \| 0.231266 / 6.500664 (-6.269398) \| 0.080893 / 0.075469 (0.005424) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.669210 / 1.841788 (-0.172577) \| 18.747144 / 8.074308 (10.672836) \| 21.084589 / 10.191392 (10.893197) \| 0.241379 / 0.680424 (-0.439045) \| 0.029473 / 0.534201 (-0.504728) \| 0.524605 / 0.579283 (-0.054678) \| 0.622852 / 0.434364 (0.188488) \| 0.604941 / 0.540337 (0.064604) \| 0.715978 / 1.386936 (-0.670958) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<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) \| 0.005973 / 0.011353 (-0.005380) \| 0.004389 / 0.011008 (-0.006620) \| 0.096076 / 0.038508 (0.057568) \| 0.031569 / 0.023109 (0.008460) \| 0.328300 / 0.275898 (0.052402) \| 0.359356 / 0.323480 (0.035876) \| 0.005378 / 0.007986 (-0.002607) \| 0.003703 / 0.004328 (-0.000625) \| 0.075251 / 0.004250 (0.071000) \| 0.042340 / 0.037052 (0.005287) \| 0.346103 / 0.258489 (0.087614) \| 0.379896 / 0.293841 (0.086055) \| 0.027493 / 0.128546 (-0.101053) \| 0.009033 / 0.075646 (-0.066613) \| 0.327829 / 0.419271 (-0.091442) \| 0.064074 / 0.043533 (0.020541) \| 0.337703 / 0.255139 (0.082564) \| 0.355335 / 0.283200 (0.072136) \| 0.101179 / 0.141683 (-0.040504) \| 1.471738 / 1.452155 (0.019584) \| 1.539031 / 1.492716 (0.046315) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194097 / 0.018006 (0.176091) \| 0.434190 / 0.000490 (0.433701) \| 0.005730 / 0.000200 (0.005530) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025634 / 0.037411 (-0.011778) \| 0.105080 / 0.014526 (0.090555) \| 0.116508 / 0.176557 (-0.060049) \| 0.173867 / 0.737135 (-0.563269) \| 0.117749 / 0.296338 (-0.178590) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401566 / 0.215209 (0.186357) \| 4.003558 / 2.077655 (1.925903) \| 1.802756 / 1.504120 (0.298636) \| 1.604222 / 1.541195 (0.063027) \| 1.656617 / 1.468490 (0.188127) \| 0.523385 / 4.584777 (-4.061392) \| 3.744292 / 3.745712 (-0.001420) \| 1.794295 / 5.269862 (-3.475567) \| 1.044690 / 4.565676 (-3.520987) \| 0.064992 / 0.424275 (-0.359284) \| 0.011542 / 0.007607 (0.003935) \| 0.507830 / 0.226044 (0.281785) \| 5.061574 / 2.268929 (2.792645) \| 2.252896 / 55.444624 (-53.191729) \| 1.912551 / 6.876477 (-4.963926) \| 2.073510 / 2.142072 (-0.068562) \| 0.642148 / 4.805227 (-4.163079) \| 0.140151 / 6.500664 (-6.360513) \| 0.062623 / 0.075469 (-0.012846) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180367 / 1.841788 (-0.661421) \| 14.263475 / 8.074308 (6.189167) \| 12.917251 / 10.191392 (2.725859) \| 0.143815 / 0.680424 (-0.536608) \| 0.017286 / 0.534201 (-0.516915) \| 0.388411 / 0.579283 (-0.190872) \| 0.430512 / 0.434364 (-0.003851) \| 0.466595 / 0.540337 (-0.073742) \| 0.564545 / 1.386936 (-0.822391) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006059 / 0.011353 (-0.005294) \| 0.004419 / 0.011008 (-0.006590) \| 0.074206 / 0.038508 (0.035697) \| 0.031180 / 0.023109 (0.008071) \| 0.380031 / 0.275898 (0.104133) \| 0.410373 / 0.323480 (0.086893) \| 0.005397 / 0.007986 (-0.002589) \| 0.003952 / 0.004328 (-0.000376) \| 0.074426 / 0.004250 (0.070176) \| 0.046256 / 0.037052 (0.009203) \| 0.385543 / 0.258489 (0.127054) \| 0.430724 / 0.293841 (0.136883) \| 0.028052 / 0.128546 (-0.100494) \| 0.008810 / 0.075646 (-0.066836) \| 0.080749 / 0.419271 (-0.338522) \| 0.046746 / 0.043533 (0.003214) \| 0.380325 / 0.255139 (0.125186) \| 0.398901 / 0.283200 (0.115701) \| 0.099607 / 0.141683 (-0.042076) \| 1.433343 / 1.452155 (-0.018812) \| 1.520447 / 1.492716 (0.027730) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202232 / 0.018006 (0.184225) \| 0.431342 / 0.000490 (0.430852) \| 0.001020 / 0.000200 (0.000820) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028762 / 0.037411 (-0.008649) \| 0.111777 / 0.014526 (0.097251) \| 0.119283 / 0.176557 (-0.057273) \| 0.168151 / 0.737135 (-0.568985) \| 0.126093 / 0.296338 (-0.170245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442689 / 0.215209 (0.227480) \| 4.369202 / 2.077655 (2.291547) \| 2.167703 / 1.504120 (0.663583) \| 1.960580 / 1.541195 (0.419385) \| 2.001459 / 1.468490 (0.532969) \| 0.527169 / 4.584777 (-4.057608) \| 3.738987 / 3.745712 (-0.006726) \| 1.819002 / 5.269862 (-3.450860) \| 1.082786 / 4.565676 (-3.482891) \| 0.066209 / 0.424275 (-0.358066) \| 0.011549 / 0.007607 (0.003942) \| 0.545959 / 0.226044 (0.319915) \| 5.466655 / 2.268929 (3.197727) \| 2.671448 / 55.444624 (-52.773176) \| 2.340968 / 6.876477 (-4.535509) \| 2.358805 / 2.142072 (0.216733) \| 0.649456 / 4.805227 (-4.155771) \| 0.142009 / 6.500664 (-6.358655) \| 0.064199 / 0.075469 (-0.011270) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259819 / 1.841788 (-0.581969) \| 14.456988 / 8.074308 (6.382680) \| 14.478982 / 10.191392 (4.287590) \| 0.163156 / 0.680424 (-0.517268) \| 0.017090 / 0.534201 (-0.517111) \| 0.391339 / 0.579283 (-0.187944) \| 0.422021 / 0.434364 (-0.012343) \| 0.465340 / 0.540337 (-0.074997) \| 0.564517 / 1.386936 (-0.822419) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]	2023-06-07T16:09:39Z	2023-06-09T10:16:58Z	2023-06-09T09:53:16Z	CONTRIBUTOR	null	Companion pr to https://github.com/huggingface/doc-builder/pull/379	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5932/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5932/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5932.diff", "html_url": "https://github.com/huggingface/datasets/pull/5932", "merged_at": "2023-06-09T09:53:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/5932.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5932" }	true
https://api.github.com/repos/huggingface/datasets/issues/5931	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5931/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5931/comments	https://api.github.com/repos/huggingface/datasets/issues/5931/events	https://github.com/huggingface/datasets/issues/5931	1,745,408,784	I_kwDODunzps5oCNMQ	5,931	`datasets.map` not reusing cached copy by default	{ "avatar_url": "https://avatars.githubusercontent.com/u/19718818?v=4", "events_url": "https://api.github.com/users/bhavitvyamalik/events{/privacy}", "followers_url": "https://api.github.com/users/bhavitvyamalik/followers", "following_url": "https://api.github.com/users/bhavitvyamalik/following{/other_user}", "gists_url": "https://api.github.com/users/bhavitvyamalik/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bhavitvyamalik", "id": 19718818, "login": "bhavitvyamalik", "node_id": "MDQ6VXNlcjE5NzE4ODE4", "organizations_url": "https://api.github.com/users/bhavitvyamalik/orgs", "received_events_url": "https://api.github.com/users/bhavitvyamalik/received_events", "repos_url": "https://api.github.com/users/bhavitvyamalik/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bhavitvyamalik/subscriptions", "type": "User", "url": "https://api.github.com/users/bhavitvyamalik" }	[]	open	false	null	[]	null	[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]	2023-06-07T09:03:33Z	2023-06-09T11:51:22Z	null	CONTRIBUTOR	null	### Describe the bug When I load the dataset from local directory, it's cached copy is picked up after first time. However, for `map` operation, the operation is applied again and cached copy is not picked up. Is there any way to pick cached copy instead of processing it again? The only solution I could think of was to use `save_to_disk` after my last transform and then use that in my DataLoader pipeline. Are there any other solutions for the same? One more thing, my dataset is occupying 6GB storage memory after I use `map`, is there any way I can reduce that memory usage? ### Steps to reproduce the bug ``` # make sure that dataset decodes audio with correct sampling rate dataset_sampling_rate = next(iter(self.raw_datasets.values())).features["audio"].sampling_rate if dataset_sampling_rate != self.feature_extractor.sampling_rate: self.raw_datasets = self.raw_datasets.cast_column( "audio", datasets.features.Audio(sampling_rate=self.feature_extractor.sampling_rate) ) vectorized_datasets = self.raw_datasets.map( self.prepare_dataset, remove_columns=next(iter(self.raw_datasets.values())).column_names, num_proc=self.num_workers, desc="preprocess datasets", ) # filter data that is longer than max_input_length self.vectorized_datasets = vectorized_datasets.filter( self.is_audio_in_length_range, num_proc=self.num_workers, input_columns=["input_length"], ) def prepare_dataset(self, batch): # load audio sample = batch["audio"] inputs = self.feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) batch["labels"] = self.tokenizer(batch["target_text"]).input_ids return batch ``` ### Expected behavior `map` to use cached copy and if possible an alternative technique to reduce memory usage after using `map` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5931/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5931/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5930	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5930/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5930/comments	https://api.github.com/repos/huggingface/datasets/issues/5930/events	https://github.com/huggingface/datasets/issues/5930	1,745,184,395	I_kwDODunzps5oBWaL	5,930	loading private custom dataset script - authentication error	{ "avatar_url": "https://avatars.githubusercontent.com/u/103381497?v=4", "events_url": "https://api.github.com/users/flckv/events{/privacy}", "followers_url": "https://api.github.com/users/flckv/followers", "following_url": "https://api.github.com/users/flckv/following{/other_user}", "gists_url": "https://api.github.com/users/flckv/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/flckv", "id": 103381497, "login": "flckv", "node_id": "U_kgDOBil5-Q", "organizations_url": "https://api.github.com/users/flckv/orgs", "received_events_url": "https://api.github.com/users/flckv/received_events", "repos_url": "https://api.github.com/users/flckv/repos", "site_admin": false, "starred_url": "https://api.github.com/users/flckv/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/flckv/subscriptions", "type": "User", "url": "https://api.github.com/users/flckv" }	[]	open	false	null	[]	null	[]	2023-06-07T06:58:23Z	2023-06-07T06:58:23Z	null	NONE	null	### Describe the bug Train model with my custom dataset stored in HuggingFace and loaded with the loading script requires authentication but I am not sure how ? I am logged in in the terminal, in the browser. I receive this error: /python3.8/site-packages/datasets/utils/file_utils.py", line 566, in get_from_cache raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels `(ConnectionError('Unauthorized for URL `https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels. Please use the parameter ``use_auth_token=True`` after logging in with ``huggingface-cli login``')) when I added: `use_auth_token=True` and logged in via terminal then I received error: or the same error in different format: raise ConnectionError(f"`Couldn't reach {url} (error {response.status_code}`)") ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels (`error 401`) ### Steps to reproduce the bug 1. cloned transformers library locally: https://huggingface.co/docs/transformers/v4.15.0/examples : > git clone https://github.com/huggingface/transformers > cd transformers > pip install . > cd /transformers/examples/pytorch/audio-classification > pip install -r requirements.txt 2. created loading script > https://huggingface.co/docs/datasets/dataset_script added next to dataset: 3. uploaded private custom dataset with loading script to HuggingFace > https://huggingface.co/docs/datasets/dataset_script 4. added dataset loading script to local directory in the above cloned transformers library: > cd /transformers/examples/pytorch/audio-classification 5. logged in to HuggingFace on local terminal with : > huggingface-cli login 6. run the model with the custom dataset stored on HuggingFace with code: https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/README.md cd /transformers/examples/pytorch/audio-classification > python run_audio_classification.py \ > --model_name_or_path facebook/wav2vec2-base \ > --output_dir l/users/flck/outputs/wav2vec2-base-s \ > --overwrite_output_dir \ > --dataset_name s \ > --dataset_config_name s \ > --remove_unused_columns False \ > --do_train \ > --do_eval \ > --fp16 \ > --learning_rate 3e-5 \ > --max_length_seconds 1 \ > --attention_mask False \ > --warmup_ratio 0.1 \ > --num_train_epochs 5 \ > --per_device_train_batch_size 32 \ > --gradient_accumulation_steps 4 \ > --per_device_eval_batch_size 32 \ > --dataloader_num_workers 4 \ > --logging_strategy steps \ > --logging_steps 10 \ > --evaluation_strategy epoch \ > --save_strategy epoch \ > --load_best_model_at_end True \ > --metric_for_best_model accuracy \ > --save_total_limit 3 \ > --seed 0 \ > --push_to_hub \ > --use_auth_token=True ### Expected behavior Be able to train a model the https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/ run_audio_classification.py with private custom dataset stored on HuggingFace. ### Environment info - datasets version: 2.12.0 - `transformers` version: 4.30.0.dev0 - Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.17 - Python version: 3.8.12 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.1 - PyTorch version (GPU?): 2.0.1+cu117 (True) Versions of relevant libraries: [pip3] numpy==1.24.3 [pip3] torch==2.0.1 [pip3] torchaudio==2.0.2 [conda] numpy 1.24.3 pypi_0 pypi [conda] torch 2.0.1 pypi_0 pypi [conda] torchaudio 2.0.2 pypi_0 pypi	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5930/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5930/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5929	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5929/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5929/comments	https://api.github.com/repos/huggingface/datasets/issues/5929/events	https://github.com/huggingface/datasets/issues/5929	1,744,478,456	I_kwDODunzps5n-qD4	5,929	Importing PyTorch reduces multiprocessing performance for map	{ "avatar_url": "https://avatars.githubusercontent.com/u/12814709?v=4", "events_url": "https://api.github.com/users/Maxscha/events{/privacy}", "followers_url": "https://api.github.com/users/Maxscha/followers", "following_url": "https://api.github.com/users/Maxscha/following{/other_user}", "gists_url": "https://api.github.com/users/Maxscha/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Maxscha", "id": 12814709, "login": "Maxscha", "node_id": "MDQ6VXNlcjEyODE0NzA5", "organizations_url": "https://api.github.com/users/Maxscha/orgs", "received_events_url": "https://api.github.com/users/Maxscha/received_events", "repos_url": "https://api.github.com/users/Maxscha/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Maxscha/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Maxscha/subscriptions", "type": "User", "url": "https://api.github.com/users/Maxscha" }	[]	open	false	null	[]	null	[]	2023-06-06T19:42:25Z	2023-06-06T20:14:46Z	null	NONE	null	### Describe the bug I noticed that the performance of my dataset preprocessing with `map(...,num_proc=32)` decreases when PyTorch is imported. ### Steps to reproduce the bug I created two example scripts to reproduce this behavior: ``` import datasets datasets.disable_caching() from datasets import Dataset import time PROC=32 if __name__ == "__main__": dataset = [True] * 10000000 dataset = Dataset.from_dict({'train': dataset}) start = time.time() dataset.map(lambda x: x, num_proc=PROC) end = time.time() print(end - start) ``` Takes around 4 seconds on my machine. While the same code, but with an `import torch`: ``` import datasets datasets.disable_caching() from datasets import Dataset import time import torch PROC=32 if __name__ == "__main__": dataset = [True] * 10000000 dataset = Dataset.from_dict({'train': dataset}) start = time.time() dataset.map(lambda x: x, num_proc=PROC) end = time.time() print(end - start) ``` takes around 22 seconds. ### Expected behavior I would expect that the import of torch to not have such a significant effect on the performance of map using multiprocessing. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35 - Python version: 3.11.3 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2 - torch: 2.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5929/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5929/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5928	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5928/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5928/comments	https://api.github.com/repos/huggingface/datasets/issues/5928/events	https://github.com/huggingface/datasets/pull/5928	1,744,098,371	PR_kwDODunzps5SUXPC	5,928	Fix link to quickstart docs in README.md	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	[ "_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) \| 0.006693 / 0.011353 (-0.004660) \| 0.004331 / 0.011008 (-0.006677) \| 0.098022 / 0.038508 (0.059514) \| 0.032764 / 0.023109 (0.009654) \| 0.295812 / 0.275898 (0.019914) \| 0.325029 / 0.323480 (0.001550) \| 0.005779 / 0.007986 (-0.002206) \| 0.005381 / 0.004328 (0.001052) \| 0.075785 / 0.004250 (0.071535) \| 0.048759 / 0.037052 (0.011707) \| 0.308986 / 0.258489 (0.050497) \| 0.348000 / 0.293841 (0.054159) \| 0.027686 / 0.128546 (-0.100860) \| 0.008839 / 0.075646 (-0.066807) \| 0.328389 / 0.419271 (-0.090883) \| 0.062173 / 0.043533 (0.018640) \| 0.312257 / 0.255139 (0.057119) \| 0.325024 / 0.283200 (0.041824) \| 0.103886 / 0.141683 (-0.037797) \| 1.440215 / 1.452155 (-0.011940) \| 1.528665 / 1.492716 (0.035948) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210082 / 0.018006 (0.192076) \| 0.442480 / 0.000490 (0.441990) \| 0.006559 / 0.000200 (0.006359) \| 0.000092 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026774 / 0.037411 (-0.010637) \| 0.108362 / 0.014526 (0.093837) \| 0.117631 / 0.176557 (-0.058926) \| 0.176657 / 0.737135 (-0.560478) \| 0.124154 / 0.296338 (-0.172184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428136 / 0.215209 (0.212927) \| 4.270287 / 2.077655 (2.192632) \| 2.014728 / 1.504120 (0.510608) \| 1.806772 / 1.541195 (0.265577) \| 1.946284 / 1.468490 (0.477794) \| 0.525542 / 4.584777 (-4.059235) \| 3.667025 / 3.745712 (-0.078687) \| 1.878751 / 5.269862 (-3.391111) \| 1.048321 / 4.565676 (-3.517356) \| 0.065550 / 0.424275 (-0.358725) \| 0.011881 / 0.007607 (0.004274) \| 0.529873 / 0.226044 (0.303829) \| 5.289641 / 2.268929 (3.020712) \| 2.489403 / 55.444624 (-52.955221) \| 2.141037 / 6.876477 (-4.735440) \| 2.230735 / 2.142072 (0.088662) \| 0.639781 / 4.805227 (-4.165447) \| 0.141410 / 6.500664 (-6.359254) \| 0.064374 / 0.075469 (-0.011095) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.159462 / 1.841788 (-0.682325) \| 14.524730 / 8.074308 (6.450422) \| 13.578070 / 10.191392 (3.386678) \| 0.152138 / 0.680424 (-0.528286) \| 0.017255 / 0.534201 (-0.516946) \| 0.387607 / 0.579283 (-0.191676) \| 0.413652 / 0.434364 (-0.020712) \| 0.453644 / 0.540337 (-0.086693) \| 0.550051 / 1.386936 (-0.836885) \|\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) \| 0.006668 / 0.011353 (-0.004685) \| 0.004677 / 0.011008 (-0.006331) \| 0.075950 / 0.038508 (0.037442) \| 0.032439 / 0.023109 (0.009329) \| 0.381839 / 0.275898 (0.105941) \| 0.419411 / 0.323480 (0.095931) \| 0.005813 / 0.007986 (-0.002172) \| 0.004090 / 0.004328 (-0.000238) \| 0.075052 / 0.004250 (0.070802) \| 0.048453 / 0.037052 (0.011401) \| 0.388076 / 0.258489 (0.129587) \| 0.431793 / 0.293841 (0.137952) \| 0.028408 / 0.128546 (-0.100138) \| 0.009028 / 0.075646 (-0.066618) \| 0.082569 / 0.419271 (-0.336702) \| 0.046772 / 0.043533 (0.003239) \| 0.380182 / 0.255139 (0.125043) \| 0.401828 / 0.283200 (0.118629) \| 0.105388 / 0.141683 (-0.036294) \| 1.453356 / 1.452155 (0.001201) \| 1.561483 / 1.492716 (0.068767) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008922 / 0.018006 (-0.009084) \| 0.444112 / 0.000490 (0.443623) \| 0.002756 / 0.000200 (0.002556) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030408 / 0.037411 (-0.007003) \| 0.112924 / 0.014526 (0.098399) \| 0.124625 / 0.176557 (-0.051932) \| 0.176915 / 0.737135 (-0.560220) \| 0.129141 / 0.296338 (-0.167198) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448197 / 0.215209 (0.232987) \| 4.476548 / 2.077655 (2.398893) \| 2.243977 / 1.504120 (0.739857) \| 2.054060 / 1.541195 (0.512865) \| 2.130680 / 1.468490 (0.662190) \| 0.526815 / 4.584777 (-4.057962) \| 3.759312 / 3.745712 (0.013600) \| 3.333618 / 5.269862 (-1.936244) \| 1.579611 / 4.565676 (-2.986065) \| 0.065714 / 0.424275 (-0.358561) \| 0.011939 / 0.007607 (0.004332) \| 0.550313 / 0.226044 (0.324269) \| 5.476946 / 2.268929 (3.208018) \| 2.726521 / 55.444624 (-52.718104) \| 2.364977 / 6.876477 (-4.511499) \| 2.450624 / 2.142072 (0.308551) \| 0.647174 / 4.805227 (-4.158053) \| 0.141265 / 6.500664 (-6.359399) \| 0.065493 / 0.075469 (-0.009976) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249702 / 1.841788 (-0.592085) \| 15.205647 / 8.074308 (7.131338) \| 14.678310 / 10.191392 (4.486918) \| 0.141539 / 0.680424 (-0.538884) \| 0.017323 / 0.534201 (-0.516878) \| 0.387602 / 0.579283 (-0.191681) \| 0.415106 / 0.434364 (-0.019258) \| 0.458146 / 0.540337 (-0.082192) \| 0.553318 / 1.386936 (-0.833618) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#55127d7bf399fd2f3a8713db9822e8cb47cdbbed \"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) \| 0.008567 / 0.011353 (-0.002786) \| 0.005245 / 0.011008 (-0.005763) \| 0.115074 / 0.038508 (0.076566) \| 0.032567 / 0.023109 (0.009458) \| 0.352297 / 0.275898 (0.076399) \| 0.393403 / 0.323480 (0.069923) \| 0.006402 / 0.007986 (-0.001583) \| 0.004353 / 0.004328 (0.000025) \| 0.087903 / 0.004250 (0.083653) \| 0.048424 / 0.037052 (0.011372) \| 0.370078 / 0.258489 (0.111588) \| 0.410192 / 0.293841 (0.116351) \| 0.042396 / 0.128546 (-0.086150) \| 0.014426 / 0.075646 (-0.061220) \| 0.411358 / 0.419271 (-0.007914) \| 0.059546 / 0.043533 (0.016013) \| 0.364721 / 0.255139 (0.109582) \| 0.385100 / 0.283200 (0.101901) \| 0.100572 / 0.141683 (-0.041111) \| 1.741457 / 1.452155 (0.289302) \| 1.933134 / 1.492716 (0.440418) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217177 / 0.018006 (0.199171) \| 0.510399 / 0.000490 (0.509909) \| 0.005542 / 0.000200 (0.005342) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026852 / 0.037411 (-0.010559) \| 0.125580 / 0.014526 (0.111054) \| 0.132164 / 0.176557 (-0.044392) \| 0.189073 / 0.737135 (-0.548063) \| 0.135980 / 0.296338 (-0.160358) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.601924 / 0.215209 (0.386715) \| 5.891397 / 2.077655 (3.813743) \| 2.389494 / 1.504120 (0.885375) \| 2.044013 / 1.541195 (0.502818) \| 2.019367 / 1.468490 (0.550877) \| 0.883807 / 4.584777 (-3.700970) \| 5.141349 / 3.745712 (1.395636) \| 2.607415 / 5.269862 (-2.662446) \| 1.567268 / 4.565676 (-2.998409) \| 0.102738 / 0.424275 (-0.321537) \| 0.013480 / 0.007607 (0.005873) \| 0.744979 / 0.226044 (0.518934) \| 7.404182 / 2.268929 (5.135254) \| 2.983406 / 55.444624 (-52.461219) \| 2.331847 / 6.876477 (-4.544630) \| 2.465119 / 2.142072 (0.323047) \| 1.106725 / 4.805227 (-3.698502) \| 0.205779 / 6.500664 (-6.294885) \| 0.081019 / 0.075469 (0.005550) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.527840 / 1.841788 (-0.313947) \| 16.989487 / 8.074308 (8.915179) \| 18.016123 / 10.191392 (7.824731) \| 0.216157 / 0.680424 (-0.464266) \| 0.025393 / 0.534201 (-0.508808) \| 0.496743 / 0.579283 (-0.082540) \| 0.575365 / 0.434364 (0.141002) \| 0.559978 / 0.540337 (0.019641) \| 0.677474 / 1.386936 (-0.709462) \|\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) \| 0.008913 / 0.011353 (-0.002440) \| 0.005540 / 0.011008 (-0.005469) \| 0.100001 / 0.038508 (0.061493) \| 0.034432 / 0.023109 (0.011323) \| 0.419824 / 0.275898 (0.143926) \| 0.443566 / 0.323480 (0.120086) \| 0.006372 / 0.007986 (-0.001614) \| 0.004405 / 0.004328 (0.000077) \| 0.094927 / 0.004250 (0.090677) \| 0.050300 / 0.037052 (0.013248) \| 0.424806 / 0.258489 (0.166317) \| 0.480793 / 0.293841 (0.186952) \| 0.050869 / 0.128546 (-0.077677) \| 0.015899 / 0.075646 (-0.059747) \| 0.111413 / 0.419271 (-0.307859) \| 0.058093 / 0.043533 (0.014560) \| 0.430575 / 0.255139 (0.175436) \| 0.483786 / 0.283200 (0.200586) \| 0.106878 / 0.141683 (-0.034805) \| 1.763576 / 1.452155 (0.311422) \| 1.837750 / 1.492716 (0.345033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011565 / 0.018006 (-0.006441) \| 0.484411 / 0.000490 (0.483922) \| 0.004869 / 0.000200 (0.004669) \| 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) \| 0.030706 / 0.037411 (-0.006706) \| 0.126901 / 0.014526 (0.112375) \| 0.130367 / 0.176557 (-0.046190) \| 0.206568 / 0.737135 (-0.530567) \| 0.146505 / 0.296338 (-0.149834) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627266 / 0.215209 (0.412057) \| 6.314049 / 2.077655 (4.236394) \| 2.582920 / 1.504120 (1.078800) \| 2.249401 / 1.541195 (0.708206) \| 2.244960 / 1.468490 (0.776470) \| 0.907770 / 4.584777 (-3.677007) \| 5.349622 / 3.745712 (1.603910) \| 4.591244 / 5.269862 (-0.678618) \| 2.301612 / 4.565676 (-2.264064) \| 0.108813 / 0.424275 (-0.315462) \| 0.013187 / 0.007607 (0.005580) \| 0.806071 / 0.226044 (0.580027) \| 7.843903 / 2.268929 (5.574974) \| 3.405968 / 55.444624 (-52.038656) \| 2.564301 / 6.876477 (-4.312176) \| 2.652208 / 2.142072 (0.510135) \| 1.168142 / 4.805227 (-3.637086) \| 0.218551 / 6.500664 (-6.282113) \| 0.078120 / 0.075469 (0.002651) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.562517 / 1.841788 (-0.279271) \| 17.519325 / 8.074308 (9.445017) \| 20.727083 / 10.191392 (10.535691) \| 0.207135 / 0.680424 (-0.473288) \| 0.028208 / 0.534201 (-0.505993) \| 0.496157 / 0.579283 (-0.083126) \| 0.569239 / 0.434364 (0.134875) \| 0.566137 / 0.540337 (0.025799) \| 0.704208 / 1.386936 (-0.682728) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8eb3f34d876da98e722d866be90d7f26135ea9e3 \"CML watermark\")\n" ]	2023-06-06T15:23:01Z	2023-06-06T15:52:34Z	2023-06-06T15:43:53Z	CONTRIBUTOR	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5928/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5928/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5928.diff", "html_url": 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https://api.github.com/repos/huggingface/datasets/issues/5927	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5927/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5927/comments	https://api.github.com/repos/huggingface/datasets/issues/5927/events	https://github.com/huggingface/datasets/issues/5927	1,744,009,032	I_kwDODunzps5n83dI	5,927	`IndexError` when indexing `Sequence` of `Array2D` with `None` values	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec" }	[]	closed	false	null	[]	null	[ "Easy fix would be to add:\r\n\r\n```python\r\nnull_indices -= np.arange(len(null_indices))\r\n```\r\n\r\nbefore L279, but I'm not sure it's the most intuitive way to fix it.", "Same issue here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7fcbe5b1575c8d162b65b9397b3dfda995a4e048/src/datasets/features/features.py#L1398\r\n\r\nFixed in #5948 " ]	2023-06-06T14:36:22Z	2023-06-13T12:39:39Z	2023-06-09T13:23:50Z	CONTRIBUTOR	null	### Describe the bug Having `None` values in a `Sequence` of `ArrayND` fails. ### Steps to reproduce the bug ```python from datasets import Array2D, Dataset, Features, Sequence data = [ [ [[0]], None, None, ] ] feature = Sequence(Array2D((1, 1), dtype="int64")) dataset = Dataset.from_dict({"a": data}, features=Features({"a": feature})) dataset[0] # error raised only when indexing ``` ``` Traceback (most recent call last): File "/Users/quentingallouedec/gia/c.py", line 13, in <module> dataset[0] # error raised only when indexing File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2658, in __getitem__ return self._getitem(key) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2643, in _getitem formatted_output = format_table( File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 634, in format_table return formatter(pa_table, query_type=query_type) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 406, in __call__ return self.format_row(pa_table) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 441, in format_row row = self.python_arrow_extractor().extract_row(pa_table) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 144, in extract_row return _unnest(pa_table.to_pydict()) File "pyarrow/table.pxi", line 4146, in pyarrow.lib.Table.to_pydict File "pyarrow/table.pxi", line 1312, in pyarrow.lib.ChunkedArray.to_pylist File "pyarrow/array.pxi", line 1521, in pyarrow.lib.Array.to_pylist File "pyarrow/scalar.pxi", line 675, in pyarrow.lib.ListScalar.as_py File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 760, in to_pylist return self.to_numpy(zero_copy_only=zero_copy_only).tolist() File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 725, in to_numpy numpy_arr = np.insert(numpy_arr.astype(np.float64), null_indices, np.nan, axis=0) File "<__array_function__ internals>", line 200, in insert File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/numpy/lib/function_base.py", line 5426, in insert old_mask[indices] = False IndexError: index 3 is out of bounds for axis 0 with size 3 ``` AFAIK, the problem only occurs when you use a `Sequence` of `ArrayND`. I strongly suspect that the problem comes from this line, or `np.insert` is misused: https://github.com/huggingface/datasets/blob/02ee418831aba68d0be93227bce8b3f42ef8980f/src/datasets/features/features.py#L729 To put t simply, you want something that do that: ```python import numpy as np numpy_arr = np.zeros((1, 1, 1)) null_indices = np.array([1, 2]) np.insert(numpy_arr, null_indices, np.nan, axis=0) # raise an error, instead of outputting # array([[[ 0.]], # [[nan]], # [[nan]]]) ``` ### Expected behavior The previous code should not raise an error. ### Environment info - Python 3.10.11 - datasets 2.10.0 - pyarrow 12.0.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5927/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5927/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5926	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5926/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5926/comments	https://api.github.com/repos/huggingface/datasets/issues/5926/events	https://github.com/huggingface/datasets/issues/5926	1,743,922,028	I_kwDODunzps5n8iNs	5,926	Uncaught exception when generating the splits from a dataset that miss data	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo" }	[]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]	2023-06-06T13:51:01Z	2023-06-07T07:53:16Z	null	CONTRIBUTOR	null	### Describe the bug Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error. But when trying to generate the split names, we get an exception which is now correctly caught. Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435 ### Steps to reproduce the bug ```python >>> from datasets import StreamingDownloadManager, load_dataset_builder >>> builder = load_dataset_builder(path="blog_authorship_corpus") Downloading builder script: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 5.60k/5.60k [00:00<00:00, 23.1MB/s] Downloading metadata: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.81k/2.81k [00:00<00:00, 14.7MB/s] Downloading readme: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7.30k/7.30k [00:00<00:00, 30.8MB/s] >>> dl_manager = StreamingDownloadManager(base_path=builder.base_path) >>> builder._split_generators(dl_manager) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators data = dl_manager.download_and_extract(_DATA_URL) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested return function(data_struct) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol with fsspec.open(urlpath, **kwargs) as f: File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open return open_files( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__ out = super().__getitem__(item) IndexError: list index out of range ``` ### Expected behavior We should have an Exception raised by the datasets library. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35 - Python version: 3.9.15 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5926/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5926/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5925	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5925/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5925/comments	https://api.github.com/repos/huggingface/datasets/issues/5925/events	https://github.com/huggingface/datasets/issues/5925	1,741,941,436	I_kwDODunzps5n0-q8	5,925	Breaking API change in datasets.list_datasets caused by change in HfApi.list_datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/78868366?v=4", "events_url": "https://api.github.com/users/mtkinit/events{/privacy}", "followers_url": "https://api.github.com/users/mtkinit/followers", "following_url": "https://api.github.com/users/mtkinit/following{/other_user}", "gists_url": "https://api.github.com/users/mtkinit/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mtkinit", "id": 78868366, "login": "mtkinit", "node_id": "MDQ6VXNlcjc4ODY4MzY2", "organizations_url": "https://api.github.com/users/mtkinit/orgs", "received_events_url": "https://api.github.com/users/mtkinit/received_events", "repos_url": "https://api.github.com/users/mtkinit/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mtkinit/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mtkinit/subscriptions", "type": "User", "url": "https://api.github.com/users/mtkinit" }	[]	open	false	null	[]	null	[]	2023-06-05T14:46:04Z	2023-06-05T14:46:04Z	null	NONE	null	### Describe the bug Hi all, after an update of the `datasets` library, we observer crashes in our code. We relied on `datasets.list_datasets` returning a `list`. Now, after the API of the HfApi.list_datasets was changed and it returns a `list` instead of an `Iterable`, the `datasets.list_datasets` now sometimes returns a `list` and somesimes an `Iterable`. It would be helpful to indicate that by the return type of the `datasets.list_datasets` function. Thanks, Martin ### Steps to reproduce the bug Here, the code crashed after we updated the `datasets` library: ```python # list_datasets no longer returns a list, which leads to an error when one tries to slice it for datasets.list_datasets(with_details=True)[:limit]: ... ``` ### Expected behavior It would be helpful to indicate that by the return type of the `datasets.list_datasets` function. ### Environment info Ubuntu 22.04 datasets 2.12.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5925/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5925/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5924	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5924/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5924/comments	https://api.github.com/repos/huggingface/datasets/issues/5924/events	https://github.com/huggingface/datasets/pull/5924	1,738,889,236	PR_kwDODunzps5SCiFv	5,924	Add parallel module using joblib for Spark	{ "avatar_url": "https://avatars.githubusercontent.com/u/12763339?v=4", "events_url": "https://api.github.com/users/es94129/events{/privacy}", "followers_url": "https://api.github.com/users/es94129/followers", "following_url": "https://api.github.com/users/es94129/following{/other_user}", "gists_url": "https://api.github.com/users/es94129/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/es94129", "id": 12763339, "login": "es94129", "node_id": "MDQ6VXNlcjEyNzYzMzM5", "organizations_url": "https://api.github.com/users/es94129/orgs", "received_events_url": "https://api.github.com/users/es94129/received_events", "repos_url": "https://api.github.com/users/es94129/repos", "site_admin": false, "starred_url": "https://api.github.com/users/es94129/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/es94129/subscriptions", "type": "User", "url": "https://api.github.com/users/es94129" }	[]	open	false	null	[]	null	[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```", "@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.", "Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)", "Hi maintainers, I've just addressed most of the comments, please take another look, thank you.", "<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) \| 0.008422 / 0.011353 (-0.002931) \| 0.005658 / 0.011008 (-0.005350) \| 0.135372 / 0.038508 (0.096864) \| 0.044766 / 0.023109 (0.021657) \| 0.417876 / 0.275898 (0.141978) \| 0.462785 / 0.323480 (0.139305) \| 0.005485 / 0.007986 (-0.002501) \| 0.005640 / 0.004328 (0.001311) \| 0.105020 / 0.004250 (0.100770) \| 0.049114 / 0.037052 (0.012062) \| 0.490450 / 0.258489 (0.231961) \| 0.467693 / 0.293841 (0.173852) \| 0.050929 / 0.128546 (-0.077617) \| 0.014644 / 0.075646 (-0.061002) \| 0.452373 / 0.419271 (0.033101) \| 0.074897 / 0.043533 (0.031364) \| 0.425816 / 0.255139 (0.170677) \| 0.420415 / 0.283200 (0.137215) \| 0.134121 / 0.141683 (-0.007561) \| 1.927744 / 1.452155 (0.475589) \| 2.014417 / 1.492716 (0.521701) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254811 / 0.018006 (0.236805) \| 0.550011 / 0.000490 (0.549521) \| 0.004913 / 0.000200 (0.004714) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032644 / 0.037411 (-0.004768) \| 0.135672 / 0.014526 (0.121146) \| 0.158984 / 0.176557 (-0.017572) \| 0.218267 / 0.737135 (-0.518869) \| 0.150348 / 0.296338 (-0.145991) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.625723 / 0.215209 (0.410514) \| 6.247559 / 2.077655 (4.169905) \| 2.626785 / 1.504120 (1.122666) \| 2.195224 / 1.541195 (0.654030) \| 2.232140 / 1.468490 (0.763650) \| 0.943082 / 4.584777 (-3.641695) \| 5.799262 / 3.745712 (2.053550) \| 2.849411 / 5.269862 (-2.420450) \| 1.744160 / 4.565676 (-2.821516) \| 0.119056 / 0.424275 (-0.305219) \| 0.014233 / 0.007607 (0.006626) \| 0.795238 / 0.226044 (0.569194) \| 7.569586 / 2.268929 (5.300657) \| 3.179481 / 55.444624 (-52.265143) \| 2.519772 / 6.876477 (-4.356704) \| 2.714570 / 2.142072 (0.572498) \| 1.107197 / 4.805227 (-3.698030) \| 0.229986 / 6.500664 (-6.270678) \| 0.087993 / 0.075469 (0.012524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.535610 / 1.841788 (-0.306178) \| 18.639369 / 8.074308 (10.565061) \| 21.081844 / 10.191392 (10.890452) \| 0.253247 / 0.680424 (-0.427177) \| 0.026711 / 0.534201 (-0.507490) \| 0.503790 / 0.579283 (-0.075493) \| 0.600124 / 0.434364 (0.165760) \| 0.617944 / 0.540337 (0.077607) \| 0.766947 / 1.386936 (-0.619989) \|\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) \| 0.007885 / 0.011353 (-0.003468) \| 0.004761 / 0.011008 (-0.006248) \| 0.097995 / 0.038508 (0.059487) \| 0.033624 / 0.023109 (0.010515) \| 0.504307 / 0.275898 (0.228409) \| 0.534803 / 0.323480 (0.211323) \| 0.006048 / 0.007986 (-0.001937) \| 0.005042 / 0.004328 (0.000714) \| 0.102288 / 0.004250 (0.098038) \| 0.048695 / 0.037052 (0.011643) \| 0.559086 / 0.258489 (0.300597) \| 0.553233 / 0.293841 (0.259392) \| 0.044596 / 0.128546 (-0.083950) \| 0.013696 / 0.075646 (-0.061950) \| 0.109875 / 0.419271 (-0.309397) \| 0.059993 / 0.043533 (0.016460) \| 0.485579 / 0.255139 (0.230440) \| 0.519835 / 0.283200 (0.236635) \| 0.123504 / 0.141683 (-0.018179) \| 1.820506 / 1.452155 (0.368351) \| 1.963448 / 1.492716 (0.470732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292663 / 0.018006 (0.274656) \| 0.557783 / 0.000490 (0.557293) \| 0.001330 / 0.000200 (0.001130) \| 0.000112 / 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) \| 0.036890 / 0.037411 (-0.000522) \| 0.140373 / 0.014526 (0.125847) \| 0.140176 / 0.176557 (-0.036381) \| 0.237378 / 0.737135 (-0.499757) \| 0.160186 / 0.296338 (-0.136152) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.673599 / 0.215209 (0.458390) \| 6.510280 / 2.077655 (4.432625) \| 2.981617 / 1.504120 (1.477497) \| 2.684664 / 1.541195 (1.143469) \| 2.760471 / 1.468490 (1.291981) \| 0.975413 / 4.584777 (-3.609364) \| 5.708933 / 3.745712 (1.963220) \| 2.772069 / 5.269862 (-2.497793) \| 1.763627 / 4.565676 (-2.802049) \| 0.111632 / 0.424275 (-0.312643) \| 0.013223 / 0.007607 (0.005616) \| 0.791545 / 0.226044 (0.565500) \| 8.063287 / 2.268929 (5.794359) \| 3.671920 / 55.444624 (-51.772704) \| 3.057248 / 6.876477 (-3.819229) \| 3.083569 / 2.142072 (0.941497) \| 1.118136 / 4.805227 (-3.687092) \| 0.214655 / 6.500664 (-6.286009) \| 0.083074 / 0.075469 (0.007605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.761731 / 1.841788 (-0.080056) \| 18.874200 / 8.074308 (10.799892) \| 22.383693 / 10.191392 (12.192301) \| 0.240292 / 0.680424 (-0.440132) \| 0.028850 / 0.534201 (-0.505351) \| 0.557334 / 0.579283 (-0.021949) \| 0.627732 / 0.434364 (0.193369) \| 0.634484 / 0.540337 (0.094146) \| 0.767372 / 1.386936 (-0.619564) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n" ]	2023-06-02T22:25:25Z	2023-06-13T16:18:53Z	null	NONE	null	Discussion in https://github.com/huggingface/datasets/issues/5798	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5924/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5924/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5924.diff", "html_url": "https://github.com/huggingface/datasets/pull/5924", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5924.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5924" }	true
https://api.github.com/repos/huggingface/datasets/issues/5923	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5923/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5923/comments	https://api.github.com/repos/huggingface/datasets/issues/5923/events	https://github.com/huggingface/datasets/issues/5923	1,737,436,227	I_kwDODunzps5njyxD	5,923	Cannot import datasets - ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility	{ "avatar_url": "https://avatars.githubusercontent.com/u/71412682?v=4", "events_url": "https://api.github.com/users/ehuangc/events{/privacy}", "followers_url": "https://api.github.com/users/ehuangc/followers", "following_url": "https://api.github.com/users/ehuangc/following{/other_user}", "gists_url": "https://api.github.com/users/ehuangc/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ehuangc", "id": 71412682, "login": "ehuangc", "node_id": "MDQ6VXNlcjcxNDEyNjgy", "organizations_url": "https://api.github.com/users/ehuangc/orgs", "received_events_url": "https://api.github.com/users/ehuangc/received_events", "repos_url": "https://api.github.com/users/ehuangc/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ehuangc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ehuangc/subscriptions", "type": "User", "url": "https://api.github.com/users/ehuangc" }	[]	open	false	null	[]	null	[ "Based on https://github.com/rapidsai/cudf/issues/10187, this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n\r\nCan you please execute the following commands in the terminal and paste the output here?\r\n```\r\nconda list \| grep arrow\r\n``` \r\n```\r\npython -c \"import pyarrow; print(pyarrow.__file__)\"\r\n```\r\n\r\n\r\n", "> Based on [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187), this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n> \r\n> Can you please execute the following commands in the terminal and paste the output here?\r\n> \r\n> ```\r\n> conda list \| grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\n\r\nHere is the output to the first command:\r\n```\r\narrow-cpp 11.0.0 py39h7f74497_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n```\r\nand the second:\r\n```\r\n/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/__init__.py\r\n```\r\nThanks!\r\n\r\n\r\n\r\n", "after installing pytesseract 0.3.10, I got the above error. FYI ", "RuntimeError: Failed to import transformers.trainer because of the following error (look up to see its traceback):\r\npyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject", "I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n\r\nDo we need to update dependencies? ", "Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291", "For conda with python3.8.16 this solved my problem! thanks!\r\n\r\n> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies? I can work on that if no one else is working on it.\r\n\r\n", "Thanks for replying. I am not sure about those environments but it seems like pyarrow-12.0.0 does not work for conda with python 3.8.16. \r\n\r\n> Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291\r\n\r\n" ]	2023-06-02T04:16:32Z	2023-06-05T15:33:20Z	null	NONE	null	### Describe the bug When trying to import datasets, I get a pyarrow ValueError: Traceback (most recent call last): File "/Users/edward/test/test.py", line 1, in <module> import datasets File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_reader.py", line 28, in <module> import pyarrow.parquet as pq File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/__init__.py", line 20, in <module> from .core import * File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 45, in <module> from pyarrow.fs import (LocalFileSystem, FileSystem, FileType, File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/fs.py", line 49, in <module> from pyarrow._gcsfs import GcsFileSystem # noqa File "pyarrow/_gcsfs.pyx", line 1, in init pyarrow._gcsfs ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug `import datasets` ### Expected behavior Successful import ### Environment info Conda environment, MacOS python 3.9.12 datasets 2.12.0	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5923/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5923/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5922	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5922/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5922/comments	https://api.github.com/repos/huggingface/datasets/issues/5922/events	https://github.com/huggingface/datasets/issues/5922	1,736,898,953	I_kwDODunzps5nhvmJ	5,922	Length of table does not accurately reflect the split	{ "avatar_url": "https://avatars.githubusercontent.com/u/8068268?v=4", "events_url": "https://api.github.com/users/amogkam/events{/privacy}", "followers_url": "https://api.github.com/users/amogkam/followers", "following_url": "https://api.github.com/users/amogkam/following{/other_user}", "gists_url": "https://api.github.com/users/amogkam/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/amogkam", "id": 8068268, "login": "amogkam", "node_id": "MDQ6VXNlcjgwNjgyNjg=", "organizations_url": "https://api.github.com/users/amogkam/orgs", "received_events_url": "https://api.github.com/users/amogkam/received_events", "repos_url": "https://api.github.com/users/amogkam/repos", "site_admin": false, "starred_url": "https://api.github.com/users/amogkam/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/amogkam/subscriptions", "type": "User", "url": "https://api.github.com/users/amogkam" }	[ { "color": "ffffff", "default": true, "description": "This will not be worked on", "id": 1935892913, "name": "wontfix", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix" } ]	closed	false	null	[]	null	[ "As already replied by @lhoestq (private channel):\r\n> `.train_test_split` (as well as `.shard`, `.select`) doesn't create a new arrow table to save time and disk space. Instead, it uses an indices mapping on top of the table that locate which examples are part of train or test.", "This is an optimization that we don't plan to \"fix\", so I'm closing this issue." ]	2023-06-01T18:56:26Z	2023-06-02T16:13:31Z	2023-06-02T16:13:31Z	NONE	null	### Describe the bug I load a Huggingface Dataset and do `train_test_split`. I'm expecting the underlying table for the dataset to also be split, but it's not. ### Steps to reproduce the bug ![image](https://github.com/huggingface/datasets/assets/8068268/83e5768f-8b4c-422a-945c-832a7585afff) ### Expected behavior The expected behavior is when `len(hf_dataset["train"].data)` should match the length of the train split, and not be the entire unsplit dataset. ### Environment info datasets 2.10.1 python 3.10.11	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5922/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5922/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5921	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5921/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5921/comments	https://api.github.com/repos/huggingface/datasets/issues/5921/events	https://github.com/huggingface/datasets/pull/5921	1,736,563,023	PR_kwDODunzps5R6j-y	5,921	Fix streaming parquet with image feature in schema	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	[ "_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) \| 0.007088 / 0.011353 (-0.004265) \| 0.005216 / 0.011008 (-0.005793) \| 0.097572 / 0.038508 (0.059064) \| 0.036510 / 0.023109 (0.013401) \| 0.316885 / 0.275898 (0.040987) \| 0.348541 / 0.323480 (0.025061) \| 0.006513 / 0.007986 (-0.001473) \| 0.004579 / 0.004328 (0.000251) \| 0.073779 / 0.004250 (0.069529) \| 0.057500 / 0.037052 (0.020448) \| 0.329840 / 0.258489 (0.071351) \| 0.357530 / 0.293841 (0.063690) \| 0.028515 / 0.128546 (-0.100031) \| 0.009156 / 0.075646 (-0.066491) \| 0.328340 / 0.419271 (-0.090932) \| 0.068400 / 0.043533 (0.024867) \| 0.313692 / 0.255139 (0.058553) \| 0.329170 / 0.283200 (0.045971) \| 0.111969 / 0.141683 (-0.029714) \| 1.422096 / 1.452155 (-0.030059) \| 1.550042 / 1.492716 (0.057326) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285113 / 0.018006 (0.267107) \| 0.546788 / 0.000490 (0.546298) \| 0.006992 / 0.000200 (0.006792) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026841 / 0.037411 (-0.010570) \| 0.108413 / 0.014526 (0.093887) \| 0.118375 / 0.176557 (-0.058181) \| 0.174889 / 0.737135 (-0.562246) \| 0.122781 / 0.296338 (-0.173558) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404187 / 0.215209 (0.188978) \| 4.039673 / 2.077655 (1.962019) \| 1.894616 / 1.504120 (0.390496) \| 1.729182 / 1.541195 (0.187987) \| 1.772917 / 1.468490 (0.304427) \| 0.524046 / 4.584777 (-4.060731) \| 3.628111 / 3.745712 (-0.117601) \| 1.866075 / 5.269862 (-3.403787) \| 1.026435 / 4.565676 (-3.539242) \| 0.065328 / 0.424275 (-0.358947) \| 0.012717 / 0.007607 (0.005110) \| 0.505821 / 0.226044 (0.279777) \| 5.049518 / 2.268929 (2.780589) \| 2.338486 / 55.444624 (-53.106139) \| 2.002874 / 6.876477 (-4.873602) \| 2.193049 / 2.142072 (0.050976) \| 0.664638 / 4.805227 (-4.140589) \| 0.151323 / 6.500664 (-6.349341) \| 0.063774 / 0.075469 (-0.011695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.168168 / 1.841788 (-0.673620) \| 15.289200 / 8.074308 (7.214891) \| 13.614249 / 10.191392 (3.422857) \| 0.167950 / 0.680424 (-0.512474) \| 0.017522 / 0.534201 (-0.516679) \| 0.393480 / 0.579283 (-0.185803) \| 0.420549 / 0.434364 (-0.013815) \| 0.461425 / 0.540337 (-0.078912) \| 0.563583 / 1.386936 (-0.823353) \|\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) \| 0.006859 / 0.011353 (-0.004493) \| 0.004864 / 0.011008 (-0.006144) \| 0.075084 / 0.038508 (0.036576) \| 0.033989 / 0.023109 (0.010880) \| 0.372512 / 0.275898 (0.096614) \| 0.394725 / 0.323480 (0.071246) \| 0.006382 / 0.007986 (-0.001604) \| 0.004521 / 0.004328 (0.000193) \| 0.076422 / 0.004250 (0.072172) \| 0.055383 / 0.037052 (0.018331) \| 0.400974 / 0.258489 (0.142485) \| 0.411570 / 0.293841 (0.117729) \| 0.028264 / 0.128546 (-0.100282) \| 0.009123 / 0.075646 (-0.066523) \| 0.081257 / 0.419271 (-0.338015) \| 0.048147 / 0.043533 (0.004614) \| 0.390735 / 0.255139 (0.135596) \| 0.376426 / 0.283200 (0.093226) \| 0.108164 / 0.141683 (-0.033518) \| 1.429667 / 1.452155 (-0.022488) \| 1.556291 / 1.492716 (0.063575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289514 / 0.018006 (0.271508) \| 0.532860 / 0.000490 (0.532370) \| 0.003810 / 0.000200 (0.003611) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031292 / 0.037411 (-0.006119) \| 0.116530 / 0.014526 (0.102005) \| 0.127624 / 0.176557 (-0.048932) \| 0.178276 / 0.737135 (-0.558859) \| 0.133742 / 0.296338 (-0.162597) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431505 / 0.215209 (0.216296) \| 4.309206 / 2.077655 (2.231551) \| 2.174779 / 1.504120 (0.670659) \| 1.998122 / 1.541195 (0.456927) \| 2.126478 / 1.468490 (0.657988) \| 0.528971 / 4.584777 (-4.055806) \| 3.797608 / 3.745712 (0.051895) \| 1.876275 / 5.269862 (-3.393586) \| 1.087458 / 4.565676 (-3.478218) \| 0.066940 / 0.424275 (-0.357335) \| 0.012432 / 0.007607 (0.004825) \| 0.538346 / 0.226044 (0.312301) \| 5.370968 / 2.268929 (3.102039) \| 2.613718 / 55.444624 (-52.830906) \| 2.246585 / 6.876477 (-4.629892) \| 2.375695 / 2.142072 (0.233622) \| 0.652227 / 4.805227 (-4.153001) \| 0.143246 / 6.500664 (-6.357418) \| 0.066163 / 0.075469 (-0.009306) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291263 / 1.841788 (-0.550524) \| 16.532281 / 8.074308 (8.457973) \| 15.038471 / 10.191392 (4.847079) \| 0.168139 / 0.680424 (-0.512285) \| 0.017724 / 0.534201 (-0.516477) \| 0.391636 / 0.579283 (-0.187648) \| 0.429690 / 0.434364 (-0.004674) \| 0.474941 / 0.540337 (-0.065396) \| 0.579461 / 1.386936 (-0.807475) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db690affa0373b08f7cef04e25fe2113ee831ef5 \"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) \| 0.006083 / 0.011353 (-0.005269) \| 0.004085 / 0.011008 (-0.006923) \| 0.098337 / 0.038508 (0.059829) \| 0.027573 / 0.023109 (0.004464) \| 0.305688 / 0.275898 (0.029790) \| 0.341767 / 0.323480 (0.018287) \| 0.005143 / 0.007986 (-0.002842) \| 0.003396 / 0.004328 (-0.000932) \| 0.076925 / 0.004250 (0.072674) \| 0.041027 / 0.037052 (0.003975) \| 0.307877 / 0.258489 (0.049388) \| 0.346559 / 0.293841 (0.052718) \| 0.025183 / 0.128546 (-0.103363) \| 0.008575 / 0.075646 (-0.067071) \| 0.319449 / 0.419271 (-0.099823) \| 0.043378 / 0.043533 (-0.000154) \| 0.304563 / 0.255139 (0.049424) \| 0.332019 / 0.283200 (0.048819) \| 0.087725 / 0.141683 (-0.053958) \| 1.484904 / 1.452155 (0.032749) \| 1.582780 / 1.492716 (0.090064) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197503 / 0.018006 (0.179497) \| 0.410370 / 0.000490 (0.409880) \| 0.003840 / 0.000200 (0.003640) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024179 / 0.037411 (-0.013232) \| 0.098876 / 0.014526 (0.084350) \| 0.106189 / 0.176557 (-0.070367) \| 0.168964 / 0.737135 (-0.568171) \| 0.109723 / 0.296338 (-0.186616) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429453 / 0.215209 (0.214244) \| 4.295584 / 2.077655 (2.217929) \| 2.014330 / 1.504120 (0.510210) \| 1.841119 / 1.541195 (0.299924) \| 1.928378 / 1.468490 (0.459888) \| 0.554571 / 4.584777 (-4.030206) \| 3.431769 / 3.745712 (-0.313943) \| 1.716204 / 5.269862 (-3.553658) \| 0.995054 / 4.565676 (-3.570622) \| 0.067374 / 0.424275 (-0.356902) \| 0.012557 / 0.007607 (0.004950) \| 0.533785 / 0.226044 (0.307740) \| 5.363360 / 2.268929 (3.094431) \| 2.535190 / 55.444624 (-52.909434) \| 2.191646 / 6.876477 (-4.684831) \| 2.400799 / 2.142072 (0.258727) \| 0.663961 / 4.805227 (-4.141266) \| 0.135992 / 6.500664 (-6.364672) \| 0.067378 / 0.075469 (-0.008092) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235110 / 1.841788 (-0.606678) \| 13.820695 / 8.074308 (5.746387) \| 13.667202 / 10.191392 (3.475810) \| 0.143025 / 0.680424 (-0.537399) \| 0.016757 / 0.534201 (-0.517444) \| 0.356262 / 0.579283 (-0.223021) \| 0.401871 / 0.434364 (-0.032493) \| 0.423928 / 0.540337 (-0.116410) \| 0.514598 / 1.386936 (-0.872338) \|\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) \| 0.006260 / 0.011353 (-0.005093) \| 0.004159 / 0.011008 (-0.006850) \| 0.076780 / 0.038508 (0.038272) \| 0.027899 / 0.023109 (0.004789) \| 0.412756 / 0.275898 (0.136858) \| 0.455145 / 0.323480 (0.131665) \| 0.005029 / 0.007986 (-0.002956) \| 0.003482 / 0.004328 (-0.000847) \| 0.076148 / 0.004250 (0.071898) \| 0.038969 / 0.037052 (0.001917) \| 0.429975 / 0.258489 (0.171486) \| 0.465880 / 0.293841 (0.172039) \| 0.025555 / 0.128546 (-0.102991) \| 0.008612 / 0.075646 (-0.067034) \| 0.082604 / 0.419271 (-0.336667) \| 0.039690 / 0.043533 (-0.003842) \| 0.403644 / 0.255139 (0.148505) \| 0.440438 / 0.283200 (0.157238) \| 0.090984 / 0.141683 (-0.050699) \| 1.465915 / 1.452155 (0.013760) \| 1.564227 / 1.492716 (0.071511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010502 / 0.018006 (-0.007504) \| 0.410573 / 0.000490 (0.410083) \| 0.000384 / 0.000200 (0.000184) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025726 / 0.037411 (-0.011686) \| 0.101760 / 0.014526 (0.087235) \| 0.110102 / 0.176557 (-0.066454) \| 0.161321 / 0.737135 (-0.575815) \| 0.112507 / 0.296338 (-0.183832) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469925 / 0.215209 (0.254716) \| 4.718740 / 2.077655 (2.641085) \| 2.466272 / 1.504120 (0.962152) \| 2.267357 / 1.541195 (0.726162) \| 2.331343 / 1.468490 (0.862853) \| 0.553448 / 4.584777 (-4.031329) \| 3.464228 / 3.745712 (-0.281484) \| 3.060957 / 5.269862 (-2.208905) \| 1.387261 / 4.565676 (-3.178415) \| 0.067989 / 0.424275 (-0.356286) \| 0.012349 / 0.007607 (0.004741) \| 0.575046 / 0.226044 (0.349001) \| 5.740322 / 2.268929 (3.471394) \| 2.925666 / 55.444624 (-52.518958) \| 2.606535 / 6.876477 (-4.269942) \| 2.658144 / 2.142072 (0.516072) \| 0.655157 / 4.805227 (-4.150071) \| 0.138520 / 6.500664 (-6.362144) \| 0.069442 / 0.075469 (-0.006027) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306523 / 1.841788 (-0.535265) \| 14.400380 / 8.074308 (6.326072) \| 14.231519 / 10.191392 (4.040127) \| 0.146194 / 0.680424 (-0.534230) \| 0.016632 / 0.534201 (-0.517569) \| 0.361151 / 0.579283 (-0.218132) \| 0.388838 / 0.434364 (-0.045526) \| 0.419337 / 0.540337 (-0.121001) \| 0.500483 / 1.386936 (-0.886453) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0429e9806bf7065d03dc5858c039a30c5af716c \"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) \| 0.009430 / 0.011353 (-0.001923) \| 0.006673 / 0.011008 (-0.004335) \| 0.125151 / 0.038508 (0.086643) \| 0.038258 / 0.023109 (0.015149) \| 0.426383 / 0.275898 (0.150485) \| 0.432327 / 0.323480 (0.108847) \| 0.006964 / 0.007986 (-0.001022) \| 0.005140 / 0.004328 (0.000811) \| 0.100767 / 0.004250 (0.096517) \| 0.058663 / 0.037052 (0.021610) \| 0.424709 / 0.258489 (0.166220) \| 0.453049 / 0.293841 (0.159208) \| 0.051042 / 0.128546 (-0.077505) \| 0.015291 / 0.075646 (-0.060355) \| 0.456549 / 0.419271 (0.037278) \| 0.067106 / 0.043533 (0.023573) \| 0.408959 / 0.255139 (0.153820) \| 0.445067 / 0.283200 (0.161867) \| 0.115590 / 0.141683 (-0.026092) \| 1.929439 / 1.452155 (0.477284) \| 2.045709 / 1.492716 (0.552992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250726 / 0.018006 (0.232720) \| 0.598976 / 0.000490 (0.598486) \| 0.007542 / 0.000200 (0.007342) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030317 / 0.037411 (-0.007094) \| 0.133177 / 0.014526 (0.118651) \| 0.152761 / 0.176557 (-0.023795) \| 0.233708 / 0.737135 (-0.503428) \| 0.147303 / 0.296338 (-0.149036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633562 / 0.215209 (0.418353) \| 6.235021 / 2.077655 (4.157366) \| 2.652573 / 1.504120 (1.148454) \| 2.223363 / 1.541195 (0.682168) \| 2.231022 / 1.468490 (0.762531) \| 0.942218 / 4.584777 (-3.642559) \| 6.068661 / 3.745712 (2.322949) \| 2.778604 / 5.269862 (-2.491257) \| 1.787939 / 4.565676 (-2.777737) \| 0.117749 / 0.424275 (-0.306526) \| 0.015613 / 0.007607 (0.008006) \| 0.810222 / 0.226044 (0.584177) \| 7.931509 / 2.268929 (5.662581) \| 3.260679 / 55.444624 (-52.183945) \| 2.609085 / 6.876477 (-4.267391) \| 2.867838 / 2.142072 (0.725766) \| 1.144672 / 4.805227 (-3.660555) \| 0.224379 / 6.500664 (-6.276285) \| 0.084490 / 0.075469 (0.009021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.650608 / 1.841788 (-0.191179) \| 18.919748 / 8.074308 (10.845440) \| 20.163162 / 10.191392 (9.971770) \| 0.229427 / 0.680424 (-0.450997) \| 0.033090 / 0.534201 (-0.501111) \| 0.535549 / 0.579283 (-0.043734) \| 0.658629 / 0.434364 (0.224265) \| 0.631526 / 0.540337 (0.091189) \| 0.748701 / 1.386936 (-0.638235) \|\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) \| 0.009157 / 0.011353 (-0.002196) \| 0.006153 / 0.011008 (-0.004856) \| 0.106294 / 0.038508 (0.067786) \| 0.040947 / 0.023109 (0.017837) \| 0.493242 / 0.275898 (0.217344) \| 0.563525 / 0.323480 (0.240045) \| 0.007256 / 0.007986 (-0.000730) \| 0.006757 / 0.004328 (0.002429) \| 0.105151 / 0.004250 (0.100901) \| 0.056262 / 0.037052 (0.019209) \| 0.573341 / 0.258489 (0.314852) \| 0.591125 / 0.293841 (0.297284) \| 0.047935 / 0.128546 (-0.080611) \| 0.015385 / 0.075646 (-0.060262) \| 0.119457 / 0.419271 (-0.299814) \| 0.066510 / 0.043533 (0.022977) \| 0.485622 / 0.255139 (0.230483) \| 0.540929 / 0.283200 (0.257730) \| 0.132619 / 0.141683 (-0.009064) \| 1.916905 / 1.452155 (0.464750) \| 2.152722 / 1.492716 (0.660006) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294823 / 0.018006 (0.276817) \| 0.569371 / 0.000490 (0.568882) \| 0.000642 / 0.000200 (0.000442) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034321 / 0.037411 (-0.003090) \| 0.134165 / 0.014526 (0.119639) \| 0.157871 / 0.176557 (-0.018685) \| 0.210753 / 0.737135 (-0.526382) \| 0.152961 / 0.296338 (-0.143377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.686810 / 0.215209 (0.471601) \| 6.890432 / 2.077655 (4.812778) \| 3.182875 / 1.504120 (1.678755) \| 2.770836 / 1.541195 (1.229641) \| 2.790785 / 1.468490 (1.322295) \| 0.938145 / 4.584777 (-3.646632) \| 5.861093 / 3.745712 (2.115381) \| 2.719862 / 5.269862 (-2.550000) \| 1.760834 / 4.565676 (-2.804842) \| 0.111317 / 0.424275 (-0.312958) \| 0.015722 / 0.007607 (0.008115) \| 0.863032 / 0.226044 (0.636988) \| 8.482433 / 2.268929 (6.213504) \| 3.892621 / 55.444624 (-51.552003) \| 3.207370 / 6.876477 (-3.669106) \| 3.344412 / 2.142072 (1.202339) \| 1.133903 / 4.805227 (-3.671324) \| 0.223456 / 6.500664 (-6.277209) \| 0.084335 / 0.075469 (0.008866) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.794116 / 1.841788 (-0.047672) \| 19.077447 / 8.074308 (11.003139) \| 23.102309 / 10.191392 (12.910917) \| 0.268806 / 0.680424 (-0.411617) \| 0.027709 / 0.534201 (-0.506492) \| 0.540488 / 0.579283 (-0.038796) \| 0.658478 / 0.434364 (0.224114) \| 0.604769 / 0.540337 (0.064431) \| 0.722768 / 1.386936 (-0.664168) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e52021c66666e6953d5be0bd45a079e3ddb8c3f \"CML watermark\")\n" ]	2023-06-01T15:23:10Z	2023-06-02T10:02:54Z	2023-06-02T09:53:11Z	MEMBER	null	It was not reading the feature type from the parquet arrow schema	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5921/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5921/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5921.diff", "html_url": "https://github.com/huggingface/datasets/pull/5921", "merged_at": "2023-06-02T09:53:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/5921.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5921" }	true
https://api.github.com/repos/huggingface/datasets/issues/5920	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5920/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5920/comments	https://api.github.com/repos/huggingface/datasets/issues/5920/events	https://github.com/huggingface/datasets/pull/5920	1,736,196,991	PR_kwDODunzps5R5TRB	5,920	Optimize IterableDataset.from_file using ArrowExamplesIterable	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	[ "_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) \| 0.007439 / 0.011353 (-0.003914) \| 0.004884 / 0.011008 (-0.006124) \| 0.098750 / 0.038508 (0.060242) \| 0.040723 / 0.023109 (0.017613) \| 0.347242 / 0.275898 (0.071344) \| 0.381202 / 0.323480 (0.057722) \| 0.006814 / 0.007986 (-0.001171) \| 0.004543 / 0.004328 (0.000215) \| 0.075338 / 0.004250 (0.071088) \| 0.058976 / 0.037052 (0.021924) \| 0.344746 / 0.258489 (0.086257) \| 0.406761 / 0.293841 (0.112920) \| 0.028961 / 0.128546 (-0.099585) \| 0.009531 / 0.075646 (-0.066115) \| 0.337324 / 0.419271 (-0.081947) \| 0.051071 / 0.043533 (0.007538) \| 0.341251 / 0.255139 (0.086112) \| 0.362773 / 0.283200 (0.079573) \| 0.109423 / 0.141683 (-0.032260) \| 1.457420 / 1.452155 (0.005266) \| 1.588824 / 1.492716 (0.096108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288620 / 0.018006 (0.270614) \| 0.568975 / 0.000490 (0.568485) \| 0.003350 / 0.000200 (0.003150) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028732 / 0.037411 (-0.008680) \| 0.117820 / 0.014526 (0.103294) \| 0.120180 / 0.176557 (-0.056376) \| 0.178736 / 0.737135 (-0.558399) \| 0.126399 / 0.296338 (-0.169939) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428357 / 0.215209 (0.213148) \| 4.251989 / 2.077655 (2.174334) \| 2.005239 / 1.504120 (0.501119) \| 1.784009 / 1.541195 (0.242815) \| 1.883763 / 1.468490 (0.415272) \| 0.555429 / 4.584777 (-4.029348) \| 3.868146 / 3.745712 (0.122434) \| 2.081896 / 5.269862 (-3.187965) \| 1.126047 / 4.565676 (-3.439629) \| 0.069496 / 0.424275 (-0.354779) \| 0.012926 / 0.007607 (0.005318) \| 0.536989 / 0.226044 (0.310944) \| 5.256052 / 2.268929 (2.987124) \| 2.526802 / 55.444624 (-52.917822) \| 2.233346 / 6.876477 (-4.643131) \| 2.389063 / 2.142072 (0.246990) \| 0.677107 / 4.805227 (-4.128120) \| 0.147212 / 6.500664 (-6.353452) \| 0.067061 / 0.075469 (-0.008408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.210651 / 1.841788 (-0.631137) \| 17.236898 / 8.074308 (9.162589) \| 14.427301 / 10.191392 (4.235909) \| 0.207194 / 0.680424 (-0.473229) \| 0.018079 / 0.534201 (-0.516122) \| 0.398355 / 0.579283 (-0.180929) \| 0.462453 / 0.434364 (0.028089) \| 0.484544 / 0.540337 (-0.055794) \| 0.590119 / 1.386936 (-0.796817) \|\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) \| 0.007392 / 0.011353 (-0.003961) \| 0.005614 / 0.011008 (-0.005394) \| 0.075587 / 0.038508 (0.037079) \| 0.040429 / 0.023109 (0.017320) \| 0.389901 / 0.275898 (0.114003) \| 0.429466 / 0.323480 (0.105986) \| 0.006790 / 0.007986 (-0.001196) \| 0.006627 / 0.004328 (0.002299) \| 0.075227 / 0.004250 (0.070976) \| 0.060298 / 0.037052 (0.023246) \| 0.391905 / 0.258489 (0.133416) \| 0.449385 / 0.293841 (0.155544) \| 0.028794 / 0.128546 (-0.099753) \| 0.009461 / 0.075646 (-0.066185) \| 0.083386 / 0.419271 (-0.335886) \| 0.057968 / 0.043533 (0.014435) \| 0.377327 / 0.255139 (0.122188) \| 0.402825 / 0.283200 (0.119626) \| 0.125477 / 0.141683 (-0.016206) \| 1.462986 / 1.452155 (0.010832) \| 1.595959 / 1.492716 (0.103243) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304179 / 0.018006 (0.286173) \| 0.543113 / 0.000490 (0.542623) \| 0.004136 / 0.000200 (0.003936) \| 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) \| 0.032617 / 0.037411 (-0.004794) \| 0.123596 / 0.014526 (0.109070) \| 0.128714 / 0.176557 (-0.047842) \| 0.176344 / 0.737135 (-0.560792) \| 0.132525 / 0.296338 (-0.163813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446041 / 0.215209 (0.230832) \| 4.438799 / 2.077655 (2.361144) \| 2.210815 / 1.504120 (0.706695) \| 2.052025 / 1.541195 (0.510830) \| 2.204687 / 1.468490 (0.736197) \| 0.535219 / 4.584777 (-4.049558) \| 3.858407 / 3.745712 (0.112695) \| 3.826043 / 5.269862 (-1.443819) \| 1.334149 / 4.565676 (-3.231527) \| 0.067454 / 0.424275 (-0.356821) \| 0.012566 / 0.007607 (0.004958) \| 0.551597 / 0.226044 (0.325553) \| 5.520054 / 2.268929 (3.251126) \| 2.817976 / 55.444624 (-52.626649) \| 2.528074 / 6.876477 (-4.348403) \| 2.622391 / 2.142072 (0.480319) \| 0.657632 / 4.805227 (-4.147595) \| 0.147039 / 6.500664 (-6.353625) \| 0.069603 / 0.075469 (-0.005866) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.300140 / 1.841788 (-0.541648) \| 17.303907 / 8.074308 (9.229599) \| 15.657887 / 10.191392 (5.466495) \| 0.168991 / 0.680424 (-0.511433) \| 0.021332 / 0.534201 (-0.512869) \| 0.487261 / 0.579283 (-0.092022) \| 0.450073 / 0.434364 (0.015709) \| 0.465865 / 0.540337 (-0.074473) \| 0.565501 / 1.386936 (-0.821435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1723ab75a6b3a5e156ea0a41651e80e91fa9cc6 \"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) \| 0.006536 / 0.011353 (-0.004817) \| 0.004254 / 0.011008 (-0.006755) \| 0.095387 / 0.038508 (0.056878) \| 0.032885 / 0.023109 (0.009776) \| 0.298580 / 0.275898 (0.022682) \| 0.319771 / 0.323480 (-0.003709) \| 0.005510 / 0.007986 (-0.002476) \| 0.003891 / 0.004328 (-0.000437) \| 0.073763 / 0.004250 (0.069513) \| 0.041625 / 0.037052 (0.004573) \| 0.294896 / 0.258489 (0.036407) \| 0.341308 / 0.293841 (0.047467) \| 0.027898 / 0.128546 (-0.100648) \| 0.008837 / 0.075646 (-0.066809) \| 0.325055 / 0.419271 (-0.094216) \| 0.050652 / 0.043533 (0.007119) \| 0.298756 / 0.255139 (0.043617) \| 0.318261 / 0.283200 (0.035061) \| 0.098927 / 0.141683 (-0.042756) \| 1.450356 / 1.452155 (-0.001798) \| 1.508034 / 1.492716 (0.015318) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209009 / 0.018006 (0.191003) \| 0.439154 / 0.000490 (0.438665) \| 0.004299 / 0.000200 (0.004099) \| 0.000142 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025938 / 0.037411 (-0.011473) \| 0.105954 / 0.014526 (0.091429) \| 0.113858 / 0.176557 (-0.062698) \| 0.168887 / 0.737135 (-0.568249) \| 0.121292 / 0.296338 (-0.175046) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402050 / 0.215209 (0.186841) \| 4.002310 / 2.077655 (1.924655) \| 1.816190 / 1.504120 (0.312070) \| 1.634404 / 1.541195 (0.093209) \| 1.713632 / 1.468490 (0.245142) \| 0.519633 / 4.584777 (-4.065144) \| 3.740291 / 3.745712 (-0.005421) \| 1.787602 / 5.269862 (-3.482260) \| 1.038844 / 4.565676 (-3.526833) \| 0.064973 / 0.424275 (-0.359302) \| 0.012475 / 0.007607 (0.004868) \| 0.498152 / 0.226044 (0.272108) \| 4.970941 / 2.268929 (2.702013) \| 2.287429 / 55.444624 (-53.157195) \| 1.998050 / 6.876477 (-4.878427) \| 2.091903 / 2.142072 (-0.050169) \| 0.630363 / 4.805227 (-4.174864) \| 0.138623 / 6.500664 (-6.362041) \| 0.063293 / 0.075469 (-0.012176) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.201802 / 1.841788 (-0.639986) \| 14.073836 / 8.074308 (5.999528) \| 12.968665 / 10.191392 (2.777273) \| 0.144653 / 0.680424 (-0.535771) \| 0.017613 / 0.534201 (-0.516588) \| 0.392067 / 0.579283 (-0.187216) \| 0.416955 / 0.434364 (-0.017409) \| 0.471492 / 0.540337 (-0.068845) \| 0.554576 / 1.386936 (-0.832360) \|\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) \| 0.006408 / 0.011353 (-0.004945) \| 0.004452 / 0.011008 (-0.006556) \| 0.073648 / 0.038508 (0.035140) \| 0.032536 / 0.023109 (0.009427) \| 0.358546 / 0.275898 (0.082648) \| 0.387330 / 0.323480 (0.063850) \| 0.005542 / 0.007986 (-0.002444) \| 0.003882 / 0.004328 (-0.000447) \| 0.073867 / 0.004250 (0.069617) \| 0.044798 / 0.037052 (0.007746) \| 0.362303 / 0.258489 (0.103814) \| 0.400496 / 0.293841 (0.106655) \| 0.028244 / 0.128546 (-0.100302) \| 0.008931 / 0.075646 (-0.066715) \| 0.080617 / 0.419271 (-0.338654) \| 0.046575 / 0.043533 (0.003043) \| 0.364283 / 0.255139 (0.109145) \| 0.373215 / 0.283200 (0.090015) \| 0.100080 / 0.141683 (-0.041603) \| 1.430047 / 1.452155 (-0.022108) \| 1.530957 / 1.492716 (0.038240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221061 / 0.018006 (0.203055) \| 0.441753 / 0.000490 (0.441263) \| 0.003626 / 0.000200 (0.003426) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029509 / 0.037411 (-0.007902) \| 0.109578 / 0.014526 (0.095053) \| 0.121009 / 0.176557 (-0.055548) \| 0.168950 / 0.737135 (-0.568185) \| 0.124475 / 0.296338 (-0.171864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431355 / 0.215209 (0.216146) \| 4.295507 / 2.077655 (2.217852) \| 2.167514 / 1.504120 (0.663394) \| 2.013073 / 1.541195 (0.471879) \| 1.973730 / 1.468490 (0.505240) \| 0.529778 / 4.584777 (-4.054999) \| 3.794702 / 3.745712 (0.048989) \| 3.062940 / 5.269862 (-2.206922) \| 1.503426 / 4.565676 (-3.062251) \| 0.066692 / 0.424275 (-0.357583) \| 0.011682 / 0.007607 (0.004075) \| 0.539311 / 0.226044 (0.313266) \| 5.406342 / 2.268929 (3.137414) \| 2.652709 / 55.444624 (-52.791916) \| 2.260066 / 6.876477 (-4.616410) \| 2.295752 / 2.142072 (0.153680) \| 0.647199 / 4.805227 (-4.158029) \| 0.142981 / 6.500664 (-6.357683) \| 0.065082 / 0.075469 (-0.010387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279788 / 1.841788 (-0.562000) \| 14.982845 / 8.074308 (6.908536) \| 14.277166 / 10.191392 (4.085774) \| 0.145082 / 0.680424 (-0.535342) \| 0.017885 / 0.534201 (-0.516316) \| 0.392071 / 0.579283 (-0.187212) \| 0.420425 / 0.434364 (-0.013939) \| 0.461244 / 0.540337 (-0.079093) \| 0.559956 / 1.386936 (-0.826980) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#651d96c1c4083a206c65f11602712d75f1f0453d \"CML watermark\")\n" ]	2023-06-01T12:14:36Z	2023-06-01T12:42:10Z	2023-06-01T12:35:14Z	MEMBER	null	following https://github.com/huggingface/datasets/pull/5893	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5920/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5920/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5920.diff", "html_url": "https://github.com/huggingface/datasets/pull/5920", "merged_at": "2023-06-01T12:35:14Z", "patch_url": "https://github.com/huggingface/datasets/pull/5920.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5920" }	true
