Datasets:

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[ "Thanks for reporting! We are working on a fix." ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6757", "html_url": "https://github.com/huggingface/datasets/pull/6757", "diff_url": "https://github.com/huggingface/datasets/pull/6757.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6757.patch", "merged_at": null }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6757). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "On slack it was mentioned that it was actually slower for `datasets`, should we close this one or am I missing something ?", "@lhoestq I converted to draft. Want to make some more tests and will let you know" ]

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[ "You can use `Dataset.from_sql(path_to_sql_file)` already. Though we haven't added the Sql dataset builder to the `_PACKAGED_DATASETS_MODULES` list or in `_EXTENSION_TO_MODULE` to map `.sqlite` to the Sql dataset builder\r\n\r\nThis would allow to load a dataset repository with a `.sqlite` file using `load_dataset` and enable the Dataset Viewer", "Considering `Dataset.from_sql`'s (extremely) low usage, I don't think many users are interested in using this format for their datasets. Also, SQLite files are hard/impossible to stream efficiently and require custom logic to define splits/subsets, so IMO we shouldn't encourage people to use SQLite on the Hub.\r\n\r\n@severo Do you have some real-world examples of datasets published in this format?", "No. Indeed, it seems better to explicitly not support sqlite" ]

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[ "Thanks for reporting @fostiropoulos! I've edited your comment to fix the link to the problematic line.\r\n" ]

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[ "I have the same problem \r\nIt seems that it only appears when you are using GPU \r\nIt seems to work fine with the 2.17 version though", "Same here." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6751). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I was mistaken on the intent of those functions, closing the PR." ]

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[ "Are you using `HF_DATASETS_OFFLINE=1` ?", "> Are you using `HF_DATASETS_OFFLINE=1` ?\r\n\r\nThis doesn't work for me. `datasets=2.18.0`\r\n\r\n`test.py`:\r\n```\r\nimport datasets\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\n\r\nds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n\r\nprint(ds)\r\n```\r\n\r\nrun `python test.py`\r\n```\r\nGenerating dataset afqmc (/home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4)\r\nDownloading and preparing dataset afqmc/default to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nhf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet not found in cache or force_download set to True, downloading to /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b.incomplete\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 240k/240k [00:01<00:00, 178kB/s]\r\nstoring hf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet in cache at /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\ncreating metadata file for /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nGenerating test split\r\nGenerating test split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3861/3861 [00:00<00:00, 3972.00 examples/s]\r\nGenerating train split\r\nGenerating train split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34334/34334 [00:00<00:00, 34355.50 examples/s]\r\nGenerating validation split\r\nGenerating validation split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4316/4316 [00:00<00:00, 4477.00 examples/s]\r\nAll the splits matched successfully.\r\nDataset afqmc downloaded and prepared to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4. Subsequent calls will reuse this data.\r\nDatasetDict({\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 3861\r\n })\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 34334\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 4316\r\n })\r\n})\r\n```\r\n\r\nThen run `HF_DATASETS_OFFLINE=1 python test.py`\r\n```\r\nTraceback (most recent call last):\r\n File \"test.py\", line 9, in <module>\r\n ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2556, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2228, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 1871, in dataset_module_factory\r\n raise ConnectionError(f\"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}\") from None\r\nConnectionError: Couldn't reach the Hugging Face Hub for dataset 'C-MTEB/AFQMC': Offline mode is enabled.\r\n```\r\n\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6749). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005017 / 0.011353 (-0.006336) | 0.002958 / 0.011008 (-0.008050) | 0.063455 / 0.038508 (0.024946) | 0.028206 / 0.023109 (0.005096) | 0.230884 / 0.275898 (-0.045014) | 0.252688 / 0.323480 (-0.070792) | 0.002995 / 0.007986 (-0.004991) | 0.002613 / 0.004328 (-0.001716) | 0.046477 / 0.004250 (0.042226) | 0.040662 / 0.037052 (0.003609) | 0.241824 / 0.258489 (-0.016665) | 0.269063 / 0.293841 (-0.024778) | 0.027336 / 0.128546 (-0.101210) | 0.010614 / 0.075646 (-0.065032) | 0.216087 / 0.419271 (-0.203184) | 0.035667 / 0.043533 (-0.007866) | 0.238657 / 0.255139 (-0.016482) | 0.253433 / 0.283200 (-0.029767) | 0.017433 / 0.141683 (-0.124250) | 1.120856 / 1.452155 (-0.331299) | 1.157415 / 1.492716 (-0.335302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088028 / 0.018006 (0.070022) | 0.277368 / 0.000490 (0.276878) | 0.000204 / 0.000200 (0.000004) | 0.000049 / 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) | 0.017956 / 0.037411 (-0.019455) | 0.061061 / 0.014526 (0.046535) | 0.073323 / 0.176557 (-0.103234) | 0.119254 / 0.737135 (-0.617881) | 0.074308 / 0.296338 (-0.222031) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285118 / 0.215209 (0.069908) | 2.785796 / 2.077655 (0.708142) | 1.476436 / 1.504120 (-0.027684) | 1.356505 / 1.541195 (-0.184690) | 1.362505 / 1.468490 (-0.105985) | 0.554064 / 4.584777 (-4.030713) | 2.395774 / 3.745712 (-1.349938) | 2.713703 / 5.269862 (-2.556159) | 1.701020 / 4.565676 (-2.864657) | 0.062370 / 0.424275 (-0.361905) | 0.004944 / 0.007607 (-0.002663) | 0.327948 / 0.226044 (0.101904) | 3.243739 / 2.268929 (0.974811) | 1.803881 / 55.444624 (-53.640743) | 1.551635 / 6.876477 (-5.324841) | 1.560627 / 2.142072 (-0.581446) | 0.628187 / 4.805227 (-4.177040) | 0.115824 / 6.500664 (-6.384840) | 0.041655 / 0.075469 (-0.033814) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968797 / 1.841788 (-0.872991) | 11.220905 / 8.074308 (3.146597) | 9.322584 / 10.191392 (-0.868808) | 0.139629 / 0.680424 (-0.540795) | 0.013823 / 0.534201 (-0.520378) | 0.286700 / 0.579283 (-0.292583) | 0.263517 / 0.434364 (-0.170847) | 0.341264 / 0.540337 (-0.199074) | 0.418834 / 1.386936 (-0.968102) |\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) | 0.005404 / 0.011353 (-0.005949) | 0.003630 / 0.011008 (-0.007378) | 0.048977 / 0.038508 (0.010469) | 0.029980 / 0.023109 (0.006871) | 0.274671 / 0.275898 (-0.001227) | 0.295671 / 0.323480 (-0.027808) | 0.004230 / 0.007986 (-0.003756) | 0.002656 / 0.004328 (-0.001672) | 0.048603 / 0.004250 (0.044353) | 0.044323 / 0.037052 (0.007271) | 0.286499 / 0.258489 (0.028010) | 0.313199 / 0.293841 (0.019358) | 0.030079 / 0.128546 (-0.098468) | 0.010480 / 0.075646 (-0.065166) | 0.058226 / 0.419271 (-0.361045) | 0.054920 / 0.043533 (0.011387) | 0.274921 / 0.255139 (0.019783) | 0.296559 / 0.283200 (0.013360) | 0.019164 / 0.141683 (-0.122519) | 1.154703 / 1.452155 (-0.297452) | 1.207015 / 1.492716 (-0.285701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089368 / 0.018006 (0.071362) | 0.301196 / 0.000490 (0.300706) | 0.000208 / 0.000200 (0.000008) | 0.000047 / 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) | 0.021355 / 0.037411 (-0.016056) | 0.074688 / 0.014526 (0.060162) | 0.085840 / 0.176557 (-0.090716) | 0.125784 / 0.737135 (-0.611351) | 0.087103 / 0.296338 (-0.209235) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296727 / 0.215209 (0.081518) | 2.884922 / 2.077655 (0.807267) | 1.586515 / 1.504120 (0.082395) | 1.474417 / 1.541195 (-0.066777) | 1.492105 / 1.468490 (0.023615) | 0.570016 / 4.584777 (-4.014761) | 2.435760 / 3.745712 (-1.309952) | 2.657999 / 5.269862 (-2.611863) | 1.740160 / 4.565676 (-2.825516) | 0.063743 / 0.424275 (-0.360532) | 0.005048 / 0.007607 (-0.002559) | 0.341279 / 0.226044 (0.115235) | 3.396185 / 2.268929 (1.127256) | 1.952825 / 55.444624 (-53.491800) | 1.676669 / 6.876477 (-5.199808) | 1.773158 / 2.142072 (-0.368915) | 0.650664 / 4.805227 (-4.154563) | 0.116815 / 6.500664 (-6.383849) | 0.040813 / 0.075469 (-0.034656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999836 / 1.841788 (-0.841952) | 11.854540 / 8.074308 (3.780232) | 10.245516 / 10.191392 (0.054124) | 0.141235 / 0.680424 (-0.539189) | 0.015562 / 0.534201 (-0.518639) | 0.287556 / 0.579283 (-0.291727) | 0.274946 / 0.434364 (-0.159418) | 0.324652 / 0.540337 (-0.215685) | 0.449204 / 1.386936 (-0.937733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed2b406d045349dad16738985c947fe743260710 \"CML watermark\")\n" ]

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[ "As explained in the [docs](https://huggingface.co/docs/datasets/v2.18.0/en/access#slicing), slicing a `Dataset` returns a dictionary that maps its column names to their values. So, `len(dataset[:300])=2` is expected, assuming your dataset has 2 columns (the returned dict has 2 keys, but each value in the dict has 300 items).\r\n` " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6747). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005129 / 0.011353 (-0.006224) | 0.003788 / 0.011008 (-0.007220) | 0.063456 / 0.038508 (0.024948) | 0.029079 / 0.023109 (0.005969) | 0.237228 / 0.275898 (-0.038670) | 0.260554 / 0.323480 (-0.062926) | 0.003090 / 0.007986 (-0.004895) | 0.002730 / 0.004328 (-0.001599) | 0.049040 / 0.004250 (0.044789) | 0.042432 / 0.037052 (0.005380) | 0.256954 / 0.258489 (-0.001535) | 0.285912 / 0.293841 (-0.007929) | 0.027568 / 0.128546 (-0.100978) | 0.010402 / 0.075646 (-0.065245) | 0.206773 / 0.419271 (-0.212499) | 0.035381 / 0.043533 (-0.008152) | 0.243147 / 0.255139 (-0.011992) | 0.259419 / 0.283200 (-0.023781) | 0.019503 / 0.141683 (-0.122180) | 1.145537 / 1.452155 (-0.306618) | 1.204070 / 1.492716 (-0.288646) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092298 / 0.018006 (0.074291) | 0.300042 / 0.000490 (0.299553) | 0.000236 / 0.000200 (0.000036) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018624 / 0.037411 (-0.018788) | 0.063832 / 0.014526 (0.049306) | 0.075849 / 0.176557 (-0.100707) | 0.120919 / 0.737135 (-0.616216) | 0.075878 / 0.296338 (-0.220461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275545 / 0.215209 (0.060336) | 2.706004 / 2.077655 (0.628349) | 1.406398 / 1.504120 (-0.097722) | 1.287154 / 1.541195 (-0.254041) | 1.298278 / 1.468490 (-0.170212) | 0.559763 / 4.584777 (-4.025014) | 2.434104 / 3.745712 (-1.311608) | 2.786338 / 5.269862 (-2.483523) | 1.720951 / 4.565676 (-2.844726) | 0.062082 / 0.424275 (-0.362193) | 0.004931 / 0.007607 (-0.002676) | 0.329998 / 0.226044 (0.103954) | 3.222105 / 2.268929 (0.953176) | 1.777539 / 55.444624 (-53.667085) | 1.533845 / 6.876477 (-5.342632) | 1.520357 / 2.142072 (-0.621715) | 0.638850 / 4.805227 (-4.166377) | 0.116718 / 6.500664 (-6.383946) | 0.042215 / 0.075469 (-0.033254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962791 / 1.841788 (-0.878997) | 11.509889 / 8.074308 (3.435581) | 9.507676 / 10.191392 (-0.683716) | 0.140780 / 0.680424 (-0.539644) | 0.014187 / 0.534201 (-0.520014) | 0.286363 / 0.579283 (-0.292920) | 0.263316 / 0.434364 (-0.171048) | 0.322099 / 0.540337 (-0.218239) | 0.415602 / 1.386936 (-0.971334) |\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) | 0.005175 / 0.011353 (-0.006178) | 0.003631 / 0.011008 (-0.007377) | 0.050277 / 0.038508 (0.011769) | 0.031879 / 0.023109 (0.008770) | 0.269966 / 0.275898 (-0.005933) | 0.297229 / 0.323480 (-0.026251) | 0.004278 / 0.007986 (-0.003707) | 0.002936 / 0.004328 (-0.001393) | 0.048686 / 0.004250 (0.044436) | 0.044262 / 0.037052 (0.007209) | 0.284578 / 0.258489 (0.026089) | 0.313681 / 0.293841 (0.019840) | 0.029064 / 0.128546 (-0.099482) | 0.010700 / 0.075646 (-0.064946) | 0.058366 / 0.419271 (-0.360905) | 0.051341 / 0.043533 (0.007809) | 0.271262 / 0.255139 (0.016123) | 0.290791 / 0.283200 (0.007591) | 0.019044 / 0.141683 (-0.122639) | 1.149514 / 1.452155 (-0.302641) | 1.209277 / 1.492716 (-0.283439) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094879 / 0.018006 (0.076872) | 0.302196 / 0.000490 (0.301707) | 0.000217 / 0.000200 (0.000018) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021715 / 0.037411 (-0.015696) | 0.075122 / 0.014526 (0.060596) | 0.087393 / 0.176557 (-0.089164) | 0.125583 / 0.737135 (-0.611553) | 0.088722 / 0.296338 (-0.207617) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295158 / 0.215209 (0.079949) | 2.930208 / 2.077655 (0.852553) | 1.590197 / 1.504120 (0.086077) | 1.459038 / 1.541195 (-0.082156) | 1.471690 / 1.468490 (0.003200) | 0.570279 / 4.584777 (-4.014498) | 2.456971 / 3.745712 (-1.288741) | 2.675315 / 5.269862 (-2.594547) | 1.750122 / 4.565676 (-2.815554) | 0.062905 / 0.424275 (-0.361370) | 0.005118 / 0.007607 (-0.002489) | 0.344263 / 0.226044 (0.118219) | 3.472460 / 2.268929 (1.203532) | 1.931707 / 55.444624 (-53.512917) | 1.658537 / 6.876477 (-5.217939) | 1.785794 / 2.142072 (-0.356278) | 0.637149 / 4.805227 (-4.168078) | 0.115838 / 6.500664 (-6.384826) | 0.040771 / 0.075469 (-0.034698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.002869 / 1.841788 (-0.838919) | 12.048825 / 8.074308 (3.974517) | 10.407979 / 10.191392 (0.216587) | 0.150300 / 0.680424 (-0.530124) | 0.015299 / 0.534201 (-0.518902) | 0.286277 / 0.579283 (-0.293006) | 0.312186 / 0.434364 (-0.122178) | 0.322633 / 0.540337 (-0.217704) | 0.438431 / 1.386936 (-0.948505) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d5468836fe94e8be1ae093397dd43d4a2503b926 \"CML watermark\")\n" ]

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[ "Hi ! We updated the `allenai/c4` repository to allow people to specify which language to load easily (the the [c4 dataset page](https://huggingface.co/datasets/allenai/c4))\r\n\r\nTo fix this issue you can update `datasets` and remove the mention of the legacy configuration name \"allenai--c4\":\r\n\r\n```python\r\ntraindata = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')\r\nvaldata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')\r\n```" ]