https://api.github.com/repos/huggingface/datasets/issues/5919	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5919/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5919/comments	https://api.github.com/repos/huggingface/datasets/issues/5919/events	https://github.com/huggingface/datasets/pull/5919	1,735,519,227	PR_kwDODunzps5R2_EK	5,919	add support for storage_options for load_dataset API	{ "avatar_url": "https://avatars.githubusercontent.com/u/59083384?v=4", "events_url": "https://api.github.com/users/janineguo/events{/privacy}", "followers_url": "https://api.github.com/users/janineguo/followers", "following_url": "https://api.github.com/users/janineguo/following{/other_user}", "gists_url": "https://api.github.com/users/janineguo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/janineguo", "id": 59083384, "login": "janineguo", "node_id": "MDQ6VXNlcjU5MDgzMzg0", "organizations_url": "https://api.github.com/users/janineguo/orgs", "received_events_url": "https://api.github.com/users/janineguo/received_events", "repos_url": "https://api.github.com/users/janineguo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/janineguo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/janineguo/subscriptions", "type": "User", "url": "https://api.github.com/users/janineguo" }	[]	open	false	null	[]	null	[ "hi @lhoestq,\r\nI saw some errors in my test and found all the failed reasons are `FileNotFoundError` about `test_load_streaming_private_dataset_with_zipped_data` and `test_load_dataset_private_zipped_images` in `test_load.py `, I run pytest on my own Wins and Ubuntu system all the test in `test_load.py ` are succeed. could you help me to check the test environment of our server?\r\n\r\n`2023-06-08T16:50:48.0828281Z FAILED tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_txt_data-16862429577813\\repo_zipped_txt_data-16862429577813.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813`\r\n`2023-06-08T16:50:48.0830602Z FAILED tests/test_load.py::test_load_dataset_private_zipped_images[False-False] - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_img_data-16862429594168\\repo_zipped_img_data-16862429594168.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168`", "I just re-ran the CI, hopefully it's fixed", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5919). All of your documentation changes will be reflected on that endpoint.", "> I just re-ran the CI, hopefully it's fixed\r\n\r\nI just checked, still has the same error, maybe need someone to fix it", "I think the issue comes from this PR somehow, since the CI fail is related to loading private repositories and this PR touches authentication related code. Let me check what's the issue, and I'll also review your PR later (sorry I don't have a ton of bandwidth atm)" ]	2023-06-01T05:52:32Z	2023-06-13T16:11:49Z	null	NONE	null	to solve the issue in #5880 1. add s3 support in the link check step, previous we only check `http` and `https`, 2. change the parameter of `use_auth_token` to `download_config` to support both `storage_options` and `use_auth_token` parameter when trying to handle(list, open, read, etc,.) the remote files. 3. integrate the check part's duplicate code to make adding or deleting other sources easier.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5919/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5919/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5919.diff", "html_url": "https://github.com/huggingface/datasets/pull/5919", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5919.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5919" }	true
https://api.github.com/repos/huggingface/datasets/issues/5918	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5918/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5918/comments	https://api.github.com/repos/huggingface/datasets/issues/5918/events	https://github.com/huggingface/datasets/issues/5918	1,735,313,549	I_kwDODunzps5nbsiN	5,918	File not found for audio dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/1783950?v=4", "events_url": "https://api.github.com/users/RobertBaruch/events{/privacy}", "followers_url": "https://api.github.com/users/RobertBaruch/followers", "following_url": "https://api.github.com/users/RobertBaruch/following{/other_user}", "gists_url": "https://api.github.com/users/RobertBaruch/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/RobertBaruch", "id": 1783950, "login": "RobertBaruch", "node_id": "MDQ6VXNlcjE3ODM5NTA=", "organizations_url": "https://api.github.com/users/RobertBaruch/orgs", "received_events_url": "https://api.github.com/users/RobertBaruch/received_events", "repos_url": "https://api.github.com/users/RobertBaruch/repos", "site_admin": false, "starred_url": "https://api.github.com/users/RobertBaruch/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RobertBaruch/subscriptions", "type": "User", "url": "https://api.github.com/users/RobertBaruch" }	[]	open	false	null	[]	null	[ "load_dataset () did not work for loading local files either " ]	2023-06-01T02:15:29Z	2023-06-11T06:02:25Z	null	NONE	null	### Describe the bug After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist. ### Steps to reproduce the bug Run bug.py: ```py import os.path from datasets import load_dataset def run() -> None: cv13 = load_dataset( "mozilla-foundation/common_voice_13_0", "hi", split="train", ) print(cv13[0]) audio_file = cv13[0]["path"] if not os.path.exists(audio_file): raise ValueError(f'File {audio_file} does not exist.') if __name__ == "__main__": run() ``` The result (on my machine): ```json {'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ..., 1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''} ``` ```txt Traceback (most recent call last): File "F:\eo-reco\bug.py", line 18, in <module> run() File "F:\eo-reco\bug.py", line 15, in run raise ValueError(f'File {audio_file} does not exist.') ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist. ``` ### Expected behavior The `path` element points to the correct file, which happens to be: ``` C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3 ``` That is, there's an extra directory `hi_train_0` that is not in the `path` element. ### Environment info - `datasets` version: 2.12.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.11.3 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 -	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5918/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5918/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5917	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5917/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5917/comments	https://api.github.com/repos/huggingface/datasets/issues/5917/events	https://github.com/huggingface/datasets/pull/5917	1,733,661,588	PR_kwDODunzps5RwoRU	5,917	Refactor extensions	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	[ "_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) \| 0.008358 / 0.011353 (-0.002995) \| 0.005673 / 0.011008 (-0.005335) \| 0.124034 / 0.038508 (0.085526) \| 0.037550 / 0.023109 (0.014441) \| 0.331301 / 0.275898 (0.055403) \| 0.383542 / 0.323480 (0.060062) \| 0.006940 / 0.007986 (-0.001046) \| 0.005959 / 0.004328 (0.001631) \| 0.084670 / 0.004250 (0.080419) \| 0.054214 / 0.037052 (0.017162) \| 0.359897 / 0.258489 (0.101408) \| 0.383260 / 0.293841 (0.089419) \| 0.047642 / 0.128546 (-0.080904) \| 0.013902 / 0.075646 (-0.061744) \| 0.380232 / 0.419271 (-0.039040) \| 0.077790 / 0.043533 (0.034257) \| 0.376648 / 0.255139 (0.121509) \| 0.387536 / 0.283200 (0.104336) \| 0.104644 / 0.141683 (-0.037038) \| 1.618560 / 1.452155 (0.166406) \| 1.742569 / 1.492716 (0.249853) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257218 / 0.018006 (0.239212) \| 0.636801 / 0.000490 (0.636311) \| 0.000634 / 0.000200 (0.000434) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037874 / 0.037411 (0.000462) \| 0.107454 / 0.014526 (0.092928) \| 0.117855 / 0.176557 (-0.058702) \| 0.204067 / 0.737135 (-0.533068) \| 0.134029 / 0.296338 (-0.162310) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583657 / 0.215209 (0.368447) \| 5.761289 / 2.077655 (3.683635) \| 2.280201 / 1.504120 (0.776081) \| 2.033442 / 1.541195 (0.492247) \| 2.035343 / 1.468490 (0.566853) \| 0.868122 / 4.584777 (-3.716655) \| 5.352591 / 3.745712 (1.606879) \| 2.432814 / 5.269862 (-2.837047) \| 1.560765 / 4.565676 (-3.004911) \| 0.098793 / 0.424275 (-0.325482) \| 0.017327 / 0.007607 (0.009720) \| 0.734676 / 0.226044 (0.508631) \| 7.070318 / 2.268929 (4.801390) \| 2.972701 / 55.444624 (-52.471924) \| 2.442189 / 6.876477 (-4.434288) \| 2.604379 / 2.142072 (0.462307) \| 1.028853 / 4.805227 (-3.776374) \| 0.210390 / 6.500664 (-6.290274) \| 0.069329 / 0.075469 (-0.006140) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.469586 / 1.841788 (-0.372202) \| 16.570305 / 8.074308 (8.495997) \| 19.187845 / 10.191392 (8.996453) \| 0.219162 / 0.680424 (-0.461262) \| 0.026356 / 0.534201 (-0.507845) \| 0.447370 / 0.579283 (-0.131913) \| 0.555893 / 0.434364 (0.121529) \| 0.574958 / 0.540337 (0.034621) \| 0.639166 / 1.386936 (-0.747770) \|\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) \| 0.008166 / 0.011353 (-0.003187) \| 0.005577 / 0.011008 (-0.005431) \| 0.103578 / 0.038508 (0.065070) \| 0.040563 / 0.023109 (0.017454) \| 0.441996 / 0.275898 (0.166098) \| 0.483594 / 0.323480 (0.160114) \| 0.007329 / 0.007986 (-0.000657) \| 0.004546 / 0.004328 (0.000218) \| 0.090471 / 0.004250 (0.086220) \| 0.052740 / 0.037052 (0.015688) \| 0.442197 / 0.258489 (0.183708) \| 0.524310 / 0.293841 (0.230469) \| 0.042487 / 0.128546 (-0.086060) \| 0.012917 / 0.075646 (-0.062730) \| 0.103992 / 0.419271 (-0.315280) \| 0.060570 / 0.043533 (0.017037) \| 0.441956 / 0.255139 (0.186817) \| 0.477084 / 0.283200 (0.193885) \| 0.103815 / 0.141683 (-0.037868) \| 1.696963 / 1.452155 (0.244809) \| 1.747849 / 1.492716 (0.255132) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292465 / 0.018006 (0.274458) \| 0.571518 / 0.000490 (0.571028) \| 0.000476 / 0.000200 (0.000276) \| 0.000077 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028697 / 0.037411 (-0.008714) \| 0.111671 / 0.014526 (0.097145) \| 0.138826 / 0.176557 (-0.037731) \| 0.189697 / 0.737135 (-0.547439) \| 0.125454 / 0.296338 (-0.170884) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.619273 / 0.215209 (0.404064) \| 6.138669 / 2.077655 (4.061015) \| 2.558622 / 1.504120 (1.054502) \| 2.201550 / 1.541195 (0.660356) \| 2.279034 / 1.468490 (0.810544) \| 0.850752 / 4.584777 (-3.734025) \| 5.438185 / 3.745712 (1.692473) \| 2.529343 / 5.269862 (-2.740518) \| 1.572178 / 4.565676 (-2.993499) \| 0.100768 / 0.424275 (-0.323507) \| 0.013902 / 0.007607 (0.006295) \| 0.726660 / 0.226044 (0.500616) \| 7.794918 / 2.268929 (5.525990) \| 3.311695 / 55.444624 (-52.132930) \| 2.729167 / 6.876477 (-4.147310) \| 2.630984 / 2.142072 (0.488911) \| 1.018534 / 4.805227 (-3.786693) \| 0.194602 / 6.500664 (-6.306062) \| 0.070876 / 0.075469 (-0.004593) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.573005 / 1.841788 (-0.268783) \| 17.042710 / 8.074308 (8.968401) \| 19.615320 / 10.191392 (9.423928) \| 0.229405 / 0.680424 (-0.451019) \| 0.027560 / 0.534201 (-0.506641) \| 0.447984 / 0.579283 (-0.131299) \| 0.598392 / 0.434364 (0.164028) \| 0.571769 / 0.540337 (0.031431) \| 0.653025 / 1.386936 (-0.733911) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9dca2ff89a8589595313e9535d16597ce10e3700 \"CML watermark\")\n" ]	2023-05-31T08:33:02Z	2023-05-31T13:34:35Z	2023-05-31T13:25:57Z	MEMBER	null	Related to: - #5850	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5917/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5917/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5917.diff", "html_url": "https://github.com/huggingface/datasets/pull/5917", "merged_at": "2023-05-31T13:25:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/5917.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5917" }	true
https://api.github.com/repos/huggingface/datasets/issues/5916	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5916/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5916/comments	https://api.github.com/repos/huggingface/datasets/issues/5916/events	https://github.com/huggingface/datasets/pull/5916	1,732,456,392	PR_kwDODunzps5RskTb	5,916	Unpin responses	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	[ "_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) \| 0.006113 / 0.011353 (-0.005239) \| 0.004195 / 0.011008 (-0.006813) \| 0.098103 / 0.038508 (0.059595) \| 0.027970 / 0.023109 (0.004860) \| 0.300992 / 0.275898 (0.025094) \| 0.335402 / 0.323480 (0.011922) \| 0.005079 / 0.007986 (-0.002906) \| 0.003516 / 0.004328 (-0.000813) \| 0.077311 / 0.004250 (0.073061) \| 0.037863 / 0.037052 (0.000810) \| 0.302638 / 0.258489 (0.044149) \| 0.346554 / 0.293841 (0.052713) \| 0.025218 / 0.128546 (-0.103328) \| 0.008630 / 0.075646 (-0.067017) \| 0.319748 / 0.419271 (-0.099523) \| 0.049182 / 0.043533 (0.005650) \| 0.306233 / 0.255139 (0.051094) \| 0.331040 / 0.283200 (0.047840) \| 0.089203 / 0.141683 (-0.052480) \| 1.496104 / 1.452155 (0.043949) \| 1.567878 / 1.492716 (0.075162) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215774 / 0.018006 (0.197768) \| 0.436810 / 0.000490 (0.436320) \| 0.000307 / 0.000200 (0.000107) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024102 / 0.037411 (-0.013310) \| 0.095459 / 0.014526 (0.080933) \| 0.106564 / 0.176557 (-0.069992) \| 0.169894 / 0.737135 (-0.567241) \| 0.109152 / 0.296338 (-0.187186) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429066 / 0.215209 (0.213857) \| 4.297385 / 2.077655 (2.219730) \| 2.054854 / 1.504120 (0.550734) \| 1.846844 / 1.541195 (0.305649) \| 1.840807 / 1.468490 (0.372317) \| 0.553193 / 4.584777 (-4.031584) \| 3.366788 / 3.745712 (-0.378924) \| 1.727337 / 5.269862 (-3.542525) \| 0.994357 / 4.565676 (-3.571319) \| 0.067790 / 0.424275 (-0.356485) \| 0.012002 / 0.007607 (0.004395) \| 0.533335 / 0.226044 (0.307291) \| 5.341341 / 2.268929 (3.072412) \| 2.543581 / 55.444624 (-52.901043) \| 2.220374 / 6.876477 (-4.656103) \| 2.321656 / 2.142072 (0.179583) \| 0.654408 / 4.805227 (-4.150819) \| 0.134693 / 6.500664 (-6.365971) \| 0.066926 / 0.075469 (-0.008544) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.209463 / 1.841788 (-0.632325) \| 13.568221 / 8.074308 (5.493913) \| 13.965418 / 10.191392 (3.774026) \| 0.145049 / 0.680424 (-0.535375) \| 0.016936 / 0.534201 (-0.517265) \| 0.371587 / 0.579283 (-0.207696) \| 0.386363 / 0.434364 (-0.048001) \| 0.437137 / 0.540337 (-0.103201) \| 0.514779 / 1.386936 (-0.872157) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006245 / 0.011353 (-0.005108) \| 0.004232 / 0.011008 (-0.006776) \| 0.075682 / 0.038508 (0.037174) \| 0.027858 / 0.023109 (0.004749) \| 0.425325 / 0.275898 (0.149427) \| 0.466732 / 0.323480 (0.143253) \| 0.005240 / 0.007986 (-0.002745) \| 0.003506 / 0.004328 (-0.000823) \| 0.075294 / 0.004250 (0.071044) \| 0.041677 / 0.037052 (0.004624) \| 0.426552 / 0.258489 (0.168063) \| 0.469452 / 0.293841 (0.175611) \| 0.025443 / 0.128546 (-0.103104) \| 0.008526 / 0.075646 (-0.067120) \| 0.082190 / 0.419271 (-0.337081) \| 0.040906 / 0.043533 (-0.002626) \| 0.428406 / 0.255139 (0.173267) \| 0.446795 / 0.283200 (0.163595) \| 0.093837 / 0.141683 (-0.047846) \| 1.518639 / 1.452155 (0.066484) \| 1.620214 / 1.492716 (0.127498) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223259 / 0.018006 (0.205253) \| 0.425077 / 0.000490 (0.424588) \| 0.001980 / 0.000200 (0.001780) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025813 / 0.037411 (-0.011599) \| 0.103062 / 0.014526 (0.088536) \| 0.108958 / 0.176557 (-0.067598) \| 0.161591 / 0.737135 (-0.575544) \| 0.112130 / 0.296338 (-0.184209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472843 / 0.215209 (0.257634) \| 4.713281 / 2.077655 (2.635626) \| 2.458216 / 1.504120 (0.954096) \| 2.272467 / 1.541195 (0.731273) \| 2.324456 / 1.468490 (0.855965) \| 0.554686 / 4.584777 (-4.030091) \| 3.445079 / 3.745712 (-0.300634) \| 3.451896 / 5.269862 (-1.817966) \| 1.431065 / 4.565676 (-3.134612) \| 0.067868 / 0.424275 (-0.356407) \| 0.012093 / 0.007607 (0.004486) \| 0.573571 / 0.226044 (0.347526) \| 5.820452 / 2.268929 (3.551523) \| 2.934858 / 55.444624 (-52.509767) \| 2.602719 / 6.876477 (-4.273758) \| 2.645999 / 2.142072 (0.503927) \| 0.660688 / 4.805227 (-4.144540) \| 0.137490 / 6.500664 (-6.363174) \| 0.068311 / 0.075469 (-0.007158) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.321709 / 1.841788 (-0.520079) \| 14.592346 / 8.074308 (6.518038) \| 14.520748 / 10.191392 (4.329356) \| 0.132689 / 0.680424 (-0.547735) \| 0.016422 / 0.534201 (-0.517779) \| 0.370071 / 0.579283 (-0.209212) \| 0.397091 / 0.434364 (-0.037273) \| 0.431979 / 0.540337 (-0.108358) \| 0.509965 / 1.386936 (-0.876971) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006182 / 0.011353 (-0.005171) \| 0.004153 / 0.011008 (-0.006855) \| 0.095715 / 0.038508 (0.057207) \| 0.032457 / 0.023109 (0.009347) \| 0.314961 / 0.275898 (0.039063) \| 0.353696 / 0.323480 (0.030216) \| 0.005256 / 0.007986 (-0.002729) \| 0.004870 / 0.004328 (0.000541) \| 0.072442 / 0.004250 (0.068192) \| 0.046102 / 0.037052 (0.009050) \| 0.324410 / 0.258489 (0.065921) \| 0.366861 / 0.293841 (0.073020) \| 0.027088 / 0.128546 (-0.101458) \| 0.008572 / 0.075646 (-0.067075) \| 0.325988 / 0.419271 (-0.093284) \| 0.049494 / 0.043533 (0.005961) \| 0.311221 / 0.255139 (0.056082) \| 0.359720 / 0.283200 (0.076521) \| 0.095101 / 0.141683 (-0.046581) \| 1.472821 / 1.452155 (0.020667) \| 1.516157 / 1.492716 (0.023441) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210456 / 0.018006 (0.192450) \| 0.439440 / 0.000490 (0.438950) \| 0.003764 / 0.000200 (0.003564) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024076 / 0.037411 (-0.013335) \| 0.104886 / 0.014526 (0.090360) \| 0.114164 / 0.176557 (-0.062393) \| 0.167289 / 0.737135 (-0.569847) \| 0.116457 / 0.296338 (-0.179882) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400039 / 0.215209 (0.184830) \| 3.973243 / 2.077655 (1.895588) \| 1.801991 / 1.504120 (0.297871) \| 1.592017 / 1.541195 (0.050822) \| 1.612564 / 1.468490 (0.144074) \| 0.527475 / 4.584777 (-4.057302) \| 3.676246 / 3.745712 (-0.069466) \| 1.806423 / 5.269862 (-3.463438) \| 1.176921 / 4.565676 (-3.388756) \| 0.065902 / 0.424275 (-0.358373) \| 0.012245 / 0.007607 (0.004638) \| 0.490883 / 0.226044 (0.264838) \| 4.905270 / 2.268929 (2.636341) \| 2.218694 / 55.444624 (-53.225930) \| 1.903074 / 6.876477 (-4.973403) \| 1.979505 / 2.142072 (-0.162567) \| 0.644415 / 4.805227 (-4.160812) \| 0.142433 / 6.500664 (-6.358231) \| 0.063564 / 0.075469 (-0.011905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.193756 / 1.841788 (-0.648032) \| 14.673103 / 8.074308 (6.598795) \| 13.410951 / 10.191392 (3.219559) \| 0.159175 / 0.680424 (-0.521249) \| 0.017076 / 0.534201 (-0.517125) \| 0.388880 / 0.579283 (-0.190403) \| 0.409974 / 0.434364 (-0.024390) \| 0.454494 / 0.540337 (-0.085844) \| 0.556873 / 1.386936 (-0.830063) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006107 / 0.011353 (-0.005246) \| 0.004433 / 0.011008 (-0.006575) \| 0.073892 / 0.038508 (0.035384) \| 0.032386 / 0.023109 (0.009277) \| 0.370339 / 0.275898 (0.094441) \| 0.388996 / 0.323480 (0.065516) \| 0.005438 / 0.007986 (-0.002548) \| 0.003875 / 0.004328 (-0.000454) \| 0.073867 / 0.004250 (0.069617) \| 0.048350 / 0.037052 (0.011298) \| 0.380328 / 0.258489 (0.121839) \| 0.411373 / 0.293841 (0.117532) \| 0.028183 / 0.128546 (-0.100363) \| 0.008924 / 0.075646 (-0.066723) \| 0.082484 / 0.419271 (-0.336787) \| 0.047321 / 0.043533 (0.003788) \| 0.371702 / 0.255139 (0.116563) \| 0.380535 / 0.283200 (0.097335) \| 0.100772 / 0.141683 (-0.040911) \| 1.475038 / 1.452155 (0.022883) \| 1.564293 / 1.492716 (0.071577) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214589 / 0.018006 (0.196583) \| 0.437193 / 0.000490 (0.436703) \| 0.003676 / 0.000200 (0.003476) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027991 / 0.037411 (-0.009421) \| 0.111154 / 0.014526 (0.096628) \| 0.120365 / 0.176557 (-0.056191) \| 0.173601 / 0.737135 (-0.563535) \| 0.126244 / 0.296338 (-0.170094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442848 / 0.215209 (0.227639) \| 4.398336 / 2.077655 (2.320681) \| 2.217058 / 1.504120 (0.712938) \| 2.011155 / 1.541195 (0.469960) \| 2.123086 / 1.468490 (0.654596) \| 0.525857 / 4.584777 (-4.058920) \| 3.730191 / 3.745712 (-0.015521) \| 3.517680 / 5.269862 (-1.752181) \| 1.557940 / 4.565676 (-3.007736) \| 0.066309 / 0.424275 (-0.357967) \| 0.011788 / 0.007607 (0.004181) \| 0.548506 / 0.226044 (0.322462) \| 5.483615 / 2.268929 (3.214687) \| 2.663784 / 55.444624 (-52.780840) \| 2.325744 / 6.876477 (-4.550732) \| 2.344179 / 2.142072 (0.202106) \| 0.644217 / 4.805227 (-4.161010) \| 0.141546 / 6.500664 (-6.359118) \| 0.063730 / 0.075469 (-0.011739) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296032 / 1.841788 (-0.545756) \| 14.903729 / 8.074308 (6.829421) \| 14.505409 / 10.191392 (4.314017) \| 0.170478 / 0.680424 (-0.509946) \| 0.017876 / 0.534201 (-0.516325) \| 0.401047 / 0.579283 (-0.178236) \| 0.417855 / 0.434364 (-0.016509) \| 0.472138 / 0.540337 (-0.068200) \| 0.570859 / 1.386936 (-0.816077) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008495 / 0.011353 (-0.002858) \| 0.005322 / 0.011008 (-0.005686) \| 0.125471 / 0.038508 (0.086962) \| 0.034604 / 0.023109 (0.011495) \| 0.419831 / 0.275898 (0.143933) \| 0.415707 / 0.323480 (0.092227) \| 0.007471 / 0.007986 (-0.000515) \| 0.005441 / 0.004328 (0.001112) \| 0.095412 / 0.004250 (0.091162) \| 0.053865 / 0.037052 (0.016812) \| 0.375257 / 0.258489 (0.116768) \| 0.438114 / 0.293841 (0.144273) \| 0.046183 / 0.128546 (-0.082363) \| 0.013663 / 0.075646 (-0.061984) \| 0.438317 / 0.419271 (0.019045) \| 0.065665 / 0.043533 (0.022133) \| 0.387640 / 0.255139 (0.132501) \| 0.431350 / 0.283200 (0.148150) \| 0.112841 / 0.141683 (-0.028842) \| 1.778639 / 1.452155 (0.326484) \| 1.891948 / 1.492716 (0.399232) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284371 / 0.018006 (0.266365) \| 0.598247 / 0.000490 (0.597758) \| 0.013674 / 0.000200 (0.013474) \| 0.000483 / 0.000054 (0.000428) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032437 / 0.037411 (-0.004974) \| 0.120547 / 0.014526 (0.106021) \| 0.129845 / 0.176557 (-0.046711) \| 0.203455 / 0.737135 (-0.533680) \| 0.140039 / 0.296338 (-0.156300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.596549 / 0.215209 (0.381340) \| 6.138766 / 2.077655 (4.061111) \| 2.515506 / 1.504120 (1.011386) \| 2.124472 / 1.541195 (0.583277) \| 2.160812 / 1.468490 (0.692322) \| 0.898965 / 4.584777 (-3.685812) \| 5.588152 / 3.745712 (1.842440) \| 2.717580 / 5.269862 (-2.552282) \| 1.683641 / 4.565676 (-2.882036) \| 0.108045 / 0.424275 (-0.316230) \| 0.014089 / 0.007607 (0.006481) \| 0.749567 / 0.226044 (0.523523) \| 7.518051 / 2.268929 (5.249123) \| 3.198238 / 55.444624 (-52.246386) \| 2.575156 / 6.876477 (-4.301321) \| 2.725818 / 2.142072 (0.583745) \| 1.149338 / 4.805227 (-3.655889) \| 0.220443 / 6.500664 (-6.280221) \| 0.081452 / 0.075469 (0.005983) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.624462 / 1.841788 (-0.217325) \| 18.204963 / 8.074308 (10.130655) \| 21.379169 / 10.191392 (11.187777) \| 0.248520 / 0.680424 (-0.431903) \| 0.030121 / 0.534201 (-0.504080) \| 0.499542 / 0.579283 (-0.079741) \| 0.599783 / 0.434364 (0.165419) \| 0.597642 / 0.540337 (0.057305) \| 0.681948 / 1.386936 (-0.704988) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008431 / 0.011353 (-0.002921) \| 0.006143 / 0.011008 (-0.004865) \| 0.107531 / 0.038508 (0.069023) \| 0.036308 / 0.023109 (0.013199) \| 0.480555 / 0.275898 (0.204657) \| 0.556407 / 0.323480 (0.232927) \| 0.007614 / 0.007986 (-0.000372) \| 0.004749 / 0.004328 (0.000421) \| 0.105734 / 0.004250 (0.101484) \| 0.051619 / 0.037052 (0.014567) \| 0.514821 / 0.258489 (0.256332) \| 0.562143 / 0.293841 (0.268302) \| 0.042957 / 0.128546 (-0.085589) \| 0.015142 / 0.075646 (-0.060505) \| 0.143161 / 0.419271 (-0.276111) \| 0.061910 / 0.043533 (0.018377) \| 0.496923 / 0.255139 (0.241784) \| 0.556302 / 0.283200 (0.273102) \| 0.136700 / 0.141683 (-0.004983) \| 1.886184 / 1.452155 (0.434029) \| 2.004087 / 1.492716 (0.511371) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235530 / 0.018006 (0.217523) \| 0.600796 / 0.000490 (0.600306) \| 0.009074 / 0.000200 (0.008874) \| 0.000203 / 0.000054 (0.000149) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036345 / 0.037411 (-0.001066) \| 0.126112 / 0.014526 (0.111586) \| 0.143369 / 0.176557 (-0.033188) \| 0.211381 / 0.737135 (-0.525755) \| 0.151095 / 0.296338 (-0.145243) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.695022 / 0.215209 (0.479813) \| 6.685981 / 2.077655 (4.608326) \| 3.104521 / 1.504120 (1.600401) \| 2.758323 / 1.541195 (1.217128) \| 2.706286 / 1.468490 (1.237796) \| 0.941182 / 4.584777 (-3.643595) \| 5.715839 / 3.745712 (1.970127) \| 5.089636 / 5.269862 (-0.180226) \| 2.594739 / 4.565676 (-1.970937) \| 0.112621 / 0.424275 (-0.311655) \| 0.014001 / 0.007607 (0.006394) \| 0.812990 / 0.226044 (0.586945) \| 8.060890 / 2.268929 (5.791961) \| 3.832506 / 55.444624 (-51.612119) \| 3.148051 / 6.876477 (-3.728425) \| 3.110096 / 2.142072 (0.968023) \| 1.105050 / 4.805227 (-3.700178) \| 0.219835 / 6.500664 (-6.280829) \| 0.078600 / 0.075469 (0.003131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.707551 / 1.841788 (-0.134237) \| 19.238194 / 8.074308 (11.163885) \| 22.167076 / 10.191392 (11.975684) \| 0.233458 / 0.680424 (-0.446966) \| 0.025131 / 0.534201 (-0.509070) \| 0.525241 / 0.579283 (-0.054042) \| 0.649666 / 0.434364 (0.215303) \| 0.602941 / 0.540337 (0.062603) \| 0.718472 / 1.386936 (-0.668464) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n" ]	2023-05-30T14:59:48Z	2023-05-30T18:03:10Z	2023-05-30T17:53:29Z	CONTRIBUTOR	null	Fix #5906	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5916/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5916/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5916.diff", "html_url": "https://github.com/huggingface/datasets/pull/5916", "merged_at": "2023-05-30T17:53:29Z", "patch_url": "https://github.com/huggingface/datasets/pull/5916.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5916" }	true
https://api.github.com/repos/huggingface/datasets/issues/5915	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5915/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5915/comments	https://api.github.com/repos/huggingface/datasets/issues/5915/events	https://github.com/huggingface/datasets/pull/5915	1,732,389,984	PR_kwDODunzps5RsVzj	5,915	Raise error in `DatasetBuilder.as_dataset` when `file_format` is not `"arrow"`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	[ "_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) \| 0.006416 / 0.011353 (-0.004937) \| 0.004278 / 0.011008 (-0.006731) \| 0.097562 / 0.038508 (0.059054) \| 0.029488 / 0.023109 (0.006379) \| 0.308648 / 0.275898 (0.032750) \| 0.339879 / 0.323480 (0.016399) \| 0.005288 / 0.007986 (-0.002697) \| 0.005033 / 0.004328 (0.000704) \| 0.074666 / 0.004250 (0.070416) \| 0.034888 / 0.037052 (-0.002164) \| 0.309960 / 0.258489 (0.051471) \| 0.344276 / 0.293841 (0.050435) \| 0.025564 / 0.128546 (-0.102982) \| 0.008579 / 0.075646 (-0.067067) \| 0.319796 / 0.419271 (-0.099476) \| 0.044786 / 0.043533 (0.001253) \| 0.308888 / 0.255139 (0.053749) \| 0.334001 / 0.283200 (0.050802) \| 0.089917 / 0.141683 (-0.051766) \| 1.456696 / 1.452155 (0.004541) \| 1.542273 / 1.492716 (0.049557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213236 / 0.018006 (0.195230) \| 0.425139 / 0.000490 (0.424650) \| 0.008831 / 0.000200 (0.008631) \| 0.000209 / 0.000054 (0.000155) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023990 / 0.037411 (-0.013421) \| 0.096787 / 0.014526 (0.082261) \| 0.105783 / 0.176557 (-0.070774) \| 0.167182 / 0.737135 (-0.569954) \| 0.108896 / 0.296338 (-0.187442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419844 / 0.215209 (0.204635) \| 4.201909 / 2.077655 (2.124254) \| 1.910784 / 1.504120 (0.406664) \| 1.685183 / 1.541195 (0.143988) \| 1.716927 / 1.468490 (0.248437) \| 0.548261 / 4.584777 (-4.036516) \| 3.414168 / 3.745712 (-0.331544) \| 1.695446 / 5.269862 (-3.574415) \| 0.989668 / 4.565676 (-3.576008) \| 0.067328 / 0.424275 (-0.356948) \| 0.012084 / 0.007607 (0.004477) \| 0.523799 / 0.226044 (0.297754) \| 5.240589 / 2.268929 (2.971661) \| 2.331618 / 55.444624 (-53.113007) \| 1.996094 / 6.876477 (-4.880383) \| 2.105450 / 2.142072 (-0.036623) \| 0.654614 / 4.805227 (-4.150613) \| 0.134721 / 6.500664 (-6.365943) \| 0.066227 / 0.075469 (-0.009242) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196266 / 1.841788 (-0.645521) \| 13.990045 / 8.074308 (5.915737) \| 13.928126 / 10.191392 (3.736734) \| 0.142600 / 0.680424 (-0.537824) \| 0.016462 / 0.534201 (-0.517739) \| 0.363113 / 0.579283 (-0.216170) \| 0.428590 / 0.434364 (-0.005773) \| 0.452594 / 0.540337 (-0.087743) \| 0.551678 / 1.386936 (-0.835258) \|\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) \| 0.005992 / 0.011353 (-0.005361) \| 0.004161 / 0.011008 (-0.006847) \| 0.076098 / 0.038508 (0.037589) \| 0.028559 / 0.023109 (0.005450) \| 0.411696 / 0.275898 (0.135798) \| 0.444519 / 0.323480 (0.121040) \| 0.004965 / 0.007986 (-0.003021) \| 0.003452 / 0.004328 (-0.000876) \| 0.075107 / 0.004250 (0.070857) \| 0.037305 / 0.037052 (0.000252) \| 0.429728 / 0.258489 (0.171239) \| 0.444313 / 0.293841 (0.150472) \| 0.025278 / 0.128546 (-0.103268) \| 0.008527 / 0.075646 (-0.067120) \| 0.081502 / 0.419271 (-0.337770) \| 0.041237 / 0.043533 (-0.002296) \| 0.417848 / 0.255139 (0.162709) \| 0.426615 / 0.283200 (0.143415) \| 0.094641 / 0.141683 (-0.047041) \| 1.525141 / 1.452155 (0.072987) \| 1.615608 / 1.492716 (0.122892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192867 / 0.018006 (0.174861) \| 0.414979 / 0.000490 (0.414490) \| 0.000815 / 0.000200 (0.000615) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025354 / 0.037411 (-0.012058) \| 0.102085 / 0.014526 (0.087559) \| 0.107930 / 0.176557 (-0.068626) \| 0.160483 / 0.737135 (-0.576652) \| 0.112341 / 0.296338 (-0.183997) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446938 / 0.215209 (0.231728) \| 4.480057 / 2.077655 (2.402402) \| 2.154825 / 1.504120 (0.650705) \| 1.942774 / 1.541195 (0.401580) \| 1.996418 / 1.468490 (0.527928) \| 0.556728 / 4.584777 (-4.028049) \| 3.441228 / 3.745712 (-0.304484) \| 3.004179 / 5.269862 (-2.265683) \| 1.314104 / 4.565676 (-3.251573) \| 0.068670 / 0.424275 (-0.355606) \| 0.011972 / 0.007607 (0.004365) \| 0.556604 / 0.226044 (0.330560) \| 5.561783 / 2.268929 (3.292855) \| 2.631262 / 55.444624 (-52.813363) \| 2.262143 / 6.876477 (-4.614333) \| 2.364243 / 2.142072 (0.222170) \| 0.660621 / 4.805227 (-4.144607) \| 0.137371 / 6.500664 (-6.363293) \| 0.069104 / 0.075469 (-0.006365) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305706 / 1.841788 (-0.536081) \| 14.015932 / 8.074308 (5.941624) \| 14.353580 / 10.191392 (4.162187) \| 0.146172 / 0.680424 (-0.534251) \| 0.016699 / 0.534201 (-0.517502) \| 0.357970 / 0.579283 (-0.221313) \| 0.389067 / 0.434364 (-0.045297) \| 0.415470 / 0.540337 (-0.124867) \| 0.501359 / 1.386936 (-0.885577) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"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) \| 0.006800 / 0.011353 (-0.004552) \| 0.004721 / 0.011008 (-0.006287) \| 0.097760 / 0.038508 (0.059252) \| 0.034192 / 0.023109 (0.011083) \| 0.298240 / 0.275898 (0.022342) \| 0.331119 / 0.323480 (0.007639) \| 0.005826 / 0.007986 (-0.002160) \| 0.003968 / 0.004328 (-0.000360) \| 0.073833 / 0.004250 (0.069582) \| 0.046288 / 0.037052 (0.009236) \| 0.303018 / 0.258489 (0.044529) \| 0.342163 / 0.293841 (0.048322) \| 0.028504 / 0.128546 (-0.100042) \| 0.009031 / 0.075646 (-0.066615) \| 0.331617 / 0.419271 (-0.087655) \| 0.060911 / 0.043533 (0.017379) \| 0.304044 / 0.255139 (0.048905) \| 0.328959 / 0.283200 (0.045759) \| 0.113174 / 0.141683 (-0.028509) \| 1.424652 / 1.452155 (-0.027502) \| 1.531392 / 1.492716 (0.038676) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206175 / 0.018006 (0.188169) \| 0.435916 / 0.000490 (0.435426) \| 0.002587 / 0.000200 (0.002387) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026996 / 0.037411 (-0.010415) \| 0.106722 / 0.014526 (0.092196) \| 0.117655 / 0.176557 (-0.058902) \| 0.176969 / 0.737135 (-0.560166) \| 0.122577 / 0.296338 (-0.173762) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396086 / 0.215209 (0.180877) \| 3.972465 / 2.077655 (1.894811) \| 1.800798 / 1.504120 (0.296678) \| 1.616747 / 1.541195 (0.075552) \| 1.680711 / 1.468490 (0.212221) \| 0.526479 / 4.584777 (-4.058298) \| 3.791528 / 3.745712 (0.045816) \| 2.989518 / 5.269862 (-2.280344) \| 1.463221 / 4.565676 (-3.102455) \| 0.065649 / 0.424275 (-0.358626) \| 0.012155 / 0.007607 (0.004548) \| 0.500241 / 0.226044 (0.274197) \| 5.008895 / 2.268929 (2.739966) \| 2.315288 / 55.444624 (-53.129336) \| 1.959409 / 6.876477 (-4.917067) \| 2.102371 / 2.142072 (-0.039701) \| 0.639611 / 4.805227 (-4.165617) \| 0.140101 / 6.500664 (-6.360563) \| 0.063599 / 0.075469 (-0.011870) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206729 / 1.841788 (-0.635059) \| 15.127250 / 8.074308 (7.052942) \| 14.397228 / 10.191392 (4.205836) \| 0.148802 / 0.680424 (-0.531622) \| 0.017628 / 0.534201 (-0.516573) \| 0.396150 / 0.579283 (-0.183133) \| 0.435826 / 0.434364 (0.001462) \| 0.471215 / 0.540337 (-0.069122) \| 0.559413 / 1.386936 (-0.827523) \|\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) \| 0.006479 / 0.011353 (-0.004874) \| 0.004520 / 0.011008 (-0.006488) \| 0.074395 / 0.038508 (0.035887) \| 0.033400 / 0.023109 (0.010291) \| 0.388411 / 0.275898 (0.112513) \| 0.396714 / 0.323480 (0.073234) \| 0.005736 / 0.007986 (-0.002250) \| 0.004038 / 0.004328 (-0.000291) \| 0.073595 / 0.004250 (0.069345) \| 0.045207 / 0.037052 (0.008155) \| 0.378096 / 0.258489 (0.119607) \| 0.417830 / 0.293841 (0.123989) \| 0.028365 / 0.128546 (-0.100181) \| 0.008887 / 0.075646 (-0.066760) \| 0.080766 / 0.419271 (-0.338505) \| 0.046923 / 0.043533 (0.003390) \| 0.376190 / 0.255139 (0.121051) \| 0.385875 / 0.283200 (0.102675) \| 0.107542 / 0.141683 (-0.034141) \| 1.409257 / 1.452155 (-0.042898) \| 1.518475 / 1.492716 (0.025759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223299 / 0.018006 (0.205292) \| 0.440640 / 0.000490 (0.440150) \| 0.000397 / 0.000200 (0.000197) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031388 / 0.037411 (-0.006024) \| 0.113078 / 0.014526 (0.098552) \| 0.124398 / 0.176557 (-0.052159) \| 0.173802 / 0.737135 (-0.563333) \| 0.129555 / 0.296338 (-0.166783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440220 / 0.215209 (0.225011) \| 4.398052 / 2.077655 (2.320398) \| 2.188396 / 1.504120 (0.684276) \| 1.997811 / 1.541195 (0.456616) \| 2.093338 / 1.468490 (0.624847) \| 0.519597 / 4.584777 (-4.065180) \| 3.885795 / 3.745712 (0.140083) \| 2.896327 / 5.269862 (-2.373534) \| 1.245785 / 4.565676 (-3.319891) \| 0.065675 / 0.424275 (-0.358600) \| 0.011729 / 0.007607 (0.004121) \| 0.541526 / 0.226044 (0.315482) \| 5.406763 / 2.268929 (3.137834) \| 2.722914 / 55.444624 (-52.721711) \| 2.471111 / 6.876477 (-4.405366) \| 2.541488 / 2.142072 (0.399415) \| 0.633566 / 4.805227 (-4.171661) \| 0.139622 / 6.500664 (-6.361042) \| 0.064220 / 0.075469 (-0.011249) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296097 / 1.841788 (-0.545690) \| 15.095320 / 8.074308 (7.021012) \| 14.300821 / 10.191392 (4.109429) \| 0.145470 / 0.680424 (-0.534954) \| 0.017496 / 0.534201 (-0.516705) \| 0.400589 / 0.579283 (-0.178694) \| 0.423091 / 0.434364 (-0.011273) \| 0.468258 / 0.540337 (-0.072079) \| 0.570873 / 1.386936 (-0.816063) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"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) \| 0.005918 / 0.011353 (-0.005435) \| 0.004393 / 0.011008 (-0.006615) \| 0.091677 / 0.038508 (0.053169) \| 0.033546 / 0.023109 (0.010437) \| 0.344682 / 0.275898 (0.068784) \| 0.388906 / 0.323480 (0.065426) \| 0.005412 / 0.007986 (-0.002574) \| 0.004909 / 0.004328 (0.000580) \| 0.082589 / 0.004250 (0.078339) \| 0.045242 / 0.037052 (0.008190) \| 0.339191 / 0.258489 (0.080702) \| 0.349673 / 0.293841 (0.055832) \| 0.026805 / 0.128546 (-0.101742) \| 0.007529 / 0.075646 (-0.068117) \| 0.319108 / 0.419271 (-0.100164) \| 0.049482 / 0.043533 (0.005949) \| 0.320013 / 0.255139 (0.064874) \| 0.342059 / 0.283200 (0.058859) \| 0.096623 / 0.141683 (-0.045060) \| 1.458204 / 1.452155 (0.006049) \| 1.571172 / 1.492716 (0.078455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235171 / 0.018006 (0.217165) \| 0.479678 / 0.000490 (0.479188) \| 0.006627 / 0.000200 (0.006427) \| 0.000257 / 0.000054 (0.000202) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025716 / 0.037411 (-0.011696) \| 0.107730 / 0.014526 (0.093204) \| 0.111595 / 0.176557 (-0.064962) \| 0.171316 / 0.737135 (-0.565819) \| 0.118962 / 0.296338 (-0.177377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376318 / 0.215209 (0.161109) \| 4.039484 / 2.077655 (1.961829) \| 1.811548 / 1.504120 (0.307428) \| 1.646728 / 1.541195 (0.105533) \| 1.688071 / 1.468490 (0.219581) \| 0.551256 / 4.584777 (-4.033520) \| 4.153931 / 3.745712 (0.408218) \| 3.424154 / 5.269862 (-1.845707) \| 1.734860 / 4.565676 (-2.830816) \| 0.067753 / 0.424275 (-0.356522) \| 0.012699 / 0.007607 (0.005092) \| 0.505722 / 0.226044 (0.279677) \| 4.997321 / 2.268929 (2.728392) \| 2.258755 / 55.444624 (-53.185869) \| 1.954382 / 6.876477 (-4.922095) \| 1.967545 / 2.142072 (-0.174527) \| 0.630489 / 4.805227 (-4.174738) \| 0.138738 / 6.500664 (-6.361926) \| 0.064907 / 0.075469 (-0.010562) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.209634 / 1.841788 (-0.632154) \| 15.055062 / 8.074308 (6.980754) \| 12.721606 / 10.191392 (2.530214) \| 0.164908 / 0.680424 (-0.515516) \| 0.019528 / 0.534201 (-0.514673) \| 0.400136 / 0.579283 (-0.179147) \| 0.451640 / 0.434364 (0.017276) \| 0.466272 / 0.540337 (-0.074065) \| 0.553258 / 1.386936 (-0.833679) \|\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) \| 0.006341 / 0.011353 (-0.005011) \| 0.004617 / 0.011008 (-0.006391) \| 0.077953 / 0.038508 (0.039445) \| 0.031104 / 0.023109 (0.007995) \| 0.360328 / 0.275898 (0.084430) \| 0.408403 / 0.323480 (0.084923) \| 0.005704 / 0.007986 (-0.002282) \| 0.003588 / 0.004328 (-0.000741) \| 0.071441 / 0.004250 (0.067190) \| 0.043520 / 0.037052 (0.006468) \| 0.375798 / 0.258489 (0.117309) \| 0.400955 / 0.293841 (0.107114) \| 0.028166 / 0.128546 (-0.100381) \| 0.008578 / 0.075646 (-0.067068) \| 0.086673 / 0.419271 (-0.332598) \| 0.046424 / 0.043533 (0.002891) \| 0.367276 / 0.255139 (0.112137) \| 0.414550 / 0.283200 (0.131351) \| 0.097355 / 0.141683 (-0.044328) \| 1.465191 / 1.452155 (0.013036) \| 1.555028 / 1.492716 (0.062312) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196642 / 0.018006 (0.178636) \| 0.464221 / 0.000490 (0.463731) \| 0.002726 / 0.000200 (0.002526) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028078 / 0.037411 (-0.009333) \| 0.110762 / 0.014526 (0.096236) \| 0.122212 / 0.176557 (-0.054344) \| 0.164758 / 0.737135 (-0.572377) \| 0.133969 / 0.296338 (-0.162370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448134 / 0.215209 (0.232925) \| 4.339335 / 2.077655 (2.261680) \| 2.129209 / 1.504120 (0.625089) \| 1.957805 / 1.541195 (0.416611) \| 1.994038 / 1.468490 (0.525548) \| 0.497101 / 4.584777 (-4.087676) \| 4.114432 / 3.745712 (0.368720) \| 3.437305 / 5.269862 (-1.832556) \| 1.692810 / 4.565676 (-2.872866) \| 0.071077 / 0.424275 (-0.353198) \| 0.012735 / 0.007607 (0.005128) \| 0.534393 / 0.226044 (0.308348) \| 5.217445 / 2.268929 (2.948517) \| 2.594858 / 55.444624 (-52.849766) \| 2.317464 / 6.876477 (-4.559012) \| 2.337974 / 2.142072 (0.195902) \| 0.622291 / 4.805227 (-4.182936) \| 0.144934 / 6.500664 (-6.355730) \| 0.068524 / 0.075469 (-0.006945) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.310601 / 1.841788 (-0.531187) \| 15.771527 / 8.074308 (7.697219) \| 13.952032 / 10.191392 (3.760640) \| 0.212473 / 0.680424 (-0.467951) \| 0.017963 / 0.534201 (-0.516238) \| 0.400755 / 0.579283 (-0.178528) \| 0.439817 / 0.434364 (0.005453) \| 0.472614 / 0.540337 (-0.067724) \| 0.558410 / 1.386936 (-0.828526) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]	2023-05-30T14:27:55Z	2023-05-31T13:31:21Z	2023-05-31T13:23:54Z	CONTRIBUTOR	null	Raise an error in `DatasetBuilder.as_dataset` when `file_format != "arrow"` (and fix the docstring) Fix #5874	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5915/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5915/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5915.diff", "html_url": "https://github.com/huggingface/datasets/pull/5915", "merged_at": "2023-05-31T13:23:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/5915.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5915" }	true
https://api.github.com/repos/huggingface/datasets/issues/5914	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5914/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5914/comments	https://api.github.com/repos/huggingface/datasets/issues/5914/events	https://github.com/huggingface/datasets/issues/5914	1,731,483,996	I_kwDODunzps5nNFlc	5,914	array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size in Datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/85110830?v=4", "events_url": "https://api.github.com/users/ravenouse/events{/privacy}", "followers_url": "https://api.github.com/users/ravenouse/followers", "following_url": "https://api.github.com/users/ravenouse/following{/other_user}", "gists_url": "https://api.github.com/users/ravenouse/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ravenouse", "id": 85110830, "login": "ravenouse", "node_id": "MDQ6VXNlcjg1MTEwODMw", "organizations_url": "https://api.github.com/users/ravenouse/orgs", "received_events_url": "https://api.github.com/users/ravenouse/received_events", "repos_url": "https://api.github.com/users/ravenouse/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ravenouse/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ravenouse/subscriptions", "type": "User", "url": "https://api.github.com/users/ravenouse" }	[]	open	false	null	[]	null	[]	2023-05-30T04:25:00Z	2023-05-30T04:25:00Z	null	NONE	null	### Describe the bug When using the `filter` or `map` function to preprocess a dataset, a ValueError is encountered with the error message "array is too big; arr.size * arr.dtype.itemsize is larger than the maximum possible size." Detailed error message: Traceback (most recent call last): File "data_processing.py", line 26, in <module> processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split],writer_batch_size = 50) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2405, in map desc=desc, File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 524, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/fingerprint.py", line 480, in wrapper out = func(self, args, *kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2756, in _map_single example = apply_function_on_filtered_inputs(example, i, offset=offset) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs processed_inputs = function(fn_args, additional_args, fn_kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2347, in decorated result = f(decorated_item, args, *kwargs) File "data_processing.py", line 11, in prepare_dataset audio = batch["audio"] File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 123, in __getitem__ value = decode_nested_example(self.features[key], value) if value is not None else None File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/features.py", line 1260, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 156, in decode_example array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 257, in _decode_non_mp3_path_like array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 176, in load y, sr_native = __soundfile_load(path, offset, duration, dtype) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 222, in __soundfile_load y = sf_desc.read(frames=frame_duration, dtype=dtype, always_2d=False).T File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 891, in read out = self._create_empty_array(frames, always_2d, dtype) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 1323, in _create_empty_array return np.empty(shape, dtype, order='C') ValueError: array is too big; `arr.size arr.dtype.itemsize` is larger than the maximum possible size. ### Steps to reproduce the bug ```python from datasets import load_dataset, DatasetDict from transformers import WhisperFeatureExtractor from transformers import WhisperTokenizer samromur_children= load_dataset("language-and-voice-lab/samromur_children") feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small") tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="icelandic", task="transcribe") def prepare_dataset(batch): # load and resample audio data from 48 to 16kHz audio = batch["audio"] # compute log-Mel input features from input audio array batch["input_features"] = feature_extractor(audio["array"], sampling_rate=16000).input_features[0] # encode target text to label ids batch["labels"] = tokenizer(batch["normalized_text"]).input_ids return batch cache_dict = {"train": "./cache/audio_train.cache", \ "validation": "./cache/audio_validation.cache", \ "test": "./cache/audio_test.cache"} filter_cache_dict = {"train": "./cache/filter_train.arrow", \ "validation": "./cache/filter_validation.arrow", \ "test": "./cache/filter_test.arrow"} print("before filtering") print(samromur_children) #filter the dataset to only include examples with more than 2 seconds of audio samromur_children = samromur_children.filter(lambda example: example["audio"]["array"].shape[0] > 16000*2, cache_file_names=filter_cache_dict) print("after filtering") print(samromur_children) processed_dataset = DatasetDict() # processed_dataset = samromur_children.map(prepare_dataset, cache_file_names=cache_dict, num_proc=10,) for split in ["train", "validation", "test"]: processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split]) ``` ### Expected behavior The dataset is successfully processed and ready to train the model. ### Environment info Python version: 3.7.13 datasets package version: 2.4.0 librosa package version: 0.10.0.post2	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5914/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5914/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5913	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5913/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5913/comments	https://api.github.com/repos/huggingface/datasets/issues/5913/events	https://github.com/huggingface/datasets/issues/5913	1,731,427,484	I_kwDODunzps5nM3yc	5,913	I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred.	{ "avatar_url": "https://avatars.githubusercontent.com/u/17508662?v=4", "events_url": "https://api.github.com/users/cjt222/events{/privacy}", "followers_url": "https://api.github.com/users/cjt222/followers", "following_url": "https://api.github.com/users/cjt222/following{/other_user}", "gists_url": "https://api.github.com/users/cjt222/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/cjt222", "id": 17508662, "login": "cjt222", "node_id": "MDQ6VXNlcjE3NTA4NjYy", "organizations_url": "https://api.github.com/users/cjt222/orgs", "received_events_url": "https://api.github.com/users/cjt222/received_events", "repos_url": "https://api.github.com/users/cjt222/repos", "site_admin": false, "starred_url": "https://api.github.com/users/cjt222/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/cjt222/subscriptions", "type": "User", "url": "https://api.github.com/users/cjt222" }	[]	open	false	null	[]	null	[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. ", "> Thanks for reporting, @cjt222.\r\n> \r\n> What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead.\r\n\r\nThanks! I have encountered similar problems. I modify the json format from list to line and works!" ]	2023-05-30T02:55:26Z	2023-06-10T08:25:12Z	null	NONE	null	### Describe the bug File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/builder.py", line 1858, in _prepare_split_single Downloading and preparing dataset json/default to /home/kas/diffusers/examples/dreambooth/cache_data/datasets/json/default-acf423d8c6ef99d0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data files: 100%\|██████████\| 1/1 [00:00<00:00, 84.35it/s] Extracting data files: 0%\| \| 0/1 [00:00<?, ?it/s] for _, table in generator: File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 114, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 258, in pyarrow._json.read_json Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 27.72it/s] Generating train split: 0 examples [00:00, ? examples/s] File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 125, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2390448764 ### Steps to reproduce the bug 1、data_files = ["1.json", "2.json", "3.json"] 2、dataset = load_dataset('json', data_files=data_files) ### Expected behavior Read the dataset normally. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.15.0-29-generic-x86_64-with-debian-buster-sid - Python version: 3.7.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 1.3.5	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5913/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5913/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5912	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5912/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5912/comments	https://api.github.com/repos/huggingface/datasets/issues/5912/events	https://github.com/huggingface/datasets/issues/5912	1,730,299,852	I_kwDODunzps5nIkfM	5,912	Missing elements in `map` a batched dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/1410927?v=4", "events_url": "https://api.github.com/users/sachinruk/events{/privacy}", "followers_url": "https://api.github.com/users/sachinruk/followers", "following_url": "https://api.github.com/users/sachinruk/following{/other_user}", "gists_url": "https://api.github.com/users/sachinruk/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sachinruk", "id": 1410927, "login": "sachinruk", "node_id": "MDQ6VXNlcjE0MTA5Mjc=", "organizations_url": "https://api.github.com/users/sachinruk/orgs", "received_events_url": "https://api.github.com/users/sachinruk/received_events", "repos_url": "https://api.github.com/users/sachinruk/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sachinruk/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sachinruk/subscriptions", "type": "User", "url": "https://api.github.com/users/sachinruk" }	[]	open	false	null	[]	null	[ "Hi ! in your code batching is only used within `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```" ]	2023-05-29T08:09:19Z	2023-05-30T17:35:33Z	null	NONE	null	### Describe the bug As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here. The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out). ```python class CollateFn: def get_image(self, url): try: response = requests.get(url) return Image.open(io.BytesIO(response.content)).convert("RGB") except PIL.UnidentifiedImageError: logger.info(f"Reading error: Could not transform f{url}") return None except requests.exceptions.ConnectionError: logger.info(f"Connection error: Could not transform f{url}") return None def __call__(self, batch): images = [self.get_image(url) for url in batch["url"]] captions = [caption for caption, image in zip(batch["caption"], images) if image is not None] images = [image for image in images if image is not None] tokenized_captions = tokenizer( captions, padding="max_length", truncation=True, max_length=tokenizer.model_max_length, return_tensors="pt", ) image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images]) # import pdb; pdb.set_trace() return {"input_ids": tokenized_captions["input_ids"], "images": image_features} collate_fn = CollateFn() laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True) laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys()) ``` ### Steps to reproduce the bug A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here. ### Expected behavior Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`. ### Environment info datasets==2.12.0 python==3.10	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5912/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5912/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5910	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5910/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5910/comments	https://api.github.com/repos/huggingface/datasets/issues/5910/events	https://github.com/huggingface/datasets/issues/5910	1,728,909,790	I_kwDODunzps5nDRHe	5,910	Cannot use both set_format and set_transform	{ "avatar_url": "https://avatars.githubusercontent.com/u/14046002?v=4", "events_url": "https://api.github.com/users/ybouane/events{/privacy}", "followers_url": "https://api.github.com/users/ybouane/followers", "following_url": "https://api.github.com/users/ybouane/following{/other_user}", "gists_url": "https://api.github.com/users/ybouane/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ybouane", "id": 14046002, "login": "ybouane", "node_id": "MDQ6VXNlcjE0MDQ2MDAy", "organizations_url": "https://api.github.com/users/ybouane/orgs", "received_events_url": "https://api.github.com/users/ybouane/received_events", "repos_url": "https://api.github.com/users/ybouane/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ybouane/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ybouane/subscriptions", "type": "User", "url": "https://api.github.com/users/ybouane" }	[]	open	false	null	[]	null	[ "Currently, it's not possible to chain `set_format`/`set_transform` calls (plus, this is a breaking change if we decide to implement it), so I see two possible solutions:\r\n* using `set_format`/`set_transform` for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n* implementing and registering a custom formatter (the relevant code is [here](https://github.com/huggingface/datasets/tree/main/src/datasets/formatting))\r\n\r\nBtw, your example requires a single `set_format` call:\r\n```python\r\nds.set_format(\"torch\", columns=[\"image\"], output_all_columns=True, dtype=torch.double)\r\n```" ]	2023-05-27T19:22:23Z	2023-06-13T17:52:17Z	null	NONE	null	### Describe the bug I need to process some data using the set_transform method but I also need the data to be formatted for pytorch before processing it. I don't see anywhere in the documentation something that says that both methods cannot be used at the same time. ### Steps to reproduce the bug ``` from datasets import load_dataset ds = load_dataset("mnist", split="train") ds.set_format(type="torch") def transform(entry): return entry["image"].double() ds.set_transform(transform) print(ds[0]) ``` ### Expected behavior It should print the pytorch tensor image as a double, but it errors because "entry" in the transform function doesn't receive a pytorch tensor to begin with, it receives a PIL Image -> entry.double() errors because entry isn't a pytorch tensor. ### Environment info Latest versions. ### Note: It would be at least handy to have access to a function that can do the dataset.set_format in the set_transform function. Something like: ``` from datasets import load_dataset, do_format ds = load_dataset("mnist", split="train") def transform(entry): entry = do_format(entry, type="torch") return entry["image"].double() ds.set_transform(transform) print(ds[0]) ```	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5910/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5910/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5909	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5909/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5909/comments	https://api.github.com/repos/huggingface/datasets/issues/5909/events	https://github.com/huggingface/datasets/pull/5909	1,728,900,068	PR_kwDODunzps5Rgga6	5,909	Use more efficient and idiomatic way to construct list.	{ "avatar_url": "https://avatars.githubusercontent.com/u/172294?v=4", "events_url": "https://api.github.com/users/ttsugriy/events{/privacy}", "followers_url": "https://api.github.com/users/ttsugriy/followers", "following_url": "https://api.github.com/users/ttsugriy/following{/other_user}", "gists_url": "https://api.github.com/users/ttsugriy/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ttsugriy", "id": 172294, "login": "ttsugriy", "node_id": "MDQ6VXNlcjE3MjI5NA==", "organizations_url": "https://api.github.com/users/ttsugriy/orgs", "received_events_url": "https://api.github.com/users/ttsugriy/received_events", "repos_url": "https://api.github.com/users/ttsugriy/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ttsugriy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ttsugriy/subscriptions", "type": "User", "url": "https://api.github.com/users/ttsugriy" }	[]	closed	false	null	[]	null	[ "_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) \| 0.008156 / 0.011353 (-0.003197) \| 0.005563 / 0.011008 (-0.005445) \| 0.118319 / 0.038508 (0.079810) \| 0.044305 / 0.023109 (0.021195) \| 0.366221 / 0.275898 (0.090323) \| 0.407585 / 0.323480 (0.084105) \| 0.006961 / 0.007986 (-0.001024) \| 0.004841 / 0.004328 (0.000513) \| 0.089949 / 0.004250 (0.085698) \| 0.062197 / 0.037052 (0.025144) \| 0.360721 / 0.258489 (0.102232) \| 0.415332 / 0.293841 (0.121491) \| 0.035709 / 0.128546 (-0.092837) \| 0.010617 / 0.075646 (-0.065030) \| 0.397454 / 0.419271 (-0.021817) \| 0.063490 / 0.043533 (0.019958) \| 0.374289 / 0.255139 (0.119150) \| 0.382827 / 0.283200 (0.099628) \| 0.121014 / 0.141683 (-0.020669) \| 1.729933 / 1.452155 (0.277779) \| 1.896222 / 1.492716 (0.403506) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254030 / 0.018006 (0.236023) \| 0.491225 / 0.000490 (0.490736) \| 0.018933 / 0.000200 (0.018734) \| 0.000413 / 0.000054 (0.000358) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033085 / 0.037411 (-0.004327) \| 0.132837 / 0.014526 (0.118311) \| 0.143275 / 0.176557 (-0.033282) \| 0.215800 / 0.737135 (-0.521335) \| 0.149802 / 0.296338 (-0.146536) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474688 / 0.215209 (0.259479) \| 4.743223 / 2.077655 (2.665569) \| 2.163107 / 1.504120 (0.658988) \| 1.946396 / 1.541195 (0.405201) \| 2.057538 / 1.468490 (0.589047) \| 0.618836 / 4.584777 (-3.965941) \| 4.605934 / 3.745712 (0.860222) \| 2.201537 / 5.269862 (-3.068324) \| 1.275758 / 4.565676 (-3.289919) \| 0.077782 / 0.424275 (-0.346493) \| 0.014830 / 0.007607 (0.007223) \| 0.593372 / 0.226044 (0.367328) \| 5.927000 / 2.268929 (3.658072) \| 2.687293 / 55.444624 (-52.757331) \| 2.301797 / 6.876477 (-4.574679) \| 2.489928 / 2.142072 (0.347856) \| 0.756779 / 4.805227 (-4.048449) \| 0.168065 / 6.500664 (-6.332600) \| 0.077276 / 0.075469 (0.001807) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.608169 / 1.841788 (-0.233619) \| 19.048790 / 8.074308 (10.974482) \| 16.100228 / 10.191392 (5.908836) \| 0.215346 / 0.680424 (-0.465077) \| 0.022293 / 0.534201 (-0.511907) \| 0.535899 / 0.579283 (-0.043384) \| 0.533729 / 0.434364 (0.099365) \| 0.562697 / 0.540337 (0.022360) \| 0.764082 / 1.386936 (-0.622854) \|\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) \| 0.010087 / 0.011353 (-0.001266) \| 0.005357 / 0.011008 (-0.005651) \| 0.092678 / 0.038508 (0.054170) \| 0.041207 / 0.023109 (0.018098) \| 0.437464 / 0.275898 (0.161566) \| 0.527867 / 0.323480 (0.204387) \| 0.006861 / 0.007986 (-0.001125) \| 0.006131 / 0.004328 (0.001802) \| 0.093741 / 0.004250 (0.089490) \| 0.064142 / 0.037052 (0.027090) \| 0.433577 / 0.258489 (0.175088) \| 0.537148 / 0.293841 (0.243307) \| 0.035339 / 0.128546 (-0.093207) \| 0.010432 / 0.075646 (-0.065214) \| 0.102838 / 0.419271 (-0.316434) \| 0.057905 / 0.043533 (0.014372) \| 0.437956 / 0.255139 (0.182817) \| 0.509562 / 0.283200 (0.226362) \| 0.120620 / 0.141683 (-0.021063) \| 1.798686 / 1.452155 (0.346531) \| 2.013290 / 1.492716 (0.520574) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249067 / 0.018006 (0.231061) \| 0.462219 / 0.000490 (0.461729) \| 0.000476 / 0.000200 (0.000276) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033988 / 0.037411 (-0.003424) \| 0.135863 / 0.014526 (0.121337) \| 0.144082 / 0.176557 (-0.032474) \| 0.201715 / 0.737135 (-0.535421) \| 0.152079 / 0.296338 (-0.144259) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522820 / 0.215209 (0.307611) \| 5.216723 / 2.077655 (3.139068) \| 2.582355 / 1.504120 (1.078235) \| 2.352799 / 1.541195 (0.811604) \| 2.451943 / 1.468490 (0.983453) \| 0.620381 / 4.584777 (-3.964396) \| 4.537841 / 3.745712 (0.792129) \| 2.206431 / 5.269862 (-3.063431) \| 1.269865 / 4.565676 (-3.295811) \| 0.078744 / 0.424275 (-0.345531) \| 0.014375 / 0.007607 (0.006768) \| 0.648215 / 0.226044 (0.422171) \| 6.482809 / 2.268929 (4.213881) \| 3.210670 / 55.444624 (-52.233954) \| 2.847485 / 6.876477 (-4.028992) \| 2.820946 / 2.142072 (0.678873) \| 0.762711 / 4.805227 (-4.042516) \| 0.171235 / 6.500664 (-6.329429) \| 0.080230 / 0.075469 (0.004761) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.646840 / 1.841788 (-0.194948) \| 19.400451 / 8.074308 (11.326142) \| 16.758845 / 10.191392 (6.567453) \| 0.171377 / 0.680424 (-0.509046) \| 0.020400 / 0.534201 (-0.513801) \| 0.467675 / 0.579283 (-0.111608) \| 0.529745 / 0.434364 (0.095381) \| 0.605989 / 0.540337 (0.065652) \| 0.694659 / 1.386936 (-0.692277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#006bf33ac5c308f9c70f4df4868abd539eb6c366 \"CML watermark\")\n", "It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom." ]	2023-05-27T18:54:47Z	2023-05-31T15:37:11Z	2023-05-31T13:28:29Z	CONTRIBUTOR	null	Using `*` is ~2X faster according to [benchmark](https://colab.research.google.com/gist/ttsugriy/c964a2604edf70c41911b10335729b6a/for-vs-mult.ipynb) with just 4 patterns. This doesn't matter much since this tiny difference is not going to be noticeable, but why not?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5909/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5909/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5909.diff", "html_url": "https://github.com/huggingface/datasets/pull/5909", "merged_at": "2023-05-31T13:28:28Z", "patch_url": "https://github.com/huggingface/datasets/pull/5909.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5909" }	true
https://api.github.com/repos/huggingface/datasets/issues/5908	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5908/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5908/comments	https://api.github.com/repos/huggingface/datasets/issues/5908/events	https://github.com/huggingface/datasets/issues/5908	1,728,653,935	I_kwDODunzps5nCSpv	5,908	Unbearably slow sorting on big mapped datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/29152154?v=4", "events_url": "https://api.github.com/users/maximxlss/events{/privacy}", "followers_url": "https://api.github.com/users/maximxlss/followers", "following_url": "https://api.github.com/users/maximxlss/following{/other_user}", "gists_url": "https://api.github.com/users/maximxlss/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/maximxlss", "id": 29152154, "login": "maximxlss", "node_id": "MDQ6VXNlcjI5MTUyMTU0", "organizations_url": "https://api.github.com/users/maximxlss/orgs", "received_events_url": "https://api.github.com/users/maximxlss/received_events", "repos_url": "https://api.github.com/users/maximxlss/repos", "site_admin": false, "starred_url": "https://api.github.com/users/maximxlss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maximxlss/subscriptions", "type": "User", "url": "https://api.github.com/users/maximxlss" }	[]	open	false	null	[]	null	[ "Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well", "@lhoestq \r\n\r\n> contiguous=True (which should be the default imo)\r\n\r\nFor `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (when we add `IterableDataset.shard`)", "Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nIf the dataset is made of one shard it's indeed not possible to shard it contiguously though", "> Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nBut sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? ", "Yes indeed !", "I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.\n\nThere are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I saw some issue like that somewhere (about the mapped datasets in general), but can't find it now.\n\nMaybe indices should be flattened before any additional processing, then." ]	2023-05-27T11:08:32Z	2023-06-13T17:45:10Z	null	CONTRIBUTOR	null	### Describe the bug For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lines at all, with flattening takes about a minute). ### Steps to reproduce the bug ```Python from datasets import load_dataset import time dataset = load_dataset("xnli", "en", split="train") dataset = dataset.shard(10, 0) print(len(dataset)) t = time.time() # dataset = dataset.flatten_indices() # uncomment this line and it's fast dataset = dataset.sort("label", reverse=True, load_from_cache_file=False) print(f"finished in {time.time() - t:.4f} seconds") ``` ### Expected behavior Expect sorting to take the same or less time than flattening and then sorting. ### Environment info - `datasets` version: 2.12.1.dev0 (same with 2.12.0 too) - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.10.10 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5908/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5908/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5907	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5907/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5907/comments	https://api.github.com/repos/huggingface/datasets/issues/5907/events	https://github.com/huggingface/datasets/pull/5907	1,728,648,560	PR_kwDODunzps5RfqUU	5,907	Add `flatten_indices` to `DatasetDict`	{ "avatar_url": "https://avatars.githubusercontent.com/u/29152154?v=4", "events_url": "https://api.github.com/users/maximxlss/events{/privacy}", "followers_url": "https://api.github.com/users/maximxlss/followers", "following_url": "https://api.github.com/users/maximxlss/following{/other_user}", "gists_url": "https://api.github.com/users/maximxlss/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/maximxlss", "id": 29152154, "login": "maximxlss", "node_id": "MDQ6VXNlcjI5MTUyMTU0", "organizations_url": "https://api.github.com/users/maximxlss/orgs", "received_events_url": "https://api.github.com/users/maximxlss/received_events", "repos_url": "https://api.github.com/users/maximxlss/repos", "site_admin": false, "starred_url": "https://api.github.com/users/maximxlss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maximxlss/subscriptions", "type": "User", "url": "https://api.github.com/users/maximxlss" }	[]	closed	false	null	[]	null	[ "_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) \| 0.006192 / 0.011353 (-0.005161) \| 0.004410 / 0.011008 (-0.006598) \| 0.095990 / 0.038508 (0.057482) \| 0.032662 / 0.023109 (0.009553) \| 0.322827 / 0.275898 (0.046929) \| 0.352542 / 0.323480 (0.029062) \| 0.005398 / 0.007986 (-0.002588) \| 0.003926 / 0.004328 (-0.000403) \| 0.075131 / 0.004250 (0.070880) \| 0.046205 / 0.037052 (0.009153) \| 0.330957 / 0.258489 (0.072468) \| 0.360166 / 0.293841 (0.066325) \| 0.027880 / 0.128546 (-0.100666) \| 0.008813 / 0.075646 (-0.066833) \| 0.327316 / 0.419271 (-0.091955) \| 0.050071 / 0.043533 (0.006539) \| 0.319939 / 0.255139 (0.064800) \| 0.331593 / 0.283200 (0.048393) \| 0.096745 / 0.141683 (-0.044938) \| 1.445165 / 1.452155 (-0.006990) \| 1.515538 / 1.492716 (0.022821) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209365 / 0.018006 (0.191358) \| 0.437007 / 0.000490 (0.436518) \| 0.003207 / 0.000200 (0.003007) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027261 / 0.037411 (-0.010151) \| 0.105101 / 0.014526 (0.090575) \| 0.117163 / 0.176557 (-0.059394) \| 0.176237 / 0.737135 (-0.560898) \| 0.122559 / 0.296338 (-0.173779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406792 / 0.215209 (0.191583) \| 4.060831 / 2.077655 (1.983176) \| 1.829691 / 1.504120 (0.325571) \| 1.633155 / 1.541195 (0.091960) \| 1.704817 / 1.468490 (0.236327) \| 0.525325 / 4.584777 (-4.059452) \| 3.752907 / 3.745712 (0.007194) \| 1.857513 / 5.269862 (-3.412349) \| 1.222237 / 4.565676 (-3.343439) \| 0.065941 / 0.424275 (-0.358334) \| 0.012498 / 0.007607 (0.004891) \| 0.495009 / 0.226044 (0.268965) \| 4.968074 / 2.268929 (2.699145) \| 2.277898 / 55.444624 (-53.166727) \| 1.936656 / 6.876477 (-4.939821) \| 1.970698 / 2.142072 (-0.171374) \| 0.635221 / 4.805227 (-4.170006) \| 0.140539 / 6.500664 (-6.360125) \| 0.064111 / 0.075469 (-0.011358) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238151 / 1.841788 (-0.603637) \| 14.681262 / 8.074308 (6.606954) \| 13.405525 / 10.191392 (3.214133) \| 0.163225 / 0.680424 (-0.517199) \| 0.017282 / 0.534201 (-0.516918) \| 0.395526 / 0.579283 (-0.183757) \| 0.429156 / 0.434364 (-0.005208) \| 0.470806 / 0.540337 (-0.069531) \| 0.571290 / 1.386936 (-0.815646) \|\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) \| 0.006444 / 0.011353 (-0.004909) \| 0.004388 / 0.011008 (-0.006621) \| 0.075004 / 0.038508 (0.036496) \| 0.032904 / 0.023109 (0.009795) \| 0.375360 / 0.275898 (0.099462) \| 0.413684 / 0.323480 (0.090204) \| 0.005854 / 0.007986 (-0.002132) \| 0.005504 / 0.004328 (0.001175) \| 0.075049 / 0.004250 (0.070799) \| 0.047973 / 0.037052 (0.010920) \| 0.377943 / 0.258489 (0.119454) \| 0.427039 / 0.293841 (0.133198) \| 0.028248 / 0.128546 (-0.100298) \| 0.008972 / 0.075646 (-0.066674) \| 0.081848 / 0.419271 (-0.337424) \| 0.047935 / 0.043533 (0.004402) \| 0.377980 / 0.255139 (0.122841) \| 0.407856 / 0.283200 (0.124656) \| 0.103454 / 0.141683 (-0.038229) \| 1.469051 / 1.452155 (0.016896) \| 1.590657 / 1.492716 (0.097941) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192380 / 0.018006 (0.174374) \| 0.440995 / 0.000490 (0.440505) \| 0.004082 / 0.000200 (0.003882) \| 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) \| 0.029584 / 0.037411 (-0.007828) \| 0.110051 / 0.014526 (0.095525) \| 0.121196 / 0.176557 (-0.055361) \| 0.172249 / 0.737135 (-0.564886) \| 0.125380 / 0.296338 (-0.170958) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435218 / 0.215209 (0.220009) \| 4.354811 / 2.077655 (2.277156) \| 2.102050 / 1.504120 (0.597930) \| 1.913454 / 1.541195 (0.372260) \| 1.974624 / 1.468490 (0.506134) \| 0.529975 / 4.584777 (-4.054802) \| 3.801605 / 3.745712 (0.055893) \| 3.162408 / 5.269862 (-2.107454) \| 1.599576 / 4.565676 (-2.966101) \| 0.066710 / 0.424275 (-0.357565) \| 0.012158 / 0.007607 (0.004551) \| 0.549187 / 0.226044 (0.323142) \| 5.489930 / 2.268929 (3.221002) \| 2.646787 / 55.444624 (-52.797837) \| 2.311915 / 6.876477 (-4.564562) \| 2.335645 / 2.142072 (0.193572) \| 0.641067 / 4.805227 (-4.164160) \| 0.142227 / 6.500664 (-6.358437) \| 0.065303 / 0.075469 (-0.010166) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283209 / 1.841788 (-0.558579) \| 15.241809 / 8.074308 (7.167501) \| 14.131471 / 10.191392 (3.940079) \| 0.143921 / 0.680424 (-0.536503) \| 0.017497 / 0.534201 (-0.516704) \| 0.402236 / 0.579283 (-0.177047) \| 0.418917 / 0.434364 (-0.015447) \| 0.461745 / 0.540337 (-0.078593) \| 0.560212 / 1.386936 (-0.826724) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"CML watermark\")\n" ]	2023-05-27T10:55:44Z	2023-06-01T11:46:35Z	2023-06-01T11:39:36Z	CONTRIBUTOR	null	Add `flatten_indices` to `DatasetDict` for convinience	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5907/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5907/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5907.diff", "html_url": "https://github.com/huggingface/datasets/pull/5907", "merged_at": "2023-06-01T11:39:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/5907.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5907" }	true
https://api.github.com/repos/huggingface/datasets/issues/5906	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5906/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5906/comments	https://api.github.com/repos/huggingface/datasets/issues/5906/events	https://github.com/huggingface/datasets/issues/5906	1,728,171,113	I_kwDODunzps5nAcxp	5,906	Could you unpin responses version?	{ "avatar_url": "https://avatars.githubusercontent.com/u/47789026?v=4", "events_url": "https://api.github.com/users/kenimou/events{/privacy}", "followers_url": "https://api.github.com/users/kenimou/followers", "following_url": "https://api.github.com/users/kenimou/following{/other_user}", "gists_url": "https://api.github.com/users/kenimou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kenimou", "id": 47789026, "login": "kenimou", "node_id": "MDQ6VXNlcjQ3Nzg5MDI2", "organizations_url": "https://api.github.com/users/kenimou/orgs", "received_events_url": "https://api.github.com/users/kenimou/received_events", "repos_url": "https://api.github.com/users/kenimou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kenimou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kenimou/subscriptions", "type": "User", "url": "https://api.github.com/users/kenimou" }	[]	closed	false	null	[]	null	[]	2023-05-26T20:02:14Z	2023-05-30T17:53:31Z	2023-05-30T17:53:31Z	NONE	null	### Describe the bug Could you unpin [this](https://github.com/huggingface/datasets/blob/main/setup.py#L139) or move it to test requirements? This is a testing library and we also use it for our tests as well. We do not want to use a very outdated version. ### Steps to reproduce the bug could not install this library due to dependency conflict. ### Expected behavior can install datasets ### Environment info linux 64	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5906/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5906/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5905	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5905/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5905/comments	https://api.github.com/repos/huggingface/datasets/issues/5905/events	https://github.com/huggingface/datasets/issues/5905	1,727,541,392	I_kwDODunzps5m-DCQ	5,905	Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently	{ "avatar_url": "https://avatars.githubusercontent.com/u/48770768?v=4", "events_url": "https://api.github.com/users/Hubert-Bonisseur/events{/privacy}", "followers_url": "https://api.github.com/users/Hubert-Bonisseur/followers", "following_url": "https://api.github.com/users/Hubert-Bonisseur/following{/other_user}", "gists_url": "https://api.github.com/users/Hubert-Bonisseur/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Hubert-Bonisseur", "id": 48770768, "login": "Hubert-Bonisseur", "node_id": "MDQ6VXNlcjQ4NzcwNzY4", "organizations_url": "https://api.github.com/users/Hubert-Bonisseur/orgs", "received_events_url": "https://api.github.com/users/Hubert-Bonisseur/received_events", "repos_url": "https://api.github.com/users/Hubert-Bonisseur/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Hubert-Bonisseur/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Hubert-Bonisseur/subscriptions", "type": "User", "url": "https://api.github.com/users/Hubert-Bonisseur" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[]	2023-05-26T12:33:02Z	2023-05-26T12:36:29Z	null	CONTRIBUTOR	null	### Feature request I would like a way to resume training from a checkpoint without waiting for a very long time when using an iterable dataset. ### Motivation I am training models on the speech-recognition task. I have very large datasets that I can't comfortably store on a disk and also quite computationally intensive audio processing to do. As a result I want to load data from my remote when it is needed and perform all processing on the fly. I am currently using the iterable dataset feature of _datasets_. It does everything I need with one exception. My issue is that when resuming training at a step n, we have to download all the data and perform the processing of steps < n, just to get the iterable at the right step. In my case it takes almost as long as training for the same steps, which make resuming training from a checkpoint useless in practice. I understand that the nature of iterators make it probably nearly impossible to quickly resume training. I thought about a possible solution nonetheless : I could in fact index my large dataset and make it a mapped dataset. Then I could use set_transform to perform the processing on the fly. Finally, if I'm not mistaken, the _accelerate_ package allows to [skip steps efficiently](https://github.com/huggingface/accelerate/blob/a73898027a211c3f6dc4460351b0ec246aa824aa/src/accelerate/data_loader.py#L827) for a mapped dataset. Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there. If not, I could do it using a plain _Pytorch_ dataset. Then I would need to convert it to a _datasets_' dataset to get all the features of _datasets_. Is it something possible ? ### Your contribution I could provide a PR to allow lazy loading of mapped dataset or the conversion of a mapped _Pytorch_ dataset into a _Datasets_ dataset if you think it is an useful new feature.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5905/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5905/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5904	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5904/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5904/comments	https://api.github.com/repos/huggingface/datasets/issues/5904/events	https://github.com/huggingface/datasets/pull/5904	1,727,415,626	PR_kwDODunzps5Rbfks	5,904	Validate name parameter in make_file_instructions	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	[ "_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) \| 0.007401 / 0.011353 (-0.003952) \| 0.005198 / 0.011008 (-0.005810) \| 0.112317 / 0.038508 (0.073809) \| 0.038406 / 0.023109 (0.015297) \| 0.358008 / 0.275898 (0.082110) \| 0.395350 / 0.323480 (0.071870) \| 0.006201 / 0.007986 (-0.001785) \| 0.004368 / 0.004328 (0.000039) \| 0.087718 / 0.004250 (0.083467) \| 0.055299 / 0.037052 (0.018247) \| 0.350481 / 0.258489 (0.091992) \| 0.419876 / 0.293841 (0.126035) \| 0.032459 / 0.128546 (-0.096087) \| 0.010635 / 0.075646 (-0.065011) \| 0.383282 / 0.419271 (-0.035989) \| 0.059241 / 0.043533 (0.015708) \| 0.365101 / 0.255139 (0.109962) \| 0.378144 / 0.283200 (0.094944) \| 0.114287 / 0.141683 (-0.027396) \| 1.680870 / 1.452155 (0.228715) \| 1.788183 / 1.492716 (0.295467) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242919 / 0.018006 (0.224913) \| 0.489850 / 0.000490 (0.489360) \| 0.011408 / 0.000200 (0.011208) \| 0.000444 / 0.000054 (0.000389) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030742 / 0.037411 (-0.006669) \| 0.123092 / 0.014526 (0.108566) \| 0.138246 / 0.176557 (-0.038311) \| 0.207299 / 0.737135 (-0.529836) \| 0.142647 / 0.296338 (-0.153691) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472553 / 0.215209 (0.257344) \| 4.671763 / 2.077655 (2.594108) \| 2.119986 / 1.504120 (0.615866) \| 1.891851 / 1.541195 (0.350656) \| 1.979094 / 1.468490 (0.510604) \| 0.617956 / 4.584777 (-3.966821) \| 4.969418 / 3.745712 (1.223706) \| 4.672083 / 5.269862 (-0.597779) \| 2.119049 / 4.565676 (-2.446627) \| 0.077466 / 0.424275 (-0.346809) \| 0.014434 / 0.007607 (0.006827) \| 0.580746 / 0.226044 (0.354701) \| 5.805458 / 2.268929 (3.536530) \| 2.622498 / 55.444624 (-52.822126) \| 2.259499 / 6.876477 (-4.616978) \| 2.362078 / 2.142072 (0.220006) \| 0.719911 / 4.805227 (-4.085317) \| 0.164939 / 6.500664 (-6.335725) \| 0.074762 / 0.075469 (-0.000707) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.496709 / 1.841788 (-0.345079) \| 18.247499 / 8.074308 (10.173191) \| 15.397075 / 10.191392 (5.205683) \| 0.181163 / 0.680424 (-0.499261) \| 0.022604 / 0.534201 (-0.511597) \| 0.462791 / 0.579283 (-0.116492) \| 0.504473 / 0.434364 (0.070109) \| 0.582254 / 0.540337 (0.041917) \| 0.673849 / 1.386936 (-0.713087) \|\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) \| 0.007633 / 0.011353 (-0.003720) \| 0.004859 / 0.011008 (-0.006149) \| 0.091194 / 0.038508 (0.052686) \| 0.038255 / 0.023109 (0.015146) \| 0.460972 / 0.275898 (0.185074) \| 0.470441 / 0.323480 (0.146961) \| 0.006482 / 0.007986 (-0.001504) \| 0.004500 / 0.004328 (0.000172) \| 0.089998 / 0.004250 (0.085748) \| 0.055470 / 0.037052 (0.018418) \| 0.459188 / 0.258489 (0.200699) \| 0.491255 / 0.293841 (0.197414) \| 0.032200 / 0.128546 (-0.096346) \| 0.010372 / 0.075646 (-0.065274) \| 0.097429 / 0.419271 (-0.321843) \| 0.052469 / 0.043533 (0.008936) \| 0.452492 / 0.255139 (0.197353) \| 0.475210 / 0.283200 (0.192010) \| 0.116976 / 0.141683 (-0.024707) \| 1.752742 / 1.452155 (0.300587) \| 1.849535 / 1.492716 (0.356819) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229822 / 0.018006 (0.211816) \| 0.472259 / 0.000490 (0.471770) \| 0.000455 / 0.000200 (0.000255) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033796 / 0.037411 (-0.003615) \| 0.136151 / 0.014526 (0.121625) \| 0.144015 / 0.176557 (-0.032542) \| 0.199337 / 0.737135 (-0.537798) \| 0.150024 / 0.296338 (-0.146315) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522737 / 0.215209 (0.307528) \| 5.165223 / 2.077655 (3.087568) \| 2.630334 / 1.504120 (1.126214) \| 2.392383 / 1.541195 (0.851188) \| 2.488966 / 1.468490 (1.020476) \| 0.608981 / 4.584777 (-3.975796) \| 4.711545 / 3.745712 (0.965833) \| 2.121537 / 5.269862 (-3.148325) \| 1.205477 / 4.565676 (-3.360199) \| 0.078277 / 0.424275 (-0.345998) \| 0.014175 / 0.007607 (0.006568) \| 0.640720 / 0.226044 (0.414675) \| 6.391173 / 2.268929 (4.122245) \| 3.265131 / 55.444624 (-52.179493) \| 2.939188 / 6.876477 (-3.937289) \| 2.919217 / 2.142072 (0.777145) \| 0.745095 / 4.805227 (-4.060132) \| 0.164065 / 6.500664 (-6.336599) \| 0.076993 / 0.075469 (0.001524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.539971 / 1.841788 (-0.301817) \| 18.597296 / 8.074308 (10.522988) \| 16.899330 / 10.191392 (6.707938) \| 0.169005 / 0.680424 (-0.511419) \| 0.020447 / 0.534201 (-0.513754) \| 0.465862 / 0.579283 (-0.113421) \| 0.522819 / 0.434364 (0.088455) \| 0.547111 / 0.540337 (0.006773) \| 0.657777 / 1.386936 (-0.729159) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56aff9ecb4e565eb95faad525558914648cc22f1 \"CML watermark\")\n" ]	2023-05-26T11:12:46Z	2023-05-31T07:43:32Z	2023-05-31T07:34:57Z	MEMBER	null	Validate `name` parameter in `make_file_instructions`. This way users get more informative error messages, instead of: ```stacktrace .../huggingface/datasets/src/datasets/arrow_reader.py in make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path) 110 name2len = {info.name: info.num_examples for info in split_infos} 111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos} --> 112 name2filenames = { 113 info.name: filenames_for_dataset_split( 114 path=prefix_path, .../huggingface/datasets/src/datasets/arrow_reader.py in <dictcomp>(.0) 111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos} 112 name2filenames = { --> 113 info.name: filenames_for_dataset_split( 114 path=prefix_path, 115 dataset_name=name, .../huggingface/datasets/src/datasets/naming.py in filenames_for_dataset_split(path, dataset_name, split, filetype_suffix, shard_lengths) 68 69 def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None): ---> 70 prefix = filename_prefix_for_split(dataset_name, split) 71 prefix = os.path.join(path, prefix) 72 .../huggingface/datasets/src/datasets/naming.py in filename_prefix_for_split(name, split) 52 53 def filename_prefix_for_split(name, split): ---> 54 if os.path.basename(name) != name: 55 raise ValueError(f"Should be a dataset name, not a path: {name}") 56 if not re.match(_split_re, split): .../lib/python3.9/posixpath.py in basename(p) 140 def basename(p): 141 """Returns the final component of a pathname""" --> 142 p = os.fspath(p) 143 sep = _get_sep(p) 144 i = p.rfind(sep) + 1 TypeError: expected str, bytes or os.PathLike object, not NoneType ``` Related to #5895.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5904/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5904/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5904.diff", "html_url": "https://github.com/huggingface/datasets/pull/5904", "merged_at": "2023-05-31T07:34:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/5904.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5904" }	true
https://api.github.com/repos/huggingface/datasets/issues/5903	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5903/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5903/comments	https://api.github.com/repos/huggingface/datasets/issues/5903/events	https://github.com/huggingface/datasets/pull/5903	1,727,372,549	PR_kwDODunzps5RbV82	5,903	Relax `ci.yml` trigger for `pull_request` based on modified paths	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[]	open	false	null	[]	null	[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint." ]	2023-05-26T10:46:52Z	2023-05-26T10:51:37Z	null	CONTRIBUTOR	null	## What's in this PR? As of a previous PR at #5902, I've seen that the CI was automatically trigger on any file, in that case when modifying a Jupyter Notebook (.ipynb), which IMO could be skipped, as the modification on the Jupyter Notebook has no effect/impact on the `ci.yml` outcome. So this PR controls the paths that trigger the `ci.yml` to avoid wasting resources when not needed. ## What's pending in this PR? I would like to confirm whether this should affect both `push` and `pull_request`, since just modifications in those files won't change the `ci.yml` outcome, so maybe it's worth skipping it too in the `push` trigger.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5903/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5903/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5903.diff", "html_url": "https://github.com/huggingface/datasets/pull/5903", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5903.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5903" }	true
https://api.github.com/repos/huggingface/datasets/issues/5902	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5902/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5902/comments	https://api.github.com/repos/huggingface/datasets/issues/5902/events	https://github.com/huggingface/datasets/pull/5902	1,727,342,194	PR_kwDODunzps5RbPS9	5,902	Align `bert-base-cased` usage, install missing `seqeval`, and re-run `Overview.ipynb`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[]	open	false	null	[]	null	[ "Random fact: previous run was showing that the Hub was hosting 13336 datasets, while the most recent run shows 36662 👀🎉", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5902). All of your documentation changes will be reflected on that endpoint.", "Thanks! \r\n\r\nHowever, I think we should stop linking this notebook and use the notebook version of the Quickstart doc page instead of it for easier maintenance (we would have the \"Open in Colab\" button in the Quickstart doc as Transformers [does](https://huggingface.co/docs/transformers/quicktour)). \r\n\r\n@stevhliu should be able to help with this. If I'm not mistaken, this can be done by adding the `[[open in colab]]` marker to the doc page.\r\n\r\nAlso, if some useful info from the Overview notebook is not in the docs, feel free to add it so we don't lose it 🙂.", "Cool, makes sense @mariosasko, then I'll check both notebooks and see whether there's something in `Overview.ipynb` worth including in the `docs/source/quickstart.mdx` and remove `Overview.ipynb` and update references in favour of `docs/source/quickstart.mdx`\r\n\r\nAre you OK if I do that @stevhliu @mariosasko? Thanks 🤗 ", "For the moment I've just updated the `quickstart.mdx` to be more similar to [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx), but regarding the `Overview.ipynb` notebook I was planning to create a PR in https://github.com/huggingface/notebooks to add it there, does that make sense @stevhliu? And then to create a `README.md` in this repository in `notebooks/` as `transformers` does to point to the related notebooks hosted in https://github.com/huggingface/notebooks, WDYT? 🤗 ", "Hi @stevhliu thanks for the feedback! Already applied your suggestions, I'll also add the pointers to both audio and image datasets in the \"What's next\" section.\r\n\r\nBesides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!", "Thanks a lot for the detailed feedback @mariosasko, I'll apply the changes today!", "> Besides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!\r\n\r\nAwesome! If you're up for it, I think you can go ahead and open a PR with the changes I've outlined [here](https://github.com/huggingface/datasets/pull/5902#pullrequestreview-1475236887) to add the notebook building workflow. " ]	2023-05-26T10:25:01Z	2023-06-13T23:42:28Z	null	CONTRIBUTOR	null	## What's in this PR? This PR solves #5887 since there was a mismatch between the tokenizer and the model used, since the tokenizer was `bert-base-cased` while the model was `distilbert-base-case` both for the PyTorch and TensorFlow alternatives. Since DistilBERT doesn't use/need the `token_type_ids`, the `**batch` was failing, as the batch contained `input_ids`, `attention_mask`, `token_type_ids`, `start_positions` and `end_positions`, and `token_type_ids` was not required. Besides that, at the end `seqeval` was being used to evaluate the model predictions, and just `evaluate` was being installed, so I've also included the `seqeval` installation. Finally, I've re-run everything in Google Colab, and every cell was successfully executed! ## What was done on top of the original PR? Based on the comments from @mariosasko and @stevhliu, I've updated the contents of this PR to also review the `quickstart.mdx` and update what was needed, besides that, we may eventually move the `Overview.ipynb` dataset to `huggingface/notebooks` following @stevhliu suggestions.	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/5902/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5902/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5902.diff", "html_url": "https://github.com/huggingface/datasets/pull/5902", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5902.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5902" }	true
https://api.github.com/repos/huggingface/datasets/issues/5901	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5901/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5901/comments	https://api.github.com/repos/huggingface/datasets/issues/5901/events	https://github.com/huggingface/datasets/pull/5901	1,727,179,016	PR_kwDODunzps5Rarux	5,901	Make prepare_split more robust if errors in metadata dataset_info splits	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	[ "_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) \| 0.008809 / 0.011353 (-0.002544) \| 0.005641 / 0.011008 (-0.005367) \| 0.124986 / 0.038508 (0.086477) \| 0.037311 / 0.023109 (0.014202) \| 0.388915 / 0.275898 (0.113017) \| 0.430123 / 0.323480 (0.106643) \| 0.007447 / 0.007986 (-0.000538) \| 0.009593 / 0.004328 (0.005264) \| 0.099148 / 0.004250 (0.094898) \| 0.052393 / 0.037052 (0.015341) \| 0.399779 / 0.258489 (0.141290) \| 0.439109 / 0.293841 (0.145268) \| 0.043409 / 0.128546 (-0.085137) \| 0.016286 / 0.075646 (-0.059360) \| 0.431198 / 0.419271 (0.011927) \| 0.064932 / 0.043533 (0.021400) \| 0.390650 / 0.255139 (0.135511) \| 0.432883 / 0.283200 (0.149684) \| 0.110978 / 0.141683 (-0.030705) \| 1.796121 / 1.452155 (0.343967) \| 1.960097 / 1.492716 (0.467381) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286292 / 0.018006 (0.268286) \| 0.659495 / 0.000490 (0.659005) \| 0.008294 / 0.000200 (0.008094) \| 0.000485 / 0.000054 (0.000431) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029325 / 0.037411 (-0.008086) \| 0.125454 / 0.014526 (0.110928) \| 0.136459 / 0.176557 (-0.040097) \| 0.221075 / 0.737135 (-0.516060) \| 0.140281 / 0.296338 (-0.156058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.602401 / 0.215209 (0.387192) \| 6.124553 / 2.077655 (4.046898) \| 2.453141 / 1.504120 (0.949021) \| 2.038611 / 1.541195 (0.497416) \| 2.073611 / 1.468490 (0.605121) \| 0.938040 / 4.584777 (-3.646737) \| 5.755972 / 3.745712 (2.010260) \| 4.450935 / 5.269862 (-0.818926) \| 2.337219 / 4.565676 (-2.228457) \| 0.107118 / 0.424275 (-0.317157) \| 0.015201 / 0.007607 (0.007594) \| 0.785833 / 0.226044 (0.559788) \| 7.732984 / 2.268929 (5.464055) \| 3.236892 / 55.444624 (-52.207733) \| 2.696402 / 6.876477 (-4.180074) \| 2.805036 / 2.142072 (0.662964) \| 1.108612 / 4.805227 (-3.696616) \| 0.221067 / 6.500664 (-6.279597) \| 0.085538 / 0.075469 (0.010068) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.600311 / 1.841788 (-0.241476) \| 18.528118 / 8.074308 (10.453810) \| 21.107199 / 10.191392 (10.915807) \| 0.219489 / 0.680424 (-0.460934) \| 0.028927 / 0.534201 (-0.505274) \| 0.503446 / 0.579283 (-0.075837) \| 0.619833 / 0.434364 (0.185469) \| 0.582454 / 0.540337 (0.042117) \| 0.709154 / 1.386936 (-0.677782) \|\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) \| 0.008516 / 0.011353 (-0.002837) \| 0.006090 / 0.011008 (-0.004918) \| 0.104574 / 0.038508 (0.066066) \| 0.042676 / 0.023109 (0.019566) \| 0.458623 / 0.275898 (0.182725) \| 0.568479 / 0.323480 (0.244999) \| 0.008374 / 0.007986 (0.000389) \| 0.004677 / 0.004328 (0.000349) \| 0.105946 / 0.004250 (0.101695) \| 0.055256 / 0.037052 (0.018204) \| 0.511036 / 0.258489 (0.252547) \| 0.598383 / 0.293841 (0.304542) \| 0.043612 / 0.128546 (-0.084934) \| 0.014707 / 0.075646 (-0.060940) \| 0.116350 / 0.419271 (-0.302921) \| 0.061413 / 0.043533 (0.017880) \| 0.477785 / 0.255139 (0.222646) \| 0.542643 / 0.283200 (0.259443) \| 0.120431 / 0.141683 (-0.021252) \| 1.994083 / 1.452155 (0.541928) \| 2.100600 / 1.492716 (0.607883) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298480 / 0.018006 (0.280474) \| 0.601921 / 0.000490 (0.601432) \| 0.000445 / 0.000200 (0.000245) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034784 / 0.037411 (-0.002627) \| 0.133555 / 0.014526 (0.119029) \| 0.138541 / 0.176557 (-0.038015) \| 0.203114 / 0.737135 (-0.534021) \| 0.153477 / 0.296338 (-0.142861) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.780484 / 0.215209 (0.565275) \| 7.150876 / 2.077655 (5.073222) \| 3.168590 / 1.504120 (1.664470) \| 2.698746 / 1.541195 (1.157552) \| 2.695678 / 1.468490 (1.227188) \| 1.037706 / 4.584777 (-3.547071) \| 5.672631 / 3.745712 (1.926918) \| 2.798137 / 5.269862 (-2.471725) \| 1.738588 / 4.565676 (-2.827088) \| 0.111160 / 0.424275 (-0.313115) \| 0.013878 / 0.007607 (0.006271) \| 0.800191 / 0.226044 (0.574146) \| 8.546676 / 2.268929 (6.277748) \| 4.116852 / 55.444624 (-51.327773) \| 3.331271 / 6.876477 (-3.545206) \| 3.307410 / 2.142072 (1.165337) \| 1.191019 / 4.805227 (-3.614208) \| 0.248953 / 6.500664 (-6.251711) \| 0.086632 / 0.075469 (0.011162) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.795057 / 1.841788 (-0.046730) \| 18.038785 / 8.074308 (9.964476) \| 21.865566 / 10.191392 (11.674174) \| 0.211058 / 0.680424 (-0.469366) \| 0.026956 / 0.534201 (-0.507245) \| 0.518855 / 0.579283 (-0.060428) \| 0.618105 / 0.434364 (0.183741) \| 0.569227 / 0.540337 (0.028889) \| 0.705431 / 1.386936 (-0.681505) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"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) \| 0.008900 / 0.011353 (-0.002453) \| 0.005726 / 0.011008 (-0.005283) \| 0.131747 / 0.038508 (0.093239) \| 0.040585 / 0.023109 (0.017476) \| 0.420531 / 0.275898 (0.144633) \| 0.459430 / 0.323480 (0.135950) \| 0.007642 / 0.007986 (-0.000344) \| 0.006750 / 0.004328 (0.002421) \| 0.099147 / 0.004250 (0.094897) \| 0.055852 / 0.037052 (0.018799) \| 0.423653 / 0.258489 (0.165164) \| 0.453304 / 0.293841 (0.159463) \| 0.045247 / 0.128546 (-0.083300) \| 0.016034 / 0.075646 (-0.059612) \| 0.443115 / 0.419271 (0.023843) \| 0.078853 / 0.043533 (0.035320) \| 0.417508 / 0.255139 (0.162369) \| 0.440936 / 0.283200 (0.157736) \| 0.115603 / 0.141683 (-0.026080) \| 1.844610 / 1.452155 (0.392456) \| 1.998497 / 1.492716 (0.505781) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272622 / 0.018006 (0.254616) \| 0.598045 / 0.000490 (0.597556) \| 0.007088 / 0.000200 (0.006888) \| 0.000159 / 0.000054 (0.000105) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032976 / 0.037411 (-0.004436) \| 0.143970 / 0.014526 (0.129444) \| 0.142172 / 0.176557 (-0.034384) \| 0.216747 / 0.737135 (-0.520389) \| 0.146004 / 0.296338 (-0.150334) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.687507 / 0.215209 (0.472298) \| 6.549524 / 2.077655 (4.471870) \| 2.924142 / 1.504120 (1.420022) \| 2.504471 / 1.541195 (0.963277) \| 2.496280 / 1.468490 (1.027790) \| 0.959054 / 4.584777 (-3.625723) \| 5.851742 / 3.745712 (2.106030) \| 4.983357 / 5.269862 (-0.286504) \| 2.627403 / 4.565676 (-1.938274) \| 0.112955 / 0.424275 (-0.311320) \| 0.016206 / 0.007607 (0.008599) \| 0.819158 / 0.226044 (0.593114) \| 8.416949 / 2.268929 (6.148020) \| 3.776765 / 55.444624 (-51.667859) \| 3.002397 / 6.876477 (-3.874080) \| 3.158852 / 2.142072 (1.016779) \| 1.197099 / 4.805227 (-3.608129) \| 0.280654 / 6.500664 (-6.220010) \| 0.099471 / 0.075469 (0.024002) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.687007 / 1.841788 (-0.154781) \| 19.411976 / 8.074308 (11.337668) \| 22.053482 / 10.191392 (11.862090) \| 0.228038 / 0.680424 (-0.452386) \| 0.028226 / 0.534201 (-0.505975) \| 0.527695 / 0.579283 (-0.051588) \| 0.635911 / 0.434364 (0.201547) \| 0.618205 / 0.540337 (0.077868) \| 0.735164 / 1.386936 (-0.651772) \|\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) \| 0.009450 / 0.011353 (-0.001903) \| 0.006566 / 0.011008 (-0.004442) \| 0.108919 / 0.038508 (0.070411) \| 0.050010 / 0.023109 (0.026900) \| 0.505168 / 0.275898 (0.229270) \| 0.552190 / 0.323480 (0.228710) \| 0.007569 / 0.007986 (-0.000417) \| 0.006807 / 0.004328 (0.002478) \| 0.116621 / 0.004250 (0.112371) \| 0.060374 / 0.037052 (0.023321) \| 0.515165 / 0.258489 (0.256676) \| 0.572125 / 0.293841 (0.278284) \| 0.046561 / 0.128546 (-0.081986) \| 0.016159 / 0.075646 (-0.059487) \| 0.114568 / 0.419271 (-0.304704) \| 0.064689 / 0.043533 (0.021157) \| 0.497870 / 0.255139 (0.242731) \| 0.567332 / 0.283200 (0.284132) \| 0.126254 / 0.141683 (-0.015429) \| 1.954074 / 1.452155 (0.501919) \| 2.057682 / 1.492716 (0.564966) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.013857 / 0.018006 (-0.004149) \| 0.601561 / 0.000490 (0.601071) \| 0.002897 / 0.000200 (0.002697) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038480 / 0.037411 (0.001069) \| 0.142480 / 0.014526 (0.127954) \| 0.160479 / 0.176557 (-0.016077) \| 0.217942 / 0.737135 (-0.519194) \| 0.159908 / 0.296338 (-0.136431) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.697926 / 0.215209 (0.482717) \| 6.869754 / 2.077655 (4.792100) \| 3.125463 / 1.504120 (1.621343) \| 2.729123 / 1.541195 (1.187928) \| 2.855747 / 1.468490 (1.387257) \| 1.015345 / 4.584777 (-3.569432) \| 5.839176 / 3.745712 (2.093463) \| 5.019678 / 5.269862 (-0.250184) \| 2.080489 / 4.565676 (-2.485187) \| 0.118884 / 0.424275 (-0.305391) \| 0.021381 / 0.007607 (0.013774) \| 0.877847 / 0.226044 (0.651803) \| 8.714561 / 2.268929 (6.445633) \| 3.933399 / 55.444624 (-51.511226) \| 3.281809 / 6.876477 (-3.594668) \| 3.330342 / 2.142072 (1.188269) \| 1.235005 / 4.805227 (-3.570222) \| 0.239686 / 6.500664 (-6.260978) \| 0.093546 / 0.075469 (0.018077) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.787916 / 1.841788 (-0.053872) \| 20.094828 / 8.074308 (12.020520) \| 22.902101 / 10.191392 (12.710709) \| 0.249315 / 0.680424 (-0.431109) \| 0.028058 / 0.534201 (-0.506143) \| 0.524960 / 0.579283 (-0.054323) \| 0.643881 / 0.434364 (0.209517) \| 0.621203 / 0.540337 (0.080866) \| 0.723337 / 1.386936 (-0.663599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"CML watermark\")\n" ]	2023-05-26T08:48:22Z	2023-06-02T06:06:38Z	2023-06-01T13:39:40Z	MEMBER	null	This PR uses `split_generator.split_info` as default value for `split_info` if any exception is raised while trying to get `split_generator.name` from `self.info.splits` (this may happen if there is any error in the metadata dataset_info splits). Please note that `split_info` is only used by the logger. Fix #5895 if passed `verification_mode="no_checks"`: ```python ds = load_dataset( "ArmelR/stack-exchange-instruction", data_dir="data/finetune", split="train", verification_mode="no_checks", revision="c609f1caade5cfbf3b9fe9cfa17d7cb000b457bd", ) ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5901/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5901/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5901.diff", "html_url": "https://github.com/huggingface/datasets/pull/5901", "merged_at": "2023-06-01T13:39:39Z", "patch_url": "https://github.com/huggingface/datasets/pull/5901.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5901" }	true
https://api.github.com/repos/huggingface/datasets/issues/5900	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5900/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5900/comments	https://api.github.com/repos/huggingface/datasets/issues/5900/events	https://github.com/huggingface/datasets/pull/5900	1,727,129,617	PR_kwDODunzps5RahTR	5,900	Fix minor typo in docs loading.mdx	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	[ "_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) \| 0.006763 / 0.011353 (-0.004589) \| 0.004548 / 0.011008 (-0.006460) \| 0.095631 / 0.038508 (0.057123) \| 0.034046 / 0.023109 (0.010936) \| 0.298064 / 0.275898 (0.022166) \| 0.330391 / 0.323480 (0.006911) \| 0.006058 / 0.007986 (-0.001928) \| 0.004163 / 0.004328 (-0.000165) \| 0.073260 / 0.004250 (0.069010) \| 0.048885 / 0.037052 (0.011832) \| 0.304651 / 0.258489 (0.046162) \| 0.345882 / 0.293841 (0.052042) \| 0.028061 / 0.128546 (-0.100485) \| 0.008823 / 0.075646 (-0.066823) \| 0.325620 / 0.419271 (-0.093651) \| 0.064480 / 0.043533 (0.020948) \| 0.303373 / 0.255139 (0.048234) \| 0.321672 / 0.283200 (0.038472) \| 0.116353 / 0.141683 (-0.025330) \| 1.442327 / 1.452155 (-0.009827) \| 1.567553 / 1.492716 (0.074837) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213042 / 0.018006 (0.195035) \| 0.457646 / 0.000490 (0.457156) \| 0.003989 / 0.000200 (0.003789) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028068 / 0.037411 (-0.009344) \| 0.114791 / 0.014526 (0.100265) \| 0.120870 / 0.176557 (-0.055686) \| 0.183006 / 0.737135 (-0.554130) \| 0.126772 / 0.296338 (-0.169567) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406438 / 0.215209 (0.191229) \| 4.041890 / 2.077655 (1.964235) \| 1.839967 / 1.504120 (0.335847) \| 1.646857 / 1.541195 (0.105662) \| 1.729372 / 1.468490 (0.260882) \| 0.525540 / 4.584777 (-4.059237) \| 3.809996 / 3.745712 (0.064284) \| 1.842598 / 5.269862 (-3.427263) \| 1.062815 / 4.565676 (-3.502862) \| 0.065301 / 0.424275 (-0.358974) \| 0.012027 / 0.007607 (0.004420) \| 0.505459 / 0.226044 (0.279415) \| 5.051177 / 2.268929 (2.782248) \| 2.354368 / 55.444624 (-53.090256) \| 2.035482 / 6.876477 (-4.840995) \| 2.120493 / 2.142072 (-0.021579) \| 0.642233 / 4.805227 (-4.162994) \| 0.141690 / 6.500664 (-6.358974) \| 0.063933 / 0.075469 (-0.011536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.186261 / 1.841788 (-0.655527) \| 14.919653 / 8.074308 (6.845345) \| 14.534003 / 10.191392 (4.342611) \| 0.183165 / 0.680424 (-0.497259) \| 0.017581 / 0.534201 (-0.516620) \| 0.397284 / 0.579283 (-0.181999) \| 0.431363 / 0.434364 (-0.003001) \| 0.510774 / 0.540337 (-0.029564) \| 0.614421 / 1.386936 (-0.772516) \|\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) \| 0.006682 / 0.011353 (-0.004671) \| 0.004558 / 0.011008 (-0.006450) \| 0.076272 / 0.038508 (0.037764) \| 0.034285 / 0.023109 (0.011176) \| 0.395594 / 0.275898 (0.119696) \| 0.402702 / 0.323480 (0.079222) \| 0.006093 / 0.007986 (-0.001893) \| 0.005538 / 0.004328 (0.001209) \| 0.075797 / 0.004250 (0.071547) \| 0.051638 / 0.037052 (0.014585) \| 0.396071 / 0.258489 (0.137582) \| 0.409282 / 0.293841 (0.115441) \| 0.028193 / 0.128546 (-0.100354) \| 0.008827 / 0.075646 (-0.066819) \| 0.083182 / 0.419271 (-0.336089) \| 0.047605 / 0.043533 (0.004072) \| 0.391148 / 0.255139 (0.136009) \| 0.386784 / 0.283200 (0.103584) \| 0.115303 / 0.141683 (-0.026380) \| 1.463666 / 1.452155 (0.011512) \| 1.566147 / 1.492716 (0.073431) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213846 / 0.018006 (0.195839) \| 0.454769 / 0.000490 (0.454279) \| 0.004767 / 0.000200 (0.004567) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030369 / 0.037411 (-0.007042) \| 0.115585 / 0.014526 (0.101059) \| 0.125181 / 0.176557 (-0.051376) \| 0.179247 / 0.737135 (-0.557888) \| 0.129336 / 0.296338 (-0.167003) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446040 / 0.215209 (0.230831) \| 4.462644 / 2.077655 (2.384989) \| 2.254511 / 1.504120 (0.750392) \| 2.062679 / 1.541195 (0.521484) \| 2.180766 / 1.468490 (0.712276) \| 0.530928 / 4.584777 (-4.053849) \| 3.781392 / 3.745712 (0.035680) \| 3.522539 / 5.269862 (-1.747322) \| 1.506960 / 4.565676 (-3.058717) \| 0.067101 / 0.424275 (-0.357174) \| 0.012011 / 0.007607 (0.004404) \| 0.546407 / 0.226044 (0.320362) \| 5.429894 / 2.268929 (3.160965) \| 2.702244 / 55.444624 (-52.742381) \| 2.367559 / 6.876477 (-4.508917) \| 2.556032 / 2.142072 (0.413960) \| 0.639690 / 4.805227 (-4.165538) \| 0.144538 / 6.500664 (-6.356126) \| 0.067822 / 0.075469 (-0.007647) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284977 / 1.841788 (-0.556811) \| 15.546489 / 8.074308 (7.472181) \| 14.747519 / 10.191392 (4.556127) \| 0.160044 / 0.680424 (-0.520380) \| 0.017746 / 0.534201 (-0.516454) \| 0.390140 / 0.579283 (-0.189143) \| 0.420342 / 0.434364 (-0.014021) \| 0.459788 / 0.540337 (-0.080549) \| 0.556360 / 1.386936 (-0.830576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d646afbac7ea3dc0996fa2cb6ffd8a98e158e742 \"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) \| 0.006493 / 0.011353 (-0.004860) \| 0.004532 / 0.011008 (-0.006476) \| 0.096509 / 0.038508 (0.058001) \| 0.033084 / 0.023109 (0.009974) \| 0.297802 / 0.275898 (0.021904) \| 0.345880 / 0.323480 (0.022400) \| 0.005461 / 0.007986 (-0.002525) \| 0.005282 / 0.004328 (0.000954) \| 0.073719 / 0.004250 (0.069469) \| 0.045035 / 0.037052 (0.007983) \| 0.295504 / 0.258489 (0.037015) \| 0.345400 / 0.293841 (0.051559) \| 0.027880 / 0.128546 (-0.100666) \| 0.008804 / 0.075646 (-0.066842) \| 0.328017 / 0.419271 (-0.091255) \| 0.050169 / 0.043533 (0.006637) \| 0.299642 / 0.255139 (0.044503) \| 0.313573 / 0.283200 (0.030374) \| 0.103359 / 0.141683 (-0.038323) \| 1.482145 / 1.452155 (0.029990) \| 1.554584 / 1.492716 (0.061867) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212860 / 0.018006 (0.194853) \| 0.444823 / 0.000490 (0.444334) \| 0.003014 / 0.000200 (0.002815) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026906 / 0.037411 (-0.010506) \| 0.108056 / 0.014526 (0.093530) \| 0.118721 / 0.176557 (-0.057835) \| 0.176646 / 0.737135 (-0.560489) \| 0.123285 / 0.296338 (-0.173053) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430157 / 0.215209 (0.214948) \| 4.279362 / 2.077655 (2.201707) \| 1.999732 / 1.504120 (0.495612) \| 1.803787 / 1.541195 (0.262592) \| 1.868322 / 1.468490 (0.399832) \| 0.529314 / 4.584777 (-4.055463) \| 3.785101 / 3.745712 (0.039389) \| 2.812608 / 5.269862 (-2.457254) \| 1.373460 / 4.565676 (-3.192216) \| 0.066208 / 0.424275 (-0.358067) \| 0.012173 / 0.007607 (0.004566) \| 0.528716 / 0.226044 (0.302672) \| 5.295003 / 2.268929 (3.026074) \| 2.450188 / 55.444624 (-52.994437) \| 2.114560 / 6.876477 (-4.761917) \| 2.268468 / 2.142072 (0.126395) \| 0.651706 / 4.805227 (-4.153521) \| 0.142185 / 6.500664 (-6.358479) \| 0.064862 / 0.075469 (-0.010607) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.184933 / 1.841788 (-0.656854) \| 14.503903 / 8.074308 (6.429595) \| 13.928965 / 10.191392 (3.737573) \| 0.156788 / 0.680424 (-0.523636) \| 0.017320 / 0.534201 (-0.516881) \| 0.391366 / 0.579283 (-0.187918) \| 0.416261 / 0.434364 (-0.018103) \| 0.461951 / 0.540337 (-0.078387) \| 0.553496 / 1.386936 (-0.833440) \|\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) \| 0.006623 / 0.011353 (-0.004730) \| 0.004617 / 0.011008 (-0.006392) \| 0.075579 / 0.038508 (0.037071) \| 0.033863 / 0.023109 (0.010754) \| 0.357097 / 0.275898 (0.081199) \| 0.396177 / 0.323480 (0.072697) \| 0.005712 / 0.007986 (-0.002274) \| 0.004232 / 0.004328 (-0.000097) \| 0.074669 / 0.004250 (0.070418) \| 0.048253 / 0.037052 (0.011201) \| 0.362453 / 0.258489 (0.103964) \| 0.405423 / 0.293841 (0.111582) \| 0.028709 / 0.128546 (-0.099837) \| 0.008884 / 0.075646 (-0.066763) \| 0.083042 / 0.419271 (-0.336230) \| 0.048074 / 0.043533 (0.004541) \| 0.355314 / 0.255139 (0.100175) \| 0.372536 / 0.283200 (0.089336) \| 0.111548 / 0.141683 (-0.030135) \| 1.466353 / 1.452155 (0.014198) \| 1.555077 / 1.492716 (0.062361) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217016 / 0.018006 (0.199010) \| 0.450145 / 0.000490 (0.449655) \| 0.001910 / 0.000200 (0.001711) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029787 / 0.037411 (-0.007624) \| 0.115282 / 0.014526 (0.100756) \| 0.121962 / 0.176557 (-0.054595) \| 0.173424 / 0.737135 (-0.563711) \| 0.127519 / 0.296338 (-0.168819) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438211 / 0.215209 (0.223002) \| 4.346352 / 2.077655 (2.268697) \| 2.140197 / 1.504120 (0.636077) \| 1.957890 / 1.541195 (0.416696) \| 2.044300 / 1.468490 (0.575810) \| 0.527958 / 4.584777 (-4.056819) \| 3.805079 / 3.745712 (0.059367) \| 2.601763 / 5.269862 (-2.668098) \| 1.359469 / 4.565676 (-3.206208) \| 0.065358 / 0.424275 (-0.358917) \| 0.011571 / 0.007607 (0.003964) \| 0.538513 / 0.226044 (0.312469) \| 5.363508 / 2.268929 (3.094580) \| 2.640495 / 55.444624 (-52.804129) \| 2.335930 / 6.876477 (-4.540547) \| 2.407782 / 2.142072 (0.265710) \| 0.641637 / 4.805227 (-4.163590) \| 0.142196 / 6.500664 (-6.358468) \| 0.065041 / 0.075469 (-0.010428) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296031 / 1.841788 (-0.545757) \| 14.950424 / 8.074308 (6.876115) \| 14.371304 / 10.191392 (4.179912) \| 0.148157 / 0.680424 (-0.532267) \| 0.017506 / 0.534201 (-0.516695) \| 0.392037 / 0.579283 (-0.187246) \| 0.423238 / 0.434364 (-0.011126) \| 0.464608 / 0.540337 (-0.075730) \| 0.563876 / 1.386936 (-0.823060) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04b1d0371408beb0c7bc587a69c382bd8d0bec36 \"CML watermark\")\n" ]	2023-05-26T08:10:54Z	2023-05-26T09:34:15Z	2023-05-26T09:25:12Z	MEMBER	null	Minor fix.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5900/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5900/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5900.diff", "html_url": "https://github.com/huggingface/datasets/pull/5900", "merged_at": "2023-05-26T09:25:12Z", "patch_url": "https://github.com/huggingface/datasets/pull/5900.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5900" }	true
https://api.github.com/repos/huggingface/datasets/issues/5899	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5899/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5899/comments	https://api.github.com/repos/huggingface/datasets/issues/5899/events	https://github.com/huggingface/datasets/pull/5899	1,726,279,011	PR_kwDODunzps5RXods	5,899	canonicalize data dir in config ID hash	{ "avatar_url": "https://avatars.githubusercontent.com/u/5044802?v=4", "events_url": "https://api.github.com/users/kylrth/events{/privacy}", "followers_url": "https://api.github.com/users/kylrth/followers", "following_url": "https://api.github.com/users/kylrth/following{/other_user}", "gists_url": "https://api.github.com/users/kylrth/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kylrth", "id": 5044802, "login": "kylrth", "node_id": "MDQ6VXNlcjUwNDQ4MDI=", "organizations_url": "https://api.github.com/users/kylrth/orgs", "received_events_url": "https://api.github.com/users/kylrth/received_events", "repos_url": "https://api.github.com/users/kylrth/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kylrth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kylrth/subscriptions", "type": "User", "url": "https://api.github.com/users/kylrth" }	[]	closed	false	null	[]	null	[ "_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) \| 0.009137 / 0.011353 (-0.002216) \| 0.006119 / 0.011008 (-0.004889) \| 0.136530 / 0.038508 (0.098022) \| 0.038434 / 0.023109 (0.015325) \| 0.427900 / 0.275898 (0.152002) \| 0.449757 / 0.323480 (0.126277) \| 0.007673 / 0.007986 (-0.000313) \| 0.007147 / 0.004328 (0.002818) \| 0.108029 / 0.004250 (0.103778) \| 0.055072 / 0.037052 (0.018020) \| 0.439245 / 0.258489 (0.180756) \| 0.477285 / 0.293841 (0.183444) \| 0.044838 / 0.128546 (-0.083708) \| 0.020814 / 0.075646 (-0.054832) \| 0.436098 / 0.419271 (0.016826) \| 0.067459 / 0.043533 (0.023926) \| 0.427470 / 0.255139 (0.172331) \| 0.443260 / 0.283200 (0.160060) \| 0.125466 / 0.141683 (-0.016216) \| 1.996756 / 1.452155 (0.544601) \| 2.100679 / 1.492716 (0.607962) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278407 / 0.018006 (0.260401) \| 0.625855 / 0.000490 (0.625365) \| 0.005544 / 0.000200 (0.005344) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033495 / 0.037411 (-0.003916) \| 0.134718 / 0.014526 (0.120192) \| 0.150151 / 0.176557 (-0.026406) \| 0.221385 / 0.737135 (-0.515751) \| 0.150932 / 0.296338 (-0.145406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668845 / 0.215209 (0.453636) \| 6.678436 / 2.077655 (4.600781) \| 2.714074 / 1.504120 (1.209954) \| 2.275784 / 1.541195 (0.734589) \| 2.332852 / 1.468490 (0.864361) \| 1.014877 / 4.584777 (-3.569900) \| 6.086455 / 3.745712 (2.340743) \| 2.990029 / 5.269862 (-2.279832) \| 1.862236 / 4.565676 (-2.703441) \| 0.122179 / 0.424275 (-0.302096) \| 0.015706 / 0.007607 (0.008099) \| 0.873473 / 0.226044 (0.647429) \| 8.580109 / 2.268929 (6.311180) \| 3.458360 / 55.444624 (-51.986264) \| 2.738801 / 6.876477 (-4.137676) \| 2.918428 / 2.142072 (0.776356) \| 1.224910 / 4.805227 (-3.580317) \| 0.243006 / 6.500664 (-6.257658) \| 0.087121 / 0.075469 (0.011652) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.757802 / 1.841788 (-0.083986) \| 19.447999 / 8.074308 (11.373691) \| 24.518157 / 10.191392 (14.326765) \| 0.245013 / 0.680424 (-0.435411) \| 0.032290 / 0.534201 (-0.501911) \| 0.542043 / 0.579283 (-0.037240) \| 0.708154 / 0.434364 (0.273790) \| 0.660584 / 0.540337 (0.120247) \| 0.794868 / 1.386936 (-0.592068) \|\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) \| 0.009496 / 0.011353 (-0.001857) \| 0.005842 / 0.011008 (-0.005166) \| 0.112813 / 0.038508 (0.074305) \| 0.039120 / 0.023109 (0.016011) \| 0.489717 / 0.275898 (0.213819) \| 0.532586 / 0.323480 (0.209107) \| 0.007681 / 0.007986 (-0.000304) \| 0.005337 / 0.004328 (0.001009) \| 0.107244 / 0.004250 (0.102994) \| 0.056847 / 0.037052 (0.019794) \| 0.499447 / 0.258489 (0.240958) \| 0.548995 / 0.293841 (0.255154) \| 0.058047 / 0.128546 (-0.070499) \| 0.015468 / 0.075646 (-0.060179) \| 0.124600 / 0.419271 (-0.294671) \| 0.060940 / 0.043533 (0.017407) \| 0.488370 / 0.255139 (0.233231) \| 0.518540 / 0.283200 (0.235341) \| 0.124147 / 0.141683 (-0.017536) \| 1.902922 / 1.452155 (0.450767) \| 2.033519 / 1.492716 (0.540803) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319527 / 0.018006 (0.301521) \| 0.629641 / 0.000490 (0.629152) \| 0.000721 / 0.000200 (0.000521) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033150 / 0.037411 (-0.004262) \| 0.134250 / 0.014526 (0.119724) \| 0.161273 / 0.176557 (-0.015283) \| 0.211471 / 0.737135 (-0.525664) \| 0.155326 / 0.296338 (-0.141012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.705244 / 0.215209 (0.490035) \| 7.043040 / 2.077655 (4.965386) \| 3.308948 / 1.504120 (1.804828) \| 2.885050 / 1.541195 (1.343855) \| 2.810260 / 1.468490 (1.341770) \| 1.027095 / 4.584777 (-3.557682) \| 6.111398 / 3.745712 (2.365686) \| 5.385545 / 5.269862 (0.115684) \| 2.521668 / 4.565676 (-2.044009) \| 0.122419 / 0.424275 (-0.301856) \| 0.016376 / 0.007607 (0.008768) \| 0.830856 / 0.226044 (0.604811) \| 8.952199 / 2.268929 (6.683271) \| 4.207875 / 55.444624 (-51.236749) \| 3.346624 / 6.876477 (-3.529853) \| 3.395316 / 2.142072 (1.253244) \| 1.351816 / 4.805227 (-3.453411) \| 0.303056 / 6.500664 (-6.197608) \| 0.098713 / 0.075469 (0.023244) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.841903 / 1.841788 (0.000116) \| 20.472125 / 8.074308 (12.397817) \| 23.433200 / 10.191392 (13.241808) \| 0.242599 / 0.680424 (-0.437825) \| 0.030701 / 0.534201 (-0.503500) \| 0.541614 / 0.579283 (-0.037669) \| 0.657827 / 0.434364 (0.223463) \| 0.652448 / 0.540337 (0.112111) \| 0.773743 / 1.386936 (-0.613193) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ee418831aba68d0be93227bce8b3f42ef8980f \"CML watermark\")\n" ]	2023-05-25T18:17:10Z	2023-06-02T16:02:15Z	2023-06-02T15:52:04Z	CONTRIBUTOR	null	fixes #5871 The second commit is optional but improves readability.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5899/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5899/timeline	null	null	false	{ "diff_url": 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https://api.github.com/repos/huggingface/datasets/issues/5898	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5898/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5898/comments	https://api.github.com/repos/huggingface/datasets/issues/5898/events	https://github.com/huggingface/datasets/issues/5898	1,726,190,481	I_kwDODunzps5m45OR	5,898	Loading The flores data set for specific language	{ "avatar_url": "https://avatars.githubusercontent.com/u/36159918?v=4", "events_url": "https://api.github.com/users/106AbdulBasit/events{/privacy}", "followers_url": "https://api.github.com/users/106AbdulBasit/followers", "following_url": "https://api.github.com/users/106AbdulBasit/following{/other_user}", "gists_url": "https://api.github.com/users/106AbdulBasit/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/106AbdulBasit", "id": 36159918, "login": "106AbdulBasit", "node_id": "MDQ6VXNlcjM2MTU5OTE4", "organizations_url": "https://api.github.com/users/106AbdulBasit/orgs", "received_events_url": "https://api.github.com/users/106AbdulBasit/received_events", "repos_url": "https://api.github.com/users/106AbdulBasit/repos", "site_admin": false, "starred_url": "https://api.github.com/users/106AbdulBasit/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/106AbdulBasit/subscriptions", "type": "User", "url": "https://api.github.com/users/106AbdulBasit" }	[]	closed	false	null	[]	null	[ "got that the syntax is like this\r\n\r\ndataset = load_dataset(\"facebook/flores\", \"ace_Arab\")" ]	2023-05-25T17:08:55Z	2023-05-25T17:21:38Z	2023-05-25T17:21:37Z	NONE	null	### Describe the bug I am trying to load the Flores data set the code which is given is ``` from datasets import load_dataset dataset = load_dataset("facebook/flores") ``` This gives the error of config name ""ValueError: Config name is missing" Now if I add some config it gives me the some error "HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'facebook/flores, 'ace_Arab''. " How I can load the data of the specific language ? Couldn't find any tutorial any one can help me out? ### Steps to reproduce the bug step one load the data set `from datasets import load_dataset dataset = load_dataset("facebook/flores")` it gives the error of config once config is given it gives the error of "HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'facebook/flores, 'ace_Arab''. " ### Expected behavior Data set should be loaded but I am receiving error ### Environment info Datasets , python ,	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5898/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5898/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5897	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5897/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5897/comments	https://api.github.com/repos/huggingface/datasets/issues/5897/events	https://github.com/huggingface/datasets/pull/5897	1,726,135,494	PR_kwDODunzps5RXJaY	5,897	Fix `FixedSizeListArray` casting	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	[ "<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) \| 0.006213 / 0.011353 (-0.005140) \| 0.004230 / 0.011008 (-0.006778) \| 0.098014 / 0.038508 (0.059506) \| 0.028659 / 0.023109 (0.005550) \| 0.303272 / 0.275898 (0.027374) \| 0.337186 / 0.323480 (0.013706) \| 0.005126 / 0.007986 (-0.002860) \| 0.003563 / 0.004328 (-0.000765) \| 0.075295 / 0.004250 (0.071045) \| 0.036836 / 0.037052 (-0.000216) \| 0.309612 / 0.258489 (0.051123) \| 0.346484 / 0.293841 (0.052643) \| 0.025714 / 0.128546 (-0.102832) \| 0.008562 / 0.075646 (-0.067085) \| 0.323475 / 0.419271 (-0.095796) \| 0.044072 / 0.043533 (0.000539) \| 0.308261 / 0.255139 (0.053122) \| 0.330903 / 0.283200 (0.047703) \| 0.091805 / 0.141683 (-0.049878) \| 1.517011 / 1.452155 (0.064856) \| 1.570815 / 1.492716 (0.078099) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211265 / 0.018006 (0.193259) \| 0.438860 / 0.000490 (0.438370) \| 0.001127 / 0.000200 (0.000927) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023337 / 0.037411 (-0.014074) \| 0.096243 / 0.014526 (0.081717) \| 0.103529 / 0.176557 (-0.073028) \| 0.161171 / 0.737135 (-0.575964) \| 0.105904 / 0.296338 (-0.190435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417042 / 0.215209 (0.201833) \| 4.155067 / 2.077655 (2.077412) \| 1.879657 / 1.504120 (0.375537) \| 1.669341 / 1.541195 (0.128146) \| 1.717623 / 1.468490 (0.249133) \| 0.556246 / 4.584777 (-4.028531) \| 3.484535 / 3.745712 (-0.261177) \| 1.728845 / 5.269862 (-3.541017) \| 0.997477 / 4.565676 (-3.568199) \| 0.068355 / 0.424275 (-0.355920) \| 0.012445 / 0.007607 (0.004837) \| 0.519023 / 0.226044 (0.292978) \| 5.173506 / 2.268929 (2.904577) \| 2.332435 / 55.444624 (-53.112190) \| 1.986348 / 6.876477 (-4.890129) \| 2.076885 / 2.142072 (-0.065187) \| 0.656738 / 4.805227 (-4.148489) \| 0.135308 / 6.500664 (-6.365356) \| 0.065486 / 0.075469 (-0.009984) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.208874 / 1.841788 (-0.632914) \| 13.994200 / 8.074308 (5.919892) \| 14.160978 / 10.191392 (3.969586) \| 0.146009 / 0.680424 (-0.534415) \| 0.016573 / 0.534201 (-0.517628) \| 0.356082 / 0.579283 (-0.223202) \| 0.387766 / 0.434364 (-0.046598) \| 0.419130 / 0.540337 (-0.121208) \| 0.508634 / 1.386936 (-0.878302) \|\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) \| 0.006238 / 0.011353 (-0.005115) \| 0.004221 / 0.011008 (-0.006788) \| 0.075155 / 0.038508 (0.036646) \| 0.028491 / 0.023109 (0.005382) \| 0.355606 / 0.275898 (0.079708) \| 0.388986 / 0.323480 (0.065506) \| 0.005941 / 0.007986 (-0.002044) \| 0.003510 / 0.004328 (-0.000819) \| 0.074905 / 0.004250 (0.070655) \| 0.039111 / 0.037052 (0.002059) \| 0.358492 / 0.258489 (0.100003) \| 0.398763 / 0.293841 (0.104922) \| 0.025535 / 0.128546 (-0.103012) \| 0.008580 / 0.075646 (-0.067067) \| 0.080461 / 0.419271 (-0.338811) \| 0.041381 / 0.043533 (-0.002152) \| 0.355498 / 0.255139 (0.100359) \| 0.379163 / 0.283200 (0.095963) \| 0.096450 / 0.141683 (-0.045233) \| 1.503248 / 1.452155 (0.051093) \| 1.595616 / 1.492716 (0.102900) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238065 / 0.018006 (0.220058) \| 0.422800 / 0.000490 (0.422311) \| 0.002274 / 0.000200 (0.002074) \| 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) \| 0.025746 / 0.037411 (-0.011665) \| 0.103319 / 0.014526 (0.088793) \| 0.112155 / 0.176557 (-0.064401) \| 0.163034 / 0.737135 (-0.574101) \| 0.113377 / 0.296338 (-0.182962) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440522 / 0.215209 (0.225313) \| 4.398123 / 2.077655 (2.320468) \| 2.143538 / 1.504120 (0.639418) \| 1.946084 / 1.541195 (0.404890) \| 1.996556 / 1.468490 (0.528066) \| 0.550108 / 4.584777 (-4.034669) \| 3.455774 / 3.745712 (-0.289938) \| 2.862474 / 5.269862 (-2.407387) \| 1.213446 / 4.565676 (-3.352230) \| 0.067987 / 0.424275 (-0.356288) \| 0.012413 / 0.007607 (0.004806) \| 0.543990 / 0.226044 (0.317945) \| 5.454807 / 2.268929 (3.185879) \| 2.669195 / 55.444624 (-52.775429) \| 2.332948 / 6.876477 (-4.543528) \| 2.383870 / 2.142072 (0.241797) \| 0.652017 / 4.805227 (-4.153210) \| 0.135508 / 6.500664 (-6.365156) \| 0.068238 / 0.075469 (-0.007231) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322669 / 1.841788 (-0.519118) \| 14.368136 / 8.074308 (6.293828) \| 14.167431 / 10.191392 (3.976039) \| 0.159371 / 0.680424 (-0.521052) \| 0.016638 / 0.534201 (-0.517563) \| 0.357106 / 0.579283 (-0.222177) \| 0.392491 / 0.434364 (-0.041873) \| 0.419458 / 0.540337 (-0.120880) \| 0.504662 / 1.386936 (-0.882274) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf764819ba6754cb7edf15899db517be0548676f \"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) \| 0.006296 / 0.011353 (-0.005057) \| 0.004185 / 0.011008 (-0.006823) \| 0.096170 / 0.038508 (0.057662) \| 0.029212 / 0.023109 (0.006102) \| 0.315356 / 0.275898 (0.039458) \| 0.335214 / 0.323480 (0.011734) \| 0.005108 / 0.007986 (-0.002877) \| 0.003634 / 0.004328 (-0.000694) \| 0.074186 / 0.004250 (0.069936) \| 0.038716 / 0.037052 (0.001663) \| 0.311041 / 0.258489 (0.052551) \| 0.341202 / 0.293841 (0.047361) \| 0.025584 / 0.128546 (-0.102962) \| 0.008499 / 0.075646 (-0.067148) \| 0.318660 / 0.419271 (-0.100611) \| 0.043745 / 0.043533 (0.000212) \| 0.314824 / 0.255139 (0.059685) \| 0.328117 / 0.283200 (0.044917) \| 0.093425 / 0.141683 (-0.048258) \| 1.478732 / 1.452155 (0.026578) \| 1.531743 / 1.492716 (0.039027) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203484 / 0.018006 (0.185478) \| 0.416131 / 0.000490 (0.415641) \| 0.007352 / 0.000200 (0.007152) \| 0.000211 / 0.000054 (0.000156) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022908 / 0.037411 (-0.014503) \| 0.098641 / 0.014526 (0.084115) \| 0.103426 / 0.176557 (-0.073131) \| 0.161658 / 0.737135 (-0.575477) \| 0.106506 / 0.296338 (-0.189832) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430781 / 0.215209 (0.215572) \| 4.315677 / 2.077655 (2.238022) \| 2.022302 / 1.504120 (0.518182) \| 1.832043 / 1.541195 (0.290849) \| 1.789302 / 1.468490 (0.320812) \| 0.560484 / 4.584777 (-4.024293) \| 3.448204 / 3.745712 (-0.297508) \| 1.725016 / 5.269862 (-3.544846) \| 1.002649 / 4.565676 (-3.563027) \| 0.068480 / 0.424275 (-0.355795) \| 0.012617 / 0.007607 (0.005010) \| 0.532291 / 0.226044 (0.306246) \| 5.319352 / 2.268929 (3.050423) \| 2.520730 / 55.444624 (-52.923894) \| 2.213881 / 6.876477 (-4.662596) \| 2.352477 / 2.142072 (0.210404) \| 0.662516 / 4.805227 (-4.142711) \| 0.136481 / 6.500664 (-6.364183) \| 0.066597 / 0.075469 (-0.008872) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224537 / 1.841788 (-0.617251) \| 13.849920 / 8.074308 (5.775612) \| 14.026358 / 10.191392 (3.834966) \| 0.131018 / 0.680424 (-0.549405) \| 0.016756 / 0.534201 (-0.517445) \| 0.358091 / 0.579283 (-0.221192) \| 0.397709 / 0.434364 (-0.036655) \| 0.450024 / 0.540337 (-0.090314) \| 0.542609 / 1.386936 (-0.844327) \|\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) \| 0.006179 / 0.011353 (-0.005174) \| 0.004145 / 0.011008 (-0.006863) \| 0.077482 / 0.038508 (0.038974) \| 0.028005 / 0.023109 (0.004896) \| 0.400010 / 0.275898 (0.124112) \| 0.408206 / 0.323480 (0.084726) \| 0.005049 / 0.007986 (-0.002937) \| 0.003608 / 0.004328 (-0.000721) \| 0.076841 / 0.004250 (0.072590) \| 0.036714 / 0.037052 (-0.000338) \| 0.406020 / 0.258489 (0.147531) \| 0.412392 / 0.293841 (0.118551) \| 0.025626 / 0.128546 (-0.102920) \| 0.008560 / 0.075646 (-0.067087) \| 0.084088 / 0.419271 (-0.335183) \| 0.039707 / 0.043533 (-0.003826) \| 0.396909 / 0.255139 (0.141770) \| 0.403623 / 0.283200 (0.120424) \| 0.095137 / 0.141683 (-0.046546) \| 1.515670 / 1.452155 (0.063515) \| 1.568379 / 1.492716 (0.075662) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181802 / 0.018006 (0.163795) \| 0.408778 / 0.000490 (0.408289) \| 0.000393 / 0.000200 (0.000193) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025940 / 0.037411 (-0.011471) \| 0.099992 / 0.014526 (0.085466) \| 0.106280 / 0.176557 (-0.070276) \| 0.161729 / 0.737135 (-0.575406) \| 0.108625 / 0.296338 (-0.187713) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459802 / 0.215209 (0.244593) \| 4.603002 / 2.077655 (2.525347) \| 2.406851 / 1.504120 (0.902732) \| 2.265422 / 1.541195 (0.724227) \| 2.306305 / 1.468490 (0.837815) \| 0.553903 / 4.584777 (-4.030874) \| 3.482052 / 3.745712 (-0.263660) \| 2.969855 / 5.269862 (-2.300007) \| 1.309285 / 4.565676 (-3.256391) \| 0.068130 / 0.424275 (-0.356145) \| 0.012189 / 0.007607 (0.004582) \| 0.571299 / 0.226044 (0.345254) \| 5.711420 / 2.268929 (3.442492) \| 2.716748 / 55.444624 (-52.727876) \| 2.369869 / 6.876477 (-4.506608) \| 2.544240 / 2.142072 (0.402167) \| 0.659955 / 4.805227 (-4.145272) \| 0.136684 / 6.500664 (-6.363980) \| 0.068962 / 0.075469 (-0.006507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.297659 / 1.841788 (-0.544129) \| 14.012758 / 8.074308 (5.938449) \| 14.324644 / 10.191392 (4.133252) \| 0.144894 / 0.680424 (-0.535530) \| 0.016751 / 0.534201 (-0.517450) \| 0.361547 / 0.579283 (-0.217736) \| 0.396595 / 0.434364 (-0.037769) \| 0.422375 / 0.540337 (-0.117962) \| 0.508209 / 1.386936 (-0.878727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba5f81357b53099b1bedfbb277211dba3952257b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006303 / 0.011353 (-0.005050) \| 0.004043 / 0.011008 (-0.006965) \| 0.096239 / 0.038508 (0.057731) \| 0.029608 / 0.023109 (0.006498) \| 0.321058 / 0.275898 (0.045160) \| 0.367066 / 0.323480 (0.043587) \| 0.005236 / 0.007986 (-0.002749) \| 0.003342 / 0.004328 (-0.000987) \| 0.074407 / 0.004250 (0.070157) \| 0.038810 / 0.037052 (0.001757) \| 0.332597 / 0.258489 (0.074108) \| 0.363562 / 0.293841 (0.069721) \| 0.025460 / 0.128546 (-0.103086) \| 0.008426 / 0.075646 (-0.067221) \| 0.316998 / 0.419271 (-0.102273) \| 0.043621 / 0.043533 (0.000088) \| 0.338043 / 0.255139 (0.082904) \| 0.366441 / 0.283200 (0.083241) \| 0.092061 / 0.141683 (-0.049622) \| 1.461531 / 1.452155 (0.009376) \| 1.538047 / 1.492716 (0.045331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206796 / 0.018006 (0.188790) \| 0.517959 / 0.000490 (0.517469) \| 0.002745 / 0.000200 (0.002545) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022902 / 0.037411 (-0.014510) \| 0.097901 / 0.014526 (0.083375) \| 0.103664 / 0.176557 (-0.072893) \| 0.163516 / 0.737135 (-0.573619) \| 0.108561 / 0.296338 (-0.187778) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418964 / 0.215209 (0.203755) \| 4.159113 / 2.077655 (2.081458) \| 1.843946 / 1.504120 (0.339827) \| 1.641083 / 1.541195 (0.099888) \| 1.686848 / 1.468490 (0.218358) \| 0.554583 / 4.584777 (-4.030194) \| 3.409862 / 3.745712 (-0.335850) \| 2.647904 / 5.269862 (-2.621958) \| 1.355424 / 4.565676 (-3.210253) \| 0.068229 / 0.424275 (-0.356046) \| 0.012217 / 0.007607 (0.004610) \| 0.515895 / 0.226044 (0.289851) \| 5.144920 / 2.268929 (2.875991) \| 2.298046 / 55.444624 (-53.146579) \| 1.964735 / 6.876477 (-4.911741) \| 2.075580 / 2.142072 (-0.066492) \| 0.657104 / 4.805227 (-4.148123) \| 0.134759 / 6.500664 (-6.365905) \| 0.067545 / 0.075469 (-0.007924) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.233075 / 1.841788 (-0.608713) \| 13.896762 / 8.074308 (5.822454) \| 14.055143 / 10.191392 (3.863751) \| 0.145507 / 0.680424 (-0.534917) \| 0.016702 / 0.534201 (-0.517499) \| 0.365157 / 0.579283 (-0.214126) \| 0.385842 / 0.434364 (-0.048522) \| 0.459993 / 0.540337 (-0.080344) \| 0.547115 / 1.386936 (-0.839821) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006174 / 0.011353 (-0.005179) \| 0.004191 / 0.011008 (-0.006817) \| 0.078311 / 0.038508 (0.039803) \| 0.028038 / 0.023109 (0.004928) \| 0.360056 / 0.275898 (0.084158) \| 0.398081 / 0.323480 (0.074602) \| 0.005069 / 0.007986 (-0.002916) \| 0.003464 / 0.004328 (-0.000864) \| 0.077858 / 0.004250 (0.073608) \| 0.039420 / 0.037052 (0.002367) \| 0.361743 / 0.258489 (0.103254) \| 0.404829 / 0.293841 (0.110988) \| 0.025604 / 0.128546 (-0.102943) \| 0.008573 / 0.075646 (-0.067074) \| 0.084944 / 0.419271 (-0.334328) \| 0.042652 / 0.043533 (-0.000881) \| 0.368549 / 0.255139 (0.113410) \| 0.385682 / 0.283200 (0.102482) \| 0.099085 / 0.141683 (-0.042598) \| 1.495815 / 1.452155 (0.043661) \| 1.548168 / 1.492716 (0.055452) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193737 / 0.018006 (0.175730) \| 0.421871 / 0.000490 (0.421381) \| 0.002306 / 0.000200 (0.002106) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025928 / 0.037411 (-0.011483) \| 0.103410 / 0.014526 (0.088885) \| 0.107931 / 0.176557 (-0.068626) \| 0.157127 / 0.737135 (-0.580008) \| 0.111892 / 0.296338 (-0.184446) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477562 / 0.215209 (0.262353) \| 4.772711 / 2.077655 (2.695056) \| 2.458725 / 1.504120 (0.954605) \| 2.269871 / 1.541195 (0.728676) \| 2.365502 / 1.468490 (0.897012) \| 0.556182 / 4.584777 (-4.028595) \| 3.408016 / 3.745712 (-0.337697) \| 1.730639 / 5.269862 (-3.539222) \| 1.000973 / 4.565676 (-3.564704) \| 0.068293 / 0.424275 (-0.355982) \| 0.012119 / 0.007607 (0.004512) \| 0.581281 / 0.226044 (0.355236) \| 5.811930 / 2.268929 (3.543001) \| 2.890337 / 55.444624 (-52.554288) \| 2.592156 / 6.876477 (-4.284321) \| 2.687764 / 2.142072 (0.545691) \| 0.664282 / 4.805227 (-4.140946) \| 0.136029 / 6.500664 (-6.364635) \| 0.067493 / 0.075469 (-0.007976) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330723 / 1.841788 (-0.511064) \| 14.379172 / 8.074308 (6.304864) \| 14.153286 / 10.191392 (3.961894) \| 0.142942 / 0.680424 (-0.537482) \| 0.016698 / 0.534201 (-0.517503) \| 0.361044 / 0.579283 (-0.218239) \| 0.393174 / 0.434364 (-0.041190) \| 0.423107 / 0.540337 (-0.117231) \| 0.514299 / 1.386936 (-0.872637) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb02285358ab4be6386e0a2aae40d267ff561fc \"CML watermark\")\n" ]	2023-05-25T16:26:33Z	2023-05-26T12:22:04Z	2023-05-26T11:57:16Z	CONTRIBUTOR	null	Fix cast on sliced `FixedSizeListArray`s. Fix #5866	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5897/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5897/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5897.diff", "html_url": "https://github.com/huggingface/datasets/pull/5897", "merged_at": "2023-05-26T11:57:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/5897.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5897" }	true
https://api.github.com/repos/huggingface/datasets/issues/5896	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5896/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5896/comments	https://api.github.com/repos/huggingface/datasets/issues/5896/events	https://github.com/huggingface/datasets/issues/5896	1,726,022,500	I_kwDODunzps5m4QNk	5,896	HuggingFace does not cache downloaded files aggressively/early enough	{ "avatar_url": "https://avatars.githubusercontent.com/u/2124157?v=4", "events_url": "https://api.github.com/users/geajack/events{/privacy}", "followers_url": "https://api.github.com/users/geajack/followers", "following_url": "https://api.github.com/users/geajack/following{/other_user}", "gists_url": "https://api.github.com/users/geajack/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/geajack", "id": 2124157, "login": "geajack", "node_id": "MDQ6VXNlcjIxMjQxNTc=", "organizations_url": "https://api.github.com/users/geajack/orgs", "received_events_url": "https://api.github.com/users/geajack/received_events", "repos_url": "https://api.github.com/users/geajack/repos", "site_admin": false, "starred_url": "https://api.github.com/users/geajack/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/geajack/subscriptions", "type": "User", "url": "https://api.github.com/users/geajack" }	[]	open	false	null	[]	null	[]	2023-05-25T15:14:36Z	2023-05-25T15:14:36Z	null	NONE	null	### Describe the bug I wrote the following script: ``` import datasets dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]") ``` I ran it and spent 90 minutes downloading a 20GB file. Then I saw: ``` Downloading: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 20.3G/20.3G [1:30:29<00:00, 3.73MB/s] Traceback (most recent call last): File "/home/jack/Code/Projects/Transformers/Codebase/main.py", line 5, in <module> dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]") File "/home/jack/.local/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 883, in download_and_prepare self._save_info() File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 2037, in _save_info import apache_beam as beam ModuleNotFoundError: No module named 'apache_beam' ``` And the 20GB of data was seemingly instantly gone forever, because when I ran the script again, it had to do the download again. ### Steps to reproduce the bug See above ### Expected behavior See above ### Environment info datasets 2.10.1 Python 3.10	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5896/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5896/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5895	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5895/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5895/comments	https://api.github.com/repos/huggingface/datasets/issues/5895/events	https://github.com/huggingface/datasets/issues/5895	1,725,467,252	I_kwDODunzps5m2Ip0	5,895	The dir name and split strings are confused when loading ArmelR/stack-exchange-instruction dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/45357817?v=4", "events_url": "https://api.github.com/users/DongHande/events{/privacy}", "followers_url": "https://api.github.com/users/DongHande/followers", "following_url": "https://api.github.com/users/DongHande/following{/other_user}", "gists_url": "https://api.github.com/users/DongHande/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/DongHande", "id": 45357817, "login": "DongHande", "node_id": "MDQ6VXNlcjQ1MzU3ODE3", "organizations_url": "https://api.github.com/users/DongHande/orgs", "received_events_url": "https://api.github.com/users/DongHande/received_events", "repos_url": "https://api.github.com/users/DongHande/repos", "site_admin": false, "starred_url": "https://api.github.com/users/DongHande/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/DongHande/subscriptions", "type": "User", "url": "https://api.github.com/users/DongHande" }	[]	closed	false	null	[]	null	[ "Thanks for reporting, @DongHande.\r\n\r\nI think the issue is caused by the metadata in the dataset card: in the header of the `README.md`, they state that the dataset has 4 splits (\"finetune\", \"reward\", \"rl\", \"evaluation\"). \r\n```yaml\r\n splits:\r\n - name: finetune\r\n num_bytes: 6674567576\r\n num_examples: 3000000\r\n - name: reward\r\n num_bytes: 6674341521\r\n num_examples: 3000000\r\n - name: rl\r\n num_bytes: 6679279968\r\n num_examples: 3000000\r\n - name: evaluation\r\n num_bytes: 4022714493\r\n num_examples: 1807695\r\n```\r\n\r\n\r\nI guess the user wanted to define these as configs, instead of splits. This is not yet supported for no-script datasets, but will be soon supported. See:\r\n- #5331\r\n\r\nI think we should contact the dataset author to inform about the issue with the split names, as you already did: https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/discussions/1\r\nLet's continue the discussion there!", "Thank you! It has been fixed. " ]	2023-05-25T09:39:06Z	2023-05-29T02:32:12Z	2023-05-29T02:32:12Z	NONE	null	### Describe the bug When I load the ArmelR/stack-exchange-instruction dataset, I encounter a bug that may be raised by confusing the dir name string and the split string about the dataset. When I use the script "datasets.load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)", it fails. But it succeeds when I add the "streaming = True" parameter. The website of the dataset is https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/ . The traceback logs are as below: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 890, in download_and_prepare self._download_and_prepare( File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 985, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 1706, in _prepare_split split_info = self.info.splits[split_generator.name] File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/splits.py", line 530, in __getitem__ instructions = make_file_instructions( File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 112, in make_file_instructions name2filenames = { File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 113, in <dictcomp> info.name: filenames_for_dataset_split( File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 70, in filenames_for_dataset_split prefix = filename_prefix_for_split(dataset_name, split) File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 54, in filename_prefix_for_split if os.path.basename(name) != name: File "/home/xxx/miniconda3/envs/code/lib/python3.9/posixpath.py", line 142, in basename p = os.fspath(p) TypeError: expected str, bytes or os.PathLike object, not NoneType ### Steps to reproduce the bug 1. import datasets library function: ```from datasets import load_dataset``` 2. load dataset: ```ds=load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)``` ### Expected behavior The dataset can be loaded successfully without the streaming setting. ### Environment info Linux, python=3.9 datasets=2.12.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5895/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5895/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5894	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5894/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5894/comments	https://api.github.com/repos/huggingface/datasets/issues/5894/events	https://github.com/huggingface/datasets/pull/5894	1,724,774,910	PR_kwDODunzps5RSjot	5,894	Force overwrite existing filesystem protocol	{ "avatar_url": "https://avatars.githubusercontent.com/u/24520725?v=4", "events_url": "https://api.github.com/users/baskrahmer/events{/privacy}", "followers_url": "https://api.github.com/users/baskrahmer/followers", "following_url": "https://api.github.com/users/baskrahmer/following{/other_user}", "gists_url": "https://api.github.com/users/baskrahmer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/baskrahmer", "id": 24520725, "login": "baskrahmer", "node_id": "MDQ6VXNlcjI0NTIwNzI1", "organizations_url": "https://api.github.com/users/baskrahmer/orgs", "received_events_url": "https://api.github.com/users/baskrahmer/received_events", "repos_url": "https://api.github.com/users/baskrahmer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/baskrahmer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/baskrahmer/subscriptions", "type": "User", "url": "https://api.github.com/users/baskrahmer" }	[]	closed	false	null	[]	null	[ "_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) \| 0.009139 / 0.011353 (-0.002214) \| 0.005634 / 0.011008 (-0.005374) \| 0.129587 / 0.038508 (0.091079) \| 0.038298 / 0.023109 (0.015189) \| 0.428149 / 0.275898 (0.152251) \| 0.443744 / 0.323480 (0.120264) \| 0.007501 / 0.007986 (-0.000485) \| 0.005999 / 0.004328 (0.001671) \| 0.100796 / 0.004250 (0.096546) \| 0.053236 / 0.037052 (0.016184) \| 0.423868 / 0.258489 (0.165379) \| 0.460110 / 0.293841 (0.166269) \| 0.041255 / 0.128546 (-0.087291) \| 0.013790 / 0.075646 (-0.061856) \| 0.438398 / 0.419271 (0.019127) \| 0.063086 / 0.043533 (0.019553) \| 0.414826 / 0.255139 (0.159687) \| 0.460652 / 0.283200 (0.177453) \| 0.121223 / 0.141683 (-0.020460) \| 1.754430 / 1.452155 (0.302275) \| 1.900037 / 1.492716 (0.407320) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027222 / 0.018006 (0.009216) \| 0.617666 / 0.000490 (0.617176) \| 0.022443 / 0.000200 (0.022243) \| 0.000820 / 0.000054 (0.000766) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030397 / 0.037411 (-0.007014) \| 0.125732 / 0.014526 (0.111206) \| 0.149805 / 0.176557 (-0.026752) \| 0.234048 / 0.737135 (-0.503087) \| 0.143108 / 0.296338 (-0.153231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.631189 / 0.215209 (0.415980) \| 6.182871 / 2.077655 (4.105216) \| 2.635730 / 1.504120 (1.131610) \| 2.231429 / 1.541195 (0.690235) \| 2.438360 / 1.468490 (0.969870) \| 0.861170 / 4.584777 (-3.723607) \| 5.785984 / 3.745712 (2.040272) \| 2.758358 / 5.269862 (-2.511504) \| 1.678095 / 4.565676 (-2.887582) \| 0.105961 / 0.424275 (-0.318314) \| 0.013659 / 0.007607 (0.006052) \| 0.762943 / 0.226044 (0.536898) \| 7.774399 / 2.268929 (5.505471) \| 3.319027 / 55.444624 (-52.125598) \| 2.700248 / 6.876477 (-4.176229) \| 3.008581 / 2.142072 (0.866509) \| 1.122522 / 4.805227 (-3.682705) \| 0.214832 / 6.500664 (-6.285832) \| 0.085281 / 0.075469 (0.009811) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.647610 / 1.841788 (-0.194177) \| 18.178316 / 8.074308 (10.104008) \| 21.199177 / 10.191392 (11.007785) \| 0.247063 / 0.680424 (-0.433361) \| 0.030443 / 0.534201 (-0.503758) \| 0.512527 / 0.579283 (-0.066757) \| 0.640758 / 0.434364 (0.206394) \| 0.639986 / 0.540337 (0.099649) \| 0.760113 / 1.386936 (-0.626823) \|\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) \| 0.008293 / 0.011353 (-0.003060) \| 0.005360 / 0.011008 (-0.005648) \| 0.102932 / 0.038508 (0.064424) \| 0.037457 / 0.023109 (0.014347) \| 0.444114 / 0.275898 (0.168216) \| 0.512855 / 0.323480 (0.189375) \| 0.007030 / 0.007986 (-0.000956) \| 0.004954 / 0.004328 (0.000625) \| 0.095757 / 0.004250 (0.091507) \| 0.051239 / 0.037052 (0.014187) \| 0.471118 / 0.258489 (0.212629) \| 0.517764 / 0.293841 (0.223923) \| 0.041953 / 0.128546 (-0.086593) \| 0.013748 / 0.075646 (-0.061898) \| 0.118089 / 0.419271 (-0.301182) \| 0.060159 / 0.043533 (0.016626) \| 0.466011 / 0.255139 (0.210872) \| 0.489180 / 0.283200 (0.205980) \| 0.123250 / 0.141683 (-0.018433) \| 1.714738 / 1.452155 (0.262584) \| 1.838571 / 1.492716 (0.345855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267792 / 0.018006 (0.249785) \| 0.624313 / 0.000490 (0.623824) \| 0.007315 / 0.000200 (0.007115) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033751 / 0.037411 (-0.003661) \| 0.122819 / 0.014526 (0.108293) \| 0.148270 / 0.176557 (-0.028286) \| 0.198581 / 0.737135 (-0.538554) \| 0.144845 / 0.296338 (-0.151494) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620631 / 0.215209 (0.405422) \| 6.224665 / 2.077655 (4.147010) \| 2.856592 / 1.504120 (1.352473) \| 2.525089 / 1.541195 (0.983894) \| 2.600198 / 1.468490 (1.131708) \| 0.872038 / 4.584777 (-3.712739) \| 5.571650 / 3.745712 (1.825937) \| 5.907643 / 5.269862 (0.637782) \| 2.348770 / 4.565676 (-2.216906) \| 0.111665 / 0.424275 (-0.312610) \| 0.013886 / 0.007607 (0.006278) \| 0.762154 / 0.226044 (0.536109) \| 7.792686 / 2.268929 (5.523758) \| 3.601122 / 55.444624 (-51.843503) \| 2.939412 / 6.876477 (-3.937064) \| 2.973430 / 2.142072 (0.831358) \| 1.065016 / 4.805227 (-3.740211) \| 0.221701 / 6.500664 (-6.278963) \| 0.088157 / 0.075469 (0.012688) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.771061 / 1.841788 (-0.070727) \| 18.826926 / 8.074308 (10.752618) \| 21.283830 / 10.191392 (11.092438) \| 0.239233 / 0.680424 (-0.441191) \| 0.026159 / 0.534201 (-0.508042) \| 0.487074 / 0.579283 (-0.092209) \| 0.623241 / 0.434364 (0.188877) \| 0.600506 / 0.540337 (0.060169) \| 0.691271 / 1.386936 (-0.695665) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bbe2c3496498a6415765b517ac4bc600a02ad06 \"CML watermark\")\n" ]	2023-05-24T21:41:53Z	2023-05-25T06:52:08Z	2023-05-25T06:42:33Z	CONTRIBUTOR	null	Fix #5876	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5894/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5894/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5894.diff", "html_url": "https://github.com/huggingface/datasets/pull/5894", "merged_at": "2023-05-25T06:42:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/5894.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5894" }	true