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[ "It's not twitter here" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6743", "html_url": "https://github.com/huggingface/datasets/pull/6743", "diff_url": "https://github.com/huggingface/datasets/pull/6743.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6743.patch", "merged_at": "2024-03-19T20:05:19" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6743). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005013 / 0.011353 (-0.006340) | 0.003228 / 0.011008 (-0.007780) | 0.062763 / 0.038508 (0.024255) | 0.028937 / 0.023109 (0.005828) | 0.240777 / 0.275898 (-0.035121) | 0.266972 / 0.323480 (-0.056508) | 0.003073 / 0.007986 (-0.004913) | 0.002769 / 0.004328 (-0.001560) | 0.049265 / 0.004250 (0.045015) | 0.042061 / 0.037052 (0.005009) | 0.261714 / 0.258489 (0.003225) | 0.284896 / 0.293841 (-0.008944) | 0.027717 / 0.128546 (-0.100829) | 0.010430 / 0.075646 (-0.065216) | 0.209022 / 0.419271 (-0.210249) | 0.035941 / 0.043533 (-0.007591) | 0.246849 / 0.255139 (-0.008290) | 0.263205 / 0.283200 (-0.019994) | 0.019489 / 0.141683 (-0.122193) | 1.102595 / 1.452155 (-0.349559) | 1.170493 / 1.492716 (-0.322223) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093611 / 0.018006 (0.075604) | 0.302041 / 0.000490 (0.301551) | 0.000223 / 0.000200 (0.000023) | 0.000052 / 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) | 0.018720 / 0.037411 (-0.018692) | 0.062199 / 0.014526 (0.047673) | 0.074888 / 0.176557 (-0.101669) | 0.120184 / 0.737135 (-0.616951) | 0.076756 / 0.296338 (-0.219583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287484 / 0.215209 (0.072275) | 2.787777 / 2.077655 (0.710123) | 1.488957 / 1.504120 (-0.015163) | 1.362678 / 1.541195 (-0.178517) | 1.364571 / 1.468490 (-0.103919) | 0.563139 / 4.584777 (-4.021638) | 2.422224 / 3.745712 (-1.323488) | 2.798011 / 5.269862 (-2.471850) | 1.751159 / 4.565676 (-2.814517) | 0.062740 / 0.424275 (-0.361536) | 0.004918 / 0.007607 (-0.002689) | 0.338285 / 0.226044 (0.112240) | 3.316012 / 2.268929 (1.047083) | 1.845975 / 55.444624 (-53.598650) | 1.553187 / 6.876477 (-5.323290) | 1.564582 / 2.142072 (-0.577490) | 0.645987 / 4.805227 (-4.159240) | 0.118216 / 6.500664 (-6.382448) | 0.041243 / 0.075469 (-0.034226) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970265 / 1.841788 (-0.871522) | 11.783152 / 8.074308 (3.708844) | 9.516584 / 10.191392 (-0.674808) | 0.148086 / 0.680424 (-0.532338) | 0.013689 / 0.534201 (-0.520512) | 0.289657 / 0.579283 (-0.289626) | 0.265966 / 0.434364 (-0.168398) | 0.328483 / 0.540337 (-0.211854) | 0.433544 / 1.386936 (-0.953392) |\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) | 0.005235 / 0.011353 (-0.006118) | 0.003515 / 0.011008 (-0.007493) | 0.049484 / 0.038508 (0.010976) | 0.029264 / 0.023109 (0.006154) | 0.278518 / 0.275898 (0.002620) | 0.298948 / 0.323480 (-0.024532) | 0.004308 / 0.007986 (-0.003678) | 0.002751 / 0.004328 (-0.001577) | 0.048952 / 0.004250 (0.044701) | 0.045379 / 0.037052 (0.008327) | 0.292633 / 0.258489 (0.034144) | 0.319405 / 0.293841 (0.025564) | 0.030201 / 0.128546 (-0.098345) | 0.010657 / 0.075646 (-0.064990) | 0.057842 / 0.419271 (-0.361430) | 0.053359 / 0.043533 (0.009826) | 0.281136 / 0.255139 (0.025997) | 0.295388 / 0.283200 (0.012188) | 0.018786 / 0.141683 (-0.122897) | 1.187181 / 1.452155 (-0.264974) | 1.198394 / 1.492716 (-0.294323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093861 / 0.018006 (0.075855) | 0.304019 / 0.000490 (0.303529) | 0.000220 / 0.000200 (0.000020) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021582 / 0.037411 (-0.015829) | 0.075381 / 0.014526 (0.060855) | 0.087886 / 0.176557 (-0.088671) | 0.125078 / 0.737135 (-0.612057) | 0.089339 / 0.296338 (-0.206999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295797 / 0.215209 (0.080588) | 2.912021 / 2.077655 (0.834367) | 1.592191 / 1.504120 (0.088071) | 1.471270 / 1.541195 (-0.069925) | 1.475535 / 1.468490 (0.007045) | 0.564114 / 4.584777 (-4.020663) | 2.442882 / 3.745712 (-1.302830) | 2.679433 / 5.269862 (-2.590428) | 1.752097 / 4.565676 (-2.813579) | 0.062748 / 0.424275 (-0.361527) | 0.005068 / 0.007607 (-0.002539) | 0.345554 / 0.226044 (0.119509) | 3.456929 / 2.268929 (1.188000) | 1.962781 / 55.444624 (-53.481844) | 1.688313 / 6.876477 (-5.188164) | 1.817392 / 2.142072 (-0.324681) | 0.639588 / 4.805227 (-4.165639) | 0.116148 / 6.500664 (-6.384516) | 0.040851 / 0.075469 (-0.034618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.009852 / 1.841788 (-0.831936) | 12.031749 / 8.074308 (3.957440) | 10.305107 / 10.191392 (0.113715) | 0.132960 / 0.680424 (-0.547464) | 0.014779 / 0.534201 (-0.519422) | 0.288903 / 0.579283 (-0.290381) | 0.275417 / 0.434364 (-0.158947) | 0.322628 / 0.540337 (-0.217709) | 0.445060 / 1.386936 (-0.941876) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f234fce40d5ffc96fac5198d8cc89817970d87ee \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6742", "html_url": "https://github.com/huggingface/datasets/pull/6742", "diff_url": "https://github.com/huggingface/datasets/pull/6742.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6742.patch", "merged_at": "2024-03-19T18:15:13" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6742). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005394 / 0.011353 (-0.005959) | 0.003780 / 0.011008 (-0.007228) | 0.063459 / 0.038508 (0.024951) | 0.028883 / 0.023109 (0.005774) | 0.239159 / 0.275898 (-0.036739) | 0.258123 / 0.323480 (-0.065357) | 0.003134 / 0.007986 (-0.004851) | 0.003452 / 0.004328 (-0.000876) | 0.049255 / 0.004250 (0.045005) | 0.042727 / 0.037052 (0.005675) | 0.257387 / 0.258489 (-0.001102) | 0.280762 / 0.293841 (-0.013079) | 0.027921 / 0.128546 (-0.100625) | 0.010867 / 0.075646 (-0.064779) | 0.207878 / 0.419271 (-0.211393) | 0.036003 / 0.043533 (-0.007530) | 0.247457 / 0.255139 (-0.007682) | 0.260231 / 0.283200 (-0.022969) | 0.019741 / 0.141683 (-0.121942) | 1.143645 / 1.452155 (-0.308510) | 1.188789 / 1.492716 (-0.303927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092065 / 0.018006 (0.074059) | 0.286021 / 0.000490 (0.285531) | 0.000220 / 0.000200 (0.000020) | 0.000048 / 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) | 0.018934 / 0.037411 (-0.018477) | 0.062474 / 0.014526 (0.047949) | 0.073384 / 0.176557 (-0.103172) | 0.121276 / 0.737135 (-0.615860) | 0.077792 / 0.296338 (-0.218546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285352 / 0.215209 (0.070143) | 2.783110 / 2.077655 (0.705456) | 1.487983 / 1.504120 (-0.016137) | 1.364264 / 1.541195 (-0.176930) | 1.388757 / 1.468490 (-0.079733) | 0.568347 / 4.584777 (-4.016430) | 2.402451 / 3.745712 (-1.343261) | 2.835577 / 5.269862 (-2.434285) | 1.754853 / 4.565676 (-2.810824) | 0.063355 / 0.424275 (-0.360920) | 0.005010 / 0.007607 (-0.002598) | 0.332061 / 0.226044 (0.106016) | 3.287121 / 2.268929 (1.018193) | 1.829520 / 55.444624 (-53.615104) | 1.542669 / 6.876477 (-5.333808) | 1.560679 / 2.142072 (-0.581393) | 0.642371 / 4.805227 (-4.162856) | 0.118636 / 6.500664 (-6.382028) | 0.042262 / 0.075469 (-0.033207) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984803 / 1.841788 (-0.856985) | 11.578044 / 8.074308 (3.503735) | 9.383428 / 10.191392 (-0.807964) | 0.141367 / 0.680424 (-0.539057) | 0.014047 / 0.534201 (-0.520154) | 0.291505 / 0.579283 (-0.287778) | 0.270199 / 0.434364 (-0.164165) | 0.329874 / 0.540337 (-0.210463) | 0.429386 / 1.386936 (-0.957550) |\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) | 0.005322 / 0.011353 (-0.006031) | 0.004023 / 0.011008 (-0.006986) | 0.050126 / 0.038508 (0.011618) | 0.029937 / 0.023109 (0.006828) | 0.275985 / 0.275898 (0.000087) | 0.297965 / 0.323480 (-0.025515) | 0.004429 / 0.007986 (-0.003557) | 0.002729 / 0.004328 (-0.001599) | 0.048995 / 0.004250 (0.044744) | 0.044940 / 0.037052 (0.007888) | 0.288397 / 0.258489 (0.029908) | 0.317716 / 0.293841 (0.023875) | 0.029705 / 0.128546 (-0.098841) | 0.010972 / 0.075646 (-0.064674) | 0.058592 / 0.419271 (-0.360680) | 0.054640 / 0.043533 (0.011108) | 0.276456 / 0.255139 (0.021317) | 0.295119 / 0.283200 (0.011919) | 0.020032 / 0.141683 (-0.121651) | 1.175740 / 1.452155 (-0.276415) | 1.227246 / 1.492716 (-0.265471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092204 / 0.018006 (0.074197) | 0.300344 / 0.000490 (0.299855) | 0.000213 / 0.000200 (0.000013) | 0.000050 / 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) | 0.021540 / 0.037411 (-0.015871) | 0.076252 / 0.014526 (0.061726) | 0.087582 / 0.176557 (-0.088975) | 0.125977 / 0.737135 (-0.611159) | 0.090649 / 0.296338 (-0.205689) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294544 / 0.215209 (0.079335) | 2.883736 / 2.077655 (0.806082) | 1.570932 / 1.504120 (0.066812) | 1.449082 / 1.541195 (-0.092113) | 1.463262 / 1.468490 (-0.005228) | 0.559625 / 4.584777 (-4.025152) | 2.448593 / 3.745712 (-1.297119) | 2.663857 / 5.269862 (-2.606005) | 1.757812 / 4.565676 (-2.807865) | 0.061999 / 0.424275 (-0.362276) | 0.005100 / 0.007607 (-0.002507) | 0.343620 / 0.226044 (0.117575) | 3.487059 / 2.268929 (1.218130) | 1.963078 / 55.444624 (-53.481546) | 1.661758 / 6.876477 (-5.214719) | 1.799130 / 2.142072 (-0.342942) | 0.650194 / 4.805227 (-4.155034) | 0.117375 / 6.500664 (-6.383289) | 0.040957 / 0.075469 (-0.034512) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.037882 / 1.841788 (-0.803906) | 12.239784 / 8.074308 (4.165476) | 10.478186 / 10.191392 (0.286794) | 0.164446 / 0.680424 (-0.515978) | 0.014901 / 0.534201 (-0.519300) | 0.302485 / 0.579283 (-0.276798) | 0.283994 / 0.434364 (-0.150370) | 0.338473 / 0.540337 (-0.201864) | 0.468901 / 1.386936 (-0.918035) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fa934e275d240d9b1228b2f598bc96390299339 \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6741", "html_url": "https://github.com/huggingface/datasets/pull/6741", "diff_url": "https://github.com/huggingface/datasets/pull/6741.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6741.patch", "merged_at": "2024-03-25T16:23:59" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6741). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005093 / 0.011353 (-0.006260) | 0.003317 / 0.011008 (-0.007692) | 0.064795 / 0.038508 (0.026287) | 0.030373 / 0.023109 (0.007263) | 0.258776 / 0.275898 (-0.017122) | 0.269768 / 0.323480 (-0.053711) | 0.004186 / 0.007986 (-0.003799) | 0.002630 / 0.004328 (-0.001699) | 0.048643 / 0.004250 (0.044392) | 0.044220 / 0.037052 (0.007168) | 0.265113 / 0.258489 (0.006624) | 0.292202 / 0.293841 (-0.001639) | 0.027468 / 0.128546 (-0.101079) | 0.010123 / 0.075646 (-0.065523) | 0.226869 / 0.419271 (-0.192402) | 0.035739 / 0.043533 (-0.007794) | 0.253193 / 0.255139 (-0.001946) | 0.271002 / 0.283200 (-0.012198) | 0.017201 / 0.141683 (-0.124482) | 1.105836 / 1.452155 (-0.346318) | 1.161559 / 1.492716 (-0.331158) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090481 / 0.018006 (0.072475) | 0.299013 / 0.000490 (0.298524) | 0.000220 / 0.000200 (0.000020) | 0.000047 / 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) | 0.017684 / 0.037411 (-0.019727) | 0.061580 / 0.014526 (0.047054) | 0.074370 / 0.176557 (-0.102186) | 0.119468 / 0.737135 (-0.617667) | 0.074671 / 0.296338 (-0.221668) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284778 / 0.215209 (0.069569) | 2.780241 / 2.077655 (0.702586) | 1.504025 / 1.504120 (-0.000095) | 1.386644 / 1.541195 (-0.154550) | 1.402038 / 1.468490 (-0.066452) | 0.555180 / 4.584777 (-4.029597) | 2.410973 / 3.745712 (-1.334740) | 2.773252 / 5.269862 (-2.496610) | 1.722784 / 4.565676 (-2.842892) | 0.062773 / 0.424275 (-0.361502) | 0.004959 / 0.007607 (-0.002648) | 0.337163 / 0.226044 (0.111119) | 3.356947 / 2.268929 (1.088019) | 1.880953 / 55.444624 (-53.563671) | 1.556049 / 6.876477 (-5.320427) | 1.578589 / 2.142072 (-0.563483) | 0.641993 / 4.805227 (-4.163234) | 0.118624 / 6.500664 (-6.382040) | 0.042202 / 0.075469 (-0.033268) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.995321 / 1.841788 (-0.846467) | 12.257597 / 8.074308 (4.183289) | 9.646214 / 10.191392 (-0.545178) | 0.131124 / 0.680424 (-0.549300) | 0.014119 / 0.534201 (-0.520082) | 0.287597 / 0.579283 (-0.291686) | 0.266983 / 0.434364 (-0.167381) | 0.328165 / 0.540337 (-0.212173) | 0.422405 / 1.386936 (-0.964531) |\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) | 0.005091 / 0.011353 (-0.006262) | 0.003358 / 0.011008 (-0.007650) | 0.049136 / 0.038508 (0.010628) | 0.031075 / 0.023109 (0.007966) | 0.275047 / 0.275898 (-0.000851) | 0.296845 / 0.323480 (-0.026635) | 0.004949 / 0.007986 (-0.003037) | 0.002586 / 0.004328 (-0.001743) | 0.048164 / 0.004250 (0.043913) | 0.040754 / 0.037052 (0.003702) | 0.288715 / 0.258489 (0.030226) | 0.312383 / 0.293841 (0.018542) | 0.029372 / 0.128546 (-0.099174) | 0.010097 / 0.075646 (-0.065549) | 0.056752 / 0.419271 (-0.362520) | 0.033128 / 0.043533 (-0.010405) | 0.274986 / 0.255139 (0.019847) | 0.292692 / 0.283200 (0.009493) | 0.018309 / 0.141683 (-0.123374) | 1.190320 / 1.452155 (-0.261834) | 1.222529 / 1.492716 (-0.270188) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091717 / 0.018006 (0.073711) | 0.300278 / 0.000490 (0.299788) | 0.000217 / 0.000200 (0.000017) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021394 / 0.037411 (-0.016018) | 0.074918 / 0.014526 (0.060392) | 0.087461 / 0.176557 (-0.089095) | 0.125499 / 0.737135 (-0.611636) | 0.087484 / 0.296338 (-0.208854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296557 / 0.215209 (0.081348) | 2.905527 / 2.077655 (0.827872) | 1.624640 / 1.504120 (0.120520) | 1.505495 / 1.541195 (-0.035700) | 1.514066 / 1.468490 (0.045576) | 0.569376 / 4.584777 (-4.015401) | 2.448575 / 3.745712 (-1.297137) | 2.772805 / 5.269862 (-2.497057) | 1.757287 / 4.565676 (-2.808390) | 0.064209 / 0.424275 (-0.360066) | 0.005688 / 0.007607 (-0.001919) | 0.353175 / 0.226044 (0.127131) | 3.481591 / 2.268929 (1.212662) | 1.995384 / 55.444624 (-53.449240) | 1.684623 / 6.876477 (-5.191854) | 1.675750 / 2.142072 (-0.466323) | 0.644463 / 4.805227 (-4.160764) | 0.115393 / 6.500664 (-6.385271) | 0.040671 / 0.075469 (-0.034799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.037487 / 1.841788 (-0.804301) | 11.902194 / 8.074308 (3.827886) | 10.148579 / 10.191392 (-0.042813) | 0.150261 / 0.680424 (-0.530163) | 0.015001 / 0.534201 (-0.519200) | 0.291008 / 0.579283 (-0.288275) | 0.278758 / 0.434364 (-0.155606) | 0.334037 / 0.540337 (-0.206301) | 0.419942 / 1.386936 (-0.966994) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dcd01046388fc052d37acc5a450bea69e3c57afc \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6739", "html_url": "https://github.com/huggingface/datasets/pull/6739", "diff_url": "https://github.com/huggingface/datasets/pull/6739.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6739.patch", "merged_at": "2024-03-19T15:29:41" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6739). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005295 / 0.011353 (-0.006058) | 0.003402 / 0.011008 (-0.007606) | 0.062860 / 0.038508 (0.024352) | 0.029627 / 0.023109 (0.006518) | 0.238359 / 0.275898 (-0.037539) | 0.262940 / 0.323480 (-0.060540) | 0.003077 / 0.007986 (-0.004909) | 0.002676 / 0.004328 (-0.001652) | 0.048731 / 0.004250 (0.044480) | 0.043989 / 0.037052 (0.006936) | 0.255702 / 0.258489 (-0.002787) | 0.282667 / 0.293841 (-0.011174) | 0.028019 / 0.128546 (-0.100527) | 0.010195 / 0.075646 (-0.065451) | 0.205472 / 0.419271 (-0.213800) | 0.036551 / 0.043533 (-0.006982) | 0.243282 / 0.255139 (-0.011857) | 0.261925 / 0.283200 (-0.021274) | 0.020506 / 0.141683 (-0.121177) | 1.137228 / 1.452155 (-0.314927) | 1.183935 / 1.492716 (-0.308782) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100290 / 0.018006 (0.082284) | 0.316279 / 0.000490 (0.315790) | 0.000239 / 0.000200 (0.000039) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017979 / 0.037411 (-0.019432) | 0.061616 / 0.014526 (0.047090) | 0.072989 / 0.176557 (-0.103568) | 0.118667 / 0.737135 (-0.618468) | 0.074266 / 0.296338 (-0.222072) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287971 / 0.215209 (0.072762) | 2.845235 / 2.077655 (0.767581) | 1.501983 / 1.504120 (-0.002137) | 1.389824 / 1.541195 (-0.151370) | 1.415616 / 1.468490 (-0.052874) | 0.568727 / 4.584777 (-4.016050) | 2.368330 / 3.745712 (-1.377382) | 2.844329 / 5.269862 (-2.425532) | 1.809038 / 4.565676 (-2.756639) | 0.063699 / 0.424275 (-0.360576) | 0.004972 / 0.007607 (-0.002635) | 0.340092 / 0.226044 (0.114048) | 3.369146 / 2.268929 (1.100217) | 1.863423 / 55.444624 (-53.581201) | 1.608334 / 6.876477 (-5.268142) | 1.624479 / 2.142072 (-0.517594) | 0.632439 / 4.805227 (-4.172788) | 0.116862 / 6.500664 (-6.383802) | 0.042558 / 0.075469 (-0.032911) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967922 / 1.841788 (-0.873866) | 11.730612 / 8.074308 (3.656304) | 9.321333 / 10.191392 (-0.870059) | 0.142604 / 0.680424 (-0.537819) | 0.013934 / 0.534201 (-0.520267) | 0.285992 / 0.579283 (-0.293292) | 0.267639 / 0.434364 (-0.166724) | 0.324972 / 0.540337 (-0.215365) | 0.427077 / 1.386936 (-0.959859) |\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) | 0.005806 / 0.011353 (-0.005547) | 0.003771 / 0.011008 (-0.007237) | 0.049542 / 0.038508 (0.011034) | 0.030182 / 0.023109 (0.007073) | 0.303923 / 0.275898 (0.028025) | 0.325623 / 0.323480 (0.002143) | 0.004327 / 0.007986 (-0.003659) | 0.002818 / 0.004328 (-0.001510) | 0.048237 / 0.004250 (0.043987) | 0.047490 / 0.037052 (0.010437) | 0.316556 / 0.258489 (0.058067) | 0.348352 / 0.293841 (0.054512) | 0.029444 / 0.128546 (-0.099102) | 0.010544 / 0.075646 (-0.065102) | 0.057382 / 0.419271 (-0.361890) | 0.056210 / 0.043533 (0.012677) | 0.305495 / 0.255139 (0.050356) | 0.321570 / 0.283200 (0.038370) | 0.019546 / 0.141683 (-0.122137) | 1.141732 / 1.452155 (-0.310423) | 1.223626 / 1.492716 (-0.269091) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093864 / 0.018006 (0.075858) | 0.309715 / 0.000490 (0.309226) | 0.000217 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022047 / 0.037411 (-0.015364) | 0.074885 / 0.014526 (0.060359) | 0.088440 / 0.176557 (-0.088117) | 0.127033 / 0.737135 (-0.610103) | 0.089048 / 0.296338 (-0.207290) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292624 / 0.215209 (0.077415) | 2.877592 / 2.077655 (0.799937) | 1.607036 / 1.504120 (0.102916) | 1.487819 / 1.541195 (-0.053376) | 1.517318 / 1.468490 (0.048828) | 0.553321 / 4.584777 (-4.031456) | 2.415577 / 3.745712 (-1.330135) | 2.691411 / 5.269862 (-2.578450) | 1.743395 / 4.565676 (-2.822282) | 0.062187 / 0.424275 (-0.362088) | 0.005073 / 0.007607 (-0.002534) | 0.342907 / 0.226044 (0.116863) | 3.402054 / 2.268929 (1.133126) | 1.979481 / 55.444624 (-53.465143) | 1.702885 / 6.876477 (-5.173592) | 1.868279 / 2.142072 (-0.273794) | 0.640095 / 4.805227 (-4.165132) | 0.117138 / 6.500664 (-6.383526) | 0.042197 / 0.075469 (-0.033272) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007495 / 1.841788 (-0.834292) | 12.037309 / 8.074308 (3.963001) | 10.227670 / 10.191392 (0.036278) | 0.149533 / 0.680424 (-0.530891) | 0.015282 / 0.534201 (-0.518919) | 0.287357 / 0.579283 (-0.291926) | 0.285109 / 0.434364 (-0.149255) | 0.324027 / 0.540337 (-0.216311) | 0.442482 / 1.386936 (-0.944454) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#19b40860acf3b3ba8db727fcf3b1b99ebb8d7e33 \"CML watermark\")\n" ]

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[ "It looks like a bug, by default every feature should be nullable.", "I've linked a PR with a fix :)", "@mariosasko awesome thank you!" ]

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[ "I couldn't reproduce the issue on my side on MacOS, I guess the issue comes from the recent `fsspec` on Windows.\r\n\r\nCan you try downgrading to `fsspec==2023.9.2` for now ? It would also be great to investigate this and see if we need a fix in `datasets` or `fsspec`" ]