https://api.github.com/repos/huggingface/datasets/issues/5893	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5893/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5893/comments	https://api.github.com/repos/huggingface/datasets/issues/5893/events	https://github.com/huggingface/datasets/pull/5893	1,722,519,056	PR_kwDODunzps5RK40K	5,893	Load cached dataset as iterable	{ "avatar_url": "https://avatars.githubusercontent.com/u/10278877?v=4", "events_url": "https://api.github.com/users/mariusz-jachimowicz-83/events{/privacy}", "followers_url": "https://api.github.com/users/mariusz-jachimowicz-83/followers", "following_url": "https://api.github.com/users/mariusz-jachimowicz-83/following{/other_user}", "gists_url": "https://api.github.com/users/mariusz-jachimowicz-83/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariusz-jachimowicz-83", "id": 10278877, "login": "mariusz-jachimowicz-83", "node_id": "MDQ6VXNlcjEwMjc4ODc3", "organizations_url": "https://api.github.com/users/mariusz-jachimowicz-83/orgs", "received_events_url": "https://api.github.com/users/mariusz-jachimowicz-83/received_events", "repos_url": "https://api.github.com/users/mariusz-jachimowicz-83/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariusz-jachimowicz-83/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariusz-jachimowicz-83/subscriptions", "type": "User", "url": "https://api.github.com/users/mariusz-jachimowicz-83" }	[]	closed	false	null	[]	null	[ "@lhoestq Could you please look into that and review?", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq I refactored the code. Could you please check is it what you requested?", "@lhoestq Thanks for a review. Excellent tips. All tips applied. ", "I think there is just PythonFormatter that needs to be imported in the test file and we should be good to merge", "@lhoestq that is weird. I have linter error when I do it.", "@lhoestq Now it should work properly.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006152 / 0.011353 (-0.005201) \| 0.004169 / 0.011008 (-0.006839) \| 0.097968 / 0.038508 (0.059460) \| 0.028325 / 0.023109 (0.005216) \| 0.308958 / 0.275898 (0.033060) \| 0.341832 / 0.323480 (0.018352) \| 0.005098 / 0.007986 (-0.002887) \| 0.004721 / 0.004328 (0.000393) \| 0.075067 / 0.004250 (0.070817) \| 0.040514 / 0.037052 (0.003462) \| 0.308355 / 0.258489 (0.049866) \| 0.351063 / 0.293841 (0.057222) \| 0.025261 / 0.128546 (-0.103285) \| 0.008483 / 0.075646 (-0.067163) \| 0.321219 / 0.419271 (-0.098052) \| 0.058258 / 0.043533 (0.014725) \| 0.312572 / 0.255139 (0.057433) \| 0.330667 / 0.283200 (0.047467) \| 0.091047 / 0.141683 (-0.050635) \| 1.536541 / 1.452155 (0.084387) \| 1.606566 / 1.492716 (0.113850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213234 / 0.018006 (0.195228) \| 0.494801 / 0.000490 (0.494311) \| 0.003764 / 0.000200 (0.003564) \| 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) \| 0.023653 / 0.037411 (-0.013758) \| 0.097176 / 0.014526 (0.082650) \| 0.102961 / 0.176557 (-0.073595) \| 0.164285 / 0.737135 (-0.572851) \| 0.107586 / 0.296338 (-0.188753) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421402 / 0.215209 (0.206193) \| 4.195828 / 2.077655 (2.118174) \| 1.884664 / 1.504120 (0.380544) \| 1.679750 / 1.541195 (0.138556) \| 1.719725 / 1.468490 (0.251235) \| 0.552290 / 4.584777 (-4.032486) \| 3.386337 / 3.745712 (-0.359375) \| 1.771527 / 5.269862 (-3.498334) \| 1.133327 / 4.565676 (-3.432349) \| 0.067911 / 0.424275 (-0.356364) \| 0.012572 / 0.007607 (0.004965) \| 0.518004 / 0.226044 (0.291960) \| 5.192381 / 2.268929 (2.923453) \| 2.316032 / 55.444624 (-53.128592) \| 1.993264 / 6.876477 (-4.883212) \| 2.071009 / 2.142072 (-0.071063) \| 0.655062 / 4.805227 (-4.150165) \| 0.135488 / 6.500664 (-6.365177) \| 0.067273 / 0.075469 (-0.008196) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.217731 / 1.841788 (-0.624056) \| 13.812927 / 8.074308 (5.738619) \| 13.137886 / 10.191392 (2.946494) \| 0.143102 / 0.680424 (-0.537322) \| 0.016884 / 0.534201 (-0.517317) \| 0.370106 / 0.579283 (-0.209178) \| 0.392349 / 0.434364 (-0.042015) \| 0.424501 / 0.540337 (-0.115837) \| 0.509830 / 1.386936 (-0.877106) \|\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) \| 0.006210 / 0.011353 (-0.005142) \| 0.004215 / 0.011008 (-0.006793) \| 0.076129 / 0.038508 (0.037621) \| 0.027825 / 0.023109 (0.004716) \| 0.403973 / 0.275898 (0.128075) \| 0.441089 / 0.323480 (0.117609) \| 0.005420 / 0.007986 (-0.002566) \| 0.004870 / 0.004328 (0.000542) \| 0.075558 / 0.004250 (0.071308) \| 0.039464 / 0.037052 (0.002411) \| 0.404329 / 0.258489 (0.145840) \| 0.447213 / 0.293841 (0.153372) \| 0.025877 / 0.128546 (-0.102669) \| 0.008660 / 0.075646 (-0.066987) \| 0.081849 / 0.419271 (-0.337422) \| 0.044551 / 0.043533 (0.001018) \| 0.379102 / 0.255139 (0.123963) \| 0.403104 / 0.283200 (0.119905) \| 0.094754 / 0.141683 (-0.046929) \| 1.460772 / 1.452155 (0.008617) \| 1.569531 / 1.492716 (0.076815) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183923 / 0.018006 (0.165917) \| 0.420708 / 0.000490 (0.420219) \| 0.002091 / 0.000200 (0.001891) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026180 / 0.037411 (-0.011231) \| 0.101529 / 0.014526 (0.087003) \| 0.108739 / 0.176557 (-0.067818) \| 0.160702 / 0.737135 (-0.576433) \| 0.111739 / 0.296338 (-0.184600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448671 / 0.215209 (0.233462) \| 4.469287 / 2.077655 (2.391632) \| 2.244335 / 1.504120 (0.740215) \| 2.107495 / 1.541195 (0.566301) \| 2.224763 / 1.468490 (0.756272) \| 0.554006 / 4.584777 (-4.030771) \| 3.390109 / 3.745712 (-0.355603) \| 1.744189 / 5.269862 (-3.525673) \| 1.008515 / 4.565676 (-3.557161) \| 0.067904 / 0.424275 (-0.356371) \| 0.012243 / 0.007607 (0.004636) \| 0.557635 / 0.226044 (0.331590) \| 5.610383 / 2.268929 (3.341454) \| 2.687326 / 55.444624 (-52.757298) \| 2.405262 / 6.876477 (-4.471214) \| 2.527300 / 2.142072 (0.385227) \| 0.662282 / 4.805227 (-4.142945) \| 0.136225 / 6.500664 (-6.364439) \| 0.068136 / 0.075469 (-0.007334) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.310791 / 1.841788 (-0.530997) \| 14.370381 / 8.074308 (6.296072) \| 14.122675 / 10.191392 (3.931283) \| 0.152302 / 0.680424 (-0.528122) \| 0.016624 / 0.534201 (-0.517577) \| 0.359395 / 0.579283 (-0.219888) \| 0.392131 / 0.434364 (-0.042233) \| 0.423796 / 0.540337 (-0.116542) \| 0.511387 / 1.386936 (-0.875549) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6a61a1af1502677a6f2333896a6ffeede9ca21b \"CML watermark\")\n" ]	2023-05-23T17:40:35Z	2023-06-01T11:58:24Z	2023-06-01T11:51:29Z	CONTRIBUTOR	null	To be used to train models it allows to load an IterableDataset from the cached Arrow file. See https://github.com/huggingface/datasets/issues/5481	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5893/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5893/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5893.diff", "html_url": "https://github.com/huggingface/datasets/pull/5893", "merged_at": "2023-06-01T11:51:29Z", "patch_url": "https://github.com/huggingface/datasets/pull/5893.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5893" }	true
https://api.github.com/repos/huggingface/datasets/issues/5892	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5892/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5892/comments	https://api.github.com/repos/huggingface/datasets/issues/5892/events	https://github.com/huggingface/datasets/issues/5892	1,722,503,824	I_kwDODunzps5mq1KQ	5,892	User access requests with manual review do not notify the dataset owner	{ "avatar_url": "https://avatars.githubusercontent.com/u/121934?v=4", "events_url": "https://api.github.com/users/leondz/events{/privacy}", "followers_url": "https://api.github.com/users/leondz/followers", "following_url": "https://api.github.com/users/leondz/following{/other_user}", "gists_url": "https://api.github.com/users/leondz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/leondz", "id": 121934, "login": "leondz", "node_id": "MDQ6VXNlcjEyMTkzNA==", "organizations_url": "https://api.github.com/users/leondz/orgs", "received_events_url": "https://api.github.com/users/leondz/received_events", "repos_url": "https://api.github.com/users/leondz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/leondz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/leondz/subscriptions", "type": "User", "url": "https://api.github.com/users/leondz" }	[]	open	false	null	[]	null	[ "cc @SBrandeis" ]	2023-05-23T17:27:46Z	2023-05-23T17:54:49Z	null	CONTRIBUTOR	null	### Describe the bug When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane. ### Steps to reproduce the bug 1. Enable a dataset's user access requests 2. Set to Manual Review 3. Ask another HF user to request access to the dataset 4. Dataset owner is not notified ### Expected behavior The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled. ### Environment info n/a	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5892/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5892/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5891	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5891/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5891/comments	https://api.github.com/repos/huggingface/datasets/issues/5891/events	https://github.com/huggingface/datasets/pull/5891	1,722,384,135	PR_kwDODunzps5RKchn	5,891	Make split slicing consisten with list slicing	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5891). 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) \| 0.006916 / 0.011353 (-0.004437) \| 0.004749 / 0.011008 (-0.006259) \| 0.096086 / 0.038508 (0.057578) \| 0.035448 / 0.023109 (0.012338) \| 0.299645 / 0.275898 (0.023747) \| 0.331279 / 0.323480 (0.007799) \| 0.006018 / 0.007986 (-0.001968) \| 0.004210 / 0.004328 (-0.000118) \| 0.072998 / 0.004250 (0.068747) \| 0.050082 / 0.037052 (0.013030) \| 0.297714 / 0.258489 (0.039225) \| 0.365523 / 0.293841 (0.071682) \| 0.028081 / 0.128546 (-0.100465) \| 0.009072 / 0.075646 (-0.066574) \| 0.327628 / 0.419271 (-0.091643) \| 0.051165 / 0.043533 (0.007633) \| 0.295091 / 0.255139 (0.039952) \| 0.320052 / 0.283200 (0.036852) \| 0.109841 / 0.141683 (-0.031842) \| 1.467867 / 1.452155 (0.015712) \| 1.572600 / 1.492716 (0.079884) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281490 / 0.018006 (0.263484) \| 0.499259 / 0.000490 (0.498770) \| 0.000691 / 0.000200 (0.000491) \| 0.000062 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027548 / 0.037411 (-0.009863) \| 0.106592 / 0.014526 (0.092066) \| 0.118654 / 0.176557 (-0.057902) \| 0.174313 / 0.737135 (-0.562822) \| 0.124491 / 0.296338 (-0.171848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399674 / 0.215209 (0.184465) \| 3.984092 / 2.077655 (1.906437) \| 1.790935 / 1.504120 (0.286815) \| 1.593612 / 1.541195 (0.052417) \| 1.694595 / 1.468490 (0.226105) \| 0.517588 / 4.584777 (-4.067189) \| 3.724353 / 3.745712 (-0.021359) \| 3.244807 / 5.269862 (-2.025054) \| 1.602929 / 4.565676 (-2.962748) \| 0.065334 / 0.424275 (-0.358941) \| 0.012259 / 0.007607 (0.004652) \| 0.501355 / 0.226044 (0.275311) \| 4.996546 / 2.268929 (2.727618) \| 2.279333 / 55.444624 (-53.165291) \| 1.940126 / 6.876477 (-4.936351) \| 2.122945 / 2.142072 (-0.019128) \| 0.626104 / 4.805227 (-4.179123) \| 0.141278 / 6.500664 (-6.359386) \| 0.064522 / 0.075469 (-0.010947) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.195351 / 1.841788 (-0.646436) \| 15.258932 / 8.074308 (7.184624) \| 14.627623 / 10.191392 (4.436231) \| 0.266897 / 0.680424 (-0.413527) \| 0.017557 / 0.534201 (-0.516644) \| 0.392932 / 0.579283 (-0.186351) \| 0.416409 / 0.434364 (-0.017955) \| 0.469100 / 0.540337 (-0.071237) \| 0.556247 / 1.386936 (-0.830689) \|\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) \| 0.006880 / 0.011353 (-0.004473) \| 0.004837 / 0.011008 (-0.006171) \| 0.074518 / 0.038508 (0.036010) \| 0.034204 / 0.023109 (0.011095) \| 0.365100 / 0.275898 (0.089202) \| 0.394976 / 0.323480 (0.071496) \| 0.006364 / 0.007986 (-0.001621) \| 0.004269 / 0.004328 (-0.000060) \| 0.073531 / 0.004250 (0.069281) \| 0.051334 / 0.037052 (0.014281) \| 0.373904 / 0.258489 (0.115415) \| 0.413662 / 0.293841 (0.119821) \| 0.028779 / 0.128546 (-0.099767) \| 0.009292 / 0.075646 (-0.066354) \| 0.081574 / 0.419271 (-0.337698) \| 0.046531 / 0.043533 (0.002998) \| 0.368995 / 0.255139 (0.113856) \| 0.376938 / 0.283200 (0.093739) \| 0.112576 / 0.141683 (-0.029107) \| 1.458880 / 1.452155 (0.006725) \| 1.550918 / 1.492716 (0.058202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319521 / 0.018006 (0.301515) \| 0.510146 / 0.000490 (0.509656) \| 0.000438 / 0.000200 (0.000238) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033082 / 0.037411 (-0.004329) \| 0.118009 / 0.014526 (0.103483) \| 0.127108 / 0.176557 (-0.049448) \| 0.176600 / 0.737135 (-0.560535) \| 0.133790 / 0.296338 (-0.162549) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437360 / 0.215209 (0.222151) \| 4.367426 / 2.077655 (2.289771) \| 2.193646 / 1.504120 (0.689526) \| 2.025002 / 1.541195 (0.483808) \| 2.142347 / 1.468490 (0.673856) \| 0.525497 / 4.584777 (-4.059280) \| 3.751275 / 3.745712 (0.005563) \| 1.912271 / 5.269862 (-3.357590) \| 1.087286 / 4.565676 (-3.478390) \| 0.066328 / 0.424275 (-0.357947) \| 0.011904 / 0.007607 (0.004297) \| 0.545870 / 0.226044 (0.319825) \| 5.434481 / 2.268929 (3.165552) \| 2.719745 / 55.444624 (-52.724880) \| 2.445001 / 6.876477 (-4.431476) \| 2.500205 / 2.142072 (0.358133) \| 0.645735 / 4.805227 (-4.159492) \| 0.144210 / 6.500664 (-6.356455) \| 0.065688 / 0.075469 (-0.009781) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.273522 / 1.841788 (-0.568265) \| 15.771778 / 8.074308 (7.697470) \| 14.685261 / 10.191392 (4.493869) \| 0.176523 / 0.680424 (-0.503900) \| 0.017877 / 0.534201 (-0.516324) \| 0.392687 / 0.579283 (-0.186596) \| 0.449992 / 0.434364 (0.015628) \| 0.462851 / 0.540337 (-0.077487) \| 0.560178 / 1.386936 (-0.826758) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fa3ef6eba906ee1214e0596d15a78fc358909f4 \"CML watermark\")\n" ]	2023-05-23T16:04:33Z	2023-05-23T16:11:12Z	null	CONTRIBUTOR	null	Fix #1774, fix #5875 TODO: a test	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5891/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5891/timeline	null	null	true	{ "diff_url": "https://github.com/huggingface/datasets/pull/5891.diff", "html_url": "https://github.com/huggingface/datasets/pull/5891", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5891.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5891" }	true
https://api.github.com/repos/huggingface/datasets/issues/5889	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5889/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5889/comments	https://api.github.com/repos/huggingface/datasets/issues/5889/events	https://github.com/huggingface/datasets/issues/5889	1,722,373,618	I_kwDODunzps5mqVXy	5,889	Token Alignment for input and output data over train and test batch/dataset.	{ "avatar_url": "https://avatars.githubusercontent.com/u/125154243?v=4", "events_url": "https://api.github.com/users/akesh1235/events{/privacy}", "followers_url": "https://api.github.com/users/akesh1235/followers", "following_url": "https://api.github.com/users/akesh1235/following{/other_user}", "gists_url": "https://api.github.com/users/akesh1235/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/akesh1235", "id": 125154243, "login": "akesh1235", "node_id": "U_kgDOB3Wzww", "organizations_url": "https://api.github.com/users/akesh1235/orgs", "received_events_url": "https://api.github.com/users/akesh1235/received_events", "repos_url": "https://api.github.com/users/akesh1235/repos", "site_admin": false, "starred_url": "https://api.github.com/users/akesh1235/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/akesh1235/subscriptions", "type": "User", "url": "https://api.github.com/users/akesh1235" }	[]	open	false	null	[]	null	[]	2023-05-23T15:58:55Z	2023-05-23T15:58:55Z	null	NONE	null	`data` > DatasetDict({ train: Dataset({ features: ['input', 'output'], num_rows: 4500 }) test: Dataset({ features: ['input', 'output'], num_rows: 500 }) }) # input (in-correct sentence) `data['train'][0]['input']` >> 'We are meet sunday 10am12pmET in Crown Heights Brooklyn New York' # output (correct sentence) `data['train'][0]['output']` >> 'We meet Sundays 10am-12pmET in Crown Heights, Brooklyn, New York.' I Want to align the output tokens with input ``` `# tokenize both inputs and targets def tokenize_fn(batch): # tokenize the input sequence first # this populates input_ids, attention_mask, etc. tokenized_inputs = tokenizer( batch['input'] ) labels_batch = tokenizer.tokenize(batch['output']) # original targets aligned_labels_batch = [] for i, labels in enumerate(labels_batch): word_ids = tokenized_inputs[i].word_ids() aligned_labels_batch.append(align_targets(labels, word_ids)) # align_targets is another user defined function which is been called here # recall: the 'target' must be stored in key called 'labels' tokenized_inputs['labels'] = aligned_labels_batch return tokenized_inputs` ``` ``` data.map( tokenize_fn, batched=True, remove_columns=data['train'].column_names, ) ``` When this user defined function is mapped to every records of train and test batch am getting following error: 1. raise DatasetTransformationNotAllowedError( 3457 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it." 2. TypeError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]]	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5889/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5889/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5887	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5887/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5887/comments	https://api.github.com/repos/huggingface/datasets/issues/5887/events	https://github.com/huggingface/datasets/issues/5887	1,722,166,382	I_kwDODunzps5mpixu	5,887	HuggingsFace dataset example give error	{ "avatar_url": "https://avatars.githubusercontent.com/u/1328316?v=4", "events_url": "https://api.github.com/users/donhuvy/events{/privacy}", "followers_url": "https://api.github.com/users/donhuvy/followers", "following_url": "https://api.github.com/users/donhuvy/following{/other_user}", "gists_url": "https://api.github.com/users/donhuvy/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/donhuvy", "id": 1328316, "login": "donhuvy", "node_id": "MDQ6VXNlcjEzMjgzMTY=", "organizations_url": "https://api.github.com/users/donhuvy/orgs", "received_events_url": "https://api.github.com/users/donhuvy/received_events", "repos_url": "https://api.github.com/users/donhuvy/repos", "site_admin": false, "starred_url": "https://api.github.com/users/donhuvy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/donhuvy/subscriptions", "type": "User", "url": "https://api.github.com/users/donhuvy" }	[]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" } ]	null	[ "Nice catch @donhuvy, that's because some models don't need the `token_type_ids`, as in this case, as the example is using `distilbert-base-cased`, and according to the DistilBert documentation at https://huggingface.co/transformers/v3.0.2/model_doc/distilbert.html, `DistilBert doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or [SEP])`. `token_type_ids` are neither required in some other well known models such as RoBERTa. \r\n\r\nHere the issue comes due to a mismatch between the tokenizer and the model, as the Colab is using a BERT tokenizer (`bert-base-cased`), while the model is a DistilBERT (`distilbert-base-cased`), so aligning the tokenizer and the model solves it!", "#self-assign", "@donhuvy I've created https://github.com/huggingface/datasets/pull/5902 to solve it! 🤗" ]	2023-05-23T14:09:05Z	2023-05-26T10:27:54Z	null	NONE	null	### Describe the bug ![image](https://github.com/huggingface/datasets/assets/1328316/1f4f0086-3db9-4c79-906b-05a375357cce) ![image](https://github.com/huggingface/datasets/assets/1328316/733ebd3d-89b9-4ece-b80a-00ab5b0a4122) ### Steps to reproduce the bug Use link as reference document written https://colab.research.google.com/github/huggingface/datasets/blob/main/notebooks/Overview.ipynb#scrollTo=biqDH9vpvSVz ```python # Now let's train our model device = 'cuda' if torch.cuda.is_available() else 'cpu' model.train().to(device) for i, batch in enumerate(dataloader): batch.to(device) outputs = model(batch) loss = outputs.loss loss.backward() optimizer.step() model.zero_grad() print(f'Step {i} - loss: {loss:.3}') if i > 5: break ``` Error ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-44-7040b885f382>](https://localhost:8080/#) in <cell line: 5>() 5 for i, batch in enumerate(dataloader): 6 batch.to(device) ----> 7 outputs = model(batch) 8 loss = outputs.loss 9 loss.backward() [/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, args, kwargs) 1499 or _global_backward_pre_hooks or _global_backward_hooks 1500 or _global_forward_hooks or _global_forward_pre_hooks): -> 1501 return forward_call(args, **kwargs) 1502 # Do not call functions when jit is used 1503 full_backward_hooks, non_full_backward_hooks = [], [] TypeError: DistilBertForQuestionAnswering.forward() got an unexpected keyword argument 'token_type_ids' ``` https://github.com/huggingface/datasets/assets/1328316/5d8b1d61-9337-4d59-8423-4f37f834c156 ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free (my Google Drive just empty about 200 MB, but I don't think it cause problem)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5887/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5887/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5886	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5886/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5886/comments	https://api.github.com/repos/huggingface/datasets/issues/5886/events	https://github.com/huggingface/datasets/issues/5886	1,721,070,225	I_kwDODunzps5mlXKR	5,886	Use work-stealing algorithm when parallel computing	{ "avatar_url": "https://avatars.githubusercontent.com/u/46060451?v=4", "events_url": "https://api.github.com/users/1014661165/events{/privacy}", "followers_url": "https://api.github.com/users/1014661165/followers", "following_url": "https://api.github.com/users/1014661165/following{/other_user}", "gists_url": "https://api.github.com/users/1014661165/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/1014661165", "id": 46060451, "login": "1014661165", "node_id": "MDQ6VXNlcjQ2MDYwNDUx", "organizations_url": "https://api.github.com/users/1014661165/orgs", "received_events_url": "https://api.github.com/users/1014661165/received_events", "repos_url": "https://api.github.com/users/1014661165/repos", "site_admin": false, "starred_url": "https://api.github.com/users/1014661165/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/1014661165/subscriptions", "type": "User", "url": "https://api.github.com/users/1014661165" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "Alternatively we could set the number of shards to be a factor than the number of processes (current they're equal) - this way it will be less likely to end up with a shard that is significantly slower than all the other ones." ]	2023-05-23T03:08:44Z	2023-05-24T15:30:09Z	null	NONE	null	### Feature request when i used Dataset.map api to process data concurrently, i found that it gets slower and slower as it gets closer to completion. Then i read the source code of arrow_dataset.py and found that it shard the dataset and use multiprocessing pool to execute each shard.It may cause the slowest task to drag out the entire program's execution time,especially when processing huge dataset. ### Motivation using work-stealing algorithm instead of sharding and parallel computing to optimize performance. ### Your contribution just an idea.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5886/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5886/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5885	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5885/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5885/comments	https://api.github.com/repos/huggingface/datasets/issues/5885/events	https://github.com/huggingface/datasets/pull/5885	1,720,954,440	PR_kwDODunzps5RFjTL	5,885	Modify `is_remote_filesystem` to return True for FUSE-mounted paths	{ "avatar_url": "https://avatars.githubusercontent.com/u/106995444?v=4", "events_url": "https://api.github.com/users/maddiedawson/events{/privacy}", "followers_url": "https://api.github.com/users/maddiedawson/followers", "following_url": "https://api.github.com/users/maddiedawson/following{/other_user}", "gists_url": "https://api.github.com/users/maddiedawson/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/maddiedawson", "id": 106995444, "login": "maddiedawson", "node_id": "U_kgDOBmCe9A", "organizations_url": "https://api.github.com/users/maddiedawson/orgs", "received_events_url": "https://api.github.com/users/maddiedawson/received_events", "repos_url": "https://api.github.com/users/maddiedawson/repos", "site_admin": false, "starred_url": "https://api.github.com/users/maddiedawson/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maddiedawson/subscriptions", "type": "User", "url": "https://api.github.com/users/maddiedawson" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5885). All of your documentation changes will be reflected on that endpoint.", "@lhoestq would you or another maintainer be able to review please? :)", "Why you do need to support FUSE mounted paths ?\r\n\r\n`datasets` uses data that live on disk for fast lookups - FUSE mounted disks would lead to poor performance and I wouldn't recomment using it.", "Fuse is commonly used to mount remote file systems (e.g. S3, DBFS) as a local directory. Since it's slower than using an actual local device, it's better to treat it as remote to reduce latency.", "I think people would be confused if they don't have the same dataset behavior depending on the disk type.\r\n\r\nIf they want to use a remote bucket they should use the remote URI instead, e.g. `s3://...`. Advancements on this are tracked at #5281 " ]	2023-05-23T01:04:54Z	2023-05-25T08:50:48Z	null	CONTRIBUTOR	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5885/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5885/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5885.diff", "html_url": "https://github.com/huggingface/datasets/pull/5885", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5885.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5885" }	true
https://api.github.com/repos/huggingface/datasets/issues/5888	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5888/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5888/comments	https://api.github.com/repos/huggingface/datasets/issues/5888/events	https://github.com/huggingface/datasets/issues/5888	1,722,290,363	I_kwDODunzps5mqBC7	5,888	A way to upload and visualize .mp4 files (millions of them) as part of a dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/AntreasAntoniou", "id": 10792502, "login": "AntreasAntoniou", "node_id": "MDQ6VXNlcjEwNzkyNTAy", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "type": "User", "url": "https://api.github.com/users/AntreasAntoniou" }	[]	open	false	null	[]	null	[ "Hi! \r\n\r\nYou want to use `push_to_hub` (creates Parquet files) instead of `save_to_disk` (creates Arrow files) when creating a Hub dataset. Parquet is designed for long-term storage and takes less space than the Arrow format, and, most importantly, `load_dataset` can parse it, which should fix the viewer. \r\n\r\nRegarding the dataset generation, `Dataset.from_generator` with the video data represented as `datasets.Value(\"binary\")` followed by `push_to_hub` should work (if the `push_to_hub` step times out, restart it to resume uploading)\r\n\r\nPS: Once the dataset is uploaded, to make working with the dataset easier, it's a good idea to add a [transform](https://huggingface.co/docs/datasets/main/en/process#format-transform) to the README that shows how to decode the binary video data into something a model can understand. Also, if you get an `ArrowInvalid` error (can happen when working with large binary data) in `Dataset.from_generator`, reduce the value of `writer_batch_size` (the default is 1000) to fix it.", "One issue here is that Dataset.from_generator can work well for the non 'infinite sampling' version of the dataset. The training set for example is often sampled dynamically given the video files that I have uploaded. I worry that storing the video data as binary means that I'll end up duplicating a lot of the data. Furthermore, storing video data as anything but .mp4 would quickly make the dataset size from 1.9TB to 1PB. ", "> storing video data as anything but .mp4\r\n\r\nWhat I mean by storing as `datasets.Value(\"binary\")` is embedding raw MP4 bytes in the Arrow table, but, indeed, this would waste a lot of space if there are duplicates.\r\n\r\nSo I see two options:\r\n* if one video is not mapped to too many samples, you can embed the video bytes and do \"group by\" on the rest of the columns (this would turn them into lists) to avoid duplicating them (then, it should be easy to define a `map` in the README that samples the video data to \"unpack\" the samples)\r\n* you can create a dataset script that downloads the video files and embeds their file paths into the Arrow file\r\n\r\nAlso, I misread MP4 as MP3. We need to add a `Video` feature to the `datasets` lib to support MP4 files in the viewer (a bit trickier to implement than the `Image` feature due to the Arrow limitations).", "I'm transferring this issue to the `datasets` repo, as it's not related to `huggingface_hub`", "@mariosasko Right. If I want my dataset to be streamable, what are the necessary requirements to achieve that within the context of .mp4 binaries like we have here? I guess your second point here would not support that right?", "The streaming would work, but the video paths would require using `fsspec.open` to get the content.", "Are there any plans to make video playable on the hub?" ]	2023-05-22T18:05:26Z	2023-05-28T21:37:45Z	null	NONE	null	Is your feature request related to a problem? Please describe. I recently chose to use huggingface hub as the home for a large multi modal dataset I've been building. https://huggingface.co/datasets/Antreas/TALI It combines images, text, audio and video. Now, I could very easily upload a dataset made via datasets.Dataset.from_generator, as long as it did not include video files. I found that including .mp4 files in the entries would not auto-upload those files. Hence I tried to upload them myself. I quickly found out that uploading many small files is a very bad way to use git lfs, and that it would take ages, so, I resorted to using 7z to pack them all up. But then I had a new problem. My dataset had a size of 1.9TB. Trying to upload such a large file with the default huggingface_hub API always resulted in time outs etc. So I decided to split the large files into chunks of 5GB each and reupload. So, eventually it all worked out. But now the dataset can't be properly and natively used by the datasets API because of all the needed preprocessing -- and furthermore the hub is unable to visualize things. Describe the solution you'd like A native way to upload large datasets that include .mp4 or other video types. Describe alternatives you've considered Already explained earlier Additional context https://huggingface.co/datasets/Antreas/TALI	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5888/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5888/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5884	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5884/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5884/comments	https://api.github.com/repos/huggingface/datasets/issues/5884/events	https://github.com/huggingface/datasets/issues/5884	1,719,548,172	I_kwDODunzps5mfjkM	5,884	`Dataset.to_tf_dataset` fails when strings cannot be encoded as `np.bytes_`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" } ]	null	[ "May eventually be solved in #5883 ", "#self-assign" ]	2023-05-22T12:03:06Z	2023-06-09T16:04:56Z	2023-06-09T16:04:55Z	CONTRIBUTOR	null	### Describe the bug When loading any dataset that contains a column with strings that are not ASCII-compatible, looping over those records raises the following exception e.g. for `é` character `UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)`. ### Steps to reproduce the bug Running the following script will eventually fail, when reaching to the batch that contains non-ASCII compatible strings. ```python from datasets import load_dataset ds = load_dataset("imdb", split="train") tfds = ds.to_tf_dataset(batch_size=16) for batch in tfds: print(batch) >>> UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128) ``` ### Expected behavior The following script to run properly, making sure that the strings are either `numpy.unicode_` or `numpy.string` instead of `numpy.bytes_` since some characters are not ASCII compatible and that would lead to an issue when applying the `map`. ```python from datasets import load_dataset ds = load_dataset("imdb", split="train") tfds = ds.to_tf_dataset(batch_size=16) for batch in tfds: print(batch) ``` ### Environment info - `datasets` version: 2.12.1.dev0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5884/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5884/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5883	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5883/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5883/comments	https://api.github.com/repos/huggingface/datasets/issues/5883/events	https://github.com/huggingface/datasets/pull/5883	1,719,527,597	PR_kwDODunzps5RAkYi	5,883	Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r\nColab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nAlso, here's a quick sample of what's happening:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"imdb\", split=\"train\")\r\ntfds = ds.to_tf_dataset(batch_size=16)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nA more detailed version of it:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"a\": [1],\r\n \"b\": [\"é\"],\r\n }\r\n)\r\ntfds = ds.to_tf_dataset(batch_size=1)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nThe original issue comes from https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#LL234C4-L234C4, which could easily be solved by replacing that line with `return result.astype(np.unicode_)` but they are mentioning that it may lead to issues.\r\n\r\nEven the following fails in `numpy`:\r\n\r\n```python\r\nimport numpy as np\r\n\r\nx = np.array([\"é\"]).astype(np.bytes_)\r\n```", "cc. @lhoestq :hugs:", "cc @Rocketknight1 ", "> Nice ! Could you add some tests to make sure that batch_size=None works as expected ?\r\n\r\nSure, I'll add the tests for everything, including the string-encoding issue to make sure it's solved!", "Thanks for the review @lhoestq and @Rocketknight1! I do understand that processing it in batches is always more efficient than processing it one-by-one, it was just to make `batch_size` optional. What we can do is default it to a certain batch size e.g. 16 as before, and that's it, but I think it can still remain optional.", "@Rocketknight1 then I'll add the integration tests for the optional `batch_size` as well as for the encoding of non-ASCII compatible characters 😄 Do we set the default `batch_size` to 16 instead of `None`?", "@alvarobartt I think 16 is a reasonable default, yep!", "I think default should be None, not 16.\r\nUsers won't expect to have it batched by default.", "Then I'll leave it as is, and add the unit/integration tests, thanks @Rocketknight1 and @lhoestq ", "Hi @Rocketknight1 @lhoestq! So the string-encoding issue is already solved, but I've got one doubt about the `batch_size` being optional in the multiprocessing approach, since in that case I assume the `batch_size` should be mandatory, for the moment I'm assuming it is/should be mandatory, but let me know if you want me to add a check to disallow `batch_size=None` when `num_workers>1`. Thanks!", "> To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n> \r\n> Colab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nI've used the Colab shared above for testing purposes, and it works fine, plus the unit/integration tests are passing. I've also trained a `KerasNLP` model with incoming data from 🤗`datasets` with no issue at all!", "> in the multiprocessing approach, since in that case I assume the batch_size should be mandatory,\r\n\r\nNo I think they're quite orthogonal, no need to have it mandatory", "> No I think they're quite orthogonal, no need to have it mandatory\r\n\r\nBut it will break if `batch_size=None` as the multiprocessing approach will aim to prepare batches and distribute those to every worker, and assuming `batch_size=1` when `batch_size=None` I guess is not a good assumption, right?", "Ah I see. Multiprocessing should support batch_size=None indeed. If you have ideas you can do it in this PR, or raise a NotImplementedError and we can see later", "Sure @lhoestq, I can add a `NotImplementedError` for the moment, and prepare the next PR straight-away to tackle the multiprocessing approach with `batch_size=None`, but not sure if that may eventually collide with @Rocketknight1 PR at https://github.com/huggingface/datasets/pull/5863", "Yes, let me merge the PR at #5863 after this one, and then we can open another to improve the behaviour with multiprocessing and `batch_size=None`!", "Sure @Rocketknight1 makes complete sense to me! Do you want me to add the `raise NotImplementedError` and then we merge this PR? Or you prefer to directly merge the current?", "`raise NotImplementedError` for now with an error telling the user that multiprocessing needs them to specify a batch size, I think!", "Since you recently approved @Rocketknight1, are we ready to merge? Thanks 🤗", "Ah actually it looks like `minimal_tf_collate_fn` doesn't support batch_size=None", "Hi @lhoestq so I didn't include the call to `collate_fn`, as we won't need to collate the incoming data e.g. \"str\" should remain a \"str\" not a [\"str\"], and the `minimal_collate_fn` was indeed putting everything into a list, so the output was not un-batched, but batched with size 1", "What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n\r\nDoes my last change look of to you ? If so I think we can merge", "> What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n> \r\n> Does my last change look of to you ? If so I think we can merge\r\n\r\nI think we're good, since it won't batch it under the scenario of `str` being provided instead of `List[str]`, and the unit/integration tests are passing, so I'm OK to merge. Maybe we can double check with Matt? cc @Rocketknight1 ", "Yes, and sorry for the delay! I'm happy to merge.", "<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) \| 0.006555 / 0.011353 (-0.004798) \| 0.004521 / 0.011008 (-0.006487) \| 0.096633 / 0.038508 (0.058125) \| 0.032859 / 0.023109 (0.009750) \| 0.294632 / 0.275898 (0.018734) \| 0.325140 / 0.323480 (0.001660) \| 0.005676 / 0.007986 (-0.002310) \| 0.005252 / 0.004328 (0.000924) \| 0.074349 / 0.004250 (0.070099) \| 0.045836 / 0.037052 (0.008784) \| 0.302919 / 0.258489 (0.044430) \| 0.340686 / 0.293841 (0.046845) \| 0.028398 / 0.128546 (-0.100148) \| 0.008942 / 0.075646 (-0.066704) \| 0.326994 / 0.419271 (-0.092278) \| 0.049556 / 0.043533 (0.006023) \| 0.293883 / 0.255139 (0.038744) \| 0.316522 / 0.283200 (0.033322) \| 0.097385 / 0.141683 (-0.044298) \| 1.405334 / 1.452155 (-0.046821) \| 1.521529 / 1.492716 (0.028812) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212269 / 0.018006 (0.194263) \| 0.445692 / 0.000490 (0.445203) \| 0.004930 / 0.000200 (0.004730) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026907 / 0.037411 (-0.010504) \| 0.108607 / 0.014526 (0.094081) \| 0.116806 / 0.176557 (-0.059751) \| 0.178428 / 0.737135 (-0.558707) \| 0.122326 / 0.296338 (-0.174012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404211 / 0.215209 (0.189002) \| 4.045374 / 2.077655 (1.967719) \| 1.877237 / 1.504120 (0.373117) \| 1.706276 / 1.541195 (0.165081) \| 1.750610 / 1.468490 (0.282120) \| 0.522331 / 4.584777 (-4.062446) \| 3.742286 / 3.745712 (-0.003426) \| 1.791285 / 5.269862 (-3.478577) \| 1.043872 / 4.565676 (-3.521805) \| 0.065176 / 0.424275 (-0.359099) \| 0.011821 / 0.007607 (0.004214) \| 0.507374 / 0.226044 (0.281329) \| 5.088803 / 2.268929 (2.819875) \| 2.282742 / 55.444624 (-53.161882) \| 1.950737 / 6.876477 (-4.925740) \| 2.042262 / 2.142072 (-0.099810) \| 0.636525 / 4.805227 (-4.168702) \| 0.140837 / 6.500664 (-6.359827) \| 0.063223 / 0.075469 (-0.012246) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.188070 / 1.841788 (-0.653718) \| 14.622681 / 8.074308 (6.548372) \| 13.247988 / 10.191392 (3.056596) \| 0.165858 / 0.680424 (-0.514566) \| 0.017476 / 0.534201 (-0.516725) \| 0.391973 / 0.579283 (-0.187310) \| 0.433326 / 0.434364 (-0.001038) \| 0.467163 / 0.540337 (-0.073175) \| 0.568359 / 1.386936 (-0.818577) \|\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) \| 0.006076 / 0.011353 (-0.005276) \| 0.004439 / 0.011008 (-0.006570) \| 0.074496 / 0.038508 (0.035988) \| 0.031396 / 0.023109 (0.008287) \| 0.372237 / 0.275898 (0.096339) \| 0.403412 / 0.323480 (0.079932) \| 0.005430 / 0.007986 (-0.002555) \| 0.003846 / 0.004328 (-0.000483) \| 0.074403 / 0.004250 (0.070153) \| 0.045398 / 0.037052 (0.008346) \| 0.394133 / 0.258489 (0.135644) \| 0.421769 / 0.293841 (0.127928) \| 0.027936 / 0.128546 (-0.100610) \| 0.008962 / 0.075646 (-0.066685) \| 0.083158 / 0.419271 (-0.336113) \| 0.044863 / 0.043533 (0.001331) \| 0.393834 / 0.255139 (0.138695) \| 0.391537 / 0.283200 (0.108337) \| 0.097971 / 0.141683 (-0.043712) \| 1.496632 / 1.452155 (0.044477) \| 1.585511 / 1.492716 (0.092795) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010094 / 0.018006 (-0.007913) \| 0.437811 / 0.000490 (0.437321) \| 0.000963 / 0.000200 (0.000763) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028864 / 0.037411 (-0.008547) \| 0.112480 / 0.014526 (0.097954) \| 0.120938 / 0.176557 (-0.055619) \| 0.170888 / 0.737135 (-0.566247) \| 0.125903 / 0.296338 (-0.170435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426716 / 0.215209 (0.211507) \| 4.238380 / 2.077655 (2.160725) \| 2.052889 / 1.504120 (0.548769) \| 1.871043 / 1.541195 (0.329848) \| 1.890405 / 1.468490 (0.421915) \| 0.522059 / 4.584777 (-4.062718) \| 3.813331 / 3.745712 (0.067619) \| 2.891651 / 5.269862 (-2.378210) \| 1.323836 / 4.565676 (-3.241841) \| 0.065124 / 0.424275 (-0.359151) \| 0.011498 / 0.007607 (0.003891) \| 0.525102 / 0.226044 (0.299057) \| 5.245190 / 2.268929 (2.976261) \| 2.531149 / 55.444624 (-52.913476) \| 2.197323 / 6.876477 (-4.679153) \| 2.197314 / 2.142072 (0.055241) \| 0.633423 / 4.805227 (-4.171804) \| 0.140248 / 6.500664 (-6.360416) \| 0.064432 / 0.075469 (-0.011037) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270639 / 1.841788 (-0.571149) \| 14.856678 / 8.074308 (6.782369) \| 14.337631 / 10.191392 (4.146239) \| 0.195319 / 0.680424 (-0.485105) \| 0.017628 / 0.534201 (-0.516573) \| 0.393984 / 0.579283 (-0.185299) \| 0.421987 / 0.434364 (-0.012376) \| 0.459245 / 0.540337 (-0.081092) \| 0.557786 / 1.386936 (-0.829150) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "Will you eventually need help with your PR @Rocketknight1? I'll be happy to help if needed 😄 ", "<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) \| 0.007577 / 0.011353 (-0.003776) \| 0.004960 / 0.011008 (-0.006048) \| 0.113622 / 0.038508 (0.075114) \| 0.037981 / 0.023109 (0.014872) \| 0.355312 / 0.275898 (0.079414) \| 0.393384 / 0.323480 (0.069904) \| 0.006575 / 0.007986 (-0.001411) \| 0.005941 / 0.004328 (0.001612) \| 0.085976 / 0.004250 (0.081726) \| 0.053784 / 0.037052 (0.016732) \| 0.369358 / 0.258489 (0.110869) \| 0.399402 / 0.293841 (0.105561) \| 0.032155 / 0.128546 (-0.096391) \| 0.010448 / 0.075646 (-0.065199) \| 0.389009 / 0.419271 (-0.030263) \| 0.057377 / 0.043533 (0.013844) \| 0.354968 / 0.255139 (0.099829) \| 0.382404 / 0.283200 (0.099204) \| 0.111056 / 0.141683 (-0.030627) \| 1.807986 / 1.452155 (0.355832) \| 1.866070 / 1.492716 (0.373354) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244439 / 0.018006 (0.226432) \| 0.491942 / 0.000490 (0.491452) \| 0.001910 / 0.000200 (0.001710) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031024 / 0.037411 (-0.006387) \| 0.129674 / 0.014526 (0.115148) \| 0.142974 / 0.176557 (-0.033583) \| 0.213568 / 0.737135 (-0.523568) \| 0.147794 / 0.296338 (-0.148545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.480333 / 0.215209 (0.265124) \| 4.792901 / 2.077655 (2.715246) \| 2.233145 / 1.504120 (0.729025) \| 2.036291 / 1.541195 (0.495096) \| 2.109631 / 1.468490 (0.641140) \| 0.624546 / 4.584777 (-3.960231) \| 4.543511 / 3.745712 (0.797799) \| 3.961345 / 5.269862 (-1.308517) \| 1.903634 / 4.565676 (-2.662042) \| 0.076584 / 0.424275 (-0.347691) \| 0.014590 / 0.007607 (0.006983) \| 0.593195 / 0.226044 (0.367151) \| 5.928740 / 2.268929 (3.659811) \| 2.781164 / 55.444624 (-52.663460) \| 2.364303 / 6.876477 (-4.512173) \| 2.510139 / 2.142072 (0.368067) \| 0.770886 / 4.805227 (-4.034341) \| 0.167995 / 6.500664 (-6.332669) \| 0.076622 / 0.075469 (0.001153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.402398 / 1.841788 (-0.439390) \| 17.921233 / 8.074308 (9.846925) \| 17.036738 / 10.191392 (6.845346) \| 0.168997 / 0.680424 (-0.511427) \| 0.020259 / 0.534201 (-0.513941) \| 0.465322 / 0.579283 (-0.113962) \| 0.500435 / 0.434364 (0.066071) \| 0.546846 / 0.540337 (0.006509) \| 0.658130 / 1.386936 (-0.728806) \|\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) \| 0.007624 / 0.011353 (-0.003729) \| 0.005265 / 0.011008 (-0.005744) \| 0.086886 / 0.038508 (0.048377) \| 0.038235 / 0.023109 (0.015126) \| 0.463969 / 0.275898 (0.188071) \| 0.502451 / 0.323480 (0.178971) \| 0.006285 / 0.007986 (-0.001701) \| 0.004525 / 0.004328 (0.000197) \| 0.086557 / 0.004250 (0.082307) \| 0.052414 / 0.037052 (0.015362) \| 0.482167 / 0.258489 (0.223678) \| 0.513684 / 0.293841 (0.219843) \| 0.032929 / 0.128546 (-0.095618) \| 0.010249 / 0.075646 (-0.065397) \| 0.093377 / 0.419271 (-0.325895) \| 0.054114 / 0.043533 (0.010582) \| 0.466116 / 0.255139 (0.210977) \| 0.488977 / 0.283200 (0.205777) \| 0.115446 / 0.141683 (-0.026237) \| 1.762912 / 1.452155 (0.310757) \| 1.874191 / 1.492716 (0.381475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012666 / 0.018006 (-0.005341) \| 0.485962 / 0.000490 (0.485473) \| 0.002621 / 0.000200 (0.002421) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033661 / 0.037411 (-0.003751) \| 0.135395 / 0.014526 (0.120869) \| 0.147230 / 0.176557 (-0.029326) \| 0.205847 / 0.737135 (-0.531288) \| 0.151496 / 0.296338 (-0.144842) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514097 / 0.215209 (0.298887) \| 5.134093 / 2.077655 (3.056438) \| 2.496775 / 1.504120 (0.992655) \| 2.268078 / 1.541195 (0.726883) \| 2.342153 / 1.468490 (0.873663) \| 0.623130 / 4.584777 (-3.961647) \| 4.601787 / 3.745712 (0.856075) \| 3.414249 / 5.269862 (-1.855613) \| 1.849603 / 4.565676 (-2.716073) \| 0.078350 / 0.424275 (-0.345925) \| 0.013785 / 0.007607 (0.006178) \| 0.638783 / 0.226044 (0.412739) \| 6.378356 / 2.268929 (4.109427) \| 3.072867 / 55.444624 (-52.371757) \| 2.668123 / 6.876477 (-4.208354) \| 2.693905 / 2.142072 (0.551833) \| 0.764583 / 4.805227 (-4.040644) \| 0.166854 / 6.500664 (-6.333810) \| 0.076883 / 0.075469 (0.001414) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.502003 / 1.841788 (-0.339784) \| 18.674205 / 8.074308 (10.599897) \| 16.837759 / 10.191392 (6.646367) \| 0.176995 / 0.680424 (-0.503428) \| 0.020126 / 0.534201 (-0.514075) \| 0.464480 / 0.579283 (-0.114803) \| 0.516477 / 0.434364 (0.082113) \| 0.549818 / 0.540337 (0.009481) \| 0.659927 / 1.386936 (-0.727009) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "@alvarobartt Yes, I'll ping you for a review once it's ready!" ]	2023-05-22T11:51:07Z	2023-06-08T11:09:03Z	2023-06-06T16:49:15Z	CONTRIBUTOR	null	## What's in this PR? This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset. The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`. Besides that, some other minor things have been fixed: * Made `batch_size` an optional parameter in `to_tf_dataset` * Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map` * Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy` * Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf` ## What's missing in this PR? I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5883/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5883/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5883.diff", "html_url": "https://github.com/huggingface/datasets/pull/5883", "merged_at": "2023-06-06T16:49:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/5883.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5883" }	true
https://api.github.com/repos/huggingface/datasets/issues/5881	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5881/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5881/comments	https://api.github.com/repos/huggingface/datasets/issues/5881/events	https://github.com/huggingface/datasets/issues/5881	1,719,402,643	I_kwDODunzps5mfACT	5,881	Split dataset by node: index error when sharding iterable dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4", "events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}", "followers_url": "https://api.github.com/users/sanchit-gandhi/followers", "following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}", "gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sanchit-gandhi", "id": 93869735, "login": "sanchit-gandhi", "node_id": "U_kgDOBZhWpw", "organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs", "received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events", "repos_url": "https://api.github.com/users/sanchit-gandhi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions", "type": "User", "url": "https://api.github.com/users/sanchit-gandhi" }	[]	open	false	null	[]	null	[ "cc @lhoestq in case you have any ideas here! Might need a multi-host set-up to debug (can give you access to a JAX one if you need)" ]	2023-05-22T10:36:13Z	2023-05-23T08:32:14Z	null	CONTRIBUTOR	null	### Describe the bug Context: we're splitting an iterable dataset by node and then passing it to a torch data loader with multiple workers When we iterate over it for 5 steps, we don't get an error When we instead iterate over it for 8 steps, we get an `IndexError` when fetching the data if we have too many workers ### Steps to reproduce the bug Here, we have 2 JAX processes (`jax.process_count() = 2`) which we split the dataset over. The dataset loading script can be found here: https://huggingface.co/datasets/distil-whisper/librispeech_asr/blob/c6a1e805cbfeed5057400ac5937327d7e30281b8/librispeech_asr.py#L310 <details> <summary> Code to reproduce </summary> ```python from datasets import load_dataset import jax from datasets.distributed import split_dataset_by_node from torch.utils.data import DataLoader from tqdm import tqdm # load an example dataset (https://huggingface.co/datasets/distil-whisper/librispeech_asr) dataset = load_dataset("distil-whisper/librispeech_asr", "all", split="train.clean.100", streaming=True) # just keep the text column -> no need to define a collator dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"}) # define some constants batch_size = 256 num_examples = 5 # works for 5 examples, doesn't for 8 num_workers = dataset_text.n_shards # try with multiple workers dataloader = DataLoader(dataset_text, batch_size=batch_size, num_workers=num_workers, drop_last=True) for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Multiple workers"): if i == num_examples: break # try splitting by node (we can't do this with `dataset_text` since `split_dataset_by_node` expects the Audio column for an ASR dataset) dataset = split_dataset_by_node(dataset, rank=jax.process_index(), world_size=jax.process_count()) # remove the text column again dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"}) dataloader = DataLoader(dataset_text, batch_size=16, num_workers=num_workers // 2, drop_last=True) for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Split by node"): if i == num_examples: break # too many workers dataloader = DataLoader(dataset_text, batch_size=256, num_workers=num_workers, drop_last=True) for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"): if i == num_examples: break ``` </details> <details> <summary> With 5 examples: </summary> ``` Multiple workers: 100%\|███████████████████████████████████████████████████████████████████\| 5/5 [00:16<00:00, 3.33s/it] Assigning 7 shards (or data sources) of the dataset to each node. Split by node: 100%\|██████████████████████████████████████████████████████████████████████\| 5/5 [00:13<00:00, 2.76s/it] Assigning 7 shards (or data sources) of the dataset to each node. Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary t o have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more files than 7. Too many workers: 100%\|███████████████████████████████████████████████████████████████████\| 5/5 [00:15<00:00, 3.03s/it] ``` </details> <details> <summary> With 7 examples: </summary> ``` Multiple workers: 100%\|███████████████████████████████████████████████████████████████████\| 8/8 [00:13<00:00, 1.71s/it] Assigning 7 shards (or data sources) of the dataset to each node. Split by node: 100%\|██████████████████████████████████████████████████████████████████████\| 8/8 [00:11<00:00, 1.38s/it] Assigning 7 shards (or data sources) of the dataset to each node. Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more files than 7. Too many workers: 88%\|██████████████████████████████████████████████████████████▋ \| 7/8 [00:13<00:01, 1.89s/it] Traceback (most recent call last): File "distil-whisper/test_librispeech.py", line 36, in <module> for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"): File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/tqdm/std.py", line 1178, in __iter__ for obj in iterable: File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1325, in _next_data return self._process_data(data) File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data data.reraise() File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/_utils.py", line 644, in reraise raise exception IndexError: Caught IndexError in DataLoader worker process 7. Original Traceback (most recent call last): File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch data.append(next(self.dataset_iter)) File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 986, in __iter__ yield from self._iter_pytorch(ex_iterable) File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 920, in _iter_pytorch for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers): File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 540, in shard_data_sources self.ex_iterable.shard_data_sources(worker_id, num_workers), File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 796, in shard_data_sources self.ex_iterable.shard_data_sources(worker_id, num_workers), File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 126, in shard_data_sources requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices]) File "/home/sanchitgandhi/datasets/src/datasets/utils/sharding.py", line 76, in _merge_gen_kwargs for key in gen_kwargs_list[0] IndexError: list index out of range ``` </details> ### Expected behavior Should pass for both 5 and 7 examples ### Environment info - `datasets` version: 2.12.1.dev0 - Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5881/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5881/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5880	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5880/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5880/comments	https://api.github.com/repos/huggingface/datasets/issues/5880/events	https://github.com/huggingface/datasets/issues/5880	1,719,090,101	I_kwDODunzps5mdzu1	5,880	load_dataset from s3 file system through streaming can't not iterate data	{ "avatar_url": "https://avatars.githubusercontent.com/u/59083384?v=4", "events_url": "https://api.github.com/users/janineguo/events{/privacy}", "followers_url": "https://api.github.com/users/janineguo/followers", "following_url": "https://api.github.com/users/janineguo/following{/other_user}", "gists_url": "https://api.github.com/users/janineguo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/janineguo", "id": 59083384, "login": "janineguo", "node_id": "MDQ6VXNlcjU5MDgzMzg0", "organizations_url": "https://api.github.com/users/janineguo/orgs", "received_events_url": "https://api.github.com/users/janineguo/received_events", "repos_url": "https://api.github.com/users/janineguo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/janineguo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/janineguo/subscriptions", "type": "User", "url": "https://api.github.com/users/janineguo" }	[]	open	false	null	[]	null	[ "This sounds related to #5281.\r\n\r\nCan you try passing `storage_options=s3_client.storage_options` instead passing it to `use_auth_token=` ?", "I tried `storage_options` before, but it doesn't work, I checked our source code and I found that we even didn't pass this parameter to the following process. if I use `storage_options` instead of `use_auth_token`, then I also need to change another place of the code. the last line of `streaming_download_manager.py`. our code only passes the `use_auth_token` to the following handler, but does nothing to the `storage_options`\r\n<img width=\"1050\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/59083384/5be90933-3331-4ecf-9e11-34f9852d8f92\">\r\n", "Cloud storage support is still experimental indeed and you can expect some bugs.\r\n\r\nI think we need to pass the storage options anywhere use_auth_token is passed in indeed. Let me know if you'd be interested in contributing a fix !", "Oh, that's great, I really like to fix it. because datasets is really useful and most of our projects need to use it, but we can store our data on the internet due to security reasons. fix it not only make our own work more efficient but also can benefit others who use it." ]	2023-05-22T07:40:27Z	2023-05-26T12:52:08Z	null	NONE	null	### Describe the bug I have a JSON file in my s3 file system(minio), I can use load_dataset to get the file link, but I can't iterate it <img width="816" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/cc0778d3-36f3-45b5-ac68-4e7c664c2ed0"> <img width="1144" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/76872af3-8b3c-42ff-9f55-528c920a7af1"> we can change 4 lines to fix this bug, you can check whether it is ok for us. <img width="941" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/5a22155a-ece7-496c-8506-047e5c235cd3"> ### Steps to reproduce the bug 1. storage a file in you s3 file system 2. use load_dataset to read it through streaming 3. iterate it ### Expected behavior can iterate it successfully ### Environment info - `datasets` version: 2.12.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5880/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5880/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5878	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5878/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5878/comments	https://api.github.com/repos/huggingface/datasets/issues/5878/events	https://github.com/huggingface/datasets/issues/5878	1,718,203,843	I_kwDODunzps5mabXD	5,878	Prefetching for IterableDataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/30946190?v=4", "events_url": "https://api.github.com/users/vyeevani/events{/privacy}", "followers_url": "https://api.github.com/users/vyeevani/followers", "following_url": "https://api.github.com/users/vyeevani/following{/other_user}", "gists_url": "https://api.github.com/users/vyeevani/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vyeevani", "id": 30946190, "login": "vyeevani", "node_id": "MDQ6VXNlcjMwOTQ2MTkw", "organizations_url": "https://api.github.com/users/vyeevani/orgs", "received_events_url": "https://api.github.com/users/vyeevani/received_events", "repos_url": "https://api.github.com/users/vyeevani/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vyeevani/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vyeevani/subscriptions", "type": "User", "url": "https://api.github.com/users/vyeevani" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "Very cool! Do you have a link to the code that you're using to eagerly fetch the data? Would also be interested in hacking around something here for pre-fetching iterable datasets", "I ended up just switching back to the pytorch dataloader and using it's multiprocessing functionality to handle this :(. I'm just not that familiar with python multiprocessing to get something to work in jupyter (kept having weird behaviors happening with zombies living after the cell finished).", "Ultimately settled on using webdataset to circumvent huggingface datasets entirely. Would definitely switch back if: https://github.com/huggingface/datasets/issues/5337 was resolved.", "Hi! You can combine `datasets` with `torchdata` to prefetch `IterableDataset`'s samples:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchdata.datapipes.iter import IterableWrapper, HuggingFaceHubReader\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(\"sst\", split=\"train\", streaming=True)\r\n# processing...\r\ndp = IterableWrapper(ds)\r\ndp = dp.prefetch(100)\r\ndl = DataLoader(dp, batch_size=8)\r\n\r\ni = iter(dl)\r\nnext(i)\r\n```", "Hey @mariosasko! Thanks for the tip here - introducing prefetch with `torchdata` didn't really give me any performance difference vs not prefetching, but the concept is definitely one that could be really beneficial. Are there any benchmarks that show the speed-up you can get with `torchdata`'s prefetch just for comparison?" ]	2023-05-20T15:25:40Z	2023-06-01T17:40:00Z	null	NONE	null	### Feature request Add support for prefetching the next n batches through iterabledataset to reduce batch loading bottleneck in training loop. ### Motivation The primary motivation behind this is to use hardware accelerators alongside a streaming dataset. This is required when you are in a low ram or low disk space setting as well as quick iteration where you're iterating though different accelerator environments (e.x changing ec2 instances quickly to figure out batch/sec for a particular architecture). Currently, using the IterableDataset results in accelerators becoming basically useless due to the massive bottleneck induced by the dataset lazy loading/transform/mapping. I've considered two alternatives: PyTorch dataloader that handles this. However, I'm using jax, and I believe this is a piece of functionality that should live in the stream class. Replicating the "num_workers" part of the PyTorch DataLoader to eagerly load batches and apply the transform so Arrow caching will automatically cache results and make them accessible. ### Your contribution I may or may not have time to do this. Currently, I've written the basic multiprocessor approach to handle the eager DataLoader for my own use case with code that's not integrated to datasets. I'd definitely see this as being the default over the regular Dataset for most people given that they wouldn't have to wait on the datasets while also not worrying about performance.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5878/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5878/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5877	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5877/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5877/comments	https://api.github.com/repos/huggingface/datasets/issues/5877/events	https://github.com/huggingface/datasets/issues/5877	1,717,983,961	I_kwDODunzps5mZlrZ	5,877	Request for text deduplication feature	{ "avatar_url": "https://avatars.githubusercontent.com/u/55043035?v=4", "events_url": "https://api.github.com/users/SupreethRao99/events{/privacy}", "followers_url": "https://api.github.com/users/SupreethRao99/followers", "following_url": "https://api.github.com/users/SupreethRao99/following{/other_user}", "gists_url": "https://api.github.com/users/SupreethRao99/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/SupreethRao99", "id": 55043035, "login": "SupreethRao99", "node_id": "MDQ6VXNlcjU1MDQzMDM1", "organizations_url": "https://api.github.com/users/SupreethRao99/orgs", "received_events_url": "https://api.github.com/users/SupreethRao99/received_events", "repos_url": "https://api.github.com/users/SupreethRao99/repos", "site_admin": false, "starred_url": "https://api.github.com/users/SupreethRao99/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/SupreethRao99/subscriptions", "type": "User", "url": "https://api.github.com/users/SupreethRao99" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "The \"exact match\" deduplication will be possible when we resolve https://github.com/huggingface/datasets/issues/2514 (first, https://github.com/apache/arrow/issues/30950 needs to be addressed on the Arrow side). In the meantime, you can use Polars or DuckDB (e.g., via [datasets-sql](https://github.com/mariosasko/datasets_sql)).\r\n\r\nFuzzy deduplication is out-of-scope for now ([splink](https://github.com/moj-analytical-services/splink) is probably the best tool for it).", "This library can be an intermediate solution : https://github.com/ChenghaoMou/text-dedup/tree/main" ]	2023-05-20T01:56:00Z	2023-06-01T20:26:18Z	null	NONE	null	### Feature request It would be great if there would be support for high performance, highly scalable text deduplication algorithms as part of the datasets library. ### Motivation Motivated by this blog post https://huggingface.co/blog/dedup and this library https://github.com/google-research/deduplicate-text-datasets, but slightly frustrated by how its not very easy to work with these tools I am proposing this feature. ### Your contribution I would be happy to contribute to the development effort of this feature. would love to collaborate with others in the development effort.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5877/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5877/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5876	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5876/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5876/comments	https://api.github.com/repos/huggingface/datasets/issues/5876/events	https://github.com/huggingface/datasets/issues/5876	1,717,978,985	I_kwDODunzps5mZkdp	5,876	Incompatibility with DataLab	{ "avatar_url": "https://avatars.githubusercontent.com/u/26192135?v=4", "events_url": "https://api.github.com/users/helpmefindaname/events{/privacy}", "followers_url": "https://api.github.com/users/helpmefindaname/followers", "following_url": "https://api.github.com/users/helpmefindaname/following{/other_user}", "gists_url": "https://api.github.com/users/helpmefindaname/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/helpmefindaname", "id": 26192135, "login": "helpmefindaname", "node_id": "MDQ6VXNlcjI2MTkyMTM1", "organizations_url": "https://api.github.com/users/helpmefindaname/orgs", "received_events_url": "https://api.github.com/users/helpmefindaname/received_events", "repos_url": "https://api.github.com/users/helpmefindaname/repos", "site_admin": false, "starred_url": "https://api.github.com/users/helpmefindaname/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/helpmefindaname/subscriptions", "type": "User", "url": "https://api.github.com/users/helpmefindaname" }	[ { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" } ]	closed	false	null	[]	null	[ "Indeed, `clobber=True` (with a warning if the existing protocol will be overwritten) should fix the issue, but maybe a better solution is to register our compression filesystem before the script is executed and unregister them afterward. WDYT @lhoestq @albertvillanova?", "I think we should use clobber and show a warning if it overwrote a registered filesystem indeed ! This way the user can re-register the filesystems if needed. Though they should probably be compatible (and maybe do the exact same thing) so I wouldn't de-register the `datasets` filesystems" ]	2023-05-20T01:39:11Z	2023-05-25T06:42:34Z	2023-05-25T06:42:34Z	NONE	null	### Describe the bug Hello, I am currently working on a project where both [DataLab](https://github.com/ExpressAI/DataLab) and [datasets](https://github.com/huggingface/datasets) are subdependencies. I noticed that I cannot import both libraries, as they both register FileSystems in `fsspec`, expecting the FileSystems not being registered before. When running the code below, I get the following error: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\__init__.py", line 28, in <module> from datalabs.arrow_dataset import concatenate_datasets, Dataset File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_dataset.py", line 60, in <module> from datalabs.arrow_writer import ArrowWriter, OptimizedTypedSequence File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_writer.py", line 28, in <module> from datalabs.features import ( File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\__init__.py", line 2, in <module> from datalabs.features.audio import Audio File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\audio.py", line 21, in <module> from datalabs.utils.streaming_download_manager import xopen File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\utils\streaming_download_manager.py", line 16, in <module> from datalabs.filesystems import COMPRESSION_FILESYSTEMS File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\filesystems\__init__.py", line 37, in <module> fsspec.register_implementation(fs_class.protocol, fs_class) File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\fsspec\registry.py", line 51, in register_implementation raise ValueError( ValueError: Name (bz2) already in the registry and clobber is False ``` I think as simple solution would be to just set `clobber=True` in https://github.com/huggingface/datasets/blob/main/src/datasets/filesystems/__init__.py#L28. This allows the register to discard previous registrations. This should work, as the datalabs FileSystems are copies of the datasets FileSystems. However, I don't know if it is guaranteed to be compatible with other libraries that might use the same protocols. I am linking the symmetric issue on [DataLab](https://github.com/ExpressAI/DataLab/issues/425) as ideally the issue is solved in both libraries the same way. Otherwise, it could lead to different behaviors depending on which library gets imported first. ### Steps to reproduce the bug 1. Run `pip install datalabs==0.4.15 datasets==2.12.0` 2. Run the following python code: ``` import datalabs import datasets ``` ### Expected behavior It should be possible to import both libraries without getting a Value Error ### Environment info datalabs==0.4.15 datasets==2.12.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5876/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5876/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5875	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5875/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5875/comments	https://api.github.com/repos/huggingface/datasets/issues/5875/events	https://github.com/huggingface/datasets/issues/5875	1,716,770,394	I_kwDODunzps5mU9Za	5,875	Why split slicing doesn't behave like list slicing ?	{ "avatar_url": "https://avatars.githubusercontent.com/u/43774355?v=4", "events_url": "https://api.github.com/users/astariul/events{/privacy}", "followers_url": "https://api.github.com/users/astariul/followers", "following_url": "https://api.github.com/users/astariul/following{/other_user}", "gists_url": "https://api.github.com/users/astariul/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/astariul", "id": 43774355, "login": "astariul", "node_id": "MDQ6VXNlcjQzNzc0MzU1", "organizations_url": "https://api.github.com/users/astariul/orgs", "received_events_url": "https://api.github.com/users/astariul/received_events", "repos_url": "https://api.github.com/users/astariul/repos", "site_admin": false, "starred_url": "https://api.github.com/users/astariul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/astariul/subscriptions", "type": "User", "url": "https://api.github.com/users/astariul" }	[ { "color": "cfd3d7", "default": true, "description": "This issue or pull request already exists", "id": 1935892865, "name": "duplicate", "node_id": "MDU6TGFiZWwxOTM1ODkyODY1", "url": "https://api.github.com/repos/huggingface/datasets/labels/duplicate" } ]	open	false	null	[]	null	[ "A duplicate of https://github.com/huggingface/datasets/issues/1774" ]	2023-05-19T07:21:10Z	2023-05-23T16:02:14Z	null	NONE	null	### Describe the bug If I want to get the first 10 samples of my dataset, I can do : ``` ds = datasets.load_dataset('mnist', split='train[:10]') ``` But if I exceed the number of samples in the dataset, an exception is raised : ``` ds = datasets.load_dataset('mnist', split='train[:999999999]') ``` > ValueError: Requested slice [:999999999] incompatible with 60000 examples. ### Steps to reproduce the bug ``` ds = datasets.load_dataset('mnist', split='train[:999999999]') ``` ### Expected behavior I would expect it to behave like python lists (no exception raised, the whole list is kept) : ``` d = list(range(1000))[:999999] print(len(d)) # > 1000 ``` ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-12.6-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5875/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5875/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5874	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5874/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5874/comments	https://api.github.com/repos/huggingface/datasets/issues/5874/events	https://github.com/huggingface/datasets/issues/5874	1,715,708,930	I_kwDODunzps5mQ6QC	5,874	Using as_dataset on a "parquet" builder	{ "avatar_url": "https://avatars.githubusercontent.com/u/9039058?v=4", "events_url": "https://api.github.com/users/rems75/events{/privacy}", "followers_url": "https://api.github.com/users/rems75/followers", "following_url": "https://api.github.com/users/rems75/following{/other_user}", "gists_url": "https://api.github.com/users/rems75/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rems75", "id": 9039058, "login": "rems75", "node_id": "MDQ6VXNlcjkwMzkwNTg=", "organizations_url": "https://api.github.com/users/rems75/orgs", "received_events_url": "https://api.github.com/users/rems75/received_events", "repos_url": "https://api.github.com/users/rems75/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rems75/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rems75/subscriptions", "type": "User", "url": "https://api.github.com/users/rems75" }	[]	closed	false	null	[]	null	[ "Hi! You can refer to [this doc](https://huggingface.co/docs/datasets/filesystems#load-and-save-your-datasets-using-your-cloud-storage-filesystem) to see the intended usage (basically, it skips the Arrow -> Parquet conversion step in `ds = load_dataset(...); ds.to_parquet(\"path/to/parquet\")`) and allows writing Parquet to remote storage unlike `to_parquet`).\r\n\r\n> I guess I'd expect as_dataset to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with load_dataset to no avail, probably due to misunderstandings on my part).\r\n\r\n`as_dataset` does not work with `file_format=\"parquet\"` files as Parquet files cannot be memory-mapped, so I think we should just raise an error in that case.\r\n" ]	2023-05-18T14:09:03Z	2023-05-31T13:23:55Z	2023-05-31T13:23:55Z	NONE	null	### Describe the bug I used a custom builder to ``download_and_prepare`` a dataset. The first (very minor) issue is that the doc seems to suggest ``download_and_prepare`` will return the dataset, while it does not ([builder.py](https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L718-L738)). ``` >>> from datasets import load_dataset_builder >>> builder = load_dataset_builder("rotten_tomatoes") >>> ds = builder.download_and_prepare("./output_dir", file_format="parquet") ``` The main issue I am facing is loading the dataset from those parquet files. I used the `as_dataset` method suggested by the doc, however it returns: ` FileNotFoundError: [Errno 2] Failed to open local file 'output_dir/__main__-train-00000-of-00245.arrow'. Detail: [errno 2] No such file or directory. ` ### Steps to reproduce the bug 1. Create a custom builder of some sort: `builder = CustomBuilder()`. 2. Run `download_and_prepare` with the parquet format: `builder.download_and_prepare("./output_dir", file_format="parquet")`. 3. Run `dataset = builder.as_dataset()`. ### Expected behavior I guess I'd expect `as_dataset` to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with `load_dataset` to no avail, probably due to misunderstandings on my part). ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-1027-gcp-x86_64-with-glibc2.31 - Python version: 3.10.0 - Huggingface_hub version: 0.14.1 - PyArrow version: 8.0.0 - Pandas version: 1.5.3 ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5874/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5874/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5873	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5873/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5873/comments	https://api.github.com/repos/huggingface/datasets/issues/5873/events	https://github.com/huggingface/datasets/issues/5873	1,713,269,724	I_kwDODunzps5mHmvc	5,873	Allow setting the environment variable for the lock file path	{ "avatar_url": "https://avatars.githubusercontent.com/u/83260933?v=4", "events_url": "https://api.github.com/users/xin3he/events{/privacy}", "followers_url": "https://api.github.com/users/xin3he/followers", "following_url": "https://api.github.com/users/xin3he/following{/other_user}", "gists_url": "https://api.github.com/users/xin3he/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/xin3he", "id": 83260933, "login": "xin3he", "node_id": "MDQ6VXNlcjgzMjYwOTMz", "organizations_url": "https://api.github.com/users/xin3he/orgs", "received_events_url": "https://api.github.com/users/xin3he/received_events", "repos_url": "https://api.github.com/users/xin3he/repos", "site_admin": false, "starred_url": "https://api.github.com/users/xin3he/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xin3he/subscriptions", "type": "User", "url": "https://api.github.com/users/xin3he" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[]	2023-05-17T07:10:02Z	2023-05-17T07:11:05Z	null	NONE	null	### Feature request Add an environment variable to replace the default lock file path. ### Motivation Usually, dataset path is a read-only path while the lock file needs to be modified each time. It would be convenient if the path can be reset individually. ### Your contribution ```/src/datasets/utils/filelock.py class UnixFileLock(BaseFileLock): def __init__(self, lock_file, timeout=-1, max_filename_length=None): #------------------- if os.getenv('DS_TMP_PATH'): file_name = str(lock_file).split('/')[-1] dataset_tmp_path = os.getenv('DS_TMP_PATH') lock_file = os.path.join(dataset_tmp_path, file_name) #------------------- max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax super().__init__(lock_file, timeout=timeout, max_filename_length=max_filename_length) ``` A simple demo is as upper. Thanks.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5873/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5873/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5872	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5872/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5872/comments	https://api.github.com/repos/huggingface/datasets/issues/5872/events	https://github.com/huggingface/datasets/pull/5872	1,713,174,662	PR_kwDODunzps5QrQ5o	5,872	Fix infer module for uppercase extensions	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007049 / 0.011353 (-0.004304) \| 0.005034 / 0.011008 (-0.005974) \| 0.097737 / 0.038508 (0.059229) \| 0.033280 / 0.023109 (0.010170) \| 0.301017 / 0.275898 (0.025119) \| 0.336593 / 0.323480 (0.013113) \| 0.005567 / 0.007986 (-0.002419) \| 0.005384 / 0.004328 (0.001056) \| 0.072980 / 0.004250 (0.068730) \| 0.045030 / 0.037052 (0.007978) \| 0.303280 / 0.258489 (0.044791) \| 0.367528 / 0.293841 (0.073687) \| 0.034131 / 0.128546 (-0.094415) \| 0.012118 / 0.075646 (-0.063528) \| 0.331677 / 0.419271 (-0.087594) \| 0.049211 / 0.043533 (0.005678) \| 0.297535 / 0.255139 (0.042396) \| 0.318136 / 0.283200 (0.034936) \| 0.101574 / 0.141683 (-0.040109) \| 1.472769 / 1.452155 (0.020615) \| 1.541724 / 1.492716 (0.049007) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014646 / 0.018006 (-0.003360) \| 0.439050 / 0.000490 (0.438560) \| 0.008575 / 0.000200 (0.008375) \| 0.000297 / 0.000054 (0.000242) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027591 / 0.037411 (-0.009820) \| 0.111639 / 0.014526 (0.097113) \| 0.117098 / 0.176557 (-0.059458) \| 0.173281 / 0.737135 (-0.563855) \| 0.123197 / 0.296338 (-0.173141) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397507 / 0.215209 (0.182298) \| 3.971457 / 2.077655 (1.893803) \| 1.781158 / 1.504120 (0.277038) \| 1.590419 / 1.541195 (0.049224) \| 1.716374 / 1.468490 (0.247884) \| 0.687150 / 4.584777 (-3.897627) \| 3.691009 / 3.745712 (-0.054703) \| 2.050900 / 5.269862 (-3.218961) \| 1.304893 / 4.565676 (-3.260784) \| 0.084507 / 0.424275 (-0.339768) \| 0.012231 / 0.007607 (0.004624) \| 0.493033 / 0.226044 (0.266988) \| 4.929957 / 2.268929 (2.661028) \| 2.209069 / 55.444624 (-53.235555) \| 1.885992 / 6.876477 (-4.990485) \| 2.007004 / 2.142072 (-0.135069) \| 0.827265 / 4.805227 (-3.977963) \| 0.168225 / 6.500664 (-6.332439) \| 0.064988 / 0.075469 (-0.010481) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.182341 / 1.841788 (-0.659447) \| 14.691983 / 8.074308 (6.617674) \| 14.350720 / 10.191392 (4.159328) \| 0.164307 / 0.680424 (-0.516117) \| 0.017480 / 0.534201 (-0.516720) \| 0.421843 / 0.579283 (-0.157441) \| 0.417481 / 0.434364 (-0.016883) \| 0.496587 / 0.540337 (-0.043751) \| 0.581208 / 1.386936 (-0.805728) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007070 / 0.011353 (-0.004283) \| 0.005083 / 0.011008 (-0.005926) \| 0.075009 / 0.038508 (0.036500) \| 0.032343 / 0.023109 (0.009234) \| 0.366788 / 0.275898 (0.090890) \| 0.392273 / 0.323480 (0.068794) \| 0.005512 / 0.007986 (-0.002474) \| 0.003999 / 0.004328 (-0.000329) \| 0.073743 / 0.004250 (0.069492) \| 0.046203 / 0.037052 (0.009151) \| 0.367874 / 0.258489 (0.109385) \| 0.409154 / 0.293841 (0.115313) \| 0.035227 / 0.128546 (-0.093319) \| 0.012223 / 0.075646 (-0.063424) \| 0.087149 / 0.419271 (-0.332122) \| 0.045648 / 0.043533 (0.002115) \| 0.362414 / 0.255139 (0.107275) \| 0.379970 / 0.283200 (0.096770) \| 0.100631 / 0.141683 (-0.041052) \| 1.439733 / 1.452155 (-0.012422) \| 1.506266 / 1.492716 (0.013550) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227071 / 0.018006 (0.209065) \| 0.451243 / 0.000490 (0.450753) \| 0.000406 / 0.000200 (0.000206) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028952 / 0.037411 (-0.008459) \| 0.111934 / 0.014526 (0.097408) \| 0.124080 / 0.176557 (-0.052477) \| 0.174022 / 0.737135 (-0.563113) \| 0.126811 / 0.296338 (-0.169527) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436423 / 0.215209 (0.221214) \| 4.331959 / 2.077655 (2.254304) \| 2.111914 / 1.504120 (0.607794) \| 1.921338 / 1.541195 (0.380143) \| 1.994425 / 1.468490 (0.525935) \| 0.699164 / 4.584777 (-3.885613) \| 3.722143 / 3.745712 (-0.023569) \| 3.516538 / 5.269862 (-1.753323) \| 1.867245 / 4.565676 (-2.698431) \| 0.085923 / 0.424275 (-0.338352) \| 0.012059 / 0.007607 (0.004452) \| 0.586147 / 0.226044 (0.360102) \| 5.395823 / 2.268929 (3.126894) \| 2.594430 / 55.444624 (-52.850194) \| 2.275021 / 6.876477 (-4.601456) \| 2.347810 / 2.142072 (0.205737) \| 0.835118 / 4.805227 (-3.970109) \| 0.167089 / 6.500664 (-6.333575) \| 0.064893 / 0.075469 (-0.010576) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291423 / 1.841788 (-0.550365) \| 14.992696 / 8.074308 (6.918388) \| 13.307842 / 10.191392 (3.116450) \| 0.163799 / 0.680424 (-0.516625) \| 0.017315 / 0.534201 (-0.516886) \| 0.461319 / 0.579283 (-0.117965) \| 0.430474 / 0.434364 (-0.003889) \| 0.568115 / 0.540337 (0.027777) \| 0.647909 / 1.386936 (-0.739027) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5161c9ecdcdde9cc99c7f212da13523d5ba6bdb \"CML watermark\")\n" ]	2023-05-17T05:56:45Z	2023-05-17T14:26:59Z	2023-05-17T14:19:18Z	MEMBER	null	Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`. Before, `None` module was returned.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5872/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5872/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5872.diff", "html_url": "https://github.com/huggingface/datasets/pull/5872", "merged_at": "2023-05-17T14:19:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/5872.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5872" }	true
https://api.github.com/repos/huggingface/datasets/issues/5871	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5871/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5871/comments	https://api.github.com/repos/huggingface/datasets/issues/5871/events	https://github.com/huggingface/datasets/issues/5871	1,712,573,073	I_kwDODunzps5mE8qR	5,871	data configuration hash suffix depends on uncanonicalized data_dir	{ "avatar_url": "https://avatars.githubusercontent.com/u/5044802?v=4", "events_url": "https://api.github.com/users/kylrth/events{/privacy}", "followers_url": "https://api.github.com/users/kylrth/followers", "following_url": "https://api.github.com/users/kylrth/following{/other_user}", "gists_url": "https://api.github.com/users/kylrth/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kylrth", "id": 5044802, "login": "kylrth", "node_id": "MDQ6VXNlcjUwNDQ4MDI=", "organizations_url": "https://api.github.com/users/kylrth/orgs", "received_events_url": "https://api.github.com/users/kylrth/received_events", "repos_url": "https://api.github.com/users/kylrth/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kylrth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kylrth/subscriptions", "type": "User", "url": "https://api.github.com/users/kylrth" }	[ { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/5044802?v=4", "events_url": "https://api.github.com/users/kylrth/events{/privacy}", "followers_url": "https://api.github.com/users/kylrth/followers", "following_url": "https://api.github.com/users/kylrth/following{/other_user}", "gists_url": "https://api.github.com/users/kylrth/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kylrth", "id": 5044802, "login": "kylrth", "node_id": "MDQ6VXNlcjUwNDQ4MDI=", "organizations_url": "https://api.github.com/users/kylrth/orgs", "received_events_url": "https://api.github.com/users/kylrth/received_events", "repos_url": "https://api.github.com/users/kylrth/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kylrth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kylrth/subscriptions", "type": "User", "url": "https://api.github.com/users/kylrth" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/5044802?v=4", "events_url": "https://api.github.com/users/kylrth/events{/privacy}", "followers_url": "https://api.github.com/users/kylrth/followers", "following_url": "https://api.github.com/users/kylrth/following{/other_user}", "gists_url": "https://api.github.com/users/kylrth/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kylrth", "id": 5044802, "login": "kylrth", "node_id": "MDQ6VXNlcjUwNDQ4MDI=", "organizations_url": "https://api.github.com/users/kylrth/orgs", "received_events_url": "https://api.github.com/users/kylrth/received_events", "repos_url": "https://api.github.com/users/kylrth/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kylrth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kylrth/subscriptions", "type": "User", "url": "https://api.github.com/users/kylrth" } ]	null	[ "It could even use `os.path.realpath` to resolve symlinks.", "Indeed, it makes sense to normalize `data_dir`. Feel free to submit a PR (this can be \"fixed\" [here](https://github.com/huggingface/datasets/blob/89f775226321ba94e5bf4670a323c0fb44f5f65c/src/datasets/builder.py#L173))", "#self-assign" ]	2023-05-16T18:56:04Z	2023-06-02T15:52:05Z	2023-06-02T15:52:05Z	CONTRIBUTOR	null	### Describe the bug I am working with the `recipe_nlg` dataset, which requires manual download. Once it's downloaded, I've noticed that the hash in the custom data configuration is different if I add a trailing `/` to my `data_dir`. It took me a while to notice that the hashes were different, and to understand that that was the cause of my dataset being processed anew instead of the cached version being used. ### Steps to reproduce the bug 1. Follow the steps to manually download the `recipe_nlg` dataset to `/data/recipenlg`. 2. Load it using `load_dataset`, once without a trailing slash and once with one: ```python >>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg") Using custom data configuration default-082278caeea85765 Downloading and preparing dataset recipe_nlg/default to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74... Dataset recipe_nlg downloaded and prepared to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74. Subsequent calls will reuse this data. 100%\|███████████████████████████████████████████████████████████████████\| 1/1 [00:01<00:00, 1.10s/it] DatasetDict({ train: Dataset({ features: ['id', 'title', 'ingredients', 'directions', 'link', 'source', 'ner'], num_rows: 2231142 }) }) >>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg/") Using custom data configuration default-83e87680785d0493 Downloading and preparing dataset recipe_nlg/default to /home/user/.cache/huggingface/datasets/recipe_nlg/default-83e87680785d0493/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74... Generating train split: 1%\| \| 12701/2231142 [00:04<13:15, 2790.25 examples/s ^C ``` 3. Observe that the hash suffix in the custom data configuration changes due to the altered string. ### Expected behavior I think I would expect the hash to remain constant if it actually points to the same location on disk. I would expect the use of `os.path.normpath` to canonicalize the paths. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5871/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5871/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5870	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5870/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5870/comments	https://api.github.com/repos/huggingface/datasets/issues/5870/events	https://github.com/huggingface/datasets/issues/5870	1,712,156,282	I_kwDODunzps5mDW56	5,870	Behaviour difference between datasets.map and IterableDatasets.map	{ "avatar_url": "https://avatars.githubusercontent.com/u/30209072?v=4", "events_url": "https://api.github.com/users/llStringll/events{/privacy}", "followers_url": "https://api.github.com/users/llStringll/followers", "following_url": "https://api.github.com/users/llStringll/following{/other_user}", "gists_url": "https://api.github.com/users/llStringll/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/llStringll", "id": 30209072, "login": "llStringll", "node_id": "MDQ6VXNlcjMwMjA5MDcy", "organizations_url": "https://api.github.com/users/llStringll/orgs", "received_events_url": "https://api.github.com/users/llStringll/received_events", "repos_url": "https://api.github.com/users/llStringll/repos", "site_admin": false, "starred_url": "https://api.github.com/users/llStringll/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/llStringll/subscriptions", "type": "User", "url": "https://api.github.com/users/llStringll" }	[]	open	false	null	[]	null	[ "PS - some work is definitely needed for 'special cases' docs, not explanations, just usages of 'functions' under mixture of special cases, like a combination of custom databuilder + iterable dataset for large size + dynamic .map() application." ]	2023-05-16T14:32:57Z	2023-05-16T14:36:05Z	null	NONE	null	### Describe the bug All the examples in all the docs mentioned throughout huggingface datasets correspond to datasets object, and not IterableDatasets object. At one point of time, they might have been in sync, but the code for datasets version >=2.9.0 is very different as compared to the docs. I basically need to .map() a transform on images in an iterable dataset, which was made using a custom databuilder config. This works very good in map-styles datasets, but the .map() fails in IterableDatasets, show behvaiour as such: "pixel_values" key not found, KeyError in examples object/dict passed into transform function for map, which works fine with map style, even as batch. In iterable style, the object/dict passed into map() paramter callable function is completely different as what is mentioned in all examples. Please look into this. Thank you My databuilder class is inherited as such: def _info(self): print ("Config: ",self.config.__dict__.keys()) return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "labels": datasets.Sequence(datasets.Value("uint16")), # "labels_name": datasets.Value("string"), # "pixel_values": datasets.Array3D(shape=(3, 1280, 960), dtype="float32"), "pixel_values": datasets.Array3D(shape=(1280, 960, 3), dtype="uint8"), "image_s3_path": datasets.Value("string"), } ), supervised_keys=None, homepage="none", citation="", ) def _split_generators(self, dl_manager): records_train = list(db.mini_set.find({'split':'train'},{'image_s3_path':1, 'ocwen_template_name':1}))[:10000] records_val = list(db.mini_set.find({'split':'val'},{'image_s3_path':1, 'ocwen_template_name':1}))[:1000] # print (len(records),self.config.num_shards) # shard_size_train = len(records_train)//self.config.num_shards # sharded_records_train = [records_train[i:i+shard_size_train] for i in range(0,len(records_train),shard_size_train)] # shard_size_val = len(records_val)//self.config.num_shards # sharded_records_val = [records_val[i:i+shard_size_val] for i in range(0,len(records_val),shard_size_val)] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"records":records_train} # passing list of records, for sharding to take over ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"records":records_val} # passing list of records, for sharding to take over ), ] def _generate_examples(self, records): # print ("Generating examples for [{}] shards".format(len(shards))) # initiate_db_connection() # records = list(db.mini_set.find({'split':split},{'image_s3_path':1, 'ocwen_template_name':1}))[:10] id_ = 0 # for records in shards: for i,rec in enumerate(records): img_local_path = fetch_file(rec['image_s3_path'],self.config.buffer_dir) # t = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.squeeze() # print (t.shape, type(t),type(t[0][0][0])) # sys.exit() pvs = np.array(Image.open(img_local_path).resize((1280,960))) # image object is wxh, so resize as per that, numpy array of it is hxwxc, transposing to cxwxh # pvs = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.astype(np.float16).squeeze() # print (type(pvs[0][0][0])) lblids = self.config.processor.tokenizer('<s_class>'+rec['ocwen_template_name']+'</s_class>'+'</s>', add_special_tokens=False, padding=False, truncation=False, return_tensors="np")["input_ids"].squeeze(0) # take padding later, as per batch collating # print (len(lblids),type(lblids[0])) # print (type(pvs),pvs.shape,type(pvs[0][0][0]), type(lblids)) yield id_, {"labels":lblids,"pixel_values":pvs,"image_s3_path":rec['image_s3_path']} id_+=1 os.remove(img_local_path) and I load it inside my trainer script as such `ds = load_dataset("/tmp/DonutDS/dataset/", split="train", streaming=True) # iterable dataset, where .map() falls` or also as `ds = load_from_disk('/tmp/DonutDS/dataset/') #map style dataset` Thank you to the team for having such a great library, and for this bug fix in advance! ### Steps to reproduce the bug Above config can allow one to reproduce the said bug ### Expected behavior .map() should show some consistency b/w map-style and iterable-style datasets, or atleast the docs should address iterable-style datasets behaviour and examples. I honestly do not figure the use of such docs. ### Environment info datasets==2.9.0 transformers==4.26.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5870/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5870/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5869	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5869/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5869/comments	https://api.github.com/repos/huggingface/datasets/issues/5869/events	https://github.com/huggingface/datasets/issues/5869	1,711,990,003	I_kwDODunzps5mCuTz	5,869	Image Encoding Issue when submitting a Parquet Dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/47530815?v=4", "events_url": "https://api.github.com/users/PhilippeMoussalli/events{/privacy}", "followers_url": "https://api.github.com/users/PhilippeMoussalli/followers", "following_url": "https://api.github.com/users/PhilippeMoussalli/following{/other_user}", "gists_url": "https://api.github.com/users/PhilippeMoussalli/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/PhilippeMoussalli", "id": 47530815, "login": "PhilippeMoussalli", "node_id": "MDQ6VXNlcjQ3NTMwODE1", "organizations_url": "https://api.github.com/users/PhilippeMoussalli/orgs", "received_events_url": "https://api.github.com/users/PhilippeMoussalli/received_events", "repos_url": "https://api.github.com/users/PhilippeMoussalli/repos", "site_admin": false, "starred_url": "https://api.github.com/users/PhilippeMoussalli/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/PhilippeMoussalli/subscriptions", "type": "User", "url": "https://api.github.com/users/PhilippeMoussalli" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	[]	null	[ "Hi @PhilippeMoussalli thanks for opening a detailed issue. It seems the issue is more related to the `datasets` library so I'll ping @lhoestq @mariosasko on this one :) \n\n(edit: also can one of you move the issue to the datasets repo? Thanks in advance 🙏)", "Hi ! The `Image()` info is stored in the schema metadata. More precisely there should be a \"huggingface\" field in the schema metadata that contains the `datasets` feature type of each column.\r\n\r\nTo fix your issue, you can use the same schema as the original Parquet files to write the new ones. You can also get the schema with metadata from a `Features` object, e.g.\r\n\r\n```python\r\nfrom datasets import Features, Image, Value\r\n\r\nfeatures = Features({\"image\": Image(), \"text\": Value(\"string\")})\r\nschema = features.arrow_schema\r\nprint(schema.metadata)\r\n# {b'huggingface': b'{\"info\": {\"features\": {\"image\": {\"_type\": \"Image\"}, \"text\": {\"dtype\": \"string\", \"_type\": \"Value\"}}}}'}\r\n```", "It appears that the parquet files at `hf://datasets/lambdalabs/pokemon-blip-captions` don't have this metadata, and it is defined in the dataset_infos.json instead (legacy).\r\n\r\nYou can get the right schema with the HF metadata this way:\r\n\r\n```python\r\nfrom datasets import load_dataset_builder\r\n\r\nfeatures = load_dataset_builder(\"lambdalabs/pokemon-blip-captions\").info.features\r\nschema = features.arrow_schema\r\n```", "Btw in the future we might add support for an dedicated Image extension type in Arrow so that you won't need to add the schema metadata anymore ;)", "Thanks @Wauplin @lhoestq for the quick reply :)! \r\n\r\nI tried your approach by passing the huggingface schema to the dask writer \r\n\r\n```\r\nfrom datasets import Features, Image, Value\r\ndf = dd.read_parquet(f\"hf://datasets/lambdalabs/pokemon-blip-captions\",index=False)\r\nfeatures = Features({\"image\": Image(), \"text\": Value(\"string\")})\r\nschema = features.arrow_schema\r\ndd.to_parquet(df, path = \"hf://datasets/philippemo/dummy_dataset/data\", schema=schema)\r\n```\r\nAt first it didn't work as I was not able to visualize the images, so then I manually added the `dataset_infos.json` from the example dataset and it worked :)\r\n\r\nHowever, It's not very ideal since there are some metadata in that file that need to be computed in order to load the data properly such as `num_of_bytes` and `num_examples` which might be unknown in my use case. \r\n\r\n![Screenshot from 2023-05-16 16-54-55](https://github.com/huggingface/datasets/assets/47530815/b2b448d2-d3d8-43a7-9682-9c0187a5192b)\r\n\r\nDo you have any pointers there? you mentioned that `datasets_info.json` will be deprecated/legacy. Could you point me to some example image datasets on the hub that are stored as parquet and don't have the `datasets_info.json`?\r\n\r\n", "You don't need the dataset_infos.json file as long as you have the schema with HF metadata ;)\r\nI could also check that it works fine myself on the git revision without the dataset_infos.json file.\r\n\r\nWhat made you think it didn't work ?", "> You don't need the dataset_infos.json file as long as you have the schema with HF metadata ;) I could also check that it works fine myself on the git revision without the dataset_infos.json file.\r\n> \r\n> What made you think it didn't work ?\r\n\r\nThose are two identical dataset repos where both were pushed with dask with the specified schema you mentioned above. I then uploaded the `dataset_infos.json` manually taken from the original example dataset into one of them. \r\n\r\n* With schema: https://huggingface.co/datasets/philippemo/dummy_dataset_with_schema\r\n* Without schema: https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nYou can see that in the examples without schema the images fail to render properly. When loaded with `datasets` they return an dict and not a Pillow Image ", "I see ! I think it's a bug on our side - it should work without the metadata - let me investigate", "Alright, it's fixed: https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nIt shows the image correctly now - even without the extra metadata :)", "Thanks @lhoestq! \r\nI tested pushing a dataset again without the metadata and it works perfectly! \r\nI appreciate the help", "Hi @lhoestq, \r\n\r\nI'v tried pushing another dataset again and I think the issue reappeared again: \r\n\r\n```\r\ndf = dd.read_parquet(f\"hf://datasets/lambdalabs/pokemon-blip-captions\")\r\nfeatures = datasets.Features({\"image\": datasets.Image(), \"text\": datasets.Value(\"string\")})\r\nschema = features.arrow_schema\r\ndd.to_parquet(df, path = \"hf://datasets/philippemo/dummy_dataset_without_schema_12_06/data\", schema=schema)\r\n```\r\n\r\nHere is the dataset: \r\n https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema_12_06\r\nThe one that was working 2 weeks ago still seems to be intact though, it might be that It rendered properly when it was initially submitted and after this something was reverted from your side:\r\nhttps://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nIt's weird because nothing really changed from the implementation, might be another issue in the hub backend. Do you have any pointers on how to resolve this? ", "We're doing some changes in the way we're handling image parquet datasets right now. We'll include the fix from https://github.com/huggingface/datasets/pull/5921 in the new datasets-server version in the coming days", "alright thanks for the update :), would that be part of the new release of datasets or is it something separate? if so, where can I track it? ", "Once the new version of `datasets` is released (tomorrow probably) we'll open an issue on https://github.com/huggingface/datasets-server to update to this version :)" ]	2023-05-16T09:42:58Z	2023-06-13T16:24:31Z	null	NONE	null	### Describe the bug Hello, I'd like to report an issue related to pushing a dataset represented as a Parquet file to a dataset repository using Dask. Here are the details: We attempted to load an example dataset in Parquet format from the Hugging Face (HF) filesystem using Dask with the following code snippet: ``` import dask.dataframe as dd df = dd.read_parquet("hf://datasets/lambdalabs/pokemon-blip-captions",index=False) ``` In this dataset, the "image" column is represented as a dictionary/struct with the format: ``` df = df.compute() df["image"].iloc[0].keys() -> dict_keys(['bytes', 'path']) ``` I think this is the format encoded by the [`Image`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Image) feature extractor from datasets to format suitable for Arrow. The next step was to push the dataset to a repository that I created: ``` dd.to_parquet(dask_df, path = "hf://datasets/philippemo/dummy_dataset/data") ``` However, after pushing the dataset using Dask, the "image" column is now represented as the encoded dictionary `(['bytes', 'path'])`, and the images are not properly visualized. You can find the dataset here: [Link to the problematic dataset](https://huggingface.co/datasets/philippemo/dummy_dataset). It's worth noting that both the original dataset and the one submitted with Dask have the same schema with minor alterations related to metadata: [ Schema of original dummy example.](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/blob/main/data/train-00000-of-00001-566cc9b19d7203f8.parquet) ``` image: struct<bytes: binary, path: null> child 0, bytes: binary child 1, path: null text: string ``` [ Schema of pushed dataset with dask](https://huggingface.co/datasets/philippemo/dummy_dataset/blob/main/data/part.0.parquet) ``` image: struct<bytes: binary, path: null> child 0, bytes: binary child 1, path: null text: string ``` This issue seems to be related to an encoding type that occurs when pushing a model to the hub. Normally, models should be represented as an HF dataset before pushing, but we are working with an example where we need to push large datasets using Dask. Could you please provide clarification on how to resolve this issue? Thank you! ### Reproduction To get the schema I downloaded the parquet files and used pyarrow.parquet to read the schema ``` import pyarrow.parquet pyarrow.parquet.read_schema(<path_to_parquet>, memory_map=True) ``` ### Logs _No response_ ### System info ```shell - huggingface_hub version: 0.14.1 - Platform: Linux-5.19.0-41-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /home/philippe/.cache/huggingface/token - Has saved token ?: True - Who am I ?: philippemo - Configured git credential helpers: cache - FastAI: N/A - Tensorflow: N/A - Torch: N/A - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 9.4.0 - hf_transfer: N/A - gradio: N/A - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /home/philippe/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /home/philippe/.cache/huggingface/assets - HF_TOKEN_PATH: /home/philippe/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5869/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5869/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5868	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5868/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5868/comments	https://api.github.com/repos/huggingface/datasets/issues/5868/events	https://github.com/huggingface/datasets/issues/5868	1,711,173,098	I_kwDODunzps5l_m3q	5,868	Is it possible to change a cached file and 're-cache' it instead of re-generating?	{ "avatar_url": "https://avatars.githubusercontent.com/u/31238754?v=4", "events_url": "https://api.github.com/users/zyh3826/events{/privacy}", "followers_url": "https://api.github.com/users/zyh3826/followers", "following_url": "https://api.github.com/users/zyh3826/following{/other_user}", "gists_url": "https://api.github.com/users/zyh3826/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zyh3826", "id": 31238754, "login": "zyh3826", "node_id": "MDQ6VXNlcjMxMjM4NzU0", "organizations_url": "https://api.github.com/users/zyh3826/orgs", "received_events_url": "https://api.github.com/users/zyh3826/received_events", "repos_url": "https://api.github.com/users/zyh3826/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zyh3826/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zyh3826/subscriptions", "type": "User", "url": "https://api.github.com/users/zyh3826" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[ "Arrow files/primitives (tables and arrays) are immutable, so re-generating them is the only option, I'm afraid.", "> \r\n\r\nGot it, thanks for your reply" ]	2023-05-16T03:45:42Z	2023-05-17T11:21:36Z	2023-05-17T11:21:36Z	NONE	null	### Feature request Hi, I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours ### Motivation For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it. ### Your contribution For now, I can't help, sorry.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5868/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5868/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5867	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5867/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5867/comments	https://api.github.com/repos/huggingface/datasets/issues/5867/events	https://github.com/huggingface/datasets/pull/5867	1,710,656,067	PR_kwDODunzps5QizOn	5,867	Add logic for hashing modules/functions optimized with `torch.compile`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	open	false	null	[]	null	[ "<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) \| 0.006598 / 0.011353 (-0.004755) \| 0.004565 / 0.011008 (-0.006443) \| 0.099063 / 0.038508 (0.060555) \| 0.028334 / 0.023109 (0.005225) \| 0.323539 / 0.275898 (0.047641) \| 0.372462 / 0.323480 (0.048982) \| 0.005120 / 0.007986 (-0.002865) \| 0.004797 / 0.004328 (0.000468) \| 0.076862 / 0.004250 (0.072611) \| 0.038021 / 0.037052 (0.000968) \| 0.337801 / 0.258489 (0.079312) \| 0.374601 / 0.293841 (0.080760) \| 0.031158 / 0.128546 (-0.097389) \| 0.011672 / 0.075646 (-0.063974) \| 0.324913 / 0.419271 (-0.094359) \| 0.051702 / 0.043533 (0.008169) \| 0.339440 / 0.255139 (0.084301) \| 0.372502 / 0.283200 (0.089303) \| 0.097590 / 0.141683 (-0.044093) \| 1.534238 / 1.452155 (0.082083) \| 1.599701 / 1.492716 (0.106985) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204101 / 0.018006 (0.186095) \| 0.416981 / 0.000490 (0.416491) \| 0.003436 / 0.000200 (0.003236) \| 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) \| 0.023527 / 0.037411 (-0.013885) \| 0.095748 / 0.014526 (0.081222) \| 0.104498 / 0.176557 (-0.072059) \| 0.164000 / 0.737135 (-0.573135) \| 0.109170 / 0.296338 (-0.187168) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418239 / 0.215209 (0.203030) \| 4.153959 / 2.077655 (2.076305) \| 1.856687 / 1.504120 (0.352567) \| 1.657818 / 1.541195 (0.116623) \| 1.715146 / 1.468490 (0.246656) \| 0.700673 / 4.584777 (-3.884103) \| 3.401060 / 3.745712 (-0.344652) \| 2.891045 / 5.269862 (-2.378816) \| 1.519433 / 4.565676 (-3.046243) \| 0.083151 / 0.424275 (-0.341124) \| 0.012352 / 0.007607 (0.004745) \| 0.523901 / 0.226044 (0.297856) \| 5.288871 / 2.268929 (3.019943) \| 2.322806 / 55.444624 (-53.121818) \| 1.982223 / 6.876477 (-4.894253) \| 2.074883 / 2.142072 (-0.067189) \| 0.812400 / 4.805227 (-3.992827) \| 0.152183 / 6.500664 (-6.348481) \| 0.066538 / 0.075469 (-0.008931) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.223220 / 1.841788 (-0.618567) \| 14.024391 / 8.074308 (5.950083) \| 14.166657 / 10.191392 (3.975265) \| 0.146017 / 0.680424 (-0.534407) \| 0.016698 / 0.534201 (-0.517503) \| 0.380779 / 0.579283 (-0.198504) \| 0.387113 / 0.434364 (-0.047251) \| 0.446329 / 0.540337 (-0.094009) \| 0.523819 / 1.386936 (-0.863118) \|\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) \| 0.006803 / 0.011353 (-0.004549) \| 0.004554 / 0.011008 (-0.006454) \| 0.077406 / 0.038508 (0.038897) \| 0.028495 / 0.023109 (0.005386) \| 0.358847 / 0.275898 (0.082949) \| 0.393256 / 0.323480 (0.069776) \| 0.005317 / 0.007986 (-0.002669) \| 0.004690 / 0.004328 (0.000362) \| 0.075842 / 0.004250 (0.071592) \| 0.041985 / 0.037052 (0.004933) \| 0.367546 / 0.258489 (0.109057) \| 0.408019 / 0.293841 (0.114178) \| 0.030712 / 0.128546 (-0.097834) \| 0.011756 / 0.075646 (-0.063891) \| 0.086002 / 0.419271 (-0.333269) \| 0.038949 / 0.043533 (-0.004583) \| 0.361045 / 0.255139 (0.105906) \| 0.381728 / 0.283200 (0.098528) \| 0.090692 / 0.141683 (-0.050991) \| 1.493251 / 1.452155 (0.041097) \| 1.584566 / 1.492716 (0.091850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217470 / 0.018006 (0.199463) \| 0.429955 / 0.000490 (0.429465) \| 0.000394 / 0.000200 (0.000194) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026223 / 0.037411 (-0.011189) \| 0.102570 / 0.014526 (0.088045) \| 0.110848 / 0.176557 (-0.065709) \| 0.162413 / 0.737135 (-0.574722) \| 0.114579 / 0.296338 (-0.181760) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.464957 / 0.215209 (0.249748) \| 4.656597 / 2.077655 (2.578942) \| 2.279755 / 1.504120 (0.775636) \| 2.230263 / 1.541195 (0.689068) \| 2.341540 / 1.468490 (0.873050) \| 0.699505 / 4.584777 (-3.885272) \| 3.389003 / 3.745712 (-0.356709) \| 1.867526 / 5.269862 (-3.402336) \| 1.167171 / 4.565676 (-3.398506) \| 0.083451 / 0.424275 (-0.340824) \| 0.012348 / 0.007607 (0.004741) \| 0.584205 / 0.226044 (0.358161) \| 5.853623 / 2.268929 (3.584694) \| 2.646650 / 55.444624 (-52.797974) \| 2.286504 / 6.876477 (-4.589973) \| 2.327536 / 2.142072 (0.185464) \| 0.811209 / 4.805227 (-3.994018) \| 0.151842 / 6.500664 (-6.348822) \| 0.067783 / 0.075469 (-0.007686) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330427 / 1.841788 (-0.511360) \| 14.668981 / 8.074308 (6.594673) \| 13.321154 / 10.191392 (3.129762) \| 0.164383 / 0.680424 (-0.516040) \| 0.016667 / 0.534201 (-0.517534) \| 0.383439 / 0.579283 (-0.195844) \| 0.392988 / 0.434364 (-0.041376) \| 0.443318 / 0.540337 (-0.097020) \| 0.537849 / 1.386936 (-0.849087) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e99bd4583bd636074b1826e2d0581161807480f1 \"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) \| 0.006379 / 0.011353 (-0.004974) \| 0.004691 / 0.011008 (-0.006317) \| 0.098047 / 0.038508 (0.059539) \| 0.028126 / 0.023109 (0.005017) \| 0.327143 / 0.275898 (0.051245) \| 0.362482 / 0.323480 (0.039002) \| 0.004953 / 0.007986 (-0.003033) \| 0.003386 / 0.004328 (-0.000943) \| 0.076222 / 0.004250 (0.071971) \| 0.037583 / 0.037052 (0.000531) \| 0.329661 / 0.258489 (0.071172) \| 0.365945 / 0.293841 (0.072104) \| 0.030455 / 0.128546 (-0.098091) \| 0.011397 / 0.075646 (-0.064249) \| 0.323889 / 0.419271 (-0.095383) \| 0.043719 / 0.043533 (0.000186) \| 0.331499 / 0.255139 (0.076360) \| 0.359357 / 0.283200 (0.076158) \| 0.088904 / 0.141683 (-0.052779) \| 1.458584 / 1.452155 (0.006429) \| 1.549375 / 1.492716 (0.056658) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195808 / 0.018006 (0.177802) \| 0.411148 / 0.000490 (0.410659) \| 0.003602 / 0.000200 (0.003402) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023278 / 0.037411 (-0.014133) \| 0.097317 / 0.014526 (0.082791) \| 0.102669 / 0.176557 (-0.073888) \| 0.168203 / 0.737135 (-0.568933) \| 0.105205 / 0.296338 (-0.191133) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424800 / 0.215209 (0.209591) \| 4.228444 / 2.077655 (2.150790) \| 1.895544 / 1.504120 (0.391424) \| 1.698793 / 1.541195 (0.157598) \| 1.717931 / 1.468490 (0.249441) \| 0.702251 / 4.584777 (-3.882526) \| 3.407013 / 3.745712 (-0.338699) \| 2.784634 / 5.269862 (-2.485228) \| 1.491317 / 4.565676 (-3.074359) \| 0.082926 / 0.424275 (-0.341350) \| 0.012320 / 0.007607 (0.004713) \| 0.524188 / 0.226044 (0.298143) \| 5.249798 / 2.268929 (2.980870) \| 2.358953 / 55.444624 (-53.085672) \| 1.985922 / 6.876477 (-4.890555) \| 2.034293 / 2.142072 (-0.107779) \| 0.815671 / 4.805227 (-3.989556) \| 0.152583 / 6.500664 (-6.348081) \| 0.066687 / 0.075469 (-0.008782) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.210901 / 1.841788 (-0.630886) \| 13.621765 / 8.074308 (5.547457) \| 14.213215 / 10.191392 (4.021823) \| 0.143346 / 0.680424 (-0.537078) \| 0.016904 / 0.534201 (-0.517297) \| 0.379795 / 0.579283 (-0.199489) \| 0.381287 / 0.434364 (-0.053077) \| 0.449086 / 0.540337 (-0.091251) \| 0.538792 / 1.386936 (-0.848144) \|\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) \| 0.006207 / 0.011353 (-0.005146) \| 0.004404 / 0.011008 (-0.006604) \| 0.076363 / 0.038508 (0.037854) \| 0.027335 / 0.023109 (0.004226) \| 0.370967 / 0.275898 (0.095069) \| 0.401936 / 0.323480 (0.078456) \| 0.004835 / 0.007986 (-0.003151) \| 0.004559 / 0.004328 (0.000231) \| 0.074964 / 0.004250 (0.070713) \| 0.038254 / 0.037052 (0.001202) \| 0.374799 / 0.258489 (0.116310) \| 0.425191 / 0.293841 (0.131350) \| 0.035290 / 0.128546 (-0.093256) \| 0.011379 / 0.075646 (-0.064267) \| 0.085911 / 0.419271 (-0.333360) \| 0.043073 / 0.043533 (-0.000460) \| 0.373557 / 0.255139 (0.118418) \| 0.395179 / 0.283200 (0.111979) \| 0.098602 / 0.141683 (-0.043081) \| 1.467234 / 1.452155 (0.015079) \| 1.571868 / 1.492716 (0.079152) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221848 / 0.018006 (0.203842) \| 0.394943 / 0.000490 (0.394454) \| 0.002983 / 0.000200 (0.002783) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024385 / 0.037411 (-0.013027) \| 0.100087 / 0.014526 (0.085561) \| 0.104897 / 0.176557 (-0.071660) \| 0.156150 / 0.737135 (-0.580985) \| 0.109113 / 0.296338 (-0.187226) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441995 / 0.215209 (0.226786) \| 4.415423 / 2.077655 (2.337769) \| 2.148791 / 1.504120 (0.644671) \| 1.947061 / 1.541195 (0.405866) \| 1.954807 / 1.468490 (0.486317) \| 0.690245 / 4.584777 (-3.894532) \| 3.372766 / 3.745712 (-0.372946) \| 1.851073 / 5.269862 (-3.418789) \| 1.155558 / 4.565676 (-3.410118) \| 0.082796 / 0.424275 (-0.341479) \| 0.012845 / 0.007607 (0.005238) \| 0.548173 / 0.226044 (0.322129) \| 5.530984 / 2.268929 (3.262056) \| 2.665360 / 55.444624 (-52.779264) \| 2.324266 / 6.876477 (-4.552211) \| 2.329397 / 2.142072 (0.187324) \| 0.801481 / 4.805227 (-4.003746) \| 0.152145 / 6.500664 (-6.348519) \| 0.067915 / 0.075469 (-0.007554) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291488 / 1.841788 (-0.550299) \| 13.912143 / 8.074308 (5.837835) \| 12.975493 / 10.191392 (2.784101) \| 0.129915 / 0.680424 (-0.550509) \| 0.016516 / 0.534201 (-0.517685) \| 0.386979 / 0.579283 (-0.192304) \| 0.389163 / 0.434364 (-0.045201) \| 0.443324 / 0.540337 (-0.097014) \| 0.533744 / 1.386936 (-0.853192) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eb48834fc2aa45cad73fe70a7ecaa0dd6015b8d0 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5867). 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) \| 0.008635 / 0.011353 (-0.002717) \| 0.006014 / 0.011008 (-0.004995) \| 0.116314 / 0.038508 (0.077806) \| 0.041113 / 0.023109 (0.018004) \| 0.358564 / 0.275898 (0.082666) \| 0.397547 / 0.323480 (0.074067) \| 0.007012 / 0.007986 (-0.000974) \| 0.004638 / 0.004328 (0.000310) \| 0.086509 / 0.004250 (0.082259) \| 0.056731 / 0.037052 (0.019678) \| 0.358859 / 0.258489 (0.100370) \| 0.425339 / 0.293841 (0.131498) \| 0.041780 / 0.128546 (-0.086767) \| 0.014203 / 0.075646 (-0.061443) \| 0.398240 / 0.419271 (-0.021031) \| 0.060180 / 0.043533 (0.016647) \| 0.352887 / 0.255139 (0.097748) \| 0.381793 / 0.283200 (0.098594) \| 0.148578 / 0.141683 (0.006895) \| 1.749483 / 1.452155 (0.297328) \| 1.869765 / 1.492716 (0.377049) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244435 / 0.018006 (0.226428) \| 0.499545 / 0.000490 (0.499055) \| 0.004576 / 0.000200 (0.004376) \| 0.000147 / 0.000054 (0.000093) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031163 / 0.037411 (-0.006249) \| 0.131082 / 0.014526 (0.116556) \| 0.137442 / 0.176557 (-0.039114) \| 0.203783 / 0.737135 (-0.533352) \| 0.144068 / 0.296338 (-0.152270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.503587 / 0.215209 (0.288378) \| 5.011953 / 2.077655 (2.934299) \| 2.366968 / 1.504120 (0.862848) \| 2.130914 / 1.541195 (0.589719) \| 2.243560 / 1.468490 (0.775070) \| 0.856719 / 4.584777 (-3.728058) \| 4.707445 / 3.745712 (0.961733) \| 2.506166 / 5.269862 (-2.763696) \| 1.590400 / 4.565676 (-2.975277) \| 0.102075 / 0.424275 (-0.322200) \| 0.014499 / 0.007607 (0.006892) \| 0.624966 / 0.226044 (0.398922) \| 6.197671 / 2.268929 (3.928742) \| 2.898481 / 55.444624 (-52.546143) \| 2.499590 / 6.876477 (-4.376886) \| 2.649690 / 2.142072 (0.507617) \| 1.012542 / 4.805227 (-3.792685) \| 0.202833 / 6.500664 (-6.297831) \| 0.078033 / 0.075469 (0.002564) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.448321 / 1.841788 (-0.393467) \| 18.084909 / 8.074308 (10.010601) \| 17.383027 / 10.191392 (7.191635) \| 0.212167 / 0.680424 (-0.468256) \| 0.020754 / 0.534201 (-0.513447) \| 0.514653 / 0.579283 (-0.064630) \| 0.543307 / 0.434364 (0.108944) \| 0.653066 / 0.540337 (0.112728) \| 0.745773 / 1.386936 (-0.641164) \|\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) \| 0.008576 / 0.011353 (-0.002777) \| 0.005834 / 0.011008 (-0.005174) \| 0.089842 / 0.038508 (0.051334) \| 0.040035 / 0.023109 (0.016926) \| 0.449329 / 0.275898 (0.173431) \| 0.471572 / 0.323480 (0.148092) \| 0.006771 / 0.007986 (-0.001215) \| 0.006129 / 0.004328 (0.001800) \| 0.090370 / 0.004250 (0.086119) \| 0.056924 / 0.037052 (0.019872) \| 0.455134 / 0.258489 (0.196645) \| 0.502670 / 0.293841 (0.208829) \| 0.041689 / 0.128546 (-0.086857) \| 0.014447 / 0.075646 (-0.061200) \| 0.104528 / 0.419271 (-0.314744) \| 0.055535 / 0.043533 (0.012003) \| 0.450667 / 0.255139 (0.195528) \| 0.453108 / 0.283200 (0.169908) \| 0.119296 / 0.141683 (-0.022387) \| 1.747359 / 1.452155 (0.295204) \| 1.839421 / 1.492716 (0.346705) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314910 / 0.018006 (0.296904) \| 0.495575 / 0.000490 (0.495085) \| 0.054702 / 0.000200 (0.054503) \| 0.000505 / 0.000054 (0.000450) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033991 / 0.037411 (-0.003420) \| 0.133268 / 0.014526 (0.118742) \| 0.142286 / 0.176557 (-0.034271) \| 0.200562 / 0.737135 (-0.536573) \| 0.147161 / 0.296338 (-0.149178) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.520288 / 0.215209 (0.305079) \| 5.227684 / 2.077655 (3.150029) \| 2.553330 / 1.504120 (1.049210) \| 2.324338 / 1.541195 (0.783143) \| 2.406790 / 1.468490 (0.938300) \| 0.850404 / 4.584777 (-3.734373) \| 4.612156 / 3.745712 (0.866444) \| 2.592546 / 5.269862 (-2.677316) \| 1.708984 / 4.565676 (-2.856692) \| 0.103751 / 0.424275 (-0.320524) \| 0.014379 / 0.007607 (0.006772) \| 0.634661 / 0.226044 (0.408616) \| 6.344939 / 2.268929 (4.076010) \| 3.179807 / 55.444624 (-52.264817) \| 2.831856 / 6.876477 (-4.044621) \| 2.866729 / 2.142072 (0.724656) \| 0.994519 / 4.805227 (-3.810708) \| 0.201566 / 6.500664 (-6.299098) \| 0.078902 / 0.075469 (0.003433) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.538738 / 1.841788 (-0.303049) \| 18.746367 / 8.074308 (10.672059) \| 16.504763 / 10.191392 (6.313371) \| 0.197898 / 0.680424 (-0.482526) \| 0.020469 / 0.534201 (-0.513732) \| 0.529106 / 0.579283 (-0.050177) \| 0.536891 / 0.434364 (0.102527) \| 0.600947 / 0.540337 (0.060610) \| 0.701713 / 1.386936 (-0.685223) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3054f66b4765a520e6fe165c44a4307d40775229 \"CML watermark\")\n" ]	2023-05-15T19:03:35Z	2023-05-17T13:41:48Z	null	CONTRIBUTOR	null	Fix https://github.com/huggingface/datasets/issues/5839 PS: The `Pickler.save` method is becoming a bit messy, so I plan to refactor the pickler a bit at some point.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5867/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5867/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5867.diff", "html_url": "https://github.com/huggingface/datasets/pull/5867", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5867.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5867" }	true
https://api.github.com/repos/huggingface/datasets/issues/5866	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5866/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5866/comments	https://api.github.com/repos/huggingface/datasets/issues/5866/events	https://github.com/huggingface/datasets/issues/5866	1,710,496,993	I_kwDODunzps5l9Bzh	5,866	Issue with Sequence features	{ "avatar_url": "https://avatars.githubusercontent.com/u/14365168?v=4", "events_url": "https://api.github.com/users/alialamiidrissi/events{/privacy}", "followers_url": "https://api.github.com/users/alialamiidrissi/followers", "following_url": "https://api.github.com/users/alialamiidrissi/following{/other_user}", "gists_url": "https://api.github.com/users/alialamiidrissi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alialamiidrissi", "id": 14365168, "login": "alialamiidrissi", "node_id": "MDQ6VXNlcjE0MzY1MTY4", "organizations_url": "https://api.github.com/users/alialamiidrissi/orgs", "received_events_url": "https://api.github.com/users/alialamiidrissi/received_events", "repos_url": "https://api.github.com/users/alialamiidrissi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alialamiidrissi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alialamiidrissi/subscriptions", "type": "User", "url": "https://api.github.com/users/alialamiidrissi" }	[]	closed	false	null	[]	null	[ "Thanks for reporting! I've opened a PR with a fix." ]	2023-05-15T17:13:29Z	2023-05-26T11:57:17Z	2023-05-26T11:57:17Z	NONE	null	### Describe the bug Sequences features sometimes causes errors when the specified length is not -1 ### Steps to reproduce the bug ```python import numpy as np from datasets import Features, ClassLabel, Sequence, Value, Dataset feats = Features(**{'target': ClassLabel(names=[0, 1]),'x': Sequence(feature=Value(dtype='float64',id=None), length=2, id=None)}) Dataset.from_dict({"target": np.ones(2000).astype(int), "x": np.random.rand(2000,2)},features = feats).flatten_indices() ``` Throws: ``` TypeError: Couldn't cast array of type fixed_size_list<item: double>[2] to Sequence(feature=Value(dtype='float64', id=None), length=2, id=None) ``` The same code works without any issues when `length = -1` EDIT: The error seems to happen only when the length of the dataset is bigger than 1000 for some reason ### Expected behavior No exception ### Environment info - `datasets` version: 2.10.1 - Python version: 3.9.5 - PyArrow version: 11.0.0 - Pandas version: 1.4.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5866/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5866/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5865	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5865/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5865/comments	https://api.github.com/repos/huggingface/datasets/issues/5865/events	https://github.com/huggingface/datasets/pull/5865	1,710,455,738	PR_kwDODunzps5QiHnw	5,865	Deprecate task api	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5865). All of your documentation changes will be reflected on that endpoint.", "If it's easy to keep supporting it we can keep it no ? There are many datasets on the hub that implement the tasks templates in dataset scripts and it's maybe easier to keep task templates than opening PRs to those datasets.", "do we know if people use the tasks api?\r\n\r\nedit: i mean, i'm fine with removing it if it's not used much, especially considering that it's not documented well." ]	2023-05-15T16:48:24Z	2023-05-15T18:21:27Z	null	CONTRIBUTOR	null	The task API is not well adopted in the ecosystem, so this PR deprecates it. The `train_eval_index` is a newer, more flexible solution that should be used instead (I think?). These are the projects that still use the task API : * the image classification example in Transformers: [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/pytorch/image-classification/run_image_classification_no_trainer.py#L262) and [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/tensorflow/image-classification/run_image_classification.py#L277) * autotrain: [here](https://github.com/huggingface/autotrain-backend/blob/455e274004b56f9377d64db4ab03671508fcc4cd/zeus/zeus/run/utils.py#L666) * api-inference-community: [here](https://github.com/huggingface/api-inference-community/blob/fb8fb29d577a5bf01c82944db745489a6d6ed3d4/manage.py#L64) (but the rest of the code does not call the `resolve_dataset` function) So we need to update these files after the merge. cc @lewtun	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5865/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5865/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5865.diff", "html_url": "https://github.com/huggingface/datasets/pull/5865", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5865.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5865" }	true