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[ "Hi ! that would be great :) Though note that `datasets` doesn't implement format-specific resuming when streaming, so in general I think it's better if users can use the mosaic-streaming library to read their MDS datasets. I wonder if they support `hf://` paths though...\r\n\r\nAnyway for those interested, the code for WebDataset is a single file here: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py.\r\n\r\nIt implements `_split_generators` that downloads files and returns the lists of splits (train/validation/test) and `_split_generators` to generate examples (dicts) from the downloaded files. Streaming is automatically supported by making download steps lazy and by extending `open()` to work with remote URLs." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6735). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005009 / 0.011353 (-0.006344) | 0.003547 / 0.011008 (-0.007461) | 0.063014 / 0.038508 (0.024506) | 0.027699 / 0.023109 (0.004589) | 0.247140 / 0.275898 (-0.028758) | 0.273610 / 0.323480 (-0.049870) | 0.003115 / 0.007986 (-0.004871) | 0.002712 / 0.004328 (-0.001616) | 0.049134 / 0.004250 (0.044883) | 0.041582 / 0.037052 (0.004530) | 0.269992 / 0.258489 (0.011503) | 0.294516 / 0.293841 (0.000675) | 0.027818 / 0.128546 (-0.100728) | 0.010568 / 0.075646 (-0.065078) | 0.207710 / 0.419271 (-0.211561) | 0.035767 / 0.043533 (-0.007766) | 0.260058 / 0.255139 (0.004919) | 0.277615 / 0.283200 (-0.005585) | 0.020192 / 0.141683 (-0.121491) | 1.116863 / 1.452155 (-0.335292) | 1.156868 / 1.492716 (-0.335848) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095087 / 0.018006 (0.077081) | 0.303249 / 0.000490 (0.302759) | 0.000215 / 0.000200 (0.000015) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018866 / 0.037411 (-0.018545) | 0.063853 / 0.014526 (0.049328) | 0.073863 / 0.176557 (-0.102693) | 0.121399 / 0.737135 (-0.615737) | 0.076014 / 0.296338 (-0.220325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289843 / 0.215209 (0.074634) | 2.844085 / 2.077655 (0.766431) | 1.528022 / 1.504120 (0.023902) | 1.397352 / 1.541195 (-0.143843) | 1.394676 / 1.468490 (-0.073814) | 0.555899 / 4.584777 (-4.028878) | 2.354010 / 3.745712 (-1.391702) | 2.737715 / 5.269862 (-2.532146) | 1.731260 / 4.565676 (-2.834416) | 0.062315 / 0.424275 (-0.361960) | 0.004920 / 0.007607 (-0.002687) | 0.342921 / 0.226044 (0.116877) | 3.416529 / 2.268929 (1.147600) | 1.862941 / 55.444624 (-53.581684) | 1.599661 / 6.876477 (-5.276816) | 1.617200 / 2.142072 (-0.524873) | 0.635129 / 4.805227 (-4.170099) | 0.121651 / 6.500664 (-6.379013) | 0.041867 / 0.075469 (-0.033602) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990825 / 1.841788 (-0.850962) | 11.435576 / 8.074308 (3.361268) | 9.490194 / 10.191392 (-0.701198) | 0.133295 / 0.680424 (-0.547129) | 0.014061 / 0.534201 (-0.520140) | 0.288648 / 0.579283 (-0.290635) | 0.268874 / 0.434364 (-0.165490) | 0.323288 / 0.540337 (-0.217049) | 0.426090 / 1.386936 (-0.960846) |\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) | 0.006712 / 0.011353 (-0.004641) | 0.003723 / 0.011008 (-0.007285) | 0.049814 / 0.038508 (0.011306) | 0.039323 / 0.023109 (0.016213) | 0.279244 / 0.275898 (0.003346) | 0.297139 / 0.323480 (-0.026341) | 0.004197 / 0.007986 (-0.003788) | 0.002753 / 0.004328 (-0.001576) | 0.048820 / 0.004250 (0.044569) | 0.049593 / 0.037052 (0.012541) | 0.287247 / 0.258489 (0.028758) | 0.338078 / 0.293841 (0.044237) | 0.029303 / 0.128546 (-0.099243) | 0.010292 / 0.075646 (-0.065354) | 0.057852 / 0.419271 (-0.361419) | 0.053390 / 0.043533 (0.009857) | 0.275155 / 0.255139 (0.020016) | 0.292891 / 0.283200 (0.009692) | 0.020007 / 0.141683 (-0.121676) | 1.161731 / 1.452155 (-0.290424) | 1.232162 / 1.492716 (-0.260555) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092848 / 0.018006 (0.074842) | 0.301180 / 0.000490 (0.300690) | 0.000236 / 0.000200 (0.000036) | 0.000050 / 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) | 0.022477 / 0.037411 (-0.014934) | 0.077012 / 0.014526 (0.062486) | 0.087335 / 0.176557 (-0.089222) | 0.126761 / 0.737135 (-0.610374) | 0.089249 / 0.296338 (-0.207090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290722 / 0.215209 (0.075513) | 2.884485 / 2.077655 (0.806830) | 1.565775 / 1.504120 (0.061656) | 1.442369 / 1.541195 (-0.098825) | 1.453995 / 1.468490 (-0.014495) | 0.563193 / 4.584777 (-4.021584) | 2.413610 / 3.745712 (-1.332102) | 2.684567 / 5.269862 (-2.585295) | 1.753322 / 4.565676 (-2.812354) | 0.061879 / 0.424275 (-0.362396) | 0.005080 / 0.007607 (-0.002527) | 0.347274 / 0.226044 (0.121229) | 3.435836 / 2.268929 (1.166907) | 1.937893 / 55.444624 (-53.506731) | 1.657824 / 6.876477 (-5.218653) | 1.777767 / 2.142072 (-0.364305) | 0.656757 / 4.805227 (-4.148471) | 0.117144 / 6.500664 (-6.383520) | 0.040691 / 0.075469 (-0.034778) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012435 / 1.841788 (-0.829353) | 12.038001 / 8.074308 (3.963693) | 10.363947 / 10.191392 (0.172555) | 0.140711 / 0.680424 (-0.539713) | 0.014937 / 0.534201 (-0.519264) | 0.291070 / 0.579283 (-0.288213) | 0.277180 / 0.434364 (-0.157184) | 0.327433 / 0.540337 (-0.212904) | 0.439767 / 1.386936 (-0.947169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b55ec53e980855d71ae22f8b3d12b2a0d476a51 \"CML watermark\")\n" ]

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[ "Hi ! First note that if the dataset is not heterogeneous / shuffled, there might be places in the data with shorter texts that are faster to tokenize.\r\n\r\nMoreover, the way `num_proc` works is by slicing the dataset and passing each slice to a process to run the `map()` function. So at the very end of `map()`, some processes might have finished transforming their slice of data while others are still running, causing the throughput to become lower.", "I did see some comments about how num_proc=None could help and outputting numpy arrays can also help in the docs, but this seems quite odd now dropping down to 1it/s\r\n\r\n```bash\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46048888/46390354 [12:33:30<4:20:32, 21.84 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46049888/46390354 [12:36:11<8:37:59, 10.95 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46050888/46390354 [12:46:35<24:56:56, 3.78 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46051888/46390354 [12:56:43<35:08:10, 2.68 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46052888/46390354 [13:06:58<42:05:41, 2.23 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46053888/46390354 [13:16:01<44:40:18, 2.09 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46054888/46390354 [13:25:11<46:35:28, 2.00 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46055888/46390354 [13:34:23<47:55:34, 1.94 examples/s]\r\n```\r\n\r\n" ]

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[ "Hi! `datasets` is not compatible with `huggingface_hub`'s cache structure, hence the error.\r\n\r\nYou can track https://github.com/huggingface/datasets/issues/5080 to get notified when this is implemented." ]

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[ "This is normal behavior in python when using `lambda`: the `i` defined in your `lambda` refers to the global variable `i` in your loop, and `i` equals to `1` when you run your `for e in res[0]` line.\r\n\r\nYou should pass `fn_kwargs` that will be passed to your `lambda` instead of using the global variable:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nres=[]\r\nfor i in [0,1]:\r\n di = load_dataset(\r\n \"json\", \r\n data_files='path_to.json', \r\n split='train',\r\n streaming=True, \r\n ).map(lambda x, source: {\"source\": source}, fn_kwargs={\"source\": i})\r\n\r\n res.append(di)\r\n\r\nfor e in res[0]:\r\n print(e)\r\n```\r\n\r\nThis doesn't happen in non-streaming since in that case `map` is executed while the variable `i` has the right value. In streaming mode, `map` is executed on-the-fly when you iterate on the dataset." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6730). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005301 / 0.011353 (-0.006052) | 0.003701 / 0.011008 (-0.007307) | 0.065830 / 0.038508 (0.027322) | 0.029791 / 0.023109 (0.006682) | 0.251676 / 0.275898 (-0.024222) | 0.283824 / 0.323480 (-0.039655) | 0.003083 / 0.007986 (-0.004903) | 0.004144 / 0.004328 (-0.000185) | 0.053670 / 0.004250 (0.049419) | 0.042020 / 0.037052 (0.004968) | 0.266389 / 0.258489 (0.007899) | 0.296740 / 0.293841 (0.002900) | 0.028320 / 0.128546 (-0.100226) | 0.010604 / 0.075646 (-0.065042) | 0.219881 / 0.419271 (-0.199390) | 0.036216 / 0.043533 (-0.007317) | 0.255718 / 0.255139 (0.000579) | 0.275808 / 0.283200 (-0.007392) | 0.018407 / 0.141683 (-0.123276) | 1.140007 / 1.452155 (-0.312148) | 1.174005 / 1.492716 (-0.318711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091230 / 0.018006 (0.073224) | 0.300704 / 0.000490 (0.300215) | 0.000207 / 0.000200 (0.000007) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018950 / 0.037411 (-0.018461) | 0.062177 / 0.014526 (0.047651) | 0.073968 / 0.176557 (-0.102589) | 0.122161 / 0.737135 (-0.614974) | 0.075001 / 0.296338 (-0.221338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285675 / 0.215209 (0.070466) | 2.794176 / 2.077655 (0.716522) | 1.478666 / 1.504120 (-0.025454) | 1.361843 / 1.541195 (-0.179351) | 1.383847 / 1.468490 (-0.084643) | 0.568610 / 4.584777 (-4.016167) | 2.402351 / 3.745712 (-1.343361) | 2.860772 / 5.269862 (-2.409089) | 1.768588 / 4.565676 (-2.797089) | 0.063257 / 0.424275 (-0.361018) | 0.004998 / 0.007607 (-0.002609) | 0.340897 / 0.226044 (0.114853) | 3.340238 / 2.268929 (1.071310) | 1.836434 / 55.444624 (-53.608190) | 1.556844 / 6.876477 (-5.319633) | 1.610685 / 2.142072 (-0.531388) | 0.644941 / 4.805227 (-4.160286) | 0.117593 / 6.500664 (-6.383072) | 0.042803 / 0.075469 (-0.032666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979181 / 1.841788 (-0.862607) | 11.901365 / 8.074308 (3.827057) | 9.587943 / 10.191392 (-0.603449) | 0.139648 / 0.680424 (-0.540776) | 0.013904 / 0.534201 (-0.520297) | 0.291249 / 0.579283 (-0.288034) | 0.260737 / 0.434364 (-0.173627) | 0.326000 / 0.540337 (-0.214338) | 0.433459 / 1.386936 (-0.953477) |\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) | 0.005503 / 0.011353 (-0.005850) | 0.003738 / 0.011008 (-0.007270) | 0.049137 / 0.038508 (0.010629) | 0.031484 / 0.023109 (0.008374) | 0.265783 / 0.275898 (-0.010115) | 0.295125 / 0.323480 (-0.028354) | 0.004074 / 0.007986 (-0.003911) | 0.002707 / 0.004328 (-0.001622) | 0.048340 / 0.004250 (0.044089) | 0.045453 / 0.037052 (0.008401) | 0.276500 / 0.258489 (0.018011) | 0.312002 / 0.293841 (0.018162) | 0.029139 / 0.128546 (-0.099408) | 0.010445 / 0.075646 (-0.065201) | 0.057486 / 0.419271 (-0.361785) | 0.052386 / 0.043533 (0.008853) | 0.267099 / 0.255139 (0.011960) | 0.283193 / 0.283200 (-0.000007) | 0.018368 / 0.141683 (-0.123315) | 1.136207 / 1.452155 (-0.315948) | 1.178418 / 1.492716 (-0.314298) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089270 / 0.018006 (0.071264) | 0.301087 / 0.000490 (0.300598) | 0.000208 / 0.000200 (0.000008) | 0.000050 / 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) | 0.021991 / 0.037411 (-0.015421) | 0.075357 / 0.014526 (0.060831) | 0.087781 / 0.176557 (-0.088775) | 0.126923 / 0.737135 (-0.610212) | 0.088491 / 0.296338 (-0.207847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293653 / 0.215209 (0.078444) | 2.872156 / 2.077655 (0.794501) | 1.559229 / 1.504120 (0.055109) | 1.441201 / 1.541195 (-0.099993) | 1.472642 / 1.468490 (0.004152) | 0.588463 / 4.584777 (-3.996314) | 2.447685 / 3.745712 (-1.298028) | 2.755752 / 5.269862 (-2.514110) | 1.796591 / 4.565676 (-2.769086) | 0.068024 / 0.424275 (-0.356252) | 0.005148 / 0.007607 (-0.002459) | 0.343572 / 0.226044 (0.117528) | 3.347856 / 2.268929 (1.078927) | 1.945977 / 55.444624 (-53.498647) | 1.648953 / 6.876477 (-5.227524) | 1.804468 / 2.142072 (-0.337604) | 0.651034 / 4.805227 (-4.154193) | 0.118130 / 6.500664 (-6.382534) | 0.041019 / 0.075469 (-0.034450) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.020461 / 1.841788 (-0.821327) | 12.514237 / 8.074308 (4.439929) | 10.696276 / 10.191392 (0.504884) | 0.154549 / 0.680424 (-0.525874) | 0.015964 / 0.534201 (-0.518237) | 0.290392 / 0.579283 (-0.288891) | 0.276074 / 0.434364 (-0.158290) | 0.326253 / 0.540337 (-0.214085) | 0.440383 / 1.386936 (-0.946553) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29ffc270da34de70cf8e28b2ebeadba1c06d8730 \"CML watermark\")\n" ]

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[ "Through debugging, I found a potential solution is to modify the code in the error handling module of `huggingface_hub`: https://github.com/huggingface/huggingface_hub/commit/56d6c798c44e83d2a3167e74c022737d8fcbe822 ", "@Wauplin ", "Thanks for investigating and reporting the bug @padeoe! I've opened a PR in `huggingface_hub` with your suggested fix! :) https://github.com/huggingface/huggingface_hub/pull/2119" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6727). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "looks like some files are missing in your google storage", "cc @mariosasko is it related to https://github.com/huggingface/datasets/pull/6474 ? The files should ideally not move for backward compatibility anyway", "@lhoestq All the files are still there.\r\n\r\nThe problem is that the `natural_questions` is now a no-code dataset, so the test's paths are no longer correct (unless the revision is pinned to the previous version). \r\n\r\n@psmyth94 This has been fixed on `main`, so you can make the CI tests green with the following:\r\n```python\r\ngit remote add upstream https://github.com/huggingface/datasets.git\r\ngit pull upstream main\r\ngit push\r\n```", "Thank you @mariosasko ! I'm updating this branch if you don't mind @psmyth94 ", "<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) | 0.004903 / 0.011353 (-0.006450) | 0.003105 / 0.011008 (-0.007903) | 0.061980 / 0.038508 (0.023471) | 0.029726 / 0.023109 (0.006617) | 0.243406 / 0.275898 (-0.032492) | 0.262530 / 0.323480 (-0.060950) | 0.003905 / 0.007986 (-0.004081) | 0.002617 / 0.004328 (-0.001712) | 0.047851 / 0.004250 (0.043601) | 0.040397 / 0.037052 (0.003345) | 0.259461 / 0.258489 (0.000972) | 0.285059 / 0.293841 (-0.008782) | 0.027321 / 0.128546 (-0.101225) | 0.009876 / 0.075646 (-0.065770) | 0.206999 / 0.419271 (-0.212273) | 0.034906 / 0.043533 (-0.008626) | 0.245120 / 0.255139 (-0.010019) | 0.270490 / 0.283200 (-0.012710) | 0.017341 / 0.141683 (-0.124342) | 1.128182 / 1.452155 (-0.323973) | 1.173024 / 1.492716 (-0.319693) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089337 / 0.018006 (0.071331) | 0.298256 / 0.000490 (0.297767) | 0.000216 / 0.000200 (0.000016) | 0.000047 / 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) | 0.018179 / 0.037411 (-0.019233) | 0.061275 / 0.014526 (0.046749) | 0.073137 / 0.176557 (-0.103419) | 0.119603 / 0.737135 (-0.617532) | 0.073969 / 0.296338 (-0.222370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283109 / 0.215209 (0.067900) | 2.765441 / 2.077655 (0.687787) | 1.471276 / 1.504120 (-0.032844) | 1.346365 / 1.541195 (-0.194830) | 1.360668 / 1.468490 (-0.107822) | 0.549947 / 4.584777 (-4.034830) | 2.344213 / 3.745712 (-1.401499) | 2.700905 / 5.269862 (-2.568956) | 1.689936 / 4.565676 (-2.875741) | 0.061985 / 0.424275 (-0.362290) | 0.004923 / 0.007607 (-0.002684) | 0.329833 / 0.226044 (0.103788) | 3.277580 / 2.268929 (1.008652) | 1.833987 / 55.444624 (-53.610638) | 1.571023 / 6.876477 (-5.305454) | 1.573259 / 2.142072 (-0.568813) | 0.627504 / 4.805227 (-4.177723) | 0.114106 / 6.500664 (-6.386558) | 0.041197 / 0.075469 (-0.034272) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967400 / 1.841788 (-0.874388) | 11.046527 / 8.074308 (2.972219) | 9.542214 / 10.191392 (-0.649178) | 0.140745 / 0.680424 (-0.539679) | 0.013627 / 0.534201 (-0.520574) | 0.288429 / 0.579283 (-0.290855) | 0.260509 / 0.434364 (-0.173855) | 0.324704 / 0.540337 (-0.215633) | 0.419366 / 1.386936 (-0.967570) |\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) | 0.005123 / 0.011353 (-0.006230) | 0.003119 / 0.011008 (-0.007890) | 0.048931 / 0.038508 (0.010423) | 0.032067 / 0.023109 (0.008958) | 0.276825 / 0.275898 (0.000927) | 0.297589 / 0.323480 (-0.025890) | 0.004075 / 0.007986 (-0.003911) | 0.002579 / 0.004328 (-0.001750) | 0.047862 / 0.004250 (0.043612) | 0.044032 / 0.037052 (0.006980) | 0.289469 / 0.258489 (0.030980) | 0.327269 / 0.293841 (0.033428) | 0.029369 / 0.128546 (-0.099177) | 0.010180 / 0.075646 (-0.065466) | 0.057111 / 0.419271 (-0.362161) | 0.051046 / 0.043533 (0.007513) | 0.276758 / 0.255139 (0.021619) | 0.296084 / 0.283200 (0.012884) | 0.017376 / 0.141683 (-0.124306) | 1.154486 / 1.452155 (-0.297669) | 1.192699 / 1.492716 (-0.300018) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.085981 / 0.018006 (0.067974) | 0.296956 / 0.000490 (0.296466) | 0.000211 / 0.000200 (0.000011) | 0.000050 / 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) | 0.021239 / 0.037411 (-0.016172) | 0.074851 / 0.014526 (0.060326) | 0.085676 / 0.176557 (-0.090881) | 0.125876 / 0.737135 (-0.611259) | 0.087573 / 0.296338 (-0.208765) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289220 / 0.215209 (0.074011) | 2.812342 / 2.077655 (0.734688) | 1.572886 / 1.504120 (0.068766) | 1.446442 / 1.541195 (-0.094752) | 1.458737 / 1.468490 (-0.009753) | 0.562010 / 4.584777 (-4.022767) | 2.422896 / 3.745712 (-1.322816) | 2.578408 / 5.269862 (-2.691454) | 1.689998 / 4.565676 (-2.875678) | 0.064782 / 0.424275 (-0.359493) | 0.005051 / 0.007607 (-0.002556) | 0.339982 / 0.226044 (0.113938) | 3.309882 / 2.268929 (1.040953) | 1.910273 / 55.444624 (-53.534351) | 1.649723 / 6.876477 (-5.226753) | 1.744073 / 2.142072 (-0.397999) | 0.651905 / 4.805227 (-4.153323) | 0.114606 / 6.500664 (-6.386058) | 0.040030 / 0.075469 (-0.035439) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.008374 / 1.841788 (-0.833414) | 11.547300 / 8.074308 (3.472992) | 9.966061 / 10.191392 (-0.225331) | 0.144874 / 0.680424 (-0.535550) | 0.014400 / 0.534201 (-0.519801) | 0.285435 / 0.579283 (-0.293848) | 0.274755 / 0.434364 (-0.159609) | 0.323105 / 0.540337 (-0.217232) | 0.439172 / 1.386936 (-0.947764) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4591ac120e9d6c082b2479d2005c04b9c36f539c \"CML watermark\")\n" ]