https://api.github.com/repos/huggingface/datasets/issues/5864	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5864/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5864/comments	https://api.github.com/repos/huggingface/datasets/issues/5864/events	https://github.com/huggingface/datasets/issues/5864	1,710,450,047	I_kwDODunzps5l82V_	5,864	Slow iteration over Torch tensors	{ "avatar_url": "https://avatars.githubusercontent.com/u/51738205?v=4", "events_url": "https://api.github.com/users/crisostomi/events{/privacy}", "followers_url": "https://api.github.com/users/crisostomi/followers", "following_url": "https://api.github.com/users/crisostomi/following{/other_user}", "gists_url": "https://api.github.com/users/crisostomi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/crisostomi", "id": 51738205, "login": "crisostomi", "node_id": "MDQ6VXNlcjUxNzM4MjA1", "organizations_url": "https://api.github.com/users/crisostomi/orgs", "received_events_url": "https://api.github.com/users/crisostomi/received_events", "repos_url": "https://api.github.com/users/crisostomi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/crisostomi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/crisostomi/subscriptions", "type": "User", "url": "https://api.github.com/users/crisostomi" }	[]	open	false	null	[]	null	[ "I am highly interested performance of dataset so I ran your example as a curious user.\r\n```python\r\ntrain_dataset.cast_column(\"x\", Array3D(shape=img_shape, dtype=\"float32\"))\r\n```\r\nhave return values and \"x\" is a new column, it shoulde be\r\n```python\r\nds=train_dataset.cast_column(\"img\", Array3D(shape=(3,32,32), dtype=\"float32\"))\r\n```\r\nI rewrite your example as\r\n```python\r\ntrain_dataset = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\ntransform_func = torchvision.transforms.Compose([\r\n ToTensor(), \r\n Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] \r\n)\r\n \r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"img\": transform_func(x[\"img\"])},\r\n)\r\nds=train_dataset.cast_column(\"img\", Array3D(shape=(3,32,32), dtype=\"float32\"))\r\nfor i in tqdm(ds):\r\n pass\r\n```\r\nthat require ~11s in my environment. While\r\n```python\r\nds = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\n\r\nfor i in tqdm(ds):\r\n pass\r\n```\r\nonly need ~6s. (So I guess it's still undesirable)" ]	2023-05-15T16:43:58Z	2023-05-16T03:27:38Z	null	NONE	null	### Describe the bug I have a problem related to this [issue](https://github.com/huggingface/datasets/issues/5841): I get a way slower iteration when using a Torch dataloader if I use vanilla Numpy tensors or if I first apply a ToTensor transform to the input. In particular, it takes 5 seconds to iterate over the vanilla input and ~30s after the transformation. ### Steps to reproduce the bug Here is the minimum code to reproduce the problem ```python import numpy as np from datasets import Dataset, DatasetDict, load_dataset, Array3D, Image, Features from torch.utils.data import DataLoader from tqdm import tqdm import torchvision from torchvision.transforms import ToTensor, Normalize ################################# # Without transform ################################# train_dataset = load_dataset( 'cifar100', split='train', use_auth_token=True, ) train_dataset.set_format(type="numpy", columns=["img", "fine_label"]) train_loader= DataLoader( train_dataset, batch_size=100, pin_memory=False, shuffle=True, num_workers=8, ) for batch in tqdm(train_loader, desc="Loading data, no transform"): pass ################################# # With transform ################################# transform_func = torchvision.transforms.Compose([ ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] ) train_dataset = train_dataset.map( desc=f"Preprocessing samples", function=lambda x: {"img": transform_func(x["img"])}, ) train_dataset.set_format(type="numpy", columns=["img", "fine_label"]) train_loader= DataLoader( train_dataset, batch_size=100, pin_memory=False, shuffle=True, num_workers=8, ) for batch in tqdm(train_loader, desc="Loading data after transform"): pass ``` I have also tried converting the Image column to an Array3D ```python img_shape = train_dataset[0]["img"].shape features = train_dataset.features.copy() features["x"] = Array3D(shape=img_shape, dtype="float32") train_dataset = train_dataset.map( desc=f"Preprocessing samples", function=lambda x: {"x": np.array(x["img"], dtype=np.uint8)}, features=features, ) train_dataset.cast_column("x", Array3D(shape=img_shape, dtype="float32")) train_dataset.set_format(type="numpy", columns=["x", "fine_label"]) ``` but to no avail. Any clue? ### Expected behavior The iteration should take approximately the same time with or without the transformation, as it doesn't change the shape of the input. What may be the issue here? ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5864/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5864/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5863	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5863/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5863/comments	https://api.github.com/repos/huggingface/datasets/issues/5863/events	https://github.com/huggingface/datasets/pull/5863	1,710,335,905	PR_kwDODunzps5QhtlM	5,863	Use a new low-memory approach for tf dataset index shuffling	{ "avatar_url": "https://avatars.githubusercontent.com/u/12866554?v=4", "events_url": "https://api.github.com/users/Rocketknight1/events{/privacy}", "followers_url": "https://api.github.com/users/Rocketknight1/followers", "following_url": "https://api.github.com/users/Rocketknight1/following{/other_user}", "gists_url": "https://api.github.com/users/Rocketknight1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Rocketknight1", "id": 12866554, "login": "Rocketknight1", "node_id": "MDQ6VXNlcjEyODY2NTU0", "organizations_url": "https://api.github.com/users/Rocketknight1/orgs", "received_events_url": "https://api.github.com/users/Rocketknight1/received_events", "repos_url": "https://api.github.com/users/Rocketknight1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Rocketknight1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Rocketknight1/subscriptions", "type": "User", "url": "https://api.github.com/users/Rocketknight1" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5863). 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) \| 0.007764 / 0.011353 (-0.003588) \| 0.005397 / 0.011008 (-0.005611) \| 0.097995 / 0.038508 (0.059487) \| 0.036360 / 0.023109 (0.013251) \| 0.312148 / 0.275898 (0.036250) \| 0.349427 / 0.323480 (0.025947) \| 0.006635 / 0.007986 (-0.001350) \| 0.004373 / 0.004328 (0.000044) \| 0.074350 / 0.004250 (0.070099) \| 0.054667 / 0.037052 (0.017614) \| 0.301621 / 0.258489 (0.043132) \| 0.364233 / 0.293841 (0.070392) \| 0.035356 / 0.128546 (-0.093191) \| 0.012512 / 0.075646 (-0.063134) \| 0.333399 / 0.419271 (-0.085873) \| 0.051363 / 0.043533 (0.007830) \| 0.302372 / 0.255139 (0.047233) \| 0.326542 / 0.283200 (0.043343) \| 0.118610 / 0.141683 (-0.023073) \| 1.438485 / 1.452155 (-0.013669) \| 1.539131 / 1.492716 (0.046415) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010920 / 0.018006 (-0.007086) \| 0.561263 / 0.000490 (0.560773) \| 0.003972 / 0.000200 (0.003772) \| 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) \| 0.030333 / 0.037411 (-0.007078) \| 0.113608 / 0.014526 (0.099083) \| 0.125802 / 0.176557 (-0.050755) \| 0.183885 / 0.737135 (-0.553250) \| 0.130242 / 0.296338 (-0.166097) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404147 / 0.215209 (0.188938) \| 4.021990 / 2.077655 (1.944335) \| 1.821450 / 1.504120 (0.317330) \| 1.619032 / 1.541195 (0.077837) \| 1.791267 / 1.468490 (0.322777) \| 0.706683 / 4.584777 (-3.878094) \| 3.819056 / 3.745712 (0.073344) \| 3.485714 / 5.269862 (-1.784147) \| 1.938968 / 4.565676 (-2.626709) \| 0.086501 / 0.424275 (-0.337774) \| 0.012300 / 0.007607 (0.004693) \| 0.503600 / 0.226044 (0.277555) \| 5.042123 / 2.268929 (2.773195) \| 2.269712 / 55.444624 (-53.174912) \| 1.944912 / 6.876477 (-4.931565) \| 2.155196 / 2.142072 (0.013123) \| 0.853434 / 4.805227 (-3.951793) \| 0.175554 / 6.500664 (-6.325110) \| 0.072005 / 0.075469 (-0.003464) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203765 / 1.841788 (-0.638022) \| 15.836634 / 8.074308 (7.762326) \| 15.707348 / 10.191392 (5.515956) \| 0.164828 / 0.680424 (-0.515596) \| 0.018115 / 0.534201 (-0.516086) \| 0.434591 / 0.579283 (-0.144692) \| 0.437858 / 0.434364 (0.003495) \| 0.524672 / 0.540337 (-0.015665) \| 0.610535 / 1.386936 (-0.776401) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007558 / 0.011353 (-0.003795) \| 0.005258 / 0.011008 (-0.005750) \| 0.075263 / 0.038508 (0.036755) \| 0.033915 / 0.023109 (0.010805) \| 0.371368 / 0.275898 (0.095470) \| 0.399239 / 0.323480 (0.075760) \| 0.006547 / 0.007986 (-0.001439) \| 0.004675 / 0.004328 (0.000347) \| 0.074230 / 0.004250 (0.069980) \| 0.054653 / 0.037052 (0.017601) \| 0.376655 / 0.258489 (0.118166) \| 0.438437 / 0.293841 (0.144596) \| 0.035838 / 0.128546 (-0.092709) \| 0.012641 / 0.075646 (-0.063005) \| 0.087279 / 0.419271 (-0.331993) \| 0.046311 / 0.043533 (0.002778) \| 0.356649 / 0.255139 (0.101510) \| 0.377876 / 0.283200 (0.094677) \| 0.108097 / 0.141683 (-0.033586) \| 1.478461 / 1.452155 (0.026306) \| 1.560375 / 1.492716 (0.067658) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316384 / 0.018006 (0.298378) \| 0.539382 / 0.000490 (0.538892) \| 0.002029 / 0.000200 (0.001829) \| 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) \| 0.029950 / 0.037411 (-0.007462) \| 0.111371 / 0.014526 (0.096846) \| 0.125254 / 0.176557 (-0.051303) \| 0.173064 / 0.737135 (-0.564071) \| 0.130446 / 0.296338 (-0.165893) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424882 / 0.215209 (0.209673) \| 4.241575 / 2.077655 (2.163920) \| 2.096216 / 1.504120 (0.592096) \| 1.916017 / 1.541195 (0.374823) \| 2.016318 / 1.468490 (0.547828) \| 0.701197 / 4.584777 (-3.883580) \| 3.762365 / 3.745712 (0.016652) \| 3.307805 / 5.269862 (-1.962057) \| 1.841752 / 4.565676 (-2.723925) \| 0.086003 / 0.424275 (-0.338272) \| 0.012247 / 0.007607 (0.004640) \| 0.532926 / 0.226044 (0.306882) \| 5.370509 / 2.268929 (3.101580) \| 2.587853 / 55.444624 (-52.856772) \| 2.264541 / 6.876477 (-4.611936) \| 2.374833 / 2.142072 (0.232760) \| 0.827751 / 4.805227 (-3.977476) \| 0.169454 / 6.500664 (-6.331210) \| 0.066340 / 0.075469 (-0.009129) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.319128 / 1.841788 (-0.522660) \| 16.702085 / 8.074308 (8.627777) \| 13.559957 / 10.191392 (3.368565) \| 0.146659 / 0.680424 (-0.533765) \| 0.017384 / 0.534201 (-0.516817) \| 0.421126 / 0.579283 (-0.158157) \| 0.422067 / 0.434364 (-0.012297) \| 0.490615 / 0.540337 (-0.049723) \| 0.587151 / 1.386936 (-0.799785) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79f4b6de25128999f5fc0a7bde9aa71c461f518f \"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) \| 0.006604 / 0.011353 (-0.004749) \| 0.004508 / 0.011008 (-0.006500) \| 0.098652 / 0.038508 (0.060144) \| 0.028172 / 0.023109 (0.005063) \| 0.366997 / 0.275898 (0.091099) \| 0.403691 / 0.323480 (0.080211) \| 0.005127 / 0.007986 (-0.002859) \| 0.003340 / 0.004328 (-0.000989) \| 0.075408 / 0.004250 (0.071157) \| 0.038049 / 0.037052 (0.000996) \| 0.367914 / 0.258489 (0.109425) \| 0.410958 / 0.293841 (0.117118) \| 0.030454 / 0.128546 (-0.098093) \| 0.011422 / 0.075646 (-0.064224) \| 0.325048 / 0.419271 (-0.094223) \| 0.042959 / 0.043533 (-0.000574) \| 0.374536 / 0.255139 (0.119397) \| 0.394738 / 0.283200 (0.111538) \| 0.090481 / 0.141683 (-0.051201) \| 1.504858 / 1.452155 (0.052703) \| 1.569072 / 1.492716 (0.076356) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010062 / 0.018006 (-0.007945) \| 0.408619 / 0.000490 (0.408130) \| 0.002307 / 0.000200 (0.002107) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022898 / 0.037411 (-0.014514) \| 0.096975 / 0.014526 (0.082449) \| 0.103032 / 0.176557 (-0.073524) \| 0.164877 / 0.737135 (-0.572259) \| 0.107324 / 0.296338 (-0.189014) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446652 / 0.215209 (0.231442) \| 4.466939 / 2.077655 (2.389285) \| 2.204590 / 1.504120 (0.700471) \| 2.004048 / 1.541195 (0.462853) \| 2.053035 / 1.468490 (0.584545) \| 0.696617 / 4.584777 (-3.888160) \| 3.391173 / 3.745712 (-0.354539) \| 1.863306 / 5.269862 (-3.406556) \| 1.160637 / 4.565676 (-3.405039) \| 0.083115 / 0.424275 (-0.341160) \| 0.012470 / 0.007607 (0.004862) \| 0.547207 / 0.226044 (0.321163) \| 5.500667 / 2.268929 (3.231739) \| 2.656615 / 55.444624 (-52.788009) \| 2.313281 / 6.876477 (-4.563195) \| 2.395632 / 2.142072 (0.253559) \| 0.815361 / 4.805227 (-3.989867) \| 0.152112 / 6.500664 (-6.348552) \| 0.067485 / 0.075469 (-0.007984) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206975 / 1.841788 (-0.634813) \| 13.684136 / 8.074308 (5.609828) \| 13.919129 / 10.191392 (3.727737) \| 0.140767 / 0.680424 (-0.539657) \| 0.016445 / 0.534201 (-0.517756) \| 0.379136 / 0.579283 (-0.200147) \| 0.385395 / 0.434364 (-0.048969) \| 0.445781 / 0.540337 (-0.094556) \| 0.522056 / 1.386936 (-0.864880) \|\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) \| 0.006370 / 0.011353 (-0.004983) \| 0.004514 / 0.011008 (-0.006495) \| 0.075671 / 0.038508 (0.037163) \| 0.026723 / 0.023109 (0.003614) \| 0.359819 / 0.275898 (0.083921) \| 0.387935 / 0.323480 (0.064456) \| 0.004888 / 0.007986 (-0.003098) \| 0.004619 / 0.004328 (0.000290) \| 0.075546 / 0.004250 (0.071295) \| 0.039024 / 0.037052 (0.001971) \| 0.361173 / 0.258489 (0.102684) \| 0.411425 / 0.293841 (0.117584) \| 0.030842 / 0.128546 (-0.097705) \| 0.011555 / 0.075646 (-0.064091) \| 0.084697 / 0.419271 (-0.334574) \| 0.039281 / 0.043533 (-0.004252) \| 0.370082 / 0.255139 (0.114943) \| 0.382113 / 0.283200 (0.098913) \| 0.091237 / 0.141683 (-0.050445) \| 1.534185 / 1.452155 (0.082030) \| 1.576488 / 1.492716 (0.083772) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226568 / 0.018006 (0.208562) \| 0.401566 / 0.000490 (0.401076) \| 0.002915 / 0.000200 (0.002715) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025357 / 0.037411 (-0.012054) \| 0.099747 / 0.014526 (0.085221) \| 0.106443 / 0.176557 (-0.070113) \| 0.157147 / 0.737135 (-0.579989) \| 0.110759 / 0.296338 (-0.185580) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444648 / 0.215209 (0.229439) \| 4.437930 / 2.077655 (2.360275) \| 2.154033 / 1.504120 (0.649913) \| 1.958351 / 1.541195 (0.417157) \| 1.991031 / 1.468490 (0.522541) \| 0.691440 / 4.584777 (-3.893337) \| 3.369087 / 3.745712 (-0.376625) \| 1.847103 / 5.269862 (-3.422758) \| 1.152509 / 4.565676 (-3.413168) \| 0.082519 / 0.424275 (-0.341756) \| 0.012609 / 0.007607 (0.005001) \| 0.547267 / 0.226044 (0.321222) \| 5.501335 / 2.268929 (3.232407) \| 2.621079 / 55.444624 (-52.823545) \| 2.281332 / 6.876477 (-4.595145) \| 2.300427 / 2.142072 (0.158354) \| 0.803611 / 4.805227 (-4.001616) \| 0.151784 / 6.500664 (-6.348880) \| 0.067801 / 0.075469 (-0.007669) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.343201 / 1.841788 (-0.498587) \| 13.901033 / 8.074308 (5.826725) \| 13.114738 / 10.191392 (2.923346) \| 0.149358 / 0.680424 (-0.531066) \| 0.016596 / 0.534201 (-0.517605) \| 0.377310 / 0.579283 (-0.201973) \| 0.387045 / 0.434364 (-0.047319) \| 0.441272 / 0.540337 (-0.099065) \| 0.525783 / 1.386936 (-0.861153) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c127e5575ab4e22648976ad268d76264ef5d04f8 \"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) \| 0.008147 / 0.011353 (-0.003205) \| 0.005531 / 0.011008 (-0.005477) \| 0.099796 / 0.038508 (0.061288) \| 0.041574 / 0.023109 (0.018465) \| 0.315752 / 0.275898 (0.039854) \| 0.369846 / 0.323480 (0.046366) \| 0.006489 / 0.007986 (-0.001497) \| 0.004339 / 0.004328 (0.000010) \| 0.074769 / 0.004250 (0.070519) \| 0.051313 / 0.037052 (0.014261) \| 0.313463 / 0.258489 (0.054974) \| 0.369918 / 0.293841 (0.076077) \| 0.035893 / 0.128546 (-0.092653) \| 0.012487 / 0.075646 (-0.063159) \| 0.336464 / 0.419271 (-0.082807) \| 0.052870 / 0.043533 (0.009337) \| 0.310795 / 0.255139 (0.055656) \| 0.333146 / 0.283200 (0.049946) \| 0.112813 / 0.141683 (-0.028870) \| 1.488192 / 1.452155 (0.036038) \| 1.563438 / 1.492716 (0.070721) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.015015 / 0.018006 (-0.002991) \| 0.531783 / 0.000490 (0.531294) \| 0.005039 / 0.000200 (0.004839) \| 0.000103 / 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) \| 0.030205 / 0.037411 (-0.007207) \| 0.115997 / 0.014526 (0.101471) \| 0.122958 / 0.176557 (-0.053599) \| 0.186956 / 0.737135 (-0.550180) \| 0.130268 / 0.296338 (-0.166071) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402648 / 0.215209 (0.187439) \| 3.996121 / 2.077655 (1.918466) \| 1.811715 / 1.504120 (0.307595) \| 1.640805 / 1.541195 (0.099610) \| 1.810478 / 1.468490 (0.341988) \| 0.699996 / 4.584777 (-3.884781) \| 3.834020 / 3.745712 (0.088308) \| 3.688364 / 5.269862 (-1.581498) \| 1.973828 / 4.565676 (-2.591849) \| 0.087085 / 0.424275 (-0.337190) \| 0.012501 / 0.007607 (0.004894) \| 0.498934 / 0.226044 (0.272889) \| 4.977608 / 2.268929 (2.708680) \| 2.258678 / 55.444624 (-53.185947) \| 1.934251 / 6.876477 (-4.942226) \| 2.177409 / 2.142072 (0.035337) \| 0.873470 / 4.805227 (-3.931757) \| 0.173132 / 6.500664 (-6.327532) \| 0.069144 / 0.075469 (-0.006325) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.181554 / 1.841788 (-0.660234) \| 15.694468 / 8.074308 (7.620160) \| 15.026954 / 10.191392 (4.835562) \| 0.167092 / 0.680424 (-0.513332) \| 0.017921 / 0.534201 (-0.516280) \| 0.425649 / 0.579283 (-0.153634) \| 0.423225 / 0.434364 (-0.011139) \| 0.522132 / 0.540337 (-0.018205) \| 0.612806 / 1.386936 (-0.774130) \|\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) \| 0.007896 / 0.011353 (-0.003457) \| 0.005581 / 0.011008 (-0.005427) \| 0.076338 / 0.038508 (0.037830) \| 0.037064 / 0.023109 (0.013954) \| 0.399706 / 0.275898 (0.123808) \| 0.431698 / 0.323480 (0.108218) \| 0.006846 / 0.007986 (-0.001140) \| 0.006010 / 0.004328 (0.001682) \| 0.075771 / 0.004250 (0.071520) \| 0.058214 / 0.037052 (0.021161) \| 0.395753 / 0.258489 (0.137264) \| 0.459925 / 0.293841 (0.166084) \| 0.036349 / 0.128546 (-0.092197) \| 0.012720 / 0.075646 (-0.062926) \| 0.087248 / 0.419271 (-0.332024) \| 0.049405 / 0.043533 (0.005872) \| 0.387576 / 0.255139 (0.132437) \| 0.409861 / 0.283200 (0.126661) \| 0.111639 / 0.141683 (-0.030043) \| 1.482840 / 1.452155 (0.030685) \| 1.574465 / 1.492716 (0.081749) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.320628 / 0.018006 (0.302622) \| 0.556338 / 0.000490 (0.555848) \| 0.000445 / 0.000200 (0.000245) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032905 / 0.037411 (-0.004507) \| 0.121253 / 0.014526 (0.106727) \| 0.127241 / 0.176557 (-0.049316) \| 0.178090 / 0.737135 (-0.559045) \| 0.143285 / 0.296338 (-0.153054) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437852 / 0.215209 (0.222643) \| 4.369770 / 2.077655 (2.292115) \| 2.219932 / 1.504120 (0.715812) \| 2.032520 / 1.541195 (0.491325) \| 2.154300 / 1.468490 (0.685810) \| 0.678942 / 4.584777 (-3.905835) \| 3.768148 / 3.745712 (0.022436) \| 2.152738 / 5.269862 (-3.117124) \| 1.341480 / 4.565676 (-3.224197) \| 0.084326 / 0.424275 (-0.339949) \| 0.012288 / 0.007607 (0.004681) \| 0.547677 / 0.226044 (0.321633) \| 5.496777 / 2.268929 (3.227848) \| 2.702267 / 55.444624 (-52.742357) \| 2.388580 / 6.876477 (-4.487897) \| 2.471673 / 2.142072 (0.329601) \| 0.833645 / 4.805227 (-3.971582) \| 0.167113 / 6.500664 (-6.333551) \| 0.067658 / 0.075469 (-0.007811) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.282050 / 1.841788 (-0.559737) \| 16.413677 / 8.074308 (8.339369) \| 14.080910 / 10.191392 (3.889518) \| 0.171782 / 0.680424 (-0.508642) \| 0.018186 / 0.534201 (-0.516015) \| 0.425244 / 0.579283 (-0.154039) \| 0.430260 / 0.434364 (-0.004104) \| 0.500838 / 0.540337 (-0.039499) \| 0.591900 / 1.386936 (-0.795036) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fc5c538de84da400118e3712077acc580ce85c4 \"CML watermark\")\n", "The approach we take here is to no longer materialize the entire index array or shuffle buffer. Instead, we do the following:\r\n\r\n1) Generate a dataset with `tf.data.Dataset.range`. This dataset is not materialized - it's basically a range iterator.\r\n2) When we begin iterating over a dataset, generate a random seed. This value is constant for each pass over the dataset, and is regenerated if we start a new iteration or epoch over the dataset.\r\n3) Map the range dataset and the random seed with `tf.random.index_shuffle`. This converts indices into the equivalent values in a permuted array. In other words `tf.random.index_shuffle(indices, maxval=50_000_000)` is equivalent to `np.random.permutation(50_000_000)[indices]`, but without ever materializing the `np.random.permutation(50_000_000)` array.\r\n\r\nUsing this approach gives us a complete iteration over the dataset that does not skip any samples, compiles in TF and also never materializes the complete index array, which should avoid the memory usage issues. I'm testing that now!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008395 / 0.011353 (-0.002958) \| 0.005893 / 0.011008 (-0.005115) \| 0.117081 / 0.038508 (0.078573) \| 0.040987 / 0.023109 (0.017878) \| 0.394234 / 0.275898 (0.118336) \| 0.447036 / 0.323480 (0.123556) \| 0.006703 / 0.007986 (-0.001283) \| 0.006085 / 0.004328 (0.001757) \| 0.086479 / 0.004250 (0.082228) \| 0.050192 / 0.037052 (0.013140) \| 0.400958 / 0.258489 (0.142469) \| 0.455551 / 0.293841 (0.161710) \| 0.041481 / 0.128546 (-0.087065) \| 0.014135 / 0.075646 (-0.061511) \| 0.399929 / 0.419271 (-0.019343) \| 0.060824 / 0.043533 (0.017291) \| 0.395946 / 0.255139 (0.140807) \| 0.428811 / 0.283200 (0.145611) \| 0.120057 / 0.141683 (-0.021626) \| 1.703244 / 1.452155 (0.251090) \| 1.841153 / 1.492716 (0.348436) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021826 / 0.018006 (0.003820) \| 0.494279 / 0.000490 (0.493789) \| 0.011258 / 0.000200 (0.011058) \| 0.000382 / 0.000054 (0.000328) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031651 / 0.037411 (-0.005760) \| 0.132871 / 0.014526 (0.118345) \| 0.137388 / 0.176557 (-0.039169) \| 0.205808 / 0.737135 (-0.531327) \| 0.147585 / 0.296338 (-0.148753) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474483 / 0.215209 (0.259274) \| 4.726568 / 2.077655 (2.648914) \| 2.136172 / 1.504120 (0.632052) \| 1.918364 / 1.541195 (0.377169) \| 2.068794 / 1.468490 (0.600304) \| 0.836481 / 4.584777 (-3.748296) \| 4.550583 / 3.745712 (0.804871) \| 2.456287 / 5.269862 (-2.813574) \| 1.563127 / 4.565676 (-3.002550) \| 0.102541 / 0.424275 (-0.321734) \| 0.014492 / 0.007607 (0.006885) \| 0.598572 / 0.226044 (0.372528) \| 5.953321 / 2.268929 (3.684392) \| 2.695210 / 55.444624 (-52.749414) \| 2.294317 / 6.876477 (-4.582160) \| 2.456585 / 2.142072 (0.314513) \| 1.019907 / 4.805227 (-3.785320) \| 0.201225 / 6.500664 (-6.299439) \| 0.077113 / 0.075469 (0.001644) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.497662 / 1.841788 (-0.344126) \| 18.216941 / 8.074308 (10.142633) \| 17.016638 / 10.191392 (6.825246) \| 0.193271 / 0.680424 (-0.487153) \| 0.020440 / 0.534201 (-0.513761) \| 0.509361 / 0.579283 (-0.069922) \| 0.513389 / 0.434364 (0.079025) \| 0.622266 / 0.540337 (0.081928) \| 0.741733 / 1.386936 (-0.645203) \|\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) \| 0.008641 / 0.011353 (-0.002712) \| 0.005792 / 0.011008 (-0.005216) \| 0.086020 / 0.038508 (0.047512) \| 0.040005 / 0.023109 (0.016896) \| 0.435120 / 0.275898 (0.159222) \| 0.480269 / 0.323480 (0.156789) \| 0.006669 / 0.007986 (-0.001317) \| 0.006039 / 0.004328 (0.001711) \| 0.083468 / 0.004250 (0.079218) \| 0.057700 / 0.037052 (0.020648) \| 0.416418 / 0.258489 (0.157929) \| 0.508286 / 0.293841 (0.214445) \| 0.041198 / 0.128546 (-0.087349) \| 0.014346 / 0.075646 (-0.061301) \| 0.100553 / 0.419271 (-0.318718) \| 0.054201 / 0.043533 (0.010668) \| 0.438232 / 0.255139 (0.183093) \| 0.454707 / 0.283200 (0.171508) \| 0.118332 / 0.141683 (-0.023351) \| 1.657607 / 1.452155 (0.205452) \| 1.825510 / 1.492716 (0.332794) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236156 / 0.018006 (0.218150) \| 0.487612 / 0.000490 (0.487123) \| 0.005747 / 0.000200 (0.005547) \| 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) \| 0.035127 / 0.037411 (-0.002284) \| 0.132013 / 0.014526 (0.117487) \| 0.142316 / 0.176557 (-0.034241) \| 0.198627 / 0.737135 (-0.538508) \| 0.145454 / 0.296338 (-0.150885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513041 / 0.215209 (0.297832) \| 5.066197 / 2.077655 (2.988542) \| 2.508779 / 1.504120 (1.004659) \| 2.273901 / 1.541195 (0.732706) \| 2.364958 / 1.468490 (0.896468) \| 0.811367 / 4.584777 (-3.773410) \| 4.504744 / 3.745712 (0.759032) \| 2.499811 / 5.269862 (-2.770050) \| 1.583349 / 4.565676 (-2.982328) \| 0.101701 / 0.424275 (-0.322574) \| 0.014379 / 0.007607 (0.006772) \| 0.669506 / 0.226044 (0.443462) \| 6.556702 / 2.268929 (4.287774) \| 3.123457 / 55.444624 (-52.321167) \| 2.731997 / 6.876477 (-4.144480) \| 2.862866 / 2.142072 (0.720794) \| 0.992956 / 4.805227 (-3.812271) \| 0.200473 / 6.500664 (-6.300191) \| 0.078780 / 0.075469 (0.003311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.540718 / 1.841788 (-0.301070) \| 18.749344 / 8.074308 (10.675036) \| 15.648983 / 10.191392 (5.457591) \| 0.174089 / 0.680424 (-0.506335) \| 0.020441 / 0.534201 (-0.513760) \| 0.503742 / 0.579283 (-0.075541) \| 0.500648 / 0.434364 (0.066284) \| 0.598558 / 0.540337 (0.058221) \| 0.712093 / 1.386936 (-0.674843) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#621554280f964b5fe87ece1a46b794406d943b1e \"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) \| 0.009940 / 0.011353 (-0.001412) \| 0.006193 / 0.011008 (-0.004815) \| 0.125874 / 0.038508 (0.087366) \| 0.038664 / 0.023109 (0.015555) \| 0.380013 / 0.275898 (0.104115) \| 0.430152 / 0.323480 (0.106672) \| 0.006961 / 0.007986 (-0.001025) \| 0.004749 / 0.004328 (0.000420) \| 0.099743 / 0.004250 (0.095492) \| 0.052349 / 0.037052 (0.015297) \| 0.433354 / 0.258489 (0.174865) \| 0.436273 / 0.293841 (0.142433) \| 0.053929 / 0.128546 (-0.074617) \| 0.019369 / 0.075646 (-0.056278) \| 0.421783 / 0.419271 (0.002511) \| 0.062746 / 0.043533 (0.019213) \| 0.377225 / 0.255139 (0.122086) \| 0.413708 / 0.283200 (0.130508) \| 0.111371 / 0.141683 (-0.030312) \| 1.819166 / 1.452155 (0.367011) \| 1.974527 / 1.492716 (0.481810) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090664 / 0.018006 (0.072658) \| 0.566166 / 0.000490 (0.565676) \| 0.079305 / 0.000200 (0.079105) \| 0.000755 / 0.000054 (0.000700) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029720 / 0.037411 (-0.007691) \| 0.126030 / 0.014526 (0.111504) \| 0.146020 / 0.176557 (-0.030537) \| 0.210354 / 0.737135 (-0.526781) \| 0.149428 / 0.296338 (-0.146910) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.624371 / 0.215209 (0.409162) \| 6.332839 / 2.077655 (4.255184) \| 2.547784 / 1.504120 (1.043664) \| 2.150508 / 1.541195 (0.609313) \| 2.240816 / 1.468490 (0.772326) \| 1.271131 / 4.584777 (-3.313646) \| 5.642726 / 3.745712 (1.897014) \| 3.212988 / 5.269862 (-2.056874) \| 2.258123 / 4.565676 (-2.307553) \| 0.149477 / 0.424275 (-0.274798) \| 0.014603 / 0.007607 (0.006996) \| 0.782155 / 0.226044 (0.556111) \| 7.855191 / 2.268929 (5.586262) \| 3.308638 / 55.444624 (-52.135986) \| 2.548142 / 6.876477 (-4.328335) \| 2.627374 / 2.142072 (0.485301) \| 1.515170 / 4.805227 (-3.290058) \| 0.262479 / 6.500664 (-6.238185) \| 0.082181 / 0.075469 (0.006712) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.573169 / 1.841788 (-0.268618) \| 18.105719 / 8.074308 (10.031411) \| 22.015179 / 10.191392 (11.823787) \| 0.254678 / 0.680424 (-0.425746) \| 0.027098 / 0.534201 (-0.507103) \| 0.578045 / 0.579283 (-0.001238) \| 0.647130 / 0.434364 (0.212766) \| 0.650522 / 0.540337 (0.110185) \| 0.797713 / 1.386936 (-0.589223) \|\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) \| 0.010376 / 0.011353 (-0.000977) \| 0.005990 / 0.011008 (-0.005018) \| 0.097144 / 0.038508 (0.058635) \| 0.038205 / 0.023109 (0.015096) \| 0.468347 / 0.275898 (0.192449) \| 0.497646 / 0.323480 (0.174166) \| 0.006916 / 0.007986 (-0.001069) \| 0.004760 / 0.004328 (0.000431) \| 0.109838 / 0.004250 (0.105587) \| 0.048321 / 0.037052 (0.011269) \| 0.437458 / 0.258489 (0.178969) \| 0.534864 / 0.293841 (0.241023) \| 0.053655 / 0.128546 (-0.074892) \| 0.021915 / 0.075646 (-0.053732) \| 0.121047 / 0.419271 (-0.298224) \| 0.059694 / 0.043533 (0.016162) \| 0.466937 / 0.255139 (0.211798) \| 0.482030 / 0.283200 (0.198831) \| 0.117458 / 0.141683 (-0.024225) \| 1.835551 / 1.452155 (0.383396) \| 1.965748 / 1.492716 (0.473031) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234885 / 0.018006 (0.216879) \| 0.529925 / 0.000490 (0.529436) \| 0.000484 / 0.000200 (0.000284) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030959 / 0.037411 (-0.006453) \| 0.128905 / 0.014526 (0.114379) \| 0.136913 / 0.176557 (-0.039643) \| 0.195133 / 0.737135 (-0.542002) \| 0.147929 / 0.296338 (-0.148410) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.715661 / 0.215209 (0.500451) \| 6.994125 / 2.077655 (4.916470) \| 3.033178 / 1.504120 (1.529058) \| 2.663709 / 1.541195 (1.122515) \| 2.707558 / 1.468490 (1.239068) \| 1.316195 / 4.584777 (-3.268582) \| 5.688264 / 3.745712 (1.942552) \| 3.260897 / 5.269862 (-2.008964) \| 2.134985 / 4.565676 (-2.430691) \| 0.153945 / 0.424275 (-0.270330) \| 0.014727 / 0.007607 (0.007119) \| 0.911339 / 0.226044 (0.685294) \| 8.902640 / 2.268929 (6.633711) \| 3.806606 / 55.444624 (-51.638018) \| 3.052238 / 6.876477 (-3.824238) \| 3.046945 / 2.142072 (0.904873) \| 1.559837 / 4.805227 (-3.245390) \| 0.272276 / 6.500664 (-6.228388) \| 0.087728 / 0.075469 (0.012259) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.712691 / 1.841788 (-0.129097) \| 18.127575 / 8.074308 (10.053267) \| 19.734063 / 10.191392 (9.542671) \| 0.235006 / 0.680424 (-0.445418) \| 0.027581 / 0.534201 (-0.506620) \| 0.551080 / 0.579283 (-0.028203) \| 0.608564 / 0.434364 (0.174200) \| 0.636578 / 0.540337 (0.096241) \| 0.732374 / 1.386936 (-0.654562) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#36911ca06d9c4e37ce36da6228cb3af8b40c2add \"CML watermark\")\n", "Looks good in testing - this should be ready for review! cc @lhoestq @massquantity", "Looks good to me, though i doubt that very few people will upgrade to TF >= 2.9 unless their memory is full:)", "Is it more efficient than using numpy to shuffle as in multiprocessing ? Why not use the same strategy ?", "Good question, honestly! The NumPy strategy works fine, but requires us to handle multiple processes instead of doing everything in `tf.data`. We could just scrap this entire code path and always use the multiprocessing NumPy approach, but I think single-threaded throughput would be lower if we did that. If you prefer it for code simplicity, though, I can do that.\r\n\r\nIn the longer term, I'm hoping that `tf.data` gets native support for our data structures and we can transition the whole pipeline to pure `tf.data`, but that still hasn't happened 🫠", "And @massquantity TF 2.13 is going to release in a couple of days, so I hope most users are at least on TF 2.9 by now!", "Unless there is a big gap in performance I think code simplicity would be appreciated ^^", "<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) \| 0.008638 / 0.011353 (-0.002715) \| 0.006013 / 0.011008 (-0.004995) \| 0.116456 / 0.038508 (0.077948) \| 0.040419 / 0.023109 (0.017310) \| 0.418374 / 0.275898 (0.142476) \| 0.447693 / 0.323480 (0.124213) \| 0.007002 / 0.007986 (-0.000984) \| 0.006175 / 0.004328 (0.001847) \| 0.087801 / 0.004250 (0.083550) \| 0.051980 / 0.037052 (0.014928) \| 0.393275 / 0.258489 (0.134786) \| 0.449601 / 0.293841 (0.155760) \| 0.041670 / 0.128546 (-0.086876) \| 0.014396 / 0.075646 (-0.061251) \| 0.399175 / 0.419271 (-0.020096) \| 0.060635 / 0.043533 (0.017102) \| 0.391449 / 0.255139 (0.136310) \| 0.420713 / 0.283200 (0.137513) \| 0.121369 / 0.141683 (-0.020314) \| 1.692630 / 1.452155 (0.240475) \| 1.815526 / 1.492716 (0.322810) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244321 / 0.018006 (0.226315) \| 0.487947 / 0.000490 (0.487458) \| 0.004563 / 0.000200 (0.004363) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033425 / 0.037411 (-0.003987) \| 0.134458 / 0.014526 (0.119932) \| 0.138810 / 0.176557 (-0.037746) \| 0.208871 / 0.737135 (-0.528264) \| 0.147964 / 0.296338 (-0.148374) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.483347 / 0.215209 (0.268138) \| 4.799550 / 2.077655 (2.721895) \| 2.174149 / 1.504120 (0.670029) \| 1.943276 / 1.541195 (0.402081) \| 2.010884 / 1.468490 (0.542394) \| 0.832030 / 4.584777 (-3.752747) \| 4.716713 / 3.745712 (0.971001) \| 4.615810 / 5.269862 (-0.654052) \| 2.379600 / 4.565676 (-2.186077) \| 0.103560 / 0.424275 (-0.320715) \| 0.014683 / 0.007607 (0.007076) \| 0.598558 / 0.226044 (0.372514) \| 5.999126 / 2.268929 (3.730197) \| 2.677819 / 55.444624 (-52.766805) \| 2.320838 / 6.876477 (-4.555639) \| 2.503684 / 2.142072 (0.361611) \| 1.016459 / 4.805227 (-3.788769) \| 0.201672 / 6.500664 (-6.298992) \| 0.079310 / 0.075469 (0.003841) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.446374 / 1.841788 (-0.395413) \| 19.219310 / 8.074308 (11.145002) \| 17.294665 / 10.191392 (7.103273) \| 0.246115 / 0.680424 (-0.434309) \| 0.021406 / 0.534201 (-0.512795) \| 0.524084 / 0.579283 (-0.055200) \| 0.511254 / 0.434364 (0.076890) \| 0.621304 / 0.540337 (0.080966) \| 0.727088 / 1.386936 (-0.659848) \|\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) \| 0.008907 / 0.011353 (-0.002446) \| 0.006165 / 0.011008 (-0.004843) \| 0.090786 / 0.038508 (0.052278) \| 0.040893 / 0.023109 (0.017784) \| 0.451252 / 0.275898 (0.175354) \| 0.477811 / 0.323480 (0.154331) \| 0.007418 / 0.007986 (-0.000568) \| 0.005789 / 0.004328 (0.001461) \| 0.087422 / 0.004250 (0.083171) \| 0.061800 / 0.037052 (0.024748) \| 0.459085 / 0.258489 (0.200596) \| 0.488897 / 0.293841 (0.195056) \| 0.048157 / 0.128546 (-0.080389) \| 0.014676 / 0.075646 (-0.060970) \| 0.104372 / 0.419271 (-0.314900) \| 0.058066 / 0.043533 (0.014534) \| 0.446131 / 0.255139 (0.190992) \| 0.460428 / 0.283200 (0.177228) \| 0.128492 / 0.141683 (-0.013191) \| 1.811419 / 1.452155 (0.359265) \| 1.894781 / 1.492716 (0.402064) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220527 / 0.018006 (0.202520) \| 0.487663 / 0.000490 (0.487173) \| 0.003864 / 0.000200 (0.003664) \| 0.000162 / 0.000054 (0.000107) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036354 / 0.037411 (-0.001057) \| 0.140469 / 0.014526 (0.125944) \| 0.149990 / 0.176557 (-0.026566) \| 0.212369 / 0.737135 (-0.524766) \| 0.154000 / 0.296338 (-0.142338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514172 / 0.215209 (0.298963) \| 5.129247 / 2.077655 (3.051593) \| 2.536773 / 1.504120 (1.032653) \| 2.317253 / 1.541195 (0.776058) \| 2.424066 / 1.468490 (0.955576) \| 0.836160 / 4.584777 (-3.748617) \| 4.906235 / 3.745712 (1.160523) \| 4.431395 / 5.269862 (-0.838467) \| 2.332845 / 4.565676 (-2.232831) \| 0.102867 / 0.424275 (-0.321409) \| 0.014851 / 0.007607 (0.007244) \| 0.644104 / 0.226044 (0.418060) \| 6.415847 / 2.268929 (4.146918) \| 3.186984 / 55.444624 (-52.257641) \| 2.774125 / 6.876477 (-4.102352) \| 2.848045 / 2.142072 (0.705972) \| 1.018757 / 4.805227 (-3.786470) \| 0.212333 / 6.500664 (-6.288331) \| 0.079405 / 0.075469 (0.003936) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.748375 / 1.841788 (-0.093412) \| 19.733829 / 8.074308 (11.659521) \| 15.766665 / 10.191392 (5.575273) \| 0.192087 / 0.680424 (-0.488337) \| 0.027641 / 0.534201 (-0.506560) \| 0.504101 / 0.579283 (-0.075182) \| 0.493815 / 0.434364 (0.059451) \| 0.583247 / 0.540337 (0.042910) \| 0.697432 / 1.386936 (-0.689504) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#95c177e02ca20bf7bb3ed8f185d2d6f05a5e5f30 \"CML watermark\")\n", "Hi @lhoestq, I tried moving everything to the NumPy path but ran into issues - the `SharedMemory` constructs it depends on were only added in Python 3.8. As a result, if we move everything to that path then `to_tf_dataset` does not work on older Python versions.\r\n\r\nFor now, how do you feel about reverting and using my original solution, which has fallbacks for all versions of Python and TensorFlow? Once our minimum versions pass Python 3.8 or TF 2.9 we can remove the older code paths.", "Gentle ping on this question @lhoestq!", "Ah yes indeed. Feel free to revert and add comments to explain why you needed to have a different approach for single process", "<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) \| 0.008395 / 0.011353 (-0.002958) \| 0.005773 / 0.011008 (-0.005235) \| 0.115702 / 0.038508 (0.077194) \| 0.039897 / 0.023109 (0.016788) \| 0.483140 / 0.275898 (0.207242) \| 0.531288 / 0.323480 (0.207808) \| 0.006739 / 0.007986 (-0.001246) \| 0.004419 / 0.004328 (0.000090) \| 0.086374 / 0.004250 (0.082124) \| 0.056498 / 0.037052 (0.019446) \| 0.491589 / 0.258489 (0.233100) \| 0.556366 / 0.293841 (0.262525) \| 0.041366 / 0.128546 (-0.087181) \| 0.014373 / 0.075646 (-0.061274) \| 0.395504 / 0.419271 (-0.023767) \| 0.094382 / 0.043533 (0.050849) \| 0.483000 / 0.255139 (0.227861) \| 0.522693 / 0.283200 (0.239494) \| 0.138804 / 0.141683 (-0.002879) \| 1.719563 / 1.452155 (0.267409) \| 1.853470 / 1.492716 (0.360753) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235616 / 0.018006 (0.217610) \| 0.483267 / 0.000490 (0.482777) \| 0.008663 / 0.000200 (0.008463) \| 0.000401 / 0.000054 (0.000347) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033124 / 0.037411 (-0.004287) \| 0.128821 / 0.014526 (0.114295) \| 0.138910 / 0.176557 (-0.037647) \| 0.213570 / 0.737135 (-0.523566) \| 0.146646 / 0.296338 (-0.149693) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479998 / 0.215209 (0.264789) \| 4.772325 / 2.077655 (2.694670) \| 2.228424 / 1.504120 (0.724304) \| 2.000915 / 1.541195 (0.459721) \| 2.105799 / 1.468490 (0.637309) \| 0.824235 / 4.584777 (-3.760542) \| 4.511902 / 3.745712 (0.766189) \| 4.723073 / 5.269862 (-0.546789) \| 2.333442 / 4.565676 (-2.232235) \| 0.101161 / 0.424275 (-0.323114) \| 0.014403 / 0.007607 (0.006796) \| 0.596395 / 0.226044 (0.370351) \| 5.961046 / 2.268929 (3.692117) \| 2.746679 / 55.444624 (-52.697946) \| 2.352085 / 6.876477 (-4.524392) \| 2.609812 / 2.142072 (0.467740) \| 0.996950 / 4.805227 (-3.808277) \| 0.197923 / 6.500664 (-6.302741) \| 0.075546 / 0.075469 (0.000077) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.529896 / 1.841788 (-0.311892) \| 18.183887 / 8.074308 (10.109578) \| 16.352332 / 10.191392 (6.160940) \| 0.213504 / 0.680424 (-0.466920) \| 0.020388 / 0.534201 (-0.513813) \| 0.497832 / 0.579283 (-0.081451) \| 0.495477 / 0.434364 (0.061113) \| 0.585984 / 0.540337 (0.045647) \| 0.688726 / 1.386936 (-0.698210) \|\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) \| 0.008422 / 0.011353 (-0.002931) \| 0.005876 / 0.011008 (-0.005132) \| 0.089310 / 0.038508 (0.050802) \| 0.039769 / 0.023109 (0.016660) \| 0.425279 / 0.275898 (0.149381) \| 0.470818 / 0.323480 (0.147338) \| 0.006519 / 0.007986 (-0.001467) \| 0.006276 / 0.004328 (0.001948) \| 0.085753 / 0.004250 (0.081503) \| 0.053867 / 0.037052 (0.016815) \| 0.429193 / 0.258489 (0.170704) \| 0.480278 / 0.293841 (0.186437) \| 0.040657 / 0.128546 (-0.087889) \| 0.014055 / 0.075646 (-0.061591) \| 0.101422 / 0.419271 (-0.317849) \| 0.053803 / 0.043533 (0.010271) \| 0.428348 / 0.255139 (0.173209) \| 0.452193 / 0.283200 (0.168994) \| 0.124914 / 0.141683 (-0.016769) \| 1.750122 / 1.452155 (0.297968) \| 1.850875 / 1.492716 (0.358159) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249958 / 0.018006 (0.231952) \| 0.485183 / 0.000490 (0.484694) \| 0.000472 / 0.000200 (0.000272) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034563 / 0.037411 (-0.002848) \| 0.135565 / 0.014526 (0.121039) \| 0.143271 / 0.176557 (-0.033285) \| 0.199080 / 0.737135 (-0.538056) \| 0.149336 / 0.296338 (-0.147003) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.526170 / 0.215209 (0.310961) \| 5.270960 / 2.077655 (3.193305) \| 2.664585 / 1.504120 (1.160465) \| 2.440027 / 1.541195 (0.898832) \| 2.612764 / 1.468490 (1.144274) \| 0.828965 / 4.584777 (-3.755812) \| 4.769983 / 3.745712 (1.024271) \| 2.441962 / 5.269862 (-2.827900) \| 1.549032 / 4.565676 (-3.016644) \| 0.100851 / 0.424275 (-0.323424) \| 0.014425 / 0.007607 (0.006818) \| 0.640908 / 0.226044 (0.414864) \| 6.399041 / 2.268929 (4.130113) \| 3.242424 / 55.444624 (-52.202200) \| 2.836317 / 6.876477 (-4.040160) \| 2.933010 / 2.142072 (0.790938) \| 1.002277 / 4.805227 (-3.802950) \| 0.201247 / 6.500664 (-6.299417) \| 0.078777 / 0.075469 (0.003308) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.620415 / 1.841788 (-0.221373) \| 19.153631 / 8.074308 (11.079323) \| 16.744068 / 10.191392 (6.552676) \| 0.167327 / 0.680424 (-0.513097) \| 0.020186 / 0.534201 (-0.514015) \| 0.503683 / 0.579283 (-0.075600) \| 0.500051 / 0.434364 (0.065687) \| 0.587188 / 0.540337 (0.046850) \| 0.699975 / 1.386936 (-0.686961) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#291d7ffa695edb4b4e818c783b16d3466246cd56 \"CML watermark\")\n", "This is probably ready, but likely conflicts with #5883. I'll wait for that PR to be merged and then rebase and merge this one.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008387 / 0.011353 (-0.002965) \| 0.005824 / 0.011008 (-0.005184) \| 0.117721 / 0.038508 (0.079213) \| 0.040420 / 0.023109 (0.017311) \| 0.404961 / 0.275898 (0.129063) \| 0.426695 / 0.323480 (0.103215) \| 0.006634 / 0.007986 (-0.001352) \| 0.006033 / 0.004328 (0.001705) \| 0.088652 / 0.004250 (0.084402) \| 0.048075 / 0.037052 (0.011022) \| 0.400683 / 0.258489 (0.142194) \| 0.432489 / 0.293841 (0.138648) \| 0.042065 / 0.128546 (-0.086482) \| 0.014071 / 0.075646 (-0.061575) \| 0.399398 / 0.419271 (-0.019873) \| 0.066034 / 0.043533 (0.022501) \| 0.400056 / 0.255139 (0.144918) \| 0.421130 / 0.283200 (0.137930) \| 0.119721 / 0.141683 (-0.021962) \| 1.752166 / 1.452155 (0.300011) \| 1.820161 / 1.492716 (0.327444) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244264 / 0.018006 (0.226258) \| 0.480882 / 0.000490 (0.480392) \| 0.005604 / 0.000200 (0.005404) \| 0.000175 / 0.000054 (0.000121) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032397 / 0.037411 (-0.005015) \| 0.131632 / 0.014526 (0.117106) \| 0.139765 / 0.176557 (-0.036792) \| 0.213135 / 0.737135 (-0.524000) \| 0.147891 / 0.296338 (-0.148447) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474534 / 0.215209 (0.259325) \| 4.730424 / 2.077655 (2.652770) \| 2.163706 / 1.504120 (0.659586) \| 1.936051 / 1.541195 (0.394857) \| 2.012185 / 1.468490 (0.543695) \| 0.826583 / 4.584777 (-3.758194) \| 4.921494 / 3.745712 (1.175782) \| 2.431401 / 5.269862 (-2.838460) \| 1.566020 / 4.565676 (-2.999656) \| 0.101255 / 0.424275 (-0.323020) \| 0.014553 / 0.007607 (0.006946) \| 0.608301 / 0.226044 (0.382256) \| 6.089801 / 2.268929 (3.820873) \| 2.691986 / 55.444624 (-52.752638) \| 2.296498 / 6.876477 (-4.579979) \| 2.455388 / 2.142072 (0.313315) \| 0.984342 / 4.805227 (-3.820885) \| 0.200447 / 6.500664 (-6.300217) \| 0.077602 / 0.075469 (0.002133) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.445067 / 1.841788 (-0.396721) \| 18.588670 / 8.074308 (10.514362) \| 16.950216 / 10.191392 (6.758824) \| 0.169688 / 0.680424 (-0.510736) \| 0.020544 / 0.534201 (-0.513657) \| 0.508506 / 0.579283 (-0.070777) \| 0.516218 / 0.434364 (0.081854) \| 0.646072 / 0.540337 (0.105734) \| 0.763227 / 1.386936 (-0.623709) \|\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) \| 0.008816 / 0.011353 (-0.002537) \| 0.006016 / 0.011008 (-0.004992) \| 0.090946 / 0.038508 (0.052438) \| 0.040189 / 0.023109 (0.017080) \| 0.446723 / 0.275898 (0.170825) \| 0.494633 / 0.323480 (0.171153) \| 0.007206 / 0.007986 (-0.000779) \| 0.004508 / 0.004328 (0.000180) \| 0.088477 / 0.004250 (0.084226) \| 0.055587 / 0.037052 (0.018535) \| 0.445349 / 0.258489 (0.186860) \| 0.504940 / 0.293841 (0.211099) \| 0.041976 / 0.128546 (-0.086570) \| 0.014296 / 0.075646 (-0.061351) \| 0.102835 / 0.419271 (-0.316436) \| 0.054786 / 0.043533 (0.011253) \| 0.444789 / 0.255139 (0.189651) \| 0.472306 / 0.283200 (0.189106) \| 0.123365 / 0.141683 (-0.018318) \| 1.725803 / 1.452155 (0.273648) \| 1.832216 / 1.492716 (0.339500) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252680 / 0.018006 (0.234674) \| 0.476719 / 0.000490 (0.476229) \| 0.000461 / 0.000200 (0.000261) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035961 / 0.037411 (-0.001450) \| 0.135399 / 0.014526 (0.120873) \| 0.147549 / 0.176557 (-0.029007) \| 0.207468 / 0.737135 (-0.529667) \| 0.151591 / 0.296338 (-0.144747) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.528143 / 0.215209 (0.312934) \| 5.270766 / 2.077655 (3.193111) \| 2.675644 / 1.504120 (1.171524) \| 2.472855 / 1.541195 (0.931660) \| 2.636020 / 1.468490 (1.167530) \| 0.841325 / 4.584777 (-3.743452) \| 4.702290 / 3.745712 (0.956578) \| 2.523537 / 5.269862 (-2.746325) \| 1.595617 / 4.565676 (-2.970059) \| 0.102095 / 0.424275 (-0.322180) \| 0.014568 / 0.007607 (0.006961) \| 0.652090 / 0.226044 (0.426046) \| 6.503086 / 2.268929 (4.234158) \| 3.277025 / 55.444624 (-52.167599) \| 2.931264 / 6.876477 (-3.945213) \| 3.021667 / 2.142072 (0.879594) \| 1.002560 / 4.805227 (-3.802668) \| 0.202621 / 6.500664 (-6.298043) \| 0.080583 / 0.075469 (0.005114) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.639281 / 1.841788 (-0.202507) \| 18.911529 / 8.074308 (10.837220) \| 17.082795 / 10.191392 (6.891403) \| 0.179456 / 0.680424 (-0.500968) \| 0.021740 / 0.534201 (-0.512460) \| 0.526426 / 0.579283 (-0.052857) \| 0.535083 / 0.434364 (0.100719) \| 0.583304 / 0.540337 (0.042967) \| 0.696733 / 1.386936 (-0.690203) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#757f19283f22eeb3e9aedefd82abc0aa2235f797 \"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) \| 0.006823 / 0.011353 (-0.004530) \| 0.004847 / 0.011008 (-0.006161) \| 0.096038 / 0.038508 (0.057530) \| 0.033037 / 0.023109 (0.009928) \| 0.298379 / 0.275898 (0.022481) \| 0.333319 / 0.323480 (0.009839) \| 0.005343 / 0.007986 (-0.002643) \| 0.003863 / 0.004328 (-0.000465) \| 0.072928 / 0.004250 (0.068678) \| 0.040898 / 0.037052 (0.003846) \| 0.303116 / 0.258489 (0.044627) \| 0.334021 / 0.293841 (0.040181) \| 0.034780 / 0.128546 (-0.093767) \| 0.011978 / 0.075646 (-0.063668) \| 0.331642 / 0.419271 (-0.087629) \| 0.052729 / 0.043533 (0.009196) \| 0.298586 / 0.255139 (0.043447) \| 0.319296 / 0.283200 (0.036097) \| 0.097711 / 0.141683 (-0.043972) \| 1.416899 / 1.452155 (-0.035256) \| 1.546008 / 1.492716 (0.053292) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234303 / 0.018006 (0.216296) \| 0.492767 / 0.000490 (0.492278) \| 0.004935 / 0.000200 (0.004736) \| 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) \| 0.030617 / 0.037411 (-0.006795) \| 0.121203 / 0.014526 (0.106677) \| 0.126677 / 0.176557 (-0.049879) \| 0.186379 / 0.737135 (-0.550756) \| 0.129849 / 0.296338 (-0.166490) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416324 / 0.215209 (0.201115) \| 4.135563 / 2.077655 (2.057908) \| 1.976182 / 1.504120 (0.472062) \| 1.807611 / 1.541195 (0.266416) \| 1.886282 / 1.468490 (0.417792) \| 0.713006 / 4.584777 (-3.871771) \| 3.899205 / 3.745712 (0.153493) \| 2.283427 / 5.269862 (-2.986435) \| 1.543088 / 4.565676 (-3.022589) \| 0.086189 / 0.424275 (-0.338087) \| 0.012908 / 0.007607 (0.005301) \| 0.516156 / 0.226044 (0.290112) \| 5.144199 / 2.268929 (2.875271) \| 2.460142 / 55.444624 (-52.984482) \| 2.209054 / 6.876477 (-4.667423) \| 2.325277 / 2.142072 (0.183204) \| 0.849890 / 4.805227 (-3.955337) \| 0.173687 / 6.500664 (-6.326977) \| 0.070178 / 0.075469 (-0.005291) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.241790 / 1.841788 (-0.599997) \| 16.047257 / 8.074308 (7.972949) \| 15.774146 / 10.191392 (5.582754) \| 0.145871 / 0.680424 (-0.534553) \| 0.018106 / 0.534201 (-0.516095) \| 0.433642 / 0.579283 (-0.145641) \| 0.425311 / 0.434364 (-0.009053) \| 0.533963 / 0.540337 (-0.006375) \| 0.638786 / 1.386936 (-0.748151) \|\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) \| 0.007242 / 0.011353 (-0.004111) \| 0.005599 / 0.011008 (-0.005410) \| 0.073443 / 0.038508 (0.034935) \| 0.033764 / 0.023109 (0.010655) \| 0.365990 / 0.275898 (0.090092) \| 0.392943 / 0.323480 (0.069463) \| 0.005987 / 0.007986 (-0.001999) \| 0.004312 / 0.004328 (-0.000016) \| 0.072831 / 0.004250 (0.068580) \| 0.048854 / 0.037052 (0.011802) \| 0.362477 / 0.258489 (0.103988) \| 0.399993 / 0.293841 (0.106152) \| 0.035602 / 0.128546 (-0.092944) \| 0.012445 / 0.075646 (-0.063202) \| 0.085768 / 0.419271 (-0.333504) \| 0.048544 / 0.043533 (0.005011) \| 0.362246 / 0.255139 (0.107107) \| 0.388753 / 0.283200 (0.105554) \| 0.109829 / 0.141683 (-0.031854) \| 1.546881 / 1.452155 (0.094726) \| 1.619454 / 1.492716 (0.126737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189926 / 0.018006 (0.171920) \| 0.447936 / 0.000490 (0.447446) \| 0.002354 / 0.000200 (0.002155) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031740 / 0.037411 (-0.005671) \| 0.122595 / 0.014526 (0.108069) \| 0.128389 / 0.176557 (-0.048168) \| 0.180570 / 0.737135 (-0.556566) \| 0.132939 / 0.296338 (-0.163399) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425073 / 0.215209 (0.209863) \| 4.238964 / 2.077655 (2.161309) \| 2.095116 / 1.504120 (0.590996) \| 1.913925 / 1.541195 (0.372730) \| 2.024669 / 1.468490 (0.556179) \| 0.699172 / 4.584777 (-3.885605) \| 3.845807 / 3.745712 (0.100094) \| 2.167502 / 5.269862 (-3.102360) \| 1.375267 / 4.565676 (-3.190410) \| 0.086739 / 0.424275 (-0.337536) \| 0.012198 / 0.007607 (0.004591) \| 0.525975 / 0.226044 (0.299931) \| 5.249449 / 2.268929 (2.980521) \| 2.550565 / 55.444624 (-52.894060) \| 2.257557 / 6.876477 (-4.618920) \| 2.298936 / 2.142072 (0.156863) \| 0.850295 / 4.805227 (-3.954932) \| 0.170506 / 6.500664 (-6.330158) \| 0.065659 / 0.075469 (-0.009810) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330556 / 1.841788 (-0.511231) \| 16.920203 / 8.074308 (8.845894) \| 15.966739 / 10.191392 (5.775347) \| 0.164000 / 0.680424 (-0.516424) \| 0.018211 / 0.534201 (-0.515990) \| 0.436253 / 0.579283 (-0.143030) \| 0.449666 / 0.434364 (0.015302) \| 0.522287 / 0.540337 (-0.018050) \| 0.615944 / 1.386936 (-0.770992) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#824f96c11a02b3817d6b1bf4dfed0abab27777f0 \"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) \| 0.007273 / 0.011353 (-0.004080) \| 0.005198 / 0.011008 (-0.005810) \| 0.114362 / 0.038508 (0.075854) \| 0.031113 / 0.023109 (0.008003) \| 0.378568 / 0.275898 (0.102670) \| 0.441695 / 0.323480 (0.118215) \| 0.006037 / 0.007986 (-0.001949) \| 0.005102 / 0.004328 (0.000774) \| 0.098682 / 0.004250 (0.094432) \| 0.042797 / 0.037052 (0.005745) \| 0.360028 / 0.258489 (0.101539) \| 0.435757 / 0.293841 (0.141916) \| 0.041438 / 0.128546 (-0.087109) \| 0.013728 / 0.075646 (-0.061918) \| 0.376154 / 0.419271 (-0.043117) \| 0.075324 / 0.043533 (0.031791) \| 0.357221 / 0.255139 (0.102082) \| 0.416378 / 0.283200 (0.133178) \| 0.110707 / 0.141683 (-0.030975) \| 1.603215 / 1.452155 (0.151061) \| 1.736843 / 1.492716 (0.244127) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249479 / 0.018006 (0.231473) \| 0.513205 / 0.000490 (0.512715) \| 0.003856 / 0.000200 (0.003656) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027750 / 0.037411 (-0.009661) \| 0.105437 / 0.014526 (0.090911) \| 0.115903 / 0.176557 (-0.060653) \| 0.179662 / 0.737135 (-0.557474) \| 0.116305 / 0.296338 (-0.180033) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.551681 / 0.215209 (0.336472) \| 5.544590 / 2.077655 (3.466935) \| 2.193933 / 1.504120 (0.689813) \| 1.898395 / 1.541195 (0.357201) \| 1.877288 / 1.468490 (0.408798) \| 0.858097 / 4.584777 (-3.726680) \| 4.920982 / 3.745712 (1.175270) \| 2.478220 / 5.269862 (-2.791641) \| 1.779608 / 4.565676 (-2.786069) \| 0.101321 / 0.424275 (-0.322954) \| 0.012627 / 0.007607 (0.005020) \| 0.674865 / 0.226044 (0.448820) \| 6.808224 / 2.268929 (4.539295) \| 2.822466 / 55.444624 (-52.622159) \| 2.170379 / 6.876477 (-4.706098) \| 2.224278 / 2.142072 (0.082205) \| 1.032763 / 4.805227 (-3.772464) \| 0.198851 / 6.500664 (-6.301813) \| 0.069249 / 0.075469 (-0.006220) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.425987 / 1.841788 (-0.415801) \| 16.212942 / 8.074308 (8.138634) \| 18.945770 / 10.191392 (8.754378) \| 0.192901 / 0.680424 (-0.487522) \| 0.025343 / 0.534201 (-0.508858) \| 0.465441 / 0.579283 (-0.113842) \| 0.540966 / 0.434364 (0.106602) \| 0.576736 / 0.540337 (0.036399) \| 0.675717 / 1.386936 (-0.711219) \|\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) \| 0.007426 / 0.011353 (-0.003927) \| 0.005023 / 0.011008 (-0.005985) \| 0.085083 / 0.038508 (0.046575) \| 0.030559 / 0.023109 (0.007449) \| 0.398461 / 0.275898 (0.122563) \| 0.418998 / 0.323480 (0.095518) \| 0.006697 / 0.007986 (-0.001288) \| 0.004665 / 0.004328 (0.000337) \| 0.087724 / 0.004250 (0.083473) \| 0.045799 / 0.037052 (0.008747) \| 0.395165 / 0.258489 (0.136676) \| 0.430172 / 0.293841 (0.136331) \| 0.040486 / 0.128546 (-0.088060) \| 0.014237 / 0.075646 (-0.061409) \| 0.099429 / 0.419271 (-0.319843) \| 0.056006 / 0.043533 (0.012473) \| 0.389046 / 0.255139 (0.133907) \| 0.419559 / 0.283200 (0.136359) \| 0.108550 / 0.141683 (-0.033132) \| 1.614052 / 1.452155 (0.161897) \| 1.677785 / 1.492716 (0.185069) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202178 / 0.018006 (0.184172) \| 0.486365 / 0.000490 (0.485875) \| 0.003844 / 0.000200 (0.003644) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027963 / 0.037411 (-0.009449) \| 0.110399 / 0.014526 (0.095873) \| 0.122266 / 0.176557 (-0.054291) \| 0.178551 / 0.737135 (-0.558585) \| 0.129259 / 0.296338 (-0.167080) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.604178 / 0.215209 (0.388969) \| 6.135943 / 2.077655 (4.058288) \| 2.547576 / 1.504120 (1.043456) \| 2.262470 / 1.541195 (0.721276) \| 2.275402 / 1.468490 (0.806912) \| 0.878804 / 4.584777 (-3.705972) \| 5.152200 / 3.745712 (1.406488) \| 2.553715 / 5.269862 (-2.716147) \| 1.580959 / 4.565676 (-2.984717) \| 0.107895 / 0.424275 (-0.316380) \| 0.012751 / 0.007607 (0.005143) \| 0.770678 / 0.226044 (0.544633) \| 7.744303 / 2.268929 (5.475374) \| 3.342037 / 55.444624 (-52.102588) \| 2.756848 / 6.876477 (-4.119629) \| 2.739357 / 2.142072 (0.597285) \| 1.086330 / 4.805227 (-3.718897) \| 0.230983 / 6.500664 (-6.269681) \| 0.073771 / 0.075469 (-0.001698) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.493441 / 1.841788 (-0.348347) \| 16.621611 / 8.074308 (8.547303) \| 19.081000 / 10.191392 (8.889608) \| 0.215623 / 0.680424 (-0.464801) \| 0.025660 / 0.534201 (-0.508541) \| 0.446490 / 0.579283 (-0.132793) \| 0.560078 / 0.434364 (0.125714) \| 0.527231 / 0.540337 (-0.013106) \| 0.636551 / 1.386936 (-0.750385) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b899ea45c0a7e724ceb5f43c3a8b9fdb081fa67a \"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) \| 0.008266 / 0.011353 (-0.003087) \| 0.005082 / 0.011008 (-0.005927) \| 0.119858 / 0.038508 (0.081350) \| 0.032907 / 0.023109 (0.009798) \| 0.362816 / 0.275898 (0.086918) \| 0.403684 / 0.323480 (0.080204) \| 0.006296 / 0.007986 (-0.001690) \| 0.006220 / 0.004328 (0.001891) \| 0.095609 / 0.004250 (0.091359) \| 0.048734 / 0.037052 (0.011682) \| 0.385724 / 0.258489 (0.127235) \| 0.424315 / 0.293841 (0.130475) \| 0.042344 / 0.128546 (-0.086202) \| 0.016147 / 0.075646 (-0.059500) \| 0.409661 / 0.419271 (-0.009610) \| 0.057900 / 0.043533 (0.014367) \| 0.387013 / 0.255139 (0.131874) \| 0.388901 / 0.283200 (0.105702) \| 0.103920 / 0.141683 (-0.037762) \| 1.732730 / 1.452155 (0.280575) \| 1.863912 / 1.492716 (0.371196) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237406 / 0.018006 (0.219400) \| 0.514398 / 0.000490 (0.513909) \| 0.005941 / 0.000200 (0.005741) \| 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) \| 0.027524 / 0.037411 (-0.009888) \| 0.116498 / 0.014526 (0.101972) \| 0.129034 / 0.176557 (-0.047522) \| 0.218272 / 0.737135 (-0.518864) \| 0.148389 / 0.296338 (-0.147950) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.604555 / 0.215209 (0.389346) \| 5.921576 / 2.077655 (3.843921) \| 2.410483 / 1.504120 (0.906363) \| 2.220286 / 1.541195 (0.679092) \| 2.138880 / 1.468490 (0.670390) \| 0.934962 / 4.584777 (-3.649815) \| 5.808855 / 3.745712 (2.063143) \| 4.881554 / 5.269862 (-0.388308) \| 2.536408 / 4.565676 (-2.029268) \| 0.124260 / 0.424275 (-0.300015) \| 0.017798 / 0.007607 (0.010190) \| 0.778991 / 0.226044 (0.552947) \| 7.899262 / 2.268929 (5.630333) \| 3.208667 / 55.444624 (-52.235957) \| 2.631182 / 6.876477 (-4.245295) \| 2.676199 / 2.142072 (0.534127) \| 1.165516 / 4.805227 (-3.639711) \| 0.228751 / 6.500664 (-6.271913) \| 0.081378 / 0.075469 (0.005909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.522156 / 1.841788 (-0.319632) \| 17.975381 / 8.074308 (9.901073) \| 18.918882 / 10.191392 (8.727490) \| 0.223984 / 0.680424 (-0.456440) \| 0.025171 / 0.534201 (-0.509030) \| 0.467894 / 0.579283 (-0.111389) \| 0.559501 / 0.434364 (0.125137) \| 0.550392 / 0.540337 (0.010055) \| 0.696923 / 1.386936 (-0.690013) \|\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) \| 0.008577 / 0.011353 (-0.002775) \| 0.006735 / 0.011008 (-0.004273) \| 0.095108 / 0.038508 (0.056600) \| 0.035059 / 0.023109 (0.011950) \| 0.448576 / 0.275898 (0.172677) \| 0.492049 / 0.323480 (0.168569) \| 0.006600 / 0.007986 (-0.001385) \| 0.004760 / 0.004328 (0.000431) \| 0.094670 / 0.004250 (0.090419) \| 0.052543 / 0.037052 (0.015491) \| 0.458927 / 0.258489 (0.200438) \| 0.511522 / 0.293841 (0.217681) \| 0.046046 / 0.128546 (-0.082500) \| 0.015227 / 0.075646 (-0.060419) \| 0.114585 / 0.419271 (-0.304686) \| 0.057569 / 0.043533 (0.014036) \| 0.441989 / 0.255139 (0.186850) \| 0.487001 / 0.283200 (0.203801) \| 0.115688 / 0.141683 (-0.025995) \| 1.777366 / 1.452155 (0.325211) \| 1.906216 / 1.492716 (0.413499) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224880 / 0.018006 (0.206874) \| 0.504153 / 0.000490 (0.503664) \| 0.001143 / 0.000200 (0.000943) \| 0.000111 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033618 / 0.037411 (-0.003793) \| 0.127396 / 0.014526 (0.112870) \| 0.135648 / 0.176557 (-0.040909) \| 0.193140 / 0.737135 (-0.543995) \| 0.142129 / 0.296338 (-0.154209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.692845 / 0.215209 (0.477636) \| 6.804897 / 2.077655 (4.727242) \| 2.851041 / 1.504120 (1.346921) \| 2.480698 / 1.541195 (0.939504) \| 2.488619 / 1.468490 (1.020129) \| 0.970439 / 4.584777 (-3.614338) \| 5.466059 / 3.745712 (1.720347) \| 2.790261 / 5.269862 (-2.479601) \| 1.727638 / 4.565676 (-2.838039) \| 0.116345 / 0.424275 (-0.307930) \| 0.014348 / 0.007607 (0.006740) \| 0.845510 / 0.226044 (0.619465) \| 8.397198 / 2.268929 (6.128270) \| 3.591998 / 55.444624 (-51.852626) \| 2.858339 / 6.876477 (-4.018137) \| 2.905075 / 2.142072 (0.763003) \| 1.193569 / 4.805227 (-3.611658) \| 0.243091 / 6.500664 (-6.257573) \| 0.082198 / 0.075469 (0.006729) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.610327 / 1.841788 (-0.231461) \| 17.191414 / 8.074308 (9.117106) \| 20.176518 / 10.191392 (9.985126) \| 0.246574 / 0.680424 (-0.433850) \| 0.024343 / 0.534201 (-0.509858) \| 0.482091 / 0.579283 (-0.097192) \| 0.585241 / 0.434364 (0.150877) \| 0.558833 / 0.540337 (0.018496) \| 0.654811 / 1.386936 (-0.732125) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81761dbfa738354a9c50309313dfe90bea26d872 \"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) \| 0.006353 / 0.011353 (-0.004999) \| 0.004393 / 0.011008 (-0.006616) \| 0.098751 / 0.038508 (0.060242) \| 0.029090 / 0.023109 (0.005981) \| 0.304169 / 0.275898 (0.028271) \| 0.339879 / 0.323480 (0.016399) \| 0.005577 / 0.007986 (-0.002408) \| 0.003516 / 0.004328 (-0.000813) \| 0.077347 / 0.004250 (0.073097) \| 0.041935 / 0.037052 (0.004882) \| 0.305865 / 0.258489 (0.047376) \| 0.357063 / 0.293841 (0.063222) \| 0.025245 / 0.128546 (-0.103301) \| 0.008753 / 0.075646 (-0.066893) \| 0.316734 / 0.419271 (-0.102538) \| 0.043464 / 0.043533 (-0.000069) \| 0.300944 / 0.255139 (0.045805) \| 0.330091 / 0.283200 (0.046891) \| 0.088593 / 0.141683 (-0.053090) \| 1.588958 / 1.452155 (0.136803) \| 1.641376 / 1.492716 (0.148660) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220290 / 0.018006 (0.202284) \| 0.445430 / 0.000490 (0.444940) \| 0.004800 / 0.000200 (0.004600) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023828 / 0.037411 (-0.013583) \| 0.103446 / 0.014526 (0.088920) \| 0.110668 / 0.176557 (-0.065889) \| 0.169604 / 0.737135 (-0.567531) \| 0.114818 / 0.296338 (-0.181520) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416951 / 0.215209 (0.201742) \| 4.138917 / 2.077655 (2.061263) \| 1.891265 / 1.504120 (0.387145) \| 1.687068 / 1.541195 (0.145873) \| 1.726618 / 1.468490 (0.258128) \| 0.546977 / 4.584777 (-4.037800) \| 3.536153 / 3.745712 (-0.209560) \| 1.795206 / 5.269862 (-3.474656) \| 1.019845 / 4.565676 (-3.545831) \| 0.067040 / 0.424275 (-0.357235) \| 0.012038 / 0.007607 (0.004431) \| 0.520583 / 0.226044 (0.294539) \| 5.211520 / 2.268929 (2.942591) \| 2.336136 / 55.444624 (-53.108488) \| 2.011262 / 6.876477 (-4.865215) \| 2.137311 / 2.142072 (-0.004762) \| 0.654779 / 4.805227 (-4.150448) \| 0.134555 / 6.500664 (-6.366109) \| 0.066427 / 0.075469 (-0.009042) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.240187 / 1.841788 (-0.601600) \| 14.104063 / 8.074308 (6.029755) \| 13.369572 / 10.191392 (3.178180) \| 0.147891 / 0.680424 (-0.532533) \| 0.016993 / 0.534201 (-0.517208) \| 0.364863 / 0.579283 (-0.214420) \| 0.398684 / 0.434364 (-0.035680) \| 0.430524 / 0.540337 (-0.109813) \| 0.520920 / 1.386936 (-0.866016) \|\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) \| 0.006845 / 0.011353 (-0.004508) \| 0.004420 / 0.011008 (-0.006588) \| 0.078334 / 0.038508 (0.039825) \| 0.030566 / 0.023109 (0.007457) \| 0.409568 / 0.275898 (0.133670) \| 0.458389 / 0.323480 (0.134910) \| 0.005739 / 0.007986 (-0.002247) \| 0.005222 / 0.004328 (0.000893) \| 0.076066 / 0.004250 (0.071816) \| 0.049239 / 0.037052 (0.012187) \| 0.409841 / 0.258489 (0.151352) \| 0.472250 / 0.293841 (0.178409) \| 0.025463 / 0.128546 (-0.103084) \| 0.008738 / 0.075646 (-0.066909) \| 0.083114 / 0.419271 (-0.336157) \| 0.041233 / 0.043533 (-0.002300) \| 0.407158 / 0.255139 (0.152019) \| 0.438724 / 0.283200 (0.155524) \| 0.097974 / 0.141683 (-0.043709) \| 1.536514 / 1.452155 (0.084360) \| 1.636704 / 1.492716 (0.143987) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240589 / 0.018006 (0.222583) \| 0.440328 / 0.000490 (0.439838) \| 0.000937 / 0.000200 (0.000737) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027559 / 0.037411 (-0.009853) \| 0.109930 / 0.014526 (0.095405) \| 0.113366 / 0.176557 (-0.063190) \| 0.166849 / 0.737135 (-0.570286) \| 0.118872 / 0.296338 (-0.177467) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474120 / 0.215209 (0.258911) \| 4.739222 / 2.077655 (2.661567) \| 2.484386 / 1.504120 (0.980266) \| 2.281937 / 1.541195 (0.740742) \| 2.362974 / 1.468490 (0.894484) \| 0.549897 / 4.584777 (-4.034879) \| 3.425540 / 3.745712 (-0.320172) \| 1.765810 / 5.269862 (-3.504051) \| 1.008277 / 4.565676 (-3.557400) \| 0.067288 / 0.424275 (-0.356987) \| 0.011954 / 0.007607 (0.004347) \| 0.577216 / 0.226044 (0.351172) \| 5.790659 / 2.268929 (3.521731) \| 2.946732 / 55.444624 (-52.497892) \| 2.608835 / 6.876477 (-4.267641) \| 2.642987 / 2.142072 (0.500915) \| 0.652798 / 4.805227 (-4.152429) \| 0.135909 / 6.500664 (-6.364755) \| 0.068480 / 0.075469 (-0.006989) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353550 / 1.841788 (-0.488237) \| 14.732084 / 8.074308 (6.657775) \| 14.439174 / 10.191392 (4.247782) \| 0.131445 / 0.680424 (-0.548979) \| 0.016608 / 0.534201 (-0.517593) \| 0.368103 / 0.579283 (-0.211180) \| 0.393918 / 0.434364 (-0.040446) \| 0.423562 / 0.540337 (-0.116776) \| 0.515041 / 1.386936 (-0.871895) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8907bdb23f78545303eb3bb0561e33ec6787f96c \"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) \| 0.006414 / 0.011353 (-0.004938) \| 0.004704 / 0.011008 (-0.006305) \| 0.096012 / 0.038508 (0.057504) \| 0.032910 / 0.023109 (0.009800) \| 0.290676 / 0.275898 (0.014778) \| 0.319646 / 0.323480 (-0.003834) \| 0.005806 / 0.007986 (-0.002180) \| 0.004008 / 0.004328 (-0.000320) \| 0.073982 / 0.004250 (0.069731) \| 0.048985 / 0.037052 (0.011933) \| 0.299498 / 0.258489 (0.041009) \| 0.338118 / 0.293841 (0.044277) \| 0.027680 / 0.128546 (-0.100866) \| 0.009051 / 0.075646 (-0.066595) \| 0.325051 / 0.419271 (-0.094221) \| 0.051011 / 0.043533 (0.007478) \| 0.292249 / 0.255139 (0.037110) \| 0.315733 / 0.283200 (0.032533) \| 0.100327 / 0.141683 (-0.041356) \| 1.481862 / 1.452155 (0.029707) \| 1.544884 / 1.492716 (0.052168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289610 / 0.018006 (0.271603) \| 0.510164 / 0.000490 (0.509675) \| 0.004726 / 0.000200 (0.004526) \| 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) \| 0.027617 / 0.037411 (-0.009794) \| 0.107593 / 0.014526 (0.093068) \| 0.122783 / 0.176557 (-0.053774) \| 0.181086 / 0.737135 (-0.556049) \| 0.128030 / 0.296338 (-0.168308) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403571 / 0.215209 (0.188362) \| 4.002881 / 2.077655 (1.925227) \| 1.805550 / 1.504120 (0.301430) \| 1.619165 / 1.541195 (0.077971) \| 1.606536 / 1.468490 (0.138046) \| 0.518917 / 4.584777 (-4.065860) \| 3.731498 / 3.745712 (-0.014214) \| 3.206645 / 5.269862 (-2.063217) \| 1.641615 / 4.565676 (-2.924062) \| 0.065100 / 0.424275 (-0.359175) \| 0.011396 / 0.007607 (0.003789) \| 0.500597 / 0.226044 (0.274553) \| 4.992293 / 2.268929 (2.723364) \| 2.278726 / 55.444624 (-53.165898) \| 1.960823 / 6.876477 (-4.915654) \| 2.038684 / 2.142072 (-0.103388) \| 0.640910 / 4.805227 (-4.164318) \| 0.140597 / 6.500664 (-6.360067) \| 0.062114 / 0.075469 (-0.013355) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.167366 / 1.841788 (-0.674422) \| 14.748193 / 8.074308 (6.673884) \| 13.592381 / 10.191392 (3.400989) \| 0.165341 / 0.680424 (-0.515083) \| 0.017360 / 0.534201 (-0.516841) \| 0.393448 / 0.579283 (-0.185836) \| 0.422951 / 0.434364 (-0.011413) \| 0.460491 / 0.540337 (-0.079847) \| 0.558238 / 1.386936 (-0.828698) \|\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) \| 0.006373 / 0.011353 (-0.004980) \| 0.004587 / 0.011008 (-0.006421) \| 0.076421 / 0.038508 (0.037913) \| 0.032162 / 0.023109 (0.009052) \| 0.385531 / 0.275898 (0.109633) \| 0.410424 / 0.323480 (0.086944) \| 0.006154 / 0.007986 (-0.001832) \| 0.005533 / 0.004328 (0.001205) \| 0.077035 / 0.004250 (0.072784) \| 0.051571 / 0.037052 (0.014519) \| 0.393283 / 0.258489 (0.134794) \| 0.433756 / 0.293841 (0.139915) \| 0.028381 / 0.128546 (-0.100165) \| 0.009034 / 0.075646 (-0.066613) \| 0.083836 / 0.419271 (-0.335435) \| 0.048246 / 0.043533 (0.004713) \| 0.385437 / 0.255139 (0.130298) \| 0.394187 / 0.283200 (0.110987) \| 0.105453 / 0.141683 (-0.036230) \| 1.459173 / 1.452155 (0.007018) \| 1.575083 / 1.492716 (0.082367) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.320324 / 0.018006 (0.302318) \| 0.502945 / 0.000490 (0.502455) \| 0.004470 / 0.000200 (0.004270) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028118 / 0.037411 (-0.009293) \| 0.111430 / 0.014526 (0.096904) \| 0.123141 / 0.176557 (-0.053415) \| 0.175215 / 0.737135 (-0.561920) \| 0.126429 / 0.296338 (-0.169909) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433407 / 0.215209 (0.218198) \| 4.329945 / 2.077655 (2.252291) \| 2.096822 / 1.504120 (0.592702) \| 1.908173 / 1.541195 (0.366978) \| 1.967167 / 1.468490 (0.498676) \| 0.529207 / 4.584777 (-4.055570) \| 3.798424 / 3.745712 (0.052712) \| 3.050716 / 5.269862 (-2.219146) \| 1.445009 / 4.565676 (-3.120668) \| 0.066467 / 0.424275 (-0.357809) \| 0.011698 / 0.007607 (0.004090) \| 0.528660 / 0.226044 (0.302615) \| 5.282069 / 2.268929 (3.013141) \| 2.535501 / 55.444624 (-52.909124) \| 2.202856 / 6.876477 (-4.673621) \| 2.293225 / 2.142072 (0.151153) \| 0.640216 / 4.805227 (-4.165011) \| 0.140884 / 6.500664 (-6.359780) \| 0.064231 / 0.075469 (-0.011238) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.292129 / 1.841788 (-0.549659) \| 15.371370 / 8.074308 (7.297062) \| 15.114854 / 10.191392 (4.923462) \| 0.176870 / 0.680424 (-0.503554) \| 0.017380 / 0.534201 (-0.516821) \| 0.398156 / 0.579283 (-0.181127) \| 0.442277 / 0.434364 (0.007913) \| 0.467093 / 0.540337 (-0.073244) \| 0.561599 / 1.386936 (-0.825337) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#323747a5ff7d9b204ea3c4989d658af7102f7bbd \"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) \| 0.009360 / 0.011353 (-0.001993) \| 0.006297 / 0.011008 (-0.004712) \| 0.133131 / 0.038508 (0.094623) \| 0.040261 / 0.023109 (0.017152) \| 0.419101 / 0.275898 (0.143203) \| 0.453087 / 0.323480 (0.129607) \| 0.007718 / 0.007986 (-0.000268) \| 0.005698 / 0.004328 (0.001369) \| 0.102261 / 0.004250 (0.098010) \| 0.055147 / 0.037052 (0.018095) \| 0.428355 / 0.258489 (0.169866) \| 0.505241 / 0.293841 (0.211400) \| 0.046745 / 0.128546 (-0.081802) \| 0.015559 / 0.075646 (-0.060088) \| 0.441775 / 0.419271 (0.022503) \| 0.070165 / 0.043533 (0.026632) \| 0.421957 / 0.255139 (0.166818) \| 0.445156 / 0.283200 (0.161957) \| 0.126321 / 0.141683 (-0.015362) \| 1.900486 / 1.452155 (0.448331) \| 2.088630 / 1.492716 (0.595913) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260244 / 0.018006 (0.242237) \| 0.606317 / 0.000490 (0.605828) \| 0.006827 / 0.000200 (0.006627) \| 0.000117 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031958 / 0.037411 (-0.005453) \| 0.139362 / 0.014526 (0.124836) \| 0.148748 / 0.176557 (-0.027809) \| 0.226269 / 0.737135 (-0.510866) \| 0.161145 / 0.296338 (-0.135194) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666287 / 0.215209 (0.451078) \| 6.588707 / 2.077655 (4.511053) \| 2.736155 / 1.504120 (1.232035) \| 2.329601 / 1.541195 (0.788406) \| 2.324991 / 1.468490 (0.856501) \| 0.943608 / 4.584777 (-3.641169) \| 6.051653 / 3.745712 (2.305941) \| 2.929150 / 5.269862 (-2.340711) \| 1.804461 / 4.565676 (-2.761216) \| 0.113302 / 0.424275 (-0.310973) \| 0.015245 / 0.007607 (0.007638) \| 0.827029 / 0.226044 (0.600984) \| 8.211536 / 2.268929 (5.942608) \| 3.445231 / 55.444624 (-51.999393) \| 2.756728 / 6.876477 (-4.119748) \| 2.904039 / 2.142072 (0.761966) \| 1.162339 / 4.805227 (-3.642888) \| 0.231168 / 6.500664 (-6.269496) \| 0.089038 / 0.075469 (0.013569) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.640619 / 1.841788 (-0.201169) \| 20.034157 / 8.074308 (11.959849) \| 22.346006 / 10.191392 (12.154614) \| 0.255300 / 0.680424 (-0.425124) \| 0.031452 / 0.534201 (-0.502749) \| 0.563290 / 0.579283 (-0.015993) \| 0.653556 / 0.434364 (0.219192) \| 0.687663 / 0.540337 (0.147326) \| 0.816432 / 1.386936 (-0.570504) \|\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) \| 0.010340 / 0.011353 (-0.001013) \| 0.006245 / 0.011008 (-0.004764) \| 0.128012 / 0.038508 (0.089504) \| 0.041799 / 0.023109 (0.018690) \| 0.533340 / 0.275898 (0.257442) \| 0.592243 / 0.323480 (0.268763) \| 0.009256 / 0.007986 (0.001271) \| 0.005310 / 0.004328 (0.000982) \| 0.110973 / 0.004250 (0.106722) \| 0.065465 / 0.037052 (0.028412) \| 0.533845 / 0.258489 (0.275356) \| 0.602190 / 0.293841 (0.308349) \| 0.060245 / 0.128546 (-0.068301) \| 0.016954 / 0.075646 (-0.058693) \| 0.119727 / 0.419271 (-0.299545) \| 0.064628 / 0.043533 (0.021095) \| 0.558229 / 0.255139 (0.303090) \| 0.563696 / 0.283200 (0.280496) \| 0.137225 / 0.141683 (-0.004458) \| 2.038605 / 1.452155 (0.586451) \| 2.158655 / 1.492716 (0.665939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.327067 / 0.018006 (0.309061) \| 0.628812 / 0.000490 (0.628323) \| 0.010259 / 0.000200 (0.010059) \| 0.000123 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037023 / 0.037411 (-0.000388) \| 0.142462 / 0.014526 (0.127936) \| 0.158165 / 0.176557 (-0.018392) \| 0.220808 / 0.737135 (-0.516328) \| 0.163608 / 0.296338 (-0.132731) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.776119 / 0.215209 (0.560910) \| 7.813044 / 2.077655 (5.735389) \| 3.610901 / 1.504120 (2.106781) \| 3.195144 / 1.541195 (1.653950) \| 3.218245 / 1.468490 (1.749755) \| 1.092732 / 4.584777 (-3.492045) \| 5.965526 / 3.745712 (2.219813) \| 2.914683 / 5.269862 (-2.355179) \| 1.848397 / 4.565676 (-2.717280) \| 0.114436 / 0.424275 (-0.309839) \| 0.014794 / 0.007607 (0.007187) \| 0.887141 / 0.226044 (0.661096) \| 9.009743 / 2.268929 (6.740815) \| 4.180143 / 55.444624 (-51.264481) \| 3.452194 / 6.876477 (-3.424283) \| 3.493520 / 2.142072 (1.351448) \| 1.233327 / 4.805227 (-3.571900) \| 0.235390 / 6.500664 (-6.265274) \| 0.099544 / 0.075469 (0.024075) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.853482 / 1.841788 (0.011694) \| 20.071177 / 8.074308 (11.996869) \| 24.507618 / 10.191392 (14.316226) \| 0.260164 / 0.680424 (-0.420260) \| 0.028433 / 0.534201 (-0.505768) \| 0.549181 / 0.579283 (-0.030102) \| 0.650069 / 0.434364 (0.215705) \| 0.629541 / 0.540337 (0.089203) \| 0.808932 / 1.386936 (-0.578004) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f39ba76af62c8037de3f464e87cbb095f8729062 \"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) \| 0.009537 / 0.011353 (-0.001816) \| 0.006036 / 0.011008 (-0.004972) \| 0.141210 / 0.038508 (0.102701) \| 0.037493 / 0.023109 (0.014384) \| 0.404285 / 0.275898 (0.128386) \| 0.458906 / 0.323480 (0.135427) \| 0.007224 / 0.007986 (-0.000761) \| 0.005148 / 0.004328 (0.000819) \| 0.103889 / 0.004250 (0.099639) \| 0.048877 / 0.037052 (0.011824) \| 0.413220 / 0.258489 (0.154731) \| 0.458153 / 0.293841 (0.164312) \| 0.046008 / 0.128546 (-0.082538) \| 0.015116 / 0.075646 (-0.060531) \| 0.439836 / 0.419271 (0.020565) \| 0.067527 / 0.043533 (0.023994) \| 0.435794 / 0.255139 (0.180656) \| 0.451687 / 0.283200 (0.168487) \| 0.121274 / 0.141683 (-0.020409) \| 1.950199 / 1.452155 (0.498044) \| 2.035589 / 1.492716 (0.542873) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247056 / 0.018006 (0.229050) \| 0.550348 / 0.000490 (0.549858) \| 0.005504 / 0.000200 (0.005305) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032171 / 0.037411 (-0.005240) \| 0.135983 / 0.014526 (0.121457) \| 0.149587 / 0.176557 (-0.026970) \| 0.233414 / 0.737135 (-0.503722) \| 0.152598 / 0.296338 (-0.143740) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.634813 / 0.215209 (0.419604) \| 6.453619 / 2.077655 (4.375964) \| 2.582070 / 1.504120 (1.077951) \| 2.214292 / 1.541195 (0.673097) \| 2.220012 / 1.468490 (0.751522) \| 0.987374 / 4.584777 (-3.597403) \| 5.543760 / 3.745712 (1.798047) \| 2.808865 / 5.269862 (-2.460996) \| 1.714713 / 4.565676 (-2.850963) \| 0.111016 / 0.424275 (-0.313259) \| 0.014688 / 0.007607 (0.007081) \| 0.842542 / 0.226044 (0.616498) \| 8.414336 / 2.268929 (6.145407) \| 3.501021 / 55.444624 (-51.943604) \| 2.665335 / 6.876477 (-4.211142) \| 2.843706 / 2.142072 (0.701633) \| 1.196398 / 4.805227 (-3.608829) \| 0.245508 / 6.500664 (-6.255156) \| 0.086970 / 0.075469 (0.011501) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.590244 / 1.841788 (-0.251544) \| 18.694141 / 8.074308 (10.619833) \| 21.752463 / 10.191392 (11.561071) \| 0.264511 / 0.680424 (-0.415913) \| 0.028713 / 0.534201 (-0.505488) \| 0.531102 / 0.579283 (-0.048181) \| 0.626302 / 0.434364 (0.191938) \| 0.624541 / 0.540337 (0.084203) \| 0.745745 / 1.386936 (-0.641191) \|\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) \| 0.010097 / 0.011353 (-0.001256) \| 0.005558 / 0.011008 (-0.005451) \| 0.111326 / 0.038508 (0.072818) \| 0.036465 / 0.023109 (0.013356) \| 0.472116 / 0.275898 (0.196218) \| 0.524479 / 0.323480 (0.200999) \| 0.007466 / 0.007986 (-0.000520) \| 0.005440 / 0.004328 (0.001112) \| 0.103482 / 0.004250 (0.099231) \| 0.053217 / 0.037052 (0.016165) \| 0.476685 / 0.258489 (0.218196) \| 0.554011 / 0.293841 (0.260170) \| 0.047157 / 0.128546 (-0.081390) \| 0.015895 / 0.075646 (-0.059751) \| 0.115997 / 0.419271 (-0.303274) \| 0.062290 / 0.043533 (0.018758) \| 0.474166 / 0.255139 (0.219027) \| 0.498854 / 0.283200 (0.215655) \| 0.121798 / 0.141683 (-0.019885) \| 1.956583 / 1.452155 (0.504428) \| 2.069620 / 1.492716 (0.576904) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278637 / 0.018006 (0.260631) \| 0.555295 / 0.000490 (0.554805) \| 0.007401 / 0.000200 (0.007201) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033576 / 0.037411 (-0.003835) \| 0.136479 / 0.014526 (0.121954) \| 0.153960 / 0.176557 (-0.022597) \| 0.203422 / 0.737135 (-0.533713) \| 0.154159 / 0.296338 (-0.142180) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.672561 / 0.215209 (0.457352) \| 6.956675 / 2.077655 (4.879020) \| 3.063636 / 1.504120 (1.559516) \| 2.668256 / 1.541195 (1.127061) \| 2.794793 / 1.468490 (1.326303) \| 0.964242 / 4.584777 (-3.620535) \| 5.785992 / 3.745712 (2.040279) \| 2.850079 / 5.269862 (-2.419782) \| 1.782491 / 4.565676 (-2.783186) \| 0.114859 / 0.424275 (-0.309416) \| 0.015229 / 0.007607 (0.007622) \| 0.858406 / 0.226044 (0.632362) \| 8.646296 / 2.268929 (6.377367) \| 3.842133 / 55.444624 (-51.602492) \| 3.180017 / 6.876477 (-3.696460) \| 3.241315 / 2.142072 (1.099243) \| 1.248988 / 4.805227 (-3.556239) \| 0.235075 / 6.500664 (-6.265589) \| 0.087192 / 0.075469 (0.011723) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.783877 / 1.841788 (-0.057910) \| 19.477223 / 8.074308 (11.402914) \| 22.926734 / 10.191392 (12.735342) \| 0.246970 / 0.680424 (-0.433454) \| 0.026386 / 0.534201 (-0.507815) \| 0.517599 / 0.579283 (-0.061684) \| 0.626504 / 0.434364 (0.192140) \| 0.606943 / 0.540337 (0.066606) \| 0.739115 / 1.386936 (-0.647821) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8f051a41454f8625091338e6b53119a5eb9b2a0 \"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) \| 0.008085 / 0.011353 (-0.003268) \| 0.005568 / 0.011008 (-0.005440) \| 0.119674 / 0.038508 (0.081166) \| 0.040452 / 0.023109 (0.017343) \| 0.360288 / 0.275898 (0.084390) \| 0.409448 / 0.323480 (0.085968) \| 0.007281 / 0.007986 (-0.000705) \| 0.004931 / 0.004328 (0.000602) \| 0.089956 / 0.004250 (0.085706) \| 0.056088 / 0.037052 (0.019036) \| 0.384708 / 0.258489 (0.126219) \| 0.423506 / 0.293841 (0.129665) \| 0.033280 / 0.128546 (-0.095266) \| 0.010696 / 0.075646 (-0.064951) \| 0.394851 / 0.419271 (-0.024421) \| 0.058412 / 0.043533 (0.014879) \| 0.361514 / 0.255139 (0.106375) \| 0.399121 / 0.283200 (0.115921) \| 0.117927 / 0.141683 (-0.023756) \| 1.791499 / 1.452155 (0.339344) \| 1.889000 / 1.492716 (0.396284) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253324 / 0.018006 (0.235318) \| 0.536151 / 0.000490 (0.535661) \| 0.010450 / 0.000200 (0.010250) \| 0.000171 / 0.000054 (0.000117) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034646 / 0.037411 (-0.002765) \| 0.145999 / 0.014526 (0.131473) \| 0.153793 / 0.176557 (-0.022763) \| 0.232871 / 0.737135 (-0.504265) \| 0.161151 / 0.296338 (-0.135188) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471407 / 0.215209 (0.256197) \| 4.715702 / 2.077655 (2.638047) \| 2.228939 / 1.504120 (0.724819) \| 2.008511 / 1.541195 (0.467317) \| 2.135182 / 1.468490 (0.666692) \| 0.620720 / 4.584777 (-3.964057) \| 4.960731 / 3.745712 (1.215019) \| 2.222469 / 5.269862 (-3.047393) \| 1.284467 / 4.565676 (-3.281209) \| 0.077931 / 0.424275 (-0.346344) \| 0.013935 / 0.007607 (0.006328) \| 0.593164 / 0.226044 (0.367120) \| 5.940829 / 2.268929 (3.671900) \| 2.664277 / 55.444624 (-52.780347) \| 2.290655 / 6.876477 (-4.585822) \| 2.496664 / 2.142072 (0.354592) \| 0.759166 / 4.805227 (-4.046061) \| 0.168011 / 6.500664 (-6.332653) \| 0.077993 / 0.075469 (0.002524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.440663 / 1.841788 (-0.401125) \| 19.105377 / 8.074308 (11.031069) \| 16.068118 / 10.191392 (5.876726) \| 0.193024 / 0.680424 (-0.487400) \| 0.022348 / 0.534201 (-0.511853) \| 0.517454 / 0.579283 (-0.061829) \| 0.528072 / 0.434364 (0.093708) \| 0.565293 / 0.540337 (0.024955) \| 0.676578 / 1.386936 (-0.710358) \|\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) \| 0.008089 / 0.011353 (-0.003264) \| 0.005287 / 0.011008 (-0.005721) \| 0.087964 / 0.038508 (0.049456) \| 0.041548 / 0.023109 (0.018439) \| 0.437733 / 0.275898 (0.161835) \| 0.487878 / 0.323480 (0.164398) \| 0.006898 / 0.007986 (-0.001087) \| 0.004649 / 0.004328 (0.000320) \| 0.086982 / 0.004250 (0.082732) \| 0.056874 / 0.037052 (0.019822) \| 0.437397 / 0.258489 (0.178908) \| 0.490636 / 0.293841 (0.196795) \| 0.033550 / 0.128546 (-0.094997) \| 0.010430 / 0.075646 (-0.065216) \| 0.096076 / 0.419271 (-0.323196) \| 0.054028 / 0.043533 (0.010495) \| 0.450262 / 0.255139 (0.195123) \| 0.465566 / 0.283200 (0.182366) \| 0.119987 / 0.141683 (-0.021696) \| 1.764428 / 1.452155 (0.312273) \| 1.841547 / 1.492716 (0.348831) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271427 / 0.018006 (0.253420) \| 0.506386 / 0.000490 (0.505896) \| 0.001213 / 0.000200 (0.001013) \| 0.000125 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036159 / 0.037411 (-0.001253) \| 0.140578 / 0.014526 (0.126053) \| 0.147517 / 0.176557 (-0.029040) \| 0.206215 / 0.737135 (-0.530921) \| 0.152560 / 0.296338 (-0.143779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522833 / 0.215209 (0.307624) \| 5.215732 / 2.077655 (3.138077) \| 2.553406 / 1.504120 (1.049286) \| 2.344815 / 1.541195 (0.803620) \| 2.422377 / 1.468490 (0.953886) \| 0.631197 / 4.584777 (-3.953580) \| 4.906216 / 3.745712 (1.160504) \| 2.212923 / 5.269862 (-3.056938) \| 1.352937 / 4.565676 (-3.212740) \| 0.079141 / 0.424275 (-0.345135) \| 0.013691 / 0.007607 (0.006084) \| 0.634939 / 0.226044 (0.408895) \| 6.578770 / 2.268929 (4.309842) \| 3.080339 / 55.444624 (-52.364286) \| 2.710243 / 6.876477 (-4.166234) \| 2.740476 / 2.142072 (0.598404) \| 0.783610 / 4.805227 (-4.021617) \| 0.171589 / 6.500664 (-6.329075) \| 0.077311 / 0.075469 (0.001842) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.584847 / 1.841788 (-0.256941) \| 19.510132 / 8.074308 (11.435824) \| 18.074572 / 10.191392 (7.883180) \| 0.173494 / 0.680424 (-0.506930) \| 0.021149 / 0.534201 (-0.513052) \| 0.469026 / 0.579283 (-0.110258) \| 0.518463 / 0.434364 (0.084099) \| 0.550363 / 0.540337 (0.010026) \| 0.667087 / 1.386936 (-0.719849) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5dfcd876c25cc0ffbd6b5b518b017419390a8ada \"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) \| 0.007144 / 0.011353 (-0.004209) \| 0.004783 / 0.011008 (-0.006225) \| 0.103991 / 0.038508 (0.065483) \| 0.039098 / 0.023109 (0.015989) \| 0.319851 / 0.275898 (0.043952) \| 0.356104 / 0.323480 (0.032625) \| 0.007077 / 0.007986 (-0.000909) \| 0.004188 / 0.004328 (-0.000141) \| 0.078360 / 0.004250 (0.074109) \| 0.050951 / 0.037052 (0.013899) \| 0.321791 / 0.258489 (0.063302) \| 0.356123 / 0.293841 (0.062283) \| 0.028967 / 0.128546 (-0.099579) \| 0.009091 / 0.075646 (-0.066555) \| 0.355265 / 0.419271 (-0.064007) \| 0.052521 / 0.043533 (0.008988) \| 0.317333 / 0.255139 (0.062194) \| 0.340747 / 0.283200 (0.057547) \| 0.104354 / 0.141683 (-0.037329) \| 1.522791 / 1.452155 (0.070636) \| 1.579835 / 1.492716 (0.087118) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260539 / 0.018006 (0.242532) \| 0.454230 / 0.000490 (0.453740) \| 0.036588 / 0.000200 (0.036388) \| 0.000289 / 0.000054 (0.000235) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028375 / 0.037411 (-0.009036) \| 0.118939 / 0.014526 (0.104413) \| 0.126553 / 0.176557 (-0.050004) \| 0.184596 / 0.737135 (-0.552539) \| 0.130583 / 0.296338 (-0.165755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417353 / 0.215209 (0.202144) \| 4.171595 / 2.077655 (2.093940) \| 1.855096 / 1.504120 (0.350976) \| 1.673941 / 1.541195 (0.132747) \| 1.761370 / 1.468490 (0.292880) \| 0.544081 / 4.584777 (-4.040696) \| 3.851877 / 3.745712 (0.106165) \| 1.896661 / 5.269862 (-3.373200) \| 1.093303 / 4.565676 (-3.472373) \| 0.067967 / 0.424275 (-0.356308) \| 0.012313 / 0.007607 (0.004706) \| 0.532316 / 0.226044 (0.306272) \| 5.336016 / 2.268929 (3.067087) \| 2.344780 / 55.444624 (-53.099845) \| 1.993909 / 6.876477 (-4.882568) \| 2.167324 / 2.142072 (0.025251) \| 0.670334 / 4.805227 (-4.134893) \| 0.147705 / 6.500664 (-6.352959) \| 0.067634 / 0.075469 (-0.007835) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251005 / 1.841788 (-0.590783) \| 15.405531 / 8.074308 (7.331223) \| 14.197019 / 10.191392 (4.005627) \| 0.144230 / 0.680424 (-0.536193) \| 0.018352 / 0.534201 (-0.515849) \| 0.427536 / 0.579283 (-0.151748) \| 0.433135 / 0.434364 (-0.001229) \| 0.502624 / 0.540337 (-0.037713) \| 0.612312 / 1.386936 (-0.774624) \|\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) \| 0.007011 / 0.011353 (-0.004342) \| 0.004857 / 0.011008 (-0.006151) \| 0.077797 / 0.038508 (0.039289) \| 0.035411 / 0.023109 (0.012302) \| 0.368234 / 0.275898 (0.092336) \| 0.408359 / 0.323480 (0.084879) \| 0.005883 / 0.007986 (-0.002102) \| 0.004311 / 0.004328 (-0.000017) \| 0.077216 / 0.004250 (0.072966) \| 0.052062 / 0.037052 (0.015010) \| 0.368502 / 0.258489 (0.110013) \| 0.428681 / 0.293841 (0.134840) \| 0.028889 / 0.128546 (-0.099657) \| 0.009146 / 0.075646 (-0.066501) \| 0.085515 / 0.419271 (-0.333756) \| 0.050216 / 0.043533 (0.006683) \| 0.359562 / 0.255139 (0.104423) \| 0.378335 / 0.283200 (0.095135) \| 0.106351 / 0.141683 (-0.035332) \| 1.538943 / 1.452155 (0.086788) \| 1.663572 / 1.492716 (0.170855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216917 / 0.018006 (0.198911) \| 0.444130 / 0.000490 (0.443641) \| 0.002640 / 0.000200 (0.002440) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032509 / 0.037411 (-0.004902) \| 0.123955 / 0.014526 (0.109430) \| 0.133236 / 0.176557 (-0.043321) \| 0.187408 / 0.737135 (-0.549727) \| 0.136696 / 0.296338 (-0.159643) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443714 / 0.215209 (0.228505) \| 4.416973 / 2.077655 (2.339318) \| 2.145279 / 1.504120 (0.641159) \| 1.946669 / 1.541195 (0.405474) \| 2.044105 / 1.468490 (0.575614) \| 0.534463 / 4.584777 (-4.050314) \| 3.824926 / 3.745712 (0.079214) \| 3.151796 / 5.269862 (-2.118066) \| 1.497513 / 4.565676 (-3.068164) \| 0.066799 / 0.424275 (-0.357476) \| 0.012408 / 0.007607 (0.004801) \| 0.544182 / 0.226044 (0.318138) \| 5.419403 / 2.268929 (3.150474) \| 2.605191 / 55.444624 (-52.839433) \| 2.285354 / 6.876477 (-4.591123) \| 2.359520 / 2.142072 (0.217448) \| 0.655489 / 4.805227 (-4.149738) \| 0.143496 / 6.500664 (-6.357168) \| 0.066782 / 0.075469 (-0.008687) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.329370 / 1.841788 (-0.512418) \| 16.058019 / 8.074308 (7.983711) \| 15.119769 / 10.191392 (4.928377) \| 0.147967 / 0.680424 (-0.532457) \| 0.018360 / 0.534201 (-0.515841) \| 0.436847 / 0.579283 (-0.142436) \| 0.435136 / 0.434364 (0.000773) \| 0.507176 / 0.540337 (-0.033161) \| 0.610627 / 1.386936 (-0.776309) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4cc3ee6d8945052283076854eb77575d52b7432 \"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) \| 0.006425 / 0.011353 (-0.004927) \| 0.003710 / 0.011008 (-0.007298) \| 0.102072 / 0.038508 (0.063564) \| 0.033974 / 0.023109 (0.010865) \| 0.273146 / 0.275898 (-0.002752) \| 0.313254 / 0.323480 (-0.010226) \| 0.004889 / 0.007986 (-0.003096) \| 0.004803 / 0.004328 (0.000475) \| 0.067359 / 0.004250 (0.063109) \| 0.040281 / 0.037052 (0.003228) \| 0.302106 / 0.258489 (0.043617) \| 0.318039 / 0.293841 (0.024198) \| 0.028839 / 0.128546 (-0.099707) \| 0.008726 / 0.075646 (-0.066921) \| 0.322532 / 0.419271 (-0.096739) \| 0.048845 / 0.043533 (0.005312) \| 0.299836 / 0.255139 (0.044697) \| 0.300983 / 0.283200 (0.017784) \| 0.103384 / 0.141683 (-0.038299) \| 1.417245 / 1.452155 (-0.034910) \| 1.538819 / 1.492716 (0.046102) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219798 / 0.018006 (0.201792) \| 0.442297 / 0.000490 (0.441807) \| 0.013792 / 0.000200 (0.013592) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024996 / 0.037411 (-0.012416) \| 0.098558 / 0.014526 (0.084032) \| 0.116423 / 0.176557 (-0.060133) \| 0.163481 / 0.737135 (-0.573654) \| 0.115031 / 0.296338 (-0.181308) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392411 / 0.215209 (0.177202) \| 4.025992 / 2.077655 (1.948337) \| 1.850809 / 1.504120 (0.346690) \| 1.668330 / 1.541195 (0.127136) \| 1.627041 / 1.468490 (0.158551) \| 0.510721 / 4.584777 (-4.074055) \| 3.841318 / 3.745712 (0.095606) \| 3.416979 / 5.269862 (-1.852883) \| 1.640796 / 4.565676 (-2.924880) \| 0.061968 / 0.424275 (-0.362307) \| 0.010281 / 0.007607 (0.002674) \| 0.485592 / 0.226044 (0.259548) \| 4.872205 / 2.268929 (2.603277) \| 2.146753 / 55.444624 (-53.297871) \| 1.832087 / 6.876477 (-5.044390) \| 1.920928 / 2.142072 (-0.221144) \| 0.606363 / 4.805227 (-4.198864) \| 0.134351 / 6.500664 (-6.366313) \| 0.057583 / 0.075469 (-0.017886) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.153048 / 1.841788 (-0.688739) \| 14.165743 / 8.074308 (6.091435) \| 12.237798 / 10.191392 (2.046406) \| 0.159815 / 0.680424 (-0.520608) \| 0.018226 / 0.534201 (-0.515975) \| 0.372390 / 0.579283 (-0.206893) \| 0.396552 / 0.434364 (-0.037811) \| 0.439445 / 0.540337 (-0.100892) \| 0.521924 / 1.386936 (-0.865012) \|\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) \| 0.006162 / 0.011353 (-0.005191) \| 0.004006 / 0.011008 (-0.007002) \| 0.067226 / 0.038508 (0.028718) \| 0.030285 / 0.023109 (0.007176) \| 0.361220 / 0.275898 (0.085322) \| 0.386783 / 0.323480 (0.063303) \| 0.005202 / 0.007986 (-0.002784) \| 0.003453 / 0.004328 (-0.000876) \| 0.068299 / 0.004250 (0.064048) \| 0.041433 / 0.037052 (0.004381) \| 0.360222 / 0.258489 (0.101733) \| 0.399327 / 0.293841 (0.105486) \| 0.026066 / 0.128546 (-0.102480) \| 0.008025 / 0.075646 (-0.067621) \| 0.079588 / 0.419271 (-0.339683) \| 0.042616 / 0.043533 (-0.000917) \| 0.347639 / 0.255139 (0.092500) \| 0.386092 / 0.283200 (0.102893) \| 0.100869 / 0.141683 (-0.040814) \| 1.386901 / 1.452155 (-0.065254) \| 1.471523 / 1.492716 (-0.021193) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217020 / 0.018006 (0.199014) \| 0.431033 / 0.000490 (0.430543) \| 0.002902 / 0.000200 (0.002702) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027396 / 0.037411 (-0.010015) \| 0.114154 / 0.014526 (0.099629) \| 0.117918 / 0.176557 (-0.058638) \| 0.173342 / 0.737135 (-0.563794) \| 0.125812 / 0.296338 (-0.170526) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424843 / 0.215209 (0.209634) \| 4.324828 / 2.077655 (2.247174) \| 2.188263 / 1.504120 (0.684143) \| 1.912288 / 1.541195 (0.371094) \| 2.011621 / 1.468490 (0.543131) \| 0.560944 / 4.584777 (-4.023833) \| 3.975047 / 3.745712 (0.229335) \| 3.130242 / 5.269862 (-2.139619) \| 1.667902 / 4.565676 (-2.897775) \| 0.062245 / 0.424275 (-0.362030) \| 0.011300 / 0.007607 (0.003692) \| 0.498571 / 0.226044 (0.272527) \| 5.024887 / 2.268929 (2.755958) \| 2.482967 / 55.444624 (-52.961657) \| 2.216125 / 6.876477 (-4.660352) \| 2.175856 / 2.142072 (0.033783) \| 0.615207 / 4.805227 (-4.190021) \| 0.133808 / 6.500664 (-6.366856) \| 0.058681 / 0.075469 (-0.016788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.370150 / 1.841788 (-0.471637) \| 14.580907 / 8.074308 (6.506599) \| 14.209955 / 10.191392 (4.018563) \| 0.139738 / 0.680424 (-0.540686) \| 0.018722 / 0.534201 (-0.515479) \| 0.375755 / 0.579283 (-0.203528) \| 0.428335 / 0.434364 (-0.006029) \| 0.438957 / 0.540337 (-0.101380) \| 0.541130 / 1.386936 (-0.845806) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c14806a42a20f44a60f3663642bae1de199ab1ec \"CML watermark\")\n" ]	2023-05-15T15:28:34Z	2023-06-08T16:40:18Z	2023-06-08T16:32:51Z	MEMBER	null	This PR tries out a new approach to generating the index tensor in `to_tf_dataset`, which should reduce memory usage for very large datasets. I'll need to do some testing before merging it! Fixes #5855	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5863/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5863/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5863.diff", "html_url": "https://github.com/huggingface/datasets/pull/5863", "merged_at": "2023-06-08T16:32:50Z", "patch_url": "https://github.com/huggingface/datasets/pull/5863.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5863" }	true
https://api.github.com/repos/huggingface/datasets/issues/5862	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5862/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5862/comments	https://api.github.com/repos/huggingface/datasets/issues/5862/events	https://github.com/huggingface/datasets/issues/5862	1,710,140,646	I_kwDODunzps5l7qzm	5,862	IndexError: list index out of range with data hosted on Zenodo	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	[ "This error is also raised when data is hosted on Google Drive:\r\n- https://huggingface.co/datasets/docred/discussions/5" ]	2023-05-15T13:47:19Z	2023-06-13T08:57:54Z	null	MEMBER	null	The dataset viewer sometimes raises an `IndexError`: ``` IndexError: list index out of range ``` See: - huggingface/datasets-server#1151 - https://huggingface.co/datasets/reddit/discussions/5 - huggingface/datasets-server#1118 - https://huggingface.co/datasets/krr-oxford/OntoLAMA/discussions/1 - https://huggingface.co/datasets/hyperpartisan_news_detection/discussions/3 - https://huggingface.co/datasets/um005/discussions/2 - https://huggingface.co/datasets/tapaco/discussions/2 - https://huggingface.co/datasets/common_language/discussions/3 After investigation: - This happens with data files hosted on Zenodo - Indeed, there is an underlying 429 HTTP error: Too Many Requests Note that some time ago, it also happened with data files hosted on Google Drive. See: - #4581 - #4580 The reason then was that there was a 403 HTTP error: Forbidden	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5862/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5862/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5861	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5861/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5861/comments	https://api.github.com/repos/huggingface/datasets/issues/5861/events	https://github.com/huggingface/datasets/pull/5861	1,709,807,340	PR_kwDODunzps5Qf55q	5,861	Better error message when combining dataset dicts instead of datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007167 / 0.011353 (-0.004185) \| 0.004914 / 0.011008 (-0.006094) \| 0.096858 / 0.038508 (0.058350) \| 0.033468 / 0.023109 (0.010359) \| 0.297276 / 0.275898 (0.021378) \| 0.344289 / 0.323480 (0.020809) \| 0.005703 / 0.007986 (-0.002282) \| 0.003972 / 0.004328 (-0.000357) \| 0.075191 / 0.004250 (0.070940) \| 0.046247 / 0.037052 (0.009194) \| 0.317857 / 0.258489 (0.059368) \| 0.347263 / 0.293841 (0.053422) \| 0.035017 / 0.128546 (-0.093529) \| 0.012036 / 0.075646 (-0.063611) \| 0.332522 / 0.419271 (-0.086750) \| 0.050188 / 0.043533 (0.006655) \| 0.296627 / 0.255139 (0.041488) \| 0.319196 / 0.283200 (0.035997) \| 0.101100 / 0.141683 (-0.040583) \| 1.484536 / 1.452155 (0.032382) \| 1.606364 / 1.492716 (0.113648) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203954 / 0.018006 (0.185948) \| 0.436505 / 0.000490 (0.436015) \| 0.003853 / 0.000200 (0.003654) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025834 / 0.037411 (-0.011578) \| 0.105759 / 0.014526 (0.091233) \| 0.114289 / 0.176557 (-0.062268) \| 0.174388 / 0.737135 (-0.562748) \| 0.122248 / 0.296338 (-0.174090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404218 / 0.215209 (0.189009) \| 4.027900 / 2.077655 (1.950245) \| 1.854757 / 1.504120 (0.350637) \| 1.668882 / 1.541195 (0.127687) \| 1.731451 / 1.468490 (0.262961) \| 0.707843 / 4.584777 (-3.876934) \| 3.756386 / 3.745712 (0.010674) \| 2.067751 / 5.269862 (-3.202110) \| 1.313039 / 4.565676 (-3.252638) \| 0.086442 / 0.424275 (-0.337833) \| 0.012329 / 0.007607 (0.004722) \| 0.505964 / 0.226044 (0.279919) \| 5.050788 / 2.268929 (2.781860) \| 2.353936 / 55.444624 (-53.090688) \| 2.055560 / 6.876477 (-4.820917) \| 2.162948 / 2.142072 (0.020876) \| 0.850532 / 4.805227 (-3.954696) \| 0.168560 / 6.500664 (-6.332104) \| 0.063143 / 0.075469 (-0.012326) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.182723 / 1.841788 (-0.659065) \| 14.779342 / 8.074308 (6.705034) \| 14.461572 / 10.191392 (4.270180) \| 0.163120 / 0.680424 (-0.517303) \| 0.017978 / 0.534201 (-0.516223) \| 0.419168 / 0.579283 (-0.160115) \| 0.420955 / 0.434364 (-0.013409) \| 0.509710 / 0.540337 (-0.030628) \| 0.619586 / 1.386936 (-0.767350) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006804 / 0.011353 (-0.004549) \| 0.005136 / 0.011008 (-0.005872) \| 0.074910 / 0.038508 (0.036402) \| 0.032552 / 0.023109 (0.009443) \| 0.374998 / 0.275898 (0.099100) \| 0.399219 / 0.323480 (0.075739) \| 0.005615 / 0.007986 (-0.002371) \| 0.004118 / 0.004328 (-0.000210) \| 0.074219 / 0.004250 (0.069969) \| 0.045924 / 0.037052 (0.008871) \| 0.383228 / 0.258489 (0.124739) \| 0.407195 / 0.293841 (0.113354) \| 0.035460 / 0.128546 (-0.093086) \| 0.012460 / 0.075646 (-0.063187) \| 0.087077 / 0.419271 (-0.332195) \| 0.050507 / 0.043533 (0.006974) \| 0.369001 / 0.255139 (0.113862) \| 0.385761 / 0.283200 (0.102561) \| 0.106999 / 0.141683 (-0.034684) \| 1.465456 / 1.452155 (0.013302) \| 1.556962 / 1.492716 (0.064246) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214926 / 0.018006 (0.196920) \| 0.436893 / 0.000490 (0.436403) \| 0.003388 / 0.000200 (0.003188) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029919 / 0.037411 (-0.007492) \| 0.110859 / 0.014526 (0.096333) \| 0.120617 / 0.176557 (-0.055939) \| 0.171781 / 0.737135 (-0.565355) \| 0.125627 / 0.296338 (-0.170712) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436024 / 0.215209 (0.220815) \| 4.359167 / 2.077655 (2.281512) \| 2.188399 / 1.504120 (0.684279) \| 2.001196 / 1.541195 (0.460001) \| 2.023710 / 1.468490 (0.555220) \| 0.713799 / 4.584777 (-3.870978) \| 3.832217 / 3.745712 (0.086504) \| 3.269351 / 5.269862 (-2.000510) \| 1.534608 / 4.565676 (-3.031068) \| 0.088505 / 0.424275 (-0.335770) \| 0.012345 / 0.007607 (0.004738) \| 0.542446 / 0.226044 (0.316401) \| 5.377757 / 2.268929 (3.108828) \| 2.659837 / 55.444624 (-52.784787) \| 2.272356 / 6.876477 (-4.604120) \| 2.297289 / 2.142072 (0.155217) \| 0.855276 / 4.805227 (-3.949952) \| 0.170666 / 6.500664 (-6.329998) \| 0.064549 / 0.075469 (-0.010920) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255938 / 1.841788 (-0.585850) \| 15.151471 / 8.074308 (7.077163) \| 12.905762 / 10.191392 (2.714370) \| 0.162425 / 0.680424 (-0.517999) \| 0.017504 / 0.534201 (-0.516697) \| 0.448671 / 0.579283 (-0.130612) \| 0.422424 / 0.434364 (-0.011940) \| 0.551772 / 0.540337 (0.011434) \| 0.649115 / 1.386936 (-0.737821) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be73d9f192149727c5542ff257df81b03024fa39 \"CML watermark\")\n", "Having those different checks helps providing an appropriate error message.\r\n\r\nIf the input is a dict, we suggest to select a split. If the input lists is a mix of iterable and non-iterable, we mention that it must be one or the other.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006559 / 0.011353 (-0.004794) \| 0.004569 / 0.011008 (-0.006439) \| 0.104503 / 0.038508 (0.065995) \| 0.028220 / 0.023109 (0.005111) \| 0.365507 / 0.275898 (0.089609) \| 0.400238 / 0.323480 (0.076758) \| 0.004968 / 0.007986 (-0.003017) \| 0.003271 / 0.004328 (-0.001057) \| 0.082804 / 0.004250 (0.078554) \| 0.036299 / 0.037052 (-0.000754) \| 0.361201 / 0.258489 (0.102712) \| 0.410962 / 0.293841 (0.117121) \| 0.030423 / 0.128546 (-0.098123) \| 0.011612 / 0.075646 (-0.064034) \| 0.331820 / 0.419271 (-0.087452) \| 0.043822 / 0.043533 (0.000289) \| 0.356242 / 0.255139 (0.101103) \| 0.393035 / 0.283200 (0.109836) \| 0.088426 / 0.141683 (-0.053257) \| 1.484139 / 1.452155 (0.031984) \| 1.566712 / 1.492716 (0.073995) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195887 / 0.018006 (0.177880) \| 0.402720 / 0.000490 (0.402231) \| 0.003516 / 0.000200 (0.003316) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023270 / 0.037411 (-0.014141) \| 0.095834 / 0.014526 (0.081308) \| 0.102924 / 0.176557 (-0.073632) \| 0.161397 / 0.737135 (-0.575738) \| 0.105225 / 0.296338 (-0.191114) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.451701 / 0.215209 (0.236491) \| 4.495171 / 2.077655 (2.417517) \| 2.223203 / 1.504120 (0.719083) \| 2.035533 / 1.541195 (0.494338) \| 2.076182 / 1.468490 (0.607692) \| 0.697317 / 4.584777 (-3.887460) \| 3.406309 / 3.745712 (-0.339403) \| 1.847179 / 5.269862 (-3.422683) \| 1.158762 / 4.565676 (-3.406914) \| 0.083067 / 0.424275 (-0.341208) \| 0.012453 / 0.007607 (0.004846) \| 0.546502 / 0.226044 (0.320458) \| 5.455712 / 2.268929 (3.186784) \| 2.654142 / 55.444624 (-52.790483) \| 2.298722 / 6.876477 (-4.577755) \| 2.383467 / 2.142072 (0.241395) \| 0.805950 / 4.805227 (-3.999278) \| 0.152479 / 6.500664 (-6.348185) \| 0.066784 / 0.075469 (-0.008685) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239129 / 1.841788 (-0.602659) \| 13.603707 / 8.074308 (5.529398) \| 14.062004 / 10.191392 (3.870612) \| 0.130928 / 0.680424 (-0.549495) \| 0.016907 / 0.534201 (-0.517294) \| 0.381614 / 0.579283 (-0.197670) \| 0.386770 / 0.434364 (-0.047594) \| 0.455792 / 0.540337 (-0.084545) \| 0.526092 / 1.386936 (-0.860844) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006202 / 0.011353 (-0.005151) \| 0.004478 / 0.011008 (-0.006531) \| 0.076492 / 0.038508 (0.037984) \| 0.026703 / 0.023109 (0.003594) \| 0.355134 / 0.275898 (0.079236) \| 0.391207 / 0.323480 (0.067727) \| 0.004852 / 0.007986 (-0.003133) \| 0.003271 / 0.004328 (-0.001057) \| 0.075080 / 0.004250 (0.070830) \| 0.038803 / 0.037052 (0.001750) \| 0.359530 / 0.258489 (0.101041) \| 0.409044 / 0.293841 (0.115203) \| 0.030366 / 0.128546 (-0.098180) \| 0.011544 / 0.075646 (-0.064102) \| 0.084849 / 0.419271 (-0.334423) \| 0.040076 / 0.043533 (-0.003457) \| 0.357359 / 0.255139 (0.102220) \| 0.384075 / 0.283200 (0.100875) \| 0.089130 / 0.141683 (-0.052552) \| 1.520400 / 1.452155 (0.068246) \| 1.604403 / 1.492716 (0.111687) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257127 / 0.018006 (0.239121) \| 0.403691 / 0.000490 (0.403202) \| 0.006894 / 0.000200 (0.006694) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024653 / 0.037411 (-0.012758) \| 0.098834 / 0.014526 (0.084309) \| 0.107276 / 0.176557 (-0.069281) \| 0.158256 / 0.737135 (-0.578879) \| 0.111339 / 0.296338 (-0.184999) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445006 / 0.215209 (0.229797) \| 4.452953 / 2.077655 (2.375299) \| 2.168291 / 1.504120 (0.664171) \| 1.969457 / 1.541195 (0.428262) \| 2.003505 / 1.468490 (0.535015) \| 0.695857 / 4.584777 (-3.888920) \| 3.433424 / 3.745712 (-0.312288) \| 2.466977 / 5.269862 (-2.802885) \| 1.528167 / 4.565676 (-3.037509) \| 0.082425 / 0.424275 (-0.341850) \| 0.012470 / 0.007607 (0.004863) \| 0.559039 / 0.226044 (0.332995) \| 5.609496 / 2.268929 (3.340568) \| 2.602898 / 55.444624 (-52.841726) \| 2.273971 / 6.876477 (-4.602506) \| 2.303370 / 2.142072 (0.161298) \| 0.803875 / 4.805227 (-4.001352) \| 0.151069 / 6.500664 (-6.349595) \| 0.067956 / 0.075469 (-0.007513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334443 / 1.841788 (-0.507345) \| 13.773252 / 8.074308 (5.698944) \| 13.007042 / 10.191392 (2.815650) \| 0.127939 / 0.680424 (-0.552485) \| 0.016412 / 0.534201 (-0.517789) \| 0.374744 / 0.579283 (-0.204539) \| 0.396912 / 0.434364 (-0.037452) \| 0.443197 / 0.540337 (-0.097140) \| 0.528338 / 1.386936 (-0.858598) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51d9f2a3064aa89a780e3d02c6cc34000c51c4fb \"CML watermark\")\n", "Just modified it to use only one loop. I think I managed to keep it readable as well", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007382 / 0.011353 (-0.003971) \| 0.005143 / 0.011008 (-0.005865) \| 0.097635 / 0.038508 (0.059127) \| 0.034726 / 0.023109 (0.011616) \| 0.315556 / 0.275898 (0.039658) \| 0.355951 / 0.323480 (0.032472) \| 0.006055 / 0.007986 (-0.001931) \| 0.004264 / 0.004328 (-0.000065) \| 0.073636 / 0.004250 (0.069386) \| 0.050480 / 0.037052 (0.013428) \| 0.316031 / 0.258489 (0.057542) \| 0.363933 / 0.293841 (0.070092) \| 0.035138 / 0.128546 (-0.093408) \| 0.012407 / 0.075646 (-0.063239) \| 0.333677 / 0.419271 (-0.085595) \| 0.050586 / 0.043533 (0.007053) \| 0.309507 / 0.255139 (0.054369) \| 0.327043 / 0.283200 (0.043844) \| 0.108975 / 0.141683 (-0.032708) \| 1.447778 / 1.452155 (-0.004377) \| 1.519971 / 1.492716 (0.027255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248770 / 0.018006 (0.230764) \| 0.603036 / 0.000490 (0.602546) \| 0.000383 / 0.000200 (0.000183) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027094 / 0.037411 (-0.010317) \| 0.104427 / 0.014526 (0.089901) \| 0.120627 / 0.176557 (-0.055929) \| 0.178790 / 0.737135 (-0.558346) \| 0.124877 / 0.296338 (-0.171461) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414442 / 0.215209 (0.199233) \| 4.138009 / 2.077655 (2.060355) \| 1.964642 / 1.504120 (0.460523) \| 1.775940 / 1.541195 (0.234745) \| 1.899719 / 1.468490 (0.431228) \| 0.695406 / 4.584777 (-3.889371) \| 3.760470 / 3.745712 (0.014758) \| 3.906958 / 5.269862 (-1.362904) \| 2.028164 / 4.565676 (-2.537513) \| 0.086704 / 0.424275 (-0.337571) \| 0.012465 / 0.007607 (0.004857) \| 0.512336 / 0.226044 (0.286292) \| 5.108587 / 2.268929 (2.839659) \| 2.435273 / 55.444624 (-53.009352) \| 2.142387 / 6.876477 (-4.734090) \| 2.258234 / 2.142072 (0.116162) \| 0.854035 / 4.805227 (-3.951193) \| 0.170443 / 6.500664 (-6.330222) \| 0.065762 / 0.075469 (-0.009707) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.187529 / 1.841788 (-0.654259) \| 15.151164 / 8.074308 (7.076856) \| 14.577545 / 10.191392 (4.386153) \| 0.166973 / 0.680424 (-0.513450) \| 0.017883 / 0.534201 (-0.516318) \| 0.427607 / 0.579283 (-0.151676) \| 0.417050 / 0.434364 (-0.017314) \| 0.508116 / 0.540337 (-0.032221) \| 0.590173 / 1.386936 (-0.796763) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007499 / 0.011353 (-0.003854) \| 0.005195 / 0.011008 (-0.005813) \| 0.073600 / 0.038508 (0.035091) \| 0.033574 / 0.023109 (0.010464) \| 0.377506 / 0.275898 (0.101608) \| 0.432752 / 0.323480 (0.109272) \| 0.006042 / 0.007986 (-0.001944) \| 0.006427 / 0.004328 (0.002098) \| 0.071666 / 0.004250 (0.067416) \| 0.053243 / 0.037052 (0.016190) \| 0.363972 / 0.258489 (0.105483) \| 0.454988 / 0.293841 (0.161147) \| 0.035118 / 0.128546 (-0.093428) \| 0.012395 / 0.075646 (-0.063251) \| 0.084308 / 0.419271 (-0.334963) \| 0.048589 / 0.043533 (0.005057) \| 0.368036 / 0.255139 (0.112897) \| 0.399414 / 0.283200 (0.116215) \| 0.109043 / 0.141683 (-0.032640) \| 1.462972 / 1.452155 (0.010817) \| 1.574443 / 1.492716 (0.081726) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215107 / 0.018006 (0.197101) \| 0.550255 / 0.000490 (0.549765) \| 0.004630 / 0.000200 (0.004430) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029948 / 0.037411 (-0.007463) \| 0.111866 / 0.014526 (0.097340) \| 0.126559 / 0.176557 (-0.049997) \| 0.181443 / 0.737135 (-0.555693) \| 0.130559 / 0.296338 (-0.165779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441410 / 0.215209 (0.226201) \| 4.403406 / 2.077655 (2.325752) \| 2.180276 / 1.504120 (0.676156) \| 2.003729 / 1.541195 (0.462534) \| 2.079394 / 1.468490 (0.610904) \| 0.706061 / 4.584777 (-3.878716) \| 3.805668 / 3.745712 (0.059956) \| 3.864941 / 5.269862 (-1.404921) \| 1.970468 / 4.565676 (-2.595208) \| 0.086033 / 0.424275 (-0.338242) \| 0.012261 / 0.007607 (0.004654) \| 0.550427 / 0.226044 (0.324383) \| 5.542270 / 2.268929 (3.273342) \| 2.717047 / 55.444624 (-52.727577) \| 2.449022 / 6.876477 (-4.427455) \| 2.549567 / 2.142072 (0.407495) \| 0.854981 / 4.805227 (-3.950247) \| 0.169756 / 6.500664 (-6.330908) \| 0.067082 / 0.075469 (-0.008387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281369 / 1.841788 (-0.560419) \| 15.445090 / 8.074308 (7.370781) \| 13.205652 / 10.191392 (3.014260) \| 0.170070 / 0.680424 (-0.510354) \| 0.017815 / 0.534201 (-0.516385) \| 0.425193 / 0.579283 (-0.154090) \| 0.425205 / 0.434364 (-0.009159) \| 0.493561 / 0.540337 (-0.046776) \| 0.588994 / 1.386936 (-0.797942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e427105fc68fce04d0f3c74efb942cbf3a65d166 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006345 / 0.011353 (-0.005008) \| 0.004330 / 0.011008 (-0.006678) \| 0.096327 / 0.038508 (0.057819) \| 0.032964 / 0.023109 (0.009855) \| 0.335600 / 0.275898 (0.059702) \| 0.365635 / 0.323480 (0.042155) \| 0.005435 / 0.007986 (-0.002551) \| 0.005005 / 0.004328 (0.000677) \| 0.071107 / 0.004250 (0.066856) \| 0.044363 / 0.037052 (0.007311) \| 0.339988 / 0.258489 (0.081498) \| 0.375575 / 0.293841 (0.081734) \| 0.028343 / 0.128546 (-0.100203) \| 0.008587 / 0.075646 (-0.067059) \| 0.324349 / 0.419271 (-0.094922) \| 0.050105 / 0.043533 (0.006573) \| 0.327398 / 0.255139 (0.072259) \| 0.348479 / 0.283200 (0.065279) \| 0.102357 / 0.141683 (-0.039326) \| 1.419905 / 1.452155 (-0.032250) \| 1.534887 / 1.492716 (0.042171) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212418 / 0.018006 (0.194412) \| 0.433183 / 0.000490 (0.432693) \| 0.000595 / 0.000200 (0.000395) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027520 / 0.037411 (-0.009891) \| 0.109503 / 0.014526 (0.094977) \| 0.118202 / 0.176557 (-0.058355) \| 0.177236 / 0.737135 (-0.559899) \| 0.123736 / 0.296338 (-0.172602) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405734 / 0.215209 (0.190525) \| 4.039566 / 2.077655 (1.961911) \| 1.838211 / 1.504120 (0.334091) \| 1.652650 / 1.541195 (0.111456) \| 1.753488 / 1.468490 (0.284998) \| 0.525258 / 4.584777 (-4.059519) \| 3.704509 / 3.745712 (-0.041203) \| 1.826794 / 5.269862 (-3.443067) \| 1.236361 / 4.565676 (-3.329315) \| 0.065619 / 0.424275 (-0.358656) \| 0.011606 / 0.007607 (0.003999) \| 0.505954 / 0.226044 (0.279910) \| 5.054140 / 2.268929 (2.785211) \| 2.352587 / 55.444624 (-53.092037) \| 2.050601 / 6.876477 (-4.825875) \| 2.097222 / 2.142072 (-0.044850) \| 0.641044 / 4.805227 (-4.164183) \| 0.140676 / 6.500664 (-6.359988) \| 0.063217 / 0.075469 (-0.012253) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.177750 / 1.841788 (-0.664038) \| 14.819346 / 8.074308 (6.745038) \| 14.085937 / 10.191392 (3.894545) \| 0.168618 / 0.680424 (-0.511806) \| 0.017189 / 0.534201 (-0.517011) \| 0.393415 / 0.579283 (-0.185868) \| 0.422879 / 0.434364 (-0.011485) \| 0.477289 / 0.540337 (-0.063048) \| 0.569078 / 1.386936 (-0.817858) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006502 / 0.011353 (-0.004850) \| 0.004640 / 0.011008 (-0.006368) \| 0.073272 / 0.038508 (0.034764) \| 0.033225 / 0.023109 (0.010116) \| 0.359165 / 0.275898 (0.083267) \| 0.391659 / 0.323480 (0.068179) \| 0.005684 / 0.007986 (-0.002302) \| 0.004045 / 0.004328 (-0.000284) \| 0.072880 / 0.004250 (0.068629) \| 0.046260 / 0.037052 (0.009208) \| 0.361772 / 0.258489 (0.103283) \| 0.402905 / 0.293841 (0.109064) \| 0.027732 / 0.128546 (-0.100814) \| 0.008864 / 0.075646 (-0.066783) \| 0.081961 / 0.419271 (-0.337310) \| 0.046170 / 0.043533 (0.002637) \| 0.364198 / 0.255139 (0.109059) \| 0.387468 / 0.283200 (0.104269) \| 0.105456 / 0.141683 (-0.036227) \| 1.457176 / 1.452155 (0.005021) \| 1.564899 / 1.492716 (0.072183) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.179129 / 0.018006 (0.161123) \| 0.439699 / 0.000490 (0.439209) \| 0.002882 / 0.000200 (0.002682) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029123 / 0.037411 (-0.008288) \| 0.112046 / 0.014526 (0.097520) \| 0.122773 / 0.176557 (-0.053784) \| 0.178404 / 0.737135 (-0.558732) \| 0.127904 / 0.296338 (-0.168434) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440413 / 0.215209 (0.225204) \| 4.407334 / 2.077655 (2.329680) \| 2.112932 / 1.504120 (0.608812) \| 1.911034 / 1.541195 (0.369840) \| 2.057168 / 1.468490 (0.588677) \| 0.525472 / 4.584777 (-4.059305) \| 3.738894 / 3.745712 (-0.006818) \| 1.807592 / 5.269862 (-3.462270) \| 1.053837 / 4.565676 (-3.511839) \| 0.066203 / 0.424275 (-0.358072) \| 0.011965 / 0.007607 (0.004358) \| 0.541137 / 0.226044 (0.315093) \| 5.415040 / 2.268929 (3.146112) \| 2.580476 / 55.444624 (-52.864148) \| 2.234144 / 6.876477 (-4.642333) \| 2.306014 / 2.142072 (0.163942) \| 0.644221 / 4.805227 (-4.161006) \| 0.142870 / 6.500664 (-6.357794) \| 0.065015 / 0.075469 (-0.010454) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303465 / 1.841788 (-0.538323) \| 14.949683 / 8.074308 (6.875375) \| 14.370871 / 10.191392 (4.179478) \| 0.142714 / 0.680424 (-0.537710) \| 0.017372 / 0.534201 (-0.516829) \| 0.403898 / 0.579283 (-0.175385) \| 0.424781 / 0.434364 (-0.009583) \| 0.465984 / 0.540337 (-0.074353) \| 0.570863 / 1.386936 (-0.816074) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22d1d533e8ab831b1aa1aab3e7d3c72ba42a83e8 \"CML watermark\")\n" ]	2023-05-15T10:36:24Z	2023-05-23T10:40:13Z	2023-05-23T10:32:58Z	MEMBER	null	close https://github.com/huggingface/datasets/issues/5851	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5861/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5861/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5861.diff", "html_url": "https://github.com/huggingface/datasets/pull/5861", "merged_at": "2023-05-23T10:32:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/5861.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5861" }	true
https://api.github.com/repos/huggingface/datasets/issues/5860	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5860/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5860/comments	https://api.github.com/repos/huggingface/datasets/issues/5860/events	https://github.com/huggingface/datasets/pull/5860	1,709,727,460	PR_kwDODunzps5QfojD	5,860	Minor tqdm optim	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006917 / 0.011353 (-0.004436) \| 0.004803 / 0.011008 (-0.006205) \| 0.097082 / 0.038508 (0.058574) \| 0.035105 / 0.023109 (0.011996) \| 0.325911 / 0.275898 (0.050013) \| 0.371858 / 0.323480 (0.048378) \| 0.006451 / 0.007986 (-0.001534) \| 0.004421 / 0.004328 (0.000093) \| 0.075738 / 0.004250 (0.071487) \| 0.053624 / 0.037052 (0.016572) \| 0.332661 / 0.258489 (0.074172) \| 0.372729 / 0.293841 (0.078888) \| 0.028279 / 0.128546 (-0.100267) \| 0.009318 / 0.075646 (-0.066328) \| 0.328505 / 0.419271 (-0.090766) \| 0.066962 / 0.043533 (0.023429) \| 0.316863 / 0.255139 (0.061724) \| 0.344296 / 0.283200 (0.061096) \| 0.120575 / 0.141683 (-0.021108) \| 1.457867 / 1.452155 (0.005712) \| 1.597361 / 1.492716 (0.104644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296399 / 0.018006 (0.278392) \| 0.507196 / 0.000490 (0.506706) \| 0.003036 / 0.000200 (0.002836) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028535 / 0.037411 (-0.008876) \| 0.110566 / 0.014526 (0.096040) \| 0.122078 / 0.176557 (-0.054479) \| 0.182926 / 0.737135 (-0.554210) \| 0.125546 / 0.296338 (-0.170792) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426952 / 0.215209 (0.211742) \| 4.255608 / 2.077655 (2.177953) \| 2.063865 / 1.504120 (0.559745) \| 1.867198 / 1.541195 (0.326004) \| 2.058236 / 1.468490 (0.589746) \| 0.525885 / 4.584777 (-4.058892) \| 3.723607 / 3.745712 (-0.022105) \| 1.919144 / 5.269862 (-3.350718) \| 1.235308 / 4.565676 (-3.330368) \| 0.066423 / 0.424275 (-0.357852) \| 0.012045 / 0.007607 (0.004438) \| 0.528432 / 0.226044 (0.302388) \| 5.268723 / 2.268929 (2.999794) \| 2.504071 / 55.444624 (-52.940553) \| 2.137999 / 6.876477 (-4.738477) \| 2.229987 / 2.142072 (0.087914) \| 0.641739 / 4.805227 (-4.163488) \| 0.142635 / 6.500664 (-6.358029) \| 0.065649 / 0.075469 (-0.009820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.182710 / 1.841788 (-0.659078) \| 15.339777 / 8.074308 (7.265469) \| 14.722308 / 10.191392 (4.530916) \| 0.145914 / 0.680424 (-0.534510) \| 0.017861 / 0.534201 (-0.516340) \| 0.393092 / 0.579283 (-0.186191) \| 0.431179 / 0.434364 (-0.003185) \| 0.485712 / 0.540337 (-0.054625) \| 0.602634 / 1.386936 (-0.784302) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006792 / 0.011353 (-0.004561) \| 0.005118 / 0.011008 (-0.005890) \| 0.073440 / 0.038508 (0.034932) \| 0.033751 / 0.023109 (0.010642) \| 0.389243 / 0.275898 (0.113345) \| 0.397083 / 0.323480 (0.073603) \| 0.005989 / 0.007986 (-0.001997) \| 0.004289 / 0.004328 (-0.000040) \| 0.073228 / 0.004250 (0.068977) \| 0.053490 / 0.037052 (0.016438) \| 0.396070 / 0.258489 (0.137581) \| 0.415134 / 0.293841 (0.121293) \| 0.028649 / 0.128546 (-0.099897) \| 0.009159 / 0.075646 (-0.066487) \| 0.080813 / 0.419271 (-0.338458) \| 0.048200 / 0.043533 (0.004667) \| 0.388009 / 0.255139 (0.132870) \| 0.382174 / 0.283200 (0.098975) \| 0.107807 / 0.141683 (-0.033876) \| 1.467276 / 1.452155 (0.015121) \| 1.568091 / 1.492716 (0.075375) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.328030 / 0.018006 (0.310024) \| 0.498058 / 0.000490 (0.497568) \| 0.002513 / 0.000200 (0.002313) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029835 / 0.037411 (-0.007576) \| 0.113859 / 0.014526 (0.099333) \| 0.130813 / 0.176557 (-0.045743) \| 0.183646 / 0.737135 (-0.553490) \| 0.136561 / 0.296338 (-0.159777) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438901 / 0.215209 (0.223692) \| 4.376426 / 2.077655 (2.298771) \| 2.220932 / 1.504120 (0.716812) \| 2.043585 / 1.541195 (0.502390) \| 2.161383 / 1.468490 (0.692893) \| 0.523224 / 4.584777 (-4.061553) \| 3.730589 / 3.745712 (-0.015123) \| 1.859602 / 5.269862 (-3.410260) \| 1.073415 / 4.565676 (-3.492261) \| 0.066363 / 0.424275 (-0.357912) \| 0.012491 / 0.007607 (0.004884) \| 0.542052 / 0.226044 (0.316008) \| 5.426246 / 2.268929 (3.157318) \| 2.673884 / 55.444624 (-52.770740) \| 2.372611 / 6.876477 (-4.503865) \| 2.482216 / 2.142072 (0.340143) \| 0.705669 / 4.805227 (-4.099558) \| 0.141075 / 6.500664 (-6.359589) \| 0.065339 / 0.075469 (-0.010130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.316403 / 1.841788 (-0.525385) \| 15.832870 / 8.074308 (7.758562) \| 13.307045 / 10.191392 (3.115653) \| 0.147258 / 0.680424 (-0.533166) \| 0.017966 / 0.534201 (-0.516235) \| 0.414396 / 0.579283 (-0.164887) \| 0.431801 / 0.434364 (-0.002563) \| 0.465483 / 0.540337 (-0.074855) \| 0.577850 / 1.386936 (-0.809086) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c795c7e332a7c850c3e725f2034d4894b5e314f7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006368 / 0.011353 (-0.004985) \| 0.004274 / 0.011008 (-0.006734) \| 0.098799 / 0.038508 (0.060291) \| 0.029096 / 0.023109 (0.005986) \| 0.308009 / 0.275898 (0.032111) \| 0.345701 / 0.323480 (0.022221) \| 0.005312 / 0.007986 (-0.002674) \| 0.003435 / 0.004328 (-0.000894) \| 0.075912 / 0.004250 (0.071662) \| 0.041993 / 0.037052 (0.004941) \| 0.320075 / 0.258489 (0.061586) \| 0.347506 / 0.293841 (0.053665) \| 0.025456 / 0.128546 (-0.103091) \| 0.008461 / 0.075646 (-0.067185) \| 0.322823 / 0.419271 (-0.096448) \| 0.044650 / 0.043533 (0.001117) \| 0.314118 / 0.255139 (0.058979) \| 0.333436 / 0.283200 (0.050237) \| 0.093811 / 0.141683 (-0.047871) \| 1.464464 / 1.452155 (0.012310) \| 1.548098 / 1.492716 (0.055382) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.015905 / 0.018006 (-0.002101) \| 0.427847 / 0.000490 (0.427357) \| 0.007600 / 0.000200 (0.007400) \| 0.000421 / 0.000054 (0.000366) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024530 / 0.037411 (-0.012882) \| 0.099907 / 0.014526 (0.085381) \| 0.107282 / 0.176557 (-0.069275) \| 0.168332 / 0.737135 (-0.568804) \| 0.109875 / 0.296338 (-0.186464) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.451064 / 0.215209 (0.235855) \| 4.491434 / 2.077655 (2.413779) \| 2.253251 / 1.504120 (0.749131) \| 2.086740 / 1.541195 (0.545545) \| 2.133288 / 1.468490 (0.664798) \| 0.558801 / 4.584777 (-4.025976) \| 3.463525 / 3.745712 (-0.282187) \| 1.747657 / 5.269862 (-3.522205) \| 1.005465 / 4.565676 (-3.560211) \| 0.068341 / 0.424275 (-0.355934) \| 0.012521 / 0.007607 (0.004914) \| 0.567002 / 0.226044 (0.340957) \| 5.689529 / 2.268929 (3.420601) \| 2.700562 / 55.444624 (-52.744062) \| 2.384888 / 6.876477 (-4.491589) \| 2.503160 / 2.142072 (0.361088) \| 0.667107 / 4.805227 (-4.138120) \| 0.137253 / 6.500664 (-6.363412) \| 0.068300 / 0.075469 (-0.007170) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.202916 / 1.841788 (-0.638872) \| 14.163393 / 8.074308 (6.089085) \| 14.402463 / 10.191392 (4.211071) \| 0.145273 / 0.680424 (-0.535151) \| 0.016996 / 0.534201 (-0.517205) \| 0.363520 / 0.579283 (-0.215763) \| 0.421595 / 0.434364 (-0.012769) \| 0.438413 / 0.540337 (-0.101925) \| 0.508615 / 1.386936 (-0.878321) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006419 / 0.011353 (-0.004934) \| 0.004346 / 0.011008 (-0.006662) \| 0.076356 / 0.038508 (0.037848) \| 0.029370 / 0.023109 (0.006260) \| 0.371046 / 0.275898 (0.095148) \| 0.398279 / 0.323480 (0.074799) \| 0.005258 / 0.007986 (-0.002728) \| 0.003528 / 0.004328 (-0.000800) \| 0.076787 / 0.004250 (0.072537) \| 0.041575 / 0.037052 (0.004522) \| 0.362319 / 0.258489 (0.103830) \| 0.402134 / 0.293841 (0.108293) \| 0.025633 / 0.128546 (-0.102913) \| 0.008826 / 0.075646 (-0.066820) \| 0.082380 / 0.419271 (-0.336892) \| 0.041655 / 0.043533 (-0.001878) \| 0.357583 / 0.255139 (0.102444) \| 0.383486 / 0.283200 (0.100287) \| 0.093682 / 0.141683 (-0.048001) \| 1.488522 / 1.452155 (0.036367) \| 1.576090 / 1.492716 (0.083373) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185556 / 0.018006 (0.167550) \| 0.431345 / 0.000490 (0.430855) \| 0.002290 / 0.000200 (0.002090) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026030 / 0.037411 (-0.011382) \| 0.102889 / 0.014526 (0.088364) \| 0.109541 / 0.176557 (-0.067015) \| 0.161050 / 0.737135 (-0.576085) \| 0.113525 / 0.296338 (-0.182814) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445301 / 0.215209 (0.230092) \| 4.437320 / 2.077655 (2.359666) \| 2.174181 / 1.504120 (0.670061) \| 1.977440 / 1.541195 (0.436245) \| 2.036323 / 1.468490 (0.567832) \| 0.554227 / 4.584777 (-4.030550) \| 3.462746 / 3.745712 (-0.282966) \| 1.765257 / 5.269862 (-3.504604) \| 1.014515 / 4.565676 (-3.551161) \| 0.068391 / 0.424275 (-0.355884) \| 0.013154 / 0.007607 (0.005546) \| 0.546696 / 0.226044 (0.320652) \| 5.490628 / 2.268929 (3.221699) \| 2.611947 / 55.444624 (-52.832677) \| 2.282659 / 6.876477 (-4.593818) \| 2.333972 / 2.142072 (0.191899) \| 0.663140 / 4.805227 (-4.142087) \| 0.137996 / 6.500664 (-6.362668) \| 0.069063 / 0.075469 (-0.006407) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.332147 / 1.841788 (-0.509641) \| 14.781592 / 8.074308 (6.707284) \| 13.399190 / 10.191392 (3.207798) \| 0.139370 / 0.680424 (-0.541054) \| 0.016742 / 0.534201 (-0.517459) \| 0.364138 / 0.579283 (-0.215146) \| 0.402479 / 0.434364 (-0.031885) \| 0.427591 / 0.540337 (-0.112746) \| 0.520864 / 1.386936 (-0.866072) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8279677b58b93f77995c7da67aea2a04b6a7395 \"CML watermark\")\n" ]	2023-05-15T09:49:37Z	2023-05-17T18:46:46Z	2023-05-17T18:39:35Z	MEMBER	null	Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`. On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5860/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5860/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5860.diff", "html_url": "https://github.com/huggingface/datasets/pull/5860", "merged_at": "2023-05-17T18:39:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/5860.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5860" }	true
https://api.github.com/repos/huggingface/datasets/issues/5859	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5859/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5859/comments	https://api.github.com/repos/huggingface/datasets/issues/5859/events	https://github.com/huggingface/datasets/pull/5859	1,709,554,829	PR_kwDODunzps5QfDLC	5,859	Raise TypeError when indexing a dataset with bool	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq any idea why this only fails (CI integration fails are unrelated) in \"Build PR Documentation / build / build_pr_documentation\" (which uses Python 3.8), with message:\r\n```\r\nTypeError: Type subscription requires python >= 3.9\r\n```\r\nwhereas the CI is green for unit tests, which use Python 3.7?", "Hmm I don't know sorry :/", "@lhoestq I am afraid I have to remove the generics I created for numpy and pandas (no subscriptable until Python 3.9) and just leave:\r\n```python\r\nListLike = Union[List[T], Tuple[T, ...]]\r\n```", "Ok sounds good - no need to spend more time on this", "I will merge once the CI is finished. The integration errors are unrelated: `502 Server Error: Bad Gateway`", "<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) \| 0.006637 / 0.011353 (-0.004716) \| 0.004578 / 0.011008 (-0.006430) \| 0.097346 / 0.038508 (0.058838) \| 0.034171 / 0.023109 (0.011062) \| 0.315060 / 0.275898 (0.039162) \| 0.354386 / 0.323480 (0.030907) \| 0.005778 / 0.007986 (-0.002207) \| 0.004123 / 0.004328 (-0.000206) \| 0.073839 / 0.004250 (0.069589) \| 0.046418 / 0.037052 (0.009366) \| 0.325910 / 0.258489 (0.067421) \| 0.368909 / 0.293841 (0.075068) \| 0.027975 / 0.128546 (-0.100571) \| 0.008885 / 0.075646 (-0.066761) \| 0.327956 / 0.419271 (-0.091316) \| 0.049911 / 0.043533 (0.006378) \| 0.309424 / 0.255139 (0.054285) \| 0.346543 / 0.283200 (0.063343) \| 0.103429 / 0.141683 (-0.038253) \| 1.517606 / 1.452155 (0.065451) \| 1.536685 / 1.492716 (0.043969) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211552 / 0.018006 (0.193546) \| 0.449583 / 0.000490 (0.449094) \| 0.002949 / 0.000200 (0.002750) \| 0.000140 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027603 / 0.037411 (-0.009808) \| 0.108873 / 0.014526 (0.094347) \| 0.117990 / 0.176557 (-0.058567) \| 0.174202 / 0.737135 (-0.562933) \| 0.123793 / 0.296338 (-0.172545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418449 / 0.215209 (0.203240) \| 4.177753 / 2.077655 (2.100099) \| 1.923446 / 1.504120 (0.419326) \| 1.720576 / 1.541195 (0.179381) \| 1.783723 / 1.468490 (0.315232) \| 0.530068 / 4.584777 (-4.054709) \| 3.709410 / 3.745712 (-0.036302) \| 1.863924 / 5.269862 (-3.405938) \| 1.149906 / 4.565676 (-3.415770) \| 0.066595 / 0.424275 (-0.357680) \| 0.011733 / 0.007607 (0.004126) \| 0.519249 / 0.226044 (0.293205) \| 5.179676 / 2.268929 (2.910748) \| 2.389488 / 55.444624 (-53.055137) \| 2.060006 / 6.876477 (-4.816471) \| 2.160668 / 2.142072 (0.018596) \| 0.641081 / 4.805227 (-4.164146) \| 0.141962 / 6.500664 (-6.358702) \| 0.063146 / 0.075469 (-0.012323) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197424 / 1.841788 (-0.644364) \| 14.915321 / 8.074308 (6.841013) \| 14.792302 / 10.191392 (4.600910) \| 0.145436 / 0.680424 (-0.534988) \| 0.017669 / 0.534201 (-0.516532) \| 0.399060 / 0.579283 (-0.180223) \| 0.416282 / 0.434364 (-0.018082) \| 0.498392 / 0.540337 (-0.041946) \| 0.600242 / 1.386936 (-0.786694) \|\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) \| 0.007246 / 0.011353 (-0.004106) \| 0.005353 / 0.011008 (-0.005656) \| 0.076357 / 0.038508 (0.037849) \| 0.037662 / 0.023109 (0.014553) \| 0.387862 / 0.275898 (0.111964) \| 0.421610 / 0.323480 (0.098130) \| 0.006424 / 0.007986 (-0.001561) \| 0.004397 / 0.004328 (0.000069) \| 0.074212 / 0.004250 (0.069961) \| 0.054147 / 0.037052 (0.017095) \| 0.393171 / 0.258489 (0.134682) \| 0.424082 / 0.293841 (0.130241) \| 0.029001 / 0.128546 (-0.099546) \| 0.009381 / 0.075646 (-0.066265) \| 0.082562 / 0.419271 (-0.336710) \| 0.048004 / 0.043533 (0.004472) \| 0.386895 / 0.255139 (0.131756) \| 0.386104 / 0.283200 (0.102904) \| 0.113714 / 0.141683 (-0.027969) \| 1.435601 / 1.452155 (-0.016553) \| 1.554940 / 1.492716 (0.062224) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.179288 / 0.018006 (0.161282) \| 0.455301 / 0.000490 (0.454811) \| 0.001469 / 0.000200 (0.001269) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030928 / 0.037411 (-0.006484) \| 0.117833 / 0.014526 (0.103307) \| 0.125088 / 0.176557 (-0.051468) \| 0.178906 / 0.737135 (-0.558230) \| 0.131264 / 0.296338 (-0.165075) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436900 / 0.215209 (0.221691) \| 4.366094 / 2.077655 (2.288439) \| 2.184398 / 1.504120 (0.680278) \| 1.992779 / 1.541195 (0.451584) \| 2.055260 / 1.468490 (0.586770) \| 0.524136 / 4.584777 (-4.060641) \| 3.750535 / 3.745712 (0.004823) \| 2.985095 / 5.269862 (-2.284767) \| 1.400291 / 4.565676 (-3.165385) \| 0.065921 / 0.424275 (-0.358354) \| 0.012110 / 0.007607 (0.004502) \| 0.538239 / 0.226044 (0.312195) \| 5.380613 / 2.268929 (3.111685) \| 2.637509 / 55.444624 (-52.807116) \| 2.352265 / 6.876477 (-4.524212) \| 2.409829 / 2.142072 (0.267756) \| 0.640428 / 4.805227 (-4.164799) \| 0.142070 / 6.500664 (-6.358594) \| 0.068171 / 0.075469 (-0.007298) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.280080 / 1.841788 (-0.561707) \| 15.588799 / 8.074308 (7.514491) \| 14.648596 / 10.191392 (4.457204) \| 0.147027 / 0.680424 (-0.533397) \| 0.018981 / 0.534201 (-0.515220) \| 0.394796 / 0.579283 (-0.184487) \| 0.423686 / 0.434364 (-0.010678) \| 0.467376 / 0.540337 (-0.072961) \| 0.562247 / 1.386936 (-0.824689) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#680162303f4c5dae6ad2edef6b3efadded7d37bd \"CML watermark\")\n" ]	2023-05-15T08:08:42Z	2023-05-25T16:31:24Z	2023-05-25T16:23:17Z	MEMBER	null	Fix #5858.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5859/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5859/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5859.diff", "html_url": "https://github.com/huggingface/datasets/pull/5859", "merged_at": "2023-05-25T16:23:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/5859.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5859" }	true
https://api.github.com/repos/huggingface/datasets/issues/5858	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5858/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5858/comments	https://api.github.com/repos/huggingface/datasets/issues/5858/events	https://github.com/huggingface/datasets/issues/5858	1,709,332,632	I_kwDODunzps5l4liY	5,858	Throw an error when dataset improperly indexed	{ "avatar_url": "https://avatars.githubusercontent.com/u/8027676?v=4", "events_url": "https://api.github.com/users/sarahwie/events{/privacy}", "followers_url": "https://api.github.com/users/sarahwie/followers", "following_url": "https://api.github.com/users/sarahwie/following{/other_user}", "gists_url": "https://api.github.com/users/sarahwie/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sarahwie", "id": 8027676, "login": "sarahwie", "node_id": "MDQ6VXNlcjgwMjc2NzY=", "organizations_url": "https://api.github.com/users/sarahwie/orgs", "received_events_url": "https://api.github.com/users/sarahwie/received_events", "repos_url": "https://api.github.com/users/sarahwie/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sarahwie/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sarahwie/subscriptions", "type": "User", "url": "https://api.github.com/users/sarahwie" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note that in `datasets` we do not have vectorized operation like `pandas`. Therefore, your equality comparisons above are `False`:\r\n- For example: `squad['question']` returns a `list`, and this list is not equal to `\"Who was the Norse leader?\"`\r\n\r\nThe `False` value is equivalent to `0` when indexing a dataset, thus the reason why you get the first element (with index 0): \r\n- For example: `squad[False]` is equivalent to `squad[0]`\r\n\r\nMaybe we should an exception instead of assuming that `False` is equivalent to `0` (and `True` is equivalent to `1`) in the context of indexing." ]	2023-05-15T05:15:53Z	2023-05-25T16:23:19Z	2023-05-25T16:23:19Z	NONE	null	### Describe the bug Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition. ### Steps to reproduce the bug Steps to reproduce the behavior: 1. `squad = datasets.load_dataset("squad_v2", split="validation")` 2. `item = squad[squad['question'] == "Who was the Norse leader?"]` or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']` 3. returns the first item in the dataset, which does not satisfy the above conditions: `{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}` ### Expected behavior Should either throw an error message, or return the dataset item that satisfies the condition. ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5858/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5858/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5857	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5857/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5857/comments	https://api.github.com/repos/huggingface/datasets/issues/5857/events	https://github.com/huggingface/datasets/issues/5857	1,709,326,622	I_kwDODunzps5l4kEe	5,857	Adding chemistry dataset/models in huggingface	{ "avatar_url": "https://avatars.githubusercontent.com/u/16902896?v=4", "events_url": "https://api.github.com/users/knc6/events{/privacy}", "followers_url": "https://api.github.com/users/knc6/followers", "following_url": "https://api.github.com/users/knc6/following{/other_user}", "gists_url": "https://api.github.com/users/knc6/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/knc6", "id": 16902896, "login": "knc6", "node_id": "MDQ6VXNlcjE2OTAyODk2", "organizations_url": "https://api.github.com/users/knc6/orgs", "received_events_url": "https://api.github.com/users/knc6/received_events", "repos_url": "https://api.github.com/users/knc6/repos", "site_admin": false, "starred_url": "https://api.github.com/users/knc6/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/knc6/subscriptions", "type": "User", "url": "https://api.github.com/users/knc6" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "Hi! \r\n\r\nThis would be a nice addition to the Hub! You can find the existing chemistry datasets/models on the Hub (using the `chemistry` tag) [here](https://huggingface.co/search/full-text?q=chemistry&type=model&type=dataset).\r\n\r\nFeel free to ping us here on the Hub if you need help adding the datasets.\r\n" ]	2023-05-15T05:09:49Z	2023-05-25T16:50:39Z	null	NONE	null	### Feature request Huggingface is really amazing platform for open science. In addition to computer vision, video and NLP, would it be of interest to add chemistry/materials science dataset/models in Huggingface? Or, if its already done, can you provide some pointers. We have been working on a comprehensive benchmark on this topic: [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard/) and I am wondering if we could contribute/integrate this project as a part of huggingface. ### Motivation Similar to the main stream AI field, there is need of large scale benchmarks/models/infrastructure for chemistry/materials data. ### Your contribution We can start adding datasets as our [benchmarks](https://github.com/usnistgov/jarvis_leaderboard/tree/main/jarvis_leaderboard/benchmarks) should be easily convertible to the dataset format.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5857/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5857/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5856	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5856/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5856/comments	https://api.github.com/repos/huggingface/datasets/issues/5856/events	https://github.com/huggingface/datasets/issues/5856	1,709,218,242	I_kwDODunzps5l4JnC	5,856	Error loading natural_questions	{ "avatar_url": "https://avatars.githubusercontent.com/u/19185508?v=4", "events_url": "https://api.github.com/users/Crownor/events{/privacy}", "followers_url": "https://api.github.com/users/Crownor/followers", "following_url": "https://api.github.com/users/Crownor/following{/other_user}", "gists_url": "https://api.github.com/users/Crownor/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Crownor", "id": 19185508, "login": "Crownor", "node_id": "MDQ6VXNlcjE5MTg1NTA4", "organizations_url": "https://api.github.com/users/Crownor/orgs", "received_events_url": "https://api.github.com/users/Crownor/received_events", "repos_url": "https://api.github.com/users/Crownor/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Crownor/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Crownor/subscriptions", "type": "User", "url": "https://api.github.com/users/Crownor" }	[]	closed	false	null	[]	null	[ "Hi! You can avoid this error by using the preprocessed version:\r\n```python\r\nimport datasets\r\nds = datasets.load_dataset('natural_questions')\r\n```\r\n\r\nPS: Once we finish https://github.com/huggingface/datasets/pull/5364, this error will no longer be a problem.", "> Hi! You can avoid this error by using the preprocessed version:\r\n> \r\n> ```python\r\n> import datasets\r\n> ds = datasets.load_dataset('natural_questions')\r\n> ```\r\n> \r\n> PS: Once we finish #5364, this error will no longer be a problem.\r\n\r\nThanks, wish #5364 finish early" ]	2023-05-15T02:46:04Z	2023-06-05T09:11:19Z	2023-06-05T09:11:18Z	NONE	null	### Describe the bug When try to load natural_questions through datasets == 2.12.0 with python == 3.8.9: ```python import datasets datasets.load_dataset('natural_questions',beam_runner='DirectRunner') ``` It failed with following info: `pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs` ### Steps to reproduce the bug In python console: ```python import datasets datasets.load_dataset('natural_questions',beam_runner='DirectRunner') ``` Then the trace is: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare self._download_and_prepare( File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 2019, in _download_and_prepare num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter)) File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 694, in finalize shard_num_bytes, _ = parquet_to_arrow(source, destination) File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 737, in parquet_to_arrow for record_batch in parquet_file.iter_batches(): File "pyarrow/_parquet.pyx", line 1323, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ### Expected behavior load natural_question questions ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-3.10.0-1160.42.2.el7.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.9 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.1 ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5856/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5856/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5855	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5855/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5855/comments	https://api.github.com/repos/huggingface/datasets/issues/5855/events	https://github.com/huggingface/datasets/issues/5855	1,708,784,943	I_kwDODunzps5l2f0v	5,855	`to_tf_dataset` consumes too much memory	{ "avatar_url": "https://avatars.githubusercontent.com/u/28751760?v=4", "events_url": "https://api.github.com/users/massquantity/events{/privacy}", "followers_url": "https://api.github.com/users/massquantity/followers", "following_url": "https://api.github.com/users/massquantity/following{/other_user}", "gists_url": "https://api.github.com/users/massquantity/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/massquantity", "id": 28751760, "login": "massquantity", "node_id": "MDQ6VXNlcjI4NzUxNzYw", "organizations_url": "https://api.github.com/users/massquantity/orgs", "received_events_url": "https://api.github.com/users/massquantity/received_events", "repos_url": "https://api.github.com/users/massquantity/repos", "site_admin": false, "starred_url": "https://api.github.com/users/massquantity/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/massquantity/subscriptions", "type": "User", "url": "https://api.github.com/users/massquantity" }	[]	closed	false	null	[]	null	[ "Cc @amyeroberts @Rocketknight1 \r\n\r\nIndded I think it's because it does something like this under the hood when there's no multiprocessing:\r\n\r\n```python\r\ntf_dataset = tf_dataset.shuffle(len(dataset))\r\n```\r\n\r\nPS: with multiprocessing it appears to be different:\r\n\r\n```python\r\nindices = np.arange(len(dataset))\r\nif shuffle:\r\n np.random.shuffle(indices)\r\n```", "Hi @massquantity, the dataset being shuffled there is not the full dataset. If you look at [the line above](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L182), the dataset is actually just a single indices array at that point, and that array is the only thing that gets fully loaded into memory and shuffled. We then load samples from the dataset by applying a transform function to the shuffled dataset, which fetches samples based on the indices it receives.\r\n\r\nIf your dataset is really gigantic, then this index tensor might be a memory issue, but since it's just an int64 tensor it will only use 1GB of memory per 125 million samples.\r\n\r\nStill, if you're encountering memory issues, there might be another cause here - can you share some code to reproduce the error, or does it depend on some internal/proprietary dataset?", "Hi @Rocketknight1, you're right and I also noticed that only indices are used in shuffling. My data has shape (50000000, 10), but really the problem doesn't relate to a specific dataset. Simply running the following code costs me 10GB of memory.\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for i in range(50000000):\r\n yield {\"data\": i}\r\n\r\nds = Dataset.from_generator(gen, cache_dir=\"./huggingface\")\r\n\r\ntf_ds = ds.to_tf_dataset(\r\n batch_size=1,\r\n shuffle=True,\r\n drop_remainder=False,\r\n prefetch=True,\r\n)\r\ntf_ds = iter(tf_ds)\r\nnext(tf_ds)\r\n# {'data': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([0])>}\r\n```\r\n\r\nI just realized maybe it was an issue from tensorflow (I'm using tf 2.12). So I tried the following code, and it used 10GB of memory too.\r\n```python\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\ndata_size = 50000000\r\ntf_dataset = tf.data.Dataset.from_tensor_slices(np.arange(data_size))\r\ntf_dataset = iter(tf_dataset.shuffle(data_size))\r\nnext(tf_dataset)\r\n# <tf.Tensor: shape=(), dtype=int64, numpy=24774043>\r\n```\r\n\r\nBy the way, as @lhoestq mentioned, multiprocessing uses numpy shuffling, and it uses less than 1 GB of memory:\r\n```python\r\ntf_ds_mp = ds.to_tf_dataset(\r\n batch_size=1,\r\n shuffle=True,\r\n drop_remainder=False,\r\n prefetch=True,\r\n num_workers=2,\r\n)\r\n```", "Thanks for that reproduction script - I've confirmed the same issue is occurring for me. Investigating it now!", "Update: The memory usage is occurring in creation of the index and shuffle buffer. You can reproduce it very simply with:\r\n\r\n```python\r\nimport tensorflow as tf\r\nindices = tf.range(50_000_000, dtype=tf.int64)\r\ndataset = tf.data.Dataset.from_tensor_slices(indices)\r\ndataset = dataset.shuffle(len(dataset))\r\nprint(next(iter(dataset))\r\n```\r\nWhen I wrote this code I thought `tf.data` had an optimization for shuffling an entire tensor that wouldn't create the entire shuffle buffer, but evidently it's just creating the enormous buffer in memory. I'll see if I can find a more efficient way to do this - we might end up moving everything to the `numpy` multiprocessing path to avoid it.", "I opened a PR to fix this - will continue the discussion there!" ]	2023-05-14T01:22:29Z	2023-06-08T16:32:52Z	2023-06-08T16:32:52Z	NONE	null	### Describe the bug Hi, I'm using `to_tf_dataset` to convert a _large_ dataset to `tf.data.Dataset`. I observed that the data loading before training took a lot of time and memory, even with `batch_size=1`. After some digging, i believe the reason lies in the shuffle behavior. The [source code](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L185) uses `len(dataset)` as the `buffer_size`, which may load all the data into the memory, and the [tf.data doc](https://www.tensorflow.org/guide/data#randomly_shuffling_input_data) also states that "While large buffer_sizes shuffle more thoroughly, they can take a lot of memory, and significant time to fill". ### Steps to reproduce the bug ```python from datasets import Dataset def gen(): # some large data for i in range(50000000): yield {"data": i} ds = Dataset.from_generator(gen, cache_dir="./huggingface") tf_ds = ds.to_tf_dataset( batch_size=64, shuffle=False, # no shuffle drop_remainder=False, prefetch=True, ) # fast and memory friendly 🤗 for batch in tf_ds: ... tf_ds_shuffle = ds.to_tf_dataset( batch_size=64, shuffle=True, drop_remainder=False, prefetch=True, ) # slow and memory hungry for simple iteration 😱 for batch in tf_ds_shuffle: ... ``` ### Expected behavior Shuffling should not load all the data into the memory. Would adding a `buffer_size` parameter in the `to_tf_dataset` API alleviate the problem? ### Environment info - `datasets` version: 2.11.0 - Platform: Linux-5.17.1-051701-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 1.4.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5855/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5855/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5854	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5854/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5854/comments	https://api.github.com/repos/huggingface/datasets/issues/5854/events	https://github.com/huggingface/datasets/issues/5854	1,708,779,300	I_kwDODunzps5l2eck	5,854	Can not load audiofolder dataset on kaggle	{ "avatar_url": "https://avatars.githubusercontent.com/u/93691919?v=4", "events_url": "https://api.github.com/users/ILG2021/events{/privacy}", "followers_url": "https://api.github.com/users/ILG2021/followers", "following_url": "https://api.github.com/users/ILG2021/following{/other_user}", "gists_url": "https://api.github.com/users/ILG2021/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ILG2021", "id": 93691919, "login": "ILG2021", "node_id": "U_kgDOBZWgDw", "organizations_url": "https://api.github.com/users/ILG2021/orgs", "received_events_url": "https://api.github.com/users/ILG2021/received_events", "repos_url": "https://api.github.com/users/ILG2021/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ILG2021/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ILG2021/subscriptions", "type": "User", "url": "https://api.github.com/users/ILG2021" }	[]	open	false	null	[]	null	[ "Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment to resolve the issue.", "> Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment to resolve the issue.\r\n\r\nI don't think it is a problem of the version. It runs ok on colab or local machine. Only on kaggle will has this bug." ]	2023-05-14T00:50:47Z	2023-05-14T13:50:54Z	null	NONE	null	### Describe the bug It's crash log: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/audiofolder/audiofolder.py or any data file in the same directory. Couldn't find 'audiofolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/audiofolder/audiofolder.py ### Steps to reproduce the bug ![image](https://github.com/huggingface/datasets/assets/93691919/a2829d27-d15c-4acc-86fb-d1987c760468) common_voice = load_dataset("audiofolder", data_dir="/kaggle/working/data") ### Expected behavior load dataset without error. It works ok on colab, but on kaggle it happends. ### Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.15.109+-x86_64-with-glibc2.31 - Python version: 3.10.10 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5854/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5854/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5853	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5853/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5853/comments	https://api.github.com/repos/huggingface/datasets/issues/5853/events	https://github.com/huggingface/datasets/pull/5853	1,708,092,786	PR_kwDODunzps5QaZLP	5,853	[docs] Redirects, migrated from nginx	{ "avatar_url": "https://avatars.githubusercontent.com/u/326577?v=4", "events_url": "https://api.github.com/users/julien-c/events{/privacy}", "followers_url": "https://api.github.com/users/julien-c/followers", "following_url": "https://api.github.com/users/julien-c/following{/other_user}", "gists_url": "https://api.github.com/users/julien-c/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/julien-c", "id": 326577, "login": "julien-c", "node_id": "MDQ6VXNlcjMyNjU3Nw==", "organizations_url": "https://api.github.com/users/julien-c/orgs", "received_events_url": "https://api.github.com/users/julien-c/received_events", "repos_url": "https://api.github.com/users/julien-c/repos", "site_admin": false, "starred_url": "https://api.github.com/users/julien-c/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/julien-c/subscriptions", "type": "User", "url": "https://api.github.com/users/julien-c" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007212 / 0.011353 (-0.004141) \| 0.005125 / 0.011008 (-0.005883) \| 0.098460 / 0.038508 (0.059952) \| 0.034040 / 0.023109 (0.010931) \| 0.320203 / 0.275898 (0.044305) \| 0.357787 / 0.323480 (0.034307) \| 0.006000 / 0.007986 (-0.001986) \| 0.005644 / 0.004328 (0.001316) \| 0.072654 / 0.004250 (0.068403) \| 0.049393 / 0.037052 (0.012341) \| 0.345686 / 0.258489 (0.087196) \| 0.362345 / 0.293841 (0.068504) \| 0.036597 / 0.128546 (-0.091949) \| 0.012303 / 0.075646 (-0.063343) \| 0.334374 / 0.419271 (-0.084897) \| 0.062010 / 0.043533 (0.018477) \| 0.312547 / 0.255139 (0.057408) \| 0.336021 / 0.283200 (0.052821) \| 0.112304 / 0.141683 (-0.029378) \| 1.446706 / 1.452155 (-0.005449) \| 1.523256 / 1.492716 (0.030540) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217658 / 0.018006 (0.199652) \| 0.449208 / 0.000490 (0.448718) \| 0.002878 / 0.000200 (0.002679) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025735 / 0.037411 (-0.011676) \| 0.105876 / 0.014526 (0.091350) \| 0.114887 / 0.176557 (-0.061669) \| 0.170984 / 0.737135 (-0.566152) \| 0.121420 / 0.296338 (-0.174918) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419670 / 0.215209 (0.204461) \| 4.189453 / 2.077655 (2.111798) \| 1.938236 / 1.504120 (0.434116) \| 1.769747 / 1.541195 (0.228553) \| 1.910919 / 1.468490 (0.442429) \| 0.705046 / 4.584777 (-3.879730) \| 3.783774 / 3.745712 (0.038062) \| 2.096504 / 5.269862 (-3.173358) \| 1.339265 / 4.565676 (-3.226412) \| 0.086670 / 0.424275 (-0.337605) \| 0.012243 / 0.007607 (0.004636) \| 0.524701 / 0.226044 (0.298657) \| 5.240689 / 2.268929 (2.971760) \| 2.473622 / 55.444624 (-52.971003) \| 2.170568 / 6.876477 (-4.705909) \| 2.289653 / 2.142072 (0.147581) \| 0.848913 / 4.805227 (-3.956314) \| 0.168332 / 6.500664 (-6.332332) \| 0.064926 / 0.075469 (-0.010543) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.193614 / 1.841788 (-0.648173) \| 14.920403 / 8.074308 (6.846095) \| 14.475059 / 10.191392 (4.283667) \| 0.164458 / 0.680424 (-0.515966) \| 0.017613 / 0.534201 (-0.516588) \| 0.426311 / 0.579283 (-0.152972) \| 0.431478 / 0.434364 (-0.002886) \| 0.520280 / 0.540337 (-0.020057) \| 0.627738 / 1.386936 (-0.759198) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007458 / 0.011353 (-0.003895) \| 0.005363 / 0.011008 (-0.005645) \| 0.076713 / 0.038508 (0.038205) \| 0.034189 / 0.023109 (0.011079) \| 0.359938 / 0.275898 (0.084040) \| 0.395532 / 0.323480 (0.072052) \| 0.005977 / 0.007986 (-0.002008) \| 0.004263 / 0.004328 (-0.000065) \| 0.075971 / 0.004250 (0.071721) \| 0.051924 / 0.037052 (0.014871) \| 0.362818 / 0.258489 (0.104329) \| 0.409897 / 0.293841 (0.116056) \| 0.035494 / 0.128546 (-0.093053) \| 0.012399 / 0.075646 (-0.063247) \| 0.088335 / 0.419271 (-0.330937) \| 0.047968 / 0.043533 (0.004435) \| 0.355744 / 0.255139 (0.100606) \| 0.376339 / 0.283200 (0.093139) \| 0.104542 / 0.141683 (-0.037141) \| 1.464826 / 1.452155 (0.012672) \| 1.600665 / 1.492716 (0.107948) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220841 / 0.018006 (0.202834) \| 0.446444 / 0.000490 (0.445954) \| 0.000392 / 0.000200 (0.000192) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029402 / 0.037411 (-0.008009) \| 0.116511 / 0.014526 (0.101986) \| 0.122959 / 0.176557 (-0.053598) \| 0.171674 / 0.737135 (-0.565462) \| 0.129871 / 0.296338 (-0.166468) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450411 / 0.215209 (0.235202) \| 4.471859 / 2.077655 (2.394205) \| 2.229439 / 1.504120 (0.725319) \| 2.053308 / 1.541195 (0.512114) \| 2.142476 / 1.468490 (0.673986) \| 0.708299 / 4.584777 (-3.876478) \| 3.797830 / 3.745712 (0.052118) \| 2.142509 / 5.269862 (-3.127352) \| 1.333357 / 4.565676 (-3.232320) \| 0.086837 / 0.424275 (-0.337439) \| 0.012102 / 0.007607 (0.004495) \| 0.548428 / 0.226044 (0.322384) \| 5.490611 / 2.268929 (3.221682) \| 2.713882 / 55.444624 (-52.730742) \| 2.399638 / 6.876477 (-4.476839) \| 2.481549 / 2.142072 (0.339477) \| 0.839812 / 4.805227 (-3.965415) \| 0.168890 / 6.500664 (-6.331774) \| 0.065564 / 0.075469 (-0.009906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.275507 / 1.841788 (-0.566281) \| 14.896343 / 8.074308 (6.822035) \| 13.159701 / 10.191392 (2.968309) \| 0.172065 / 0.680424 (-0.508359) \| 0.017507 / 0.534201 (-0.516694) \| 0.420031 / 0.579283 (-0.159252) \| 0.438835 / 0.434364 (0.004471) \| 0.490597 / 0.540337 (-0.049741) \| 0.583952 / 1.386936 (-0.802984) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48c9755d0ae9abe4c4d6cd8c1ce76eff849f0e5c \"CML watermark\")\n" ]	2023-05-12T19:19:27Z	2023-05-15T10:37:19Z	2023-05-15T10:30:14Z	MEMBER	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5853/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5853/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5853.diff", "html_url": "https://github.com/huggingface/datasets/pull/5853", "merged_at": "2023-05-15T10:30:14Z", "patch_url": "https://github.com/huggingface/datasets/pull/5853.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5853" }	true