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[ "FWIW I debugged this while waiting for it to go", "Oh I forgot to mention you can also cache resolve_pattern, and that seemed to also substantially improves things, if you want to load a dataset twice for whatever reason." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6723). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005658 / 0.011353 (-0.005694) | 0.003883 / 0.011008 (-0.007125) | 0.064007 / 0.038508 (0.025499) | 0.030370 / 0.023109 (0.007261) | 0.246677 / 0.275898 (-0.029221) | 0.270846 / 0.323480 (-0.052634) | 0.003102 / 0.007986 (-0.004884) | 0.002931 / 0.004328 (-0.001397) | 0.049446 / 0.004250 (0.045196) | 0.043555 / 0.037052 (0.006503) | 0.261810 / 0.258489 (0.003321) | 0.289705 / 0.293841 (-0.004136) | 0.028676 / 0.128546 (-0.099870) | 0.010778 / 0.075646 (-0.064868) | 0.210604 / 0.419271 (-0.208667) | 0.035987 / 0.043533 (-0.007546) | 0.248034 / 0.255139 (-0.007105) | 0.265019 / 0.283200 (-0.018181) | 0.018522 / 0.141683 (-0.123161) | 1.096364 / 1.452155 (-0.355791) | 1.152750 / 1.492716 (-0.339966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093987 / 0.018006 (0.075981) | 0.306143 / 0.000490 (0.305653) | 0.000218 / 0.000200 (0.000018) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018727 / 0.037411 (-0.018685) | 0.061983 / 0.014526 (0.047457) | 0.074254 / 0.176557 (-0.102303) | 0.121256 / 0.737135 (-0.615880) | 0.076756 / 0.296338 (-0.219582) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278824 / 0.215209 (0.063615) | 2.815960 / 2.077655 (0.738305) | 1.472946 / 1.504120 (-0.031174) | 1.349722 / 1.541195 (-0.191473) | 1.327844 / 1.468490 (-0.140646) | 0.574964 / 4.584777 (-4.009813) | 2.403458 / 3.745712 (-1.342254) | 2.769293 / 5.269862 (-2.500569) | 1.736970 / 4.565676 (-2.828706) | 0.063144 / 0.424275 (-0.361131) | 0.004983 / 0.007607 (-0.002625) | 0.331212 / 0.226044 (0.105168) | 3.231496 / 2.268929 (0.962567) | 1.798487 / 55.444624 (-53.646138) | 1.523010 / 6.876477 (-5.353467) | 1.559973 / 2.142072 (-0.582099) | 0.657036 / 4.805227 (-4.148191) | 0.119084 / 6.500664 (-6.381580) | 0.042982 / 0.075469 (-0.032487) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976433 / 1.841788 (-0.865355) | 11.475946 / 8.074308 (3.401638) | 9.339369 / 10.191392 (-0.852023) | 0.141761 / 0.680424 (-0.538662) | 0.014506 / 0.534201 (-0.519695) | 0.289944 / 0.579283 (-0.289340) | 0.273667 / 0.434364 (-0.160697) | 0.326682 / 0.540337 (-0.213655) | 0.458946 / 1.386936 (-0.927990) |\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) | 0.005194 / 0.011353 (-0.006159) | 0.003713 / 0.011008 (-0.007295) | 0.049297 / 0.038508 (0.010789) | 0.029723 / 0.023109 (0.006614) | 0.278664 / 0.275898 (0.002766) | 0.296387 / 0.323480 (-0.027093) | 0.004215 / 0.007986 (-0.003771) | 0.002680 / 0.004328 (-0.001648) | 0.048276 / 0.004250 (0.044025) | 0.044454 / 0.037052 (0.007402) | 0.290510 / 0.258489 (0.032021) | 0.319028 / 0.293841 (0.025187) | 0.029177 / 0.128546 (-0.099369) | 0.010361 / 0.075646 (-0.065285) | 0.056993 / 0.419271 (-0.362279) | 0.050765 / 0.043533 (0.007232) | 0.278234 / 0.255139 (0.023095) | 0.295848 / 0.283200 (0.012649) | 0.018776 / 0.141683 (-0.122906) | 1.134866 / 1.452155 (-0.317288) | 1.204083 / 1.492716 (-0.288634) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094397 / 0.018006 (0.076391) | 0.304693 / 0.000490 (0.304203) | 0.000207 / 0.000200 (0.000007) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021322 / 0.037411 (-0.016090) | 0.075384 / 0.014526 (0.060859) | 0.086961 / 0.176557 (-0.089596) | 0.124424 / 0.737135 (-0.612711) | 0.087802 / 0.296338 (-0.208536) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305542 / 0.215209 (0.090333) | 2.980678 / 2.077655 (0.903023) | 1.632348 / 1.504120 (0.128228) | 1.501466 / 1.541195 (-0.039728) | 1.517681 / 1.468490 (0.049191) | 0.579318 / 4.584777 (-4.005459) | 2.460734 / 3.745712 (-1.284978) | 2.650164 / 5.269862 (-2.619697) | 1.752061 / 4.565676 (-2.813615) | 0.064561 / 0.424275 (-0.359714) | 0.005097 / 0.007607 (-0.002510) | 0.359613 / 0.226044 (0.133569) | 3.518549 / 2.268929 (1.249620) | 1.962575 / 55.444624 (-53.482050) | 1.686108 / 6.876477 (-5.190369) | 1.787873 / 2.142072 (-0.354199) | 0.653715 / 4.805227 (-4.151512) | 0.117617 / 6.500664 (-6.383048) | 0.040359 / 0.075469 (-0.035110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.021533 / 1.841788 (-0.820255) | 11.974817 / 8.074308 (3.900509) | 10.073530 / 10.191392 (-0.117862) | 0.141477 / 0.680424 (-0.538947) | 0.015081 / 0.534201 (-0.519120) | 0.292622 / 0.579283 (-0.286661) | 0.291043 / 0.434364 (-0.143321) | 0.347822 / 0.540337 (-0.192516) | 0.443647 / 1.386936 (-0.943289) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6fb6c834f008996c994b0a86c3808d0a33d44525 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6722). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi ! I opened a PR to fix an issue in the Features defined in your code\r\n\r\nBasically changing\r\n```python\r\nSequence(\"float32\")\r\n```\r\n\r\nto\r\n```python\r\nSequence(Value(\"float32\"))\r\n```\r\n\r\n\r\nhttps://huggingface.co/datasets/BramVanroy/hplt_mono_v1_2/discussions/1", "D'oh! Was wondering why the `str() is not callable` was in there. Glad the error is my end though, and not related to zstandard (which I had not used in the past).\r\n\r\nThanks a lot!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6718). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005074 / 0.011353 (-0.006279) | 0.003505 / 0.011008 (-0.007503) | 0.063683 / 0.038508 (0.025175) | 0.029308 / 0.023109 (0.006199) | 0.246648 / 0.275898 (-0.029250) | 0.265546 / 0.323480 (-0.057933) | 0.004108 / 0.007986 (-0.003878) | 0.002683 / 0.004328 (-0.001646) | 0.048634 / 0.004250 (0.044383) | 0.043786 / 0.037052 (0.006733) | 0.262197 / 0.258489 (0.003708) | 0.291582 / 0.293841 (-0.002259) | 0.027472 / 0.128546 (-0.101074) | 0.010213 / 0.075646 (-0.065434) | 0.206744 / 0.419271 (-0.212527) | 0.036195 / 0.043533 (-0.007337) | 0.249090 / 0.255139 (-0.006049) | 0.280002 / 0.283200 (-0.003198) | 0.018568 / 0.141683 (-0.123115) | 1.124844 / 1.452155 (-0.327311) | 1.159358 / 1.492716 (-0.333359) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093186 / 0.018006 (0.075180) | 0.302331 / 0.000490 (0.301842) | 0.000217 / 0.000200 (0.000017) | 0.000046 / 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) | 0.018727 / 0.037411 (-0.018684) | 0.061730 / 0.014526 (0.047204) | 0.074330 / 0.176557 (-0.102226) | 0.119769 / 0.737135 (-0.617366) | 0.075611 / 0.296338 (-0.220727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285063 / 0.215209 (0.069854) | 2.824809 / 2.077655 (0.747155) | 1.481858 / 1.504120 (-0.022262) | 1.350193 / 1.541195 (-0.191002) | 1.358012 / 1.468490 (-0.110478) | 0.557842 / 4.584777 (-4.026935) | 2.380729 / 3.745712 (-1.364983) | 2.798891 / 5.269862 (-2.470970) | 1.719288 / 4.565676 (-2.846388) | 0.061705 / 0.424275 (-0.362570) | 0.005431 / 0.007607 (-0.002176) | 0.343233 / 0.226044 (0.117189) | 3.375223 / 2.268929 (1.106295) | 1.838188 / 55.444624 (-53.606436) | 1.570015 / 6.876477 (-5.306461) | 1.573157 / 2.142072 (-0.568915) | 0.650678 / 4.805227 (-4.154549) | 0.116412 / 6.500664 (-6.384252) | 0.041754 / 0.075469 (-0.033715) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970431 / 1.841788 (-0.871357) | 11.317128 / 8.074308 (3.242819) | 9.691240 / 10.191392 (-0.500152) | 0.142260 / 0.680424 (-0.538164) | 0.014131 / 0.534201 (-0.520070) | 0.289910 / 0.579283 (-0.289373) | 0.265648 / 0.434364 (-0.168715) | 0.323130 / 0.540337 (-0.217208) | 0.447005 / 1.386936 (-0.939931) |\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) | 0.005322 / 0.011353 (-0.006031) | 0.003755 / 0.011008 (-0.007253) | 0.049646 / 0.038508 (0.011138) | 0.029669 / 0.023109 (0.006560) | 0.284151 / 0.275898 (0.008253) | 0.298351 / 0.323480 (-0.025128) | 0.004183 / 0.007986 (-0.003803) | 0.002683 / 0.004328 (-0.001645) | 0.048814 / 0.004250 (0.044563) | 0.045017 / 0.037052 (0.007965) | 0.287358 / 0.258489 (0.028869) | 0.317394 / 0.293841 (0.023553) | 0.030025 / 0.128546 (-0.098521) | 0.010854 / 0.075646 (-0.064793) | 0.058694 / 0.419271 (-0.360578) | 0.052287 / 0.043533 (0.008754) | 0.279038 / 0.255139 (0.023899) | 0.295442 / 0.283200 (0.012242) | 0.019413 / 0.141683 (-0.122270) | 1.146106 / 1.452155 (-0.306048) | 1.197777 / 1.492716 (-0.294939) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092191 / 0.018006 (0.074184) | 0.302672 / 0.000490 (0.302182) | 0.000623 / 0.000200 (0.000423) | 0.000048 / 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) | 0.022067 / 0.037411 (-0.015345) | 0.081760 / 0.014526 (0.067235) | 0.087548 / 0.176557 (-0.089009) | 0.126405 / 0.737135 (-0.610730) | 0.089331 / 0.296338 (-0.207008) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295821 / 0.215209 (0.080612) | 2.897930 / 2.077655 (0.820276) | 1.604500 / 1.504120 (0.100380) | 1.471502 / 1.541195 (-0.069692) | 1.497918 / 1.468490 (0.029428) | 0.576179 / 4.584777 (-4.008598) | 2.452103 / 3.745712 (-1.293609) | 2.668043 / 5.269862 (-2.601818) | 1.753544 / 4.565676 (-2.812133) | 0.064410 / 0.424275 (-0.359865) | 0.005027 / 0.007607 (-0.002580) | 0.351509 / 0.226044 (0.125465) | 3.479208 / 2.268929 (1.210280) | 1.990356 / 55.444624 (-53.454269) | 1.684920 / 6.876477 (-5.191556) | 1.794251 / 2.142072 (-0.347821) | 0.662692 / 4.805227 (-4.142535) | 0.118589 / 6.500664 (-6.382076) | 0.040813 / 0.075469 (-0.034656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.002390 / 1.841788 (-0.839398) | 12.004617 / 8.074308 (3.930309) | 10.216005 / 10.191392 (0.024613) | 0.154354 / 0.680424 (-0.526070) | 0.015554 / 0.534201 (-0.518647) | 0.288741 / 0.579283 (-0.290542) | 0.276774 / 0.434364 (-0.157590) | 0.327055 / 0.540337 (-0.213282) | 0.435121 / 1.386936 (-0.951815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f45bc6caa25115a04c41b278671a5a89457eb66c \"CML watermark\")\n" ]

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[ "And it also works well with `dataset = dataset.select_columns([\"audio\"])`" ]

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[ "When `rotten_tomatoes` is printed out, the following warning message is also printed out:\r\n\r\n```\r\nYou can avoid this message in future by passing the argument `trust_remote_code=True`.\r\nPassing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\r\n```", "Hi ! This behavior happens because the dataset was originakky created using a dataset script [rotten_tomatoes.py](https://huggingface.co/datasets/rotten_tomatoes/blob/26f40d324d7b281d8b3fb1c47f30f8b9957f206b/rotten_tomatoes.py) and because we added features recently allowing to download the dataset directly from Parquet files (parquet builder) without running the dataset script (rotten_tomatoes). The flakiness must come from the availability of the Parquet files (we automatically export them in the refs/convert/parquet branch and we recently had to move some files).\r\n\r\nAnyway the easy fix on our side is to remove the dataset script completely, let me open a PR at https://huggingface.co/datasets/rotten_tomatoes\r\n\r\nEDIT: opened https://huggingface.co/datasets/rotten_tomatoes/discussions/6, feel free to comment there if you're ok with that change", "@lhoestq Thanks for the comment, explanation, and patch!", "> we automatically export them in the refs/convert/parquet branch\r\n\r\nWhen this operation is in progress, the parquet files become temporarily unavailable?", "> When this operation is in progress, the parquet files become temporarily unavailable?\r\n\r\nYes correct. I just merged the patch btw :)", "@lhoestq Thanks for merging the PR! I think this issue can be closed." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6715). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005294 / 0.011353 (-0.006059) | 0.003598 / 0.011008 (-0.007411) | 0.062798 / 0.038508 (0.024290) | 0.027479 / 0.023109 (0.004370) | 0.247146 / 0.275898 (-0.028752) | 0.272103 / 0.323480 (-0.051377) | 0.002979 / 0.007986 (-0.005007) | 0.002701 / 0.004328 (-0.001628) | 0.049384 / 0.004250 (0.045134) | 0.041562 / 0.037052 (0.004510) | 0.269924 / 0.258489 (0.011435) | 0.290749 / 0.293841 (-0.003092) | 0.028285 / 0.128546 (-0.100261) | 0.010464 / 0.075646 (-0.065183) | 0.207000 / 0.419271 (-0.212272) | 0.036186 / 0.043533 (-0.007347) | 0.254524 / 0.255139 (-0.000615) | 0.274843 / 0.283200 (-0.008356) | 0.020044 / 0.141683 (-0.121638) | 1.119223 / 1.452155 (-0.332931) | 1.156557 / 1.492716 (-0.336159) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092014 / 0.018006 (0.074008) | 0.297349 / 0.000490 (0.296859) | 0.000205 / 0.000200 (0.000005) | 0.000048 / 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) | 0.018617 / 0.037411 (-0.018794) | 0.061879 / 0.014526 (0.047354) | 0.072877 / 0.176557 (-0.103680) | 0.121850 / 0.737135 (-0.615286) | 0.074686 / 0.296338 (-0.221653) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281204 / 0.215209 (0.065995) | 2.728688 / 2.077655 (0.651033) | 1.469659 / 1.504120 (-0.034461) | 1.355306 / 1.541195 (-0.185889) | 1.350598 / 1.468490 (-0.117892) | 0.563669 / 4.584777 (-4.021108) | 2.377177 / 3.745712 (-1.368535) | 2.767402 / 5.269862 (-2.502460) | 1.720188 / 4.565676 (-2.845489) | 0.062594 / 0.424275 (-0.361681) | 0.005004 / 0.007607 (-0.002603) | 0.333017 / 0.226044 (0.106972) | 3.354543 / 2.268929 (1.085615) | 1.840031 / 55.444624 (-53.604593) | 1.545548 / 6.876477 (-5.330929) | 1.569858 / 2.142072 (-0.572214) | 0.642680 / 4.805227 (-4.162547) | 0.117463 / 6.500664 (-6.383201) | 0.042472 / 0.075469 (-0.032997) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977436 / 1.841788 (-0.864351) | 11.285982 / 8.074308 (3.211673) | 9.441848 / 10.191392 (-0.749544) | 0.140773 / 0.680424 (-0.539650) | 0.013783 / 0.534201 (-0.520418) | 0.292304 / 0.579283 (-0.286979) | 0.275011 / 0.434364 (-0.159353) | 0.339094 / 0.540337 (-0.201244) | 0.447593 / 1.386936 (-0.939343) |\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) | 0.005258 / 0.011353 (-0.006095) | 0.003539 / 0.011008 (-0.007469) | 0.049920 / 0.038508 (0.011412) | 0.029789 / 0.023109 (0.006680) | 0.277187 / 0.275898 (0.001288) | 0.296817 / 0.323480 (-0.026663) | 0.004133 / 0.007986 (-0.003852) | 0.002679 / 0.004328 (-0.001649) | 0.048999 / 0.004250 (0.044749) | 0.044087 / 0.037052 (0.007034) | 0.290359 / 0.258489 (0.031870) | 0.319572 / 0.293841 (0.025731) | 0.030248 / 0.128546 (-0.098298) | 0.010453 / 0.075646 (-0.065194) | 0.058734 / 0.419271 (-0.360537) | 0.051216 / 0.043533 (0.007683) | 0.278667 / 0.255139 (0.023528) | 0.298792 / 0.283200 (0.015592) | 0.019131 / 0.141683 (-0.122552) | 1.131814 / 1.452155 (-0.320340) | 1.167208 / 1.492716 (-0.325508) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088316 / 0.018006 (0.070309) | 0.297143 / 0.000490 (0.296653) | 0.000207 / 0.000200 (0.000007) | 0.000048 / 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) | 0.022457 / 0.037411 (-0.014954) | 0.075251 / 0.014526 (0.060726) | 0.086747 / 0.176557 (-0.089809) | 0.124975 / 0.737135 (-0.612161) | 0.087320 / 0.296338 (-0.209019) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292339 / 0.215209 (0.077130) | 2.860196 / 2.077655 (0.782541) | 1.599058 / 1.504120 (0.094938) | 1.476104 / 1.541195 (-0.065091) | 1.509109 / 1.468490 (0.040619) | 0.564056 / 4.584777 (-4.020721) | 2.388870 / 3.745712 (-1.356842) | 2.582356 / 5.269862 (-2.687506) | 1.726033 / 4.565676 (-2.839644) | 0.061788 / 0.424275 (-0.362487) | 0.005021 / 0.007607 (-0.002586) | 0.345644 / 0.226044 (0.119600) | 3.384000 / 2.268929 (1.115071) | 1.946591 / 55.444624 (-53.498033) | 1.693485 / 6.876477 (-5.182992) | 1.790300 / 2.142072 (-0.351773) | 0.654637 / 4.805227 (-4.150590) | 0.116271 / 6.500664 (-6.384393) | 0.040710 / 0.075469 (-0.034759) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007367 / 1.841788 (-0.834421) | 11.868065 / 8.074308 (3.793757) | 10.146212 / 10.191392 (-0.045180) | 0.128902 / 0.680424 (-0.551522) | 0.015259 / 0.534201 (-0.518942) | 0.288087 / 0.579283 (-0.291196) | 0.281516 / 0.434364 (-0.152848) | 0.325755 / 0.540337 (-0.214583) | 0.424814 / 1.386936 (-0.962122) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8247202a7ed1c3164c88f8f183513c5f003aa2af \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6714). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005237 / 0.011353 (-0.006116) | 0.003614 / 0.011008 (-0.007394) | 0.063349 / 0.038508 (0.024841) | 0.027297 / 0.023109 (0.004187) | 0.236203 / 0.275898 (-0.039695) | 0.260029 / 0.323480 (-0.063451) | 0.003096 / 0.007986 (-0.004889) | 0.003342 / 0.004328 (-0.000987) | 0.048703 / 0.004250 (0.044453) | 0.043121 / 0.037052 (0.006069) | 0.257491 / 0.258489 (-0.000998) | 0.282861 / 0.293841 (-0.010980) | 0.027701 / 0.128546 (-0.100845) | 0.010634 / 0.075646 (-0.065012) | 0.207369 / 0.419271 (-0.211903) | 0.035799 / 0.043533 (-0.007734) | 0.240445 / 0.255139 (-0.014694) | 0.261977 / 0.283200 (-0.021223) | 0.018175 / 0.141683 (-0.123508) | 1.143964 / 1.452155 (-0.308191) | 1.230057 / 1.492716 (-0.262659) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096656 / 0.018006 (0.078650) | 0.303434 / 0.000490 (0.302944) | 0.000225 / 0.000200 (0.000025) | 0.000051 / 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) | 0.018454 / 0.037411 (-0.018957) | 0.061792 / 0.014526 (0.047266) | 0.073384 / 0.176557 (-0.103172) | 0.120148 / 0.737135 (-0.616988) | 0.074221 / 0.296338 (-0.222118) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290291 / 0.215209 (0.075082) | 2.822908 / 2.077655 (0.745254) | 1.483139 / 1.504120 (-0.020981) | 1.349619 / 1.541195 (-0.191576) | 1.356588 / 1.468490 (-0.111902) | 0.571723 / 4.584777 (-4.013054) | 2.402696 / 3.745712 (-1.343016) | 2.832215 / 5.269862 (-2.437647) | 1.794962 / 4.565676 (-2.770714) | 0.062707 / 0.424275 (-0.361568) | 0.004997 / 0.007607 (-0.002610) | 0.343093 / 0.226044 (0.117049) | 3.383028 / 2.268929 (1.114100) | 1.818624 / 55.444624 (-53.626000) | 1.549859 / 6.876477 (-5.326618) | 1.667838 / 2.142072 (-0.474235) | 0.648574 / 4.805227 (-4.156653) | 0.119181 / 6.500664 (-6.381484) | 0.042074 / 0.075469 (-0.033395) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982039 / 1.841788 (-0.859748) | 11.411759 / 8.074308 (3.337451) | 9.783405 / 10.191392 (-0.407987) | 0.129577 / 0.680424 (-0.550847) | 0.014091 / 0.534201 (-0.520110) | 0.297925 / 0.579283 (-0.281358) | 0.263884 / 0.434364 (-0.170480) | 0.346032 / 0.540337 (-0.194305) | 0.444806 / 1.386936 (-0.942130) |\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) | 0.005527 / 0.011353 (-0.005826) | 0.003677 / 0.011008 (-0.007332) | 0.050245 / 0.038508 (0.011737) | 0.030070 / 0.023109 (0.006961) | 0.272640 / 0.275898 (-0.003258) | 0.296555 / 0.323480 (-0.026925) | 0.004247 / 0.007986 (-0.003738) | 0.003833 / 0.004328 (-0.000495) | 0.049341 / 0.004250 (0.045091) | 0.046604 / 0.037052 (0.009552) | 0.282765 / 0.258489 (0.024276) | 0.314924 / 0.293841 (0.021084) | 0.029749 / 0.128546 (-0.098797) | 0.010524 / 0.075646 (-0.065122) | 0.057859 / 0.419271 (-0.361412) | 0.053172 / 0.043533 (0.009640) | 0.274906 / 0.255139 (0.019767) | 0.290566 / 0.283200 (0.007366) | 0.019299 / 0.141683 (-0.122384) | 1.164092 / 1.452155 (-0.288062) | 1.205074 / 1.492716 (-0.287642) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093943 / 0.018006 (0.075936) | 0.298746 / 0.000490 (0.298256) | 0.000232 / 0.000200 (0.000032) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022098 / 0.037411 (-0.015313) | 0.075523 / 0.014526 (0.060997) | 0.086784 / 0.176557 (-0.089773) | 0.124610 / 0.737135 (-0.612525) | 0.087743 / 0.296338 (-0.208595) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298555 / 0.215209 (0.083346) | 2.951493 / 2.077655 (0.873838) | 1.611448 / 1.504120 (0.107328) | 1.481503 / 1.541195 (-0.059692) | 1.497937 / 1.468490 (0.029447) | 0.580402 / 4.584777 (-4.004375) | 2.433308 / 3.745712 (-1.312404) | 2.712717 / 5.269862 (-2.557145) | 1.766286 / 4.565676 (-2.799391) | 0.063973 / 0.424275 (-0.360303) | 0.005006 / 0.007607 (-0.002601) | 0.354541 / 0.226044 (0.128497) | 3.486448 / 2.268929 (1.217519) | 1.972779 / 55.444624 (-53.471846) | 1.709018 / 6.876477 (-5.167458) | 1.864242 / 2.142072 (-0.277831) | 0.678213 / 4.805227 (-4.127014) | 0.119525 / 6.500664 (-6.381140) | 0.041387 / 0.075469 (-0.034082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.021337 / 1.841788 (-0.820451) | 12.049563 / 8.074308 (3.975255) | 10.424701 / 10.191392 (0.233309) | 0.131444 / 0.680424 (-0.548980) | 0.015644 / 0.534201 (-0.518557) | 0.293712 / 0.579283 (-0.285571) | 0.279160 / 0.434364 (-0.155204) | 0.327991 / 0.540337 (-0.212346) | 0.435455 / 1.386936 (-0.951481) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fe9483acc1ccaf19f3c199b99391921a8526215 \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6713", "html_url": "https://github.com/huggingface/datasets/pull/6713", "diff_url": "https://github.com/huggingface/datasets/pull/6713.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6713.patch", "merged_at": "2024-03-04T18:06:05" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6713). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq if you agree, I could make a patch release tomorrow morning.", "sure :)", "<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) | 0.005086 / 0.011353 (-0.006267) | 0.003695 / 0.011008 (-0.007313) | 0.063430 / 0.038508 (0.024922) | 0.026798 / 0.023109 (0.003689) | 0.253761 / 0.275898 (-0.022138) | 0.301301 / 0.323480 (-0.022179) | 0.004160 / 0.007986 (-0.003825) | 0.002783 / 0.004328 (-0.001545) | 0.050698 / 0.004250 (0.046448) | 0.040899 / 0.037052 (0.003846) | 0.269024 / 0.258489 (0.010535) | 0.323467 / 0.293841 (0.029626) | 0.027756 / 0.128546 (-0.100791) | 0.010684 / 0.075646 (-0.064963) | 0.207128 / 0.419271 (-0.212144) | 0.035874 / 0.043533 (-0.007659) | 0.251620 / 0.255139 (-0.003519) | 0.268668 / 0.283200 (-0.014532) | 0.017387 / 0.141683 (-0.124296) | 1.139230 / 1.452155 (-0.312925) | 1.183613 / 1.492716 (-0.309103) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096337 / 0.018006 (0.078331) | 0.305014 / 0.000490 (0.304524) | 0.000219 / 0.000200 (0.000019) | 0.000050 / 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) | 0.018086 / 0.037411 (-0.019325) | 0.061626 / 0.014526 (0.047100) | 0.072598 / 0.176557 (-0.103959) | 0.119944 / 0.737135 (-0.617192) | 0.074549 / 0.296338 (-0.221789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282661 / 0.215209 (0.067452) | 2.804473 / 2.077655 (0.726818) | 1.444602 / 1.504120 (-0.059517) | 1.313977 / 1.541195 (-0.227217) | 1.319426 / 1.468490 (-0.149064) | 0.570176 / 4.584777 (-4.014601) | 2.397895 / 3.745712 (-1.347818) | 2.760208 / 5.269862 (-2.509654) | 1.732457 / 4.565676 (-2.833220) | 0.062743 / 0.424275 (-0.361533) | 0.004950 / 0.007607 (-0.002657) | 0.338500 / 0.226044 (0.112456) | 3.287249 / 2.268929 (1.018320) | 1.777495 / 55.444624 (-53.667130) | 1.521255 / 6.876477 (-5.355222) | 1.517317 / 2.142072 (-0.624756) | 0.642202 / 4.805227 (-4.163025) | 0.116501 / 6.500664 (-6.384163) | 0.042418 / 0.075469 (-0.033052) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968966 / 1.841788 (-0.872822) | 11.490531 / 8.074308 (3.416223) | 9.507803 / 10.191392 (-0.683589) | 0.141570 / 0.680424 (-0.538854) | 0.014000 / 0.534201 (-0.520201) | 0.284237 / 0.579283 (-0.295046) | 0.269341 / 0.434364 (-0.165022) | 0.321654 / 0.540337 (-0.218683) | 0.446914 / 1.386936 (-0.940022) |\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) | 0.005280 / 0.011353 (-0.006072) | 0.003794 / 0.011008 (-0.007214) | 0.050328 / 0.038508 (0.011820) | 0.029756 / 0.023109 (0.006647) | 0.273403 / 0.275898 (-0.002495) | 0.297346 / 0.323480 (-0.026133) | 0.004310 / 0.007986 (-0.003676) | 0.002858 / 0.004328 (-0.001470) | 0.048833 / 0.004250 (0.044583) | 0.045696 / 0.037052 (0.008644) | 0.291034 / 0.258489 (0.032545) | 0.318899 / 0.293841 (0.025058) | 0.029809 / 0.128546 (-0.098737) | 0.010710 / 0.075646 (-0.064936) | 0.058183 / 0.419271 (-0.361089) | 0.051761 / 0.043533 (0.008228) | 0.275022 / 0.255139 (0.019883) | 0.291614 / 0.283200 (0.008414) | 0.017975 / 0.141683 (-0.123708) | 1.148489 / 1.452155 (-0.303666) | 1.218111 / 1.492716 (-0.274605) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091806 / 0.018006 (0.073799) | 0.299413 / 0.000490 (0.298923) | 0.000219 / 0.000200 (0.000019) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021506 / 0.037411 (-0.015905) | 0.075537 / 0.014526 (0.061011) | 0.087020 / 0.176557 (-0.089536) | 0.125270 / 0.737135 (-0.611865) | 0.088038 / 0.296338 (-0.208300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300401 / 0.215209 (0.085192) | 2.932571 / 2.077655 (0.854916) | 1.609502 / 1.504120 (0.105383) | 1.480078 / 1.541195 (-0.061117) | 1.514902 / 1.468490 (0.046412) | 0.575591 / 4.584777 (-4.009186) | 2.461873 / 3.745712 (-1.283839) | 2.728099 / 5.269862 (-2.541762) | 1.760054 / 4.565676 (-2.805622) | 0.064371 / 0.424275 (-0.359904) | 0.004990 / 0.007607 (-0.002617) | 0.350134 / 0.226044 (0.124090) | 3.453249 / 2.268929 (1.184321) | 1.979760 / 55.444624 (-53.464865) | 1.741128 / 6.876477 (-5.135348) | 1.825734 / 2.142072 (-0.316339) | 0.654902 / 4.805227 (-4.150325) | 0.116989 / 6.500664 (-6.383676) | 0.040800 / 0.075469 (-0.034669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.033352 / 1.841788 (-0.808436) | 12.196711 / 8.074308 (4.122403) | 10.315114 / 10.191392 (0.123722) | 0.132541 / 0.680424 (-0.547882) | 0.016455 / 0.534201 (-0.517746) | 0.289025 / 0.579283 (-0.290258) | 0.281464 / 0.434364 (-0.152900) | 0.325302 / 0.540337 (-0.215036) | 0.428469 / 1.386936 (-0.958467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7093b4b1a69f413e452119c87669af9e8ceaf749 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6712). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005151 / 0.011353 (-0.006202) | 0.003813 / 0.011008 (-0.007196) | 0.062957 / 0.038508 (0.024449) | 0.028282 / 0.023109 (0.005173) | 0.246036 / 0.275898 (-0.029862) | 0.290024 / 0.323480 (-0.033456) | 0.004009 / 0.007986 (-0.003977) | 0.002749 / 0.004328 (-0.001580) | 0.049351 / 0.004250 (0.045101) | 0.041143 / 0.037052 (0.004090) | 0.264782 / 0.258489 (0.006293) | 0.290711 / 0.293841 (-0.003130) | 0.027248 / 0.128546 (-0.101298) | 0.010691 / 0.075646 (-0.064955) | 0.205926 / 0.419271 (-0.213345) | 0.035652 / 0.043533 (-0.007880) | 0.246357 / 0.255139 (-0.008782) | 0.267851 / 0.283200 (-0.015348) | 0.018498 / 0.141683 (-0.123185) | 1.135996 / 1.452155 (-0.316159) | 1.181841 / 1.492716 (-0.310875) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094054 / 0.018006 (0.076048) | 0.305470 / 0.000490 (0.304980) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018842 / 0.037411 (-0.018569) | 0.061532 / 0.014526 (0.047006) | 0.073483 / 0.176557 (-0.103073) | 0.119426 / 0.737135 (-0.617709) | 0.075385 / 0.296338 (-0.220954) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285544 / 0.215209 (0.070335) | 2.774256 / 2.077655 (0.696601) | 1.475719 / 1.504120 (-0.028401) | 1.353841 / 1.541195 (-0.187353) | 1.381891 / 1.468490 (-0.086599) | 0.570619 / 4.584777 (-4.014158) | 2.380300 / 3.745712 (-1.365412) | 2.788767 / 5.269862 (-2.481095) | 1.741790 / 4.565676 (-2.823886) | 0.061810 / 0.424275 (-0.362465) | 0.005004 / 0.007607 (-0.002603) | 0.334963 / 0.226044 (0.108918) | 3.286388 / 2.268929 (1.017459) | 1.831669 / 55.444624 (-53.612955) | 1.523372 / 6.876477 (-5.353105) | 1.581551 / 2.142072 (-0.560521) | 0.639642 / 4.805227 (-4.165585) | 0.117356 / 6.500664 (-6.383308) | 0.043277 / 0.075469 (-0.032192) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973005 / 1.841788 (-0.868782) | 11.590148 / 8.074308 (3.515839) | 9.521262 / 10.191392 (-0.670130) | 0.143243 / 0.680424 (-0.537181) | 0.013529 / 0.534201 (-0.520672) | 0.285724 / 0.579283 (-0.293559) | 0.265642 / 0.434364 (-0.168721) | 0.366098 / 0.540337 (-0.174239) | 0.444410 / 1.386936 (-0.942526) |\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) | 0.005347 / 0.011353 (-0.006006) | 0.003797 / 0.011008 (-0.007212) | 0.050441 / 0.038508 (0.011933) | 0.032812 / 0.023109 (0.009703) | 0.281278 / 0.275898 (0.005379) | 0.304524 / 0.323480 (-0.018956) | 0.005039 / 0.007986 (-0.002946) | 0.002735 / 0.004328 (-0.001594) | 0.049184 / 0.004250 (0.044933) | 0.046751 / 0.037052 (0.009698) | 0.292093 / 0.258489 (0.033604) | 0.322087 / 0.293841 (0.028246) | 0.029775 / 0.128546 (-0.098771) | 0.010540 / 0.075646 (-0.065106) | 0.057927 / 0.419271 (-0.361345) | 0.054240 / 0.043533 (0.010707) | 0.281537 / 0.255139 (0.026398) | 0.298386 / 0.283200 (0.015186) | 0.019773 / 0.141683 (-0.121910) | 1.157161 / 1.452155 (-0.294994) | 1.210395 / 1.492716 (-0.282321) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095098 / 0.018006 (0.077091) | 0.306952 / 0.000490 (0.306462) | 0.000211 / 0.000200 (0.000011) | 0.000050 / 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) | 0.022602 / 0.037411 (-0.014809) | 0.075242 / 0.014526 (0.060716) | 0.087134 / 0.176557 (-0.089422) | 0.127923 / 0.737135 (-0.609212) | 0.088645 / 0.296338 (-0.207693) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304187 / 0.215209 (0.088978) | 2.977120 / 2.077655 (0.899465) | 1.663592 / 1.504120 (0.159473) | 1.527601 / 1.541195 (-0.013594) | 1.540121 / 1.468490 (0.071631) | 0.562492 / 4.584777 (-4.022285) | 2.473836 / 3.745712 (-1.271876) | 2.656782 / 5.269862 (-2.613080) | 1.754212 / 4.565676 (-2.811464) | 0.062330 / 0.424275 (-0.361945) | 0.005149 / 0.007607 (-0.002459) | 0.354905 / 0.226044 (0.128860) | 3.503587 / 2.268929 (1.234659) | 2.015682 / 55.444624 (-53.428943) | 1.744421 / 6.876477 (-5.132056) | 1.923120 / 2.142072 (-0.218952) | 0.652209 / 4.805227 (-4.153018) | 0.119406 / 6.500664 (-6.381258) | 0.042840 / 0.075469 (-0.032630) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.009164 / 1.841788 (-0.832624) | 12.379654 / 8.074308 (4.305346) | 10.408696 / 10.191392 (0.217304) | 0.141674 / 0.680424 (-0.538750) | 0.016815 / 0.534201 (-0.517386) | 0.292453 / 0.579283 (-0.286830) | 0.277577 / 0.434364 (-0.156787) | 0.325024 / 0.540337 (-0.215313) | 0.433181 / 1.386936 (-0.953755) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a16a08c940e65397305aec5f1b484d91cee75a \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6711", "html_url": "https://github.com/huggingface/datasets/pull/6711", "diff_url": "https://github.com/huggingface/datasets/pull/6711.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6711.patch", "merged_at": null }