https://api.github.com/repos/huggingface/datasets/issues/5852	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5852/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5852/comments	https://api.github.com/repos/huggingface/datasets/issues/5852/events	https://github.com/huggingface/datasets/pull/5852	1,707,927,165	PR_kwDODunzps5QZ1lj	5,852	Iterable torch formatting	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	[ "<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) \| 0.006567 / 0.011353 (-0.004786) \| 0.004479 / 0.011008 (-0.006530) \| 0.028286 / 0.038508 (-0.010222) \| 0.033137 / 0.023109 (0.010028) \| 0.305249 / 0.275898 (0.029351) \| 0.330306 / 0.323480 (0.006826) \| 0.003747 / 0.007986 (-0.004238) \| 0.004409 / 0.004328 (0.000081) \| 0.004742 / 0.004250 (0.000491) \| 0.040780 / 0.037052 (0.003728) \| 0.302879 / 0.258489 (0.044390) \| 0.346880 / 0.293841 (0.053039) \| 0.032908 / 0.128546 (-0.095638) \| 0.010617 / 0.075646 (-0.065029) \| 0.257996 / 0.419271 (-0.161275) \| 0.051044 / 0.043533 (0.007511) \| 0.306113 / 0.255139 (0.050974) \| 0.324444 / 0.283200 (0.041244) \| 0.100820 / 0.141683 (-0.040863) \| 1.478402 / 1.452155 (0.026248) \| 1.599398 / 1.492716 (0.106682) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216540 / 0.018006 (0.198534) \| 0.433480 / 0.000490 (0.432991) \| 0.004032 / 0.000200 (0.003832) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027807 / 0.037411 (-0.009604) \| 0.107225 / 0.014526 (0.092699) \| 0.120157 / 0.176557 (-0.056400) \| 0.174130 / 0.737135 (-0.563005) \| 0.128902 / 0.296338 (-0.167437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395996 / 0.215209 (0.180787) \| 3.936254 / 2.077655 (1.858599) \| 1.808864 / 1.504120 (0.304744) \| 1.608935 / 1.541195 (0.067741) \| 1.646427 / 1.468490 (0.177937) \| 0.716026 / 4.584777 (-3.868751) \| 3.815045 / 3.745712 (0.069333) \| 2.271534 / 5.269862 (-2.998327) \| 1.548728 / 4.565676 (-3.016948) \| 0.076743 / 0.424275 (-0.347532) \| 0.011575 / 0.007607 (0.003968) \| 0.499202 / 0.226044 (0.273158) \| 4.983754 / 2.268929 (2.714825) \| 2.239319 / 55.444624 (-53.205306) \| 1.919427 / 6.876477 (-4.957050) \| 2.019664 / 2.142072 (-0.122408) \| 0.866318 / 4.805227 (-3.938910) \| 0.157309 / 6.500664 (-6.343355) \| 0.063341 / 0.075469 (-0.012128) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180817 / 1.841788 (-0.660971) \| 14.579869 / 8.074308 (6.505561) \| 14.277848 / 10.191392 (4.086456) \| 0.182560 / 0.680424 (-0.497863) \| 0.017402 / 0.534201 (-0.516799) \| 0.411549 / 0.579283 (-0.167734) \| 0.432938 / 0.434364 (-0.001426) \| 0.545067 / 0.540337 (0.004730) \| 0.642173 / 1.386936 (-0.744763) \|\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) \| 0.006753 / 0.011353 (-0.004600) \| 0.004590 / 0.011008 (-0.006418) \| 0.006111 / 0.038508 (-0.032397) \| 0.032763 / 0.023109 (0.009654) \| 0.401001 / 0.275898 (0.125103) \| 0.428063 / 0.323480 (0.104583) \| 0.003730 / 0.007986 (-0.004255) \| 0.004617 / 0.004328 (0.000289) \| 0.004770 / 0.004250 (0.000519) \| 0.049718 / 0.037052 (0.012666) \| 0.399724 / 0.258489 (0.141235) \| 0.440292 / 0.293841 (0.146451) \| 0.032846 / 0.128546 (-0.095700) \| 0.010842 / 0.075646 (-0.064804) \| 0.012642 / 0.419271 (-0.406630) \| 0.046043 / 0.043533 (0.002510) \| 0.390862 / 0.255139 (0.135723) \| 0.407027 / 0.283200 (0.123828) \| 0.099349 / 0.141683 (-0.042334) \| 1.455739 / 1.452155 (0.003584) \| 1.572214 / 1.492716 (0.079497) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227186 / 0.018006 (0.209180) \| 0.447404 / 0.000490 (0.446914) \| 0.000400 / 0.000200 (0.000200) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029830 / 0.037411 (-0.007581) \| 0.112365 / 0.014526 (0.097839) \| 0.125736 / 0.176557 (-0.050821) \| 0.174781 / 0.737135 (-0.562354) \| 0.129439 / 0.296338 (-0.166900) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444438 / 0.215209 (0.229229) \| 4.459381 / 2.077655 (2.381726) \| 2.264541 / 1.504120 (0.760421) \| 2.075257 / 1.541195 (0.534062) \| 2.181289 / 1.468490 (0.712799) \| 0.725279 / 4.584777 (-3.859498) \| 3.863253 / 3.745712 (0.117541) \| 2.132498 / 5.269862 (-3.137364) \| 1.402003 / 4.565676 (-3.163673) \| 0.084268 / 0.424275 (-0.340007) \| 0.011762 / 0.007607 (0.004155) \| 0.556239 / 0.226044 (0.330194) \| 5.617998 / 2.268929 (3.349070) \| 2.754789 / 55.444624 (-52.689835) \| 2.418418 / 6.876477 (-4.458059) \| 2.479696 / 2.142072 (0.337624) \| 0.870037 / 4.805227 (-3.935190) \| 0.160480 / 6.500664 (-6.340184) \| 0.064464 / 0.075469 (-0.011005) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.290916 / 1.841788 (-0.550872) \| 14.783173 / 8.074308 (6.708865) \| 13.355883 / 10.191392 (3.164491) \| 0.169963 / 0.680424 (-0.510461) \| 0.017657 / 0.534201 (-0.516544) \| 0.409218 / 0.579283 (-0.170065) \| 0.422942 / 0.434364 (-0.011422) \| 0.494968 / 0.540337 (-0.045369) \| 0.587044 / 1.386936 (-0.799892) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2051e912d9525bc38a1caf295df0620619c488eb \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007183 / 0.011353 (-0.004169) \| 0.004586 / 0.011008 (-0.006423) \| 0.032668 / 0.038508 (-0.005840) \| 0.040896 / 0.023109 (0.017787) \| 0.358225 / 0.275898 (0.082327) \| 0.395063 / 0.323480 (0.071583) \| 0.004540 / 0.007986 (-0.003446) \| 0.003849 / 0.004328 (-0.000480) \| 0.005521 / 0.004250 (0.001271) \| 0.053314 / 0.037052 (0.016262) \| 0.362417 / 0.258489 (0.103928) \| 0.414337 / 0.293841 (0.120496) \| 0.030698 / 0.128546 (-0.097849) \| 0.008823 / 0.075646 (-0.066823) \| 0.303583 / 0.419271 (-0.115689) \| 0.060277 / 0.043533 (0.016744) \| 0.365938 / 0.255139 (0.110799) \| 0.379554 / 0.283200 (0.096354) \| 0.122545 / 0.141683 (-0.019138) \| 1.712098 / 1.452155 (0.259943) \| 1.802036 / 1.492716 (0.309319) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239508 / 0.018006 (0.221502) \| 0.492194 / 0.000490 (0.491704) \| 0.003280 / 0.000200 (0.003081) \| 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) \| 0.033301 / 0.037411 (-0.004110) \| 0.125851 / 0.014526 (0.111325) \| 0.137757 / 0.176557 (-0.038799) \| 0.207603 / 0.737135 (-0.529533) \| 0.143507 / 0.296338 (-0.152831) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470662 / 0.215209 (0.255453) \| 4.736017 / 2.077655 (2.658363) \| 2.154152 / 1.504120 (0.650032) \| 1.954243 / 1.541195 (0.413048) \| 2.080186 / 1.468490 (0.611696) \| 0.622884 / 4.584777 (-3.961893) \| 4.385885 / 3.745712 (0.640173) \| 2.262085 / 5.269862 (-3.007776) \| 1.454215 / 4.565676 (-3.111462) \| 0.067342 / 0.424275 (-0.356933) \| 0.012913 / 0.007607 (0.005306) \| 0.600676 / 0.226044 (0.374631) \| 5.915093 / 2.268929 (3.646164) \| 2.664915 / 55.444624 (-52.779709) \| 2.286986 / 6.876477 (-4.589490) \| 2.387776 / 2.142072 (0.245704) \| 0.757067 / 4.805227 (-4.048160) \| 0.154625 / 6.500664 (-6.346039) \| 0.074632 / 0.075469 (-0.000838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.413229 / 1.841788 (-0.428558) \| 17.433012 / 8.074308 (9.358704) \| 16.980340 / 10.191392 (6.788948) \| 0.218943 / 0.680424 (-0.461481) \| 0.020525 / 0.534201 (-0.513676) \| 0.451847 / 0.579283 (-0.127436) \| 0.495587 / 0.434364 (0.061223) \| 0.548739 / 0.540337 (0.008402) \| 0.662120 / 1.386936 (-0.724816) \|\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) \| 0.006775 / 0.011353 (-0.004577) \| 0.004556 / 0.011008 (-0.006452) \| 0.006462 / 0.038508 (-0.032046) \| 0.039073 / 0.023109 (0.015964) \| 0.429249 / 0.275898 (0.153351) \| 0.469946 / 0.323480 (0.146467) \| 0.004402 / 0.007986 (-0.003584) \| 0.003798 / 0.004328 (-0.000530) \| 0.005347 / 0.004250 (0.001097) \| 0.053743 / 0.037052 (0.016691) \| 0.434635 / 0.258489 (0.176146) \| 0.475661 / 0.293841 (0.181820) \| 0.029891 / 0.128546 (-0.098656) \| 0.009058 / 0.075646 (-0.066588) \| 0.010987 / 0.419271 (-0.408284) \| 0.053877 / 0.043533 (0.010344) \| 0.434428 / 0.255139 (0.179289) \| 0.449637 / 0.283200 (0.166437) \| 0.124331 / 0.141683 (-0.017352) \| 1.736083 / 1.452155 (0.283928) \| 1.831632 / 1.492716 (0.338916) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248428 / 0.018006 (0.230422) \| 0.493113 / 0.000490 (0.492623) \| 0.000429 / 0.000200 (0.000229) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031337 / 0.037411 (-0.006074) \| 0.132360 / 0.014526 (0.117834) \| 0.134734 / 0.176557 (-0.041822) \| 0.193811 / 0.737135 (-0.543324) \| 0.146883 / 0.296338 (-0.149456) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.510876 / 0.215209 (0.295666) \| 5.170198 / 2.077655 (3.092543) \| 2.572105 / 1.504120 (1.067985) \| 2.316918 / 1.541195 (0.775723) \| 2.449316 / 1.468490 (0.980826) \| 0.612219 / 4.584777 (-3.972558) \| 4.456740 / 3.745712 (0.711028) \| 2.099757 / 5.269862 (-3.170105) \| 1.293017 / 4.565676 (-3.272660) \| 0.067922 / 0.424275 (-0.356353) \| 0.013467 / 0.007607 (0.005860) \| 0.634240 / 0.226044 (0.408196) \| 6.373111 / 2.268929 (4.104182) \| 3.171567 / 55.444624 (-52.273057) \| 2.763411 / 6.876477 (-4.113066) \| 2.845557 / 2.142072 (0.703485) \| 0.763431 / 4.805227 (-4.041797) \| 0.155949 / 6.500664 (-6.344715) \| 0.076264 / 0.075469 (0.000795) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.468075 / 1.841788 (-0.373713) \| 17.582354 / 8.074308 (9.508046) \| 16.565964 / 10.191392 (6.374572) \| 0.163779 / 0.680424 (-0.516644) \| 0.020472 / 0.534201 (-0.513728) \| 0.444416 / 0.579283 (-0.134867) \| 0.488471 / 0.434364 (0.054107) \| 0.550661 / 0.540337 (0.010323) \| 0.667230 / 1.386936 (-0.719706) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3655cbf1c627c945e393641d35298a166f1e4bf5 \"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) \| 0.006160 / 0.011353 (-0.005193) \| 0.004093 / 0.011008 (-0.006915) \| 0.056485 / 0.038508 (0.017977) \| 0.033637 / 0.023109 (0.010528) \| 0.296448 / 0.275898 (0.020550) \| 0.332532 / 0.323480 (0.009052) \| 0.003864 / 0.007986 (-0.004122) \| 0.003446 / 0.004328 (-0.000883) \| 0.034808 / 0.004250 (0.030558) \| 0.048567 / 0.037052 (0.011514) \| 0.296090 / 0.258489 (0.037601) \| 0.336067 / 0.293841 (0.042226) \| 0.026081 / 0.128546 (-0.102465) \| 0.007875 / 0.075646 (-0.067771) \| 0.286049 / 0.419271 (-0.133222) \| 0.050411 / 0.043533 (0.006878) \| 0.297016 / 0.255139 (0.041877) \| 0.320030 / 0.283200 (0.036830) \| 0.110374 / 0.141683 (-0.031308) \| 1.432470 / 1.452155 (-0.019684) \| 1.492479 / 1.492716 (-0.000238) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.262352 / 0.018006 (0.244346) \| 0.557956 / 0.000490 (0.557467) \| 0.010296 / 0.000200 (0.010096) \| 0.000315 / 0.000054 (0.000260) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028801 / 0.037411 (-0.008611) \| 0.109844 / 0.014526 (0.095318) \| 0.122333 / 0.176557 (-0.054224) \| 0.180571 / 0.737135 (-0.556564) \| 0.125990 / 0.296338 (-0.170348) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401643 / 0.215209 (0.186434) \| 4.020993 / 2.077655 (1.943338) \| 1.815256 / 1.504120 (0.311136) \| 1.619579 / 1.541195 (0.078384) \| 1.708889 / 1.468490 (0.240398) \| 0.537847 / 4.584777 (-4.046930) \| 3.743331 / 3.745712 (-0.002381) \| 1.779891 / 5.269862 (-3.489970) \| 1.021423 / 4.565676 (-3.544253) \| 0.058869 / 0.424275 (-0.365406) \| 0.011826 / 0.007607 (0.004218) \| 0.499665 / 0.226044 (0.273621) \| 4.980928 / 2.268929 (2.712000) \| 2.285664 / 55.444624 (-53.158960) \| 1.936553 / 6.876477 (-4.939923) \| 2.090428 / 2.142072 (-0.051645) \| 0.655218 / 4.805227 (-4.150009) \| 0.133178 / 6.500664 (-6.367486) \| 0.062991 / 0.075469 (-0.012478) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.168895 / 1.841788 (-0.672892) \| 14.656773 / 8.074308 (6.582465) \| 13.737921 / 10.191392 (3.546529) \| 0.145383 / 0.680424 (-0.535041) \| 0.017614 / 0.534201 (-0.516587) \| 0.386499 / 0.579283 (-0.192784) \| 0.425626 / 0.434364 (-0.008738) \| 0.389572 / 0.540337 (-0.150766) \| 0.386753 / 1.386936 (-1.000183) \|\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) \| 0.005998 / 0.011353 (-0.005355) \| 0.004265 / 0.011008 (-0.006743) \| 0.034743 / 0.038508 (-0.003766) \| 0.033929 / 0.023109 (0.010820) \| 0.405535 / 0.275898 (0.129636) \| 0.407235 / 0.323480 (0.083755) \| 0.003972 / 0.007986 (-0.004013) \| 0.003616 / 0.004328 (-0.000712) \| 0.035278 / 0.004250 (0.031027) \| 0.052990 / 0.037052 (0.015937) \| 0.405228 / 0.258489 (0.146739) \| 0.415007 / 0.293841 (0.121166) \| 0.025951 / 0.128546 (-0.102595) \| 0.007990 / 0.075646 (-0.067656) \| 0.040492 / 0.419271 (-0.378779) \| 0.049123 / 0.043533 (0.005591) \| 0.399282 / 0.255139 (0.144143) \| 0.384303 / 0.283200 (0.101103) \| 0.115234 / 0.141683 (-0.026448) \| 1.476904 / 1.452155 (0.024749) \| 1.627191 / 1.492716 (0.134475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209211 / 0.018006 (0.191205) \| 0.566718 / 0.000490 (0.566228) \| 0.002094 / 0.000200 (0.001894) \| 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) \| 0.030885 / 0.037411 (-0.006526) \| 0.110777 / 0.014526 (0.096251) \| 0.124382 / 0.176557 (-0.052174) \| 0.175081 / 0.737135 (-0.562054) \| 0.130263 / 0.296338 (-0.166075) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448091 / 0.215209 (0.232882) \| 4.484404 / 2.077655 (2.406749) \| 2.278438 / 1.504120 (0.774318) \| 2.087933 / 1.541195 (0.546738) \| 2.186709 / 1.468490 (0.718219) \| 0.534822 / 4.584777 (-4.049955) \| 3.778229 / 3.745712 (0.032517) \| 3.312334 / 5.269862 (-1.957528) \| 1.557209 / 4.565676 (-3.008467) \| 0.058923 / 0.424275 (-0.365352) \| 0.011350 / 0.007607 (0.003743) \| 0.550470 / 0.226044 (0.324426) \| 5.480347 / 2.268929 (3.211419) \| 2.781709 / 55.444624 (-52.662915) \| 2.478729 / 6.876477 (-4.397748) \| 2.492001 / 2.142072 (0.349929) \| 0.652649 / 4.805227 (-4.152578) \| 0.131334 / 6.500664 (-6.369330) \| 0.065619 / 0.075469 (-0.009850) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253998 / 1.841788 (-0.587790) \| 15.207433 / 8.074308 (7.133124) \| 14.627842 / 10.191392 (4.436450) \| 0.146947 / 0.680424 (-0.533477) \| 0.017533 / 0.534201 (-0.516668) \| 0.391627 / 0.579283 (-0.187656) \| 0.431113 / 0.434364 (-0.003251) \| 0.413886 / 0.540337 (-0.126451) \| 0.414483 / 1.386936 (-0.972453) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f4e98701590a4922050051eb0f4d63e6125723d \"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) \| 0.007741 / 0.011353 (-0.003612) \| 0.004584 / 0.011008 (-0.006424) \| 0.067869 / 0.038508 (0.029361) \| 0.041612 / 0.023109 (0.018503) \| 0.377878 / 0.275898 (0.101980) \| 0.421633 / 0.323480 (0.098153) \| 0.004614 / 0.007986 (-0.003371) \| 0.003824 / 0.004328 (-0.000504) \| 0.041479 / 0.004250 (0.037229) \| 0.053309 / 0.037052 (0.016256) \| 0.390147 / 0.258489 (0.131658) \| 0.437706 / 0.293841 (0.143865) \| 0.035951 / 0.128546 (-0.092595) \| 0.009231 / 0.075646 (-0.066415) \| 0.357572 / 0.419271 (-0.061699) \| 0.081332 / 0.043533 (0.037799) \| 0.370076 / 0.255139 (0.114937) \| 0.423653 / 0.283200 (0.140453) \| 0.141401 / 0.141683 (-0.000282) \| 1.722744 / 1.452155 (0.270589) \| 1.914668 / 1.492716 (0.421952) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256568 / 0.018006 (0.238562) \| 0.512243 / 0.000490 (0.511753) \| 0.019913 / 0.000200 (0.019713) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031742 / 0.037411 (-0.005670) \| 0.128537 / 0.014526 (0.114011) \| 0.139962 / 0.176557 (-0.036594) \| 0.210711 / 0.737135 (-0.526424) \| 0.147162 / 0.296338 (-0.149177) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.509518 / 0.215209 (0.294309) \| 5.083788 / 2.077655 (3.006134) \| 2.455381 / 1.504120 (0.951262) \| 2.208078 / 1.541195 (0.666883) \| 2.341807 / 1.468490 (0.873317) \| 0.580014 / 4.584777 (-4.004763) \| 4.599492 / 3.745712 (0.853780) \| 2.403249 / 5.269862 (-2.866612) \| 1.559177 / 4.565676 (-3.006500) \| 0.072846 / 0.424275 (-0.351429) \| 0.017327 / 0.007607 (0.009720) \| 0.627747 / 0.226044 (0.401703) \| 6.242586 / 2.268929 (3.973657) \| 2.982875 / 55.444624 (-52.461750) \| 2.588645 / 6.876477 (-4.287832) \| 2.765915 / 2.142072 (0.623843) \| 0.720455 / 4.805227 (-4.084772) \| 0.157474 / 6.500664 (-6.343190) \| 0.074295 / 0.075469 (-0.001174) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.540799 / 1.841788 (-0.300988) \| 18.054632 / 8.074308 (9.980324) \| 16.544036 / 10.191392 (6.352644) \| 0.201423 / 0.680424 (-0.479001) \| 0.020497 / 0.534201 (-0.513704) \| 0.496275 / 0.579283 (-0.083008) \| 0.547380 / 0.434364 (0.113017) \| 0.614605 / 0.540337 (0.074267) \| 0.749889 / 1.386936 (-0.637047) \|\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) \| 0.006963 / 0.011353 (-0.004389) \| 0.004543 / 0.011008 (-0.006465) \| 0.039530 / 0.038508 (0.001022) \| 0.038420 / 0.023109 (0.015311) \| 0.454885 / 0.275898 (0.178987) \| 0.491731 / 0.323480 (0.168251) \| 0.004211 / 0.007986 (-0.003775) \| 0.003673 / 0.004328 (-0.000655) \| 0.038735 / 0.004250 (0.034484) \| 0.052085 / 0.037052 (0.015032) \| 0.448924 / 0.258489 (0.190435) \| 0.499254 / 0.293841 (0.205413) \| 0.030069 / 0.128546 (-0.098477) \| 0.009082 / 0.075646 (-0.066565) \| 0.047181 / 0.419271 (-0.372090) \| 0.054758 / 0.043533 (0.011225) \| 0.445035 / 0.255139 (0.189896) \| 0.475090 / 0.283200 (0.191891) \| 0.122641 / 0.141683 (-0.019042) \| 1.706514 / 1.452155 (0.254360) \| 1.855726 / 1.492716 (0.363010) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246028 / 0.018006 (0.228022) \| 0.486382 / 0.000490 (0.485892) \| 0.003038 / 0.000200 (0.002838) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034298 / 0.037411 (-0.003113) \| 0.135364 / 0.014526 (0.120838) \| 0.146102 / 0.176557 (-0.030455) \| 0.207997 / 0.737135 (-0.529139) \| 0.153119 / 0.296338 (-0.143219) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.528758 / 0.215209 (0.313549) \| 5.243303 / 2.077655 (3.165648) \| 2.617194 / 1.504120 (1.113074) \| 2.400740 / 1.541195 (0.859545) \| 2.534692 / 1.468490 (1.066202) \| 0.585825 / 4.584777 (-3.998952) \| 4.879766 / 3.745712 (1.134054) \| 2.377419 / 5.269862 (-2.892443) \| 1.460711 / 4.565676 (-3.104966) \| 0.075572 / 0.424275 (-0.348703) \| 0.013650 / 0.007607 (0.006042) \| 0.697103 / 0.226044 (0.471058) \| 6.444984 / 2.268929 (4.176055) \| 3.227662 / 55.444624 (-52.216963) \| 2.875163 / 6.876477 (-4.001314) \| 2.860953 / 2.142072 (0.718881) \| 0.718908 / 4.805227 (-4.086319) \| 0.158005 / 6.500664 (-6.342659) \| 0.077581 / 0.075469 (0.002112) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.653027 / 1.841788 (-0.188760) \| 18.789342 / 8.074308 (10.715034) \| 16.762678 / 10.191392 (6.571286) \| 0.238920 / 0.680424 (-0.441504) \| 0.020698 / 0.534201 (-0.513502) \| 0.512634 / 0.579283 (-0.066649) \| 0.542235 / 0.434364 (0.107871) \| 0.626634 / 0.540337 (0.086297) \| 0.753324 / 1.386936 (-0.633612) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f978ad8bec6e5e77868c6ffcc6f514354a03901d \"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) \| 0.005737 / 0.011353 (-0.005616) \| 0.003767 / 0.011008 (-0.007241) \| 0.097792 / 0.038508 (0.059284) \| 0.028466 / 0.023109 (0.005356) \| 0.317703 / 0.275898 (0.041805) \| 0.359512 / 0.323480 (0.036032) \| 0.003428 / 0.007986 (-0.004558) \| 0.002848 / 0.004328 (-0.001481) \| 0.075668 / 0.004250 (0.071418) \| 0.037165 / 0.037052 (0.000113) \| 0.329539 / 0.258489 (0.071050) \| 0.361365 / 0.293841 (0.067524) \| 0.024777 / 0.128546 (-0.103769) \| 0.008324 / 0.075646 (-0.067323) \| 0.317346 / 0.419271 (-0.101926) \| 0.043296 / 0.043533 (-0.000237) \| 0.315318 / 0.255139 (0.060179) \| 0.347641 / 0.283200 (0.064441) \| 0.089551 / 0.141683 (-0.052132) \| 1.506335 / 1.452155 (0.054180) \| 1.573931 / 1.492716 (0.081215) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208041 / 0.018006 (0.190034) \| 0.428198 / 0.000490 (0.427708) \| 0.002568 / 0.000200 (0.002369) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023745 / 0.037411 (-0.013667) \| 0.096256 / 0.014526 (0.081730) \| 0.104917 / 0.176557 (-0.071639) \| 0.164341 / 0.737135 (-0.572794) \| 0.107972 / 0.296338 (-0.188367) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453995 / 0.215209 (0.238786) \| 4.546892 / 2.077655 (2.469238) \| 2.185498 / 1.504120 (0.681378) \| 1.989156 / 1.541195 (0.447962) \| 2.053443 / 1.468490 (0.584953) \| 0.559940 / 4.584777 (-4.024837) \| 3.420759 / 3.745712 (-0.324954) \| 1.771528 / 5.269862 (-3.498333) \| 1.139692 / 4.565676 (-3.425984) \| 0.067686 / 0.424275 (-0.356589) \| 0.011729 / 0.007607 (0.004122) \| 0.558001 / 0.226044 (0.331957) \| 5.583886 / 2.268929 (3.314957) \| 2.678726 / 55.444624 (-52.765899) \| 2.324127 / 6.876477 (-4.552350) \| 2.472805 / 2.142072 (0.330733) \| 0.663163 / 4.805227 (-4.142065) \| 0.134892 / 6.500664 (-6.365772) \| 0.066722 / 0.075469 (-0.008747) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.195200 / 1.841788 (-0.646587) \| 13.602517 / 8.074308 (5.528209) \| 14.036344 / 10.191392 (3.844952) \| 0.143759 / 0.680424 (-0.536665) \| 0.017215 / 0.534201 (-0.516986) \| 0.383749 / 0.579283 (-0.195534) \| 0.388229 / 0.434364 (-0.046134) \| 0.469366 / 0.540337 (-0.070971) \| 0.560408 / 1.386936 (-0.826528) \|\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) \| 0.005953 / 0.011353 (-0.005400) \| 0.003840 / 0.011008 (-0.007168) \| 0.077481 / 0.038508 (0.038973) \| 0.028318 / 0.023109 (0.005209) \| 0.403991 / 0.275898 (0.128093) \| 0.433374 / 0.323480 (0.109894) \| 0.003572 / 0.007986 (-0.004414) \| 0.003033 / 0.004328 (-0.001295) \| 0.075873 / 0.004250 (0.071623) \| 0.039321 / 0.037052 (0.002269) \| 0.416790 / 0.258489 (0.158301) \| 0.459368 / 0.293841 (0.165527) \| 0.025270 / 0.128546 (-0.103276) \| 0.008574 / 0.075646 (-0.067072) \| 0.083376 / 0.419271 (-0.335896) \| 0.043206 / 0.043533 (-0.000327) \| 0.404831 / 0.255139 (0.149692) \| 0.418559 / 0.283200 (0.135360) \| 0.099135 / 0.141683 (-0.042548) \| 1.501315 / 1.452155 (0.049160) \| 1.583912 / 1.492716 (0.091195) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241510 / 0.018006 (0.223504) \| 0.410473 / 0.000490 (0.409983) \| 0.001857 / 0.000200 (0.001657) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025366 / 0.037411 (-0.012045) \| 0.103353 / 0.014526 (0.088828) \| 0.107934 / 0.176557 (-0.068622) \| 0.162388 / 0.737135 (-0.574747) \| 0.113550 / 0.296338 (-0.182789) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463529 / 0.215209 (0.248320) \| 4.657688 / 2.077655 (2.580034) \| 2.455088 / 1.504120 (0.950968) \| 2.304833 / 1.541195 (0.763638) \| 2.317520 / 1.468490 (0.849029) \| 0.563395 / 4.584777 (-4.021382) \| 3.408489 / 3.745712 (-0.337223) \| 2.636379 / 5.269862 (-2.633482) \| 1.425355 / 4.565676 (-3.140322) \| 0.068335 / 0.424275 (-0.355940) \| 0.011713 / 0.007607 (0.004106) \| 0.550230 / 0.226044 (0.324186) \| 5.519843 / 2.268929 (3.250915) \| 2.864986 / 55.444624 (-52.579639) \| 2.604821 / 6.876477 (-4.271655) \| 2.701501 / 2.142072 (0.559428) \| 0.668193 / 4.805227 (-4.137034) \| 0.134739 / 6.500664 (-6.365925) \| 0.067110 / 0.075469 (-0.008359) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.326358 / 1.841788 (-0.515430) \| 14.184172 / 8.074308 (6.109864) \| 14.139245 / 10.191392 (3.947853) \| 0.151881 / 0.680424 (-0.528542) \| 0.016718 / 0.534201 (-0.517483) \| 0.367035 / 0.579283 (-0.212248) \| 0.393512 / 0.434364 (-0.040852) \| 0.441261 / 0.540337 (-0.099076) \| 0.533907 / 1.386936 (-0.853029) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#54098759d023f0b3e8eccd2dd98d46a1c6d19cce \"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) \| 0.006275 / 0.011353 (-0.005078) \| 0.003980 / 0.011008 (-0.007028) \| 0.097617 / 0.038508 (0.059109) \| 0.034089 / 0.023109 (0.010980) \| 0.297381 / 0.275898 (0.021483) \| 0.330106 / 0.323480 (0.006626) \| 0.003838 / 0.007986 (-0.004148) \| 0.004042 / 0.004328 (-0.000287) \| 0.074305 / 0.004250 (0.070055) \| 0.048318 / 0.037052 (0.011265) \| 0.295585 / 0.258489 (0.037096) \| 0.346924 / 0.293841 (0.053083) \| 0.027397 / 0.128546 (-0.101150) \| 0.008452 / 0.075646 (-0.067194) \| 0.326837 / 0.419271 (-0.092435) \| 0.049515 / 0.043533 (0.005982) \| 0.303931 / 0.255139 (0.048792) \| 0.317647 / 0.283200 (0.034447) \| 0.098280 / 0.141683 (-0.043403) \| 1.442603 / 1.452155 (-0.009552) \| 1.524050 / 1.492716 (0.031334) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215095 / 0.018006 (0.197089) \| 0.437662 / 0.000490 (0.437173) \| 0.009771 / 0.000200 (0.009571) \| 0.000401 / 0.000054 (0.000346) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027169 / 0.037411 (-0.010243) \| 0.111383 / 0.014526 (0.096857) \| 0.116163 / 0.176557 (-0.060394) \| 0.173134 / 0.737135 (-0.564001) \| 0.122376 / 0.296338 (-0.173962) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398332 / 0.215209 (0.183123) \| 3.974166 / 2.077655 (1.896511) \| 1.793847 / 1.504120 (0.289727) \| 1.615117 / 1.541195 (0.073922) \| 1.660288 / 1.468490 (0.191798) \| 0.523833 / 4.584777 (-4.060944) \| 3.704273 / 3.745712 (-0.041439) \| 1.873308 / 5.269862 (-3.396554) \| 1.203546 / 4.565676 (-3.362131) \| 0.064949 / 0.424275 (-0.359326) \| 0.011830 / 0.007607 (0.004223) \| 0.497294 / 0.226044 (0.271250) \| 4.948663 / 2.268929 (2.679735) \| 2.233391 / 55.444624 (-53.211234) \| 1.903208 / 6.876477 (-4.973269) \| 2.067908 / 2.142072 (-0.074164) \| 0.644256 / 4.805227 (-4.160971) \| 0.142798 / 6.500664 (-6.357866) \| 0.064734 / 0.075469 (-0.010735) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.172313 / 1.841788 (-0.669475) \| 14.665853 / 8.074308 (6.591545) \| 13.147051 / 10.191392 (2.955659) \| 0.139338 / 0.680424 (-0.541086) \| 0.017452 / 0.534201 (-0.516749) \| 0.395660 / 0.579283 (-0.183623) \| 0.410138 / 0.434364 (-0.024226) \| 0.460357 / 0.540337 (-0.079980) \| 0.555670 / 1.386936 (-0.831266) \|\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) \| 0.006247 / 0.011353 (-0.005106) \| 0.004098 / 0.011008 (-0.006910) \| 0.075050 / 0.038508 (0.036542) \| 0.033232 / 0.023109 (0.010122) \| 0.384139 / 0.275898 (0.108241) \| 0.420865 / 0.323480 (0.097385) \| 0.003889 / 0.007986 (-0.004096) \| 0.003336 / 0.004328 (-0.000993) \| 0.073837 / 0.004250 (0.069587) \| 0.048775 / 0.037052 (0.011723) \| 0.386373 / 0.258489 (0.127884) \| 0.421718 / 0.293841 (0.127878) \| 0.027553 / 0.128546 (-0.100993) \| 0.008724 / 0.075646 (-0.066922) \| 0.080970 / 0.419271 (-0.338302) \| 0.045981 / 0.043533 (0.002448) \| 0.364381 / 0.255139 (0.109242) \| 0.391203 / 0.283200 (0.108004) \| 0.101681 / 0.141683 (-0.040002) \| 1.469533 / 1.452155 (0.017378) \| 1.562016 / 1.492716 (0.069300) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222318 / 0.018006 (0.204312) \| 0.441395 / 0.000490 (0.440905) \| 0.000408 / 0.000200 (0.000208) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030291 / 0.037411 (-0.007120) \| 0.114053 / 0.014526 (0.099527) \| 0.123124 / 0.176557 (-0.053433) \| 0.173474 / 0.737135 (-0.563661) \| 0.129946 / 0.296338 (-0.166393) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430342 / 0.215209 (0.215133) \| 4.309782 / 2.077655 (2.232128) \| 2.110668 / 1.504120 (0.606548) \| 1.922881 / 1.541195 (0.381687) \| 1.993562 / 1.468490 (0.525072) \| 0.523682 / 4.584777 (-4.061095) \| 3.774152 / 3.745712 (0.028440) \| 3.354783 / 5.269862 (-1.915079) \| 1.489793 / 4.565676 (-3.075884) \| 0.065169 / 0.424275 (-0.359107) \| 0.011626 / 0.007607 (0.004019) \| 0.539126 / 0.226044 (0.313081) \| 5.372593 / 2.268929 (3.103664) \| 2.570652 / 55.444624 (-52.873973) \| 2.253353 / 6.876477 (-4.623123) \| 2.312876 / 2.142072 (0.170804) \| 0.644241 / 4.805227 (-4.160986) \| 0.138326 / 6.500664 (-6.362338) \| 0.064491 / 0.075469 (-0.010979) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.344164 / 1.841788 (-0.497624) \| 15.124679 / 8.074308 (7.050371) \| 14.799310 / 10.191392 (4.607918) \| 0.149054 / 0.680424 (-0.531370) \| 0.017564 / 0.534201 (-0.516637) \| 0.394593 / 0.579283 (-0.184690) \| 0.428768 / 0.434364 (-0.005596) \| 0.468235 / 0.540337 (-0.072103) \| 0.557384 / 1.386936 (-0.829552) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8bfac259e2b5047bb8a0cdcefc8357477ebf93c \"CML watermark\")\n", "@albertvillanova could you take a look at this one ? It directly follows the arrow formatting PR", "I added tests for the `__array__` case which lets you go from any tensor format to any other tensor format.\r\n\r\nI also properly deprecated format_type and added a warning message.", "<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) \| 0.007838 / 0.011353 (-0.003515) \| 0.005177 / 0.011008 (-0.005831) \| 0.131058 / 0.038508 (0.092550) \| 0.035959 / 0.023109 (0.012850) \| 0.414071 / 0.275898 (0.138173) \| 0.429628 / 0.323480 (0.106148) \| 0.005151 / 0.007986 (-0.002834) \| 0.003979 / 0.004328 (-0.000349) \| 0.103209 / 0.004250 (0.098958) \| 0.046200 / 0.037052 (0.009148) \| 0.414020 / 0.258489 (0.155531) \| 0.475748 / 0.293841 (0.181907) \| 0.041031 / 0.128546 (-0.087515) \| 0.014462 / 0.075646 (-0.061185) \| 0.423706 / 0.419271 (0.004434) \| 0.063488 / 0.043533 (0.019955) \| 0.404937 / 0.255139 (0.149798) \| 0.404973 / 0.283200 (0.121773) \| 0.114982 / 0.141683 (-0.026701) \| 1.911867 / 1.452155 (0.459713) \| 1.925274 / 1.492716 (0.432557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284656 / 0.018006 (0.266650) \| 0.588329 / 0.000490 (0.587840) \| 0.007092 / 0.000200 (0.006892) \| 0.000143 / 0.000054 (0.000089) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025136 / 0.037411 (-0.012275) \| 0.109514 / 0.014526 (0.094988) \| 0.117953 / 0.176557 (-0.058603) \| 0.195454 / 0.737135 (-0.541682) \| 0.134243 / 0.296338 (-0.162096) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584045 / 0.215209 (0.368836) \| 6.456922 / 2.077655 (4.379267) \| 2.759728 / 1.504120 (1.255608) \| 2.260913 / 1.541195 (0.719718) \| 2.292535 / 1.468490 (0.824045) \| 0.906873 / 4.584777 (-3.677904) \| 5.554455 / 3.745712 (1.808743) \| 4.881557 / 5.269862 (-0.388305) \| 2.509121 / 4.565676 (-2.056555) \| 0.107191 / 0.424275 (-0.317084) \| 0.014684 / 0.007607 (0.007077) \| 0.761625 / 0.226044 (0.535580) \| 7.582708 / 2.268929 (5.313780) \| 3.150160 / 55.444624 (-52.294464) \| 2.792284 / 6.876477 (-4.084193) \| 2.881321 / 2.142072 (0.739248) \| 1.108353 / 4.805227 (-3.696874) \| 0.220129 / 6.500664 (-6.280535) \| 0.075877 / 0.075469 (0.000408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.465743 / 1.841788 (-0.376045) \| 17.679219 / 8.074308 (9.604911) \| 18.929399 / 10.191392 (8.738007) \| 0.219488 / 0.680424 (-0.460935) \| 0.028435 / 0.534201 (-0.505766) \| 0.512623 / 0.579283 (-0.066660) \| 0.619983 / 0.434364 (0.185619) \| 0.603430 / 0.540337 (0.063092) \| 0.730416 / 1.386936 (-0.656520) \|\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) \| 0.008285 / 0.011353 (-0.003068) \| 0.005771 / 0.011008 (-0.005237) \| 0.106444 / 0.038508 (0.067936) \| 0.035078 / 0.023109 (0.011969) \| 0.441198 / 0.275898 (0.165300) \| 0.536279 / 0.323480 (0.212800) \| 0.004561 / 0.007986 (-0.003424) \| 0.006623 / 0.004328 (0.002294) \| 0.102392 / 0.004250 (0.098142) \| 0.051736 / 0.037052 (0.014684) \| 0.479113 / 0.258489 (0.220624) \| 0.535088 / 0.293841 (0.241247) \| 0.041805 / 0.128546 (-0.086741) \| 0.014031 / 0.075646 (-0.061615) \| 0.115795 / 0.419271 (-0.303477) \| 0.057913 / 0.043533 (0.014380) \| 0.435847 / 0.255139 (0.180708) \| 0.524831 / 0.283200 (0.241632) \| 0.119419 / 0.141683 (-0.022263) \| 1.835577 / 1.452155 (0.383423) \| 1.936990 / 1.492716 (0.444273) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288422 / 0.018006 (0.270416) \| 0.569776 / 0.000490 (0.569287) \| 0.005652 / 0.000200 (0.005452) \| 0.000139 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034632 / 0.037411 (-0.002779) \| 0.136217 / 0.014526 (0.121691) \| 0.139468 / 0.176557 (-0.037089) \| 0.206804 / 0.737135 (-0.530331) \| 0.148733 / 0.296338 (-0.147606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.667728 / 0.215209 (0.452518) \| 6.548972 / 2.077655 (4.471317) \| 3.051537 / 1.504120 (1.547417) \| 2.581173 / 1.541195 (1.039978) \| 2.653443 / 1.468490 (1.184953) \| 0.906606 / 4.584777 (-3.678171) \| 5.704384 / 3.745712 (1.958672) \| 2.848618 / 5.269862 (-2.421244) \| 1.821402 / 4.565676 (-2.744274) \| 0.118018 / 0.424275 (-0.306257) \| 0.014821 / 0.007607 (0.007214) \| 0.821967 / 0.226044 (0.595923) \| 8.165818 / 2.268929 (5.896889) \| 3.744509 / 55.444624 (-51.700116) \| 2.901097 / 6.876477 (-3.975380) \| 3.018068 / 2.142072 (0.875996) \| 1.106155 / 4.805227 (-3.699072) \| 0.263118 / 6.500664 (-6.237546) \| 0.088508 / 0.075469 (0.013039) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.725860 / 1.841788 (-0.115928) \| 19.411246 / 8.074308 (11.336938) \| 20.807499 / 10.191392 (10.616107) \| 0.238417 / 0.680424 (-0.442007) \| 0.026550 / 0.534201 (-0.507651) \| 0.500715 / 0.579283 (-0.078568) \| 0.615547 / 0.434364 (0.181183) \| 0.614361 / 0.540337 (0.074023) \| 0.720365 / 1.386936 (-0.666571) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae2e77f8344cdcc1c4c876f67936bec33087b19a \"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) \| 0.006640 / 0.011353 (-0.004713) \| 0.004079 / 0.011008 (-0.006930) \| 0.100555 / 0.038508 (0.062046) \| 0.037318 / 0.023109 (0.014209) \| 0.320050 / 0.275898 (0.044152) \| 0.358860 / 0.323480 (0.035380) \| 0.003828 / 0.007986 (-0.004158) \| 0.003215 / 0.004328 (-0.001113) \| 0.076577 / 0.004250 (0.072326) \| 0.048080 / 0.037052 (0.011028) \| 0.324759 / 0.258489 (0.066270) \| 0.361862 / 0.293841 (0.068021) \| 0.030759 / 0.128546 (-0.097787) \| 0.008998 / 0.075646 (-0.066648) \| 0.329105 / 0.419271 (-0.090167) \| 0.051407 / 0.043533 (0.007875) \| 0.311067 / 0.255139 (0.055928) \| 0.334401 / 0.283200 (0.051201) \| 0.098307 / 0.141683 (-0.043376) \| 1.500931 / 1.452155 (0.048776) \| 1.574646 / 1.492716 (0.081930) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219080 / 0.018006 (0.201073) \| 0.447117 / 0.000490 (0.446627) \| 0.009091 / 0.000200 (0.008891) \| 0.000396 / 0.000054 (0.000341) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026048 / 0.037411 (-0.011363) \| 0.112714 / 0.014526 (0.098188) \| 0.116426 / 0.176557 (-0.060131) \| 0.172187 / 0.737135 (-0.564948) \| 0.121707 / 0.296338 (-0.174632) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.358898 / 0.215209 (0.143689) \| 3.589212 / 2.077655 (1.511557) \| 1.677927 / 1.504120 (0.173807) \| 1.515861 / 1.541195 (-0.025334) \| 1.598479 / 1.468490 (0.129989) \| 0.478265 / 4.584777 (-4.106512) \| 3.834982 / 3.745712 (0.089270) \| 1.933815 / 5.269862 (-3.336047) \| 1.122769 / 4.565676 (-3.442908) \| 0.066984 / 0.424275 (-0.357291) \| 0.011276 / 0.007607 (0.003669) \| 0.512530 / 0.226044 (0.286486) \| 5.112667 / 2.268929 (2.843739) \| 2.266336 / 55.444624 (-53.178288) \| 1.929671 / 6.876477 (-4.946806) \| 2.127231 / 2.142072 (-0.014842) \| 0.671307 / 4.805227 (-4.133920) \| 0.143919 / 6.500664 (-6.356745) \| 0.066086 / 0.075469 (-0.009383) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.208767 / 1.841788 (-0.633021) \| 15.008415 / 8.074308 (6.934106) \| 14.085442 / 10.191392 (3.894050) \| 0.184164 / 0.680424 (-0.496260) \| 0.017619 / 0.534201 (-0.516582) \| 0.394443 / 0.579283 (-0.184840) \| 0.457653 / 0.434364 (0.023289) \| 0.473169 / 0.540337 (-0.067169) \| 0.571332 / 1.386936 (-0.815604) \|\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) \| 0.007009 / 0.011353 (-0.004344) \| 0.004330 / 0.011008 (-0.006678) \| 0.077462 / 0.038508 (0.038954) \| 0.034780 / 0.023109 (0.011671) \| 0.395573 / 0.275898 (0.119675) \| 0.425444 / 0.323480 (0.101964) \| 0.004119 / 0.007986 (-0.003866) \| 0.003597 / 0.004328 (-0.000731) \| 0.075209 / 0.004250 (0.070958) \| 0.050871 / 0.037052 (0.013819) \| 0.402990 / 0.258489 (0.144500) \| 0.445334 / 0.293841 (0.151493) \| 0.032492 / 0.128546 (-0.096054) \| 0.009066 / 0.075646 (-0.066581) \| 0.083073 / 0.419271 (-0.336198) \| 0.051661 / 0.043533 (0.008128) \| 0.395207 / 0.255139 (0.140068) \| 0.409556 / 0.283200 (0.126356) \| 0.106035 / 0.141683 (-0.035648) \| 1.506255 / 1.452155 (0.054101) \| 1.598724 / 1.492716 (0.106008) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194733 / 0.018006 (0.176727) \| 0.444920 / 0.000490 (0.444431) \| 0.002402 / 0.000200 (0.002202) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030464 / 0.037411 (-0.006947) \| 0.119153 / 0.014526 (0.104627) \| 0.126081 / 0.176557 (-0.050476) \| 0.179692 / 0.737135 (-0.557444) \| 0.131834 / 0.296338 (-0.164504) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440153 / 0.215209 (0.224944) \| 4.397504 / 2.077655 (2.319850) \| 2.138320 / 1.504120 (0.634200) \| 1.950596 / 1.541195 (0.409402) \| 2.079792 / 1.468490 (0.611302) \| 0.537606 / 4.584777 (-4.047171) \| 3.689420 / 3.745712 (-0.056292) \| 2.960732 / 5.269862 (-2.309129) \| 1.585652 / 4.565676 (-2.980024) \| 0.066102 / 0.424275 (-0.358173) \| 0.011429 / 0.007607 (0.003821) \| 0.537011 / 0.226044 (0.310967) \| 5.342171 / 2.268929 (3.073242) \| 2.624446 / 55.444624 (-52.820179) \| 2.313311 / 6.876477 (-4.563166) \| 2.389166 / 2.142072 (0.247094) \| 0.657547 / 4.805227 (-4.147681) \| 0.141640 / 6.500664 (-6.359025) \| 0.066102 / 0.075469 (-0.009367) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.130471 / 1.841788 (-0.711317) \| 14.824792 / 8.074308 (6.750484) \| 13.436463 / 10.191392 (3.245071) \| 0.155688 / 0.680424 (-0.524736) \| 0.015811 / 0.534201 (-0.518390) \| 0.355623 / 0.579283 (-0.223660) \| 0.450604 / 0.434364 (0.016241) \| 0.472542 / 0.540337 (-0.067796) \| 0.563584 / 1.386936 (-0.823352) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#963ff6de6eae80a6de4aabf0092eb3dfbe43096e \"CML watermark\")\n" ]	2023-05-12T16:48:49Z	2023-06-13T16:04:05Z	2023-06-13T15:57:05Z	MEMBER	null	Used the TorchFormatter to get torch tensors in iterable dataset with format set to "torch". It uses the data from Arrow if possible, otherwise applies recursive_tensorize. When set back to format_type=None, cast_to_python_objects is used. requires https://github.com/huggingface/datasets/pull/5821 close https://github.com/huggingface/datasets/issues/5793	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5852/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5852/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5852.diff", "html_url": "https://github.com/huggingface/datasets/pull/5852", "merged_at": "2023-06-13T15:57:05Z", "patch_url": "https://github.com/huggingface/datasets/pull/5852.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5852" }	true
https://api.github.com/repos/huggingface/datasets/issues/5850	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5850/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5850/comments	https://api.github.com/repos/huggingface/datasets/issues/5850/events	https://github.com/huggingface/datasets/pull/5850	1,707,678,911	PR_kwDODunzps5QZALv	5,850	Make packaged builders skip non-supported file formats	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5850). All of your documentation changes will be reflected on that endpoint.", "Good idea. @mariosasko!!!\r\n\r\nPlease note that before this PR, the files are not evenly distributed for archives: `_generate_examples` gets a list of iterators, one for each archive (uncompressed to a directory).", "This change could create silent problems when loading files with extensions that are not listed here. For example\r\n\r\n```python\r\nload_dataset(\"text\", data_files=[\"20230515.log\"])\r\n```\r\n\r\nwouldn't even log anything to say that the file was ignored.\r\n\r\nMaybe it's possible to do this at data files patterns resolution ?\r\n\r\ne.g. in get_data_patterns_in_dataset_repository / get_data_patterns_locally we could return patterns that include the most common extension", "@lhoestq the issue you evoke (.log files skipped by text builder if .log is not added to .txt as supported extension) persists whether you perform the skip at the pattern resolution or in the builder itself.\r\n\r\nThe solution is to add the .log extension (besides the .txt) as supported by text, independently of where we perform the skip (at pattern resolution or in the builder itself).\r\n\r\nAdditionally, at the time we call for pattern resolution, we do not know the builder class yet, so that we cannot pass specific file extensions. First we call data files pattern resolution, and afterwards we call `infer_module_for_data_files` and then know the builder class.", "> @lhoestq the issue you evoke (.log files skipped by text builder if .log is not added to .txt as supported extension) persists whether you perform the skip at the pattern resolution or in the builder itself.\r\n\r\nNo I simply think it's a bad breaking change to not support\r\n\r\n```python\r\nload_dataset(\"<builder_name>\", data_files=[\"path/to/file_with_unknown_or_no_extension\"])\r\n# or\r\nload_dataset(\"<builder_name>\", data_files=[\"https://url.to/file_with_unknown_or_no_extension\"])\r\n```\r\n\r\nIdk if it's the easiest solution, but maybe it's possible to do the change only when inferring the patterns of dataset repositories. This should avoid this breaking change.\r\n\r\nFor example it could do something like that in `get_data_patterns_locally`\r\n\r\n```python\r\n Input:\r\n\r\n my_dataset_repository/\r\n ├── README.md\r\n ├── banner.png\r\n ├── data0.csv\r\n ├── data1.csv\r\n └── data2.csv\r\n\r\n Output:\r\n\r\n {\"train\": [\".csv\"]}\r\n```\r\n\r\ninstead of \r\n\r\n```python\r\n Output:\r\n\r\n {\"train\": [\"\"]}\r\n```", "I agree with @lhoestq - it should still be possible to request parsing a file with a specific builder even if the file's extension is \"invalid\" for the builder, and only ignore non-supported file formats when inferring the patterns.", "Therefore, if I understand correctly, what you suggest is:\r\n- if the user passes a packaged builder to `load_dataset` (e.g. `load_dataset(\"csv\",...`), then the passed `data_files` should not be filtered to remove unsupported extensions. No breaking change in this case\r\n- if the user passes a no-script repo/folder to `load_dataset` (e.g. `load_dataset(\"my_dataset_repository\",...`), then the inferred data files should be filtered to remove the extensions that are not supported by the inferred module name builder\r\n - if the user passes `data_files` as well, then I guess these should not be filtered, to avoid any breaking change as in the first case above", "Yes that would be ideal imo !", "I think this now fulfills all the requirements.", "I find it a bit confusing to still be able to pass data_files that are going to be silently ignored based on the value of `only_supported_extensions`. My suggestion was to have the right data files pattern, not to filter a posteriori (sorry if my last message was confusing).\r\n\r\nHaving the right data files pattern would also allow users to inspect what's actually being loaded with\r\n```\r\nload_dataset_builder(...).config.data_files\r\n```\r\nand it would list exactly what data files are used." ]	2023-05-12T13:52:34Z	2023-06-07T12:26:38Z	null	MEMBER	null	This PR makes packaged builders skip non-supported file formats: - Csv builder skips non-CSV files - Analogously for the other builders Fix #5849.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5850/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5850/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5850.diff", "html_url": "https://github.com/huggingface/datasets/pull/5850", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5850.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5850" }	true
https://api.github.com/repos/huggingface/datasets/issues/5849	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5849/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5849/comments	https://api.github.com/repos/huggingface/datasets/issues/5849/events	https://github.com/huggingface/datasets/issues/5849	1,707,551,511	I_kwDODunzps5lxysX	5,849	CSV datasets should only read the CSV data files in the repo	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	[]	2023-05-12T12:29:53Z	2023-05-12T12:31:22Z	null	MEMBER	null	When a no-script dataset has many CSV files and a JPG file, the library infers to use the Csv builder, but tries to read as CSV all files in the repo, also the JPG file. I think the Csv builder should filter out non-CSV files when reading. An analogue solution should be implemented for other packaged builders. Related to: - https://huggingface.co/datasets/abidlabs/img2text/discussions/1 - https://github.com/gradio-app/gradio/pull/3973#issuecomment-1545409061 CC: @abidlabs @severo	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/5849/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5849/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5848	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5848/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5848/comments	https://api.github.com/repos/huggingface/datasets/issues/5848/events	https://github.com/huggingface/datasets/pull/5848	1,707,506,734	PR_kwDODunzps5QYa1B	5,848	Add `accelerate` as metric's test dependency to fix CI error	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007565 / 0.011353 (-0.003788) \| 0.005361 / 0.011008 (-0.005647) \| 0.098963 / 0.038508 (0.060455) \| 0.034271 / 0.023109 (0.011162) \| 0.323421 / 0.275898 (0.047523) \| 0.348495 / 0.323480 (0.025015) \| 0.006244 / 0.007986 (-0.001741) \| 0.004215 / 0.004328 (-0.000113) \| 0.073614 / 0.004250 (0.069364) \| 0.049334 / 0.037052 (0.012282) \| 0.315277 / 0.258489 (0.056788) \| 0.354325 / 0.293841 (0.060484) \| 0.035001 / 0.128546 (-0.093545) \| 0.012149 / 0.075646 (-0.063497) \| 0.335614 / 0.419271 (-0.083657) \| 0.050532 / 0.043533 (0.006999) \| 0.308500 / 0.255139 (0.053361) \| 0.324620 / 0.283200 (0.041421) \| 0.110241 / 0.141683 (-0.031442) \| 1.443923 / 1.452155 (-0.008232) \| 1.559289 / 1.492716 (0.066573) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207629 / 0.018006 (0.189622) \| 0.433251 / 0.000490 (0.432762) \| 0.003021 / 0.000200 (0.002821) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028312 / 0.037411 (-0.009100) \| 0.111829 / 0.014526 (0.097303) \| 0.127099 / 0.176557 (-0.049458) \| 0.184702 / 0.737135 (-0.552433) \| 0.125062 / 0.296338 (-0.171277) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399451 / 0.215209 (0.184242) \| 3.966528 / 2.077655 (1.888874) \| 1.826004 / 1.504120 (0.321884) \| 1.669547 / 1.541195 (0.128353) \| 1.751584 / 1.468490 (0.283094) \| 0.688308 / 4.584777 (-3.896469) \| 3.813275 / 3.745712 (0.067562) \| 3.181554 / 5.269862 (-2.088307) \| 1.750566 / 4.565676 (-2.815111) \| 0.085038 / 0.424275 (-0.339237) \| 0.011992 / 0.007607 (0.004385) \| 0.502374 / 0.226044 (0.276330) \| 4.970614 / 2.268929 (2.701686) \| 2.309617 / 55.444624 (-53.135007) \| 2.012427 / 6.876477 (-4.864050) \| 2.156348 / 2.142072 (0.014276) \| 0.834415 / 4.805227 (-3.970812) \| 0.167912 / 6.500664 (-6.332752) \| 0.065711 / 0.075469 (-0.009758) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.223132 / 1.841788 (-0.618656) \| 15.126753 / 8.074308 (7.052445) \| 14.829184 / 10.191392 (4.637792) \| 0.142582 / 0.680424 (-0.537842) \| 0.017483 / 0.534201 (-0.516718) \| 0.429768 / 0.579283 (-0.149516) \| 0.422745 / 0.434364 (-0.011619) \| 0.508813 / 0.540337 (-0.031525) \| 0.618716 / 1.386936 (-0.768220) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007749 / 0.011353 (-0.003604) \| 0.005433 / 0.011008 (-0.005576) \| 0.076223 / 0.038508 (0.037715) \| 0.036334 / 0.023109 (0.013225) \| 0.375339 / 0.275898 (0.099441) \| 0.413674 / 0.323480 (0.090194) \| 0.006207 / 0.007986 (-0.001778) \| 0.004085 / 0.004328 (-0.000244) \| 0.076154 / 0.004250 (0.071904) \| 0.050324 / 0.037052 (0.013271) \| 0.382919 / 0.258489 (0.124429) \| 0.442508 / 0.293841 (0.148667) \| 0.035951 / 0.128546 (-0.092595) \| 0.012067 / 0.075646 (-0.063580) \| 0.087649 / 0.419271 (-0.331623) \| 0.048786 / 0.043533 (0.005253) \| 0.373541 / 0.255139 (0.118402) \| 0.400437 / 0.283200 (0.117237) \| 0.102622 / 0.141683 (-0.039061) \| 1.472443 / 1.452155 (0.020288) \| 1.580178 / 1.492716 (0.087462) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222105 / 0.018006 (0.204098) \| 0.445465 / 0.000490 (0.444975) \| 0.003671 / 0.000200 (0.003471) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030808 / 0.037411 (-0.006603) \| 0.116687 / 0.014526 (0.102161) \| 0.124972 / 0.176557 (-0.051584) \| 0.175621 / 0.737135 (-0.561514) \| 0.129029 / 0.296338 (-0.167310) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434627 / 0.215209 (0.219418) \| 4.330268 / 2.077655 (2.252613) \| 2.140266 / 1.504120 (0.636146) \| 1.960705 / 1.541195 (0.419510) \| 2.035949 / 1.468490 (0.567459) \| 0.696830 / 4.584777 (-3.887947) \| 3.790468 / 3.745712 (0.044756) \| 3.194112 / 5.269862 (-2.075750) \| 1.577728 / 4.565676 (-2.987948) \| 0.085445 / 0.424275 (-0.338830) \| 0.012207 / 0.007607 (0.004600) \| 0.555199 / 0.226044 (0.329154) \| 5.551539 / 2.268929 (3.282610) \| 2.630917 / 55.444624 (-52.813707) \| 2.383362 / 6.876477 (-4.493114) \| 2.476301 / 2.142072 (0.334229) \| 0.845773 / 4.805227 (-3.959455) \| 0.169229 / 6.500664 (-6.331435) \| 0.066064 / 0.075469 (-0.009405) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277543 / 1.841788 (-0.564245) \| 15.775637 / 8.074308 (7.701329) \| 13.528588 / 10.191392 (3.337196) \| 0.167428 / 0.680424 (-0.512996) \| 0.017581 / 0.534201 (-0.516620) \| 0.454472 / 0.579283 (-0.124811) \| 0.427987 / 0.434364 (-0.006377) \| 0.551512 / 0.540337 (0.011175) \| 0.650811 / 1.386936 (-0.736125) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#96a6f5f526cc90330df597ae0097274742d5b84f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009800 / 0.011353 (-0.001552) \| 0.006443 / 0.011008 (-0.004565) \| 0.144137 / 0.038508 (0.105629) \| 0.037493 / 0.023109 (0.014383) \| 0.482306 / 0.275898 (0.206408) \| 0.467625 / 0.323480 (0.144145) \| 0.006812 / 0.007986 (-0.001174) \| 0.004810 / 0.004328 (0.000481) \| 0.109047 / 0.004250 (0.104796) \| 0.047169 / 0.037052 (0.010116) \| 0.451253 / 0.258489 (0.192764) \| 0.511339 / 0.293841 (0.217498) \| 0.055583 / 0.128546 (-0.072963) \| 0.021810 / 0.075646 (-0.053836) \| 0.426522 / 0.419271 (0.007250) \| 0.070282 / 0.043533 (0.026749) \| 0.469631 / 0.255139 (0.214492) \| 0.484951 / 0.283200 (0.201751) \| 0.117370 / 0.141683 (-0.024313) \| 1.809917 / 1.452155 (0.357763) \| 1.882659 / 1.492716 (0.389943) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223843 / 0.018006 (0.205837) \| 0.549216 / 0.000490 (0.548726) \| 0.007120 / 0.000200 (0.006920) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033057 / 0.037411 (-0.004354) \| 0.128242 / 0.014526 (0.113716) \| 0.140906 / 0.176557 (-0.035650) \| 0.213122 / 0.737135 (-0.524013) \| 0.148115 / 0.296338 (-0.148224) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.638712 / 0.215209 (0.423503) \| 6.383684 / 2.077655 (4.306029) \| 2.477020 / 1.504120 (0.972900) \| 2.129190 / 1.541195 (0.587996) \| 2.230503 / 1.468490 (0.762013) \| 1.367167 / 4.584777 (-3.217610) \| 5.570586 / 3.745712 (1.824873) \| 5.462857 / 5.269862 (0.192996) \| 2.990604 / 4.565676 (-1.575073) \| 0.146543 / 0.424275 (-0.277732) \| 0.016060 / 0.007607 (0.008453) \| 0.812691 / 0.226044 (0.586646) \| 7.928041 / 2.268929 (5.659112) \| 3.329494 / 55.444624 (-52.115130) \| 2.523452 / 6.876477 (-4.353025) \| 2.672374 / 2.142072 (0.530302) \| 1.598554 / 4.805227 (-3.206673) \| 0.284727 / 6.500664 (-6.215937) \| 0.080359 / 0.075469 (0.004889) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.501112 / 1.841788 (-0.340675) \| 17.553644 / 8.074308 (9.479335) \| 22.704062 / 10.191392 (12.512670) \| 0.225575 / 0.680424 (-0.454849) \| 0.026531 / 0.534201 (-0.507670) \| 0.520129 / 0.579283 (-0.059154) \| 0.626220 / 0.434364 (0.191856) \| 0.631740 / 0.540337 (0.091403) \| 0.750611 / 1.386936 (-0.636325) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009866 / 0.011353 (-0.001487) \| 0.005733 / 0.011008 (-0.005275) \| 0.111529 / 0.038508 (0.073021) \| 0.042001 / 0.023109 (0.018891) \| 0.458578 / 0.275898 (0.182680) \| 0.507796 / 0.323480 (0.184316) \| 0.006547 / 0.007986 (-0.001438) \| 0.005611 / 0.004328 (0.001282) \| 0.115321 / 0.004250 (0.111070) \| 0.048741 / 0.037052 (0.011689) \| 0.447611 / 0.258489 (0.189122) \| 0.531830 / 0.293841 (0.237989) \| 0.052176 / 0.128546 (-0.076370) \| 0.022431 / 0.075646 (-0.053216) \| 0.120709 / 0.419271 (-0.298562) \| 0.067301 / 0.043533 (0.023769) \| 0.460577 / 0.255139 (0.205438) \| 0.497805 / 0.283200 (0.214605) \| 0.121830 / 0.141683 (-0.019853) \| 1.876436 / 1.452155 (0.424281) \| 1.983491 / 1.492716 (0.490775) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230982 / 0.018006 (0.212976) \| 0.540643 / 0.000490 (0.540153) \| 0.004646 / 0.000200 (0.004446) \| 0.000131 / 0.000054 (0.000077) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034230 / 0.037411 (-0.003181) \| 0.136454 / 0.014526 (0.121928) \| 0.143370 / 0.176557 (-0.033187) \| 0.206752 / 0.737135 (-0.530384) \| 0.148722 / 0.296338 (-0.147617) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.704667 / 0.215209 (0.489458) \| 7.112079 / 2.077655 (5.034424) \| 3.083916 / 1.504120 (1.579797) \| 2.606388 / 1.541195 (1.065193) \| 2.738505 / 1.468490 (1.270015) \| 1.314897 / 4.584777 (-3.269880) \| 5.764442 / 3.745712 (2.018729) \| 3.491890 / 5.269862 (-1.777972) \| 2.299983 / 4.565676 (-2.265693) \| 0.169655 / 0.424275 (-0.254620) \| 0.015251 / 0.007607 (0.007643) \| 0.977230 / 0.226044 (0.751186) \| 9.697773 / 2.268929 (7.428844) \| 3.826928 / 55.444624 (-51.617697) \| 3.108238 / 6.876477 (-3.768239) \| 3.103242 / 2.142072 (0.961169) \| 1.586645 / 4.805227 (-3.218582) \| 0.287181 / 6.500664 (-6.213483) \| 0.107332 / 0.075469 (0.031863) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.712710 / 1.841788 (-0.129077) \| 19.169403 / 8.074308 (11.095095) \| 21.777301 / 10.191392 (11.585909) \| 0.216918 / 0.680424 (-0.463506) \| 0.026551 / 0.534201 (-0.507650) \| 0.570383 / 0.579283 (-0.008900) \| 0.643885 / 0.434364 (0.209521) \| 0.673906 / 0.540337 (0.133568) \| 0.824573 / 1.386936 (-0.562363) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ead18b6921c9576a3078d2fb685c38f1e1a4b8a \"CML watermark\")\n" ]	2023-05-12T12:01:01Z	2023-05-12T13:48:47Z	2023-05-12T13:39:06Z	CONTRIBUTOR	null	The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently). Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5848/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5848/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5848.diff", "html_url": "https://github.com/huggingface/datasets/pull/5848", "merged_at": "2023-05-12T13:39:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/5848.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5848" }	true
https://api.github.com/repos/huggingface/datasets/issues/5847	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5847/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5847/comments	https://api.github.com/repos/huggingface/datasets/issues/5847/events	https://github.com/huggingface/datasets/issues/5847	1,706,616,634	I_kwDODunzps5luOc6	5,847	Streaming IterableDataset not working with translation pipeline	{ "avatar_url": "https://avatars.githubusercontent.com/u/826841?v=4", "events_url": "https://api.github.com/users/jlquinn/events{/privacy}", "followers_url": "https://api.github.com/users/jlquinn/followers", "following_url": "https://api.github.com/users/jlquinn/following{/other_user}", "gists_url": "https://api.github.com/users/jlquinn/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jlquinn", "id": 826841, "login": "jlquinn", "node_id": "MDQ6VXNlcjgyNjg0MQ==", "organizations_url": "https://api.github.com/users/jlquinn/orgs", "received_events_url": "https://api.github.com/users/jlquinn/received_events", "repos_url": "https://api.github.com/users/jlquinn/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jlquinn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jlquinn/subscriptions", "type": "User", "url": "https://api.github.com/users/jlquinn" }	[]	open	false	null	[]	null	[ "I wasn't sure to file this against transformers or datasets.", "[`KeyDataset`](https://github.com/huggingface/transformers/blob/7f8b909189547944617741d8d3c6c84504701693/src/transformers/pipelines/pt_utils.py#L296) doesn't support iterable datasets, so you either need to implement a version that does (and also indexing nested (translation) fields):\r\n\r\n```python\r\nfrom torch.utils.data import Dataset, IterableDataset\r\n\r\ndef build_key_fetcher(key: str):\r\n def _key_fetcher(item):\r\n for sub_key in key.split(\".\"):\r\n item = item[sub_key]\r\n return item\r\n return _key_fetcher\r\n\r\nclass KeyDataset(Dataset):\r\n def __new__(cls, dataset: Dataset, key: str):\r\n cls = _KeyIterableDataset if isinstance(dataset, IterableDataset) else _KeyMapDataset\r\n self = object.__new__(cls)\r\n self.dataset = dataset\r\n self.key = key\r\n self._key_fetcher = build_key_fetcher(key)\r\n return self\r\n\r\nclass _KeyMapDataset(KeyDataset):\r\n def __getitem__(self, i):\r\n return self._key_fetcher(self.dataset[i])\r\n \r\n def __len__(self):\r\n return len(self.dataset)\r\n\r\n\r\nclass _KeyIterableDataset(KeyDataset):\r\n def __iter__(self):\r\n for ex in self.dataset:\r\n yield self._key_fetcher(ex)\r\n\r\nks = KeyDataset(ds, \"translation.en\")\r\n```\r\n\r\nor use `IterableDataset`'s `map`:\r\n```python\r\ndef fetch_en_translation(ex):\r\n return {\"en\": ex[\"translation\"][\"en\"]}\r\nks = ds.map(fetch_en_translation, remove_columns=ds.column_names) \r\n```\r\n\r\ncc @sgugger: Perhaps the `KeyDataset` + PyTorch `IterableDataset` case should be supported by Transformers", "@mariosasko The map snippet didn't quite work, but gave me enough of a clue to get it working. The following snippet does work:\r\n```\r\ndef en_translation(x):\r\n return {\"en\":x['translation']['en']}\r\nks = ds.map(en_translation, remove_columns=['translation'])\r\ntest=[]\r\nfor x in iter(ks):\r\n test.append(x['en'])\r\nxx= mt(test)\r\nfor x in xx:\r\n print(x)\r\n```\r\n\r\nI tried just returning `x['translation']['en`]` in the helper function instead of the dict, but that didn't give me an iterator over strings that pipeline would work with either.\r\n\r\n\r\nThe snippet as is gives the following error:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/pdb.py\", line 1704, in main\r\n pdb._runscript(mainpyfile)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/pdb.py\", line 1573, in _runscript\r\n self.run(statement)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/bdb.py\", line 580, in run\r\n exec(cmd, globals, locals)\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/jlquinn/models/hf/ende.t5.pipe.py\", line 1, in <module>\r\n from transformers import pipeline\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 335, in __call__\r\n return super().__call__(args, kwargs)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 138, in __call__\r\n result = super().__call__(args, kwargs)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/base.py\", line 1027, in __call__\r\n return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/base.py\", line 1033, in run_single\r\n model_inputs = self.preprocess(inputs, preprocess_params)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 287, in preprocess\r\n return super()._parse_and_tokenize(*args, truncation=truncation)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 100, in _parse_and_tokenize\r\n raise ValueError(\r\nValueError: `args[0]`: <datasets.iterable_dataset.IterableDataset object at 0x7f5fd38ef1c0> have the wrong format. The should be either of type `str` or type `list`\r\nUncaught exception. Entering post mortem debugging\r\nRunning 'cont' or 'step' will restart the program\r\n```\r\n", "So perhaps there's no bug exactly, but I would love to see two things: 1) improve the documentation to better understand what's really getting returned. 2) update the example provided of using transformer pipeline with a dataset to include the oddball case that translation appears to be.", "cc @Narsil ", "Hi,\r\n\r\nfor the original snippet, the issue is that `streaming` datasets are not countable (they have no len) and therefore `KeyDataset` cannot work with them ( KeyDataset is a dataset and therefore requires a length).\r\n\r\nI modified slightly the original snippet to make it work:\r\n\r\n```python\r\nfrom transformers import pipeline\r\nfrom transformers.pipelines.pt_utils import KeyDataset\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(path=\"wmt14\", name=\"fr-en\", split=\"test\", streaming=True)\r\nbs = 1\r\nmt = pipeline(\r\n \"translation_en_to_fr\", model=\"hf-internal-testing/tiny-random-T5ForConditionalGeneration\", batch_size=bs\r\n)\r\n\r\n\r\ndef ks(ds):\r\n for item in ds:\r\n yield item[\"translation\"][\"en\"]\r\n\r\n\r\n# print(f\"{ks}\")\r\nxx = mt(ks(ds))\r\nfor x in xx:\r\n print(x)\r\n```\r\n\r\nThis is what the first example in the docs suggests to use (as it's the most flexible): https://huggingface.co/docs/transformers/v4.29.1/en/pipeline_tutorial#using-pipelines-on-a-dataset\r\n\r\n`KeyDataset` really exists only to get a `sized` dataset to work nicer with `tqdm` for instance.\r\n\r\n@sgugger should we update the docs to remove `KeyDataset` entirely ? (We can add a note to pass manually the length of the data to tqdm so that the progress bar option can still be easy to use ?)\r\n", "Maybe moving `KeyDataset` later on in the guide and specify it's mostly for streaming then? Or is it also necessary for batch_size>1 (which is what the current doc implies)?", "Hmm\r\n\r\nIterator (`yield`) :\r\n- Not countable\r\n- Super flexible\r\n- Cannot use `num_workers>1` (threading requires indexing at the correct location, iterators require to iterate in order,so each thread would iterate over the full thing being genuinely a bad idea)\r\n- Can batch\r\n- tqdm doesn't show a nice progress bar (it has no total)\r\n\r\nKeyDataset (Or any PyTorch like Dataset returning the correct object for the pipeline):\r\n- Countable\r\n- Less flexible (not applicable to datasets with streaming), can only work on single keys. But should be easy to read and write your own (like @mariosasko did)\r\n- Works with `num_workers > 1` (Every worker can fetch exactly what's needed)\r\n- Can batch \r\n- tqdm shows a nice progress bar\r\n\r\nIn the docs, if we update all the examples to use iterators, and include an example with\r\n\r\n```\r\nfor item in tqdm.tqdm(pipe(iterator(), total=len(dataset))))\r\n```\r\n\r\nWe can save the biggest feature that doesn't work out of the box with iterators which is the tqdm progress bar.\r\n\r\n`num_workers>1` we can mention it, but it tends to be an issues only on CPU intensive loads, like image (and maybe audio)\r\n" ]	2023-05-11T21:52:38Z	2023-05-16T15:59:55Z	null	NONE	null	### Describe the bug I'm trying to use a streaming dataset for translation inference to avoid downloading the training data. I'm using a pipeline and a dataset, and following the guidance in the tutorial. Instead I get an exception that IterableDataset has no len(). ### Steps to reproduce the bug CODE: ``` from transformers import pipeline from transformers.pipelines.pt_utils import KeyDataset from datasets import load_dataset ds = load_dataset(path="wmt14", name="fr-en", split="test", streaming=True) bs=1 mt = pipeline("translation_en_to_fr", model="t5-base", batch_size=bs) #print(mt("hello")) THIS WORKS ks = KeyDataset(ds, "translation") print(f"{ks}") xx= mt(ks) for x in xx: print(x) ``` RUN: ``` (watnlp) [jlquinn@bertdev01 hf]$ python ende.t5.pipe.py 2023-05-11 16:48:08.817572: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-05-11 16:48:08.821388: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2023-05-11 16:48:08.821407: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. <transformers.pipelines.pt_utils.KeyDataset object at 0x7f61ed5da9d0> Traceback (most recent call last): File "/home/jlquinn/models/hf/ende.t5.pipe.py", line 11, in <module> for x in xx: File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__ item = next(self.iterator) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__ item = next(self.iterator) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__ data = self._next_data() File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 720, in _next_data index = self._next_index() # may raise StopIteration File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 671, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 247, in __iter__ for idx in self.sampler: File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 76, in __iter__ return iter(range(len(self.data_source))) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 13, in __len__ return len(self.dataset) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 289, in __len__ return len(self.dataset) TypeError: object of type 'IterableDataset' has no len() ``` ### Expected behavior I'm expecting french translations of the english test set to be printed. ### Environment info Run on CPU with no GPU. RHEL 8.7 x86_64 python 3.9.0 transformers 4.17.0 datasets 2.0.0 tokenizers 0.12.1 ``` (watnlp) [jlquinn@bertdev01 hf]$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-372.19.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.0 - PyArrow version: 8.0.0 - Pandas version: 1.4.4 ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5847/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5847/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5851	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5851/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5851/comments	https://api.github.com/repos/huggingface/datasets/issues/5851/events	https://github.com/huggingface/datasets/issues/5851	1,707,907,048	I_kwDODunzps5lzJfo	5,851	Error message not clear in interleaving datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/17240858?v=4", "events_url": "https://api.github.com/users/surya-narayanan/events{/privacy}", "followers_url": "https://api.github.com/users/surya-narayanan/followers", "following_url": "https://api.github.com/users/surya-narayanan/following{/other_user}", "gists_url": "https://api.github.com/users/surya-narayanan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/surya-narayanan", "id": 17240858, "login": "surya-narayanan", "node_id": "MDQ6VXNlcjE3MjQwODU4", "organizations_url": "https://api.github.com/users/surya-narayanan/orgs", "received_events_url": "https://api.github.com/users/surya-narayanan/received_events", "repos_url": "https://api.github.com/users/surya-narayanan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/surya-narayanan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/surya-narayanan/subscriptions", "type": "User", "url": "https://api.github.com/users/surya-narayanan" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" } ]	null	[]	2023-05-11T20:52:13Z	2023-05-23T10:32:59Z	2023-05-23T10:32:59Z	NONE	null	### System Info standard env ### Who can help? _No response_ ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful- ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3 [41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %% ----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted") File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy) [122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]: [123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122) if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)): --> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123) raise ValueError( [125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124) f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects." [126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125) ) [127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]: [128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127) raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.") ValueError: Unable to interleave a with a . Expected a list of Dataset objects or a list of IterableDataset objects. ``` ### Expected behavior the error message should hopefully be more clear	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5851/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5851/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5846	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5846/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5846/comments	https://api.github.com/repos/huggingface/datasets/issues/5846/events	https://github.com/huggingface/datasets/issues/5846	1,706,289,290	I_kwDODunzps5ls-iK	5,846	load_dataset('bigcode/the-stack-dedup', streaming=True) very slow!	{ "avatar_url": "https://avatars.githubusercontent.com/u/4241811?v=4", "events_url": "https://api.github.com/users/tbenthompson/events{/privacy}", "followers_url": "https://api.github.com/users/tbenthompson/followers", "following_url": "https://api.github.com/users/tbenthompson/following{/other_user}", "gists_url": "https://api.github.com/users/tbenthompson/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/tbenthompson", "id": 4241811, "login": "tbenthompson", "node_id": "MDQ6VXNlcjQyNDE4MTE=", "organizations_url": "https://api.github.com/users/tbenthompson/orgs", "received_events_url": "https://api.github.com/users/tbenthompson/received_events", "repos_url": "https://api.github.com/users/tbenthompson/repos", "site_admin": false, "starred_url": "https://api.github.com/users/tbenthompson/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tbenthompson/subscriptions", "type": "User", "url": "https://api.github.com/users/tbenthompson" }	[]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" } ]	null	[ "This is due to the slow resolution of the data files: https://github.com/huggingface/datasets/issues/5537.\r\n\r\nWe plan to switch to `huggingface_hub`'s `HfFileSystem` soon to make the resolution faster (will be up to 20x faster once we merge https://github.com/huggingface/huggingface_hub/pull/1443)\r\n\r\n", "You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.", "> You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.\r\n\r\nThat's unrelated to the problem discussed in this issue. ", "> > You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.\r\n> \r\n> That's unrelated to the problem discussed in this issue.\r\n\r\nSorry, I misunderstood it." ]	2023-05-11T17:58:57Z	2023-05-16T03:23:46Z	null	NONE	null	### Describe the bug Running ``` import datasets ds = datasets.load_dataset('bigcode/the-stack-dedup', streaming=True) ``` takes about 2.5 minutes! I would expect this to be near instantaneous. With other datasets, the runtime is one or two seconds. ### Environment info - `datasets` version: 2.11.0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.10 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5846/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5846/timeline	null	null	null	null	false