[ "Unfortunately, that won't improve the performance. StringZilla repository has extensive benchmarks comparing against different built-in functionality of several programming languages. Using `re.finditer` for tokenization is practically the slowest anti-pattern I've encountered in any language. The gap between that and a SIMD-accelerated kernel can be as big as 10 MB/s vs 10 GB/s.\n\nI understand the need to keep the dependencies minimal. It helps the package remain small and portable. At this point, StringZilla provides 105 binaries for different OS and hardware versions (more portable than NumPy) and the [binary size generally ranges from 50 KB to 250 KB](https://pypi.org/project/stringzilla/), smaller than a single JPEG. \n", "The `text` builder is not very popular, so I'm also not a fan of introducing a dependency for it.\r\n\r\nMoreover, I couldn't find any projects of this size/usage depending on StringZilla (with GitHub search), so we should at least wait for its greater adoption to merge this PR.\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6710). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6709). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005081 / 0.011353 (-0.006272) | 0.004182 / 0.011008 (-0.006826) | 0.063377 / 0.038508 (0.024869) | 0.027880 / 0.023109 (0.004770) | 0.247260 / 0.275898 (-0.028638) | 0.273580 / 0.323480 (-0.049900) | 0.002995 / 0.007986 (-0.004991) | 0.002804 / 0.004328 (-0.001524) | 0.049669 / 0.004250 (0.045418) | 0.042469 / 0.037052 (0.005417) | 0.268606 / 0.258489 (0.010117) | 0.292867 / 0.293841 (-0.000973) | 0.028077 / 0.128546 (-0.100469) | 0.011031 / 0.075646 (-0.064615) | 0.210225 / 0.419271 (-0.209047) | 0.035723 / 0.043533 (-0.007810) | 0.252131 / 0.255139 (-0.003008) | 0.272895 / 0.283200 (-0.010304) | 0.019809 / 0.141683 (-0.121874) | 1.138500 / 1.452155 (-0.313655) | 1.167752 / 1.492716 (-0.324964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094881 / 0.018006 (0.076875) | 0.300168 / 0.000490 (0.299678) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 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) | 0.017917 / 0.037411 (-0.019494) | 0.061854 / 0.014526 (0.047328) | 0.074481 / 0.176557 (-0.102075) | 0.120075 / 0.737135 (-0.617061) | 0.074627 / 0.296338 (-0.221711) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287888 / 0.215209 (0.072679) | 2.770165 / 2.077655 (0.692510) | 1.500071 / 1.504120 (-0.004049) | 1.374857 / 1.541195 (-0.166338) | 1.427291 / 1.468490 (-0.041200) | 0.558431 / 4.584777 (-4.026346) | 2.439352 / 3.745712 (-1.306361) | 2.787471 / 5.269862 (-2.482391) | 1.742636 / 4.565676 (-2.823041) | 0.061716 / 0.424275 (-0.362559) | 0.004961 / 0.007607 (-0.002646) | 0.345209 / 0.226044 (0.119164) | 3.360253 / 2.268929 (1.091325) | 1.847945 / 55.444624 (-53.596680) | 1.595733 / 6.876477 (-5.280744) | 1.642350 / 2.142072 (-0.499723) | 0.638639 / 4.805227 (-4.166588) | 0.116918 / 6.500664 (-6.383746) | 0.042132 / 0.075469 (-0.033338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980602 / 1.841788 (-0.861185) | 11.545402 / 8.074308 (3.471094) | 9.452471 / 10.191392 (-0.738921) | 0.129930 / 0.680424 (-0.550494) | 0.014143 / 0.534201 (-0.520058) | 0.290302 / 0.579283 (-0.288981) | 0.263785 / 0.434364 (-0.170579) | 0.339580 / 0.540337 (-0.200758) | 0.450355 / 1.386936 (-0.936581) |\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) | 0.005565 / 0.011353 (-0.005788) | 0.003764 / 0.011008 (-0.007244) | 0.050082 / 0.038508 (0.011574) | 0.030354 / 0.023109 (0.007245) | 0.250609 / 0.275898 (-0.025289) | 0.277200 / 0.323480 (-0.046280) | 0.004276 / 0.007986 (-0.003710) | 0.002805 / 0.004328 (-0.001523) | 0.048765 / 0.004250 (0.044514) | 0.045477 / 0.037052 (0.008425) | 0.267704 / 0.258489 (0.009215) | 0.303214 / 0.293841 (0.009373) | 0.029393 / 0.128546 (-0.099153) | 0.010623 / 0.075646 (-0.065023) | 0.058201 / 0.419271 (-0.361070) | 0.053131 / 0.043533 (0.009599) | 0.258682 / 0.255139 (0.003543) | 0.276069 / 0.283200 (-0.007131) | 0.018260 / 0.141683 (-0.123423) | 1.141542 / 1.452155 (-0.310613) | 1.185780 / 1.492716 (-0.306936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096857 / 0.018006 (0.078850) | 0.300656 / 0.000490 (0.300167) | 0.000450 / 0.000200 (0.000250) | 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) | 0.022416 / 0.037411 (-0.014995) | 0.074781 / 0.014526 (0.060255) | 0.087299 / 0.176557 (-0.089257) | 0.127616 / 0.737135 (-0.609519) | 0.088382 / 0.296338 (-0.207957) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298639 / 0.215209 (0.083430) | 2.940002 / 2.077655 (0.862347) | 1.709707 / 1.504120 (0.205587) | 1.556502 / 1.541195 (0.015307) | 1.592841 / 1.468490 (0.124351) | 0.570237 / 4.584777 (-4.014539) | 2.467576 / 3.745712 (-1.278137) | 2.741021 / 5.269862 (-2.528840) | 1.776526 / 4.565676 (-2.789151) | 0.063999 / 0.424275 (-0.360276) | 0.005068 / 0.007607 (-0.002539) | 0.360727 / 0.226044 (0.134682) | 3.535404 / 2.268929 (1.266476) | 2.035345 / 55.444624 (-53.409279) | 1.755916 / 6.876477 (-5.120561) | 1.889281 / 2.142072 (-0.252791) | 0.649025 / 4.805227 (-4.156202) | 0.118210 / 6.500664 (-6.382454) | 0.040815 / 0.075469 (-0.034654) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005650 / 1.841788 (-0.836138) | 12.228314 / 8.074308 (4.154006) | 10.147363 / 10.191392 (-0.044029) | 0.159258 / 0.680424 (-0.521166) | 0.015288 / 0.534201 (-0.518913) | 0.288144 / 0.579283 (-0.291139) | 0.281319 / 0.434364 (-0.153045) | 0.323380 / 0.540337 (-0.216958) | 0.426887 / 1.386936 (-0.960049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b04ccb486f3831b4b0d2474119823efa3815709 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6708). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005442 / 0.011353 (-0.005910) | 0.003796 / 0.011008 (-0.007213) | 0.063663 / 0.038508 (0.025155) | 0.028901 / 0.023109 (0.005792) | 0.256742 / 0.275898 (-0.019156) | 0.279555 / 0.323480 (-0.043925) | 0.004128 / 0.007986 (-0.003858) | 0.002789 / 0.004328 (-0.001539) | 0.049463 / 0.004250 (0.045213) | 0.043461 / 0.037052 (0.006409) | 0.272975 / 0.258489 (0.014486) | 0.299057 / 0.293841 (0.005216) | 0.029030 / 0.128546 (-0.099516) | 0.010453 / 0.075646 (-0.065193) | 0.207611 / 0.419271 (-0.211660) | 0.037200 / 0.043533 (-0.006332) | 0.258327 / 0.255139 (0.003188) | 0.279746 / 0.283200 (-0.003454) | 0.018940 / 0.141683 (-0.122743) | 1.150379 / 1.452155 (-0.301776) | 1.217621 / 1.492716 (-0.275095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095115 / 0.018006 (0.077109) | 0.299393 / 0.000490 (0.298903) | 0.000223 / 0.000200 (0.000023) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018972 / 0.037411 (-0.018439) | 0.061669 / 0.014526 (0.047143) | 0.075605 / 0.176557 (-0.100951) | 0.125695 / 0.737135 (-0.611440) | 0.076654 / 0.296338 (-0.219684) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286431 / 0.215209 (0.071222) | 2.763554 / 2.077655 (0.685899) | 1.489902 / 1.504120 (-0.014218) | 1.375082 / 1.541195 (-0.166113) | 1.418903 / 1.468490 (-0.049587) | 0.555646 / 4.584777 (-4.029131) | 2.410578 / 3.745712 (-1.335134) | 2.827453 / 5.269862 (-2.442408) | 1.764381 / 4.565676 (-2.801295) | 0.062937 / 0.424275 (-0.361339) | 0.004989 / 0.007607 (-0.002619) | 0.342115 / 0.226044 (0.116071) | 3.354660 / 2.268929 (1.085732) | 1.858418 / 55.444624 (-53.586206) | 1.586403 / 6.876477 (-5.290074) | 1.625762 / 2.142072 (-0.516311) | 0.643678 / 4.805227 (-4.161550) | 0.116764 / 6.500664 (-6.383900) | 0.042198 / 0.075469 (-0.033271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974953 / 1.841788 (-0.866835) | 11.748419 / 8.074308 (3.674111) | 9.753700 / 10.191392 (-0.437692) | 0.131330 / 0.680424 (-0.549094) | 0.018876 / 0.534201 (-0.515325) | 0.290078 / 0.579283 (-0.289205) | 0.264676 / 0.434364 (-0.169688) | 0.340285 / 0.540337 (-0.200052) | 0.445340 / 1.386936 (-0.941596) |\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) | 0.005513 / 0.011353 (-0.005840) | 0.003665 / 0.011008 (-0.007344) | 0.049368 / 0.038508 (0.010860) | 0.032045 / 0.023109 (0.008936) | 0.280955 / 0.275898 (0.005057) | 0.299804 / 0.323480 (-0.023675) | 0.004391 / 0.007986 (-0.003594) | 0.002896 / 0.004328 (-0.001432) | 0.048914 / 0.004250 (0.044663) | 0.045448 / 0.037052 (0.008396) | 0.298779 / 0.258489 (0.040289) | 0.322012 / 0.293841 (0.028171) | 0.029449 / 0.128546 (-0.099097) | 0.010410 / 0.075646 (-0.065236) | 0.057867 / 0.419271 (-0.361405) | 0.053944 / 0.043533 (0.010411) | 0.278139 / 0.255139 (0.023000) | 0.297453 / 0.283200 (0.014254) | 0.018746 / 0.141683 (-0.122937) | 1.137890 / 1.452155 (-0.314264) | 1.206109 / 1.492716 (-0.286607) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091741 / 0.018006 (0.073735) | 0.300415 / 0.000490 (0.299925) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022097 / 0.037411 (-0.015314) | 0.076853 / 0.014526 (0.062327) | 0.088440 / 0.176557 (-0.088116) | 0.127176 / 0.737135 (-0.609959) | 0.088976 / 0.296338 (-0.207363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300754 / 0.215209 (0.085545) | 2.917713 / 2.077655 (0.840058) | 1.619338 / 1.504120 (0.115218) | 1.501543 / 1.541195 (-0.039652) | 1.506032 / 1.468490 (0.037542) | 0.579481 / 4.584777 (-4.005296) | 2.458917 / 3.745712 (-1.286795) | 2.754621 / 5.269862 (-2.515241) | 1.796440 / 4.565676 (-2.769237) | 0.067547 / 0.424275 (-0.356728) | 0.005001 / 0.007607 (-0.002606) | 0.351030 / 0.226044 (0.124985) | 3.466282 / 2.268929 (1.197353) | 1.954661 / 55.444624 (-53.489964) | 1.688737 / 6.876477 (-5.187740) | 1.836762 / 2.142072 (-0.305311) | 0.656441 / 4.805227 (-4.148786) | 0.118258 / 6.500664 (-6.382406) | 0.041608 / 0.075469 (-0.033861) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999696 / 1.841788 (-0.842092) | 12.383471 / 8.074308 (4.309162) | 10.338488 / 10.191392 (0.147096) | 0.150214 / 0.680424 (-0.530210) | 0.014997 / 0.534201 (-0.519204) | 0.288949 / 0.579283 (-0.290334) | 0.272012 / 0.434364 (-0.162352) | 0.327253 / 0.540337 (-0.213084) | 0.427594 / 1.386936 (-0.959342) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca8409a8bec4508255b9c3e808d0751eb1005260 \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6707", "html_url": "https://github.com/huggingface/datasets/pull/6707", "diff_url": "https://github.com/huggingface/datasets/pull/6707.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6707.patch", "merged_at": "2024-03-01T17:25:46" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6707). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.004728 / 0.011353 (-0.006624) | 0.002941 / 0.011008 (-0.008067) | 0.058270 / 0.038508 (0.019762) | 0.027418 / 0.023109 (0.004309) | 0.224993 / 0.275898 (-0.050905) | 0.243103 / 0.323480 (-0.080377) | 0.004668 / 0.007986 (-0.003318) | 0.002499 / 0.004328 (-0.001829) | 0.045020 / 0.004250 (0.040770) | 0.038006 / 0.037052 (0.000953) | 0.240807 / 0.258489 (-0.017682) | 0.264554 / 0.293841 (-0.029287) | 0.027018 / 0.128546 (-0.101529) | 0.009866 / 0.075646 (-0.065780) | 0.196578 / 0.419271 (-0.222694) | 0.034536 / 0.043533 (-0.008997) | 0.236535 / 0.255139 (-0.018604) | 0.248879 / 0.283200 (-0.034321) | 0.017140 / 0.141683 (-0.124543) | 1.046927 / 1.452155 (-0.405228) | 1.121209 / 1.492716 (-0.371507) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088267 / 0.018006 (0.070261) | 0.279774 / 0.000490 (0.279284) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 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) | 0.016521 / 0.037411 (-0.020890) | 0.056499 / 0.014526 (0.041974) | 0.067264 / 0.176557 (-0.109293) | 0.117270 / 0.737135 (-0.619865) | 0.069284 / 0.296338 (-0.227055) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.260679 / 0.215209 (0.045470) | 2.608971 / 2.077655 (0.531316) | 1.363139 / 1.504120 (-0.140981) | 1.262128 / 1.541195 (-0.279067) | 1.273619 / 1.468490 (-0.194871) | 0.523417 / 4.584777 (-4.061360) | 2.291145 / 3.745712 (-1.454567) | 2.540603 / 5.269862 (-2.729258) | 1.599090 / 4.565676 (-2.966586) | 0.058170 / 0.424275 (-0.366105) | 0.004556 / 0.007607 (-0.003051) | 0.308361 / 0.226044 (0.082316) | 3.069269 / 2.268929 (0.800340) | 1.698064 / 55.444624 (-53.746560) | 1.426631 / 6.876477 (-5.449846) | 1.463913 / 2.142072 (-0.678160) | 0.595234 / 4.805227 (-4.209993) | 0.107202 / 6.500664 (-6.393462) | 0.038183 / 0.075469 (-0.037286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.905999 / 1.841788 (-0.935789) | 10.828492 / 8.074308 (2.754184) | 8.705635 / 10.191392 (-1.485757) | 0.121203 / 0.680424 (-0.559221) | 0.013789 / 0.534201 (-0.520412) | 0.268172 / 0.579283 (-0.311111) | 0.254277 / 0.434364 (-0.180086) | 0.310280 / 0.540337 (-0.230057) | 0.410490 / 1.386936 (-0.976446) |\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) | 0.004696 / 0.011353 (-0.006657) | 0.002982 / 0.011008 (-0.008026) | 0.045333 / 0.038508 (0.006825) | 0.027483 / 0.023109 (0.004374) | 0.253438 / 0.275898 (-0.022460) | 0.272657 / 0.323480 (-0.050823) | 0.004060 / 0.007986 (-0.003926) | 0.002574 / 0.004328 (-0.001754) | 0.045462 / 0.004250 (0.041212) | 0.041260 / 0.037052 (0.004208) | 0.267919 / 0.258489 (0.009430) | 0.290935 / 0.293841 (-0.002906) | 0.026674 / 0.128546 (-0.101873) | 0.009370 / 0.075646 (-0.066276) | 0.053543 / 0.419271 (-0.365729) | 0.047390 / 0.043533 (0.003857) | 0.255774 / 0.255139 (0.000635) | 0.273909 / 0.283200 (-0.009291) | 0.017252 / 0.141683 (-0.124431) | 1.064298 / 1.452155 (-0.387857) | 1.125374 / 1.492716 (-0.367342) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091506 / 0.018006 (0.073499) | 0.298570 / 0.000490 (0.298080) | 0.000741 / 0.000200 (0.000541) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020232 / 0.037411 (-0.017179) | 0.069640 / 0.014526 (0.055114) | 0.081360 / 0.176557 (-0.095197) | 0.116955 / 0.737135 (-0.620180) | 0.080920 / 0.296338 (-0.215418) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283000 / 0.215209 (0.067791) | 2.802526 / 2.077655 (0.724871) | 1.534631 / 1.504120 (0.030511) | 1.407260 / 1.541195 (-0.133935) | 1.409111 / 1.468490 (-0.059379) | 0.534892 / 4.584777 (-4.049885) | 2.350516 / 3.745712 (-1.395196) | 2.550444 / 5.269862 (-2.719418) | 1.661747 / 4.565676 (-2.903930) | 0.060978 / 0.424275 (-0.363297) | 0.005300 / 0.007607 (-0.002308) | 0.367418 / 0.226044 (0.141373) | 3.338046 / 2.268929 (1.069117) | 1.883914 / 55.444624 (-53.560710) | 1.638561 / 6.876477 (-5.237916) | 1.751547 / 2.142072 (-0.390526) | 0.633318 / 4.805227 (-4.171910) | 0.114971 / 6.500664 (-6.385693) | 0.040202 / 0.075469 (-0.035267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962053 / 1.841788 (-0.879735) | 11.600643 / 8.074308 (3.526334) | 9.526461 / 10.191392 (-0.664931) | 0.123909 / 0.680424 (-0.556515) | 0.015944 / 0.534201 (-0.518257) | 0.271542 / 0.579283 (-0.307741) | 0.254366 / 0.434364 (-0.179998) | 0.300499 / 0.540337 (-0.239838) | 0.409122 / 1.386936 (-0.977814) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0acb27347a3c03efde612023235201a777e08e72 \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6706", "html_url": "https://github.com/huggingface/datasets/pull/6706", "diff_url": "https://github.com/huggingface/datasets/pull/6706.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6706.patch", "merged_at": "2024-03-01T16:52:17" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6706). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005014 / 0.011353 (-0.006339) | 0.003324 / 0.011008 (-0.007685) | 0.062501 / 0.038508 (0.023993) | 0.027633 / 0.023109 (0.004524) | 0.245693 / 0.275898 (-0.030205) | 0.271963 / 0.323480 (-0.051517) | 0.003062 / 0.007986 (-0.004923) | 0.002646 / 0.004328 (-0.001683) | 0.049020 / 0.004250 (0.044769) | 0.042381 / 0.037052 (0.005328) | 0.269729 / 0.258489 (0.011240) | 0.289052 / 0.293841 (-0.004789) | 0.027138 / 0.128546 (-0.101408) | 0.010246 / 0.075646 (-0.065400) | 0.205378 / 0.419271 (-0.213893) | 0.035792 / 0.043533 (-0.007741) | 0.247204 / 0.255139 (-0.007935) | 0.271805 / 0.283200 (-0.011394) | 0.019541 / 0.141683 (-0.122142) | 1.129335 / 1.452155 (-0.322820) | 1.174088 / 1.492716 (-0.318629) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091340 / 0.018006 (0.073334) | 0.300037 / 0.000490 (0.299547) | 0.000214 / 0.000200 (0.000014) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018360 / 0.037411 (-0.019051) | 0.061239 / 0.014526 (0.046713) | 0.072304 / 0.176557 (-0.104253) | 0.118883 / 0.737135 (-0.618253) | 0.073562 / 0.296338 (-0.222777) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284478 / 0.215209 (0.069269) | 2.761819 / 2.077655 (0.684165) | 1.443757 / 1.504120 (-0.060363) | 1.315221 / 1.541195 (-0.225974) | 1.333930 / 1.468490 (-0.134560) | 0.581470 / 4.584777 (-4.003307) | 2.422530 / 3.745712 (-1.323183) | 2.869898 / 5.269862 (-2.399963) | 1.789159 / 4.565676 (-2.776517) | 0.063708 / 0.424275 (-0.360567) | 0.004922 / 0.007607 (-0.002685) | 0.337352 / 0.226044 (0.111307) | 3.290192 / 2.268929 (1.021263) | 1.840192 / 55.444624 (-53.604432) | 1.543008 / 6.876477 (-5.333469) | 1.548947 / 2.142072 (-0.593125) | 0.655129 / 4.805227 (-4.150098) | 0.119010 / 6.500664 (-6.381654) | 0.042583 / 0.075469 (-0.032886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981333 / 1.841788 (-0.860455) | 11.349564 / 8.074308 (3.275256) | 9.397603 / 10.191392 (-0.793789) | 0.142151 / 0.680424 (-0.538273) | 0.013850 / 0.534201 (-0.520351) | 0.286323 / 0.579283 (-0.292960) | 0.265223 / 0.434364 (-0.169141) | 0.335322 / 0.540337 (-0.205015) | 0.441727 / 1.386936 (-0.945209) |\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) | 0.005134 / 0.011353 (-0.006219) | 0.003216 / 0.011008 (-0.007792) | 0.049401 / 0.038508 (0.010893) | 0.031509 / 0.023109 (0.008400) | 0.262211 / 0.275898 (-0.013687) | 0.284814 / 0.323480 (-0.038665) | 0.004165 / 0.007986 (-0.003821) | 0.002693 / 0.004328 (-0.001636) | 0.048088 / 0.004250 (0.043838) | 0.043609 / 0.037052 (0.006557) | 0.271126 / 0.258489 (0.012637) | 0.301374 / 0.293841 (0.007533) | 0.028891 / 0.128546 (-0.099655) | 0.009911 / 0.075646 (-0.065735) | 0.057334 / 0.419271 (-0.361938) | 0.050936 / 0.043533 (0.007403) | 0.258883 / 0.255139 (0.003744) | 0.282884 / 0.283200 (-0.000315) | 0.017475 / 0.141683 (-0.124208) | 1.167562 / 1.452155 (-0.284593) | 1.214081 / 1.492716 (-0.278636) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096890 / 0.018006 (0.078884) | 0.315819 / 0.000490 (0.315329) | 0.000218 / 0.000200 (0.000018) | 0.000054 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021775 / 0.037411 (-0.015637) | 0.075816 / 0.014526 (0.061290) | 0.086992 / 0.176557 (-0.089564) | 0.125816 / 0.737135 (-0.611319) | 0.090343 / 0.296338 (-0.205995) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295204 / 0.215209 (0.079995) | 2.903129 / 2.077655 (0.825475) | 1.629838 / 1.504120 (0.125718) | 1.531862 / 1.541195 (-0.009332) | 1.504614 / 1.468490 (0.036123) | 0.572910 / 4.584777 (-4.011867) | 2.482555 / 3.745712 (-1.263157) | 2.637259 / 5.269862 (-2.632603) | 1.733049 / 4.565676 (-2.832628) | 0.063239 / 0.424275 (-0.361036) | 0.005037 / 0.007607 (-0.002570) | 0.346657 / 0.226044 (0.120612) | 3.446469 / 2.268929 (1.177540) | 2.017864 / 55.444624 (-53.426761) | 1.688704 / 6.876477 (-5.187773) | 1.790813 / 2.142072 (-0.351259) | 0.660769 / 4.805227 (-4.144458) | 0.115582 / 6.500664 (-6.385082) | 0.040111 / 0.075469 (-0.035358) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.058089 / 1.841788 (-0.783699) | 11.998171 / 8.074308 (3.923863) | 10.459128 / 10.191392 (0.267736) | 0.149653 / 0.680424 (-0.530771) | 0.015015 / 0.534201 (-0.519186) | 0.289973 / 0.579283 (-0.289310) | 0.274217 / 0.434364 (-0.160147) | 0.351057 / 0.540337 (-0.189281) | 0.434295 / 1.386936 (-0.952641) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb33582eb2262cec5ec6f238b50a7d043ab3ca94 \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6705", "html_url": "https://github.com/huggingface/datasets/pull/6705", "diff_url": "https://github.com/huggingface/datasets/pull/6705.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6705.patch", "merged_at": "2024-03-01T18:52:49" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6705). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005014 / 0.011353 (-0.006339) | 0.003371 / 0.011008 (-0.007637) | 0.063622 / 0.038508 (0.025114) | 0.026551 / 0.023109 (0.003442) | 0.244602 / 0.275898 (-0.031296) | 0.269981 / 0.323480 (-0.053499) | 0.003959 / 0.007986 (-0.004027) | 0.002678 / 0.004328 (-0.001650) | 0.049421 / 0.004250 (0.045170) | 0.039926 / 0.037052 (0.002873) | 0.256609 / 0.258489 (-0.001881) | 0.281934 / 0.293841 (-0.011907) | 0.027794 / 0.128546 (-0.100752) | 0.010130 / 0.075646 (-0.065516) | 0.207471 / 0.419271 (-0.211800) | 0.035423 / 0.043533 (-0.008110) | 0.246987 / 0.255139 (-0.008152) | 0.265413 / 0.283200 (-0.017787) | 0.018287 / 0.141683 (-0.123396) | 1.117550 / 1.452155 (-0.334604) | 1.151713 / 1.492716 (-0.341003) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095632 / 0.018006 (0.077626) | 0.304315 / 0.000490 (0.303825) | 0.000214 / 0.000200 (0.000014) | 0.000049 / 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) | 0.018591 / 0.037411 (-0.018820) | 0.062081 / 0.014526 (0.047555) | 0.075137 / 0.176557 (-0.101420) | 0.119116 / 0.737135 (-0.618020) | 0.075254 / 0.296338 (-0.221085) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286161 / 0.215209 (0.070952) | 2.793824 / 2.077655 (0.716169) | 1.492523 / 1.504120 (-0.011597) | 1.372158 / 1.541195 (-0.169037) | 1.385921 / 1.468490 (-0.082569) | 0.568700 / 4.584777 (-4.016077) | 2.340451 / 3.745712 (-1.405262) | 2.712022 / 5.269862 (-2.557840) | 1.712479 / 4.565676 (-2.853197) | 0.060906 / 0.424275 (-0.363369) | 0.004909 / 0.007607 (-0.002698) | 0.338227 / 0.226044 (0.112182) | 3.331329 / 2.268929 (1.062400) | 1.845646 / 55.444624 (-53.598978) | 1.559384 / 6.876477 (-5.317093) | 1.577683 / 2.142072 (-0.564390) | 0.629367 / 4.805227 (-4.175860) | 0.118645 / 6.500664 (-6.382019) | 0.041517 / 0.075469 (-0.033952) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962237 / 1.841788 (-0.879551) | 11.232566 / 8.074308 (3.158258) | 9.627141 / 10.191392 (-0.564251) | 0.129732 / 0.680424 (-0.550692) | 0.013701 / 0.534201 (-0.520500) | 0.291869 / 0.579283 (-0.287414) | 0.269298 / 0.434364 (-0.165066) | 0.342502 / 0.540337 (-0.197835) | 0.455891 / 1.386936 (-0.931045) |\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) | 0.005256 / 0.011353 (-0.006097) | 0.003419 / 0.011008 (-0.007589) | 0.049681 / 0.038508 (0.011173) | 0.029566 / 0.023109 (0.006457) | 0.268010 / 0.275898 (-0.007888) | 0.293721 / 0.323480 (-0.029759) | 0.004249 / 0.007986 (-0.003737) | 0.002643 / 0.004328 (-0.001685) | 0.048758 / 0.004250 (0.044508) | 0.044294 / 0.037052 (0.007241) | 0.279584 / 0.258489 (0.021095) | 0.311150 / 0.293841 (0.017309) | 0.029443 / 0.128546 (-0.099103) | 0.010314 / 0.075646 (-0.065333) | 0.057770 / 0.419271 (-0.361501) | 0.050953 / 0.043533 (0.007420) | 0.268283 / 0.255139 (0.013144) | 0.289155 / 0.283200 (0.005956) | 0.017742 / 0.141683 (-0.123941) | 1.163963 / 1.452155 (-0.288192) | 1.200580 / 1.492716 (-0.292136) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096365 / 0.018006 (0.078359) | 0.307257 / 0.000490 (0.306767) | 0.000265 / 0.000200 (0.000065) | 0.000049 / 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) | 0.021862 / 0.037411 (-0.015550) | 0.075502 / 0.014526 (0.060976) | 0.087800 / 0.176557 (-0.088756) | 0.125468 / 0.737135 (-0.611667) | 0.088207 / 0.296338 (-0.208132) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.324184 / 0.215209 (0.108975) | 3.198442 / 2.077655 (1.120787) | 1.862801 / 1.504120 (0.358682) | 1.728637 / 1.541195 (0.187443) | 1.727997 / 1.468490 (0.259507) | 0.571590 / 4.584777 (-4.013187) | 2.448661 / 3.745712 (-1.297051) | 2.665943 / 5.269862 (-2.603919) | 1.731718 / 4.565676 (-2.833958) | 0.063644 / 0.424275 (-0.360631) | 0.004989 / 0.007607 (-0.002619) | 0.364543 / 0.226044 (0.138498) | 3.615859 / 2.268929 (1.346930) | 2.131637 / 55.444624 (-53.312987) | 1.857317 / 6.876477 (-5.019159) | 1.992813 / 2.142072 (-0.149260) | 0.654662 / 4.805227 (-4.150565) | 0.117631 / 6.500664 (-6.383034) | 0.040934 / 0.075469 (-0.034535) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.013802 / 1.841788 (-0.827985) | 11.899873 / 8.074308 (3.825565) | 10.291297 / 10.191392 (0.099905) | 0.155245 / 0.680424 (-0.525179) | 0.014449 / 0.534201 (-0.519752) | 0.286331 / 0.579283 (-0.292952) | 0.273111 / 0.434364 (-0.161253) | 0.321182 / 0.540337 (-0.219155) | 0.433406 / 1.386936 (-0.953530) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e5406f9a9a453f2c0614c2ee26975e5973edc278 \"CML watermark\")\n" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6704", "html_url": "https://github.com/huggingface/datasets/pull/6704", "diff_url": "https://github.com/huggingface/datasets/pull/6704.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6704.patch", "merged_at": "2024-03-15T15:22:03" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6704). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Awesome !\r\n\r\nNote that it can still create duplicates if a path matches several dir patterns, e.g.\r\n\r\n```\r\ndata/train-train/data/txt\r\n```\r\nmatches two dir patterns:\r\n```\r\n**/{keyword}[{sep}]*/**\r\n**/*[{sep}]{keyword}/**\r\n```\r\n\r\nPS: feel free to update your branch, I just updated ruff on `main`", "Yes, I didn't mention that case on purpose 🙂. One solution would be deprecating the `**/*[{sep}]{keyword}/**` pattern (and eventually removing it). This way, the directory patterns would align more with the filename ones. Or do you think this is too big of a breaking change?", "I think it's too big of a breaking change yes :/ (and would make the docs / logic more complex for users to get imo) Though I think your approach is already a nice step in the right direction", "These changes to the `resolve_pattern` function lead to 20-30x faster local file resolution in my benchmarks.", "Nice ! Though since `fsspec` caches the filesystem, is there a risk when adding new files and reloading a dataset ?\r\n\r\n\r\n```python\r\nwith open(\"my/local/dir/0000.txt\", \"w\") as f:\r\n f.write(\"Hello there\")\r\nd1 = load_dataset(\"my/local/dir\")\r\nwith open(\"my/local/dir/0001.txt\", \"w\") as f:\r\n f.write(\"General Kenobi\")\r\nd2 = load_dataset(\"my/local/dir\")\r\nassert list(d1) != list(d2)\r\n```", "Yes. But I think I have a solution for this.", "I'm not satisfied with the context manager approach...\r\n\r\nA clean solution would require a bigger rewrite of the resolution logic (e.g., merging `get_data_patterns` and `DataFilesDict.from_patterns` into a `get_data_files` function that would build the `DataFilesDict` by matching the paths using `fs.find` and `fsspec.utils.glob_translate` (available in `fsspec>=2023.12.0`))\r\n\r\nThe current changes make the local resolution 2-3x faster, which is good enough for now, I think.", "<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) | 0.004888 / 0.011353 (-0.006465) | 0.003267 / 0.011008 (-0.007742) | 0.065117 / 0.038508 (0.026609) | 0.029416 / 0.023109 (0.006306) | 0.232021 / 0.275898 (-0.043877) | 0.258053 / 0.323480 (-0.065427) | 0.003971 / 0.007986 (-0.004014) | 0.002550 / 0.004328 (-0.001779) | 0.049126 / 0.004250 (0.044876) | 0.040620 / 0.037052 (0.003568) | 0.253437 / 0.258489 (-0.005052) | 0.273583 / 0.293841 (-0.020258) | 0.026775 / 0.128546 (-0.101771) | 0.010073 / 0.075646 (-0.065573) | 0.219089 / 0.419271 (-0.200183) | 0.035047 / 0.043533 (-0.008486) | 0.247661 / 0.255139 (-0.007478) | 0.258674 / 0.283200 (-0.024525) | 0.018428 / 0.141683 (-0.123255) | 1.130394 / 1.452155 (-0.321761) | 1.173167 / 1.492716 (-0.319549) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092581 / 0.018006 (0.074574) | 0.303657 / 0.000490 (0.303167) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 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) | 0.018640 / 0.037411 (-0.018771) | 0.062032 / 0.014526 (0.047506) | 0.073982 / 0.176557 (-0.102575) | 0.121499 / 0.737135 (-0.615636) | 0.076780 / 0.296338 (-0.219559) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279411 / 0.215209 (0.064202) | 2.737977 / 2.077655 (0.660322) | 1.454135 / 1.504120 (-0.049985) | 1.343144 / 1.541195 (-0.198051) | 1.339876 / 1.468490 (-0.128614) | 0.567306 / 4.584777 (-4.017471) | 2.372569 / 3.745712 (-1.373143) | 2.716810 / 5.269862 (-2.553052) | 1.697895 / 4.565676 (-2.867782) | 0.061804 / 0.424275 (-0.362471) | 0.004986 / 0.007607 (-0.002622) | 0.332721 / 0.226044 (0.106676) | 3.274572 / 2.268929 (1.005644) | 1.789900 / 55.444624 (-53.654725) | 1.536346 / 6.876477 (-5.340131) | 1.551940 / 2.142072 (-0.590132) | 0.634539 / 4.805227 (-4.170688) | 0.115860 / 6.500664 (-6.384805) | 0.041737 / 0.075469 (-0.033732) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.024469 / 1.841788 (-0.817319) | 11.327496 / 8.074308 (3.253188) | 9.265855 / 10.191392 (-0.925537) | 0.142200 / 0.680424 (-0.538224) | 0.013945 / 0.534201 (-0.520256) | 0.289670 / 0.579283 (-0.289614) | 0.269240 / 0.434364 (-0.165124) | 0.324748 / 0.540337 (-0.215590) | 0.421393 / 1.386936 (-0.965543) |\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) | 0.005284 / 0.011353 (-0.006069) | 0.003351 / 0.011008 (-0.007658) | 0.049973 / 0.038508 (0.011465) | 0.030257 / 0.023109 (0.007148) | 0.273660 / 0.275898 (-0.002238) | 0.300328 / 0.323480 (-0.023152) | 0.004133 / 0.007986 (-0.003852) | 0.002614 / 0.004328 (-0.001715) | 0.048055 / 0.004250 (0.043804) | 0.044731 / 0.037052 (0.007678) | 0.290257 / 0.258489 (0.031768) | 0.321243 / 0.293841 (0.027402) | 0.029542 / 0.128546 (-0.099004) | 0.010074 / 0.075646 (-0.065573) | 0.057944 / 0.419271 (-0.361327) | 0.051267 / 0.043533 (0.007734) | 0.276278 / 0.255139 (0.021139) | 0.302464 / 0.283200 (0.019264) | 0.018231 / 0.141683 (-0.123452) | 1.140782 / 1.452155 (-0.311373) | 1.182991 / 1.492716 (-0.309725) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092325 / 0.018006 (0.074319) | 0.302610 / 0.000490 (0.302121) | 0.000202 / 0.000200 (0.000002) | 0.000049 / 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) | 0.021458 / 0.037411 (-0.015954) | 0.074883 / 0.014526 (0.060357) | 0.085747 / 0.176557 (-0.090809) | 0.125506 / 0.737135 (-0.611629) | 0.086921 / 0.296338 (-0.209417) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290485 / 0.215209 (0.075276) | 2.853898 / 2.077655 (0.776243) | 1.615606 / 1.504120 (0.111486) | 1.491797 / 1.541195 (-0.049397) | 1.515981 / 1.468490 (0.047491) | 0.566760 / 4.584777 (-4.018017) | 2.462593 / 3.745712 (-1.283119) | 2.765516 / 5.269862 (-2.504345) | 1.755078 / 4.565676 (-2.810598) | 0.063614 / 0.424275 (-0.360661) | 0.005040 / 0.007607 (-0.002567) | 0.347957 / 0.226044 (0.121912) | 3.464258 / 2.268929 (1.195330) | 1.992273 / 55.444624 (-53.452351) | 1.699147 / 6.876477 (-5.177330) | 1.868438 / 2.142072 (-0.273635) | 0.660756 / 4.805227 (-4.144471) | 0.118142 / 6.500664 (-6.382522) | 0.041974 / 0.075469 (-0.033495) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012206 / 1.841788 (-0.829581) | 12.343735 / 8.074308 (4.269427) | 10.321975 / 10.191392 (0.130583) | 0.140007 / 0.680424 (-0.540417) | 0.015755 / 0.534201 (-0.518446) | 0.291978 / 0.579283 (-0.287305) | 0.278792 / 0.434364 (-0.155572) | 0.325366 / 0.540337 (-0.214972) | 0.439403 / 1.386936 (-0.947533) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d1d3c06a651c6ad5142f331cb5dc0008ddcade33 \"CML watermark\")\n" ]

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[ "`save_to_disk` uses a special serialization that can only be read using `load_from_disk`.\r\n\r\nContrary to `load_dataset`, `load_from_disk` directly loads Arrow files and uses the dataset directory as cache.\r\n\r\nOn the other hand `load_dataset` does a conversion step to get Arrow files from the raw data files (could be in JSON, CSV, Parquet etc.) and caches them in the `datasets` cache directory (default is `~/.cache/huggingface/datasets`). We haven't implemented any logic in `load_dataset` to support datasets saved with `save_to_disk` because they don't use the same cache.\r\n\r\nEDIT: note that you can save your dataset in Parquet format locally using `.to.parquet()` (make sure to shard in multiple files your dataset if it's multiple GBs - you can use `.shard()` + `.to_parquet()` to do that) and you'll be able to reload it using `load_dataset`", "@lhoestq, so is it correctly understood that if I run `to_parquet()` and then `save_to_disk()`, I can load it with `load_dataset`? If yes, then it would resolve this issue (and should probably be documented somewhere 😄)", "Here is an example:\r\n```python\r\nds.to_parquet(\"my/local/dir/data.parquet\")\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\nand for bigger datasets:\r\n```python\r\nnum_shards = 1024 # set number of files to save (e.g. try to have files smaller than 5GB)\r\nfor shard_idx in num_shards:\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"my/local/dir/{shard_idx:05d}.parquet\") # 00000.parquet to 01023.parquet\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\n\r\nI hope this helps :)", "Thanks for helping out! Does this approach work with `s3fs`? e.g. something like this:\r\n\r\n```python\r\nimport s3fs\r\ns3 = s3fs.S3FileSystem(anon=True)\r\nwith s3.open('mybucket/new-file.parquet', 'w') as f:\r\n ds.to_parquet(f)\r\n```\r\n\r\nThis is instead of `save_to_disk` to save to an S3 bucket.\r\n\r\nOtherwise, I am not sure how to make this work when saving the dataset to an S3 bucket. Would `dataset.set_format(\"arrow\")` work as a replacement?", "`load_dataset` does't support S3 buckets unfortunately :/", "> `load_dataset` does't support S3 buckets unfortunately :/\r\n\r\nI am aware but I have some code that downloads it to disk before using that method. The most important part is to store it in a format that load_dataset is compatible with. ", "Feel free to use Parquet then :)", "I ended up with this. Not ideal to save to local disk, but it works and loads via `load_datasets` after downloading from S3 with another method.\r\n\r\n```python\r\nwith tempfile.TemporaryDirectory() as dir:\r\n dataset_nbytes = ds._estimate_nbytes()\r\n max_shard_size_local = convert_file_size_to_int(max_shard_size)\r\n num_shards = int(dataset_nbytes / max_shard_size_local) + 1\r\n\r\n for shard_idx in range(num_shards):\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"{dir}/{shard_idx:05d}.parquet\")\r\n \r\n fs.upload(\r\n lpath=dir,\r\n rpath=s3_path,\r\n recursive=True,\r\n )\r\n```" ]

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[ "Hi ! For now I would recommend creating a new Parquet file using `dataset_new.to_parquet()` and upload it to HF using `huggingface_hub` every time you get a new batch of data. You can name the Parquet files `0000.parquet`, `0001.parquet`, etc.\r\n\r\nThough maybe make sure to not upload one file per sample since that would be inefficient. You can buffer your data and upload when you have enough new samples for example", "This is excellent, thanks!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6701). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005490 / 0.011353 (-0.005863) | 0.003709 / 0.011008 (-0.007299) | 0.064192 / 0.038508 (0.025684) | 0.029581 / 0.023109 (0.006472) | 0.251086 / 0.275898 (-0.024812) | 0.267306 / 0.323480 (-0.056174) | 0.003074 / 0.007986 (-0.004912) | 0.003340 / 0.004328 (-0.000988) | 0.048820 / 0.004250 (0.044569) | 0.045370 / 0.037052 (0.008318) | 0.260384 / 0.258489 (0.001895) | 0.284558 / 0.293841 (-0.009283) | 0.027732 / 0.128546 (-0.100814) | 0.010661 / 0.075646 (-0.064986) | 0.213403 / 0.419271 (-0.205868) | 0.036283 / 0.043533 (-0.007250) | 0.250107 / 0.255139 (-0.005032) | 0.265220 / 0.283200 (-0.017980) | 0.021021 / 0.141683 (-0.120661) | 1.112058 / 1.452155 (-0.340096) | 1.169039 / 1.492716 (-0.323678) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095008 / 0.018006 (0.077002) | 0.303509 / 0.000490 (0.303019) | 0.000233 / 0.000200 (0.000033) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018224 / 0.037411 (-0.019187) | 0.061366 / 0.014526 (0.046841) | 0.073584 / 0.176557 (-0.102972) | 0.119869 / 0.737135 (-0.617266) | 0.074228 / 0.296338 (-0.222111) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288147 / 0.215209 (0.072938) | 2.824419 / 2.077655 (0.746764) | 1.478530 / 1.504120 (-0.025590) | 1.350127 / 1.541195 (-0.191067) | 1.349622 / 1.468490 (-0.118868) | 0.568058 / 4.584777 (-4.016719) | 2.377494 / 3.745712 (-1.368218) | 2.720767 / 5.269862 (-2.549094) | 1.710763 / 4.565676 (-2.854914) | 0.061498 / 0.424275 (-0.362778) | 0.004893 / 0.007607 (-0.002715) | 0.335633 / 0.226044 (0.109588) | 3.380646 / 2.268929 (1.111717) | 1.802436 / 55.444624 (-53.642188) | 1.562737 / 6.876477 (-5.313739) | 1.566267 / 2.142072 (-0.575806) | 0.629058 / 4.805227 (-4.176169) | 0.116307 / 6.500664 (-6.384357) | 0.042174 / 0.075469 (-0.033295) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950945 / 1.841788 (-0.890842) | 11.279009 / 8.074308 (3.204701) | 9.433251 / 10.191392 (-0.758141) | 0.138964 / 0.680424 (-0.541460) | 0.014155 / 0.534201 (-0.520046) | 0.284065 / 0.579283 (-0.295218) | 0.263301 / 0.434364 (-0.171063) | 0.331932 / 0.540337 (-0.208406) | 0.441656 / 1.386936 (-0.945280) |\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) | 0.005132 / 0.011353 (-0.006221) | 0.003484 / 0.011008 (-0.007524) | 0.049040 / 0.038508 (0.010532) | 0.030254 / 0.023109 (0.007145) | 0.277141 / 0.275898 (0.001243) | 0.295242 / 0.323480 (-0.028238) | 0.004295 / 0.007986 (-0.003690) | 0.002632 / 0.004328 (-0.001696) | 0.048540 / 0.004250 (0.044290) | 0.044787 / 0.037052 (0.007734) | 0.287736 / 0.258489 (0.029247) | 0.313146 / 0.293841 (0.019305) | 0.029340 / 0.128546 (-0.099206) | 0.010204 / 0.075646 (-0.065442) | 0.059058 / 0.419271 (-0.360214) | 0.051033 / 0.043533 (0.007500) | 0.274086 / 0.255139 (0.018947) | 0.293048 / 0.283200 (0.009848) | 0.019573 / 0.141683 (-0.122110) | 1.174032 / 1.452155 (-0.278123) | 1.227107 / 1.492716 (-0.265609) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094896 / 0.018006 (0.076890) | 0.303519 / 0.000490 (0.303029) | 0.000223 / 0.000200 (0.000023) | 0.000049 / 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) | 0.021495 / 0.037411 (-0.015917) | 0.074234 / 0.014526 (0.059708) | 0.086212 / 0.176557 (-0.090345) | 0.125052 / 0.737135 (-0.612084) | 0.087464 / 0.296338 (-0.208874) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297098 / 0.215209 (0.081889) | 2.970944 / 2.077655 (0.893289) | 1.650101 / 1.504120 (0.145981) | 1.532694 / 1.541195 (-0.008501) | 1.513652 / 1.468490 (0.045162) | 0.559614 / 4.584777 (-4.025163) | 2.404848 / 3.745712 (-1.340865) | 2.627851 / 5.269862 (-2.642011) | 1.707550 / 4.565676 (-2.858127) | 0.061821 / 0.424275 (-0.362454) | 0.005012 / 0.007607 (-0.002595) | 0.342462 / 0.226044 (0.116417) | 3.401703 / 2.268929 (1.132774) | 1.991632 / 55.444624 (-53.452993) | 1.737706 / 6.876477 (-5.138771) | 1.837457 / 2.142072 (-0.304616) | 0.638845 / 4.805227 (-4.166383) | 0.114773 / 6.500664 (-6.385891) | 0.040175 / 0.075469 (-0.035294) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.038286 / 1.841788 (-0.803501) | 11.885757 / 8.074308 (3.811448) | 10.061530 / 10.191392 (-0.129862) | 0.140824 / 0.680424 (-0.539600) | 0.015080 / 0.534201 (-0.519121) | 0.287992 / 0.579283 (-0.291291) | 0.273498 / 0.434364 (-0.160866) | 0.326478 / 0.540337 (-0.213860) | 0.426900 / 1.386936 (-0.960036) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b02be21047087c5ffc11cf1c072a5aceab517eba \"CML watermark\")\n" ]

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[ "Good catch! I've opened a PR with a fix in the `transformers` repo.", "@mariosasko Thanks!\r\n\r\nWill the doc of `datasets` be updated?\r\n\r\nI find some possible mistakes in doc about whether `remove_columns` is in-place.\r\n1. [You can also remove a column using map() with remove_columns but the present method is in-place (doesn’t copy the data to a new dataset) and is thus faster.](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.Dataset.remove_columns)\r\n2. [You can also remove a column using Dataset.map() with remove_columns but the present method is in-place (doesn’t copy the data to a new dataset) and is thus faster.](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.DatasetDict.remove_columns)\r\n3. [🤗 Datasets also has a remove_columns() function which is faster because it doesn’t copy the data of the remaining columns.](https://huggingface.co/docs/datasets/v2.17.1/en/process#map)" ]

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[ "If `test.jsonl` contains more lines like:\r\n```\r\n{\"id\": 0, \"indexs\": {\"-1\": [0, 10]}}\r\n{\"id\": 1, \"indexs\": {\"-1\": [0, 10]}}\r\n{\"id\": 2, \"indexs\": {\"-2\": [0, 10]}}\r\n...\r\n{\"id\": n, \"indexs\": {\"-9999\": [0, 10]}}\r\n```\r\n\r\n`Dataset.from_json` will just raise an error:\r\n```\r\nAn error occurred while generating the dataset\r\nTypeError: Couldn't cast array of type\r\nstruct<-5942: list<item: int64>, -5943: list<item: int64>, -5944: list<item: int64>, -5945: list<item: int64>, -5946: list<item: int64>, -5947: list<item: int64>, -5948: list<item: int64>, -5949: list<item: int64>: ...\r\nto\r\n{... '-5312': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '-5313': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/scruel/Code/Python/Working/llm-memory/data_reader.py\", line 120, in <module>\r\n reader = SnippetReader(jsonl_path, npy_path)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/Code/Python/Working/llm-memory/data_reader.py\", line 85, in __init__\r\n self._dataset = Dataset.from_json(jsonl_path, features=)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/arrow_dataset.py\", line 1130, in from_json\r\n ).read()\r\n ^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/io/json.py\", line 59, in read\r\n self.builder.download_and_prepare(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1860, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 2016, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n```", "Hi! Our JSON parser expects all examples/rows to share the same set of columns (applies to nested columns, too), hence the error. \r\n\r\nTo read the `index` column, we would have to manually cast the input to PyArrow's `pa.map_` type, but this requires a more thorough investigation, as `pa.map_` has limited support in PyArrow." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6698). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "CI failure is unrelated to the changes.", "<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) | 0.005499 / 0.011353 (-0.005854) | 0.003824 / 0.011008 (-0.007184) | 0.064230 / 0.038508 (0.025722) | 0.028962 / 0.023109 (0.005853) | 0.283540 / 0.275898 (0.007642) | 0.300774 / 0.323480 (-0.022706) | 0.003405 / 0.007986 (-0.004581) | 0.002796 / 0.004328 (-0.001532) | 0.049834 / 0.004250 (0.045584) | 0.045924 / 0.037052 (0.008872) | 0.274818 / 0.258489 (0.016328) | 0.306189 / 0.293841 (0.012348) | 0.028304 / 0.128546 (-0.100242) | 0.011496 / 0.075646 (-0.064150) | 0.208236 / 0.419271 (-0.211036) | 0.035720 / 0.043533 (-0.007813) | 0.261190 / 0.255139 (0.006051) | 0.281545 / 0.283200 (-0.001655) | 0.019388 / 0.141683 (-0.122295) | 1.134999 / 1.452155 (-0.317156) | 1.203053 / 1.492716 (-0.289663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096007 / 0.018006 (0.078000) | 0.316958 / 0.000490 (0.316469) | 0.000210 / 0.000200 (0.000010) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018330 / 0.037411 (-0.019081) | 0.063299 / 0.014526 (0.048773) | 0.073833 / 0.176557 (-0.102723) | 0.122285 / 0.737135 (-0.614850) | 0.077352 / 0.296338 (-0.218987) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304487 / 0.215209 (0.089278) | 3.017666 / 2.077655 (0.940012) | 1.664292 / 1.504120 (0.160172) | 1.448446 / 1.541195 (-0.092748) | 1.435612 / 1.468490 (-0.032878) | 0.569704 / 4.584777 (-4.015073) | 2.362015 / 3.745712 (-1.383698) | 2.910380 / 5.269862 (-2.359481) | 1.814560 / 4.565676 (-2.751116) | 0.063986 / 0.424275 (-0.360289) | 0.005022 / 0.007607 (-0.002585) | 0.363528 / 0.226044 (0.137483) | 3.641940 / 2.268929 (1.373011) | 1.961589 / 55.444624 (-53.483035) | 1.603683 / 6.876477 (-5.272793) | 1.663144 / 2.142072 (-0.478928) | 0.645628 / 4.805227 (-4.159599) | 0.118759 / 6.500664 (-6.381905) | 0.042631 / 0.075469 (-0.032838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985648 / 1.841788 (-0.856140) | 13.082558 / 8.074308 (5.008250) | 9.909811 / 10.191392 (-0.281581) | 0.131340 / 0.680424 (-0.549083) | 0.013983 / 0.534201 (-0.520218) | 0.289869 / 0.579283 (-0.289414) | 0.271775 / 0.434364 (-0.162589) | 0.334853 / 0.540337 (-0.205485) | 0.457017 / 1.386936 (-0.929919) |\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) | 0.005580 / 0.011353 (-0.005773) | 0.003788 / 0.011008 (-0.007221) | 0.049401 / 0.038508 (0.010893) | 0.030372 / 0.023109 (0.007263) | 0.278554 / 0.275898 (0.002655) | 0.302462 / 0.323480 (-0.021018) | 0.004412 / 0.007986 (-0.003573) | 0.002825 / 0.004328 (-0.001504) | 0.047826 / 0.004250 (0.043576) | 0.047903 / 0.037052 (0.010851) | 0.293098 / 0.258489 (0.034609) | 0.322777 / 0.293841 (0.028936) | 0.030010 / 0.128546 (-0.098536) | 0.011187 / 0.075646 (-0.064459) | 0.057639 / 0.419271 (-0.361632) | 0.059693 / 0.043533 (0.016160) | 0.280288 / 0.255139 (0.025149) | 0.294022 / 0.283200 (0.010823) | 0.019635 / 0.141683 (-0.122048) | 1.154733 / 1.452155 (-0.297422) | 1.200808 / 1.492716 (-0.291908) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099682 / 0.018006 (0.081676) | 0.319521 / 0.000490 (0.319031) | 0.000224 / 0.000200 (0.000024) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022042 / 0.037411 (-0.015370) | 0.078842 / 0.014526 (0.064317) | 0.088715 / 0.176557 (-0.087841) | 0.126832 / 0.737135 (-0.610303) | 0.089217 / 0.296338 (-0.207122) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300099 / 0.215209 (0.084890) | 2.907746 / 2.077655 (0.830092) | 1.619418 / 1.504120 (0.115298) | 1.495693 / 1.541195 (-0.045501) | 1.544956 / 1.468490 (0.076466) | 0.556652 / 4.584777 (-4.028124) | 2.414408 / 3.745712 (-1.331304) | 2.737227 / 5.269862 (-2.532635) | 1.763187 / 4.565676 (-2.802490) | 0.062207 / 0.424275 (-0.362069) | 0.005076 / 0.007607 (-0.002531) | 0.349880 / 0.226044 (0.123836) | 3.425355 / 2.268929 (1.156427) | 1.972094 / 55.444624 (-53.472531) | 1.710650 / 6.876477 (-5.165827) | 1.902218 / 2.142072 (-0.239855) | 0.640699 / 4.805227 (-4.164529) | 0.117879 / 6.500664 (-6.382785) | 0.042412 / 0.075469 (-0.033057) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.030131 / 1.841788 (-0.811656) | 12.750637 / 8.074308 (4.676329) | 10.352636 / 10.191392 (0.161244) | 0.141139 / 0.680424 (-0.539285) | 0.015343 / 0.534201 (-0.518858) | 0.294931 / 0.579283 (-0.284352) | 0.275237 / 0.434364 (-0.159127) | 0.336669 / 0.540337 (-0.203668) | 0.429945 / 1.386936 (-0.956991) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c424fa517a1b8517c89545f979e0c8c7d90c3e3 \"CML watermark\")\n" ]

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[ "FWIW, I run `load_dataset(\"llm-blender/mix-instruct\")` and it ran successfully.\r\nCan you clear your cache and try again?\r\n\r\n\r\n### Environment Info\r\n\r\n- `datasets` version: 2.17.0\r\n- Platform: Linux-6.2.6-76060206-generic-x86_64-with-glibc2.35\r\n- Python version: 3.9.13\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 1.5.3\r\n- `fsspec` version: 2023.10.0", "It is working on the Kaggle GPU instance but gives this same error when running on the CPU instance. Still to run it on Kaggle you require to install the latest versions of datasets and transformers.", "This error means that `fsspec>=2023.12.0` is installed, which is incompatible with the current releases (the next `datasets` release will be the first to support it). In the meantime, downgrading `fsspec` (`pip install fsspec<=2023.12.0`) should fix the issue.", "@mariosasko Thanks I got it to work with installing that version of fsspec." ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6696", "html_url": "https://github.com/huggingface/datasets/pull/6696", "diff_url": "https://github.com/huggingface/datasets/pull/6696.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6696.patch", "merged_at": "2024-02-28T06:39:12" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6696). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005057 / 0.011353 (-0.006296) | 0.003665 / 0.011008 (-0.007343) | 0.063217 / 0.038508 (0.024709) | 0.028789 / 0.023109 (0.005679) | 0.233597 / 0.275898 (-0.042301) | 0.254792 / 0.323480 (-0.068687) | 0.003065 / 0.007986 (-0.004921) | 0.002686 / 0.004328 (-0.001642) | 0.050182 / 0.004250 (0.045932) | 0.042204 / 0.037052 (0.005151) | 0.254262 / 0.258489 (-0.004227) | 0.277099 / 0.293841 (-0.016742) | 0.027564 / 0.128546 (-0.100982) | 0.010768 / 0.075646 (-0.064878) | 0.207302 / 0.419271 (-0.211969) | 0.035737 / 0.043533 (-0.007796) | 0.242388 / 0.255139 (-0.012751) | 0.259833 / 0.283200 (-0.023367) | 0.019833 / 0.141683 (-0.121850) | 1.135928 / 1.452155 (-0.316227) | 1.162851 / 1.492716 (-0.329865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089209 / 0.018006 (0.071202) | 0.300493 / 0.000490 (0.300003) | 0.000216 / 0.000200 (0.000016) | 0.000049 / 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) | 0.017968 / 0.037411 (-0.019444) | 0.061773 / 0.014526 (0.047247) | 0.073835 / 0.176557 (-0.102722) | 0.118592 / 0.737135 (-0.618544) | 0.073606 / 0.296338 (-0.222732) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287858 / 0.215209 (0.072649) | 2.822917 / 2.077655 (0.745262) | 1.485259 / 1.504120 (-0.018861) | 1.355922 / 1.541195 (-0.185273) | 1.364008 / 1.468490 (-0.104482) | 0.557713 / 4.584777 (-4.027064) | 2.378972 / 3.745712 (-1.366741) | 2.737218 / 5.269862 (-2.532643) | 1.718317 / 4.565676 (-2.847359) | 0.062362 / 0.424275 (-0.361913) | 0.004992 / 0.007607 (-0.002615) | 0.350765 / 0.226044 (0.124721) | 3.387579 / 2.268929 (1.118650) | 1.860408 / 55.444624 (-53.584216) | 1.569355 / 6.876477 (-5.307122) | 1.593013 / 2.142072 (-0.549059) | 0.639325 / 4.805227 (-4.165902) | 0.121769 / 6.500664 (-6.378895) | 0.042148 / 0.075469 (-0.033322) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990594 / 1.841788 (-0.851194) | 11.460904 / 8.074308 (3.386596) | 9.438691 / 10.191392 (-0.752701) | 0.141884 / 0.680424 (-0.538540) | 0.013725 / 0.534201 (-0.520476) | 0.288847 / 0.579283 (-0.290436) | 0.278815 / 0.434364 (-0.155549) | 0.337108 / 0.540337 (-0.203229) | 0.441659 / 1.386936 (-0.945277) |\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) | 0.005265 / 0.011353 (-0.006088) | 0.003734 / 0.011008 (-0.007274) | 0.049365 / 0.038508 (0.010857) | 0.030483 / 0.023109 (0.007373) | 0.275085 / 0.275898 (-0.000813) | 0.296004 / 0.323480 (-0.027475) | 0.004964 / 0.007986 (-0.003022) | 0.002542 / 0.004328 (-0.001787) | 0.048734 / 0.004250 (0.044483) | 0.044098 / 0.037052 (0.007046) | 0.292517 / 0.258489 (0.034028) | 0.319992 / 0.293841 (0.026151) | 0.029552 / 0.128546 (-0.098994) | 0.010669 / 0.075646 (-0.064977) | 0.058887 / 0.419271 (-0.360385) | 0.051163 / 0.043533 (0.007630) | 0.277266 / 0.255139 (0.022127) | 0.295347 / 0.283200 (0.012147) | 0.018403 / 0.141683 (-0.123280) | 1.151979 / 1.452155 (-0.300176) | 1.204583 / 1.492716 (-0.288134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091157 / 0.018006 (0.073151) | 0.300109 / 0.000490 (0.299619) | 0.000211 / 0.000200 (0.000011) | 0.000052 / 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) | 0.021521 / 0.037411 (-0.015890) | 0.074954 / 0.014526 (0.060428) | 0.087010 / 0.176557 (-0.089546) | 0.125853 / 0.737135 (-0.611282) | 0.087877 / 0.296338 (-0.208461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297890 / 0.215209 (0.082681) | 2.912159 / 2.077655 (0.834504) | 1.619311 / 1.504120 (0.115192) | 1.501726 / 1.541195 (-0.039468) | 1.494143 / 1.468490 (0.025652) | 0.566744 / 4.584777 (-4.018033) | 2.497594 / 3.745712 (-1.248118) | 2.631403 / 5.269862 (-2.638459) | 1.727896 / 4.565676 (-2.837780) | 0.065937 / 0.424275 (-0.358339) | 0.005023 / 0.007607 (-0.002585) | 0.345747 / 0.226044 (0.119702) | 3.417615 / 2.268929 (1.148686) | 1.949970 / 55.444624 (-53.494654) | 1.680019 / 6.876477 (-5.196457) | 1.789879 / 2.142072 (-0.352193) | 0.648053 / 4.805227 (-4.157174) | 0.117408 / 6.500664 (-6.383256) | 0.040681 / 0.075469 (-0.034788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012535 / 1.841788 (-0.829252) | 11.935819 / 8.074308 (3.861511) | 10.241452 / 10.191392 (0.050060) | 0.130956 / 0.680424 (-0.549468) | 0.015396 / 0.534201 (-0.518805) | 0.289166 / 0.579283 (-0.290117) | 0.274149 / 0.434364 (-0.160215) | 0.325844 / 0.540337 (-0.214493) | 0.424919 / 1.386936 (-0.962017) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb834d9c63ab8cb14725ae8e4fc2da8672892a6d \"CML watermark\")\n" ]

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[ "https://huggingface.co/datasets/CausalLM/Refined-Anime-Text/discussions/1 has been fixed, but how can we check if there are other datasets with the same error, in datasets-server's database? I don't know how to get the list of erroneous cache entries, since we only copied `Error code: JobManagerCrashedError`, but not the traceback in `details`... Do you remember the error message, or the underlying exception, we had?" ]

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[ "Hi! You can find a reason why we are against this feature in https://github.com/huggingface/datasets/issues/3449. \r\n\r\n> It's too cumbersome to write this command every time we perform a dataset merging operation\r\n\r\nExplicit is better than implicit, so this isn't a good enough reason. \r\n\r\nThanks for the effort nonetheless :)!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6693). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005069 / 0.011353 (-0.006284) | 0.003682 / 0.011008 (-0.007326) | 0.063733 / 0.038508 (0.025225) | 0.030377 / 0.023109 (0.007268) | 0.242962 / 0.275898 (-0.032936) | 0.262865 / 0.323480 (-0.060615) | 0.004760 / 0.007986 (-0.003225) | 0.002772 / 0.004328 (-0.001557) | 0.049094 / 0.004250 (0.044843) | 0.041093 / 0.037052 (0.004041) | 0.260423 / 0.258489 (0.001934) | 0.283908 / 0.293841 (-0.009933) | 0.027409 / 0.128546 (-0.101138) | 0.010548 / 0.075646 (-0.065098) | 0.208637 / 0.419271 (-0.210634) | 0.035386 / 0.043533 (-0.008147) | 0.242352 / 0.255139 (-0.012787) | 0.264201 / 0.283200 (-0.018999) | 0.017822 / 0.141683 (-0.123860) | 1.140792 / 1.452155 (-0.311363) | 1.166782 / 1.492716 (-0.325934) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094727 / 0.018006 (0.076720) | 0.308548 / 0.000490 (0.308059) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 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) | 0.018106 / 0.037411 (-0.019305) | 0.062057 / 0.014526 (0.047531) | 0.073821 / 0.176557 (-0.102735) | 0.121269 / 0.737135 (-0.615867) | 0.074062 / 0.296338 (-0.222277) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282978 / 0.215209 (0.067768) | 2.788626 / 2.077655 (0.710971) | 1.479756 / 1.504120 (-0.024364) | 1.360620 / 1.541195 (-0.180575) | 1.363996 / 1.468490 (-0.104494) | 0.571646 / 4.584777 (-4.013131) | 2.430630 / 3.745712 (-1.315083) | 2.783909 / 5.269862 (-2.485953) | 1.744617 / 4.565676 (-2.821060) | 0.062771 / 0.424275 (-0.361504) | 0.004978 / 0.007607 (-0.002629) | 0.347929 / 0.226044 (0.121884) | 3.368837 / 2.268929 (1.099908) | 1.855635 / 55.444624 (-53.588990) | 1.581555 / 6.876477 (-5.294922) | 1.589888 / 2.142072 (-0.552184) | 0.655821 / 4.805227 (-4.149406) | 0.118990 / 6.500664 (-6.381674) | 0.042191 / 0.075469 (-0.033278) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.991099 / 1.841788 (-0.850688) | 11.627919 / 8.074308 (3.553611) | 9.554180 / 10.191392 (-0.637212) | 0.140541 / 0.680424 (-0.539882) | 0.014264 / 0.534201 (-0.519937) | 0.288465 / 0.579283 (-0.290818) | 0.266400 / 0.434364 (-0.167964) | 0.324400 / 0.540337 (-0.215938) | 0.423158 / 1.386936 (-0.963778) |\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) | 0.005588 / 0.011353 (-0.005765) | 0.003784 / 0.011008 (-0.007224) | 0.049961 / 0.038508 (0.011453) | 0.031215 / 0.023109 (0.008105) | 0.280859 / 0.275898 (0.004961) | 0.306416 / 0.323480 (-0.017063) | 0.004310 / 0.007986 (-0.003676) | 0.002884 / 0.004328 (-0.001445) | 0.049662 / 0.004250 (0.045412) | 0.046611 / 0.037052 (0.009559) | 0.293353 / 0.258489 (0.034864) | 0.327839 / 0.293841 (0.033998) | 0.050784 / 0.128546 (-0.077763) | 0.010890 / 0.075646 (-0.064757) | 0.059612 / 0.419271 (-0.359659) | 0.033175 / 0.043533 (-0.010358) | 0.281085 / 0.255139 (0.025946) | 0.302746 / 0.283200 (0.019547) | 0.019201 / 0.141683 (-0.122481) | 1.126722 / 1.452155 (-0.325433) | 1.225678 / 1.492716 (-0.267038) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094335 / 0.018006 (0.076329) | 0.304774 / 0.000490 (0.304285) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 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) | 0.021648 / 0.037411 (-0.015763) | 0.077920 / 0.014526 (0.063394) | 0.087125 / 0.176557 (-0.089432) | 0.125481 / 0.737135 (-0.611654) | 0.089415 / 0.296338 (-0.206924) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304955 / 0.215209 (0.089746) | 2.992587 / 2.077655 (0.914932) | 1.654609 / 1.504120 (0.150490) | 1.509114 / 1.541195 (-0.032081) | 1.530906 / 1.468490 (0.062416) | 0.572092 / 4.584777 (-4.012685) | 2.477902 / 3.745712 (-1.267810) | 2.731363 / 5.269862 (-2.538498) | 1.750000 / 4.565676 (-2.815677) | 0.063662 / 0.424275 (-0.360613) | 0.005008 / 0.007607 (-0.002600) | 0.353066 / 0.226044 (0.127022) | 3.528309 / 2.268929 (1.259380) | 2.009238 / 55.444624 (-53.435387) | 1.717792 / 6.876477 (-5.158685) | 1.861699 / 2.142072 (-0.280373) | 0.667392 / 4.805227 (-4.137835) | 0.119197 / 6.500664 (-6.381467) | 0.041131 / 0.075469 (-0.034338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032182 / 1.841788 (-0.809605) | 12.042613 / 8.074308 (3.968305) | 10.256293 / 10.191392 (0.064901) | 0.141180 / 0.680424 (-0.539244) | 0.015005 / 0.534201 (-0.519196) | 0.290081 / 0.579283 (-0.289202) | 0.281081 / 0.434364 (-0.153283) | 0.331425 / 0.540337 (-0.208912) | 0.418674 / 1.386936 (-0.968262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ad5b221c01a183a66cbf52a6d708f94e0cff0b53 \"CML watermark\")\n" ]

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[ "Hi @harsh1504660,\r\n\r\nThanks for your work, but this functionality already exists. See my comment in the corresponding issue: https://github.com/huggingface/datasets/issues/6691#issuecomment-1963449923\r\n\r\nNext time you would like to contribute, I would suggest you take on an issue that is previously validated by one of the maintainers. Thanks anyway." ]

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